Hi Andrew
We should discuss different options for the implementation. The
namespace is fairly cluttered, and it may be that we want to implement
gradient3 some time in the future as well. Maybe something like
gradient(f, 1, 2, 3, order=2)
would work -- then we can combine gradient and
Hi,
I looked at this ticket and whipped up two alternative patches, like mentioned
in
the description: in-memory or temporary dir on disk. On my computer
the second one is slightly faster.
I think it is important to merge some version of this fix. In it's current form,
someone using
I agree that the gradient functions should be combined, especially considering
how much redundant code would be added by keeping them separate. Here is one
possible implementation, but I don't like the signature yet as it departs from
the current behaviour. At the risk of demonstrating my
On Tue, Oct 28, 2008 at 16:28, Andrew Hawryluk [EMAIL PROTECTED] wrote:
I agree that the gradient functions should be combined, especially
considering how much redundant code would be added by keeping them separate.
Here is one possible implementation, but I don't like the signature yet as it
Le mardi 28 octobre 2008 à 15:28 -0600, Andrew Hawryluk a écrit :
I agree that the gradient functions should be combined, especially
considering how much redundant code would be added by keeping them
separate. Here is one possible implementation, but I don't like the
signature yet as it
On 28-Oct-08, at 5:57 PM, Fabrice Silva wrote:
Are there some parts of the code that may be used only once to
calculate
both the gradient and the second derivative (isn't it called the
hessian, at least in the N-d case) ?
Probably. I'd imagine depends on your differencing scheme; central
Hi numpy group,
I have a problem I know there is an elegant solution to, but I can't
wrap my head around the right way to do the indexing.
The problem:
I have a 2D array that has been chopped up into 3 dimensions - it was [ time
X detectors ], it is now [ scans X time X detectors ]. During
