On Fri, Nov 4, 2011 at 1:20 PM, Benjamin Root <ben.r...@ou.edu> wrote:
> For np.gradient(), one can specify a sample distance for each axis to > apply to the gradient. But, all this does is just divides the gradient by > the sample distance. I could easily do that myself with the output from > gradient. Wouldn't it be more valuable to be able to specify the width of > the central difference (or is there another function that does that)? > > Thanks, > Ben Root > Nevermind, I should have realized the difficulty in coordinating the various divisions when dealing with multiple dimensions. My other question remains, though. Is there a function somewhere that allows me to perform central differences of varying widths. Preferably something that works with masks? Thanks, Ben Root
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