Hi,
I am doing a simple function callback from fortran to python for which
the actual function call in fortran has repeated arguments.
! callback_error.f90:
subroutine testfun(x)
double precision, intent(in) :: x
double precision :: y
!f2py intent(callback) foo
!f2py double precision :: arg
Hi,
> I changed this a while ago in the documentation editor, but it hasn't
> been merged yet to the source docstring
>
> http://docs.scipy.org/numpy/docs/numpy.random.mtrand.RandomState.exponential/
>
> There is also an open ticket for this
> http://projects.scipy.org/numpy/ticket/987
>
> Can yo
Hi,
I noticed a problem with numpy.random.exponential. Apparently, the
samples generated by numpy.random.exponential(scale=scale) follow the
distribution f(x)=1/scale*exp(-x/scale) (and not
f(x)=scale*exp(-x*scale) as stated by the docstring).
The script below illustrates this.
--
import numpy a
Hi,
> I am also looking to verify the vendor-libs being used.
>
> What does numpy.__config__.show() tell you ?
>
In the case of the ACML compilation, I get:
0 [EMAIL PROTECTED] .../u0050015 $ python -c "import numpy;
numpy.show_config()"
atlas_threads_info:
libraries = ['lapack', 'a
Hi all,
I have managed to compile numpy using pathscale and ACML on a 64 bit AMD
system. Now I wanted to verify that numpy.dot indeed uses the ACML
libs. The example for dot()
(http://www.scipy.org/Numpy_Example_List?highlight=%28example%29#head-c7a573f030ff7cbaea62baf219599b3976136bac)
suggest a
