[Numpy-discussion] Adding .abs() method to the array object
josef.pktd@gmai...
josef.pktd@gmai...
Mon Feb 25 21:04:54 CST 2013
On Mon, Feb 25, 2013 at 9:58 PM, <josef.pktd@gmail.com> wrote:
> On Mon, Feb 25, 2013 at 9:20 PM, Sebastian Berg
> <sebastian@sipsolutions.net> wrote:
>> On Mon, 2013-02-25 at 10:50 -0500, Skipper Seabold wrote:
>>> On Mon, Feb 25, 2013 at 10:43 AM, Till Stensitzki <mail.till@gmx.de>
>>> wrote:
>>> >
>>> > First, sorry that i didnt search for an old thread, but because i
>>> disagree with
>>> > conclusion i would at least address my reason:
>>> >
>>> >> I don't like
>>> >> np.abs(arr).max()
>>> >> because I have to concentrate to much on the braces, especially if
>>> arr
>>> >> is a calculation
>>> >
>>> > This exactly, adding an abs into an old expression is always a
>>> little annoyance
>>> > due to the parenthesis. The argument that np.abs() also works is
>>> true for
>>> > (almost?) every other method. The fact that so many methods already
>>> exists,
>>> > especially for most of the commonly used functions (min, max, dot,
>>> mean, std,
>>> > argmin, argmax, conj, T) makes me missing abs. Of course, if one
>>> would redesign
>>> > the api, one would drop most methods (i am looking at you ptp and
>>> byteswap). But
>>> > the objected is already cluttered and adding abs is imo logical
>>> application of
>>> > "practicality beats purity".
>>> >
>>>
>>> I tend to agree here. The situation isn't all that dire for the number
>>> of methods in an array. No scrolling at reasonably small terminal
>>> sizes.
>>>
>>> [~/]
>>> [3]: x.
>>> x.T x.copy x.getfield x.put x.std
>>> x.all x.ctypes x.imag x.ravel
>>> x.strides
>>> x.any x.cumprod x.item x.real x.sum
>>> x.argmax x.cumsum x.itemset x.repeat
>>> x.swapaxes
>>> x.argmin x.data x.itemsize x.reshape x.take
>>> x.argsort x.diagonal x.max x.resize
>>> x.tofile
>>> x.astype x.dot x.mean x.round
>>> x.tolist
>>> x.base x.dtype x.min x.searchsorted
>>> x.tostring
>>> x.byteswap x.dump x.nbytes x.setfield
>>> x.trace
>>> x.choose x.dumps x.ndim x.setflags
>>> x.transpose
>>> x.clip x.fill x.newbyteorder x.shape x.var
>>> x.compress x.flags x.nonzero x.size x.view
>>> x.conj x.flat x.prod x.sort
>>> x.conjugate x.flatten x.ptp x.squeeze
>>>
>>>
>> Two small things (not sure if it matters much). But first almost all of
>> these methods are related to the container and not the elements. Second
>> actually using a method arr.abs() has a tiny pitfall, since abs would
>> work on numpy types, but not on python types. This means that:
>>
>> np.array([1, 2, 3]).max().abs()
>>
>> works, but
>>
>> np.array([1, 2, 3], dtype=object).max().abs()
>>
>> breaks. Python has a safe name for abs already...
>
>>>> (np.array([1, 2, 3], dtype=object)).max()
> 3
>>>> (np.array([1, 2, 3], dtype=object)).__abs__().max()
> 3
>>>> (np.array([1, 2, '3'], dtype=object)).__abs__()
> Traceback (most recent call last):
> File "<stdin>", line 1, in <module>
> TypeError: bad operand type for abs(): 'str'
>
>>>> map(abs, [1, 2, 3])
> [1, 2, 3]
>>>> map(abs, [1, 2, '3'])
> Traceback (most recent call last):
> File "<stdin>", line 1, in <module>
> TypeError: bad operand type for abs(): 'str'
or maybe more useful
>>> from decimal import Decimal
>>> d = [Decimal(str(k)) for k in np.linspace(-1, 1, 5)]
>>> map(abs, d)
[Decimal('1.0'), Decimal('0.5'), Decimal('0.0'), Decimal('0.5'), Decimal('1.0')]
>>> np.asarray(d).__abs__()
array([1.0, 0.5, 0.0, 0.5, 1.0], dtype=object)
>>> np.asarray(d).__abs__()[0]
Decimal('1.0')
Josef
>
> I don't see a difference.
>
> (I don't expect to use max abs on anything else than numbers.)
>
> Josef
>>
>>
>>> I find myself typing things like
>>>
>>> arr.abs()
>>>
>>> and
>>>
>>> arr.unique()
>>>
>>> quite often.
>>>
>>> Skipper
>>> _______________________________________________
>>> NumPy-Discussion mailing list
>>> NumPy-Discussion@scipy.org
>>> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>>
>>
>> _______________________________________________
>> NumPy-Discussion mailing list
>> NumPy-Discussion@scipy.org
>> http://mail.scipy.org/mailman/listinfo/numpy-discussion
More information about the NumPy-Discussion
mailing list