In NumPy 1.6.0, I get the following behaviour:
Python 2.7.2 |EPD 7.1-1 (32-bit)| (default, Jul 3 2011, 15:40:35)
[GCC 4.0.1 (Apple Inc. build 5493)] on darwin
Type packages, demo or enthought for more information.
import numpy
numpy.nanmin(numpy.ma.masked_array([1,2,3,4]))
Traceback (most
On Wed, Jul 27, 2011 at 2:49 AM, Mark Dickinson mdickin...@enthought.comwrote:
In NumPy 1.6.0, I get the following behaviour:
Python 2.7.2 |EPD 7.1-1 (32-bit)| (default, Jul 3 2011, 15:40:35)
[GCC 4.0.1 (Apple Inc. build 5493)] on darwin
Type packages, demo or enthought for more
The return type of PyArray_BYTES in the old API compatibility code seems
to have changed recently to (void *) which breaks matplotlib builds.
This pull request changes it back. Is this correct?
https://github.com/numpy/numpy/pull/121
Mike
___
Looks good. It might be good to change it back to (void *) for the
PyArray_DATA inline function as well, I changed that during lots of tweaking
to get things to build properly.
-Mark
On Wed, Jul 27, 2011 at 11:46 AM, Michael Droettboom md...@stsci.eduwrote:
The return type of PyArray_BYTES in
MacOS Lion:
numpy.sqrt([complex(numpy.nan, numpy.inf)])
array([ nan+infj])
Other all system:
array([ inf+infj])
This causes a few numpy tests to fail on Lion. The numpy
was not compiled using the new LLVM based gcc, it is the
same numpy binary I used on other MacOS systems, which
was compiled
Hi,
I see that (current trunk):
In [9]: np.ones((1,), dtype=bool)
Out[9]: array([ True], dtype='bool')
- whereas (1.5.1):
In [2]: np.ones((1,), dtype=bool)
Out[2]: array([ True], dtype=bool)
That is breaking quite a few doctests. What is the reason for the
change? Something to do with more
This was the most consistent way to deal with the parameterized dtype in the
repr, making it more future-proof at the same time. It was producing reprs
like array(['2011-01-01'], dtype=datetime64[D]), which is clearly wrong,
and putting quotes around it makes it work in general for all possible
On Wed, Jul 27, 2011 at 7:17 PM, Ilan Schnell ischn...@enthought.comwrote:
MacOS Lion:
numpy.sqrt([complex(numpy.nan, numpy.inf)])
array([ nan+infj])
Other all system:
array([ inf+infj])
This causes a few numpy tests to fail on Lion. The numpy
was not compiled using the new LLVM based
Hi,
On Wed, Jul 27, 2011 at 6:54 PM, Mark Wiebe mwwi...@gmail.com wrote:
This was the most consistent way to deal with the parameterized dtype in the
repr, making it more future-proof at the same time. It was producing reprs
like array(['2011-01-01'], dtype=datetime64[D]), which is clearly
Thanks for you quick response Ralf. Regarding binaries, we are
trying to avoid to different EPD binaries for different versions of OSX,
as maintaining/distributing/testing more binaries is quite expensive.
- Ilan
On Wed, Jul 27, 2011 at 12:58 PM, Ralf Gommers
ralf.gomm...@googlemail.com wrote:
On Wed, Jul 27, 2011 at 8:18 PM, Ilan Schnell ischn...@enthought.comwrote:
Thanks for you quick response Ralf. Regarding binaries, we are
trying to avoid to different EPD binaries for different versions of OSX,
as maintaining/distributing/testing more binaries is quite expensive.
Agreed, it
In article
cabl7cqj4i6stf_qjndvch66fsfc5bjq9etpx3ukczaxyyuw...@mail.gmail.com,
Ralf Gommers ralf.gomm...@googlemail.com wrote:
On Wed, Jul 27, 2011 at 7:17 PM, Ilan Schnell ischn...@enthought.comwrote:
MacOS Lion:
numpy.sqrt([complex(numpy.nan, numpy.inf)])
array([ nan+infj])
On Wed, Jul 27, 2011 at 6:58 AM, Charles R Harris charlesr.har...@gmail.com
wrote:
On Wed, Jul 27, 2011 at 2:49 AM, Mark Dickinson
mdickin...@enthought.comwrote:
In NumPy 1.6.0, I get the following behaviour:
Python 2.7.2 |EPD 7.1-1 (32-bit)| (default, Jul 3 2011, 15:40:35)
[GCC
On Wed, Jul 27, 2011 at 1:01 PM, Matthew Brett matthew.br...@gmail.comwrote:
Hi,
On Wed, Jul 27, 2011 at 6:54 PM, Mark Wiebe mwwi...@gmail.com wrote:
This was the most consistent way to deal with the parameterized dtype in
the
repr, making it more future-proof at the same time. It was
On Wed, Jul 27, 2011 at 2:20 PM, Charles R Harris charlesr.har...@gmail.com
wrote:
On Wed, Jul 27, 2011 at 6:58 AM, Charles R Harris
charlesr.har...@gmail.com wrote:
On Wed, Jul 27, 2011 at 2:49 AM, Mark Dickinson mdickin...@enthought.com
wrote:
In NumPy 1.6.0, I get the following
On Wed, Jul 27, 2011 at 9:00 PM, Russell E. Owen ro...@uw.edu wrote:
In article
cabl7cqj4i6stf_qjndvch66fsfc5bjq9etpx3ukczaxyyuw...@mail.gmail.com,
Ralf Gommers ralf.gomm...@googlemail.com wrote:
On Wed, Jul 27, 2011 at 7:17 PM, Ilan Schnell ischn...@enthought.com
wrote:
MacOS Lion:
Hi,
On Wed, Jul 27, 2011 at 12:25 PM, Mark Wiebe mwwi...@gmail.com wrote:
On Wed, Jul 27, 2011 at 1:01 PM, Matthew Brett matthew.br...@gmail.com
wrote:
Hi,
On Wed, Jul 27, 2011 at 6:54 PM, Mark Wiebe mwwi...@gmail.com wrote:
This was the most consistent way to deal with the parameterized
On Wed, Jul 27, 2011 at 2:44 PM, Matthew Brett matthew.br...@gmail.comwrote:
Hi,
On Wed, Jul 27, 2011 at 12:25 PM, Mark Wiebe mwwi...@gmail.com wrote:
On Wed, Jul 27, 2011 at 1:01 PM, Matthew Brett matthew.br...@gmail.com
wrote:
Hi,
On Wed, Jul 27, 2011 at 6:54 PM, Mark Wiebe
On Wed, Jul 27, 2011 at 3:09 PM, Robert Kern robert.k...@gmail.com wrote:
On Wed, Jul 27, 2011 at 14:47, Mark Wiebe mwwi...@gmail.com wrote:
On Wed, Jul 27, 2011 at 2:44 PM, Matthew Brett matthew.br...@gmail.com
wrote:
Hi,
On Wed, Jul 27, 2011 at 12:25 PM, Mark Wiebe
Please don't distribute a different numpy binary for each version of
MacOS X.
+1
Maybe I should mention that I just finished testing all Python
packages in EPD under 10.7, and everything (execpt numpy.sqr
for weird complex values such as inf/nan) works fine!
In particular building C and
Hi,
On Wed, Jul 27, 2011 at 1:12 PM, Mark Wiebe mwwi...@gmail.com wrote:
On Wed, Jul 27, 2011 at 3:09 PM, Robert Kern robert.k...@gmail.com wrote:
On Wed, Jul 27, 2011 at 14:47, Mark Wiebe mwwi...@gmail.com wrote:
On Wed, Jul 27, 2011 at 2:44 PM, Matthew Brett matthew.br...@gmail.com
When applying two different slicing operations in succession (e.g. select a
sub-range, then select using a binary mask) it seems that numpy arrays can
be inconsistent with respect to assignment:
For example, in this case an array is modified:
In [6]: *A = np.arange(5)*
In [8]: *A[:][A2] = 0*
In
Dear experts,
is there a C-API function for numpy which implements Python's
multidimensional indexing? Say, I have a 2d-array
PyArrayObject * M;
and an index
int i;
how do I extract the i-th row or column M[i,:] respectively M[:,i]?
I am looking for a function which gives again a
On Wed, Jul 27, 2011 at 3:09 PM, Robert Kern robert.k...@gmail.com wrote:
On Wed, Jul 27, 2011 at 14:47, Mark Wiebe mwwi...@gmail.com wrote:
On Wed, Jul 27, 2011 at 2:44 PM, Matthew Brett matthew.br...@gmail.com
wrote:
Hi,
On Wed, Jul 27, 2011 at 12:25 PM, Mark Wiebe
On Wed, Jul 27, 2011 at 5:36 PM, Alex Flint alex.fl...@gmail.com wrote:
When applying two different slicing operations in succession (e.g. select a
sub-range, then select using a binary mask) it seems that numpy arrays can
be inconsistent with respect to assignment:
For example, in this case
On Wed, Jul 27, 2011 at 4:32 PM, Matthew Brett matthew.br...@gmail.comwrote:
Hi,
On Wed, Jul 27, 2011 at 1:12 PM, Mark Wiebe mwwi...@gmail.com wrote:
On Wed, Jul 27, 2011 at 3:09 PM, Robert Kern robert.k...@gmail.com
wrote:
On Wed, Jul 27, 2011 at 14:47, Mark Wiebe mwwi...@gmail.com
On Wed, Jul 27, 2011 at 04:59:17PM -0500, Mark Wiebe wrote:
but ultimately NumPy needs the ability to change its repr and other
details like it in order to progress as a software project.
You have to understand that numpy is the core layer on which people have
build pretty huge scientific
On Wed, Jul 27, 2011 at 5:07 PM, Gael Varoquaux
gael.varoqu...@normalesup.org wrote:
On Wed, Jul 27, 2011 at 04:59:17PM -0500, Mark Wiebe wrote:
but ultimately NumPy needs the ability to change its repr and other
details like it in order to progress as a software project.
You have
Hi,
I was trying to compile matplotlib against current trunk, and hit an
error with this line:
char* row0 = PyArray_BYTES(matrix);
https://github.com/matplotlib/matplotlib/blob/master/src/agg_py_transforms.cpp
The error is:
src/agg_py_transforms.cpp:30:26: error: invalid
On Wed, Jul 27, 2011 at 5:35 PM, Matthew Brett matthew.br...@gmail.comwrote:
Hi,
I was trying to compile matplotlib against current trunk, and hit an
error with this line:
char* row0 = PyArray_BYTES(matrix);
Hi,
On Wed, Jul 27, 2011 at 3:23 PM, Mark Wiebe mwwi...@gmail.com wrote:
On Wed, Jul 27, 2011 at 5:07 PM, Gael Varoquaux
gael.varoqu...@normalesup.org wrote:
On Wed, Jul 27, 2011 at 04:59:17PM -0500, Mark Wiebe wrote:
but ultimately NumPy needs the ability to change its repr and other
Thanks, Mark! Problem solved.
Johann
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On Wed, Jul 27, 2011 at 5:47 PM, Matthew Brett matthew.br...@gmail.comwrote:
Hi,
On Wed, Jul 27, 2011 at 3:23 PM, Mark Wiebe mwwi...@gmail.com wrote:
On Wed, Jul 27, 2011 at 5:07 PM, Gael Varoquaux
gael.varoqu...@normalesup.org wrote:
On Wed, Jul 27, 2011 at 04:59:17PM -0500, Mark
On Wed, Jul 27, 2011 at 4:07 PM, Gael Varoquaux
gael.varoqu...@normalesup.org wrote:
On Wed, Jul 27, 2011 at 04:59:17PM -0500, Mark Wiebe wrote:
but ultimately NumPy needs the ability to change its repr and other
details like it in order to progress as a software project.
You have
On Wed, Jul 27, 2011 at 05:25:20PM -0600, Charles R Harris wrote:
Well, doc tests are just a losing proposition, no one should be using them
for writing tests. It's not like this is a new discovery, doc tests have
been known to be unstable for years.
Untested documentation is broken
Hi Travis, hi Olivier,
Thanks for your replies last month about the choose() issue.
I did some further investigation into this. I ran out of time in that
project to come up with a patch, but here's what I found, which may be of
interest:
The compile-time constant NPY_MAXARGS is indeed limiting
To use quaternions I find I often need conversion to/from matrices and to/from
Euler angles. Will you add that functionality? Will you handle the left
versor and right versor versions?
I have a set of pure python code I've sketched out for my needs (aerospace) but
would be happy to have an
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