[Numpy-discussion] Fast clip for native types, 2d version

[Robert Kern]

David Cournapeau wrote:
> Robert Kern wrote:
>> David Cournapeau wrote:
>>> Robert Kern wrote:
>>>> David Cournapeau wrote:
>>>>
>>>>>   2: the old implementation does not upcast the input array. If the
>>>>> input is int32, and min/max are float32, the function fails; if input is
>>>>> float32, and min/max float64, the output is still float32. Again, this
>>>>> seems against the expected numpy behaviour ?
>>>> The latter is expected. As discussed previously here, 
>>> Could you tell me where this was discussed, I think I missed it.
>> http://projects.scipy.org/pipermail/numpy-discussion/2006-November/024402.html
> Ok, thank you.
>>> Thanks for the precision. Is this the expected behaviour for endianness, 
>>> too ?
>> What endianness behaviour?
> This (assuming your machine is little endian):
> 
> In [1]: import numpy
> 
> In [2]: a = numpy.random.randn(3, 2)
> 
> In [3]: b = a.astype(a.dtype.newbyteorder('>'))
> 
> In [4]: a.clip(0., 1.).dtype.isnative
> Out[4]: True
> 
> In [5]: b.clip(0., 1.).dtype.isnative
> Out[5]: False
> 
> In [6]: b.clip(numpy.zeros(b.shape), 1.).dtype.isnative
> Out[6]: True
> 
> If input is byteswapped, the output is also byteswapped for scalar 
> min/max, but native if a native array is given as a min/max argument.

Yes, that seems to fit in with the rule.

-- 
Robert Kern

"I have come to believe that the whole world is an enigma, a harmless enigma
 that is made terrible by our own mad attempt to interpret it as though it had
 an underlying truth."
  -- Umberto Eco