def read_amira_spatialGraph(filename=None, resolution=1.0, lUnit='um',
**kwargs):
"""Reads an AMIRA spatial graph file (AmiraMesh 3D ASCII 2.0 format) and
constructs the corresponding vascular graph from it.
INPUT: filename: AMIRA spatial graph file (including path). If not
provided, a graphical file-selection dialog will display.
resolution: The resolution of the srXTM scan, i.e. voxel size
(isotropic resolution is assumend). The default is 1.0.
lUnit: The unit of length properties in the spatialGraph (i.e.
radii, and position vectors). The default is microns.
**kwargs:
defaultUnits: The defaultUnits of the VascularGraph created (see
__init__ of the VascularGraph class for details).
OUTPUT: G: Vascular graph in iGraph format.
"""
#if filename is None:
# filename = guiTools.uigetfile('Select Amira spatialGraph (.am) file')
f = open(filename,'r')
def advanceToToken(token,searchdepth=2):
done = False
while not done:
l = f.readline()
if l == '': # eof
break
if l[0:searchdepth] == token:
done = True
log.debug('Found ' + token)
return l
# Get file statistics
l = advanceToToken('define VERTEX',13)
n_vertices = int(l.split()[-1])
l = advanceToToken('define EDGE',11)
n_edges = int(l.split()[-1])
l = advanceToToken('define POINT',12)
n_points = int(l.split()[-1])
log.info("Vertices: " + str(n_vertices))
log.info("Edges: " + str(n_edges))
log.info("Points: " + str(n_points))
# Create VascularGraph and compute scaling factor
G = VascularGraph(n_vertices, **kwargs)
sf = units.scaling_factor_du(lUnit, G['defaultUnits'])
# Add vertices
log.info("reading and adding vertices")
advanceToToken('@1')
edgeConnectivity = []
numEdgePoints = []
r = [[] for i in xrange(n_vertices)]
for i in xrange(n_vertices):
l = f.readline()
l = l.split()
r[i] = sp.array([float(l[0]),float(l[1]),float(l[2])]) * resolution * sf
G.vs['r'] = r
# Read connectivity information
log.info("reading connectivity information")
advanceToToken('@2')
for i in xrange(n_edges):
l = f.readline()
l = l.split()
edgeConnectivity.append((int(l[0]),int(l[1])))
# Read number of edge points
log.info("reading number of edge points")
advanceToToken('@3')
for i in xrange(n_edges):
l = f.readline()
numEdgePoints.append(int(l))
# Read edge point coordinates
# Compute length
log.info("reading point coordinates")
advanceToToken('@4')
l_edge = []
edgepoints = []
for edge in xrange(n_edges):
tmppoints = sp.zeros([numEdgePoints[edge],3])
for point in xrange(numEdgePoints[edge]):
l = f.readline()
tmppoints[point] = map(float, l.split())
l_edge.append([ np.linalg.norm(tmppoints[i]-tmppoints[i+1])
for i in xrange(len(tmppoints)-1) ])
l_edge[edge].insert(0, 0.0)
l_edge[edge].extend([0.0])
l_edge[edge] = [(l_edge[edge][i-1] + l_edge[edge][i]) / 2.0
for i in xrange(1,len(l_edge[edge]))]
# iGraph, unlike Amira orders edge vertices by index. Make sure that
# this is reflected in the points that make up the edge:
if edgeConnectivity[edge][0] > edgeConnectivity[edge][1]:
tmppoints = tmppoints[::-1,:]
edgepoints.append(tmppoints)
if sp.mod(edge+1,sp.floor(n_edges/10)) == 0:
log.info(str(10*(edge+1)/sp.floor(n_edges/10)) + "%")
# Read edge radii
log.info("reading radii")
advanceToToken('@5')
d_edge = []
for edge in xrange(n_edges):
tmpdiameters = []
for point in xrange(numEdgePoints[edge]):
l = f.readline()
tmpdiameters.append(float(l)*2.0) # conversion radius to diameter
d_edge.append(tmpdiameters)
f.close()
# Add edges
log.info("adding edges")
G.add_edges(edgeConnectivity)
d_edge = [sp.array(d_edge[i],dtype=float) * resolution * sf
for i in xrange(n_edges)]
l_edge = [sp.array(l_edge[i],dtype=float) * resolution * sf
for i in xrange(n_edges)]
G.es['diameters'] = d_edge
G.es['points'] = [p * resolution * sf for p in edgepoints]
# Compute average diameter, weighted by length:
# - volume = pi * d**2 /4 * length
# - morphologically representative because of length-weighting
# - individual diameters scale with identical factors as average diameter
G.es['diameter'] = [np.sqrt(np.average(d_edge[i]**2, weights=l_edge[i]))
for i in xrange(n_edges)]
G.es['lengths'] = l_edge
G.es['length'] = [sum(l) for l in l_edge]
return G
def read_amira_spatialGraph(filename=None,
resolution=1.0,
lUnit='um',
**kwargs):
"""Reads an AMIRA spatial graph file (AmiraMesh 3D ASCII 2.0 format) and
constructs the corresponding vascular graph from it.
INPUT: filename: AMIRA spatial graph file (including path). If not
provided, a graphical file-selection dialog will display.
resolution: The resolution of the srXTM scan, i.e. voxel size
(isotropic resolution is assumend). The default is 1.0.
lUnit: The unit of length properties in the spatialGraph (i.e.
radii, and position vectors). The default is microns.
**kwargs:
defaultUnits: The defaultUnits of the VascularGraph created (see
__init__ of the VascularGraph class for details).
OUTPUT: G: Vascular graph in iGraph format.
"""
#if filename is None:
# filename = guiTools.uigetfile('Select Amira spatialGraph (.am) file')
f = open(filename, 'r')
def advanceToToken(token, searchdepth=2):
done = False
while not done:
l = f.readline()
if l == '': # eof
break
if l[0:searchdepth] == token:
done = True
log.debug('Found ' + token)
return l
# Get file statistics
l = advanceToToken('define VERTEX', 13)
n_vertices = int(l.split()[-1])
l = advanceToToken('define EDGE', 11)
n_edges = int(l.split()[-1])
l = advanceToToken('define POINT', 12)
n_points = int(l.split()[-1])
log.info("Vertices: " + str(n_vertices))
log.info("Edges: " + str(n_edges))
log.info("Points: " + str(n_points))
# Create VascularGraph and compute scaling factor
G = VascularGraph(n_vertices, **kwargs)
sf = units.scaling_factor_du(lUnit, G['defaultUnits'])
# Add vertices
log.info("reading and adding vertices")
advanceToToken('@1')
edgeConnectivity = []
numEdgePoints = []
r = [[] for i in xrange(n_vertices)]
for i in xrange(n_vertices):
l = f.readline()
l = l.split()
r[i] = sp.array([float(l[0]), float(l[1]),
float(l[2])]) * resolution * sf
G.vs['r'] = r
# Read connectivity information
log.info("reading connectivity information")
advanceToToken('@2')
for i in xrange(n_edges):
l = f.readline()
l = l.split()
edgeConnectivity.append((int(l[0]), int(l[1])))
# Read number of edge points
log.info("reading number of edge points")
advanceToToken('@3')
for i in xrange(n_edges):
l = f.readline()
numEdgePoints.append(int(l))
# Read edge point coordinates
# Compute length
log.info("reading point coordinates")
advanceToToken('@4')
l_edge = []
edgepoints = []
for edge in xrange(n_edges):
tmppoints = sp.zeros([numEdgePoints[edge], 3])
for point in xrange(numEdgePoints[edge]):
l = f.readline()
tmppoints[point] = map(float, l.split())
l_edge.append([
np.linalg.norm(tmppoints[i] - tmppoints[i + 1])
for i in xrange(len(tmppoints) - 1)
])
l_edge[edge].insert(0, 0.0)
l_edge[edge].extend([0.0])
l_edge[edge] = [(l_edge[edge][i - 1] + l_edge[edge][i]) / 2.0
for i in xrange(1, len(l_edge[edge]))]
# iGraph, unlike Amira orders edge vertices by index. Make sure that
# this is reflected in the points that make up the edge:
if edgeConnectivity[edge][0] > edgeConnectivity[edge][1]:
tmppoints = tmppoints[::-1, :]
edgepoints.append(tmppoints)
if sp.mod(edge + 1, sp.floor(n_edges / 10)) == 0:
log.info(str(10 * (edge + 1) / sp.floor(n_edges / 10)) + "%")
# Read edge radii
log.info("reading radii")
advanceToToken('@5')
d_edge = []
for edge in xrange(n_edges):
tmpdiameters = []
for point in xrange(numEdgePoints[edge]):
l = f.readline()
tmpdiameters.append(float(l) *
2.0) # conversion radius to diameter
d_edge.append(tmpdiameters)
f.close()
# Add edges
log.info("adding edges")
G.add_edges(edgeConnectivity)
d_edge = [
sp.array(d_edge[i], dtype=float) * resolution * sf
for i in xrange(n_edges)
]
l_edge = [
sp.array(l_edge[i], dtype=float) * resolution * sf
for i in xrange(n_edges)
]
G.es['diameters'] = d_edge
G.es['points'] = [p * resolution * sf for p in edgepoints]
# Compute average diameter, weighted by length:
# - volume = pi * d**2 /4 * length
# - morphologically representative because of length-weighting
# - individual diameters scale with identical factors as average diameter
G.es['diameter'] = [
np.sqrt(np.average(d_edge[i]**2, weights=l_edge[i]))
for i in xrange(n_edges)
]
G.es['lengths'] = l_edge
G.es['length'] = [sum(l) for l in l_edge]
return G
def read_amira_spatialGraph_v2(filename=None, resolution=1.0, lUnit='um',
**kwargs):
"""Reads an AMIRA spatial graph file (AmiraMesh 3D ASCII 2.0 format) and
constructs the corresponding vascular graph from it. This version accounts
for the fact that some spatialGraph files have colocalized vertices, which
are actually identical (this happens especially after conversion from an
mv3d file).
INPUT: filename: AMIRA spatial graph file (including path). If not
provided, a graphical file-selection dialog will display.
resolution: The resolution of the srXTM scan, i.e. voxel size
(isotropic resolution is assumend). The default is 1.0.
lUnit: The unit of length properties in the spatialGraph (i.e.
radii, and position vectors). The default is microns.
**kwargs:
defaultUnits: The defaultUnits of the VascularGraph created (see
__init__ of the VascularGraph class for details).
OUTPUT: G: Vascular graph in iGraph format.
"""
#if filename is None:
# filename = guiTools.uigetfile('Select Amira spatialGraph (.am) file')
f = open(filename,'r')
def advanceToToken(token,searchdepth=2):
done = False
while not done:
l = f.readline()
if l == '': # eof
break
if l[0:searchdepth] == token:
done = True
log.debug('Found ' + token)
return l
# Get file statistics
l = advanceToToken('define VERTEX',13)
n_vertices = int(l.split()[-1])
l = advanceToToken('define EDGE',11)
n_edges = int(l.split()[-1])
l = advanceToToken('define POINT',12)
n_points = int(l.split()[-1])
log.info("Vertices: " + str(n_vertices))
log.info("Edges: " + str(n_edges))
log.info("Points: " + str(n_points))
log.info("reading and adding vertices")
# Add vertices
advanceToToken('@1')
edgeConnectivity = []
numEdgePoints = []
rDict = {} # relates coordinates to first vertex occurence
vDict = {} # relates later vertex occurences to first one
vCounter = 0
for i in xrange(n_vertices):
l = f.readline()
l = l.split()
rTmp = (float(l[0]),float(l[1]),float(l[2]))
if rDict.has_key(rTmp):
vDict[i] = rDict[rTmp]
else:
rDict[rTmp] = vCounter
vDict[i] = vCounter
vCounter += 1
# Create VascularGraph and compute scaling factor
G = VascularGraph(len(rDict), **kwargs) # len(rDict) <= n_vertices
sf = units.scaling_factor_du(lUnit, G['defaultUnits'])
# sort r by vertex number and scale with 'resolution' and 'sf':
G.vs['r'] = sp.array([x[0] for x in sorted(rDict.items(),
key=itemgetter(1))]) * resolution * sf
# Read connectivity information
log.info("reading connectivity information")
advanceToToken('@2')
for i in xrange(n_edges):
l = f.readline()
l = l.split()
edgeConnectivity.append((vDict[int(l[0])],vDict[int(l[1])]))
# Read number of edge points
log.info("reading number of edge points")
advanceToToken('@3')
for i in xrange(n_edges):
l = f.readline()
numEdgePoints.append(int(l))
# Read edge point coordinates
# Compute length
log.info("reading point coordinates")
advanceToToken('@4')
# Assign a length to each point (i.e.: #lengths == #points):
l_edge = []
edgepoints = []
for edge in xrange(n_edges):
tmppoints = sp.zeros([numEdgePoints[edge],3])
for point in xrange(numEdgePoints[edge]):
l = f.readline()
tmppoints[point] = map(float, l.split())
l_edge.append([ np.linalg.norm(tmppoints[i]-tmppoints[i+1])
for i in xrange(len(tmppoints)-1) ])
l_edge[edge].insert(0, 0.0)
l_edge[edge].extend([0.0])
l_edge[edge] = [(l_edge[edge][i-1] + l_edge[edge][i]) / 2.0
for i in xrange(1,len(l_edge[edge]))]
# iGraph, unlike Amira orders edge vertices by index. Make sure that
# this is reflected in the points that make up the edge:
if edgeConnectivity[edge][0] > edgeConnectivity[edge][1]:
tmppoints = tmppoints[::-1,:]
edgepoints.append(tmppoints)
if sp.mod(edge+1,sp.floor(n_edges/10)) == 0:
log.info(str(10*(edge+1)/sp.floor(n_edges/10)) + "%")
# Read edge radii
log.info("reading radii")
advanceToToken('@5')
d_edge = []
for edge in xrange(n_edges):
tmpdiameters = []
for point in xrange(numEdgePoints[edge]):
l = f.readline()
tmpdiameters.append(float(l)*2.0) # conversion radius to diameter
d_edge.append(tmpdiameters)
f.close()
# Add edges:
log.info("adding edges")
G.add_edges(edgeConnectivity)
d_edge = [sp.array(d_edge[i],dtype=float) * resolution * sf
for i in xrange(n_edges)]
l_edge = [sp.array(l_edge[i],dtype=float) * resolution * sf
for i in xrange(n_edges)]
# Compute average diameter, weighted by length:
# - volume = pi * d**2 /4 * length
# - morphologically representative because of length-weighting
# - individual diameters scale with identical (multiplicative) factors as
# the average diameter
try:
G.es['diameter'] = [np.sqrt(np.average(d_edge[i]**2, weights=l_edge[i]))
for i in xrange(n_edges)]
except:
# Length-weighting is not possible for zero-length edges. These
# pathological edges need to be removed:
length = [sum(l) for l in l_edge]
zeroLength = np.nonzero(np.array(length) == 0.0)[0]
nonzeroLength = np.nonzero(np.array(length) != 0.0)[0]
d_edge = [x for i,x in enumerate(d_edge) if i in nonzeroLength]
l_edge = [x for i,x in enumerate(l_edge) if i in nonzeroLength]
edgepoints = [x for i,x in enumerate(edgepoints) if i in nonzeroLength]
G.delete_edges(zeroLength.tolist())
n_edges = G.ecount()
log.warning('%i zero-length edges were removed. %i edges remaining' % \
(len(zeroLength), n_edges))
G.es['diameter'] = [np.sqrt(np.average(d_edge[i]**2, weights=l_edge[i]))
for i in xrange(n_edges)]
G.es['diameters'] = d_edge
G.es['points'] = [p * resolution * sf for p in edgepoints]
G.es['lengths'] = l_edge
G.es['length'] = [sum(l) for l in l_edge]
return G
def read_amira_spatialGraph_v2(filename=None,
resolution=1.0,
lUnit='um',
**kwargs):
"""Reads an AMIRA spatial graph file (AmiraMesh 3D ASCII 2.0 format) and
constructs the corresponding vascular graph from it. This version accounts
for the fact that some spatialGraph files have colocalized vertices, which
are actually identical (this happens especially after conversion from an
mv3d file).
INPUT: filename: AMIRA spatial graph file (including path). If not
provided, a graphical file-selection dialog will display.
resolution: The resolution of the srXTM scan, i.e. voxel size
(isotropic resolution is assumend). The default is 1.0.
lUnit: The unit of length properties in the spatialGraph (i.e.
radii, and position vectors). The default is microns.
**kwargs:
defaultUnits: The defaultUnits of the VascularGraph created (see
__init__ of the VascularGraph class for details).
OUTPUT: G: Vascular graph in iGraph format.
"""
#if filename is None:
# filename = guiTools.uigetfile('Select Amira spatialGraph (.am) file')
f = open(filename, 'r')
def advanceToToken(token, searchdepth=2):
done = False
while not done:
l = f.readline()
if l == '': # eof
break
if l[0:searchdepth] == token:
done = True
log.debug('Found ' + token)
return l
# Get file statistics
l = advanceToToken('define VERTEX', 13)
n_vertices = int(l.split()[-1])
l = advanceToToken('define EDGE', 11)
n_edges = int(l.split()[-1])
l = advanceToToken('define POINT', 12)
n_points = int(l.split()[-1])
log.info("Vertices: " + str(n_vertices))
log.info("Edges: " + str(n_edges))
log.info("Points: " + str(n_points))
log.info("reading and adding vertices")
# Add vertices
advanceToToken('@1')
edgeConnectivity = []
numEdgePoints = []
rDict = {} # relates coordinates to first vertex occurence
vDict = {} # relates later vertex occurences to first one
vCounter = 0
for i in xrange(n_vertices):
l = f.readline()
l = l.split()
rTmp = (float(l[0]), float(l[1]), float(l[2]))
if rDict.has_key(rTmp):
vDict[i] = rDict[rTmp]
else:
rDict[rTmp] = vCounter
vDict[i] = vCounter
vCounter += 1
# Create VascularGraph and compute scaling factor
G = VascularGraph(len(rDict), **kwargs) # len(rDict) <= n_vertices
sf = units.scaling_factor_du(lUnit, G['defaultUnits'])
# sort r by vertex number and scale with 'resolution' and 'sf':
G.vs['r'] = sp.array(
[x[0]
for x in sorted(rDict.items(), key=itemgetter(1))]) * resolution * sf
# Read connectivity information
log.info("reading connectivity information")
advanceToToken('@2')
for i in xrange(n_edges):
l = f.readline()
l = l.split()
edgeConnectivity.append((vDict[int(l[0])], vDict[int(l[1])]))
# Read number of edge points
log.info("reading number of edge points")
advanceToToken('@3')
for i in xrange(n_edges):
l = f.readline()
numEdgePoints.append(int(l))
# Read edge point coordinates
# Compute length
log.info("reading point coordinates")
advanceToToken('@4')
# Assign a length to each point (i.e.: #lengths == #points):
l_edge = []
edgepoints = []
for edge in xrange(n_edges):
tmppoints = sp.zeros([numEdgePoints[edge], 3])
for point in xrange(numEdgePoints[edge]):
l = f.readline()
tmppoints[point] = map(float, l.split())
l_edge.append([
np.linalg.norm(tmppoints[i] - tmppoints[i + 1])
for i in xrange(len(tmppoints) - 1)
])
l_edge[edge].insert(0, 0.0)
l_edge[edge].extend([0.0])
l_edge[edge] = [(l_edge[edge][i - 1] + l_edge[edge][i]) / 2.0
for i in xrange(1, len(l_edge[edge]))]
# iGraph, unlike Amira orders edge vertices by index. Make sure that
# this is reflected in the points that make up the edge:
if edgeConnectivity[edge][0] > edgeConnectivity[edge][1]:
tmppoints = tmppoints[::-1, :]
edgepoints.append(tmppoints)
if sp.mod(edge + 1, sp.floor(n_edges / 10)) == 0:
log.info(str(10 * (edge + 1) / sp.floor(n_edges / 10)) + "%")
# Read edge radii
log.info("reading radii")
advanceToToken('@5')
d_edge = []
for edge in xrange(n_edges):
tmpdiameters = []
for point in xrange(numEdgePoints[edge]):
l = f.readline()
tmpdiameters.append(float(l) *
2.0) # conversion radius to diameter
d_edge.append(tmpdiameters)
f.close()
# Add edges:
log.info("adding edges")
G.add_edges(edgeConnectivity)
d_edge = [
sp.array(d_edge[i], dtype=float) * resolution * sf
for i in xrange(n_edges)
]
l_edge = [
sp.array(l_edge[i], dtype=float) * resolution * sf
for i in xrange(n_edges)
]
# Compute average diameter, weighted by length:
# - volume = pi * d**2 /4 * length
# - morphologically representative because of length-weighting
# - individual diameters scale with identical (multiplicative) factors as
# the average diameter
try:
G.es['diameter'] = [
np.sqrt(np.average(d_edge[i]**2, weights=l_edge[i]))
for i in xrange(n_edges)
]
except:
# Length-weighting is not possible for zero-length edges. These
# pathological edges need to be removed:
length = [sum(l) for l in l_edge]
zeroLength = np.nonzero(np.array(length) == 0.0)[0]
nonzeroLength = np.nonzero(np.array(length) != 0.0)[0]
d_edge = [x for i, x in enumerate(d_edge) if i in nonzeroLength]
l_edge = [x for i, x in enumerate(l_edge) if i in nonzeroLength]
edgepoints = [
x for i, x in enumerate(edgepoints) if i in nonzeroLength
]
G.delete_edges(zeroLength.tolist())
n_edges = G.ecount()
log.warning('%i zero-length edges were removed. %i edges remaining' % \
(len(zeroLength), n_edges))
G.es['diameter'] = [
np.sqrt(np.average(d_edge[i]**2, weights=l_edge[i]))
for i in xrange(n_edges)
]
G.es['diameters'] = d_edge
G.es['points'] = [p * resolution * sf for p in edgepoints]
G.es['lengths'] = l_edge
G.es['length'] = [sum(l) for l in l_edge]
return G