def testprofiling(ID):
tree = Display.getFront().getLayerSet().findById(ID)
coords = Matrix(getNodeCoordinates(tree))
#Define axis vectors and origin
xnorm = Matrix([[1,0,0]])
ynorm = Matrix([[0,1,0]])
znorm = Matrix([[0,0,1]])
center= Matrix([[0,0,0]])
#Project nodes onto axis
dpx=[]
dpy=[]
dpz=[]
m=coords.getRowDimension()
for i in range(0, m):
xstore = Matrix([coords.getRow( i )]) * xnorm.transpose()
ystore = Matrix([coords.getRow( i )]) * ynorm.transpose()
zstore = Matrix([coords.getRow( i )]) * znorm.transpose()
dpx.append(xstore.getRow(0))
dpy.append(ystore.getRow(0))
dpz.append(zstore.getRow(0))
#get it back in array form (get rid of list in list of Jarray)
dpx=[ x[0] for x in dpx]
dpy=[ x[0] for x in dpy]
dpz=[ x[0] for x in dpz]
zipped=zip(dpx, dpy, dpz)
return zipped
def profileDistances(ID, biniter):
histo=getDendriticProfiles(ID, biniter)
xhisto=histo[0]
yhisto=histo[1]
zhisto=histo[2]
xdistance=[]
ydistance=[]
zdistance=[]
tree = Display.getFront().getLayerSet().findById(ID)
coords = Matrix(getNodeCoordinates(tree))
m=coords.getRowDimension()
for i in range(0, m):
xdist = coords.get(i, 1 )
if xdist > 0 :
xdistance.append(xdist)
else:
xdistance.append((-1)*xdist)
for i in range(0, m):
ydist = coords.get(i, 0 )
if ydist > 0 :
ydistance.append(ydist)
else:
ydistance.append((-1)*ydist)
for i in range(0, m):
zdist = sqrt(coords.get(i, 0)**2 + coords.get(i,1)**2)
zdistance.append(zdist)
return xdistance, ydistance, zdistance
def pca(ID, biniter):
tree = Display.getFront().getLayerSet().findById(ID)
#Calculate the center of mass
center = Matrix( [[0,0,0]] )
coords = Matrix(getNodeCoordinates(tree))
m=coords.getRowDimension()
for i in range(0, coords.getRowDimension()):
center += Matrix([coords.getRow( i )])
center /= float(m)
#print center
"""
x, y, z = 0, 0, 0
nc = getNodeCoordinates(tree)
for c in nc:
x += c[0]
y += c[1]
z += c[2]
print "center:", x/len(nc), y/len(nc), z/len(nc)
"""
#Define covvariance matrix
cova = Matrix([[0,0,0],[0,0,0],[0,0,0]])
diff= Matrix ( [[0,0,0]])
for i in range(0, m):
diff = Matrix([coords.getRow( i )]) - center
cova += diff.transpose() * diff
cova /= float(m)
#Evaluedecomposition
evaluedecomp = cova.eig()
evalues = evaluedecomp.getRealEigenvalues()
evectors = evaluedecomp.getV() # is a Matrix instance
#Find maximum Eigenvector for maximal Eigenvalue
maxevaluepos = -1
maxevalue = 0
for i in range(0, len(evalues)):
if evalues[i] > maxevalue or maxevaluepos==-1:
maxevalue=evalues[i]
maxevaluepos=i
maxevector=evectors.getColumn(maxevaluepos) #HAVE TO GET VALUES OF COLUMN of evector matrix i NOT ROW
#--------------------TEST if Eigenvaluedecomposition is correct-----------
#vectormatrix=Matrix([evectors.getColumn(maxevaluepos)])
#test=cova * vectormatrix.transpose()
#test2=vectormatrix*maxevalue
#print test
#print test2
#-------------------------------------------------------------------------
#Define a vector over the point cloud and count points in defined interval
#Normalize vector
length=sqrt(maxevector[0]**2 + maxevector[1]**2 + maxevector[2]**2)
normvector = map(lambda x: x / length, maxevector)
normvectormatrix = Matrix([normvector])
pca=[]
m=coords.getRowDimension()
for i in range(0, m):
pcastore = (Matrix([coords.getRow( i )]) - center ) * normvectormatrix.transpose()
pca.append(pcastore.getRow(0))
#Count number of nodes which fall in defined interval of pca projection -> has to be fixed globally when to compare PCAs!
counter = 0
histovector = []
pca=[ x[0] for x in pca] #get it back in array form (get rid of list in list)
binleft = -12500 #binleft = min(pca)
iterations = biniter
length = int(25000/iterations + 0.5) #int((max(pca)-min(pca))/iterations + 0.5)
binright = binleft + length
for i in range(0,iterations):
for i in range(0, len(pca)):
if pca[i] <= binright and pca[i] >= binleft:
counter += 1
histovector.append(counter)
counter=0
binleft+=length
binright+=length
#print "The histogram vector is:", histovector
return histovector
def getDendriticProfiles(ID, biniter):
tree = Display.getFront().getLayerSet().findById(ID)
coords = Matrix(getNodeCoordinates(tree))
# Define axis vectors and origin
xnorm = Matrix([[1, 0, 0]])
ynorm = Matrix([[0, 1, 0]])
znorm = Matrix([[0, 0, 1]])
center = Matrix([[0, 0, 0]])
# Project nodes onto axis
dpx = []
dpy = []
dpz = []
m = coords.getRowDimension()
for i in range(0, m):
xstore = Matrix([coords.getRow(i)]) * xnorm.transpose()
ystore = Matrix([coords.getRow(i)]) * ynorm.transpose()
zstore = Matrix([coords.getRow(i)]) * znorm.transpose()
dpx.append(xstore.getRow(0))
dpy.append(ystore.getRow(0))
dpz.append(zstore.getRow(0))
# Count number of nodes which fall in defined interval of pca projection
xhistovector = []
yhistovector = []
zhistovector = []
# get it back in array form (get rid of list in list of Jarray)
dpx = [x[0] for x in dpx]
dpy = [x[0] for x in dpy]
dpz = [x[0] for x in dpz]
# Initialize interval size and fix iterations to a specific number
xbinleft = -34600.0
ybinleft = -24200.0
zbinleft = 0
# --------PARAMETER FOR MACHINE LEARNING-----
iterations = biniter
# -------------------------------------------
xlength = int((35600.0 + 34600.0) / iterations + 0.5)
ylength = int((21300.0 + 24200.0) / iterations + 0.5)
zlength = int((22500) / iterations + 0.5)
xbinright = xbinleft + xlength
ybinright = ybinleft + ylength
zbinright = zbinleft + zlength
# Count elements in bins
xaxis = []
# Count lengths in bins
xdist = []
ydist = []
zdist = []
for i in range(0, iterations):
counter = 0
xdiff = 0
for i in range(0, len(dpx)):
if dpx[i] <= xbinright and dpx[i] >= xbinleft:
counter += 1
xdiff += sqrt(dpy[i] ** 2 + dpz[i] ** 2) # sqrt(dpy[i]**2) xdist is yvalue
if counter != 0:
xdiff /= counter
xdist.append(xdiff)
xhistovector.append(counter)
xaxis.append(xbinleft)
xbinleft += xlength
xbinright += xlength
yaxis = []
for i in range(0, iterations):
counter = 0
ydiff = 0
for i in range(0, len(dpy)):
if dpy[i] <= ybinright and dpy[i] >= ybinleft:
counter += 1
ydiff += sqrt(dpx[i] ** 2 + dpz[i] ** 2)
if counter != 0:
ydiff /= counter
ydist.append(ydiff)
yhistovector.append(counter)
yaxis.append(ybinleft)
ybinleft += ylength
ybinright += ylength
zaxis = []
for i in range(0, iterations):
counter = 0
zdiff = 0
for i in range(0, len(dpz)):
if dpz[i] <= zbinright and dpz[i] >= zbinleft:
counter += 1
zdiff += sqrt(dpx[i] ** 2 + dpy[i] ** 2)
if counter != 0:
zdiff /= counter
zdist.append(zdiff)
zhistovector.append(counter)
zaxis.append(zbinleft)
zbinleft += zlength
zbinright += zlength
# print xdist
# print ydist
# print zdist
"""
#Normalize
for i in range(0, iterations):
xhistovector[i]/=float(xlength)
yhistovector[i]/=float(ylength)
zhistovector[i]/=float(zlength)
"""
return xhistovector, yhistovector, zhistovector, xdist, ydist, zdist
def getDendriticProfiles(ID):
tree = Display.getFront().getLayerSet().findById(ID)
coords = Matrix(getNodeCoordinates(tree))
#Define axis vectors and origin
xnorm = Matrix([[1,0,0]])
ynorm = Matrix([[0,1,0]])
znorm = Matrix([[0,0,1]])
center= Matrix([[0,0,0]])
#Project nodes onto axis
dpx=[]
dpy=[]
dpz=[]
m=coords.getRowDimension()
for i in range(0, m):
xstore = Matrix([coords.getRow( i )]) * xnorm.transpose()
ystore = Matrix([coords.getRow( i )]) * ynorm.transpose()
zstore = Matrix([coords.getRow( i )]) * znorm.transpose()
dpx.append(xstore.getRow(0))
dpy.append(ystore.getRow(0))
dpz.append(zstore.getRow(0))
#Count number of nodes which fall in defined interval of pca projection
counter = 0
xhistovector = []
yhistovector = []
zhistovector = []
#get it back in array form (get rid of list in list of Jarray)
dpx=[ x[0] for x in dpx]
dpy=[ x[0] for x in dpy]
dpz=[ x[0] for x in dpz]
#Initialize interval size and fix iterations to a specific number
xbinleft = min(dpx)
ybinleft = min(dpy)
zbinleft = min(dpz)
#-------PARAMETER FOR MACHINE LEARNING--------------
iterations =100
#---------------------------------------------------
xlength = int((max(dpx)-min(dpx))/iterations + 0.5)
ylength = int((max(dpy)-min(dpy))/iterations + 0.5)
zlength = int((max(dpz)-min(dpz))/iterations + 0.5)
xbinright = xbinleft + xlength
ybinright = ybinleft + ylength
zbinright = zbinleft + zlength
#Count elements in bins
xaxis=[]
for i in range(0,iterations):
for i in range(0, len(dpx)):
if dpx[i] <= xbinright and dpx[i] >= xbinleft:
counter += 1
xhistovector.append(counter)
counter=0
xbinleft += xlength
xbinright += xlength
xaxis.append(xbinleft)
yaxis=[]
for i in range(0,iterations):
for i in range(0, len(dpy)):
if dpy[i] <= ybinright and dpy[i] >= ybinleft:
counter += 1
yhistovector.append(ylength)
counter=0
ybinleft += ylength
ybinright += ylength
yaxis.append(ybinleft)
zaxis=[]
for i in range(0,iterations):
for i in range(0, len(dpz)):
if dpz[i] <= zbinright and dpz[i] >= zbinleft:
counter += 1
zhistovector.append(counter)
counter=0
zbinleft += zlength
zbinright += zlength
zaxis.append(zbinleft)
#Normalize bin-count by division through length
for i in range(0, iterations):
xhistovector[i]/=float(xlength)
yhistovector[i]/=float(ylength)
zhistovector[i]/=float(zlength)
return xhistovector, yhistovector, zhistovector
# data may be a radius or a java.awt.geom.Area
coords.append( correct([x, y, z]) )
#print coords[len(coords)-1]
return coords
#---------Select tree-----------------
#tree = Display.getFront().getActive()
ID = 74329
tree = Display.getFront().getLayerSet().findById(ID)
#-------------------------------------
#Calculate the center of mass
center = Matrix( [[0,0,0]] )
coords = Matrix(getNodeCoordinates(tree))
m=coords.getRowDimension()
for i in range(0, coords.getRowDimension()):
center += Matrix([coords.getRow( i )])
center /= float(m)
print center
"""
x, y, z = 0, 0, 0
nc = getNodeCoordinates(tree)
for c in nc:
x += c[0]
y += c[1]
z += c[2]
print "center:", x/len(nc), y/len(nc), z/len(nc)
ID=75408 tree = Display.getFront().getLayerSet().findById(ID) coords = Matrix(getNodeCoordinates(tree)) #Define axis vectors and origin xnorm = Matrix([[1,0,0]]) ynorm = Matrix([[0,1,0]]) znorm = Matrix([[0,0,1]]) center= Matrix([[0,0,0]]) #Project nodes onto axis dpx=[] dpy=[] dpz=[] m=coords.getRowDimension() for i in range(0, m): xstore = Matrix([coords.getRow( i )]) * xnorm.transpose() ystore = Matrix([coords.getRow( i )]) * ynorm.transpose() zstore = Matrix([coords.getRow( i )]) * znorm.transpose() dpx.append(xstore.getRow(0)) dpy.append(ystore.getRow(0)) dpz.append(zstore.getRow(0)) #Count number of nodes which fall in defined interval of pca projection counter = 0 xhistovector = [] yhistovector = [] zhistovector = [] #get it back in array form (get rid of list in list of Jarray)
def sphereCount(ID, biniter):
#find center of mass
tree = Display.getFront().getLayerSet().findById(ID)
coords = Matrix(getNodeCoordinates(tree))
center = Matrix( [[0,0,0]] )
m=coords.getRowDimension()
for i in range(0, m):
center += Matrix([coords.getRow( i )])
center /= float(m)
#calculate all distances of all points to center of mass and put them in a list
xdist=[]
ydist=[]
zdist=[]
for i in range(0, m):
xdiff=[]
if coords.get(i,0) > center.get(0,0):
xdiff = coords.get(i,0) - center.get(0,0)
else:
xdiff = center.get(0,0) - coords.get(i,0)
xdist.append(xdiff)
for i in range(0, m):
ydiff=[]
if coords.get(i,1) > center.get(0,1):
ydiff = coords.get(i,1) - center.get(0,1)
else:
ydiff = center.get(0,1) - coords.get(i,1)
ydist.append(ydiff)
for i in range(0, m):
zdiff=[]
if coords.get(i,2) > center.get(0,2):
zdiff = coords.get(i,2) - center.get(0,2)
else:
zdiff = center.get(0,2) - coords.get(i,2)
zdist.append(zdiff)
nodeDist=[]
dist=[]
for i in range(0, m):
dist=sqrt(xdist[i]**2 + ydist[i]**2 + zdist[i]**2)
nodeDist.append(dist)
iterations=biniter
Ri=0 #inner radius
"""
The maximum distance can only be achieved in the xy-plane
With "fixed" coordinates this will approx. be 35000nm
To make the scholl-profiles comparable this length has to be used.
"""
scholl=[]
xaxis=[]
counter=0
length= int(35000 / biniter + 0.5)
Ra=length #outer radius
for i in range(0, biniter):
for i in range(0, len(nodeDist)):
if nodeDist[i] <= Ra and nodeDist[i] >= Ri:
counter += 1
scholl.append(counter)
counter=0
xaxis.append(Ri) # adapt THIS position in all other scripts! Otherwise 1 datapoint is missing!!
Ri += length
Ra += length
return scholl, xaxis
def sumDist(id1, biniter):
distance=profileDistances(id1, biniter)
xdistance=distance[0]
ydistance=distance[1]
zdistance=distance[2]
tree = Display.getFront().getLayerSet().findById(id1)
coords = Matrix(getNodeCoordinates(tree))
m=coords.getRowDimension()
xdist=[]
ydist=[]
zdist=[]
#Initialize interval size and fix iterations to a specific coordinate position
xbinleft = -34600.0
ybinleft = -24200.0
zbinleft = 0
#----------PARAMETER FOR MACHINE LEARNING-----
iterations =biniter
#---------------------------------------------
xlength = int((35600.0+34600.0)/iterations + 0.5)
ylength = int((21300.0+24200.0)/iterations + 0.5)
zlength = int((22500)/iterations + 0.5)
xbinright = xbinleft + xlength
ybinright = ybinleft + ylength
zbinright = zbinleft + zlength
sumdist=0.0
for i in range(0, iterations):
for i in range(0, len(xdistance)):
if xdistance[i] <= xbinright and xdistance[i] >= xbinleft:
sumdist += xdistance[i]
xdist.append(sumdist)
sumdist=0.0
xbinleft += xlength
xbinright += xlength
for i in range(0, iterations):
for i in range(0, len(ydistance)):
if ydistance[i] <= ybinright and ydistance[i] >= ybinleft:
sumdist += ydistance[i]
ydist.append(sumdist)
sumdist=0.0
ybinleft += ylength
ybinright += ylength
for i in range(0, iterations):
for i in range(0, len(zdistance)):
if zdistance[i] <= zbinright and zdistance[i] >= zbinleft:
sumdist += zdistance[i]
zdist.append(sumdist)
sumdist=0.0
zbinleft += zlength
zbinright += zlength
sumdistance=[]
sumdistance= [xdist, ydist, zdist]
return sumdistance