[Numpy-discussion] what is the point of dx for np.gradient()?
Sat Nov 5 11:37:06 CDT 2011
On Fri, Nov 4, 2011 at 1:20 PM, Benjamin Root <email@example.com> 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)?
> 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?
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