Hi group, I'm trying to generate the diffusion pattern of a bivariate_normal. However, so far a main obstacle is to form the distribution on top of a 2D mesh. The bivariate_normal function from matplotlib.mlab takes axial arguments that are spit out by numpy.meshgrid. But it's clear that the underlying mesh construction of meshgrid is different from Grid2D from fipy:
sample meshgrid config: N = 3 > x = np.linspace(-1.0, 1.0, N) > y = np.linspace(-1.0, 1.0, N) > X, Y = np.meshgrid(x, y) > > z = bivariate_normal(X, Y, 1., 1., 0., 0.) # matplotlib.mlab. > bivariate_normal(*X*, *Y*, *sigmax=1.0*, *sigmay=1.0*, *mux=0.0*, *muy=0.0 > *, *sigmaxy=0.0*) Is there a way to bridge the two packages or perhaps there's an alternative to generating 2D distributions under Fipy (maybe manually)? Thanks, Yun -- Graduate Group of Ecology Doctoral Candidate Department of Environmental Science and Policy Center for Population Biology University of California, Davis
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