Hi,
you are right. I am using two different versions, Numpy 1.10.4 and 1.9.0
Both show this behavior.
Numpy 1.11.1 also does, but now raises VisibleDeprecationWarning:
using a boolean instead of an integer will result in an error in the
future
Thanks!
Am 14.12.2017 21:37 schrieb Sebastian Berg:
On Thu, 2017-12-14 at 16:24 +0000, Eric Wieser wrote:
It sounds like you're using an old version of numpy, where boolean
scalars were interpreted as integers.
What version are you using?
Eric
Indeed, you are maybe using a pre 1.9 version (post 1.9 should at least
have a DeprecationWarning or some such, though you might not notice it
IIRC).
For newer versions you should get boolean indexing, the result of it
may be a bit confusing. It is advanced indexing, basically with False
giving you an empty array (with an extra dimension of size 0) and True
being much like an `np.newaxis`.
It all makes perfect sense if you think of it of a 0-d array
picking....
The same thing is true for example for lists of booleans.
- Sebastian
On Thu, Dec 14, 2017, 04:27 Joe <solar...@posteo.org> wrote:
> Hello,
> thanks for you feedback.
>
> Sorry, if thie question is stupid and the case below does not make
> sense.
> I am just trying to understand the logic.
> For
>
> x = np.random.rand(2,3)
>
> x[True]
> x[(True,)]
>
> or
>
> x[False]
> x[(False,)]
>
> where True and False are not arrays,
> it will pick the first or second row.
>
> Is this basic indexing then with one the rules
> - obj is an integer
> - obj is a tuple of slice objects and integers.
> ?
>
>
> Am 13.12.2017 21:49 schrieb Eric Wieser:
> > Increasingly, NumPy does not considers booleans to be integer
> types,
> > and indexing is one of these cases.
> >
> > So no, it will not be treated as a tuple of integers, but as a 0d
> mask
> >
> > Eric
> >
> > On Wed, 13 Dec 2017 at 12:44 Joe <solar...@posteo.org> wrote:
> >
> >> Hi,
> >>
> >> yet another question.
> >>
> >> I looked through the indexing rules in the
> >> documentation but I count not find which one
> >> applies to x[True] and x[False]
> >>
> >> that might e.g result from
> >>
> >> import numpy as np
> >> x = np.array(3)
> >> x[x>5]
> >> x[x<1]
> >> x[True]
> >> x[False]
> >>
> >> x = np.random.rand(2,3)
> >> x[x>5]
> >> x[x<1]
> >> x[True]
> >> x[False]
> >>
> >> I understood that they are equivalent to
> >>
> >> x[(False,)]
> >>
> >> I tested it and it looks like advanced indexing,
> >> but I try to unterstand the logic behind this,
> >> if there is any :)
> >>
> >> In x[x<1] the x<1 is a mask and thus I guess it is a
> >> "tuple with at least one sequence object or ndarray (of data
> type
> >> integer or bool)", right?
> >>
> >> Or will x[True] trigger basic indexing as it is "a tuple of
> >> integers"
> >> because True will be converted to Int?
> >>
> >> Cheers,
> >> Joe
> >> _______________________________________________
> >> NumPy-Discussion mailing list
> >> NumPy-Discussion@python.org
> >> https://mail.python.org/mailman/listinfo/numpy-discussion [1]
> >
> >
> > Links:
> > ------
> > [1] https://mail.python.org/mailman/listinfo/numpy-discussion
> >
> > _______________________________________________
> > NumPy-Discussion mailing list
> > NumPy-Discussion@python.org
> > https://mail.python.org/mailman/listinfo/numpy-discussion
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion@python.org
> https://mail.python.org/mailman/listinfo/numpy-discussion
>
_______________________________________________
NumPy-Discussion mailing list
NumPy-Discussion@python.org
https://mail.python.org/mailman/listinfo/numpy-discussion
_______________________________________________
NumPy-Discussion mailing list
NumPy-Discussion@python.org
https://mail.python.org/mailman/listinfo/numpy-discussion
_______________________________________________
NumPy-Discussion mailing list
NumPy-Discussion@python.org
https://mail.python.org/mailman/listinfo/numpy-discussion
