On Di, 2014-02-18 at 09:05 -0700, Charles R Harris wrote:
Hi All,
There is an old ticket, #1499, that suggest adding a segment_axis
function.
def segment_axis(a, length, overlap=0, axis=None, end='cut', endvalue=0):
Generate a new array that chops the given array along the given
On Di, 2014-02-18 at 17:09 +0100, Sebastian Berg wrote:
Hey all,
snip
Now also NumPy commonly uses lists here to build up indexing tuples
(since they are mutable), however would it really be so bad if we had to
do `arr[tuple(slice_list)]` in the end to resolve this issue? So the
proposal
On Sa, 2014-02-15 at 16:37 -0500, alex wrote:
Hello list,
Here's another idea resurrection from numpy github comments that I've
been advised could be posted here for re-discussion.
The proposal would be to make np.linalg.svd more like scipy.linalg.svd
with respect to input checking. The
On Sa, 2014-02-15 at 17:35 -0500, josef.p...@gmail.com wrote:
On Sat, Feb 15, 2014 at 5:12 PM, Skipper Seabold jsseab...@gmail.com wrote:
On Sat, Feb 15, 2014 at 5:08 PM, josef.p...@gmail.com wrote:
On Sat, Feb 15, 2014 at 4:56 PM, Sebastian Berg
sebast...@sipsolutions.net wrote:
On Sa
On Sa, 2014-02-15 at 18:20 -0500, alex wrote:
snip
I'm not sure exactly what you mean by this. You are suggesting that
if the svd fails with some kind of exception (possibly poorly or
misleadingly worded) then it could be cleaned-up after the fact by
checking the input, and that this would
On Mon, 2014-02-03 at 00:41 -0800, Dinesh Vadhia wrote:
Does the numpy indexing refactorizing address the performance of fancy
indexing highlighted in wes mckinney's blog some years back -
http://wesmckinney.com/blog/?p=215 - where numpy.take() was shown to
be preferable than fancy indexing?
On Sun, 2014-02-02 at 13:11 -0600, Travis Oliphant wrote:
This sounds like a great and welcome work and improvements.
Does it make sense to also do something about the behavior of advanced
indexing when slices are interleaved between lists and integers.
I know that jay borque has some
On Sat, 2014-01-25 at 00:18 +, Nathaniel Smith wrote:
On 25 Jan 2014 00:05, Sebastian Berg sebast...@sipsolutions.net
wrote:
Hi all,
in https://github.com/numpy/numpy/pull/3514 I proposed some changes
to
the comparison operators. This includes:
1. Comparison with None
On Fri, 2014-01-24 at 06:13 -0800, Dinesh Vadhia wrote:
When using vstack or hstack for large arrays, are there any
performance penalties eg. takes longer time-wise or makes a copy of an
array during operation ?
No, they all use concatenate. There are only constant overheads on top
of the
Hi all,
in https://github.com/numpy/numpy/pull/3514 I proposed some changes to
the comparison operators. This includes:
1. Comparison with None will broadcast in the future, so that `arr ==
None` will actually compare all elements to None. (A FutureWarning for
now)
2. I added that == and !=
On Wed, 2014-01-22 at 07:58 +0100, Dr. Leo wrote:
Hi,
thanks. Both recarray and itertools.chain work just fine in the example
case.
However, the real purpose of this is to read strings from a large xml
file into a pandas DataFrame. But fromiter cannot create arrays of dtype
'object'.
On Wed, 2014-01-22 at 17:23 +, Ralf Juengling wrote:
Executing the following code,
import numpy as np
a = np.zeros((3,))
w = np.array([0, 1, 0, 1, 2])
v = np.array([10.0, 1, 10.0, 2, 9])
a[w] += v
I was expecting ‘a’ to be array([20., 3., 9.]. Instead I get
On Tue, 2014-01-21 at 07:48 -0700, Charles R Harris wrote:
On Tue, Jan 21, 2014 at 7:37 AM, Aldcroft, Thomas
aldcr...@head.cfa.harvard.edu wrote:
On Tue, Jan 21, 2014 at 8:55 AM, Charles R Harris
charlesr.har...@gmail.com wrote:
Hey,
fixing a corner case indexing regression in 1.8, I noticed/fixed
accidentally this behavior of returning a scalar when indexing a 0-d
array with fields (see also [1]):
arr = np.array((1,), dtype=[('a', 'f8')])
arr['a'] # Returns an array
arr[['a']] # Currently returns a *scalar*
I think
On Tue, 2013-12-17 at 16:41 +0100, Pierre Haessig wrote:
Le 13/12/2013 13:45, Sebastian Berg a écrit :
What are the other options for such a repeat ?
No, I don't think there are any other options. stride tricks are a bit
hidden, since in many cases it is more dangerous than helping
Hey,
On Thu, 2013-12-12 at 15:20 +0100, Pierre Haessig wrote:
Hello,
In order to repeat rows or columns of an array as
http://stackoverflow.com/questions/1550130/cloning-row-or-column-vectors
I can use np.repeat as suggested by pv. However, looking at the flags of
the resulting array, data
On Thu, 2013-12-05 at 23:02 -0500, josef.p...@gmail.com wrote:
On Thu, Dec 5, 2013 at 10:56 PM, Alexander Belopolsky ndar...@mac.com wrote:
On Thu, Dec 5, 2013 at 5:37 PM, Sebastian Berg sebast...@sipsolutions.net
wrote:
there was a discussion that for numpy booleans math operators
On Fri, 2013-12-06 at 15:30 -0500, josef.p...@gmail.com wrote:
On Fri, Dec 6, 2013 at 2:59 PM, Nathaniel Smith n...@pobox.com wrote:
On Fri, Dec 6, 2013 at 11:55 AM, Alexander Belopolsky ndar...@mac.com
wrote:
On Fri, Dec 6, 2013 at 1:46 PM, Alan G Isaac alan.is...@gmail.com wrote:
Hey,
there was a discussion that for numpy booleans math operators +,-,* (and
the unary -), while defined, are not very helpful. I have set up a quick
PR with start (needs some fixes inside numpy still):
https://github.com/numpy/numpy/pull/4105
The idea is to deprecate these, since the binary
On Mon, 2013-12-02 at 14:51 -0500, Neal Becker wrote:
The software I'm using, which is
https://github.com/ndarray/ndarray
does depend on this. Am I the only one who thinks that this
behavior is not desirable?
Well, this is not meant to be the way for a release version of numpy.
The
On Mon, 2013-12-02 at 18:15 -0500, Jim Bosch wrote:
If your arrays are contiguous, you don't really need the strides
(use the itemsize instead). How is ndarray broken by this?
ndarray is broken by this change because it expects the stride to be a
multiple of the itemsize (I think; I'm just
On Tue, 2013-10-29 at 16:47 +, Henry Gomersall wrote:
Is there a way to extract the size of array that would be created by
doing 1j*array?
There is np.result_type. It does the handling of scalars as normal,
dtypes will be handled like arrays (scalars are allowed to lose
precision).
-
On Wed, 2013-10-16 at 11:50 -0400, Benjamin Root wrote:
On Wed, Oct 16, 2013 at 11:39 AM, Chad Kidder cckid...@gmail.com
wrote:
Just found what should be a bug in 1.7.1. I'm running
python(x,y) on windows here:
dataMatrix[ii,:,mask].shape
On Wed, 2013-10-02 at 10:04 +0100, Nathaniel Smith wrote:
This is a complicated issue to describe but i think the bottom line is
that the test is just wonky here. the behaviour it's checking for is:
wrong in old numpy, but we do it anyway (bug)
wrong in current numpy without RELAXED_STRIDES,
On Wed, 2013-10-02 at 12:54 +0200, Sebastian Berg wrote:
On Wed, 2013-10-02 at 10:04 +0100, Nathaniel Smith wrote:
This is a complicated issue to describe but i think the bottom line is
that the test is just wonky here. the behaviour it's checking for is:
wrong in old numpy, but we do
On Tue, 2013-10-01 at 12:00 +0200, Jens Jørgen Mortensen wrote:
Den 30-09-2013 17:17, Charles R Harris skrev:
Hi All,
NumPy 1.8.0rc1 is up now on sourceforge .The binary builds are
included except for Python 3.3 on windows, which will arrive later.
Many thanks to Ralf for the
Hey,
since I am working on the indexing. I was wondering about a few smaller
things:
* 0-d boolean array, `np.array(0)[True]` (will work now) would
give np.array([0]) as a copy, instead of the original array.
I guess I could add a FutureWarning or so, but I am not sure
and overall
On Fri, 2013-09-27 at 09:26 -0400, Benjamin Root wrote:
snip
Boolean indexing could use a facelift. First, consider the following
(albeit minor) annoyance:
Done. Well will be deprecation warnings for the time being, though.
snip
Next, it would be nice if boolean indexing returned a
On Fri, 2013-09-27 at 08:45 -0700, Jaime Fernández del Río wrote:
On Fri, Sep 27, 2013 at 5:27 AM, Sebastian Berg
sebast...@sipsolutions.net wrote:
And most importantly, is there any behaviour thing in the
index
machinery that is bugging you, which I may
On Sun, 2013-09-22 at 10:21 -0400, David Reed wrote:
Hi,
I am getting a strange error when finding the minimum of a matrix.
The weird thing is I get this while running within iPython shell, and
if I do %debug and go to the line where this fails and run the command
`a = np.min(D,
22, 2013 at 10:42 AM, Sebastian Berg
sebast...@sipsolutions.net wrote:
On Sun, 2013-09-22 at 10:21 -0400, David Reed wrote:
Hi,
I am getting a strange error when finding the
minimum
On Wed, 2013-09-04 at 22:08 -0700, Christoph Gohlke wrote:
On 9/1/2013 9:54 AM, Charles R Harris wrote:
snip
Hello,
is this IndexError intentional in numpy 1.8? Matplotlib 1.3 fails some
tests because of this.
numpy.zeros(1)[[0], :]
Traceback (most recent call last):
File
On Fri, 2013-08-23 at 07:59 -0700, Chris Barker - NOAA Federal wrote:
On Aug 22, 2013, at 11:57 PM, David Cournapeau courn...@gmail.com
wrote:
snip
arch -32 python -c import numpy as np; print np.dtype(np.int);
print np.dtype(np.long)
int32
int64
So this is giving us a 64 bit
On Sat, 2013-07-13 at 11:28 -0400, josef.p...@gmail.com wrote:
On Sat, Jul 13, 2013 at 9:14 AM, Nathaniel Smith n...@pobox.com wrote:
snip
I'm now +1 on the exception that Sebastian proposed.
I like consistency, and having a more straightforward mental model of
the numpy behavior for
On Tue, 2013-07-23 at 10:22 -0600, Charles R Harris wrote:
On Tue, Jul 23, 2013 at 8:46 AM, Pauli Virtanen p...@iki.fi wrote:
23.07.2013 17:34, Benjamin Root kirjoitti:
[clip]
Don't assume .flat is not commonly used. A common idiom in
matlab is
On Fri, 2013-07-19 at 16:31 +0100, Nathaniel Smith wrote:
On Thu, Jul 18, 2013 at 2:23 PM, Sebastian Berg
sebast...@sipsolutions.net wrote:
It is so difficult because of the fact that dot is basically a
combination of many functions:
o vector * vector - vector
o vector * matrix
On Fri, 2013-07-19 at 16:14 +0100, Nathaniel Smith wrote:
On Thu, Jul 18, 2013 at 2:23 PM, Sebastian Berg
sebast...@sipsolutions.net wrote:
On Thu, 2013-07-18 at 13:52 +0100, Nathaniel Smith wrote:
Hi all,
snip
What I mean is: Suppose we wrote a gufunc for 'sum', where the
intrinsic
On Thu, 2013-07-18 at 13:52 +0100, Nathaniel Smith wrote:
Hi all,
snip
So:
QUESTION 1: does that sound right: that in a perfect world, the
current gufunc convention would be the only one, and that's what we
should work towards, at least in the cases where that's possible?
Sounds
!
Ben Root
On Fri, Jul 12, 2013 at 8:38 AM, Sebastian Berg
sebast...@sipsolutions.net wrote:
Hey,
the array comparisons
On Mon, 2013-07-15 at 07:52 -0600, Charles R Harris wrote:
On Sun, Jul 14, 2013 at 3:35 PM, Charles R Harris
charlesr.har...@gmail.com wrote:
snip
For nansum, I would expect 0 even in the case of all
nans. The point
On Mon, 2013-07-15 at 08:47 -0600, Charles R Harris wrote:
On Mon, Jul 15, 2013 at 8:34 AM, Sebastian Berg
sebast...@sipsolutions.net wrote:
On Mon, 2013-07-15 at 07:52 -0600, Charles R Harris wrote:
On Sun, Jul 14, 2013 at 3:35 PM, Charles R Harris
On Mon, 2013-07-15 at 17:12 +0200, bruno Piguet wrote:
2013/7/15 Frédéric Bastien no...@nouiz.org
Just a question, should == behave like a ufunc or like python
== for tuple?
That's what I was also wondering.
I am not sure I understand the question. Of
On Fri, 2013-07-12 at 19:29 -0400, josef.p...@gmail.com wrote:
On Fri, Jul 12, 2013 at 3:35 PM, Frédéric Bastien no...@nouiz.org wrote:
I also don't like that idea, but I'm not able to come to a good reasoning
like Benjamin.
I don't see advantage to this change and the reason isn't good
Hey,
the array comparisons == and != never raise errors but instead simply
return False for invalid comparisons.
The main example are arrays of non-matching dimensions, and object
arrays with invalid element-wise comparisons:
In [1]: np.array([1,2,3]) == np.array([1,2])
Out[1]: False
In [2]:
On Tue, 2013-07-09 at 15:14 +0200, Stéfan van der Walt wrote:
On Tue, Jul 9, 2013 at 2:55 PM, Chao YUE chaoyue...@gmail.com wrote:
I am using 1.7.1 version of numpy and np.ma.argmax is not repecting the
mask?
In [96]: d3
Out[96]:
masked_array(data =
[[-- -- -- -- 4]
[5 -- 7 8
On Tue, 2013-07-02 at 20:44 -0700, Bradley M. Froehle wrote:
A colleague just showed me this indexing behavior and I was at a loss
to explain what was going on. Can anybody else chime in and help me
understand this indexing behavior?
import numpy as np
np.__version__
'1.7.1'
On Mon, 2013-07-01 at 14:11 +0200, Félix Hartmann wrote:
Hi all,
I recently upgraded from Numpy 1.6.2 to 1.7.1 on my Debian testing, and
then got a bug in a program that was previously working. It turned out
that the problem comes from the np.insert function when the argument
`axis=-1` is
On Mon, 2013-07-01 at 17:54 +0200, Sebastian Berg wrote:
On Mon, 2013-07-01 at 14:11 +0200, Félix Hartmann wrote:
Hi all,
I recently upgraded from Numpy 1.6.2 to 1.7.1 on my Debian testing, and
then got a bug in a program that was previously working. It turned out
that the problem
On Fri, 2013-06-28 at 07:44 -0500, David Cournapeau wrote:
Hi there,
It is very last minute, but I have set up a page to coordinate a bit
scipy 2013's numpy sprints (Friday 28 and Saturday 29th, although I
may not be there the second day).
Depending on the audience, I will also look
On Wed, 2013-06-26 at 11:30 -0400, josef.p...@gmail.com wrote:
Is there a change in the behavior of boolean slicing in current master?
Yes, but I think this is probably a bug in statsmodel. I would expect
you should be using ... and not : here, because : requires the
dimension to actually
On Wed, 2013-06-26 at 12:52 -0400, josef.p...@gmail.com wrote:
On Wed, Jun 26, 2013 at 12:01 PM, Sebastian Berg
sebast...@sipsolutions.net wrote:
On Wed, 2013-06-26 at 11:30 -0400, josef.p...@gmail.com wrote:
Is there a change in the behavior of boolean slicing in current master?
Yes
Just to note, I disabled most github notifications for time reasons for
the next months, so if something pops up that you think I should look
at, use @mention.
- Sebastian
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
On Thu, 2013-06-13 at 16:50 +0200, Pietro Bonfa' wrote:
Dear Numpy users,
I have a memory leak in my code. A simple way to reproduce my problem is:
import numpy
class test():
def __init__(self):
pass
def t(self):
temp = numpy.zeros([200,100,100])
A
On Tue, 2013-06-11 at 09:24 -0700, Jaime Fernández del Río wrote:
I noticed today that the documentation for np.transpose states, for
the return value, that A view is returned whenever possible.
I guess a subclass could cause a copy (the code looks like subclassing
doing something fancy is
On Sat, 2013-06-08 at 00:48 +0100, Nathaniel Smith wrote:
On 7 Jun 2013 21:58, josef.p...@gmail.com wrote:
Interesting observation, (while lurking on a pull request)
np.add.reduce(np.arange(5)3)
3
np.add((np.arange(5)3), (np.arange(5)3))
array([ True, True, True, False, False],
On Fri, 2013-06-07 at 20:29 -0400, josef.p...@gmail.com wrote:
On Fri, Jun 7, 2013 at 8:08 PM, josef.p...@gmail.com wrote:
On Fri, Jun 7, 2013 at 7:48 PM, Nathaniel Smith n...@pobox.com wrote:
On 7 Jun 2013 21:58, josef.p...@gmail.com wrote:
Interesting observation, (while lurking on a
On Sat, 2013-06-08 at 08:52 -0400, josef.p...@gmail.com wrote:
Is there anything to require a numpy array with a minimum numeric dtype?
To avoid lower precision calculations and be upwards compatible, something
like
x = np.asarray(x, =np.float64)
np.result_type(arr, np.float64) uses the
On Mon, 2013-06-03 at 10:44 +0200, Chao YUE wrote:
Dear all,
I have an array with 4 dim:
In [24]: dd.shape
Out[24]: (12, 13, 120, 170)
I would like to collapse the last two dim for applying np.sum(axis=-1)
If you use Numpy = 1.7. you can also just use dd.sum(axis=(-1,-2))
- Sebastian
On Sat, 2013-06-01 at 17:47 -0600, Charles R Harris wrote:
On Sat, Jun 1, 2013 at 4:50 PM, Warren Weckesser
warren.weckes...@gmail.com wrote:
I'm getting a failure and two errors with the latest master
branch:
$ python -c import numpy; numpy.test('full')
On Fri, 2013-05-31 at 16:32 -0400, josef.p...@gmail.com wrote:
On Fri, May 31, 2013 at 2:08 PM, Sebastian Berg
sebast...@sipsolutions.net wrote:
Hi,
the current numpy master has deprecated non-integers for the use of
indexing (not-fancy yet). However I think this should be moved further
Hi,
the current numpy master has deprecated non-integers for the use of
indexing (not-fancy yet). However I think this should be moved further
down in the numpy machinery which means that the conversion utils
provided by numpy would generally raise warnings for non-integers.
This means that for
into problems.
- Sebastian
thanks
Fred
On Sat, May 11, 2013 at 11:41 AM, Sebastian Berg
sebast...@sipsolutions.net wrote:
Hey,
(this is only interesting if you know what MapIter and
actually use it)
In case anyone already uses
On Fri, 2013-05-10 at 19:57 -0400, Neal Becker wrote:
It would be convenient if in arithmetic 0-d arrays were just ignored - it
would
seem to me to be convenient in generic code where a degenerate array is
treated
as nothing
Small naming detail. A 0-d array is an array with exactly one
On Sat, 2013-05-11 at 08:30 -0400, Neal Becker wrote:
Sebastian Berg wrote:
On Fri, 2013-05-10 at 19:57 -0400, Neal Becker wrote:
It would be convenient if in arithmetic 0-d arrays were just ignored - it
would seem to me to be convenient in generic code where a degenerate array
On Fri, 2013-05-10 at 15:35 -0400, Frédéric Bastien wrote:
I'm trying to do it, but each time I want to test something, it takes
a long time to rebuild numpy to test it. Is there a way to don't
recompile everything for each test?
Are you using current master? It defaults to use
On Mon, 2013-05-06 at 12:11 -0400, Yaroslav Halchenko wrote:
On Mon, 06 May 2013, Sebastian Berg wrote:
if you care to tune it up/extend and then I could fire it up again on
that box (which doesn't do anything else ATM AFAIK). Since majority of
time is spent actually building it (did
On Mon, 2013-05-06 at 11:39 +0200, Daniele Nicolodi wrote:
On 06/05/2013 11:01, Robert Kern wrote:
np.roll() copies all of the data every time. It does not return a
view.
Are you sure about that? Either I'm missing something, or it returns a
view in my testing (with a fairly old numpy,
On Mon, 2013-05-06 at 10:32 -0400, Yaroslav Halchenko wrote:
On Wed, 01 May 2013, Sebastian Berg wrote:
btw -- is there something like panda's vbench for numpy? i.e. where
it would be possible to track/visualize such performance
improvements/hits?
Sorry if it seemed harsh
On Thu, 2013-05-02 at 07:03 -0400, Nathaniel Smith wrote:
On 1 May 2013 23:12, Charles R Harris charlesr.har...@gmail.com
wrote:
On Wed, May 1, 2013 at 7:10 PM, Benjamin Root ben.r...@ou.edu
wrote:
So, to summarize the thread so far:
Consensus:
np.nanmean()
np.nanstd()
On Tue, 2013-04-30 at 22:20 -0700, Matthew Brett wrote:
Hi,
On Tue, Apr 30, 2013 at 9:16 PM, Matthew Brett matthew.br...@gmail.com
wrote:
Hi,
On Tue, Apr 30, 2013 at 8:08 PM, Yaroslav Halchenko
li...@onerussian.com wrote:
could anyone on 32bit system with fresh numpy (1.7.1) test
On Wed, 2013-05-01 at 15:29 -0400, Yaroslav Halchenko wrote:
just for completeness... I haven't yet double checked if I have done it
correctly but here is the bisected commit:
aed9925a9d5fe9a407d0ca2c65cb577116c4d0f1 is the first bad commit
commit aed9925a9d5fe9a407d0ca2c65cb577116c4d0f1
On Wed, 2013-05-01 at 16:37 -0400, Yaroslav Halchenko wrote:
On Wed, 01 May 2013, Sebastian Berg wrote:
There really is no point discussing here, this has to do with numpy
doing iteration order optimization, and you actually *want* this. Lets
for a second assume that the old behavior
On Mon, 2013-04-29 at 11:15 -0400, josef.p...@gmail.com wrote:
Is there a available function to convert an int to binary
representation as sequence of 0 and 1?
Maybe unpackbits/packbits? It only supports the uint8 type, but you can
view anything as that (being aware of endianess where
On Thu, 2013-04-25 at 09:16 -0600, Charles R Harris wrote:
Hi All,
I think it is time to start the runup to the 1.8 release. I don't know
of any outstanding blockers but if anyone has a PR/issue that they
feel needs to be in the next Numpy release now is the time to make it
known.
Sounds
On Thu, 2013-04-25 at 14:04 -0600, Charles R Harris wrote:
On Thu, Apr 25, 2013 at 1:51 PM, josef.p...@gmail.com wrote:
On Thu, Apr 25, 2013 at 3:40 PM, Robert Kern
robert.k...@gmail.com wrote:
On Thu, Apr 25, 2013 at 8:21 PM, Andrew Giessel
On Tue, 2013-04-23 at 23:33 -0400, josef.p...@gmail.com wrote:
On Tue, Apr 23, 2013 at 6:16 PM, Sebastian Berg
sebast...@sipsolutions.net wrote:
On Tue, 2013-04-23 at 12:13 -0500, Jonathan Helmus wrote:
Back in December it was pointed out on the scipy-user list[1] that
numpy has
that you don't remove the part that we use for the
next 1.8 release.
thanks
Frédéric
On Tue, Apr 16, 2013 at 9:54 AM, Nathaniel Smith n...@pobox.com
wrote:
On Mon, Apr 15, 2013 at 5:29 PM, Sebastian Berg
sebast...@sipsolutions.net wrote:
Hey
moved to enhancing the Numpy version with
Pull Request 2970 [3]. With some input from Sebastian Berg the
percentile function was rewritten with further vectorization, but
neither of us felt fully comfortable with the final product. Can
someone look at implementation in the PR and suggest
Hi,
just something that has been spooking around in my mind. Considering
that matrix indexing does not really support fancy indexing, I was
wondering about introducing a KeepDims flag. Maybe it is not worth it,
at least not unless other subclasses could make use of it, too. And a
big reason for
On Fri, 2013-04-19 at 08:03 -0700, Chris Barker - NOAA Federal wrote:
On Apr 18, 2013, at 11:33 PM, Nathaniel Smith n...@pobox.com wrote:
On 18 Apr 2013 01:29, Chris Barker - NOAA Federal
chris.bar...@noaa.gov wrote:
This has been annoying, particular as rank-zero scalars are kind
On Fri, 2013-04-19 at 23:02 +0530, Robert Kern wrote:
On Fri, Apr 19, 2013 at 9:40 PM, Sebastian Berg
sebast...@sipsolutions.net wrote:
Fun fact, array[()] will convert a 0-d array to a scalar, but do nothing
(or currently create a view) for other arrays. Which is actually a good
Hey,
so I ignored trying to redo MapIter (likely it is lobotomized at this
time though). But actually got a working new index parsing (still needs
cleanup, etc.). Also some of the fast paths are not yet put back. For
most pure integer indices it got a bit slower, if it actually gets too
much one
On Wed, 2013-04-17 at 09:07 -0700, Chris Barker - NOAA Federal wrote:
On Wed, Apr 17, 2013 at 9:04 AM, Chris Barker - NOAA Federal
chris.bar...@noaa.gov wrote:
On Tue, Apr 16, 2013 at 8:23 PM, Zachary Ploskey zplos...@gmail.com wrote:
I'd say we need some more unit-tests!
speaking of
On Mon, 2013-04-15 at 13:36 -0600, Charles R Harris wrote:
On Mon, Apr 15, 2013 at 1:27 PM, Sebastian Berg
sebast...@sipsolutions.net wrote:
On Mon, 2013-04-15 at 11:16 -0600, Charles R Harris wrote:
On Mon, Apr 15, 2013 at 10:29 AM, Sebastian Berg
Hey,
the MapIter API has only been made public in master right? So it is no
problem at all to change at least the mapiter struct, right?
I got annoyed at all those special cases that make things difficult to
get an idea where to put i.e. to fix the boolean array-like stuff. So
actually started
On Mon, 2013-04-15 at 11:16 -0600, Charles R Harris wrote:
On Mon, Apr 15, 2013 at 10:29 AM, Sebastian Berg
sebast...@sipsolutions.net wrote:
Hey,
the MapIter API has only been made public in master right? So
it is no
problem at all to change
On Fri, 2013-04-12 at 10:50 -0400, Andrew Nelson wrote:
I have written a differential evolution optimiser that i use for
curvefitting. As a genetic optimisation technique it is stochastic and
relies heavily on random number generators to do the minimisation. As
part
of the
Hey all,
just revisiting non-integer (index) deprecations (basically
https://github.com/numpy/numpy/pull/2891). I believe for all natural
integer arguments, it is correct to do a deprecation if the input is not
an integer. (Technically most of these go through PyArray_PyIntAsIntp,
and if not
On Wed, 2013-03-06 at 11:43 -0700, Charles R Harris wrote:
Hi All,
snip
The development branch has been accumulating stuff since last summer,
I suggest we look to get it out in May, branching at the end of this
month.
Hey,
maybe it is a bit early, but I was wondering. What are the things
On Wed, 2013-04-10 at 11:45 +0200, Sebastian Berg wrote:
On Wed, 2013-04-10 at 11:54 +0300, Dmitrey wrote:
On 04/10/2013 10:31 AM, Robert Kern wrote:
snip
This is all good and nice, but Robert is still right. For dictionaries
to work predictable you need to ensure two things.
First
On Wed, 2013-04-10 at 11:45 +0200, Sebastian Berg wrote:
On Wed, 2013-04-10 at 11:54 +0300, Dmitrey wrote:
On 04/10/2013 10:31 AM, Robert Kern wrote:
You think comparing tracked bug counts across different projects
means anything? That's adorable. I admire your diligence at
addressing
On Thu, 2013-04-04 at 16:56 +0300, Jaakko Luttinen wrote:
I don't quite understand how einsum handles broadcasting. I get the
following error, but I don't understand why:
In [8]: import numpy as np
In [9]: A = np.arange(12).reshape((4,3))
In [10]: B = np.arange(6).reshape((3,2))
In [11]:
On Thu, 2013-04-04 at 12:40 -0700, Matthew Brett wrote:
Hi,
snip
So - to restate in other words - this :
np.reshape(a, (3, 4), order='F')
could reasonably mean one of two orthogonal things
1) Retrieve data from the array using first-to-last indexing, return
any memory layout you
On Sun, 2013-03-31 at 14:04 -0700, Matthew Brett wrote:
Hi,
On Sun, Mar 31, 2013 at 1:43 PM, josef.p...@gmail.com wrote:
On Sun, Mar 31, 2013 at 3:54 PM, Matthew Brett matthew.br...@gmail.com
wrote:
Hi,
On Sat, Mar 30, 2013 at 10:38 PM, josef.p...@gmail.com wrote:
On Sun, Mar
On Fri, 2013-03-29 at 19:08 -0700, Matthew Brett wrote:
Hi,
We were teaching today, and found ourselves getting very confused
about ravel and shape in numpy.
Summary
--
There are two separate ideas needed to understand ordering in ravel and
reshape:
Idea 1): ravel /
On Sat, 2013-03-30 at 12:45 -0700, Matthew Brett wrote:
Hi,
On Sat, Mar 30, 2013 at 11:55 AM, Sebastian Berg
sebast...@sipsolutions.net wrote:
On Fri, 2013-03-29 at 19:08 -0700, Matthew Brett wrote:
Hi,
We were teaching today, and found ourselves getting very confused
about ravel
On Thu, 2013-03-21 at 22:20 +0100, Ralf Gommers wrote:
Hi all,
It is the time of the year for Google Summer of Code applications. If
we want to participate with Numpy and/or Scipy, we need two things:
enough mentors and ideas for projects. If we get those, we'll apply
under the PSF
Hey,
how would I go about making a compile time flag for numpy to use as a
macro?
The reason is: https://github.com/numpy/numpy/pull/2735
so that it would be possible to compile numpy differently for debugging
if software depending on numpy is broken by this change.
Regards,
Sebastian
On Sat, 2013-03-09 at 17:17 +0100, Sebastian Berg wrote:
Hey,
how would I go about making a compile time flag for numpy to use as a
macro?
To be clear I mean an environment variable.
The reason is: https://github.com/numpy/numpy/pull/2735
so that it would be possible to compile numpy
On Wed, 2013-03-06 at 12:42 -0600, Kurt Smith wrote:
On Wed, Mar 6, 2013 at 12:12 PM, Kurt Smith kwmsm...@gmail.com wrote:
On Wed, Mar 6, 2013 at 4:29 AM, Francesc Alted franc...@continuum.io
wrote:
I would not run too much. The example above takes 9 bytes to host the
structure, while
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