On Tue, Feb 7, 2012 at 9:01 PM, Travis Oliphant tra...@continuum.io wrote:
like so: x[ind1, :, ind2], the question is what should the shape of the
output me. If ind1 is a scalar there is no ambiguity (and this should be
special cased --- but unfortunately isn't).
If
x.shape == (a0, a1,
Le 8 février 2012 00:01, Travis Oliphant tra...@continuum.io a écrit :
On Feb 7, 2012, at 12:24 PM, Sturla Molden wrote:
On 07.02.2012 19:17, Benjamin Root wrote:
print x.shape
(2, 3, 4)
print x[0, :, :].shape
(3, 4)
print x[0, :, idx].shape
(2, 3)
That looks like a bug to
On 08.02.2012 06:01, Travis Oliphant wrote:
Recall that the shape of the output with fancy indexing is determined by
broadcasting together the indexing objects and using that as the shape of the
output:
x[ind1, ind2] will produce an output with the shape of broadcast(ind1,
ind2) whose
On Feb 8, 2012, at 8:29 AM, Sturla Molden wrote:
On 08.02.2012 06:01, Travis Oliphant wrote:
Recall that the shape of the output with fancy indexing is determined by
broadcasting together the indexing objects and using that as the shape of
the output:
x[ind1, ind2] will produce an
On 08.02.2012 15:11, Olivier Delalleau wrote:
From a user perspective, I would definitely prefer cross-product
semantics for fancy indexing. In fact, I had never used fancy indexing
with more than one array index, so the behavior described in this thread
totally baffled me. If for instance x
On 08.02.2012 15:49, Travis Oliphant wrote:
This sort of thing would take time, but is not out of the question in my mind
because I suspect the number of users and use-cases of broadcasted
fancy-indexing is small.
In Matlab this (misfeature?) is generally used to compensate for the
lack of
On Wed, Feb 8, 2012 at 10:29 AM, Sturla Molden stu...@molden.no wrote:
On 08.02.2012 15:49, Travis Oliphant wrote:
This sort of thing would take time, but is not out of the question in my
mind because I suspect the number of users and use-cases of broadcasted
fancy-indexing is small.
I
On 08.02.2012 18:17, josef.p...@gmail.com wrote:
I think I use it quite a bit, and I like that the broadcasting in
indexing is as flexible as the broadcasting of numpy arrays
themselves.
x[np.arange(len(x)), np.arange(len(x))] gives the diagonal for example.
Personally I would prefer that
On Wed, Feb 8, 2012 at 8:49 AM, Travis Oliphant tra...@continuum.io wrote:
On Feb 8, 2012, at 8:29 AM, Sturla Molden wrote:
On 08.02.2012 06:01, Travis Oliphant wrote:
Recall that the shape of the output with fancy indexing is determined
by broadcasting together the indexing objects and
On Feb 8, 2012, at 11:17 AM, josef.p...@gmail.com wrote:
On Wed, Feb 8, 2012 at 10:29 AM, Sturla Molden stu...@molden.no wrote:
On 08.02.2012 15:49, Travis Oliphant wrote:
This sort of thing would take time, but is not out of the question in my
mind because I suspect the number of users
On Wed, Feb 8, 2012 at 22:11, Travis Oliphant tra...@continuum.io wrote:
On Feb 8, 2012, at 11:17 AM, josef.p...@gmail.com wrote:
On Wed, Feb 8, 2012 at 10:29 AM, Sturla Molden stu...@molden.no wrote:
On 08.02.2012 15:49, Travis Oliphant wrote:
This sort of thing would take time, but is not
On Feb 8, 2012, at 4:19 PM, Robert Kern wrote:
On Wed, Feb 8, 2012 at 22:11, Travis Oliphant tra...@continuum.io wrote:
On Feb 8, 2012, at 11:17 AM, josef.p...@gmail.com wrote:
On Wed, Feb 8, 2012 at 10:29 AM, Sturla Molden stu...@molden.no wrote:
On 08.02.2012 15:49, Travis Oliphant
On Wed, Feb 8, 2012 at 6:49 AM, Travis Oliphant tra...@continuum.io wrote:
There are also some very nice applications where you can select out of a 3-d
volume a depth-surface defined by indexes like so:
arr[ i[:,newaxis], j, depth]
where arr is a 3-d array, i and j are 1-d index
Consider the following. Is this a bug?
Thanks,
- Jordi G. H.
---
#!/usr/bin/python
import numpy as np
x = np.reshape(np.random.uniform(size=2*3*4), [2,3,4])
idx = np.array([False, True, False, True])
y = x[0,:,:];
## Why is this transposed?
print
On Tue, Feb 7, 2012 at 11:11 AM, Jordi Gutiérrez Hermoso jord...@octave.org
wrote:
Consider the following. Is this a bug?
Thanks,
- Jordi G. H.
---
#!/usr/bin/python
import numpy as np
x = np.reshape(np.random.uniform(size=2*3*4), [2,3,4])
On 07.02.2012 19:17, Benjamin Root wrote:
print x.shape
(2, 3, 4)
print x[0, :, :].shape
(3, 4)
print x[0, :, idx].shape
(2, 3)
That looks like a bug to me. The length of the first dimension should be
the same.
Sturla
___
On 07.02.2012 19:24, Sturla Molden wrote:
On 07.02.2012 19:17, Benjamin Root wrote:
print x.shape
(2, 3, 4)
print x[0, :, :].shape
(3, 4)
print x[0, :, idx].shape
(2, 3)
That looks like a bug to me. The length of the first dimension should be
the same.
I can reproduce this
On Tue, Feb 7, 2012 at 10:41 AM, Sturla Molden stu...@molden.no wrote:
It's the combination of a single index and fancy indexing that does
this, not the slicing.
There are some quirks in the broadcasting machinery that makes it
almost impossible to guess what the outcome of mixed indexing is
This comes up from time to time.This is an example of what is described at
the top of page 84 of Guide to NumPy. Also read Chapter 17 to get the
explanation of how fancy indexing is implemented if you really want to
understand the issues.
When you mix fancy-indexing with simple indexing,
On Feb 7, 2012, at 12:24 PM, Sturla Molden wrote:
On 07.02.2012 19:17, Benjamin Root wrote:
print x.shape
(2, 3, 4)
print x[0, :, :].shape
(3, 4)
print x[0, :, idx].shape
(2, 3)
That looks like a bug to me. The length of the first dimension should be
the same.
What you are
20 matches
Mail list logo
