Hi all,
disclaimer: pardon my vast ignorance on the subject of ufuncs, that
will explain the naivety of the following questions
This morning I was looking at this line of code, which was running
quite slow for me and making me think
data_has_nan = numpy.isnan(data_array).any()
I knew that the a
On Thu, Apr 1, 2010 at 11:53 AM, Warren Weckesser
wrote:
> M Trumpis wrote:
>> Hi all,
>>
>>
>>
>> And a last mini question, it doesn't appear that any() is doing short
>> circuit evaluation. It runs in appx the same time whether an array is
>&
On Wed, Jul 21, 2010 at 10:08 AM, Keith Goodman wrote:
> On Wed, Jul 21, 2010 at 9:56 AM, John Salvatier
> wrote:
>> I don't really know much about this topic, but what about a flag at array
>> creation time (or whenever you define labels) that says whether valid
>> indexes will be treated as lab
Just a heads up that I'm going to do a quick pitch for datarray this
Wed. at the NYC Python Meetup thing.
If any collaborator is attending, please introduce yourself!
Mike
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On Mon, Oct 18, 2010 at 5:18 PM, Fernando Perez wrote:
> On Mon, Oct 18, 2010 at 11:21 AM, M Trumpis wrote:
>> Just a heads up that I'm going to do a quick pitch for datarray this
>> Wed. at the NYC Python Meetup thing.
>>
>> If any collaborator is atte
I played around with a C translation of that test program, and found
that dgesvd (but not dgesdd) happens to converge and return all
non-negative singular values for both operators I was having trouble
with. I'm also looking at the Octave code just now, and I think
they're using dgesvd also. Any on
I ran into this problem as well a few months back.
The reason for the empty residual array when M==N is that the LAPACK
routine for Ax = b puts the solution for x in b. When M>N, the
norm-squared is parceled out into the unused (M-N) points in the b
array. When M==N, there's no room for the resids
Hi Nadav.. if you want a lower resolution 2d function with the same
field of view (or whatever term is appropriate to your case), then in
principle you can truncate your higher frequencies and do this:
sig = ifft2_func(sig[N/2 - M/2:N/2 + M/2, N/2 - M/2:N/2+M/2])
I like to use an fft that transfo
