Oops, don't know how I missed that. Guess bivariate_normal()'s more flexible than I thought. Thanks!
On Mon, May 14, 2012 at 6:37 AM, Jonathan Guyer <[email protected]> wrote: > On May 11, 2012, at 6:01 PM, Yun Tao wrote: > > > I've now successfully simulated a diffusing bivariate normal (see > attachment), thanks to your help. For those who're curious, the crucial > thing to do is to first reshape the axial variables, feed it through > bivariate normal, and finally flatten the result for CellVariable. To > illustrate: > > > > x, y = mesh.getCellCenters() > > > > xr = np.reshape(x, (nx, nx)) > > yr = np.reshape(y, (ny, ny)) > > > > zf = bivariate_normal(xr, yr, 1., 1., 0., 0.) > > > > zflat = zf.flatten() > > phi = CellVariable(mesh=mesh, value=zflat) > > > I don't believe you need to reshape and flatten. bivariate_normal() > returns the same shape that you pass in, so all you need to do is > > x, y = mesh.getCellCenters() > > z = bivariate_normal(x, y, 1., 1., 0., 0.) > > phi = CellVariable(mesh=mesh, value=z) > > > _______________________________________________ > fipy mailing list > [email protected] > http://www.ctcms.nist.gov/fipy > [ NIST internal ONLY: https://email.nist.gov/mailman/listinfo/fipy ] > -- Graduate Group of Ecology Doctoral Candidate Department of Environmental Science and Policy Center for Population Biology University of California, Davis
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