[Numpy-discussion] nanmean(), nanstd() and other "missing" functions for 1.8
Robert Kern
robert.kern at gmail.com
Thu May 2 09:47:05 EDT 2013
On Thu, May 2, 2013 at 2:38 PM, Charles R Harris
<charlesr.harris at gmail.com> wrote:
>
> On Thu, May 2, 2013 at 7:28 AM, Robert Kern <robert.kern at gmail.com> wrote:
>>
>> On Thu, May 2, 2013 at 12:03 PM, Nathaniel Smith <njs at pobox.com> wrote:
>> > On 1 May 2013 23:12, "Charles R Harris" <charlesr.harris at gmail.com>
>> > wrote:
>> >>
>> >> On Wed, May 1, 2013 at 7:10 PM, Benjamin Root <ben.root at ou.edu> wrote:
>> >>>
>> >>> So, to summarize the thread so far:
>> >>>
>> >>> Consensus:
>> >>> np.nanmean()
>> >>> np.nanstd()
>> >>> np.minmax()
>> >>> np.argminmax()
>> >>>
>> >>> Vague Consensus:
>> >>> np.sincos()
>> >>>
>> >>
>> >> If the return of sincos (cossin?) is an array, then it could be
>> >> reshaped
>> >> to be exp(1j*x), which together with exp(2*pi*1j*x) would cover some
>> >> pretty
>> >> common cases.
>>
>> It couldn't be a mere reshape, since the complex dtype requires the
>> real and imag components to be adjacent to each other. They wouldn't
>> be so if sincos's return type is an array (nor even the cossin
>> alternative). It always requires a memory copy (except in the "who
>> cares?" case of a scalar). Composition with an efficient
>> np.tocomplex(real, imag) implementation would cover those use cases
>> whether sincos returns tuples or arrays.
>
> I would assume the basic return type would be complex, i.e., the cos/sin
> adjacent. The cos/sin parts would then be real/imag views into the array.
You mean that the implementation of cossin (to make things easier on
ourselves) would create an (N,2) contiguous array, fill it with the
cos and sin results, then reshape it to return the expected (2,N)
array (or 2-tuple)? How would the user then reconstitute the exp(1j*x)
result efficiently? If the use case is that important, I would just
make exp(1j*x) into its own ufunc and have it use the C sincos()
function internally.
--
Robert Kern
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