Re: [Numpy-discussion] ANN: NumPy 1.9.0 beta release

2014-06-12 Thread Sebastian Berg
On Mo, 2014-06-09 at 16:21 -0700, Christoph Gohlke wrote:
 On 6/8/2014 1:34 PM, Julian Taylor wrote:
  Hello,
snip
 
 
 Hello,
 
 I tested numpy-MKL-1.9.0b1 (msvc9, Intel MKL build) on win-amd64-py2.7 
 against a few other packages that were built against numpy-MKL-1.8.x.
 
 While numpy and scipy pass all tests, some other packages (matplotlib, 
 statsmodels, skimage, pandas, pytables, sklearn...) show a few new test 
 failures (compared to testing with numpy-MKL-1.8.1). Many test errors 
 are of kind:
 
  ValueError: shape mismatch: value array of shape (24,) could not be 
 broadcast to indexing result of shape (8,3)
 
 I have attached a list of failing tests. The full test results are at 
 http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20140609-win-amd64-py2.7-numpy-1.9.0b1/
  
 (compare to 
 http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20140609-win-amd64-py2.7/)
 
 I have not investigated any further...
 

I have put up a sketch for the indexing fix at
https://github.com/numpy/numpy/pull/4804 not sure when I will have time
to finish it off, so if someone has, go ahead ;). It should fix all
those indexing errors (though numpy has harmless test failures with it
currently). Deprecationwarnings (and not there yet FutureWarnings for
the error type change) should be given then.

- Sebastian

 Christoph
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Re: [Numpy-discussion] ANN: NumPy 1.9.0 beta release

2014-06-10 Thread Sebastian Berg
On Mo, 2014-06-09 at 18:21 -0600, Charles R Harris wrote:
 
 
 
 On Mon, Jun 9, 2014 at 6:10 PM, Charles R Harris
 charlesr.har...@gmail.com wrote:
 

snip
 
 
 
 
 Other errors are of the type:
 TypeError: NumPy boolean array indexing assignment requires a 0 or 
 1-dimensionalinput, input has 2 dimensions
  This one looks to arise is from stricter rules for boolean indexing.
 

H, I may have removed an if size of the boolean index matches the
size of the output array ignore the shape (dimensions) special cased, I
guess we can put that in again.

The other error looks a bit different because of the nonzero logic, but
probably is the same, i.e. also boolean indexing. The last one is the
change that `arr[[1,2,3,4]] = [1,2]` does not work anymore. A workaround
(maybe also for the rest possibly) is `arr.flat[[1,2,3,4]] = [1,2]`, but
I guess workarounds are not an option with matplotlib, so have to think
about it.

- Sebastian

 Chuck
 
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Re: [Numpy-discussion] ANN: NumPy 1.9.0 beta release

2014-06-10 Thread Sebastian Berg
On Di, 2014-06-10 at 10:43 +0200, Sebastian Berg wrote:
 On Mo, 2014-06-09 at 18:21 -0600, Charles R Harris wrote:
  
  
  
  On Mon, Jun 9, 2014 at 6:10 PM, Charles R Harris
  charlesr.har...@gmail.com wrote:
  
 
 snip
  
  
  
  
  Other errors are of the type:
  TypeError: NumPy boolean array indexing assignment requires a 0 or 
  1-dimensionalinput, input has 2 dimensions
   This one looks to arise is from stricter rules for boolean indexing.
  
 
 H, I may have removed an if size of the boolean index matches the
 size of the output array ignore the shape (dimensions) special cased, I
 guess we can put that in again.
 
 The other error looks a bit different because of the nonzero logic, but
 probably is the same, i.e. also boolean indexing. The last one is the
 change that `arr[[1,2,3,4]] = [1,2]` does not work anymore. A workaround
 (maybe also for the rest possibly) is `arr.flat[[1,2,3,4]] = [1,2]`, but
 I guess workarounds are not an option with matplotlib, so have to think
 about it.

Correction: I think all (indexing) errors are the second case since the
boolean special case is not taken.

 
 - Sebastian
 
  Chuck
  
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Re: [Numpy-discussion] ANN: NumPy 1.9.0 beta release

2014-06-10 Thread Nathaniel Smith
On 10 Jun 2014 09:44, Sebastian Berg sebast...@sipsolutions.net wrote:
 The other error looks a bit different because of the nonzero logic, but
 probably is the same, i.e. also boolean indexing. The last one is the
 change that `arr[[1,2,3,4]] = [1,2]` does not work anymore. A workaround
 (maybe also for the rest possibly) is `arr.flat[[1,2,3,4]] = [1,2]`, but
 I guess workarounds are not an option with matplotlib, so have to think
 about it.

If the beta is breaking code then let's put the change off to 1.10 or so
and raise a deprecation warning in 1.9.

-n
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Re: [Numpy-discussion] ANN: NumPy 1.9.0 beta release

2014-06-10 Thread Sebastian Berg
On Di, 2014-06-10 at 10:50 +0100, Nathaniel Smith wrote:
 On 10 Jun 2014 09:44, Sebastian Berg sebast...@sipsolutions.net
 wrote:
  The other error looks a bit different because of the nonzero logic,
 but
  probably is the same, i.e. also boolean indexing. The last one is
 the
  change that `arr[[1,2,3,4]] = [1,2]` does not work anymore. A
 workaround
  (maybe also for the rest possibly) is `arr.flat[[1,2,3,4]] = [1,2]`,
 but
  I guess workarounds are not an option with matplotlib, so have to
 think
  about it.
 
 If the beta is breaking code then let's put the change off to 1.10 or
 so and raise a deprecation warning in 1.9.
 

Yes, unfortunately it is a bit more complicating than that.

 -n
 
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Re: [Numpy-discussion] ANN: NumPy 1.9.0 beta release

2014-06-10 Thread Nathaniel Smith
On 10 Jun 2014 11:15, Sebastian Berg sebast...@sipsolutions.net wrote:

 On Di, 2014-06-10 at 10:50 +0100, Nathaniel Smith wrote:
  On 10 Jun 2014 09:44, Sebastian Berg sebast...@sipsolutions.net
  wrote:
   The other error looks a bit different because of the nonzero logic,
  but
   probably is the same, i.e. also boolean indexing. The last one is
  the
   change that `arr[[1,2,3,4]] = [1,2]` does not work anymore. A
  workaround
   (maybe also for the rest possibly) is `arr.flat[[1,2,3,4]] = [1,2]`,
  but
   I guess workarounds are not an option with matplotlib, so have to
  think
   about it.
 
  If the beta is breaking code then let's put the change off to 1.10 or
  so and raise a deprecation warning in 1.9.
 

 Yes, unfortunately it is a bit more complicating than that.

Is it impossible to emulate the old arr[[1, 2, 3, 4]] = [1, 2] behavior for
some reason? Or what do you mean? (I'm not suggesting we literally go back
to the 1.8 indexing code.)

-n
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Re: [Numpy-discussion] ANN: NumPy 1.9.0 beta release

2014-06-10 Thread Sebastian Berg
On Di, 2014-06-10 at 11:24 +0100, Nathaniel Smith wrote:
 On 10 Jun 2014 11:15, Sebastian Berg sebast...@sipsolutions.net
 wrote:
 
  On Di, 2014-06-10 at 10:50 +0100, Nathaniel Smith wrote:
   On 10 Jun 2014 09:44, Sebastian Berg
 sebast...@sipsolutions.net
   wrote:
The other error looks a bit different because of the nonzero
 logic,
   but
probably is the same, i.e. also boolean indexing. The last one
 is
   the
change that `arr[[1,2,3,4]] = [1,2]` does not work anymore. A
   workaround
(maybe also for the rest possibly) is `arr.flat[[1,2,3,4]] =
 [1,2]`,
   but
I guess workarounds are not an option with matplotlib, so have
 to
   think
about it.
  
   If the beta is breaking code then let's put the change off to 1.10
 or
   so and raise a deprecation warning in 1.9.
  
 
  Yes, unfortunately it is a bit more complicating than that.
 
 Is it impossible to emulate the old arr[[1, 2, 3, 4]] = [1, 2]
 behavior for some reason? Or what do you mean? (I'm not suggesting we
 literally go back to the 1.8 indexing code.)
 

Yeah, just have to check carefully. Maybe easiest is to just try/except
it in C-code, throw a warning, and (if applicable) try calling the
`arr.flat[...] = ...` code (which still exists).

- Sebastian

 -n
 
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Re: [Numpy-discussion] ANN: NumPy 1.9.0 beta release

2014-06-10 Thread Sankarshan Mudkavi
Still working on it, it's unfortunately taking much more time than I'd 
anticipated.

Cheers,
Sankarshan Mudkavi

On Jun 9, 2014, at 12:10 PM, Chris Barker chris.bar...@noaa.gov wrote:

 I take nothing ever happened to clean up the datetime64 timezone mess?
 
 sigh.
 
 -Chris
 
 
 
 On Sun, Jun 8, 2014 at 1:34 PM, Julian Taylor jtaylor.deb...@googlemail.com 
 wrote:
 Hello,
 
 I'm happy to announce the fist beta release of Numpy 1.9.0.
 1.9.0 will be a new feature release supporting Python 2.6 - 2.7 and 3.2
 - 3.4.
 Due to low demand windows binaries for the beta are only available for
 Python 2.7, 3.3 and 3.4.
 Please try it and report any issues to the numpy-discussion mailing list
 or on github.
 
 The 1.9 release will consists of mainly of many small improvements and
 bugfixes. The highlights are:
 
 * Addition of __numpy_ufunc__ to allow overriding ufuncs in ndarray
 subclasses. Please note that there are still some known issues with this
 mechanism which we hope to resolve before the final release (e.g. #4753)
 * Numerous performance improvements in various areas, most notably
 indexing and operations on small arrays are significantly faster.
 Indexing operations now also release the GIL.
 * Addition of nanmedian and nanpercentile rounds out the nanfunction set.
 
 The changes involve a lot of small changes that might affect some
 applications, please read the release notes for the full details on all
 changes:
 https://github.com/numpy/numpy/blob/maintenance/1.9.x/doc/release/1.9.0-notes.rst
 Please also take special note of the future changes section which will
 apply to the following release 1.10.0 and make sure to check if your
 applications would be affected by them.
 
 Source tarballs, windows installers and release notes can be found at
 https://sourceforge.net/projects/numpy/files/NumPy/1.9.0b1
 
 Cheers,
 Julian Taylor
 
 
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 NOAA/NOS/ORR(206) 526-6959   voice
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Re: [Numpy-discussion] ANN: NumPy 1.9.0 beta release

2014-06-09 Thread Jaime Fernández del Río
On Sun, Jun 8, 2014 at 1:43 PM, Charles R Harris charlesr.har...@gmail.com
wrote:




 On Sun, Jun 8, 2014 at 2:34 PM, Julian Taylor 
 jtaylor.deb...@googlemail.com wrote:

 Hello,

 I'm happy to announce the fist beta release of Numpy 1.9.0.
 1.9.0 will be a new feature release supporting Python 2.6 - 2.7 and 3.2
 - 3.4.
 Due to low demand windows binaries for the beta are only available for
 Python 2.7, 3.3 and 3.4.
 Please try it and report any issues to the numpy-discussion mailing list
 or on github.

 The 1.9 release will consists of mainly of many small improvements and
 bugfixes. The highlights are:

 * Addition of __numpy_ufunc__ to allow overriding ufuncs in ndarray
 subclasses. Please note that there are still some known issues with this
 mechanism which we hope to resolve before the final release (e.g. #4753)
 * Numerous performance improvements in various areas, most notably
 indexing and operations on small arrays are significantly faster.
 Indexing operations now also release the GIL.
 * Addition of nanmedian and nanpercentile rounds out the nanfunction set.

 The changes involve a lot of small changes that might affect some
 applications, please read the release notes for the full details on all
 changes:

 https://github.com/numpy/numpy/blob/maintenance/1.9.x/doc/release/1.9.0-notes.rst
 Please also take special note of the future changes section which will
 apply to the following release 1.10.0 and make sure to check if your
 applications would be affected by them.

 Source tarballs, windows installers and release notes can be found at
 https://sourceforge.net/projects/numpy/files/NumPy/1.9.0b1


 Thanks for this, good job.

 Chuck


This is the first release of numpy I have had any involvement with, and I
am truly amazed at the amount of talent and dedication you guys put into it.

Big, big thank you to everyone!

Jaime



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Re: [Numpy-discussion] ANN: NumPy 1.9.0 beta release

2014-06-09 Thread Chris Barker
I take nothing ever happened to clean up the datetime64 timezone mess?

sigh.

-Chris



On Sun, Jun 8, 2014 at 1:34 PM, Julian Taylor jtaylor.deb...@googlemail.com
 wrote:

 Hello,

 I'm happy to announce the fist beta release of Numpy 1.9.0.
 1.9.0 will be a new feature release supporting Python 2.6 - 2.7 and 3.2
 - 3.4.
 Due to low demand windows binaries for the beta are only available for
 Python 2.7, 3.3 and 3.4.
 Please try it and report any issues to the numpy-discussion mailing list
 or on github.

 The 1.9 release will consists of mainly of many small improvements and
 bugfixes. The highlights are:

 * Addition of __numpy_ufunc__ to allow overriding ufuncs in ndarray
 subclasses. Please note that there are still some known issues with this
 mechanism which we hope to resolve before the final release (e.g. #4753)
 * Numerous performance improvements in various areas, most notably
 indexing and operations on small arrays are significantly faster.
 Indexing operations now also release the GIL.
 * Addition of nanmedian and nanpercentile rounds out the nanfunction set.

 The changes involve a lot of small changes that might affect some
 applications, please read the release notes for the full details on all
 changes:

 https://github.com/numpy/numpy/blob/maintenance/1.9.x/doc/release/1.9.0-notes.rst
 Please also take special note of the future changes section which will
 apply to the following release 1.10.0 and make sure to check if your
 applications would be affected by them.

 Source tarballs, windows installers and release notes can be found at
 https://sourceforge.net/projects/numpy/files/NumPy/1.9.0b1

 Cheers,
 Julian Taylor


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Re: [Numpy-discussion] ANN: NumPy 1.9.0 beta release

2014-06-09 Thread Christoph Gohlke

On 6/8/2014 1:34 PM, Julian Taylor wrote:

Hello,

I'm happy to announce the fist beta release of Numpy 1.9.0.
1.9.0 will be a new feature release supporting Python 2.6 - 2.7 and 3.2
- 3.4.
Due to low demand windows binaries for the beta are only available for
Python 2.7, 3.3 and 3.4.
Please try it and report any issues to the numpy-discussion mailing list
or on github.

The 1.9 release will consists of mainly of many small improvements and
bugfixes. The highlights are:

* Addition of __numpy_ufunc__ to allow overriding ufuncs in ndarray
subclasses. Please note that there are still some known issues with this
mechanism which we hope to resolve before the final release (e.g. #4753)
* Numerous performance improvements in various areas, most notably
indexing and operations on small arrays are significantly faster.
Indexing operations now also release the GIL.
* Addition of nanmedian and nanpercentile rounds out the nanfunction set.

The changes involve a lot of small changes that might affect some
applications, please read the release notes for the full details on all
changes:
https://github.com/numpy/numpy/blob/maintenance/1.9.x/doc/release/1.9.0-notes.rst
Please also take special note of the future changes section which will
apply to the following release 1.10.0 and make sure to check if your
applications would be affected by them.

Source tarballs, windows installers and release notes can be found at
https://sourceforge.net/projects/numpy/files/NumPy/1.9.0b1

Cheers,
Julian Taylor



Hello,

I tested numpy-MKL-1.9.0b1 (msvc9, Intel MKL build) on win-amd64-py2.7 
against a few other packages that were built against numpy-MKL-1.8.x.


While numpy and scipy pass all tests, some other packages (matplotlib, 
statsmodels, skimage, pandas, pytables, sklearn...) show a few new test 
failures (compared to testing with numpy-MKL-1.8.1). Many test errors 
are of kind:


ValueError: shape mismatch: value array of shape (24,) could not be 
broadcast to indexing result of shape (8,3)


I have attached a list of failing tests. The full test results are at 
http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20140609-win-amd64-py2.7-numpy-1.9.0b1/ 
(compare to 
http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20140609-win-amd64-py2.7/)


I have not investigated any further...

Christoph
matplotlib 1.3.1


==
ERROR: test suite for class 
'matplotlib.tests.test_triangulation.test_tri_smooth_contouring'
--
Traceback (most recent call last):
  File X:\Python27-x64\lib\site-packages\nose\suite.py, line 208, in run
self.setUp()
  File X:\Python27-x64\lib\site-packages\nose\suite.py, line 291, in setUp
self.setupContext(ancestor)
  File X:\Python27-x64\lib\site-packages\nose\suite.py, line 314, in 
setupContext
try_run(context, names)
  File X:\Python27-x64\lib\site-packages\nose\util.py, line 470, in try_run
return func()
  File X:\Python27-x64\lib\site-packages\matplotlib\testing\decorators.py, 
line 102, in setup_class
cls._func()
  File 
X:\Python27-x64\lib\site-packages\matplotlib\tests\test_triangulation.py, 
line 715, in test_tri_smooth_contouring
tri_refi, z_test_refi = refiner.refine_field(z0, subdiv=4)
  File X:\Python27-x64\lib\site-packages\matplotlib\tri\trirefine.py, line 
179, in refine_field
subdiv=subdiv, return_tri_index=True)
  File X:\Python27-x64\lib\site-packages\matplotlib\tri\trirefine.py, line 
125, in refine_triangulation
] = np.repeat(ancestors[ancestor_mask], 3)
ValueError: shape mismatch: value array of shape (13824,) could not be 
broadcast to indexing result of shape (4608,3)

==
ERROR: test suite for class 
'matplotlib.tests.test_triangulation.test_tri_smooth_gradient'
--
Traceback (most recent call last):
  File X:\Python27-x64\lib\site-packages\nose\suite.py, line 208, in run
self.setUp()
  File X:\Python27-x64\lib\site-packages\nose\suite.py, line 291, in setUp
self.setupContext(ancestor)
  File X:\Python27-x64\lib\site-packages\nose\suite.py, line 314, in 
setupContext
try_run(context, names)
  File X:\Python27-x64\lib\site-packages\nose\util.py, line 470, in try_run
return func()
  File X:\Python27-x64\lib\site-packages\matplotlib\testing\decorators.py, 
line 102, in setup_class
cls._func()
  File 
X:\Python27-x64\lib\site-packages\matplotlib\tests\test_triangulation.py, 
line 752, in test_tri_smooth_gradient
tri_refi, z_test_refi = refiner.refine_field(V, subdiv=3)
  File X:\Python27-x64\lib\site-packages\matplotlib\tri\trirefine.py, line 
179, in refine_field
subdiv=subdiv, return_tri_index=True)
  File X:\Python27-x64\lib\site-packages\matplotlib\tri\trirefine.py, line 
125, in refine_triangulation
] = np.repeat(ancestors[ancestor_mask], 3)

Re: [Numpy-discussion] ANN: NumPy 1.9.0 beta release

2014-06-09 Thread Charles R Harris
On Mon, Jun 9, 2014 at 5:21 PM, Christoph Gohlke cgoh...@uci.edu wrote:

 On 6/8/2014 1:34 PM, Julian Taylor wrote:

 Hello,

 I'm happy to announce the fist beta release of Numpy 1.9.0.
 1.9.0 will be a new feature release supporting Python 2.6 - 2.7 and 3.2
 - 3.4.
 Due to low demand windows binaries for the beta are only available for
 Python 2.7, 3.3 and 3.4.
 Please try it and report any issues to the numpy-discussion mailing list
 or on github.

 The 1.9 release will consists of mainly of many small improvements and
 bugfixes. The highlights are:

 * Addition of __numpy_ufunc__ to allow overriding ufuncs in ndarray
 subclasses. Please note that there are still some known issues with this
 mechanism which we hope to resolve before the final release (e.g. #4753)
 * Numerous performance improvements in various areas, most notably
 indexing and operations on small arrays are significantly faster.
 Indexing operations now also release the GIL.
 * Addition of nanmedian and nanpercentile rounds out the nanfunction set.

 The changes involve a lot of small changes that might affect some
 applications, please read the release notes for the full details on all
 changes:
 https://github.com/numpy/numpy/blob/maintenance/1.9.x/
 doc/release/1.9.0-notes.rst
 Please also take special note of the future changes section which will
 apply to the following release 1.10.0 and make sure to check if your
 applications would be affected by them.

 Source tarballs, windows installers and release notes can be found at
 https://sourceforge.net/projects/numpy/files/NumPy/1.9.0b1

 Cheers,
 Julian Taylor


 Hello,

 I tested numpy-MKL-1.9.0b1 (msvc9, Intel MKL build) on win-amd64-py2.7
 against a few other packages that were built against numpy-MKL-1.8.x.

 While numpy and scipy pass all tests, some other packages (matplotlib,
 statsmodels, skimage, pandas, pytables, sklearn...) show a few new test
 failures (compared to testing with numpy-MKL-1.8.1). Many test errors are
 of kind:

 ValueError: shape mismatch: value array of shape (24,) could not be
 broadcast to indexing result of shape (8,3)

 I have attached a list of failing tests. The full test results are at 
 http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20140609-win-amd64-py2.7-
 numpy-1.9.0b1/ (compare to http://www.lfd.uci.edu/~
 gohlke/pythonlibs/tests/20140609-win-amd64-py2.7/)

 I have not investigated any further...


One of the matplotlib failures, and I suspect the others, comes from the
assignment

found_index[refi_triangles[ancestor_mask, :]
] = np.repeat(ancestors[ancestor_mask], 3)

This fails with the error

ValueError: shape mismatch: value array of shape (13824,)
could not be broadcast to indexing result of shape (4608,3)

I confess I find the construction odd, but the error probably results from
stricter indexing rules. Indeed,  (13824,) does not broadcast to  (4608,3).
Apart from considerations of backward compatibility, should it?

Chuck
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Re: [Numpy-discussion] ANN: NumPy 1.9.0 beta release

2014-06-09 Thread Jeff Reback
The one pandas test failure that is valid: ERROR:
test_interp_regression (pandas.tests.test_generic.TestSeries)

has been fixed in pandas master / 0.14.1 (prob releasing in 1 month).

(the other test failures are for clipboard / network issues)




On Mon, Jun 9, 2014 at 7:21 PM, Christoph Gohlke cgoh...@uci.edu wrote:

 On 6/8/2014 1:34 PM, Julian Taylor wrote:

 Hello,

 I'm happy to announce the fist beta release of Numpy 1.9.0.
 1.9.0 will be a new feature release supporting Python 2.6 - 2.7 and 3.2
 - 3.4.
 Due to low demand windows binaries for the beta are only available for
 Python 2.7, 3.3 and 3.4.
 Please try it and report any issues to the numpy-discussion mailing list
 or on github.

 The 1.9 release will consists of mainly of many small improvements and
 bugfixes. The highlights are:

 * Addition of __numpy_ufunc__ to allow overriding ufuncs in ndarray
 subclasses. Please note that there are still some known issues with this
 mechanism which we hope to resolve before the final release (e.g. #4753)
 * Numerous performance improvements in various areas, most notably
 indexing and operations on small arrays are significantly faster.
 Indexing operations now also release the GIL.
 * Addition of nanmedian and nanpercentile rounds out the nanfunction set.

 The changes involve a lot of small changes that might affect some
 applications, please read the release notes for the full details on all
 changes:
 https://github.com/numpy/numpy/blob/maintenance/1.9.x/
 doc/release/1.9.0-notes.rst
 Please also take special note of the future changes section which will
 apply to the following release 1.10.0 and make sure to check if your
 applications would be affected by them.

 Source tarballs, windows installers and release notes can be found at
 https://sourceforge.net/projects/numpy/files/NumPy/1.9.0b1

 Cheers,
 Julian Taylor


 Hello,

 I tested numpy-MKL-1.9.0b1 (msvc9, Intel MKL build) on win-amd64-py2.7
 against a few other packages that were built against numpy-MKL-1.8.x.

 While numpy and scipy pass all tests, some other packages (matplotlib,
 statsmodels, skimage, pandas, pytables, sklearn...) show a few new test
 failures (compared to testing with numpy-MKL-1.8.1). Many test errors are
 of kind:

 ValueError: shape mismatch: value array of shape (24,) could not be
 broadcast to indexing result of shape (8,3)

 I have attached a list of failing tests. The full test results are at 
 http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20140609-win-amd64-py2.7-
 numpy-1.9.0b1/ (compare to http://www.lfd.uci.edu/~
 gohlke/pythonlibs/tests/20140609-win-amd64-py2.7/)

 I have not investigated any further...

 Christoph

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Re: [Numpy-discussion] ANN: NumPy 1.9.0 beta release

2014-06-09 Thread Charles R Harris
On Mon, Jun 9, 2014 at 6:10 PM, Charles R Harris charlesr.har...@gmail.com
wrote:




 On Mon, Jun 9, 2014 at 5:21 PM, Christoph Gohlke cgoh...@uci.edu wrote:

 On 6/8/2014 1:34 PM, Julian Taylor wrote:

 Hello,

 I'm happy to announce the fist beta release of Numpy 1.9.0.
 1.9.0 will be a new feature release supporting Python 2.6 - 2.7 and 3.2
 - 3.4.
 Due to low demand windows binaries for the beta are only available for
 Python 2.7, 3.3 and 3.4.
 Please try it and report any issues to the numpy-discussion mailing list
 or on github.

 The 1.9 release will consists of mainly of many small improvements and
 bugfixes. The highlights are:

 * Addition of __numpy_ufunc__ to allow overriding ufuncs in ndarray
 subclasses. Please note that there are still some known issues with this
 mechanism which we hope to resolve before the final release (e.g. #4753)
 * Numerous performance improvements in various areas, most notably
 indexing and operations on small arrays are significantly faster.
 Indexing operations now also release the GIL.
 * Addition of nanmedian and nanpercentile rounds out the nanfunction set.

 The changes involve a lot of small changes that might affect some
 applications, please read the release notes for the full details on all
 changes:
 https://github.com/numpy/numpy/blob/maintenance/1.9.x/
 doc/release/1.9.0-notes.rst
 Please also take special note of the future changes section which will
 apply to the following release 1.10.0 and make sure to check if your
 applications would be affected by them.

 Source tarballs, windows installers and release notes can be found at
 https://sourceforge.net/projects/numpy/files/NumPy/1.9.0b1

 Cheers,
 Julian Taylor


 Hello,

 I tested numpy-MKL-1.9.0b1 (msvc9, Intel MKL build) on win-amd64-py2.7
 against a few other packages that were built against numpy-MKL-1.8.x.

 While numpy and scipy pass all tests, some other packages (matplotlib,
 statsmodels, skimage, pandas, pytables, sklearn...) show a few new test
 failures (compared to testing with numpy-MKL-1.8.1). Many test errors are
 of kind:

 ValueError: shape mismatch: value array of shape (24,) could not be
 broadcast to indexing result of shape (8,3)

 I have attached a list of failing tests. The full test results are at 
 http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20140609-win-amd64-py2.7-
 numpy-1.9.0b1/ (compare to http://www.lfd.uci.edu/~
 gohlke/pythonlibs/tests/20140609-win-amd64-py2.7/)

 I have not investigated any further...


 One of the matplotlib failures, and I suspect the others, comes from the
 assignment

 found_index[refi_triangles[ancestor_mask, :]
 ] = np.repeat(ancestors[ancestor_mask], 3)

 This fails with the error

 ValueError: shape mismatch: value array of shape (13824,)

 could not be broadcast to indexing result of shape (4608,3)

 I confess I find the construction odd, but the error probably results from
 stricter indexing rules. Indeed,  (13824,) does not broadcast to  (4608,3).
 Apart from considerations of backward compatibility, should it?


Other errors are of the type:

TypeError: NumPy boolean array indexing assignment requires a 0 or
1-dimensionalinput, input has 2 dimensions

 This one looks to arise is from stricter rules for boolean indexing.

Chuck
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Re: [Numpy-discussion] ANN: NumPy 1.9.0 beta release

2014-06-09 Thread Charles R Harris
On Mon, Jun 9, 2014 at 6:21 PM, Jeff Reback jeffreb...@gmail.com wrote:

 The one pandas test failure that is valid: ERROR: test_interp_regression 
 (pandas.tests.test_generic.TestSeries)

 has been fixed in pandas master / 0.14.1 (prob releasing in 1 month).

 (the other test failures are for clipboard / network issues)




Thanks for the feedback.

snip

Chuck
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Re: [Numpy-discussion] ANN: NumPy 1.9.0 beta release

2014-06-09 Thread Charles R Harris
On Mon, Jun 9, 2014 at 6:23 PM, Charles R Harris charlesr.har...@gmail.com
wrote:




 On Mon, Jun 9, 2014 at 6:21 PM, Jeff Reback jeffreb...@gmail.com wrote:

 The one pandas test failure that is valid: ERROR: test_interp_regression 
 (pandas.tests.test_generic.TestSeries)

 has been fixed in pandas master / 0.14.1 (prob releasing in 1 month).

 (the other test failures are for clipboard / network issues)




 Thanks for the feedback.

 snip


Looks to me like a fair number of the other test failures are actually bugs
in the tests, or could be argued to be so.

Chuck
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Re: [Numpy-discussion] ANN: NumPy 1.9.0 beta release

2014-06-09 Thread Charles R Harris
On Mon, Jun 9, 2014 at 6:47 PM, Charles R Harris charlesr.har...@gmail.com
wrote:




 On Mon, Jun 9, 2014 at 6:23 PM, Charles R Harris 
 charlesr.har...@gmail.com wrote:




 On Mon, Jun 9, 2014 at 6:21 PM, Jeff Reback jeffreb...@gmail.com wrote:

 The one pandas test failure that is valid: ERROR: test_interp_regression 
 (pandas.tests.test_generic.TestSeries)

 has been fixed in pandas master / 0.14.1 (prob releasing in 1 month).

 (the other test failures are for clipboard / network issues)




 Thanks for the feedback.

 snip


 Looks to me like a fair number of the other test failures are actually
 bugs in the tests, or could be argued to be so.


Or rather, actual bugs, but not in numpy. I can see this will take a while
to settle ;)

Chuck
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Re: [Numpy-discussion] ANN: NumPy 1.9.0 beta release

2014-06-09 Thread josef . pktd
On Mon, Jun 9, 2014 at 8:49 PM, Charles R Harris
charlesr.har...@gmail.com wrote:



 On Mon, Jun 9, 2014 at 6:47 PM, Charles R Harris charlesr.har...@gmail.com
 wrote:




 On Mon, Jun 9, 2014 at 6:23 PM, Charles R Harris
 charlesr.har...@gmail.com wrote:




 On Mon, Jun 9, 2014 at 6:21 PM, Jeff Reback jeffreb...@gmail.com wrote:

 The one pandas test failure that is valid: ERROR: test_interp_regression
 (pandas.tests.test_generic.TestSeries)

 has been fixed in pandas master / 0.14.1 (prob releasing in 1 month).

 (the other test failures are for clipboard / network issues)




 Thanks for the feedback.

 snip


 Looks to me like a fair number of the other test failures are actually
 bugs in the tests, or could be argued to be so.


 Or rather, actual bugs, but not in numpy. I can see this will take a while
 to settle ;)

not really a bug on our side given the old numpy behavior
I was surprised about the failure because it still produces the correct result.

params[nuis_param_index] = nuisance_params
ValueError: shape mismatch: value array of shape (2,) could not be
broadcast to indexing result of shape (0,)

As far as I have figured out based on Christoph's test failures

`nuis_param_index`  is array([], dtype=int32)
and because numpy didn't assign anything, `nuisance_params` was picked
essentially arbitrary along this code path

 x = np.arange(5.)
 we_dont_care_what_this_is = np.random.randn(10)
 x[[]] = we_dont_care_what_this_is
 x
array([ 0.,  1.,  2.,  3.,  4.])

Does x[[]] = [] work with numpy 1.9?

Josef


 Chuck

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Re: [Numpy-discussion] ANN: NumPy 1.9.0 beta release

2014-06-09 Thread josef . pktd
On Mon, Jun 9, 2014 at 11:10 PM,  josef.p...@gmail.com wrote:
 On Mon, Jun 9, 2014 at 8:49 PM, Charles R Harris
 charlesr.har...@gmail.com wrote:



 On Mon, Jun 9, 2014 at 6:47 PM, Charles R Harris charlesr.har...@gmail.com
 wrote:




 On Mon, Jun 9, 2014 at 6:23 PM, Charles R Harris
 charlesr.har...@gmail.com wrote:




 On Mon, Jun 9, 2014 at 6:21 PM, Jeff Reback jeffreb...@gmail.com wrote:

 The one pandas test failure that is valid: ERROR: test_interp_regression
 (pandas.tests.test_generic.TestSeries)

 has been fixed in pandas master / 0.14.1 (prob releasing in 1 month).

 (the other test failures are for clipboard / network issues)




 Thanks for the feedback.

 snip


 Looks to me like a fair number of the other test failures are actually
 bugs in the tests, or could be argued to be so.


 Or rather, actual bugs, but not in numpy. I can see this will take a while
 to settle ;)

 not really a bug on our side given the old numpy behavior

forgot to define: our side = statsmodels

 I was surprised about the failure because it still produces the correct 
 result.

 params[nuis_param_index] = nuisance_params
 ValueError: shape mismatch: value array of shape (2,) could not be
 broadcast to indexing result of shape (0,)

 As far as I have figured out based on Christoph's test failures

 `nuis_param_index`  is array([], dtype=int32)
 and because numpy didn't assign anything, `nuisance_params` was picked
 essentially arbitrary along this code path

 x = np.arange(5.)
 we_dont_care_what_this_is = np.random.randn(10)
 x[[]] = we_dont_care_what_this_is
 x
 array([ 0.,  1.,  2.,  3.,  4.])

 Does x[[]] = [] work with numpy 1.9?

 Josef


 Chuck

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[Numpy-discussion] ANN: NumPy 1.9.0 beta release

2014-06-08 Thread Julian Taylor
Hello,

I'm happy to announce the fist beta release of Numpy 1.9.0.
1.9.0 will be a new feature release supporting Python 2.6 - 2.7 and 3.2
- 3.4.
Due to low demand windows binaries for the beta are only available for
Python 2.7, 3.3 and 3.4.
Please try it and report any issues to the numpy-discussion mailing list
or on github.

The 1.9 release will consists of mainly of many small improvements and
bugfixes. The highlights are:

* Addition of __numpy_ufunc__ to allow overriding ufuncs in ndarray
subclasses. Please note that there are still some known issues with this
mechanism which we hope to resolve before the final release (e.g. #4753)
* Numerous performance improvements in various areas, most notably
indexing and operations on small arrays are significantly faster.
Indexing operations now also release the GIL.
* Addition of nanmedian and nanpercentile rounds out the nanfunction set.

The changes involve a lot of small changes that might affect some
applications, please read the release notes for the full details on all
changes:
https://github.com/numpy/numpy/blob/maintenance/1.9.x/doc/release/1.9.0-notes.rst
Please also take special note of the future changes section which will
apply to the following release 1.10.0 and make sure to check if your
applications would be affected by them.

Source tarballs, windows installers and release notes can be found at
https://sourceforge.net/projects/numpy/files/NumPy/1.9.0b1

Cheers,
Julian Taylor



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Re: [Numpy-discussion] ANN: NumPy 1.9.0 beta release

2014-06-08 Thread Charles R Harris
On Sun, Jun 8, 2014 at 2:34 PM, Julian Taylor jtaylor.deb...@googlemail.com
 wrote:

 Hello,

 I'm happy to announce the fist beta release of Numpy 1.9.0.
 1.9.0 will be a new feature release supporting Python 2.6 - 2.7 and 3.2
 - 3.4.
 Due to low demand windows binaries for the beta are only available for
 Python 2.7, 3.3 and 3.4.
 Please try it and report any issues to the numpy-discussion mailing list
 or on github.

 The 1.9 release will consists of mainly of many small improvements and
 bugfixes. The highlights are:

 * Addition of __numpy_ufunc__ to allow overriding ufuncs in ndarray
 subclasses. Please note that there are still some known issues with this
 mechanism which we hope to resolve before the final release (e.g. #4753)
 * Numerous performance improvements in various areas, most notably
 indexing and operations on small arrays are significantly faster.
 Indexing operations now also release the GIL.
 * Addition of nanmedian and nanpercentile rounds out the nanfunction set.

 The changes involve a lot of small changes that might affect some
 applications, please read the release notes for the full details on all
 changes:

 https://github.com/numpy/numpy/blob/maintenance/1.9.x/doc/release/1.9.0-notes.rst
 Please also take special note of the future changes section which will
 apply to the following release 1.10.0 and make sure to check if your
 applications would be affected by them.

 Source tarballs, windows installers and release notes can be found at
 https://sourceforge.net/projects/numpy/files/NumPy/1.9.0b1


Thanks for this, good job.

Chuck
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