I often find a need to do the type of comparison done by function shown
below. I suspect that this would be more efficient for large arrays if
implemented direction in C. Is there any possibility of adding something
like this to NumPy?
def three_way(x, y):
"""
This function performs a 3-wa
The ufunc approach makes sense.
Something like abs2 is essential for anyone who does signal processing
simulations using NumPy.
Phillip
On Sat, Oct 10, 2015 at 11:29 AM, Nathaniel Smith wrote:
> On Oct 10, 2015 10:50 AM, "Charles R Harris"
> wrote:
> >
> > On Sat, Oct 10, 2015 at 11:14 AM, Ma
tremely
> difficult to do well, and may well be impossible to do perfectly.
>
> Good luck,
> -n
> On Oct 5, 2015 21:08, "Phillip Feldman"
> wrote:
>
>> My apologies for the slow response; I was experiencing some technical
>> problems with e-mail.
>&
My apologies for the slow response; I was experiencing some technical
problems with e-mail.
In answer to Antoine's question, my main desire is for a numpy ndarray
method, for the convenience, with a secondary goal being improved
performance.
I have added the function `magsq` to my library, but wo
In communications and signal processing, it is frequently necessary to
calculate the power of a signal. This can be done with a function like the
following:
def magsq(z):
"""
Return the magnitude squared of the real- or complex-valued input.
"""
return z.real**2 + z.imag**2
A high pe
It seems odd that `nanmin` and `nanmax` are in NumPy, while `nanmean` is in
SciPy.stats. I'd like to propose that a `nanmean` function be added to
NumPy.
Phillip
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/
numpy.unique behaves as I would expect for small inputs like the following:
In [12]: x= [0, 0, 1, 0, 1, 2, 0, 1, 2, 3]
In [13]: unique(x, return_index=True)
Out[13]: (array([0, 1, 2, 3]), array([0, 2, 5, 9], dtype=int64))
But, when I give it something larger, the return index values do not alway
