Numpy chose to violate the principal of equality by having __eq__ not
return a bool. So a numpy type can't be used reliably outside of the numpy
DSL.
-gps
On Tue, Dec 22, 2020, 11:51 AM Alan G. Isaac <alan.is...@gmail.com> wrote:
> Here, `seq1 == seq2` produces a boolean array (i.e., an array of boolean
> values).
> hth, Alan Isaac
>
>
> On 12/22/2020 2:28 PM, Ivan Pozdeev via Python-Dev wrote:
> >
> > You sure about that? For me, bool(np.array) raises an exception:
> >
> > In [12]: np.__version__ Out[12]: '1.19.4'
> >
> > In [11]: if [False, False]==np.array([False, False]): print("foo") <...>
> ValueError: The truth value of an array with more than one element is
> ambiguous.
> > Use a.any() or a.all()
> >
> >
>
>
>
> > On 22.12.2020 21:52, Alan G. Isaac wrote:
> >> The following test fails because because `seq1 == seq2` returns a
> (boolean) NumPy array whenever either seq is a NumPy array.
> >>
> >> import unittest import numpy as np
> unittest.TestCase().assertSequenceEqual([1.,2.,3.], np.array([1.,2.,3.]))
> >>
> >> I expected `unittest` to rely only on features of a
> `collections.abc.Sequence`, which based on
> https://docs.python.org/3/glossary.html#term-sequence, I
> >> believe are satisfied by a NumPy array. Specifically, I see no
> requirement that a sequence implement __eq__ at all much less in any
> particular way.
> >>
> >> In short: a test named `assertSequenceEqual` should, I would think,
> work for any sequence and therefore (based on the available documentation)
> should not
> >> depend on the class-specific implementation of __eq__.
> >>
> >> Is that wrong?
> >>
> >> Thank you, Alan Isaac _______________________________________________
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