def pca3d(pointList):
'''sets up the pca for a list of points in 3d. solves eig problem'''
matrixList = [[0,0,0],[0,0,0],[0,0,0]] #init to 0
avgPt = geometry_basic.getAverage(pointList)
diffs = [0,0,0]
for point in pointList:
for index in range(3):
diffs[index] = point[index] - avgPt[index]
#matrix is old plus a2 ab ac
# ba b2 bc
# ca cb c2 only compute upper diagonal now
matrixList[0][0] += diffs[0]*diffs[0] #just hardcode it
matrixList[0][1] += diffs[0]*diffs[1]
matrixList[0][2] += diffs[0]*diffs[2]
matrixList[1][1] += diffs[1]*diffs[1]
matrixList[1][2] += diffs[1]*diffs[2]
matrixList[2][2] += diffs[2]*diffs[2]
#make symmetric
matrixList[1][0] = matrixList[0][1]
matrixList[2][0] = matrixList[0][2]
matrixList[2][1] = matrixList[1][2]
actualMatrix = Matrix(matrixList)
val,vec = eigenvectors(actualMatrix)
return val, vec
def _findAdditionalMatchSpheres(self, numSpheres=5, cutoff=2.5):
'''for each cluster, find a couple matching spheres for distant atoms
that are relatively localized in space.
data ends up in dict self.clusterSpheres.
numSpheres is the max# of spheres to add for each cluster. will not always
find as many as requested.
cutoff is used as a cutoff to decide
whether or not to add a sphere for that atom, mean pairwise dist?'''
atomDists = self.mol2data.distFromAtoms(self.rigidComponent) #useful
possibleAtoms = set(self.heavyAtomNums) #only heavy can be matching
possibleAtoms.difference_update(self.rigidComponent) #no need to repeat
possAtomDist = [] #useful for sorting by distance
for possibleAtom in possibleAtoms:
possAtomDist.append((possibleAtom, atomDists[possibleAtom]))
possAtomDist.sort(key=operator.itemgetter(1), reverse=True)
#use possAtomDist for each cluster now to find the best candidates
self.clusterSpheres = {} #indexed by clusterIndex just like self.clusters
for clusterIndex in self.clusters.keys():
cluster = self.clusters[clusterIndex] #cluster is a tuple of conformations
#print "cluster", cluster #debugging
self.clusterSpheres[clusterIndex] = []
for possibleAtom, atomDist in possAtomDist:
xyzPositions = self.mol2data.getXyzManyConfs(cluster, possibleAtom)
okayToAdd = False
if 1 == len(xyzPositions): #singleton cluster, definitely okay
okayToAdd = True
else:
longDist, meanDist = geometry_basic.longestAndMeanDist(xyzPositions)
if meanDist <= cutoff: #passes cutoff
okayToAdd = True
if okayToAdd: #either singleton or passes cutoff
avgPoint = geometry_basic.getAverage(xyzPositions)
self.clusterSpheres[clusterIndex].append((possibleAtom, avgPoint))
#print possibleAtom #debugging
if len(self.clusterSpheres[clusterIndex]) == numSpheres: #done
break #out of for loop, no need to go on
