28/02/10 @ 01:56 (-0500), thus spake David Warde-Farley:
> On 26-Feb-10, at 8:12 AM, Ernest Adrogué wrote:
>
> > Thanks for the tip. I didn't know that...
> > Also, frompyfunc appears to crash python when the last argument is 0:
> >
> > In [9]: func=np.frompyfunc(lambda x: x, 1, 0)
> >
> > In [10]
On 26-Feb-10, at 8:12 AM, Ernest Adrogué wrote:
> Thanks for the tip. I didn't know that...
> Also, frompyfunc appears to crash python when the last argument is 0:
>
> In [9]: func=np.frompyfunc(lambda x: x, 1, 0)
>
> In [10]: func(np.arange(5))
> Violació de segment
>
> This with Python 2.5.5, Nu
26/02/10 @ 13:51 (+0200), thus spake Pauli Virtanen:
> pe, 2010-02-26 kello 12:43 +0100, Ernest Adrogué kirjoitti:
> [clip]
> > Or if you want to produce a different array of the same shape
> > as the original, then you probably need a vectorised function.
> >
> > def myfunc(x):
> > print 'myf
26/02/10 @ 13:31 (+0100), thus spake Ole Streicher:
> Hello Ernest,
>
> Ernest Adrogué writes:
> > It depends on what exactly you want to do. If you just want
> > to iterate over the array, try something liks this
> > for element in a[a > 0.8]:
> > myfunc(element)
>
> No; I need to iterate o
Hello Ernest,
Ernest Adrogué writes:
> It depends on what exactly you want to do. If you just want
> to iterate over the array, try something liks this
> for element in a[a > 0.8]:
> myfunc(element)
No; I need to iterate over the *indices*, not over the elements.
a = numpy.random.random((
pe, 2010-02-26 kello 12:43 +0100, Ernest Adrogué kirjoitti:
[clip]
> Or if you want to produce a different array of the same shape
> as the original, then you probably need a vectorised function.
>
> def myfunc(x):
> print 'myfunc of', x
> if x > 0.8:
> return x + 2
> els
Hi,
26/02/10 @ 11:23 (+0100), thus spake Ole Streicher:
> Hi,
>
> I want to apply a function to all indices of an array that fullfill a
> certain condition.
>
> What I tried:
>
> -8<
> import numpy
>
> def myfunc(x):
> print 'myfunc of', x
Hi,
I want to apply a function to all indices of an array that fullfill a
certain condition.
What I tried:
-8<
import numpy
def myfunc(x):
print 'myfunc of', x
a = numpy.random.random((2,3,4))
numpy.apply_along_axis(myfunc, 0, numpy.where
