[Python-ideas] Boolean ABC similar to what's provided in the 'numbers' module

[Sylvain MARIE]

I see :)

This does not seem to happen with PyCharm IDE + Anaconda distribution. Is PyCharm relying on MyPy under the hood ?
I actually have no knowledge at all about MyPy and how it relates to PyCharm static code analysis warnings. I’m pretty sure though that the runtime checkers (enforce, pytypes) are not dependent on MyPy.

Sylvain

De : gvanrossum at gmail.com [mailto:gvanrossum at gmail.com] De la part de Guido van Rossum
Envoyé : mercredi 14 février 2018 19:47
À : Sylvain MARIE <sylvain.marie at schneider-electric.com>
Cc : python-ideas <python-ideas at python.org>
Objet : Re: [Python-ideas] Boolean ABC similar to what's provided in the 'numbers' module

I am mystified how you can be using the numbers package with mypy. Example:

import numbers
def f(a: numbers.Integral, b: numbers.Integral) -> numbers.Integral:
  return a + b
f(12, 12)
This gives an two errors on the last line when checked by mypy:

_.py:10: error: Argument 1 to "f" has incompatible type "int"; expected "Integral"
_.py:10: error: Argument 2 to "f" has incompatible type "int"; expected "Integral"

On Tue, Feb 13, 2018 at 1:21 AM, Sylvain MARIE <sylvain.marie at schneider-electric.com<mailto:sylvain.marie at schneider-electric.com>> wrote:
The main use case I had in mind was PEP484-based type hinting/checking actually:

def my_function(foo: Boolean):
    pass

explicitly states that my_function accepts any Boolean value, whether it is a python bool or a np.bool that would come from a numpy array or pandas dataframe.
Note that type hinting is also the use case for which I make extensive use of the types from the ‘numbers’ package, for the same reasons.

Sylvain

De : Python-ideas [mailto:python-ideas-bounces+sylvain.marie<mailto:python-ideas-bounces%2Bsylvain.marie>=schneider-electric.com at python.org<mailto:schneider-electric.com at python.org>] De la part de David Mertz
Envoyé : mardi 13 février 2018 07:08
À : Nick Coghlan <ncoghlan at gmail.com<mailto:ncoghlan at gmail.com>>
Cc : python-ideas <python-ideas at python.org<mailto:python-ideas at python.org>>
Objet : Re: [Python-ideas] Boolean ABC similar to what's provided in the 'numbers' module

I'm not sure I'm convinced by Sylvain that Boolean needs to be an ABC in the standard library; Guido expresses skepticism.  Of course it is possible to define it in some other library that actually needs to use `isinstance(x, Boolean)` as Sylvain demonstraits in his post.  I'm not sure I'm unconvinced either, I can see a certain value to saying a given value is "fully round-trippable to bool" (as is np.bool_).

But just for anyone who doesn't know NumPy, here's a quick illustration of what I alluded to:

In [1]: import numpy as np
In [2]: arr = np.array([7,8,12,33])
In [3]: ndx1 = np.array([0,1,1,0], dtype=int)
In [4]: ndx2 = np.array([0,1,1,0], dtype=bool)
In [5]: arr[ndx1]
Out[5]: array([7, 8, 8, 7])
In [6]: arr[ndx2]
Out[6]: array([ 8, 12])

ndx1 and ndx2 are both nice things (and are both often programmatically constructed by operations in NumPy).  But indexing using ndx1 gives us an array of the things in the listed positions in arr.  In this case, we happen to choose two each of the things an index 0 and index 1 in the result.

Indexing by ndx2 gives us a filter of only those positions in arr corresponding to 'True's.  These are both nice things to be able to do, but if NumPy's True was a special kind of 1, it wouldn't work out unambiguously.  However, recent versions of NumPy have gotten a bit smarter about recognizing the special type of Python bools, so it's less of a trap than it used to be.  Still, contrast these (using actual Python lists for the indexes:

In [10]: arr[[False, True, True, False]]
Out[10]: array([ 8, 12])
In [11]: arr[[False, True, 1, 0]]
Out[11]: array([7, 8, 8, 7])



On Mon, Feb 12, 2018 at 7:50 PM, Nick Coghlan <ncoghlan at gmail.com<mailto:ncoghlan at gmail.com>> wrote:
On 13 February 2018 at 02:14, David Mertz <mertz at gnosis.cx<mailto:mertz at gnosis.cx>> wrote:
> NumPy np.bool_ is specifically not a subclass of any np.int_.  If it we're,
> there would be an ambiguity between indexing with a Boolean array and an
> array of ints. Both are meaningful, but they mean different things (mask vs
> collection of indices).
>
> Do we have other examples a Python ABC that exists to accommodate something
> outside the standard library or builtins? Even if not, NumPy is special...
> the actual syntax for '@' exists primarily for that library!

collections.abc.Sequence and collections.abc.Mapping come to mind -
the standard library doesn't tend to distinguish between different
kinds of subscriptable objects, but it's a distinction some third
party libraries and tools want to be able to make reliably.

The other comparison that comes to mind would be the distinction
between "__int__" ("can be coerced to an integer, but may lose
information in the process") and "__index__" ("can be losslessly
converted to and from a builtin integer").

Right now, we only define boolean coercion via "__bool__" - there's no
mechanism to say "this *is* a boolean value that can be losslessly
converted to and from the builtin boolean constants". That isn't a
distinction the standard library makes, but it sounds like it's one
that NumPy cares about (and NumPy was also the main driver for
introducing __index__).

Cheers,
Nick.

--
Nick Coghlan   |   ncoghlan at gmail.com<mailto:ncoghlan at gmail.com>   |   Brisbane, Australia



--
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from the bellies of the hungry; books from the hands of the
uneducated; technology from the underdeveloped; and putting
advocates of freedom in prisons.  Intellectual property is
to the 21st century what the slave trade was to the 16th.

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