[Numpy-discussion] unique return_index order?

[josef.pktd at gmail.com]

On Fri, Mar 21, 2014 at 9:27 PM, <josef.pktd at gmail.com> wrote:

>
>
>
> On Fri, Mar 21, 2014 at 9:01 PM, Charles R Harris <
> charlesr.harris at gmail.com> wrote:
>
>>
>>
>>
>> On Fri, Mar 21, 2014 at 6:49 PM, <josef.pktd at gmail.com> wrote:
>>
>>>
>>>
>>>
>>> On Fri, Mar 21, 2014 at 8:46 PM, Charles R Harris <
>>> charlesr.harris at gmail.com> wrote:
>>>
>>>>
>>>>
>>>>
>>>> On Fri, Mar 21, 2014 at 6:26 PM, Alan G Isaac <alan.isaac at gmail.com>wrote:
>>>>
>>>>> The documentation of numpy.unique
>>>>> http://docs.scipy.org/doc/numpy/reference/generated/numpy.unique.html
>>>>> does not seem to promise that return_index=True will always index the
>>>>> *first* occurrence of each unique item, which I believe is the current
>>>>> behavior.
>>>>>
>>>>> A promise would be nice.
>>>>> Is it intended?
>>>>>
>>>>>
>>>> Yes, it is intended, although the required mergesort wasn't available
>>>> for all types before numpy 1.7.
>>>>
>>>
summary, AFAICS: since numpy 1.6.2 np.unique used mergesort if
return_index=True and provides a stable sort.

Josef



>
>>> Does this mean return_inverse works again for all cases, even with
>>> return_index?
>>>
>>> I removed return_index from my code in statsmodels because I make
>>> frequent use of return_inverse, which was broken. We don't have any
>>> unittests in statsmodels anymore that use both return_xxx.
>>>
>>>
>> I don't know, needs checking. Seems to work now with a simple trial array
>> of integers.
>>
>
> my example from may 2012,  thread "1.6.2 no more unique for rows"
> works fine on  python 3.3  numpy 1.7.1
>
> >>> groups = np.random.randint(0,4,size=(10,2))
> >>> groups_ = groups.view([('',groups.dtype)]*groups.shape[1]).flatten()
> >>> uni, uni_idx, uni_inv = np.unique(groups_, return_index=True,
> return_inverse=True)
> >>> uni
> array([(0, 2), (0, 3), (1, 0), (2, 1), (2, 2), (3, 2), (3, 3)],
>       dtype=[('f0', '<i4'), ('f1', '<i4')])
> >>> uni_inv
> array([1, 6, 3, 4, 5, 3, 2, 5, 0, 2], dtype=int32)
> >>> np.__version__
> '1.7.1'
>
> Thanks,
>
> Josef
>
>
>> Chuck
>>
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>>
>>
>
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