On Mon, Jun 11, 2012 at 1:31 AM, Travis Oliphant wrote:
> It is unfortunate that this was committed to master. This should be backed
> out and is a blocker for 1.7. Can someone help me identify which commit
> made the change?
>
> This is a rather significant change and changes the documented
It is unfortunate that this was committed to master. This should be backed out
and is a blocker for 1.7. Can someone help me identify which commit made the
change?
This is a rather significant change and changes the documented behavior of
NumPy substantially. This should definitely not o
Something that we just ran into trying to merge a scipy PR:
With 1.5.1:
>>> np.arange(10)[np.array([0,1,0,1,2,3]) > 0]
array([1, 3, 4, 5])
With current master:
In [1]: np.arange(10)[np.array([0,1,0,1,2,3]) > 0]
---
ValueError
Not sure if this is a bug or not. I am using a fairly recent master branch.
>>> # Setting up...
>>> import numpy as np
>>> a = np.zeros((10, 1), dtype=[('foo', 'f4'), ('bar', 'f4'), ('spam',
'f4')])
>>> a['foo'] = np.random.random((10, 1))
>>> a['bar'] = np.random.random((10, 1))
>>> a['spam'] =
Hi,
for a description of the problem see here:
http://stackoverflow.com/questions/7820809/understanding-weird-boolean-2d-array-indexing-behavior-in-numpy
I really think, that the current way of handling two boolean indices is
missleading, is there any reason behind that?
greetings
Till
___
On Tue, Mar 8, 2011 at 07:59, Sam Tygier
wrote:
> Hi
>
> I am having an issue with boolean slicing. it seems to work fine for
> reading a value, but I can use it to set a value:
>
> import numpy
> b = numpy.array([[1,2],[3,4],[5,6],[7,8],[9,10]])
> m = numpy.array([0,1,0,0,0], dtype=bool)
> prin
2011-03-08 14:29:07 GMT, Sturla Molden:
>A "boolean slice" cannot be indexed with the dot product of dimensions
and strides, hence the copy.
>You probably want to use masked arrays instead.
Masked array does not seem to help. when i do:
am = numpy.ma.array(a, mask=a[n]['name']=="foo")
am['x'] +=
Den 08.03.2011 14:59, skrev Sam Tygier:
> I think the boolean slicing is making a copy instead of a view.
Yes.
> is there
> a way around this?
A "boolean slice" cannot be indexed with the dot product of dimensions
and strides, hence the copy.
You probably want to use masked arrays instead.
St
Hi
I am having an issue with boolean slicing. it seems to work fine for
reading a value, but I can use it to set a value:
import numpy
b = numpy.array([[1,2],[3,4],[5,6],[7,8],[9,10]])
m = numpy.array([0,1,0,0,0], dtype=bool)
print b[m]
print b[m][0,0]
b[m][0,0] = -1
print b[m][0,0]
I think th
Hi,
Ticket #721 is titled "0-dimensional boolean arrays should work as
masks for array scalars". It is not quite clear to me what the right
behaviour is.
We are generally trying to make zero-dimensional arrays behave the
same as array scalars, but I'm not sure how that should work in this
case:
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