[Numpy-discussion] Fast clip for native types, 2d version
Robert Kern
robert.kern at gmail.com
Sun Jan 14 22:47:41 CST 2007
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
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