Hello, We are proud to announce the 1.9.0 release of NumPy. This release includes numerous performance improvements, most significantly the indexing code has been rewritten be a lot times faster for most cases and performance of using small arrays and scalars has almost doubled. Plenty of other functions have been improved too, nonzero, where, bincount, searchsorted, count_nonzero, floating point min/max, boolean argmin/argmax, triu/tril, masked sorting can be expected to perform significantly better in many cases. Also NumPy 1.9.0 releases the GIL for more functions, most notably indexing now releases it and the random modules state object has a private lock instead of using the GIL. This allows leveraging pure python threads more efficiently. In order to make working with arrays containing NaN values easier nanmedian and nanpercentile have been added which ignore these values. These functions and the regular median and percentile now also support generalized axis arguments that ufuncs already have, these allow reducing along multiple axis in one call. Please see the release notes for all the details. Please also take not of the many small compatibility changes and deprecation in the notes. https://github.com/numpy/numpy/blob/maintenance/1.9.x/doc/release/1.9.0-note... The source tarballs and win32 binaries can be downloaded here: https://sourceforge.net/projects/numpy/files/NumPy/1.9.0 Cheers, The NumPy Development Team
On So, 2014-09-07 at 12:33 +0200, Julian Taylor wrote:
Hello,
We are proud to announce the 1.9.0 release of NumPy.
Awesome, thanks for the release management! - Sebastian
This release includes numerous performance improvements, most significantly the indexing code has been rewritten be a lot times faster for most cases and performance of using small arrays and scalars has almost doubled. Plenty of other functions have been improved too, nonzero, where, bincount, searchsorted, count_nonzero, floating point min/max, boolean argmin/argmax, triu/tril, masked sorting can be expected to perform significantly better in many cases.
Also NumPy 1.9.0 releases the GIL for more functions, most notably indexing now releases it and the random modules state object has a private lock instead of using the GIL. This allows leveraging pure python threads more efficiently.
In order to make working with arrays containing NaN values easier nanmedian and nanpercentile have been added which ignore these values. These functions and the regular median and percentile now also support generalized axis arguments that ufuncs already have, these allow reducing along multiple axis in one call.
Please see the release notes for all the details. Please also take not of the many small compatibility changes and deprecation in the notes. https://github.com/numpy/numpy/blob/maintenance/1.9.x/doc/release/1.9.0-note...
The source tarballs and win32 binaries can be downloaded here: https://sourceforge.net/projects/numpy/files/NumPy/1.9.0
Cheers, The NumPy Development Team
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
On Sun, Sep 7, 2014 at 4:33 AM, Julian Taylor <jtaylor.debian@googlemail.com
wrote:
Hello,
We are proud to announce the 1.9.0 release of NumPy.
This release includes numerous performance improvements, most significantly the indexing code has been rewritten be a lot times faster for most cases and performance of using small arrays and scalars has almost doubled. Plenty of other functions have been improved too, nonzero, where, bincount, searchsorted, count_nonzero, floating point min/max, boolean argmin/argmax, triu/tril, masked sorting can be expected to perform significantly better in many cases.
Also NumPy 1.9.0 releases the GIL for more functions, most notably indexing now releases it and the random modules state object has a private lock instead of using the GIL. This allows leveraging pure python threads more efficiently.
In order to make working with arrays containing NaN values easier nanmedian and nanpercentile have been added which ignore these values. These functions and the regular median and percentile now also support generalized axis arguments that ufuncs already have, these allow reducing along multiple axis in one call.
Please see the release notes for all the details. Please also take not of the many small compatibility changes and deprecation in the notes.
https://github.com/numpy/numpy/blob/maintenance/1.9.x/doc/release/1.9.0-note...
The source tarballs and win32 binaries can be downloaded here: https://sourceforge.net/projects/numpy/files/NumPy/1.9.0
Cheers, The NumPy Development Team
Great! Thanks for all the work you did getting this out. Chuck
participants (3)
-
Charles R Harris -
Julian Taylor -
Sebastian Berg