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