Re: [Numpy-discussion] ANN: NumPy 1.7.0rc2 release
On Sat, Feb 9, 2013 at 12:58 PM, Ondřej Čertík ondrej.cer...@gmail.comwrote: Thanks Frédéric and Christoph for the feedback. Looks like there are no further problems, so I will go ahead and do the final release today. Ondrej Congratulations on your second child ;) And so soon after the first ... Chuck ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] ANN: NumPy 1.7.0rc2 release
Hi, As expected all Theano's tests passed. thanks Fred On Wed, Feb 6, 2013 at 10:10 PM, Ondřej Čertík ondrej.cer...@gmail.comwrote: Hi, I'm pleased to announce the availability of the second release candidate of NumPy 1.7.0rc2. Sources and binary installers can be found at https://sourceforge.net/projects/numpy/files/NumPy/1.7.0rc2/ We have fixed all issues known to us since the 1.7.0rc1 release. Please test this release and report any issues on the numpy-discussion mailing list. If there are no further problems, I plan to release the final version in a few days. I would like to thank Sandro Tosi, Sebastian Berg, Charles Harris, Marcin Juszkiewicz, Mark Wiebe, Ralf Gommers and Nathaniel J. Smith for sending patches, fixes and helping with reviews for this release since 1.7.0rc1, and Vincent Davis for providing the Mac build machine. Cheers, Ondrej = NumPy 1.7.0 Release Notes = This release includes several new features as well as numerous bug fixes and refactorings. It supports Python 2.4 - 2.7 and 3.1 - 3.3 and is the last release that supports Python 2.4 - 2.5. Highlights == * ``where=`` parameter to ufuncs (allows the use of boolean arrays to choose where a computation should be done) * ``vectorize`` improvements (added 'excluded' and 'cache' keyword, general cleanup and bug fixes) * ``numpy.random.choice`` (random sample generating function) Compatibility notes === In a future version of numpy, the functions np.diag, np.diagonal, and the diagonal method of ndarrays will return a view onto the original array, instead of producing a copy as they do now. This makes a difference if you write to the array returned by any of these functions. To facilitate this transition, numpy 1.7 produces a FutureWarning if it detects that you may be attempting to write to such an array. See the documentation for np.diagonal for details. Similar to np.diagonal above, in a future version of numpy, indexing a record array by a list of field names will return a view onto the original array, instead of producing a copy as they do now. As with np.diagonal, numpy 1.7 produces a FutureWarning if it detects that you may be attempting to write to such an array. See the documentation for array indexing for details. In a future version of numpy, the default casting rule for UFunc out= parameters will be changed from 'unsafe' to 'same_kind'. (This also applies to in-place operations like a += b, which is equivalent to np.add(a, b, out=a).) Most usages which violate the 'same_kind' rule are likely bugs, so this change may expose previously undetected errors in projects that depend on NumPy. In this version of numpy, such usages will continue to succeed, but will raise a DeprecationWarning. Full-array boolean indexing has been optimized to use a different, optimized code path. This code path should produce the same results, but any feedback about changes to your code would be appreciated. Attempting to write to a read-only array (one with ``arr.flags.writeable`` set to ``False``) used to raise either a RuntimeError, ValueError, or TypeError inconsistently, depending on which code path was taken. It now consistently raises a ValueError. The ufunc.reduce functions evaluate some reductions in a different order than in previous versions of NumPy, generally providing higher performance. Because of the nature of floating-point arithmetic, this may subtly change some results, just as linking NumPy to a different BLAS implementations such as MKL can. If upgrading from 1.5, then generally in 1.6 and 1.7 there have been substantial code added and some code paths altered, particularly in the areas of type resolution and buffered iteration over universal functions. This might have an impact on your code particularly if you relied on accidental behavior in the past. New features Reduction UFuncs Generalize axis= Parameter --- Any ufunc.reduce function call, as well as other reductions like sum, prod, any, all, max and min support the ability to choose a subset of the axes to reduce over. Previously, one could say axis=None to mean all the axes or axis=# to pick a single axis. Now, one can also say axis=(#,#) to pick a list of axes for reduction. Reduction UFuncs New keepdims= Parameter There is a new keepdims= parameter, which if set to True, doesn't throw away the reduction axes but instead sets them to have size one. When this option is set, the reduction result will broadcast correctly to the original operand which was reduced. Datetime support .. note:: The datetime API is *experimental* in 1.7.0, and may undergo changes in future versions of NumPy. There have been a lot of fixes and enhancements to datetime64 compared to NumPy
Re: [Numpy-discussion] ANN: NumPy 1.7.0rc2 release
On 2/6/2013 7:10 PM, Ondřej Čertík wrote: Hi, I'm pleased to announce the availability of the second release candidate of NumPy 1.7.0rc2. Sources and binary installers can be found at https://sourceforge.net/projects/numpy/files/NumPy/1.7.0rc2/ We have fixed all issues known to us since the 1.7.0rc1 release. Please test this release and report any issues on the numpy-discussion mailing list. If there are no further problems, I plan to release the final version in a few days. I would like to thank Sandro Tosi, Sebastian Berg, Charles Harris, Marcin Juszkiewicz, Mark Wiebe, Ralf Gommers and Nathaniel J. Smith for sending patches, fixes and helping with reviews for this release since 1.7.0rc1, and Vincent Davis for providing the Mac build machine. Cheers, Ondrej Thanks. It works well on win-amd64-py2.7 http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20130206-win-amd64-py2.7-numpy-1.7.0rc2/. Christoph ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] ANN: NumPy 1.7.0rc2 release
Hi, I'm pleased to announce the availability of the second release candidate of NumPy 1.7.0rc2. Sources and binary installers can be found at https://sourceforge.net/projects/numpy/files/NumPy/1.7.0rc2/ We have fixed all issues known to us since the 1.7.0rc1 release. Please test this release and report any issues on the numpy-discussion mailing list. If there are no further problems, I plan to release the final version in a few days. I would like to thank Sandro Tosi, Sebastian Berg, Charles Harris, Marcin Juszkiewicz, Mark Wiebe, Ralf Gommers and Nathaniel J. Smith for sending patches, fixes and helping with reviews for this release since 1.7.0rc1, and Vincent Davis for providing the Mac build machine. Cheers, Ondrej = NumPy 1.7.0 Release Notes = This release includes several new features as well as numerous bug fixes and refactorings. It supports Python 2.4 - 2.7 and 3.1 - 3.3 and is the last release that supports Python 2.4 - 2.5. Highlights == * ``where=`` parameter to ufuncs (allows the use of boolean arrays to choose where a computation should be done) * ``vectorize`` improvements (added 'excluded' and 'cache' keyword, general cleanup and bug fixes) * ``numpy.random.choice`` (random sample generating function) Compatibility notes === In a future version of numpy, the functions np.diag, np.diagonal, and the diagonal method of ndarrays will return a view onto the original array, instead of producing a copy as they do now. This makes a difference if you write to the array returned by any of these functions. To facilitate this transition, numpy 1.7 produces a FutureWarning if it detects that you may be attempting to write to such an array. See the documentation for np.diagonal for details. Similar to np.diagonal above, in a future version of numpy, indexing a record array by a list of field names will return a view onto the original array, instead of producing a copy as they do now. As with np.diagonal, numpy 1.7 produces a FutureWarning if it detects that you may be attempting to write to such an array. See the documentation for array indexing for details. In a future version of numpy, the default casting rule for UFunc out= parameters will be changed from 'unsafe' to 'same_kind'. (This also applies to in-place operations like a += b, which is equivalent to np.add(a, b, out=a).) Most usages which violate the 'same_kind' rule are likely bugs, so this change may expose previously undetected errors in projects that depend on NumPy. In this version of numpy, such usages will continue to succeed, but will raise a DeprecationWarning. Full-array boolean indexing has been optimized to use a different, optimized code path. This code path should produce the same results, but any feedback about changes to your code would be appreciated. Attempting to write to a read-only array (one with ``arr.flags.writeable`` set to ``False``) used to raise either a RuntimeError, ValueError, or TypeError inconsistently, depending on which code path was taken. It now consistently raises a ValueError. The ufunc.reduce functions evaluate some reductions in a different order than in previous versions of NumPy, generally providing higher performance. Because of the nature of floating-point arithmetic, this may subtly change some results, just as linking NumPy to a different BLAS implementations such as MKL can. If upgrading from 1.5, then generally in 1.6 and 1.7 there have been substantial code added and some code paths altered, particularly in the areas of type resolution and buffered iteration over universal functions. This might have an impact on your code particularly if you relied on accidental behavior in the past. New features Reduction UFuncs Generalize axis= Parameter --- Any ufunc.reduce function call, as well as other reductions like sum, prod, any, all, max and min support the ability to choose a subset of the axes to reduce over. Previously, one could say axis=None to mean all the axes or axis=# to pick a single axis. Now, one can also say axis=(#,#) to pick a list of axes for reduction. Reduction UFuncs New keepdims= Parameter There is a new keepdims= parameter, which if set to True, doesn't throw away the reduction axes but instead sets them to have size one. When this option is set, the reduction result will broadcast correctly to the original operand which was reduced. Datetime support .. note:: The datetime API is *experimental* in 1.7.0, and may undergo changes in future versions of NumPy. There have been a lot of fixes and enhancements to datetime64 compared to NumPy 1.6: * the parser is quite strict about only accepting ISO 8601 dates, with a few convenience extensions * converts between units correctly * datetime arithmetic works correctly * business day functionality (allows the datetime
