Hi Andrew,
I have no clue what future versions of Python will do, but on my setup
there is no optimization done on this (I have taken arrays as float32, just
to show the differences better):
In [19]: N = 10
In [20]: x0 = numpy.random.uniform(size=(N, )).astype(numpy.float32)
In [21]: h = numpy.ra
On Mon, 25 Mar 2024 at 20:09, Charles R Harris
wrote:
>
>
> On Mon, Mar 25, 2024 at 11:28 AM Luca Bertolotti <
> luca72.bertolo...@gmail.com> wrote:
>
>> Hello
>> in a vb program they use 3rd degree approx and get this value including
>> displacement:(SC)
>> [image: image.png]
>>
>> Ii think that
Hi,
On Tue, 23 Feb 2021 at 19.11, Neal Becker wrote:
> I have code that performs dot product of a 2D matrix of size (on the
> order of) [1000,16] with a vector of size [1000]. The matrix is
> float64 and the vector is complex128. I was using numpy.dot but it
> turned out to be a bottleneck.
>
Hi,
On Mon, 12 Oct 2020 at 16.22, Hongyi Zhao wrote:
> On Mon, Oct 12, 2020 at 9:33 PM Andrea Gavana
> wrote:
> >
> > Hi,
> >
> > On Mon, 12 Oct 2020 at 14:38, Hongyi Zhao wrote:
> >>
> >> On Sun, Oct 11, 2020 at 3:42 PM Evgeni Burovski
> &g
ifting your
implementation to C/Cython or Fortran/f2py. I had much better results
myself using Fortran/f2py than pure NumPy or C/Cython, but this is mostly
because my knowledge of Cython is quite limited. That said, your problem
should be fairly easy to implement in a compiled language.
Andrea.
>
On Sun, 11 Oct 2020 at 07.52, Hongyi Zhao wrote:
> On Sun, Oct 11, 2020 at 1:33 PM Andrea Gavana
> wrote:
> >
> >
> >
> > On Sun, 11 Oct 2020 at 07.14, Andrea Gavana
> wrote:
> >>
> >> Hi,
> >>
> >> On Sun, 11 Oct 2020 at 00.
On Sun, 11 Oct 2020 at 07.14, Andrea Gavana wrote:
> Hi,
>
> On Sun, 11 Oct 2020 at 00.27, Hongyi Zhao wrote:
>
>> On Sun, Oct 11, 2020 at 1:48 AM Robert Kern
>> wrote:
>> >
>> > You don't need to use vectorize() on fermi(). fermi() will work j
Hi,
On Sun, 11 Oct 2020 at 00.27, Hongyi Zhao wrote:
> On Sun, Oct 11, 2020 at 1:48 AM Robert Kern wrote:
> >
> > You don't need to use vectorize() on fermi(). fermi() will work just
> fine on arrays and should be much faster.
>
> Yes, it really does the trick. See the following for the benchma
On Wed, 1 Jul 2020 at 21.23, gyro funch wrote:
> Hello,
>
> I greatly respect the intention, but this is a very slippery slope.
>
> Will you exempt groups within these companies that are working on
> 'green' technologies (e.g., biofuels)?
>
> Will you add to the license restrictions companies who
Hi,
On Tue, 3 Jul 2018 at 09.20, Gael Varoquaux
wrote:
> On Tue, Jul 03, 2018 at 08:54:51AM +0200, Andrea Gavana wrote:
> > This sound so very powerful... it’s such a pity that these type of gems
> won’t
> > be backported to Python 2 - we have so many legacy applications sm
On Tue, 3 Jul 2018 at 07.35, Gael Varoquaux
wrote:
> On Mon, Jul 02, 2018 at 05:31:05PM -0600, Charles R Harris wrote:
> > ISTR that some parallel processing applications sent pickled arrays
> around to
> > different processes, I don't know if that is still the case, but if so,
> no copy
> > migh
unbeatable, thanks for the analysis!
Andrea.
> Cheers,
>
>
>
> --
> Nicholas Nadeau, P.Eng., AVS
>
> On 7 October 2017 at 05:56, Andrea Gavana wrote:
>
>> Apologies, correct timeit code this time (I had gotten the wrong shape
>> for the output matrix in the loop case):
&
print method, ': MIN: %0.2f ms , MAX: %0.2f ms , MEAN: %0.2f ms ,
BEST OF 3: %0.2f ms'%tuple(result.tolist())
Results are the same as before...
On 7 October 2017 at 11:52, Andrea Gavana wrote:
> Hi All,
>
> I have this little snippet of code:
>
> import timeit
> impor
Hi All,
I have this little snippet of code:
import timeit
import numpy
class Item(object):
def __init__(self, name):
self.name = name
self.values = numpy.random.rand(8, 1)
def do_something(self):
sv = self.values.sum(axis=0)
array = numpy.empty((8,
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