<br><div class="gmail_quote">On Fri, Nov 4, 2011 at 1:20 PM, Benjamin Root <span dir="ltr"><<a href="mailto:ben.root@ou.edu">ben.root@ou.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">
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)?<br>
<br>Thanks,<br>Ben Root<br>
</blockquote></div><br>Nevermind, I should have realized the difficulty in coordinating the various divisions when dealing with multiple dimensions.<br><br>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?<br>
<br>Thanks,<br>Ben Root<br>