[Numpy-discussion] Gaussian Quadrature routine Numpy-ization :)

[Rob]

I finally got it to work, but the Numpy-ized version runs slower than
the plain Python one.  I think that I can transpose the NodeCord matrix
once in the program and feed that in, rather than the scratch matrix
that is generated here.  Evidently transpose is not very fast even for
smal matrices. Here is my test program:


from Numeric import *



Qpnt=array(([20,21,22],[23,24,25],[26,27,28]))
NodeCord=array(([1,2,3],[4,5,6],[7,8,9]))
TrngleNode=array((1,2,0))


#the original routine
def ComputeGaussQuadPoint(QuadPoint,TrngleNode,Qpnt,NodeCord):
    
    SrcPointCol=zeros((3))
    
    
    SrcPointCol[0] =   Qpnt[QuadPoint,0]*NodeCord[TrngleNode[0],0]\
                     + Qpnt[QuadPoint,1]*NodeCord[TrngleNode[1],0]\
                     + Qpnt[QuadPoint,2]*NodeCord[TrngleNode[2],0]

    SrcPointCol[1] =   Qpnt[QuadPoint,0]*NodeCord[TrngleNode[0],1]\
                     + Qpnt[QuadPoint,1]*NodeCord[TrngleNode[1],1]\
                     + Qpnt[QuadPoint,2]*NodeCord[TrngleNode[2],1]

    SrcPointCol[2] =   Qpnt[QuadPoint,0]*NodeCord[TrngleNode[0],2]\
                     + Qpnt[QuadPoint,1]*NodeCord[TrngleNode[1],2]\
                     + Qpnt[QuadPoint,2]*NodeCord[TrngleNode[2],2]

    return SrcPointCol


#the yet-to-be-faster routine

def Quad(QuadPoint, TrngleNode, Qpnt,NodeCord):
    s = Qpnt[QuadPoint,:]
    c= take(NodeCord, TrngleNode)
    SrcPointCol= add.reduce(s *
transpose(c),1)                               

    return SrcPointCol
  
QuadPoint=1

print "The Correct:"
print ComputeGaussQuadPoint(QuadPoint,TrngleNode,Qpnt,NodeCord)

print "The New"
print  Quad(QuadPoint,TrngleNode,Qpnt,NodeCord)





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