Ok... I'm using Traits and numpy.
I have a 3D grid with directions I,J and K.
I have NI,NJ,NK cells in the I,J,K directions so I have NI*NJ*NK cells
overall.
I have data arrays with a value for each cell in the grid.
I'm going to store this as a 1D array, i.e. 1....ncells where
ncells=NI*NJ*NK rather than as a 3D array
Apart from lots of other data arrays that will be read in from external
files, I want to create I, J and K data arrays
where the 'I' array contains the I index for the cell, the 'J' array the
J index etc.
I haven't used numpy extensively and I'm coming from a Fortran/C
background so I'm hideously inefficient.
At the moment I have something like....
self.iarray=zeros(self.ncells,dtype=int)
for icell in arange(1,self.ncells+2):
# icell=i+(j-1)*ni+(k-1)*ni*nj
i,j,k=rn2ijk(icell,self.ni,self.nj)
self.yarray[icell]=i
rn2ijk is defined as...
def rn2ijk(n,ni,nj):
nij=ni*nj
k=(n+nij-1)/nij
ij=n-(k-1)*nij
j=(ij+ni-1)/ni
i=ij-(j-1)*ni
return i,j,k
Obviously I can improve my use of rn2ijk as for a start I'm
recalculating nij for every cell (and I can have anything from a few
thousand to a few million cells).
But I'm sure I could do this more efficiently by probably starting off
with a 3d array, looping over i,j,k and then reshaping it into a 1d array.
Ideas?
Brennan
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