108 lines
4.0 KiB
ReStructuredText
108 lines
4.0 KiB
ReStructuredText
==========================
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NumPy 1.16.1 Release Notes
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==========================
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The NumPy 1.16.1 release fixes bugs reported against the 1.16.0 release, and
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also backports several enhancements from master that seem appropriate for a
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release series that is the last to support Python 2.7. The wheels on PyPI are
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linked with OpenBLAS v0.3.4+, which should fix the known threading issues
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found in previous OpenBLAS versions.
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Downstream developers building this release should use Cython >= 0.29.2 and, if
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using OpenBLAS, OpenBLAS > v0.3.4.
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If you are installing using pip, you may encounter a problem with older
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installed versions of NumPy that pip did not delete becoming mixed with the
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current version, resulting in an ``ImportError``. That problem is particularly
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common on Debian derived distributions due to a modified pip. The fix is to
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make sure all previous NumPy versions installed by pip have been removed. See
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`#12736 <https://github.com/numpy/numpy/issues/12736>`__ for discussion of the
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issue. Note that previously this problem resulted in an ``AttributeError``.
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Contributors
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============
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A total of 16 people contributed to this release. People with a "+" by their
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names contributed a patch for the first time.
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* Antoine Pitrou
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* Arcesio Castaneda Medina +
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* Charles Harris
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* Chris Markiewicz +
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* Christoph Gohlke
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* Christopher J. Markiewicz +
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* Daniel Hrisca +
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* EelcoPeacs +
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* Eric Wieser
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* Kevin Sheppard
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* Matti Picus
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* OBATA Akio +
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* Ralf Gommers
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* Sebastian Berg
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* Stephan Hoyer
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* Tyler Reddy
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Enhancements
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============
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* `#12767 <https://github.com/numpy/numpy/pull/12767>`__: ENH: add mm->q floordiv
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* `#12768 <https://github.com/numpy/numpy/pull/12768>`__: ENH: port np.core.overrides to C for speed
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* `#12769 <https://github.com/numpy/numpy/pull/12769>`__: ENH: Add np.ctypeslib.as_ctypes_type(dtype), improve `np.ctypeslib.as_ctypes`
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* `#12773 <https://github.com/numpy/numpy/pull/12773>`__: ENH: add "max difference" messages to np.testing.assert_array_equal...
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* `#12820 <https://github.com/numpy/numpy/pull/12820>`__: ENH: Add mm->qm divmod
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* `#12890 <https://github.com/numpy/numpy/pull/12890>`__: ENH: add _dtype_ctype to namespace for freeze analysis
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Compatibility notes
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===================
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* The changed error message emitted by array comparison testing functions may
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affect doctests. See below for detail.
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* Casting from double and single denormals to float16 has been corrected. In
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some rare cases, this may result in results being rounded up instead of down,
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changing the last bit (ULP) of the result.
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New Features
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============
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divmod operation is now supported for two ``timedelta64`` operands
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------------------------------------------------------------------
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The divmod operator now handles two ``np.timedelta64`` operands, with
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type signature ``mm->qm``.
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Improvements
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============
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Further improvements to ``ctypes`` support in ``np.ctypeslib``
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--------------------------------------------------------------
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A new `numpy.ctypeslib.as_ctypes_type` function has been added, which can be
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used to converts a `dtype` into a best-guess `ctypes` type. Thanks to this
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new function, `numpy.ctypeslib.as_ctypes` now supports a much wider range of
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array types, including structures, booleans, and integers of non-native
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endianness.
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Array comparison assertions include maximum differences
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-------------------------------------------------------
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Error messages from array comparison tests such as
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`np.testing.assert_allclose` now include "max absolute difference" and
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"max relative difference," in addition to the previous "mismatch" percentage.
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This information makes it easier to update absolute and relative error
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tolerances.
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Changes
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=======
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``timedelta64 % 0`` behavior adjusted to return ``NaT``
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-------------------------------------------------------
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The modulus operation with two ``np.timedelta64`` operands now returns
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``NaT`` in the case of division by zero, rather than returning zero
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