On Sun, Aug 30, 2015 at 2:44 PM, David Cournapeau wrote:
> Hi there,
>
> Reading Nathaniel summary from the numpy dev meeting, it looks like there is
> a consensus on using cython in numpy for the Python-C interfaces.
>
> This has been on my radar for a long time: that was one of my rationale for
On Sun, Aug 30, 2015 at 9:12 PM, Marten van Kerkwijk
wrote:
> Hi Nathaniel, others,
>
> I read the discussion of plans with interest. One item that struck me is
> that while there are great plans to have a proper extensible and presumably
> subclassable dtype, it is discouraged to subclass ndarray
Hi,
I cannot see how the following would work when it is np.fft.fft() that takes a
long time based on the length of data. In my case my data is non-periodic.>
from numpy.fft import fft> from numpy.random import rand> from math import log,
ceil> seq_A = rand(2649674)> seq_B = rand(2646070)> fft
On Tue, Sep 1, 2015 at 8:16 AM, Nathaniel Smith wrote:
> On Sun, Aug 30, 2015 at 2:44 PM, David Cournapeau
> wrote:
> > Hi there,
> >
> > Reading Nathaniel summary from the numpy dev meeting, it looks like
> there is
> > a consensus on using cython in numpy for the Python-C interfaces.
> >
> > T
On 1 September 2015 at 11:38, Joseph Codadeen wrote:
>
>> And while you zero-pad, you can zero-pad to a sequence that is a power of
>> two, thus preventing awkward factorizations.
>
> Does numpy have an easy way to do this, i.e. for a given number, find the
> next highest number (within a range) t
Ah, looking back I see what Pierre-André did, the penny drops. Sorry I
miss-read.I'm only interested in this part;>zeropadded_fft_A = fft(seq_A,
n=2**(ceil(log(len(seq_A),2))+1))
>zeropadded_fft_B = fft(seq_B, n=2**(ceil(log(len(seq_B),2))+1))>You could
>remove the "+1" above to get faste
On 09/01/2015 11:14 AM, Oscar Benjamin wrote:
> Just use the next power of 2. Pure powers of 2 are the most efficient
> for FFT algorithms so it potentially works out better than finding a
> smaller but similarly composite size to pad to. Finding the next power
> of 2 is easy to code and never a ba
On Tue, 1 Sep 2015 18:43 Phil Hodge wrote:
On 09/01/2015 11:14 AM, Oscar Benjamin wrote:
> Just use the next power of 2. Pure powers of 2 are the most efficient
> for FFT algorithms so it potentially works out better than finding a
> smaller but similarly composite size to pad to. Finding the nex
On Tue, Sep 1, 2015 at 12:06 PM, Oscar Benjamin
wrote:
>
> On Tue, 1 Sep 2015 18:43 Phil Hodge wrote:
>
> On 09/01/2015 11:14 AM, Oscar Benjamin wrote:
> > Just use the next power of 2. Pure powers of 2 are the most efficient
> > for FFT algorithms so it potentially works out better than finding
> Googling for a way to print UTC out of the box, the best thing I could
> find is:
>
> In [40]: [str(i.item()) for i in np.array([t], dtype="datetime64[s]")]
> Out[40]: ['2015-08-26 11:52:10']
>
> Now, is there a better way to specify that I want the datetimes printed
> always in UTC?
>
maybe, bu
On Tue, Sep 01, 2015 at 05:28:56PM -0700, Chris Barker wrote:
> > Googling for a way to print UTC out of the box, the best thing I could
> > find is:
> >
> > In [40]: [str(i.item()) for i in np.array([t], dtype="datetime64[s]")]
> > Out[40]: ['2015-08-26 11:52:10']
> >
> > Now, is there a better wa
On Mon, Aug 31, 2015 at 11:49 PM, Nathaniel Smith wrote:
> On Sun, Aug 30, 2015 at 9:09 PM, Jaime Fernández del Río <
> jaime.f...@gmail.com> wrote:
>
>>
>> There are three ways of fixing this that I see:
>>
>>1. Arbitrarily choose a value to set the return to. This is
>>equivalent to cho
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