[Numpy-discussion] what is the point of dx for np.gradient()?
Fri Nov 4 13:20:49 CDT 2011
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)?
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