@charris charris released this Aug 21, 2018 · 545 commits to master since this release

Assets 6

==========================
NumPy 1.15.1 Release Notes

This is a bugfix release for bugs and regressions reported following the 1.15.0
release.

  • The annoying but harmless RuntimeWarning that "numpy.dtype size changed" has
    been suppressed. The long standing suppression was lost in the transition to
    pytest.
  • The update to Cython 0.28.3 exposed a problematic use of a gcc attribute used
    to prefer code size over speed in module initialization, possibly resulting in
    incorrect compiled code. This has been fixed in latest Cython but has been
    disabled here for safety.
  • Support for big-endian and ARMv8 architectures has been improved.

The Python versions supported by this release are 2.7, 3.4-3.7. The wheels are
linked with OpenBLAS v0.3.0, which should fix some of the linalg problems
reported for NumPy 1.14.

Compatibility Note

The NumPy 1.15.x OS X wheels released on PyPI no longer contain 32-bit
binaries. That will also be the case in future releases. See
#11625 <https://github.com/numpy/numpy/issues/11625>__ for the related
discussion. Those needing 32-bit support should look elsewhere or build
from source.

Contributors

A total of 7 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

  • Charles Harris
  • Chris Billington
  • Elliott Sales de Andrade +
  • Eric Wieser
  • Jeremy Manning +
  • Matti Picus
  • Ralf Gommers

Pull requests merged

A total of 24 pull requests were merged for this release.

  • #11647: MAINT: Filter Cython warnings in __init__.py
  • #11648: BUG: Fix doc source links to unwrap decorators
  • #11657: BUG: Ensure singleton dimensions are not dropped when converting...
  • #11661: BUG: Warn on Nan in minimum,maximum for scalars
  • #11665: BUG: cython sometimes emits invalid gcc attribute
  • #11682: BUG: Fix regression in void_getitem
  • #11698: BUG: Make matrix_power again work for object arrays.
  • #11700: BUG: Add missing PyErr_NoMemory after failing malloc
  • #11719: BUG: Fix undefined functions on big-endian systems.
  • #11720: MAINT: Make einsum optimize default to False.
  • #11746: BUG: Fix regression in loadtxt for bz2 text files in Python 2.
  • #11757: BUG: Revert use of console_scripts.
  • #11758: BUG: Fix Fortran kind detection for aarch64 & s390x.
  • #11759: BUG: Fix printing of longdouble on ppc64le.
  • #11760: BUG: Fixes for unicode field names in Python 2
  • #11761: BUG: Increase required cython version on python 3.7
  • #11763: BUG: check return value of _buffer_format_string
  • #11775: MAINT: Make assert_array_compare more generic.
  • #11776: TST: Fix urlopen stubbing.
  • #11777: BUG: Fix regression in intersect1d.
  • #11779: BUG: Fix test sensitive to platform byte order.
  • #11781: BUG: Avoid signed overflow in histogram
  • #11785: BUG: Fix pickle and memoryview for datetime64, timedelta64 scalars
  • #11786: BUG: Deprecation triggers segfault

Checksums

MD5

8e894e6873420259fa13bc685ca922a7  numpy-1.15.1-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
75154de03468c18c8b8d337b75d29bad  numpy-1.15.1-cp27-cp27m-manylinux1_i686.whl
50e3db64b9be2d399f7035ea71e16092  numpy-1.15.1-cp27-cp27m-manylinux1_x86_64.whl
35e15be82a5fc807572c7723171902b4  numpy-1.15.1-cp27-cp27mu-manylinux1_i686.whl
315cc1fb777c5251f27e49075b4d13fb  numpy-1.15.1-cp27-cp27mu-manylinux1_x86_64.whl
7b6fbdca75eeb0a0c28c09bfaf2e17c2  numpy-1.15.1-cp27-none-win32.whl
8bc75bc94bd189a4cc3ded0f0e9b1353  numpy-1.15.1-cp27-none-win_amd64.whl
3c8950f10241185376ae6dd425209543  numpy-1.15.1-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
3e488ea8de86391335a56e7e2b2c47de  numpy-1.15.1-cp34-cp34m-manylinux1_i686.whl
0edee0d56ea5670b93b47410e66fa337  numpy-1.15.1-cp34-cp34m-manylinux1_x86_64.whl
67670224f931699c3836a1c9e4e8230b  numpy-1.15.1-cp34-none-win32.whl
5b9e984e562aac63b7549e456bd89dfe  numpy-1.15.1-cp34-none-win_amd64.whl
063f6a86f0713211b69050545e7c6c2c  numpy-1.15.1-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
4afe4fd3ea108a967bd0b9425305b979  numpy-1.15.1-cp35-cp35m-manylinux1_i686.whl
e1ebc2bc6d0947159b33f208e844251a  numpy-1.15.1-cp35-cp35m-manylinux1_x86_64.whl
910aab0be682f29a182239e4bd4631cf  numpy-1.15.1-cp35-none-win32.whl
bfaac6c5f4e8ab65cd76b010ea5c5dfe  numpy-1.15.1-cp35-none-win_amd64.whl
ce48f8b807c9ac8b7d00301584ab7976  numpy-1.15.1-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
d7d0c86acb89a86894811b8a792fba89  numpy-1.15.1-cp36-cp36m-manylinux1_i686.whl
3cd21facc099e72ab56a957978207c8c  numpy-1.15.1-cp36-cp36m-manylinux1_x86_64.whl
04471e530164dd25c7a9c1309712cc64  numpy-1.15.1-cp36-none-win32.whl
013ea5fbb8a953c2112acaa591c675a8  numpy-1.15.1-cp36-none-win_amd64.whl
3fdd39812b8fe172824d2cc52cb807c4  numpy-1.15.1-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
381bd5ea598b17333264b1cbc9f62fac  numpy-1.15.1-cp37-cp37m-manylinux1_i686.whl
e600bd09303c622ff4d16ed63fefb205  numpy-1.15.1-cp37-cp37m-manylinux1_x86_64.whl
c05625370ff437b3e1a4f08cf194e3e4  numpy-1.15.1-cp37-none-win32.whl
f476babe66c6104c00accbf0bcfafce5  numpy-1.15.1-cp37-none-win_amd64.whl
e369ffae42ab89c7d1be5fe786e27702  numpy-1.15.1.tar.gz
898004d5be091fde59ae353e3008fe9b  numpy-1.15.1.zip

SHA256

5e359e9c531075220785603e5966eef20ccae9b3b6b8a06fdfb66c084361ce92  numpy-1.15.1-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
419e6faee16097124ee627ed31572c7e80a1070efa25260b78097cca240e219a  numpy-1.15.1-cp27-cp27m-manylinux1_i686.whl
719b6789acb2bc86ea9b33a701d7c43dc2fc56d95107fd3c5b0a8230164d4dfb  numpy-1.15.1-cp27-cp27m-manylinux1_x86_64.whl
62d55e96ec7b117d3d5e618c15efcf769e70a6effaee5842857b64fb4883887a  numpy-1.15.1-cp27-cp27mu-manylinux1_i686.whl
df0b02c6705c5d1c25cc35c7b5d6b6f9b3b30833f9d178843397ae55ecc2eebb  numpy-1.15.1-cp27-cp27mu-manylinux1_x86_64.whl
dae8618c0bcbfcf6cf91350f8abcdd84158323711566a8c5892b5c7f832af76f  numpy-1.15.1-cp27-none-win32.whl
a3bd01d6d3ed3d7c06d7f9979ba5d68281f15383fafd53b81aa44b9191047cf8  numpy-1.15.1-cp27-none-win_amd64.whl
1c362ad12dd09a43b348bb28dd2295dd9cdf77f41f0f45965e04ba97f525b864  numpy-1.15.1-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
83b8fc18261b70f45bece2d392537c93dc81eb6c539a16c9ac994c47fc79f09a  numpy-1.15.1-cp34-cp34m-manylinux1_i686.whl
ce75ed495a746e3e78cfa22a77096b3bff2eda995616cb7a542047f233091268  numpy-1.15.1-cp34-cp34m-manylinux1_x86_64.whl
340ec1697d9bb3a9c464028af7a54245298502e91178bddb4c37626d36e197b7  numpy-1.15.1-cp34-none-win32.whl
2156a06bd407918df4ac0122df6497a9c137432118f585e5b17d543e593d1587  numpy-1.15.1-cp34-none-win_amd64.whl
549f3e9778b148a47f4fb4682955ed88057eb627c9fe5467f33507c536deda9d  numpy-1.15.1-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
378378973546ecc1dfaf9e24c160d683dd04df871ecd2dcc86ce658ca20f92c0  numpy-1.15.1-cp35-cp35m-manylinux1_i686.whl
35db8d419345caa4eeaa65cd63f34a15208acd87530a30f0bc25fc84f55c8c80  numpy-1.15.1-cp35-cp35m-manylinux1_x86_64.whl
4287104c24e6a09b9b418761a1e7b1bbde65105f110690ca46a23600a3c606b8  numpy-1.15.1-cp35-none-win32.whl
7a70f2b60d48828cba94a54a8776b61a9c2657a803d47f5785f8062e3a9c7c55  numpy-1.15.1-cp35-none-win_amd64.whl
e3660744cda0d94b90141cdd0db9308b958a372cfeee8d7188fdf5ad9108ea82  numpy-1.15.1-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
5ee7f3dbbdba0da75dec7e94bd7a2b10fe57a83e1b38e678200a6ad8e7b14fdc  numpy-1.15.1-cp36-cp36m-manylinux1_i686.whl
36e8dcd1813ca92ce7e4299120cee6c03adad33d89b54862c1b1a100443ac399  numpy-1.15.1-cp36-cp36m-manylinux1_x86_64.whl
9473ad28375710ab18378e72b59422399b27e957e9339c413bf00793b4b12df0  numpy-1.15.1-cp36-none-win32.whl
c81a6afc1d2531a9ada50b58f8c36197f8418ef3d0611d4c1d7af93fdcda764f  numpy-1.15.1-cp36-none-win_amd64.whl
98b86c62c08c2e5dc98a9c856d4a95329d11b1c6058cb9b5191d5ea6891acd09  numpy-1.15.1-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
24e4149c38489b51fc774b1e1faa9103e82f73344d7a00ba66f6845ab4769f3f  numpy-1.15.1-cp37-cp37m-manylinux1_i686.whl
95b085b253080e5d09f7826f5e27dce067bae813a132023a77b739614a29de6e  numpy-1.15.1-cp37-cp37m-manylinux1_x86_64.whl
361370e9b7f5e44c41eee29f2bb5cb3b755abb4b038bce6d6cbe08db7ff9cb74  numpy-1.15.1-cp37-none-win32.whl
f2362d0ca3e16c37782c1054d7972b8ad2729169567e3f0f4e5dd3cdf85f188e  numpy-1.15.1-cp37-none-win_amd64.whl
3c1ccce5d935ef8df16ae0595b459ef08a5cdb05aee195ebc04b9d89a72be7fa  numpy-1.15.1.tar.gz
7b9e37f194f8bcdca8e9e6af92e2cbad79e360542effc2dd6b98d63955d8d8a3  numpy-1.15.1.zip

@charris charris released this Jul 23, 2018 · 547 commits to master since this release

Assets 6

==========================
NumPy 1.15.0 Release Notes

NumPy 1.15.0 is a release with an unusual number of cleanups, many deprecations
of old functions, and improvements to many existing functions. Please read the
detailed descriptions below to see if you are affected.

For testing, we have switched to pytest as a replacement for the no longer
maintained nose framework. The old nose based interface remains for downstream
projects who may still be using it.

The Python versions supported by this release are 2.7, 3.4-3.7. The wheels are
linked with OpenBLAS v0.3.0, which should fix some of the linalg problems
reported for NumPy 1.14.

Highlights

  • NumPy has switched to pytest for testing.
  • A new numpy.printoptions context manager.
  • Many improvements to the histogram functions.
  • Support for unicode field names in python 2.7.
  • Improved support for PyPy.
  • Fixes and improvements to numpy.einsum.

New functions

  • numpy.gcd and numpy.lcm, to compute the greatest common divisor and least
    common multiple.

  • numpy.ma.stack, the numpy.stack array-joining function generalized to
    masked arrays.

  • numpy.quantile function, an interface to percentile without factors of
    100

  • numpy.nanquantile function, an interface to nanpercentile without
    factors of 100

  • numpy.printoptions, a context manager that sets print options temporarily
    for the scope of the with block::

    with np.printoptions(precision=2):
    ... print(np.array([2.0]) / 3)
    [0.67]

  • numpy.histogram_bin_edges, a function to get the edges of the bins used by a
    histogram without needing to calculate the histogram.

  • C functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier
    have been added to deal with compiler optimization changing the order of
    operations. See below for details.

Deprecations

  • Aliases of builtin pickle functions are deprecated, in favor of their
    unaliased pickle.<func> names:

    • numpy.loads
    • numpy.core.numeric.load
    • numpy.core.numeric.loads
    • numpy.ma.loads, numpy.ma.dumps
    • numpy.ma.load, numpy.ma.dump - these functions already failed on
      python 3 when called with a string.
  • Multidimensional indexing with anything but a tuple is deprecated. This means
    that the index list in ind = [slice(None), 0]; arr[ind] should be changed
    to a tuple, e.g., ind = [slice(None), 0]; arr[tuple(ind)] or
    arr[(slice(None), 0)]. That change is necessary to avoid ambiguity in
    expressions such as arr[[[0, 1], [0, 1]]], currently interpreted as
    arr[array([0, 1]), array([0, 1])], that will be interpreted
    as arr[array([[0, 1], [0, 1]])] in the future.

  • Imports from the following sub-modules are deprecated, they will be removed
    at some future date.

    • numpy.testing.utils
    • numpy.testing.decorators
    • numpy.testing.nosetester
    • numpy.testing.noseclasses
    • numpy.core.umath_tests
  • Giving a generator to numpy.sum is now deprecated. This was undocumented
    behavior, but worked. Previously, it would calculate the sum of the generator
    expression. In the future, it might return a different result. Use
    np.sum(np.from_iter(generator)) or the built-in Python sum instead.

  • Users of the C-API should call PyArrayResolveWriteBackIfCopy or
    PyArray_DiscardWritbackIfCopy on any array with the WRITEBACKIFCOPY
    flag set, before deallocating the array. A deprecation warning will be
    emitted if those calls are not used when needed.

  • Users of nditer should use the nditer object as a context manager
    anytime one of the iterator operands is writeable, so that numpy can
    manage writeback semantics, or should call it.close(). A
    RuntimeWarning may be emitted otherwise in these cases.

  • The normed argument of np.histogram, deprecated long ago in 1.6.0,
    now emits a DeprecationWarning.

Future Changes

  • NumPy 1.16 will drop support for Python 3.4.
  • NumPy 1.17 will drop support for Python 2.7.

Compatibility notes

Compiled testing modules renamed and made private

The following compiled modules have been renamed and made private:

  • umath_tests -> _umath_tests
  • test_rational -> _rational_tests
  • multiarray_tests -> _multiarray_tests
  • struct_ufunc_test -> _struct_ufunc_tests
  • operand_flag_tests -> _operand_flag_tests

The umath_tests module is still available for backwards compatibility, but
will be removed in the future.

The NpzFile returned by np.savez is now a collections.abc.Mapping

This means it behaves like a readonly dictionary, and has a new .values()
method and len() implementation.

For python 3, this means that .iteritems(), .iterkeys() have been
deprecated, and .keys() and .items() now return views and not lists.
This is consistent with how the builtin dict type changed between python 2
and python 3.

Under certain conditions, nditer must be used in a context manager

When using an numpy.nditer with the "writeonly" or "readwrite" flags, there
are some circumstances where nditer doesn't actually give you a view of the
writable array. Instead, it gives you a copy, and if you make changes to the
copy, nditer later writes those changes back into your actual array. Currently,
this writeback occurs when the array objects are garbage collected, which makes
this API error-prone on CPython and entirely broken on PyPy. Therefore,
nditer should now be used as a context manager whenever it is used
with writeable arrays, e.g., with np.nditer(...) as it: .... You may also
explicitly call it.close() for cases where a context manager is unusable,
for instance in generator expressions.

Numpy has switched to using pytest instead of nose for testing

The last nose release was 1.3.7 in June, 2015, and development of that tool has
ended, consequently NumPy has now switched to using pytest. The old decorators
and nose tools that were previously used by some downstream projects remain
available, but will not be maintained. The standard testing utilities,
assert_almost_equal and such, are not be affected by this change except for
the nose specific functions import_nose and raises. Those functions are
not used in numpy, but are kept for downstream compatibility.

Numpy no longer monkey-patches ctypes with __array_interface__

Previously numpy added __array_interface__ attributes to all the integer
types from ctypes.

np.ma.notmasked_contiguous and np.ma.flatnotmasked_contiguous always return lists

This is the documented behavior, but previously the result could be any of
slice, None, or list.

All downstream users seem to check for the None result from
flatnotmasked_contiguous and replace it with []. Those callers will
continue to work as before.

np.squeeze restores old behavior of objects that cannot handle an axis argument

Prior to version 1.7.0, numpy.squeeze did not have an axis argument and
all empty axes were removed by default. The incorporation of an axis
argument made it possible to selectively squeeze single or multiple empty axes,
but the old API expectation was not respected because axes could still be
selectively removed (silent success) from an object expecting all empty axes to
be removed. That silent, selective removal of empty axes for objects expecting
the old behavior has been fixed and the old behavior restored.

unstructured void array's .item method now returns a bytes object

.item now returns a bytes object instead of a buffer or byte array.
This may affect code which assumed the return value was mutable, which is no
longer the case.

copy.copy and copy.deepcopy no longer turn masked into an array

Since np.ma.masked is a readonly scalar, copying should be a no-op. These
functions now behave consistently with np.copy().

Multifield Indexing of Structured Arrays will still return a copy

The change that multi-field indexing of structured arrays returns a view
instead of a copy is pushed back to 1.16. A new method
numpy.lib.recfunctions.repack_fields has been introduced to help mitigate
the effects of this change, which can be used to write code compatible with
both numpy 1.15 and 1.16. For more information on how to update code to account
for this future change see the "accessing multiple fields" section of the
user guide <https://docs.scipy.org/doc/numpy/user/basics.rec.html>__.

C API changes

New functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier

Functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier
have been added and should be used in place of the npy_get_floatstatusand
npy_clear_status functions. Optimizing compilers like GCC 8.1 and Clang
were rearranging the order of operations when the previous functions were used
in the ufunc SIMD functions, resulting in the floatstatus flags being checked
before the operation whose status we wanted to check was run. See #10339 <https://github.com/numpy/numpy/issues/10370>__.

Changes to PyArray_GetDTypeTransferFunction

PyArray_GetDTypeTransferFunction now defaults to using user-defined
copyswapn / copyswap for user-defined dtypes. If this causes a
significant performance hit, consider implementing copyswapn to reflect the
implementation of PyArray_GetStridedCopyFn. See #10898 <https://github.com/numpy/numpy/pull/10898>__.

  • Functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier
    have been added and should be used in place of the npy_get_floatstatusand
    npy_clear_status functions. Optimizing compilers like GCC 8.1 and Clang
    were rearranging the order of operations when the previous functions were
    used in the ufunc SIMD functions, resulting in the floatstatus flags being '
    checked before the operation whose status we wanted to check was run.
    See #10339 <https://github.com/numpy/numpy/issues/10370>__.

New Features

np.gcd and np.lcm ufuncs added for integer and objects types

These compute the greatest common divisor, and lowest common multiple,
respectively. These work on all the numpy integer types, as well as the
builtin arbitrary-precision Decimal and long types.

Support for cross-platform builds for iOS

The build system has been modified to add support for the
_PYTHON_HOST_PLATFORM environment variable, used by distutils when
compiling on one platform for another platform. This makes it possible to
compile NumPy for iOS targets.

This only enables you to compile NumPy for one specific platform at a time.
Creating a full iOS-compatible NumPy package requires building for the 5
architectures supported by iOS (i386, x86_64, armv7, armv7s and arm64), and
combining these 5 compiled builds products into a single "fat" binary.

return_indices keyword added for np.intersect1d

New keyword return_indices returns the indices of the two input arrays
that correspond to the common elements.

np.quantile and np.nanquantile

Like np.percentile and np.nanpercentile, but takes quantiles in [0, 1]
rather than percentiles in [0, 100]. np.percentile is now a thin wrapper
around np.quantile with the extra step of dividing by 100.

Build system

Added experimental support for the 64-bit RISC-V architecture.

Improvements

np.einsum updates

Syncs einsum path optimization tech between numpy and opt_einsum. In
particular, the greedy path has received many enhancements by @jcmgray. A
full list of issues fixed are:

  • Arbitrary memory can be passed into the greedy path. Fixes gh-11210.
  • The greedy path has been updated to contain more dynamic programming ideas
    preventing a large number of duplicate (and expensive) calls that figure out
    the actual pair contraction that takes place. Now takes a few seconds on
    several hundred input tensors. Useful for matrix product state theories.
  • Reworks the broadcasting dot error catching found in gh-11218 gh-10352 to be
    a bit earlier in the process.
  • Enhances the can_dot functionality that previous missed an edge case (part
    of gh-11308).

np.ufunc.reduce and related functions now accept an initial value

np.ufunc.reduce, np.sum, np.prod, np.min and np.max all
now accept an initial keyword argument that specifies the value to start
the reduction with.

np.flip can operate over multiple axes

np.flip now accepts None, or tuples of int, in its axis argument. If
axis is None, it will flip over all the axes.

histogram and histogramdd functions have moved to np.lib.histograms

These were originally found in np.lib.function_base. They are still
available under their un-scoped np.histogram(dd) names, and
to maintain compatibility, aliased at np.lib.function_base.histogram(dd).

Code that does from np.lib.function_base import * will need to be updated
with the new location, and should consider not using import * in future.

histogram will accept NaN values when explicit bins are given

Previously it would fail when trying to compute a finite range for the data.
Since the range is ignored anyway when the bins are given explicitly, this error
was needless.

Note that calling histogram on NaN values continues to raise the
RuntimeWarning s typical of working with nan values, which can be silenced
as usual with errstate.

histogram works on datetime types, when explicit bin edges are given

Dates, times, and timedeltas can now be histogrammed. The bin edges must be
passed explicitly, and are not yet computed automatically.

histogram "auto" estimator handles limited variance better

No longer does an IQR of 0 result in n_bins=1, rather the number of bins
chosen is related to the data size in this situation.

The edges retuned by `histogramandhistogramdd`` now match the data float type

When passed np.float16, np.float32, or np.longdouble data, the
returned edges are now of the same dtype. Previously, histogram would only
return the same type if explicit bins were given, and histogram would
produce float64 bins no matter what the inputs.

histogramdd allows explicit ranges to be given in a subset of axes

The range argument of numpy.histogramdd can now contain None values to
indicate that the range for the corresponding axis should be computed from the
data. Previously, this could not be specified on a per-axis basis.

The normed arguments of histogramdd and histogram2d have been renamed

These arguments are now called density, which is consistent with
histogram. The old argument continues to work, but the new name should be
preferred.

np.r_ works with 0d arrays, and np.ma.mr_ works with np.ma.masked

0d arrays passed to the r_ and mr_ concatenation helpers are now treated as
though they are arrays of length 1. Previously, passing these was an error.
As a result, numpy.ma.mr_ now works correctly on the masked constant.

np.ptp accepts a keepdims argument, and extended axis tuples

np.ptp (peak-to-peak) can now work over multiple axes, just like np.max
and np.min.

MaskedArray.astype now is identical to ndarray.astype

This means it takes all the same arguments, making more code written for
ndarray work for masked array too.

Enable AVX2/AVX512 at compile time

Change to simd.inc.src to allow use of AVX2 or AVX512 at compile time. Previously
compilation for avx2 (or 512) with -march=native would still use the SSE
code for the simd functions even when the rest of the code got AVX2.

nan_to_num always returns scalars when receiving scalar or 0d inputs

Previously an array was returned for integer scalar inputs, which is
inconsistent with the behavior for float inputs, and that of ufuncs in general.
For all types of scalar or 0d input, the result is now a scalar.

np.flatnonzero works on numpy-convertible types

np.flatnonzero now uses np.ravel(a) instead of a.ravel(), so it
works for lists, tuples, etc.

np.interp returns numpy scalars rather than builtin scalars

Previously np.interp(0.5, [0, 1], [10, 20]) would return a float, but
now it returns a np.float64 object, which more closely matches the behavior
of other functions.

Additionally, the special case of np.interp(object_array_0d, ...) is no
longer supported, as np.interp(object_array_nd) was never supported anyway.

As a result of this change, the period argument can now be used on 0d
arrays.

Allow dtype field names to be unicode in Python 2

Previously np.dtype([(u'name', float)]) would raise a TypeError in
Python 2, as only bytestrings were allowed in field names. Now any unicode
string field names will be encoded with the ascii codec, raising a
UnicodeEncodeError upon failure.

This change makes it easier to write Python 2/3 compatible code using
from __future__ import unicode_literals, which previously would cause
string literal field names to raise a TypeError in Python 2.

Comparison ufuncs accept dtype=object, overriding the default bool

This allows object arrays of symbolic types, which override == and other
operators to return expressions, to be compared elementwise with
np.equal(a, b, dtype=object).

sort functions accept kind='stable'

Up until now, to perform a stable sort on the data, the user must do:

>>> np.sort([5, 2, 6, 2, 1], kind='mergesort')
[1, 2, 2, 5, 6]

because merge sort is the only stable sorting algorithm available in
NumPy. However, having kind='mergesort' does not make it explicit that
the user wants to perform a stable sort thus harming the readability.

This change allows the user to specify kind='stable' thus clarifying
the intent.

Do not make temporary copies for in-place accumulation

When ufuncs perform accumulation they no longer make temporary copies because
of the overlap between input an output, that is, the next element accumulated
is added before the accumulated result is stored in its place, hence the
overlap is safe. Avoiding the copy results in faster execution.

linalg.matrix_power can now handle stacks of matrices

Like other functions in linalg, matrix_power can now deal with arrays
of dimension larger than 2, which are treated as stacks of matrices. As part
of the change, to further improve consistency, the name of the first argument
has been changed to a (from M), and the exceptions for non-square
matrices have been changed to LinAlgError (from ValueError).

Increased performance in random.permutation for multidimensional arrays

permutation uses the fast path in random.shuffle for all input
array dimensions. Previously the fast path was only used for 1-d arrays.

Generalized ufuncs now accept axes, axis and keepdims arguments

One can control over which axes a generalized ufunc operates by passing in an
axes argument, a list of tuples with indices of particular axes. For
instance, for a signature of (i,j),(j,k)->(i,k) appropriate for matrix
multiplication, the base elements are two-dimensional matrices and these are
taken to be stored in the two last axes of each argument. The corresponding
axes keyword would be [(-2, -1), (-2, -1), (-2, -1)]. If one wanted to
use leading dimensions instead, one would pass in [(0, 1), (0, 1), (0, 1)].

For simplicity, for generalized ufuncs that operate on 1-dimensional arrays
(vectors), a single integer is accepted instead of a single-element tuple, and
for generalized ufuncs for which all outputs are scalars, the (empty) output
tuples can be omitted. Hence, for a signature of (i),(i)->() appropriate
for an inner product, one could pass in axes=[0, 0] to indicate that the
vectors are stored in the first dimensions of the two inputs arguments.

As a short-cut for generalized ufuncs that are similar to reductions, i.e.,
that act on a single, shared core dimension such as the inner product example
above, one can pass an axis argument. This is equivalent to passing in
axes with identical entries for all arguments with that core dimension
(e.g., for the example above, axes=[(axis,), (axis,)]).

Furthermore, like for reductions, for generalized ufuncs that have inputs that
all have the same number of core dimensions and outputs with no core dimension,
one can pass in keepdims to leave a dimension with size 1 in the outputs,
thus allowing proper broadcasting against the original inputs. The location of
the extra dimension can be controlled with axes. For instance, for the
inner-product example, keepdims=True, axes=[-2, -2, -2] would act on the
inner-product example, keepdims=True, axis=-2 would act on the
one-but-last dimension of the input arguments, and leave a size 1 dimension in
that place in the output.

float128 values now print correctly on ppc systems

Previously printing float128 values was buggy on ppc, since the special
double-double floating-point-format on these systems was not accounted for.
float128s now print with correct rounding and uniqueness.

Warning to ppc users: You should upgrade glibc if it is version <=2.23,
especially if using float128. On ppc, glibc's malloc in these version often
misaligns allocated memory which can crash numpy when using float128 values.

New np.take_along_axis and np.put_along_axis functions

When used on multidimensional arrays, argsort, argmin, argmax, and
argpartition return arrays that are difficult to use as indices.
take_along_axis provides an easy way to use these indices to lookup values
within an array, so that::

np.take_along_axis(a, np.argsort(a, axis=axis), axis=axis)

is the same as::

np.sort(a, axis=axis)

np.put_along_axis acts as the dual operation for writing to these indices
within an array.

Checksums

MD5

4957a50c1125fdecb4cb51829f5feba1  numpy-1.15.0-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
d5ffa73c6a3eeba8cfcab283e7db3c2f  numpy-1.15.0-cp27-cp27m-manylinux1_i686.whl
a6f7aa33d4d1598dc33831a4bb36570d  numpy-1.15.0-cp27-cp27m-manylinux1_x86_64.whl
cbdd2291782deb29f41c9b7d121264e0  numpy-1.15.0-cp27-cp27mu-manylinux1_i686.whl
0bd79da73435161850099bfcacc75fae  numpy-1.15.0-cp27-cp27mu-manylinux1_x86_64.whl
73f930c046ac09e518d0b4cf2f8ff642  numpy-1.15.0-cp27-none-win32.whl
7ba5b463728a792dced42fd6259e511f  numpy-1.15.0-cp27-none-win_amd64.whl
badfc9f713510d59f478037c88b3d963  numpy-1.15.0-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
662f2536cac7b841f86e9b7488e52371  numpy-1.15.0-cp34-cp34m-manylinux1_i686.whl
346d9239f7f12bb7042f8bc847928dc1  numpy-1.15.0-cp34-cp34m-manylinux1_x86_64.whl
fd03012584359cd05cee08408df5897d  numpy-1.15.0-cp34-none-win32.whl
1032db03cefd82e87f72f2b04b15b7ae  numpy-1.15.0-cp34-none-win_amd64.whl
cc463ee62af94c8410fdf95ce9933c3c  numpy-1.15.0-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
77655199a4e18719dd5a0b348c44fc92  numpy-1.15.0-cp35-cp35m-manylinux1_i686.whl
d76c54272549cf3a2165d40d3fea5e30  numpy-1.15.0-cp35-cp35m-manylinux1_x86_64.whl
956c6f7c216b677b27628a97150cd069  numpy-1.15.0-cp35-none-win32.whl
2ab8080576932775167a6f9c772b91e4  numpy-1.15.0-cp35-none-win_amd64.whl
1a01c8d089d488565acc2836d03a7482  numpy-1.15.0-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
5606fa1c1e13e789b802102699d613e2  numpy-1.15.0-cp36-cp36m-manylinux1_i686.whl
5635343a70f7cdd17f372966db1526d3  numpy-1.15.0-cp36-cp36m-manylinux1_x86_64.whl
166e901c1a86da5ffb8c6d3090ed917e  numpy-1.15.0-cp36-none-win32.whl
6423497ad5a610c1deed606ce44893bd  numpy-1.15.0-cp36-none-win_amd64.whl
e232fbba29585812bf7fa547f671b768  numpy-1.15.0-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
b2fc4551651fae84eb01b8a37f2e1e69  numpy-1.15.0-cp37-cp37m-manylinux1_i686.whl
36ed60bef7c5cb252b9d0e8dc5029e08  numpy-1.15.0-cp37-cp37m-manylinux1_x86_64.whl
4482a89fa4540c8bbf76028621931266  numpy-1.15.0-cp37-none-win32.whl
cfef18ee246468752f1686147c70bd0a  numpy-1.15.0-cp37-none-win_amd64.whl
5cf4daff88042326334266f80ad38884  numpy-1.15.0.tar.gz
20e13185089011116a98e11c9bf8aa07  numpy-1.15.0.zip

SHA256

a17a8fd5df4fec5b56b4d11c9ba8b9ebfb883c90ec361628d07be00aaa4f009a  numpy-1.15.0-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
c3ac359ace241707e5a48fe2922e566ac666aacacf4f8031f2994ac429c31344  numpy-1.15.0-cp27-cp27m-manylinux1_i686.whl
e2317cf091c2e7f0dacdc2e72c693cc34403ca1f8e3807622d0bb653dc978616  numpy-1.15.0-cp27-cp27m-manylinux1_x86_64.whl
64c6acf5175745fd1b7b7e17c74fdbfb7191af3b378bc54f44560279f41238d3  numpy-1.15.0-cp27-cp27mu-manylinux1_i686.whl
924f37e66db78464b4b85ed4b6d2e5cda0c0416e657cac7ccbef14b9fa2c40b5  numpy-1.15.0-cp27-cp27mu-manylinux1_x86_64.whl
674ea7917f0657ddb6976bd102ac341bc493d072c32a59b98e5b8c6eaa2d5ec0  numpy-1.15.0-cp27-none-win32.whl
ae3864816287d0e86ead580b69921daec568fe680857f07ee2a87bf7fd77ce24  numpy-1.15.0-cp27-none-win_amd64.whl
78c35dc7ad184aebf3714dbf43f054714c6e430e14b9c06c49a864fb9e262030  numpy-1.15.0-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
c7c660cc0209fdf29a4e50146ca9ac9d8664acaded6b6ae2f5d0ae2e91a0f0cd  numpy-1.15.0-cp34-cp34m-manylinux1_i686.whl
3fbccb399fe9095b1c1d7b41e7c7867db8aa0d2347fc44c87a7a180cedda112b  numpy-1.15.0-cp34-cp34m-manylinux1_x86_64.whl
aaa519335a71f87217ca8a680c3b66b61960e148407bdf5c209c42f50fe30f49  numpy-1.15.0-cp34-none-win32.whl
62cb836506f40ce2529bfba9d09edc4b2687dd18c56cf4457e51c3e7145402fd  numpy-1.15.0-cp34-none-win_amd64.whl
55daf757e5f69aa75b4477cf4511bf1f96325c730e4ad32d954ccb593acd2585  numpy-1.15.0-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
b5f8c15cb9173f6cdf0f994955e58d1265331029ae26296232379461a297e5f2  numpy-1.15.0-cp35-cp35m-manylinux1_i686.whl
24f3bb9a5f6c3936a8ccd4ddfc1210d9511f4aeb879a12efd2e80bec647b8695  numpy-1.15.0-cp35-cp35m-manylinux1_x86_64.whl
34033b581bc01b1135ca2e3e93a94daea7c739f21a97a75cca93e29d9f0c8e71  numpy-1.15.0-cp35-none-win32.whl
f5a758252502b466b9c2b201ea397dae5a914336c987f3a76c3741a82d43c96e  numpy-1.15.0-cp35-none-win_amd64.whl
14fb76bde161c87dcec52d91c78f65aa8a23aa2e1530a71f412dabe03927d917  numpy-1.15.0-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
816645178f2180be257a576b735d3ae245b1982280b97ae819550ce8bcdf2b6b  numpy-1.15.0-cp36-cp36m-manylinux1_i686.whl
f2a778dd9bb3e4590dbe3bbac28e7c7134280c4ec97e3bf8678170ee58c67b21  numpy-1.15.0-cp36-cp36m-manylinux1_x86_64.whl
7f17efe9605444fcbfd990ba9b03371552d65a3c259fc2d258c24fb95afdd728  numpy-1.15.0-cp36-none-win32.whl
73a816e441dace289302e04a7a34ec4772ed234ab6885c968e3ca2fc2d06fe2d  numpy-1.15.0-cp36-none-win_amd64.whl
21041014b7529237994a6b578701c585703fbb3b1bea356cdb12a5ea7804241c  numpy-1.15.0-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
d690a2ff49f6c3bc35336693c9924fe5916be3cc0503fe1ea6c7e2bf951409ee  numpy-1.15.0-cp37-cp37m-manylinux1_i686.whl
50718eea8e77a1bedcc85befd22c8dbf5a24c9d2c0c1e36bbb8d7a38da847eb3  numpy-1.15.0-cp37-cp37m-manylinux1_x86_64.whl
fb4c33a404d9eff49a0cdc8ead0af6453f62f19e071b60d283f9dc05581e4134  numpy-1.15.0-cp37-none-win32.whl
61efc65f325770bbe787f34e00607bc124f08e6c25fdf04723848585e81560dc  numpy-1.15.0-cp37-none-win_amd64.whl
259934a941663e93fdd5d28ce3f6aa2a81ce7dda85c395dd07b1f1edff2e0236  numpy-1.15.0.tar.gz
f28e73cf18d37a413f7d5de35d024e6b98f14566a10d82100f9dc491a7d449f9  numpy-1.15.0.zip
Pre-release
Pre-release

@charris charris released this Jul 9, 2018 · 547 commits to master since this release

Assets 6

==========================
NumPy 1.15.0 Release Notes

NumPy 1.15.0 is a release with an unusual number of cleanups, many deprecations
of old functions, and improvements to many existing functions. Please read the
detailed descriptions below to see if you are affected.

For testing, we have switched to pytest as a replacement for the no longer
maintained nose framework. The old nose based interface remains for downstream
projects who may still be using it.

The Python versions supported by this release are 2.7, 3.4-3.7. The wheels are
linked with OpenBLAS v0.3.0, which should fix some of the linalg problems
reported for NumPy 1.14.

Highlights

  • NumPy has switched to pytest for testing.
  • A new numpy.printoptions context manager.
  • Many improvements to the histogram functions.
  • Support for unicode field names in python 2.7.
  • Improved support for PyPy.
  • Fixes and improvements to numpy.einsum.

New functions

  • numpy.gcd and numpy.lcm, to compute the greatest common divisor and least
    common multiple.

  • numpy.ma.stack, the numpy.stack array-joining function generalized to
    masked arrays.

  • numpy.quantile function, an interface to percentile without factors of
    100

  • numpy.nanquantile function, an interface to nanpercentile without
    factors of 100

  • numpy.printoptions, a context manager that sets print options temporarily
    for the scope of the with block::

    with np.printoptions(precision=2):
    ... print(np.array([2.0]) / 3)
    [0.67]

  • numpy.histogram_bin_edges, a function to get the edges of the bins used by a
    histogram without needing to calculate the histogram.

  • C functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier
    have been added to deal with compiler optimization changing the order of
    operations. See below for details.

Deprecations

  • Aliases of builtin pickle functions are deprecated, in favor of their
    unaliased pickle.<func> names:

    • numpy.loads
    • numpy.core.numeric.load
    • numpy.core.numeric.loads
    • numpy.ma.loads, numpy.ma.dumps
    • numpy.ma.load, numpy.ma.dump - these functions already failed on
      python 3 when called with a string.
  • Multidimensional indexing with anything but a tuple is deprecated. This means
    that the index list in ind = [slice(None), 0]; arr[ind] should be changed
    to a tuple, e.g., ind = [slice(None), 0]; arr[tuple(ind)] or
    arr[(slice(None), 0)]. That change is necessary to avoid ambiguity in
    expressions such as arr[[[0, 1], [0, 1]]], currently interpreted as
    arr[array([0, 1]), array([0, 1])], that will be interpreted
    as arr[array([[0, 1], [0, 1]])] in the future.

  • Imports from the following sub-modules are deprecated, they will be removed
    at some future date.

    • numpy.testing.utils
    • numpy.testing.decorators
    • numpy.testing.nosetester
    • numpy.testing.noseclasses
    • numpy.core.umath_tests
  • Giving a generator to numpy.sum is now deprecated. This was undocumented
    behavior, but worked. Previously, it would calculate the sum of the generator
    expression. In the future, it might return a different result. Use
    np.sum(np.from_iter(generator)) or the built-in Python sum instead.

  • Users of the C-API should call PyArrayResolveWriteBackIfCopy or
    PyArray_DiscardWritbackIfCopy on any array with the WRITEBACKIFCOPY
    flag set, before deallocating the array. A deprecation warning will be
    emitted if those calls are not used when needed.

  • Users of nditer should use the nditer object as a context manager
    anytime one of the iterator operands is writeable, so that numpy can
    manage writeback semantics, or should call it.close(). A
    RuntimeWarning may be emitted otherwise in these cases.

  • The normed argument of np.histogram, deprecated long ago in 1.6.0,
    now emits a DeprecationWarning.

Future Changes

  • NumPy 1.16 will drop support for Python 3.4.
  • NumPy 1.17 will drop support for Python 2.7.

Compatibility notes

Compiled testing modules renamed and made private

The following compiled modules have been renamed and made private:

  • umath_tests -> _umath_tests
  • test_rational -> _rational_tests
  • multiarray_tests -> _multiarray_tests
  • struct_ufunc_test -> _struct_ufunc_tests
  • operand_flag_tests -> _operand_flag_tests

The umath_tests module is still available for backwards compatibility, but
will be removed in the future.

The NpzFile returned by np.savez is now a collections.abc.Mapping

This means it behaves like a readonly dictionary, and has a new .values()
method and len() implementation.

For python 3, this means that .iteritems(), .iterkeys() have been
deprecated, and .keys() and .items() now return views and not lists.
This is consistent with how the builtin dict type changed between python 2
and python 3.

Under certain conditions, nditer must be used in a context manager

When using an numpy.nditer with the "writeonly" or "readwrite" flags, there
are some circumstances where nditer doesn't actually give you a view of the
writable array. Instead, it gives you a copy, and if you make changes to the
copy, nditer later writes those changes back into your actual array. Currently,
this writeback occurs when the array objects are garbage collected, which makes
this API error-prone on CPython and entirely broken on PyPy. Therefore,
nditer should now be used as a context manager whenever it is used
with writeable arrays, e.g., with np.nditer(...) as it: .... You may also
explicitly call it.close() for cases where a context manager is unusable,
for instance in generator expressions.

Numpy has switched to using pytest instead of nose for testing

The last nose release was 1.3.7 in June, 2015, and development of that tool has
ended, consequently NumPy has now switched to using pytest. The old decorators
and nose tools that were previously used by some downstream projects remain
available, but will not be maintained. The standard testing utilities,
assert_almost_equal and such, are not be affected by this change except for
the nose specific functions import_nose and raises. Those functions are
not used in numpy, but are kept for downstream compatibility.

Numpy no longer monkey-patches ctypes with __array_interface__

Previously numpy added __array_interface__ attributes to all the integer
types from ctypes.

np.ma.notmasked_contiguous and np.ma.flatnotmasked_contiguous always return lists

This is the documented behavior, but previously the result could be any of
slice, None, or list.

All downstream users seem to check for the None result from
flatnotmasked_contiguous and replace it with []. Those callers will
continue to work as before.

np.squeeze restores old behavior of objects that cannot handle an axis argument

Prior to version 1.7.0, numpy.squeeze did not have an axis argument and
all empty axes were removed by default. The incorporation of an axis
argument made it possible to selectively squeeze single or multiple empty axes,
but the old API expectation was not respected because axes could still be
selectively removed (silent success) from an object expecting all empty axes to
be removed. That silent, selective removal of empty axes for objects expecting
the old behavior has been fixed and the old behavior restored.

unstructured void array's .item method now returns a bytes object

.item now returns a bytes object instead of a buffer or byte array.
This may affect code which assumed the return value was mutable, which is no
longer the case.

copy.copy and copy.deepcopy no longer turn masked into an array

Since np.ma.masked is a readonly scalar, copying should be a no-op. These
functions now behave consistently with np.copy().

Multifield Indexing of Structured Arrays will still return a copy

The change that multi-field indexing of structured arrays returns a view
instead of a copy is pushed back to 1.16. A new method
numpy.lib.recfunctions.repack_fields has been introduced to help mitigate
the effects of this change, which can be used to write code compatible with
both numpy 1.15 and 1.16. For more information on how to update code to account
for this future change see the "accessing multiple fields" section of the
user guide <https://docs.scipy.org/doc/numpy/user/basics.rec.html>__.

C API changes

New functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier

Functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier
have been added and should be used in place of the npy_get_floatstatusand
npy_clear_status functions. Optimizing compilers like GCC 8.1 and Clang
were rearranging the order of operations when the previous functions were used
in the ufunc SIMD functions, resulting in the floatstatus flags being checked
before the operation whose status we wanted to check was run. See #10339 <https://github.com/numpy/numpy/issues/10370>__.

Changes to PyArray_GetDTypeTransferFunction

PyArray_GetDTypeTransferFunction now defaults to using user-defined
copyswapn / copyswap for user-defined dtypes. If this causes a
significant performance hit, consider implementing copyswapn to reflect the
implementation of PyArray_GetStridedCopyFn. See #10898 <https://github.com/numpy/numpy/pull/10898>__.

  • Functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier
    have been added and should be used in place of the npy_get_floatstatusand
    npy_clear_status functions. Optimizing compilers like GCC 8.1 and Clang
    were rearranging the order of operations when the previous functions were
    used in the ufunc SIMD functions, resulting in the floatstatus flags being '
    checked before the operation whose status we wanted to check was run.
    See #10339 <https://github.com/numpy/numpy/issues/10370>__.

New Features

np.gcd and np.lcm ufuncs added for integer and objects types

These compute the greatest common divisor, and lowest common multiple,
respectively. These work on all the numpy integer types, as well as the
builtin arbitrary-precision Decimal and long types.

Support for cross-platform builds for iOS

The build system has been modified to add support for the
_PYTHON_HOST_PLATFORM environment variable, used by distutils when
compiling on one platform for another platform. This makes it possible to
compile NumPy for iOS targets.

This only enables you to compile NumPy for one specific platform at a time.
Creating a full iOS-compatible NumPy package requires building for the 5
architectures supported by iOS (i386, x86_64, armv7, armv7s and arm64), and
combining these 5 compiled builds products into a single "fat" binary.

return_indices keyword added for np.intersect1d

New keyword return_indices returns the indices of the two input arrays
that correspond to the common elements.

np.quantile and np.nanquantile

Like np.percentile and np.nanpercentile, but takes quantiles in [0, 1]
rather than percentiles in [0, 100]. np.percentile is now a thin wrapper
around np.quantile with the extra step of dividing by 100.

Build system

Added experimental support for the 64-bit RISC-V architecture.

Improvements

np.einsum updates

Syncs einsum path optimization tech between numpy and opt_einsum. In
particular, the greedy path has received many enhancements by @jcmgray. A
full list of issues fixed are:

  • Arbitrary memory can be passed into the greedy path. Fixes gh-11210.
  • The greedy path has been updated to contain more dynamic programming ideas
    preventing a large number of duplicate (and expensive) calls that figure out
    the actual pair contraction that takes place. Now takes a few seconds on
    several hundred input tensors. Useful for matrix product state theories.
  • Reworks the broadcasting dot error catching found in gh-11218 gh-10352 to be
    a bit earlier in the process.
  • Enhances the can_dot functionality that previous missed an edge case (part
    of gh-11308).

np.ufunc.reduce and related functions now accept an initial value

np.ufunc.reduce, np.sum, np.prod, np.min and np.max all
now accept an initial keyword argument that specifies the value to start
the reduction with.

np.flip can operate over multiple axes

np.flip now accepts None, or tuples of int, in its axis argument. If
axis is None, it will flip over all the axes.

histogram and histogramdd functions have moved to np.lib.histograms

These were originally found in np.lib.function_base. They are still
available under their un-scoped np.histogram(dd) names, and
to maintain compatibility, aliased at np.lib.function_base.histogram(dd).

Code that does from np.lib.function_base import * will need to be updated
with the new location, and should consider not using import * in future.

histogram will accept NaN values when explicit bins are given

Previously it would fail when trying to compute a finite range for the data.
Since the range is ignored anyway when the bins are given explicitly, this error
was needless.

Note that calling histogram on NaN values continues to raise the
RuntimeWarning s typical of working with nan values, which can be silenced
as usual with errstate.

histogram works on datetime types, when explicit bin edges are given

Dates, times, and timedeltas can now be histogrammed. The bin edges must be
passed explicitly, and are not yet computed automatically.

histogram "auto" estimator handles limited variance better

No longer does an IQR of 0 result in n_bins=1, rather the number of bins
chosen is related to the data size in this situation.

The edges retuned by `histogramandhistogramdd`` now match the data float type

When passed np.float16, np.float32, or np.longdouble data, the
returned edges are now of the same dtype. Previously, histogram would only
return the same type if explicit bins were given, and histogram would
produce float64 bins no matter what the inputs.

histogramdd allows explicit ranges to be given in a subset of axes

The range argument of numpy.histogramdd can now contain None values to
indicate that the range for the corresponding axis should be computed from the
data. Previously, this could not be specified on a per-axis basis.

The normed arguments of histogramdd and histogram2d have been renamed

These arguments are now called density, which is consistent with
histogram. The old argument continues to work, but the new name should be
preferred.

np.r_ works with 0d arrays, and np.ma.mr_ works with np.ma.masked

0d arrays passed to the r_ and mr_ concatenation helpers are now treated as
though they are arrays of length 1. Previously, passing these was an error.
As a result, numpy.ma.mr_ now works correctly on the masked constant.

np.ptp accepts a keepdims argument, and extended axis tuples

np.ptp (peak-to-peak) can now work over multiple axes, just like np.max
and np.min.

MaskedArray.astype now is identical to ndarray.astype

This means it takes all the same arguments, making more code written for
ndarray work for masked array too.

Enable AVX2/AVX512 at compile time

Change to simd.inc.src to allow use of AVX2 or AVX512 at compile time. Previously
compilation for avx2 (or 512) with -march=native would still use the SSE
code for the simd functions even when the rest of the code got AVX2.

nan_to_num always returns scalars when receiving scalar or 0d inputs

Previously an array was returned for integer scalar inputs, which is
inconsistent with the behavior for float inputs, and that of ufuncs in general.
For all types of scalar or 0d input, the result is now a scalar.

np.flatnonzero works on numpy-convertible types

np.flatnonzero now uses np.ravel(a) instead of a.ravel(), so it
works for lists, tuples, etc.

np.interp returns numpy scalars rather than builtin scalars

Previously np.interp(0.5, [0, 1], [10, 20]) would return a float, but
now it returns a np.float64 object, which more closely matches the behavior
of other functions.

Additionally, the special case of np.interp(object_array_0d, ...) is no
longer supported, as np.interp(object_array_nd) was never supported anyway.

As a result of this change, the period argument can now be used on 0d
arrays.

Allow dtype field names to be unicode in Python 2

Previously np.dtype([(u'name', float)]) would raise a TypeError in
Python 2, as only bytestrings were allowed in field names. Now any unicode
string field names will be encoded with the ascii codec, raising a
UnicodeEncodeError upon failure.

This change makes it easier to write Python 2/3 compatible code using
from __future__ import unicode_literals, which previously would cause
string literal field names to raise a TypeError in Python 2.

Comparison ufuncs accept dtype=object, overriding the default bool

This allows object arrays of symbolic types, which override == and other
operators to return expressions, to be compared elementwise with
np.equal(a, b, dtype=object).

sort functions accept kind='stable'

Up until now, to perform a stable sort on the data, the user must do:

>>> np.sort([5, 2, 6, 2, 1], kind='mergesort')
[1, 2, 2, 5, 6]

because merge sort is the only stable sorting algorithm available in
NumPy. However, having kind='mergesort' does not make it explicit that
the user wants to perform a stable sort thus harming the readability.

This change allows the user to specify kind='stable' thus clarifying
the intent.

Do not make temporary copies for in-place accumulation

When ufuncs perform accumulation they no longer make temporary copies because
of the overlap between input an output, that is, the next element accumulated
is added before the accumulated result is stored in its place, hence the
overlap is safe. Avoiding the copy results in faster execution.

linalg.matrix_power can now handle stacks of matrices

Like other functions in linalg, matrix_power can now deal with arrays
of dimension larger than 2, which are treated as stacks of matrices. As part
of the change, to further improve consistency, the name of the first argument
has been changed to a (from M), and the exceptions for non-square
matrices have been changed to LinAlgError (from ValueError).

Increased performance in random.permutation for multidimensional arrays

permutation uses the fast path in random.shuffle for all input
array dimensions. Previously the fast path was only used for 1-d arrays.

Generalized ufuncs now accept axes, axis and keepdims arguments

One can control over which axes a generalized ufunc operates by passing in an
axes argument, a list of tuples with indices of particular axes. For
instance, for a signature of (i,j),(j,k)->(i,k) appropriate for matrix
multiplication, the base elements are two-dimensional matrices and these are
taken to be stored in the two last axes of each argument. The corresponding
axes keyword would be [(-2, -1), (-2, -1), (-2, -1)]. If one wanted to
use leading dimensions instead, one would pass in [(0, 1), (0, 1), (0, 1)].

For simplicity, for generalized ufuncs that operate on 1-dimensional arrays
(vectors), a single integer is accepted instead of a single-element tuple, and
for generalized ufuncs for which all outputs are scalars, the (empty) output
tuples can be omitted. Hence, for a signature of (i),(i)->() appropriate
for an inner product, one could pass in axes=[0, 0] to indicate that the
vectors are stored in the first dimensions of the two inputs arguments.

As a short-cut for generalized ufuncs that are similar to reductions, i.e.,
that act on a single, shared core dimension such as the inner product example
above, one can pass an axis argument. This is equivalent to passing in
axes with identical entries for all arguments with that core dimension
(e.g., for the example above, axes=[(axis,), (axis,)]).

Furthermore, like for reductions, for generalized ufuncs that have inputs that
all have the same number of core dimensions and outputs with no core dimension,
one can pass in keepdims to leave a dimension with size 1 in the outputs,
thus allowing proper broadcasting against the original inputs. The location of
the extra dimension can be controlled with axes. For instance, for the
inner-product example, keepdims=True, axes=[-2, -2, -2] would act on the
inner-product example, keepdims=True, axis=-2 would act on the
one-but-last dimension of the input arguments, and leave a size 1 dimension in
that place in the output.

float128 values now print correctly on ppc systems

Previously printing float128 values was buggy on ppc, since the special
double-double floating-point-format on these systems was not accounted for.
float128s now print with correct rounding and uniqueness.

Warning to ppc users: You should upgrade glibc if it is version <=2.23,
especially if using float128. On ppc, glibc's malloc in these version often
misaligns allocated memory which can crash numpy when using float128 values.

New np.take_along_axis and np.put_along_axis functions

When used on multidimensional arrays, argsort, argmin, argmax, and
argpartition return arrays that are difficult to use as indices.
take_along_axis provides an easy way to use these indices to lookup values
within an array, so that::

np.take_along_axis(a, np.argsort(a, axis=axis), axis=axis)

is the same as::

np.sort(a, axis=axis)

np.put_along_axis acts as the dual operation for writing to these indices
within an array.

Checksums

MD5

8316260e8887fc8bcf5f0b05f63c5019  numpy-1.15.0rc2-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
2891ad8dbc338eba71726d460542a081  numpy-1.15.0rc2-cp27-cp27m-manylinux1_i686.whl
6d222985f3f97c5e2fc1153eb8373808  numpy-1.15.0rc2-cp27-cp27m-manylinux1_x86_64.whl
a07824b238cfb6a28997802e5196b59a  numpy-1.15.0rc2-cp27-cp27mu-manylinux1_i686.whl
bdcdf5b7af86c3c9e7986443f958088a  numpy-1.15.0rc2-cp27-cp27mu-manylinux1_x86_64.whl
03402a54a4160992470418bb2d8b80ce  numpy-1.15.0rc2-cp27-none-win32.whl
8d43fa19d44eb1984103a3d6a36ca1d8  numpy-1.15.0rc2-cp27-none-win_amd64.whl
584955fb44be27fdeb98403fc7377570  numpy-1.15.0rc2-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
85f2e7d543d164fbd3fd8eaebc8e37b0  numpy-1.15.0rc2-cp34-cp34m-manylinux1_i686.whl
305290d750a6d3d6b0730faac3bdd918  numpy-1.15.0rc2-cp34-cp34m-manylinux1_x86_64.whl
ca67388f3ab2de181caaf35b219e4bb0  numpy-1.15.0rc2-cp34-none-win32.whl
112a63e88d58a841e286015f213246e5  numpy-1.15.0rc2-cp34-none-win_amd64.whl
fd8aa9b7261e96694546956f6580e930  numpy-1.15.0rc2-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
a95d9721714724c129a804d37e226b8c  numpy-1.15.0rc2-cp35-cp35m-manylinux1_i686.whl
199229506044c8d47b8dba19a093c7a6  numpy-1.15.0rc2-cp35-cp35m-manylinux1_x86_64.whl
cd560aae25afd852181a9f89c82eecac  numpy-1.15.0rc2-cp35-none-win32.whl
981140d5b42ad1457ddff565576073a3  numpy-1.15.0rc2-cp35-none-win_amd64.whl
1b83a71e5021a582d9f77427394c36e1  numpy-1.15.0rc2-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
791f0204b92772284472af1769ecbcba  numpy-1.15.0rc2-cp36-cp36m-manylinux1_i686.whl
24679506658bee564e9c4c96de5cffaf  numpy-1.15.0rc2-cp36-cp36m-manylinux1_x86_64.whl
daeb5f3144795c51039f79fdf15da10d  numpy-1.15.0rc2-cp36-none-win32.whl
8a32e40e2d2a82bb86a2887fe7b72120  numpy-1.15.0rc2-cp36-none-win_amd64.whl
a6856a9ee8e6faae5d6d7424029d5390  numpy-1.15.0rc2-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
566a8a2fd66be5fec607254abdc001be  numpy-1.15.0rc2-cp37-cp37m-manylinux1_i686.whl
ed94c261b602b88fabd9de497adfe461  numpy-1.15.0rc2-cp37-cp37m-manylinux1_x86_64.whl
6045f7e99b4e29ec5e5255aafafa9546  numpy-1.15.0rc2-cp37-none-win32.whl
d0cafbac87ba71cea7b3617931f1a71e  numpy-1.15.0rc2-cp37-none-win_amd64.whl
c2e3c18e470506059b44e50d5153609a  numpy-1.15.0rc2.tar.gz
1709c599dcc04f37316df85df451b3d9  numpy-1.15.0rc2.zip

SHA256

5e4f9a3ea77cfeae16e1227ddd887c33b24dcc38e90add9630d71bd3ad96c13a  numpy-1.15.0rc2-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
8d1f49ea7f50aeddbe5bead5af75aba20e15237908aedc6f35e3cd8548372783  numpy-1.15.0rc2-cp27-cp27m-manylinux1_i686.whl
28cb017a0502e46d27a1f4af25c199e4609e3193c1f39b24b4a677d7a737f477  numpy-1.15.0rc2-cp27-cp27m-manylinux1_x86_64.whl
8c02ccade177d82a366dba221eb6fcc85f63c3147817526883084c8b50aba471  numpy-1.15.0rc2-cp27-cp27mu-manylinux1_i686.whl
bc2073c9a97821b8bbf8cc58a3480aaf5897ee3c812427410aa03bd0615ed24e  numpy-1.15.0rc2-cp27-cp27mu-manylinux1_x86_64.whl
316122b90b48dc7cd93232b8d0e6b82d73ef34e18f96d9c4d49f7be3f2b86759  numpy-1.15.0rc2-cp27-none-win32.whl
e2c57051f9126291b82043aefd01aa3f3523a7db27f20f6e0959e02983ee601f  numpy-1.15.0rc2-cp27-none-win_amd64.whl
04847c434f6a9dce1785f4f6adf6d15d057677ec86ad7139e089f505040fc02f  numpy-1.15.0rc2-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
714b87ffc1cb76eb658898f0105cfec673615458b472f0c838a69d1de28ddaaf  numpy-1.15.0rc2-cp34-cp34m-manylinux1_i686.whl
8ddb2abf160e25b1c0a0dd648c686332c33d52df88eb7ed1df248eb25ffd2191  numpy-1.15.0rc2-cp34-cp34m-manylinux1_x86_64.whl
558e102282d330234cfcc1b68e163e9ceb5baac26585a5506fd12c8ae406e0d2  numpy-1.15.0rc2-cp34-none-win32.whl
7db1973b8dd352a923c875c522c3f414c3e286fd12278e806ad635430cf7e906  numpy-1.15.0rc2-cp34-none-win_amd64.whl
8c962a352744e1f1df47665d12c24b59a8f30d6c1c492b6a1fb0a4be5a1a383c  numpy-1.15.0rc2-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
3f9fa0257a0aab9ec18a205b3d5a10bf6c727913065d62fa61344604729559f4  numpy-1.15.0rc2-cp35-cp35m-manylinux1_i686.whl
79049cfc7222de51bd8f58eddcd99196851a0401d91a82df293c6e9451b44b3e  numpy-1.15.0rc2-cp35-cp35m-manylinux1_x86_64.whl
0014321634f10f96f135b25183eaaa7fe595067534374f8ba80b9099ca90d74e  numpy-1.15.0rc2-cp35-none-win32.whl
b12e5339885f291ff42c308aa1e0dd643899e2df73fa96b41121e2a921a0ac08  numpy-1.15.0rc2-cp35-none-win_amd64.whl
1a10572879d88786303e6ba12a9045b44a08d796b81a4fed3eaaaffa055c3730  numpy-1.15.0rc2-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
2d2a599d500e1654a8b1ee6e2ff255a8b6a2aff432bed1507a43f0adce2d0793  numpy-1.15.0rc2-cp36-cp36m-manylinux1_i686.whl
c8d1495be39a8f04ebf0dd6ef9a0c6818d68f572da884b9b8e860cff99066701  numpy-1.15.0rc2-cp36-cp36m-manylinux1_x86_64.whl
8a858dc23e49ae8cc4f8a9ee4806e2a271c4fb897c88ffcc1e72407e6331ec43  numpy-1.15.0rc2-cp36-none-win32.whl
4f20759b5244e80a063aa2224d8fe7a14802572d6a96ebd95a13ba72b6d3d5cc  numpy-1.15.0rc2-cp36-none-win_amd64.whl
0a1d8a25093527532109405482522e61eaa77d5618897a5783beeb0f04d27de8  numpy-1.15.0rc2-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
c3a0fee0fa2228288cfd0f5dc63f529dac46791fa8f489d3ad2ef99a3e2f37bd  numpy-1.15.0rc2-cp37-cp37m-manylinux1_i686.whl
5227554a410cf6dbefb76f63b53381a4761cf8c9e9ca2f0e3bab6a27110ef9c6  numpy-1.15.0rc2-cp37-cp37m-manylinux1_x86_64.whl
67005ca9a63c65d7bef2c9200bc15165dee30b7c977fe1d8eef1c78f3270d3e5  numpy-1.15.0rc2-cp37-none-win32.whl
9886b43c330e122855b8b6573db8444dd0fe5a4b407f156f08305b7b441a564d  numpy-1.15.0rc2-cp37-none-win_amd64.whl
917faceae30edd62f29a9cc16a6f70200fa07ae15228f3eda3d4555e16285484  numpy-1.15.0rc2.tar.gz
e402d58467cec78c6fd7f60bacf7105bf31e2b863e4982bdf6b3c8f9d6ca9b23  numpy-1.15.0rc2.zip
Pre-release
Pre-release

@charris charris released this Jun 21, 2018 · 550 commits to master since this release

Assets 6

==========================
NumPy 1.15.0 Release Notes

NumPy 1.15.0 is a release with an unusual number of cleanups, many deprecations
of old functions, and improvements to many existing functions. Please read the
detailed descriptions below to see if you are affected.

For testing, we have switched to pytest as a replacement for the no longer
maintained nose framework. The old nose based interface remains for downstream
projects who may still be using it.

The Python versions supported by this release are 2.7, 3.4-3.6. The upcoming
3.7 release should also work, but you will need to compile from source using
Cython 0.28.2 or later. The wheels will be linked with OpenBLAS 3.0, which
should fix some of the linalg problems reported for NumPy 1.14.

Highlights

  • NumPy has switched to pytest for testing.
  • A new numpy.printoptions context manager.
  • Many improvements to the histogram functions.
  • Support for unicode field names in python 2.7.
  • Improved support for PyPy.

New functions

  • numpy.gcd and numpy.lcm, to compute the greatest common divisor and least
    common multiple.

  • numpy.ma.stack, the numpy.stack array-joining function generalized to
    masked arrays.

  • numpy.quantile function, an interface to percentile without factors of
    100

  • numpy.nanquantile function, an interface to nanpercentile without
    factors of 100

  • numpy.printoptions, a context manager that sets print options temporarily
    for the scope of the with block::

    with np.printoptions(precision=2):
    ... print(np.array([2.0]) / 3)
    [0.67]

  • numpy.histogram_bin_edges, a function to get the edges of the bins used by a
    histogram without needing to calculate the histogram.

  • C functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier
    have been added to deal with compiler optimization changing the order of
    operations. See below for details.

Deprecations

  • Aliases of builtin pickle functions are deprecated, in favor of their
    unaliased pickle.<func> names:

    • numpy.loads
    • numpy.core.numeric.load
    • numpy.core.numeric.loads
    • numpy.ma.loads, numpy.ma.dumps
    • numpy.ma.load, numpy.ma.dump - these functions already failed on
      python 3 when called with a string.
  • Multidimensional indexing with anything but a tuple is deprecated. This means
    that the index list in ind = [slice(None), 0]; arr[ind] should be changed
    to a tuple, e.g., ind = [slice(None), 0]; arr[tuple(ind)] or
    arr[(slice(None), 0)]. That change is necessary to avoid ambiguity in
    expressions such as arr[[[0, 1], [0, 1]]], currently interpreted as
    arr[array([0, 1]), array([0, 1])], that will be interpreted
    as arr[array([[0, 1], [0, 1]])] in the future.

  • Imports from the following sub-modules are deprecated, they will be removed
    at some future date.

    • numpy.testing.utils
    • numpy.testing.decorators
    • numpy.testing.nosetester
    • numpy.testing.noseclasses
    • numpy.core.umath_tests
  • Giving a generator to numpy.sum is now deprecated. This was undocumented
    behavior, but worked. Previously, it would calculate the sum of the generator
    expression. In the future, it might return a different result. Use
    np.sum(np.from_iter(generator)) or the built-in Python sum instead.

  • Users of the C-API should call PyArrayResolveWriteBackIfCopy or
    PyArray_DiscardWritbackIfCopy on any array with the WRITEBACKIFCOPY
    flag set, before deallocating the array. A deprecation warning will be
    emitted if those calls are not used when needed.

  • Users of numpy.nditer should use the nditer object as a context manager
    whenever one of the iterator operands is writeable so that numpy can manage
    writeback semantics, or alternately, one can call it.close() to trigger a
    writeback. A RuntimeWarning will otherwise be raised in those cases. Users
    of the C-API should call NpyIter_Close before NpyIter_Deallocate.

  • Users of nditer should use the nditer object as a context manager
    anytime one of the iterator operands is writeable, so that numpy can
    manage writeback semantics, or should call it.close(). A
    RuntimeWarning may be emitted otherwise in these cases.

  • The normed argument of np.histogram, deprecated long ago in 1.6.0,
    now emits a DeprecationWarning.

Future Changes

  • NumPy 1.16 will drop support for Python 3.4.
  • NumPy 1.17 will drop support for Python 2.7.

Compatibility notes

Compiled testing modules renamed and made private

The following compiled modules have been renamed and made private:

  • umath_tests -> _umath_tests
  • test_rational -> _rational_tests
  • multiarray_tests -> _multiarray_tests
  • struct_ufunc_test -> _struct_ufunc_tests
  • operand_flag_tests -> _operand_flag_tests

The umath_tests module is still available for backwards compatibility, but
will be removed in the future.

The NpzFile returned by np.savez is now a collections.abc.Mapping

This means it behaves like a readonly dictionary, and has a new .values()
method and len() implementation.

For python 3, this means that .iteritems(), .iterkeys() have been
deprecated, and .keys() and .items() now return views and not lists.
This is consistent with how the builtin dict type changed between python 2
and python 3.

Under certain conditions, nditer must be used in a context manager

When using an numpy.nditer with the "writeonly" or "readwrite" flags, there
are some circumstances where nditer doesn't actually give you a view of the
writable array. Instead, it gives you a copy, and if you make changes to the
copy, nditer later writes those changes back into your actual array. Currently,
this writeback occurs when the array objects are garbage collected, which makes
this API error-prone on CPython and entirely broken on PyPy. Therefore,
nditer should now be used as a context manager whenever it is used
with writeable arrays, e.g., with np.nditer(...) as it: .... You may also
explicitly call it.close() for cases where a context manager is unusable,
for instance in generator expressions.

Numpy has switched to using pytest instead of nose for testing

The last nose release was 1.3.7 in June, 2015, and development of that tool has
ended, consequently NumPy has now switched to using pytest. The old decorators
and nose tools that were previously used by some downstream projects remain
available, but will not be maintained. The standard testing utilities,
assert_almost_equal and such, are not be affected by this change except for
the nose specific functions import_nose and raises. Those functions are
not used in numpy, but are kept for downstream compatibility.

Numpy no longer monkey-patches ctypes with __array_interface__

Previously numpy added __array_interface__ attributes to all the integer
types from ctypes.

np.ma.notmasked_contiguous and np.ma.flatnotmasked_contiguous always return lists

This is the documented behavior, but previously the result could be any of
slice, None, or list.

All downstream users seem to check for the None result from
flatnotmasked_contiguous and replace it with []. Those callers will
continue to work as before.

np.squeeze restores old behavior of objects that cannot handle an axis argument

Prior to version 1.7.0, numpy.squeeze did not have an axis argument and
all empty axes were removed by default. The incorporation of an axis
argument made it possible to selectively squeeze single or multiple empty axes,
but the old API expectation was not respected because axes could still be
selectively removed (silent success) from an object expecting all empty axes to
be removed. That silent, selective removal of empty axes for objects expecting
the old behavior has been fixed and the old behavior restored.

unstructured void array's .item method now returns a bytes object

.item now returns a bytes object instead of a buffer or byte array.
This may affect code which assumed the return value was mutable, which is no
longer the case.

copy.copy and copy.deepcopy no longer turn masked into an array

Since np.ma.masked is a readonly scalar, copying should be a no-op. These
functions now behave consistently with np.copy().

Multifield Indexing of Structured Arrays will still return a copy

The change that multi-field indexing of structured arrays returns a view
instead of a copy is pushed back to 1.16. A new method
numpy.lib.recfunctions.repack_fields has been introduced to help mitigate
the effects of this change, which can be used to write code compatible with
both numpy 1.15 and 1.16. For more information on how to update code to account
for this future change see the "accessing multiple fields" section of the
user guide <https://docs.scipy.org/doc/numpy/user/basics.rec.html>__.

C API changes

New function NpyIter_Close

The function NpyIter_Close has been added and should be called before
NpyIter_Deallocate to resolve possible writeback-enabled arrays.

New functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier

Functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier
have been added and should be used in place of the npy_get_floatstatusand
npy_clear_status functions. Optimizing compilers like GCC 8.1 and Clang
were rearranging the order of operations when the previous functions were used
in the ufunc SIMD functions, resulting in the floatstatus flags being checked
before the operation whose status we wanted to check was run. See #10339 <https://github.com/numpy/numpy/issues/10370>__.

Changes to PyArray_GetDTypeTransferFunction

PyArray_GetDTypeTransferFunction now defaults to using user-defined
copyswapn / copyswap for user-defined dtypes. If this causes a
significant performance hit, consider implementing copyswapn to reflect the
implementation of PyArray_GetStridedCopyFn. See #10898 <https://github.com/numpy/numpy/pull/10898>__.

  • Functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier
    have been added and should be used in place of the npy_get_floatstatusand
    npy_clear_status functions. Optimizing compilers like GCC 8.1 and Clang
    were rearranging the order of operations when the previous functions were
    used in the ufunc SIMD functions, resulting in the floatstatus flags being '
    checked before the operation whose status we wanted to check was run.
    See #10339 <https://github.com/numpy/numpy/issues/10370>__.

New Features

np.gcd and np.lcm ufuncs added for integer and objects types

These compute the greatest common divisor, and lowest common multiple,
respectively. These work on all the numpy integer types, as well as the
builtin arbitrary-precision Decimal and long types.

Support for cross-platform builds for iOS

The build system has been modified to add support for the
_PYTHON_HOST_PLATFORM environment variable, used by distutils when
compiling on one platform for another platform. This makes it possible to
compile NumPy for iOS targets.

This only enables you to compile NumPy for one specific platform at a time.
Creating a full iOS-compatible NumPy package requires building for the 5
architectures supported by iOS (i386, x86_64, armv7, armv7s and arm64), and
combining these 5 compiled builds products into a single "fat" binary.

return_indices keyword added for np.intersect1d

New keyword return_indices returns the indices of the two input arrays
that correspond to the common elements.

np.quantile and np.nanquantile

Like np.percentile and np.nanpercentile, but takes quantiles in [0, 1]
rather than percentiles in [0, 100]. np.percentile is now a thin wrapper
around np.quantile with the extra step of dividing by 100.

Build system

Added experimental support for the 64-bit RISC-V architecture.

Improvements

np.ufunc.reduce and related functions now accept an initial value

np.ufunc.reduce, np.sum, np.prod, np.min and np.max all
now accept an initial keyword argument that specifies the value to start
the reduction with.

np.flip can operate over multiple axes

np.flip now accepts None, or tuples of int, in its axis argument. If
axis is None, it will flip over all the axes.

histogram and histogramdd functions have moved to np.lib.histograms

These were originally found in np.lib.function_base. They are still
available under their un-scoped np.histogram(dd) names, and
to maintain compatibility, aliased at np.lib.function_base.histogram(dd).

Code that does from np.lib.function_base import * will need to be updated
with the new location, and should consider not using import * in future.

histogram will accept NaN values when explicit bins are given

Previously it would fail when trying to compute a finite range for the data.
Since the range is ignored anyway when the bins are given explicitly, this error
was needless.

Note that calling histogram on NaN values continues to raise the
RuntimeWarning s typical of working with nan values, which can be silenced
as usual with errstate.

histogram works on datetime types, when explicit bin edges are given

Dates, times, and timedeltas can now be histogrammed. The bin edges must be
passed explicitly, and are not yet computed automatically.

histogram "auto" estimator handles limited variance better

No longer does an IQR of 0 result in n_bins=1, rather the number of bins
chosen is related to the data size in this situation.

The edges retuned by `histogramandhistogramdd`` now match the data float type

When passed np.float16, np.float32, or np.longdouble data, the
returned edges are now of the same dtype. Previously, histogram would only
return the same type if explicit bins were given, and histogram would
produce float64 bins no matter what the inputs.

histogramdd allows explicit ranges to be given in a subset of axes

The range argument of numpy.histogramdd can now contain None values to
indicate that the range for the corresponding axis should be computed from the
data. Previously, this could not be specified on a per-axis basis.

np.r_ works with 0d arrays, and np.ma.mr_ works with np.ma.masked

0d arrays passed to the r_ and mr_ concatenation helpers are now treated as
though they are arrays of length 1. Previously, passing these was an error.
As a result, numpy.ma.mr_ now works correctly on the masked constant.

np.ptp accepts a keepdims argument, and extended axis tuples

np.ptp (peak-to-peak) can now work over multiple axes, just like np.max
and np.min.

MaskedArray.astype now is identical to ndarray.astype

This means it takes all the same arguments, making more code written for
ndarray work for masked array too.

Enable AVX2/AVX512 at compile time

Change to simd.inc.src to allow use of AVX2 or AVX512 at compile time. Previously
compilation for avx2 (or 512) with -march=native would still use the SSE
code for the simd functions even when the rest of the code got AVX2.

nan_to_num always returns scalars when receiving scalar or 0d inputs

Previously an array was returned for integer scalar inputs, which is
inconsistent with the behavior for float inputs, and that of ufuncs in general.
For all types of scalar or 0d input, the result is now a scalar.

np.flatnonzero works on numpy-convertible types

np.flatnonzero now uses np.ravel(a) instead of a.ravel(), so it
works for lists, tuples, etc.

np.interp returns numpy scalars rather than builtin scalars

Previously np.interp(0.5, [0, 1], [10, 20]) would return a float, but
now it returns a np.float64 object, which more closely matches the behavior
of other functions.

Additionally, the special case of np.interp(object_array_0d, ...) is no
longer supported, as np.interp(object_array_nd) was never supported anyway.

As a result of this change, the period argument can now be used on 0d
arrays.

Allow dtype field names to be unicode in Python 2

Previously np.dtype([(u'name', float)]) would raise a TypeError in
Python 2, as only bytestrings were allowed in field names. Now any unicode
string field names will be encoded with the ascii codec, raising a
UnicodeEncodeError upon failure.

This change makes it easier to write Python 2/3 compatible code using
from __future__ import unicode_literals, which previously would cause
string literal field names to raise a TypeError in Python 2.

Comparison ufuncs accept dtype=object, overriding the default bool

This allows object arrays of symbolic types, which override == and other
operators to return expressions, to be compared elementwise with
np.equal(a, b, dtype=object).

sort functions accept kind='stable'

Up until now, to perform a stable sort on the data, the user must do:

>>> np.sort([5, 2, 6, 2, 1], kind='mergesort')
[1, 2, 2, 5, 6]

because merge sort is the only stable sorting algorithm available in
NumPy. However, having kind='mergesort' does not make it explicit that
the user wants to perform a stable sort thus harming the readability.

This change allows the user to specify kind='stable' thus clarifying
the intent.

Do not make temporary copies for in-place accumulation

When ufuncs perform accumulation they no longer make temporary copies because
of the overlap between input an output, that is, the next element accumulated
is added before the accumulated result is stored in its place, hence the
overlap is safe. Avoiding the copy results in faster execution.

linalg.matrix_power can now handle stacks of matrices

Like other functions in linalg, matrix_power can now deal with arrays
of dimension larger than 2, which are treated as stacks of matrices. As part
of the change, to further improve consistency, the name of the first argument
has been changed to a (from M), and the exceptions for non-square
matrices have been changed to LinAlgError (from ValueError).

Increased performance in random.permutation for multidimensional arrays

permutation uses the fast path in random.shuffle for all input
array dimensions. Previously the fast path was only used for 1-d arrays.

Generalized ufuncs now accept axes, axis and keepdims arguments

One can control over which axes a generalized ufunc operates by passing in an
axes argument, a list of tuples with indices of particular axes. For
instance, for a signature of (i,j),(j,k)->(i,k) appropriate for matrix
multiplication, the base elements are two-dimensional matrices and these are
taken to be stored in the two last axes of each argument. The corresponding
axes keyword would be [(-2, -1), (-2, -1), (-2, -1)]. If one wanted to
use leading dimensions instead, one would pass in [(0, 1), (0, 1), (0, 1)].

For simplicity, for generalized ufuncs that operate on 1-dimensional arrays
(vectors), a single integer is accepted instead of a single-element tuple, and
for generalized ufuncs for which all outputs are scalars, the (empty) output
tuples can be omitted. Hence, for a signature of (i),(i)->() appropriate
for an inner product, one could pass in axes=[0, 0] to indicate that the
vectors are stored in the first dimensions of the two inputs arguments.

As a short-cut for generalized ufuncs that are similar to reductions, i.e.,
that act on a single, shared core dimension such as the inner product example
above, one can pass an axis argument. This is equivalent to passing in
axes with identical entries for all arguments with that core dimension
(e.g., for the example above, axes=[(axis,), (axis,)]).

Furthermore, like for reductions, for generalized ufuncs that have inputs that
all have the same number of core dimensions and outputs with no core dimension,
one can pass in keepdims to leave a dimension with size 1 in the outputs,
thus allowing proper broadcasting against the original inputs. The location of
the extra dimension can be controlled with axes. For instance, for the
inner-product example, keepdims=True, axes=[-2, -2, -2] would act on the
inner-product example, keepdims=True, axis=-2 would act on the
one-but-last dimension of the input arguments, and leave a size 1 dimension in
that place in the output.

float128 values now print correctly on ppc systems

Previously printing float128 values was buggy on ppc, since the special
double-double floating-point-format on these systems was not accounted for.
float128s now print with correct rounding and uniqueness.

Warning to ppc users: You should upgrade glibc if it is version <=2.23,
especially if using float128. On ppc, glibc's malloc in these version often
misaligns allocated memory which can crash numpy when using float128 values.

New np.take_along_axis and np.put_along_axis functions

When used on multidimensional arrays, argsort, argmin, argmax, and
argpartition return arrays that are difficult to use as indices.
take_along_axis provides an easy way to use these indices to lookup values
within an array, so that::

np.take_along_axis(a, np.argsort(a, axis=axis), axis=axis)

is the same as::

np.sort(a, axis=axis)

np.put_along_axis acts as the dual operation for writing to these indices
within an array.

Checksums

MD5

ae603a7948555f5a877aa9e62d4de4a5  numpy-1.15.0rc1-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
2b2bdf4a2e2d2be118740c036952b12a  numpy-1.15.0rc1-cp27-cp27m-manylinux1_i686.whl
d0f34f5a96b108e39c5f7c349de4b079  numpy-1.15.0rc1-cp27-cp27m-manylinux1_x86_64.whl
91156245b11f2606fb9679ad11cd3788  numpy-1.15.0rc1-cp27-cp27mu-manylinux1_i686.whl
34f0cc01a35fd61bbf25ee64f2dac5ff  numpy-1.15.0rc1-cp27-cp27mu-manylinux1_x86_64.whl
3d003bfc970de7364c7a509f1905b017  numpy-1.15.0rc1-cp27-none-win32.whl
2e63c6cef05817b00aec3550075e4a32  numpy-1.15.0rc1-cp27-none-win_amd64.whl
976c3898f88d06f2fd4e7e4acd454e37  numpy-1.15.0rc1-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
faa3445ef691307efc371c82d34dbbfb  numpy-1.15.0rc1-cp34-cp34m-manylinux1_i686.whl
fe5ef50a0f8b21dd16283cbfd05ff438  numpy-1.15.0rc1-cp34-cp34m-manylinux1_x86_64.whl
77bad7aeff1472e6777aedd62f6b7a06  numpy-1.15.0rc1-cp34-none-win32.whl
e8890f675820efb7718a02b071edb3d3  numpy-1.15.0rc1-cp34-none-win_amd64.whl
5a095e7fef444bb70cf43e33fd71a2b0  numpy-1.15.0rc1-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
cf92df0e604414593f21976b998f97ba  numpy-1.15.0rc1-cp35-cp35m-manylinux1_i686.whl
0cb3a14f65a556652a143fd8cdff21e6  numpy-1.15.0rc1-cp35-cp35m-manylinux1_x86_64.whl
e146ae6eb6bd78b928ae624a9db6a323  numpy-1.15.0rc1-cp35-none-win32.whl
e77c2df92eec2f87a89b5ecffe31de8e  numpy-1.15.0rc1-cp35-none-win_amd64.whl
ed423c6807ae7b5488546436259d281f  numpy-1.15.0rc1-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
d7455ee275bae39c68e130e4848ebb80  numpy-1.15.0rc1-cp36-cp36m-manylinux1_i686.whl
a7e88e6d9c6f5bd59b354b16538a3cc5  numpy-1.15.0rc1-cp36-cp36m-manylinux1_x86_64.whl
e5f8e934ff03979bdb3dc6041ef40859  numpy-1.15.0rc1-cp36-none-win32.whl
a0a932cc07062bb70ad9ff893b57d2b7  numpy-1.15.0rc1-cp36-none-win_amd64.whl
f6c1139993cce7bea5b78418ecf56ed1  numpy-1.15.0rc1.tar.gz
ff5045d88b409bfeff664b13a110400e  numpy-1.15.0rc1.zip

SHA256

d5e4c5c34745d626d8f6613e4dd9b8d88b2dd9a52ea8764ed934cc5a8cb9cc22  numpy-1.15.0rc1-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
744d8c46272a5f2240fc750f7da1d831d90d0f1ce938d5540bcaabfb0f762a53  numpy-1.15.0rc1-cp27-cp27m-manylinux1_i686.whl
9fe328aa046c5c9359d07f5161bdcdaaae8cd6d4fcf0761a41db424628f805d3  numpy-1.15.0rc1-cp27-cp27m-manylinux1_x86_64.whl
85dbc672c5f2683147f5ee1af0793a5159411340f84fea99469dfd699def5bfd  numpy-1.15.0rc1-cp27-cp27mu-manylinux1_i686.whl
9870ddc0055dbe5d77f1a7f5abd493737f0728f88e15fdafc459ea5e64a82efa  numpy-1.15.0rc1-cp27-cp27mu-manylinux1_x86_64.whl
23ef8080613e5f8743b94d6e075e5894418a4a8a95a2eb7da3e524d180370512  numpy-1.15.0rc1-cp27-none-win32.whl
647dfec3cad0ab6a443a5661ad09cc35c2dd317f843909535d4183b05fea1860  numpy-1.15.0rc1-cp27-none-win_amd64.whl
fb78cb51abd23395a320b3608f35b8a2afcd76f32954184cf84b5db07e3c8649  numpy-1.15.0rc1-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
baf9a2aa084e184cbf9b91702a26491fa263826ae941160527645cff84bf96b5  numpy-1.15.0rc1-cp34-cp34m-manylinux1_i686.whl
2afb65982702da7905640afc63688b6b38c183f410e8df9dfb88c45a430d34f3  numpy-1.15.0rc1-cp34-cp34m-manylinux1_x86_64.whl
00fa0b94566db5512fd402150dbadaeb38e0e78521673242f8ef687c343efcfe  numpy-1.15.0rc1-cp34-none-win32.whl
fc1fa913fd0b3d80aed6744b7837176d755e0a6e023364db006c8d17679d64aa  numpy-1.15.0rc1-cp34-none-win_amd64.whl
2d17cf40bd5b97c504fb4da00f1c0686c1ef9ebf363b8d5667269e66b07a2544  numpy-1.15.0rc1-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
01f46c1149d7571860f758299e5a0b6153d6d34f8821adf0c87d8d6f3e9517c5  numpy-1.15.0rc1-cp35-cp35m-manylinux1_i686.whl
d80d04f9ea96c5d8fc27b0bc47122ad3459946f63fb49d26259decda25677221  numpy-1.15.0rc1-cp35-cp35m-manylinux1_x86_64.whl
f2394a623f83c75dec54ab366ccac879ae01dcff9fa3391c97bda40c2b4b41b3  numpy-1.15.0rc1-cp35-none-win32.whl
3cff020d7beba3668c7e67a5921a4596a6abd917815691b93a5c87c5ae2f2ed3  numpy-1.15.0rc1-cp35-none-win_amd64.whl
ce9295610854f1dc749cc05a1f705539c983cfd4e99f3e7c01106f2d0f49d132  numpy-1.15.0rc1-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
8a211172894d6b21e8d2133dbdced490b95492707982cffce32b97fcada718ca  numpy-1.15.0rc1-cp36-cp36m-manylinux1_i686.whl
e16b6b066a19fb595eea0bd29ec0f9429af6bda3d0f7be3f31e57bff2b735ac5  numpy-1.15.0rc1-cp36-cp36m-manylinux1_x86_64.whl
b1dcad0333986cbbbfa547d57807047d9777d5e1fd56c47eb47c59050e4485b1  numpy-1.15.0rc1-cp36-none-win32.whl
0e1871bb9307fbc3173c198f5536792185d20750cd5ad9907a14d49f767464b5  numpy-1.15.0rc1-cp36-none-win_amd64.whl
c57f33f83f61ad819e3fcb41afa54d224d03377d6f33be57289924cc193ead63  numpy-1.15.0rc1.tar.gz
592657828982b13ff48a56c5f75fb2f286709f7b830ec211029ac7970027b54d  numpy-1.15.0rc1.zip

@mattip mattip released this Jun 12, 2018 · 1583 commits to master since this release

Assets 6

NumPy 1.14.5 Release Notes

This is a bugfix release for bugs reported following the 1.14.4 release. The
most significant fixes are:

  • fixes for compilation errors on alpine and NetBSD

The Python versions supported in this release are 2.7 and 3.4 - 3.6. The Python
3.6 wheels available from PIP are built with Python 3.6.2 and should be
compatible with all previous versions of Python 3.6. The source releases were
cythonized with Cython 0.28.2 and should work for the upcoming Python 3.7.

Contributors

A total of 1 person contributed to this release. People with a "+" by their
names contributed a patch for the first time.

  • Charles Harris

Pull requests merged

A total of 2 pull requests were merged for this release.

  • #11274 <https://github.com/numpy/numpy/pull/11274>__: BUG: Correct use of NPY_UNUSED.
  • #11294 <https://github.com/numpy/numpy/pull/11294>__: BUG: Remove extra trailing parentheses.

Checksums

MD5


429afa5c8720016214a79779f774d3a4  numpy-1.14.5-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
de8f5c6c0e46eedf8d92c1a7ba3fccf7  numpy-1.14.5-cp27-cp27m-manylinux1_i686.whl
6315999b5142d22ce7bd9e74b1b4e3ab  numpy-1.14.5-cp27-cp27m-manylinux1_x86_64.whl
397a64608b5809983ff07842ebe0d353  numpy-1.14.5-cp27-cp27mu-manylinux1_i686.whl
6759e2f4bd57727f1ab9d6c9611b3f9d  numpy-1.14.5-cp27-cp27mu-manylinux1_x86_64.whl
2d5609f384fccf9fe4e6172dd4fed3d0  numpy-1.14.5-cp27-none-win32.whl
c0d5fc38ab45f19cbd12200ff4ea45dd  numpy-1.14.5-cp27-none-win_amd64.whl
0a77f36af749e5c3546c3d310f571256  numpy-1.14.5-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
ae15c8254a4a3ebfc45894617ce030a2  numpy-1.14.5-cp34-cp34m-manylinux1_i686.whl
78c67b4b4f8f3f8bd9c2f897f9d40f60  numpy-1.14.5-cp34-cp34m-manylinux1_x86_64.whl
5263ec59028d508992c15263993698d0  numpy-1.14.5-cp34-none-win32.whl
193365c9f1bb2086b47afe9c797ff415  numpy-1.14.5-cp34-none-win_amd64.whl
90caeba061eec5dbebadad5c8bad3a0c  numpy-1.14.5-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
129848206c41b68071fe9cb469a66846  numpy-1.14.5-cp35-cp35m-manylinux1_i686.whl
395c0058b7ec0ae0cad1e052362e9aeb  numpy-1.14.5-cp35-cp35m-manylinux1_x86_64.whl
a542ea0d9047df0da8ab69e90d60dbdc  numpy-1.14.5-cp35-none-win32.whl
c5c86e11b5071c0ca0bb11f6a84f20e6  numpy-1.14.5-cp35-none-win_amd64.whl
350120bd20a0a45857b4c39e901af41b  numpy-1.14.5-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
5a0682a984fcf6f87a9f10760d896b70  numpy-1.14.5-cp36-cp36m-manylinux1_i686.whl
c5596c3d232345d0f0176cd02e6efe92  numpy-1.14.5-cp36-cp36m-manylinux1_x86_64.whl
c0306cbad68f8084e977121ba104b634  numpy-1.14.5-cp36-none-win32.whl
01b5bd7897e1306660c7ea6a30391cc4  numpy-1.14.5-cp36-none-win_amd64.whl
e3189ee851c3a0e2e6e4c6e80a711ec8  numpy-1.14.5.tar.gz
02d940a6931703de2c41fa5590ac7e98  numpy-1.14.5.zip

SHA256


e1864a4e9f93ddb2dc6b62ccc2ec1f8250ff4ac0d3d7a15c8985dd4e1fbd6418  numpy-1.14.5-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
085afac75bbc97a096744fcfc97a4b321c5a87220286811e85089ae04885acdd  numpy-1.14.5-cp27-cp27m-manylinux1_i686.whl
6c57f973218b776195d0356e556ec932698f3a563e2f640cfca7020086383f50  numpy-1.14.5-cp27-cp27m-manylinux1_x86_64.whl
589336ba5199c8061239cf446ee2f2f1fcc0c68e8531ee1382b6fc0c66b2d388  numpy-1.14.5-cp27-cp27mu-manylinux1_i686.whl
5edf1acc827ed139086af95ce4449b7b664f57a8c29eb755411a634be280d9f2  numpy-1.14.5-cp27-cp27mu-manylinux1_x86_64.whl
6b82b81c6b3b70ed40bc6d0b71222ebfcd6b6c04a6e7945a936e514b9113d5a3  numpy-1.14.5-cp27-none-win32.whl
385f1ce46e08676505b692bfde918c1e0b350963a15ef52d77691c2cf0f5dbf6  numpy-1.14.5-cp27-none-win_amd64.whl
758d1091a501fd2d75034e55e7e98bfd1370dc089160845c242db1c760d944d9  numpy-1.14.5-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
c725d11990a9243e6ceffe0ab25a07c46c1cc2c5dc55e305717b5afe856c9608  numpy-1.14.5-cp34-cp34m-manylinux1_i686.whl
07379fe0b450f6fd6e5934a9bc015025bb4ce1c8fbed3ca8bef29328b1bc9570  numpy-1.14.5-cp34-cp34m-manylinux1_x86_64.whl
9e1f53afae865cc32459ad211493cf9e2a3651a7295b7a38654ef3d123808996  numpy-1.14.5-cp34-none-win32.whl
4d278c2261be6423c5e63d8f0ceb1b0c6db3ff83f2906f4b860db6ae99ca1bb5  numpy-1.14.5-cp34-none-win_amd64.whl
d696a8c87315a83983fc59dd27efe034292b9e8ad667aeae51a68b4be14690d9  numpy-1.14.5-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
2df854df882d322d5c23087a4959e145b953dfff2abe1774fec4f639ac2f3160  numpy-1.14.5-cp35-cp35m-manylinux1_i686.whl
baadc5f770917ada556afb7651a68176559f4dca5f4b2d0947cd15b9fb84fb51  numpy-1.14.5-cp35-cp35m-manylinux1_x86_64.whl
2d6481c6bdab1c75affc0fc71eb1bd4b3ecef620d06f2f60c3f00521d54be04f  numpy-1.14.5-cp35-none-win32.whl
51c5dcb51cf88b34b7d04c15f600b07c6ccbb73a089a38af2ab83c02862318da  numpy-1.14.5-cp35-none-win_amd64.whl
8b8dcfcd630f1981f0f1e3846fae883376762a0c1b472baa35b145b911683b7b  numpy-1.14.5-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
9d69967673ab7b028c2df09cae05ba56bf4e39e3cb04ebe452b6035c3b49848e  numpy-1.14.5-cp36-cp36m-manylinux1_i686.whl
8622db292b766719810e0cb0f62ef6141e15fe32b04e4eb2959888319e59336b  numpy-1.14.5-cp36-cp36m-manylinux1_x86_64.whl
97fa8f1dceffab782069b291e38c4c2227f255cdac5f1e3346666931df87373e  numpy-1.14.5-cp36-none-win32.whl
381ad13c30cd1d0b2f3da8a0c1a4aa697487e8bb0e9e0cbeb7439776bcb645f8  numpy-1.14.5-cp36-none-win_amd64.whl
1b4a02758fb68a65ea986d808867f1d6383219c234aef553a8741818e795b529  numpy-1.14.5.tar.gz
a4a433b3a264dbc9aa9c7c241e87c0358a503ea6394f8737df1683c7c9a102ac  numpy-1.14.5.zip

@charris charris released this Jun 6, 2018 · 1583 commits to master since this release

Assets 6

==========================
NumPy 1.14.4 Release Notes

This is a bugfix release for bugs reported following the 1.14.3 release. The
most significant fixes are:

  • fixes for compiler instruction reordering that resulted in NaN's not being
    properly propagated in np.max and np.min,

  • fixes for bus faults on SPARC and older ARM due to incorrect alignment
    checks.

There are also improvements to printing of long doubles on PPC platforms. All
is not yet perfect on that platform, the whitespace padding is still incorrect
and is to be fixed in numpy 1.15, consequently NumPy still fails some
printing-related (and other) unit tests on ppc systems. However, the printed
values are now correct.

Note that NumPy will error on import if it detects incorrect float32 dot
results. This problem has been seen on the Mac when working in the Anaconda
enviroment and is due to a subtle interaction between MKL and PyQt5. It is not
strictly a NumPy problem, but it is best that users be aware of it. See the
gh-8577 NumPy issue for more information.

The Python versions supported in this release are 2.7 and 3.4 - 3.6. The Python
3.6 wheels available from PIP are built with Python 3.6.2 and should be
compatible with all previous versions of Python 3.6. The source releases were
cythonized with Cython 0.28.2 and should work for the upcoming Python 3.7.

Contributors

A total of 7 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

  • Allan Haldane
  • Charles Harris
  • Marten van Kerkwijk
  • Matti Picus
  • Pauli Virtanen
  • Ryan Soklaski +
  • Sebastian Berg

Pull requests merged

A total of 11 pull requests were merged for this release.

  • #11104: BUG: str of DOUBLE_DOUBLE format wrong on ppc64
  • #11170: TST: linalg: add regression test for gh-8577
  • #11174: MAINT: add sanity-checks to be run at import time
  • #11181: BUG: void dtype setup checked offset not actual pointer for alignment
  • #11194: BUG: Python2 doubles don't print correctly in interactive shell.
  • #11198: BUG: optimizing compilers can reorder call to npy_get_floatstatus
  • #11199: BUG: reduce using SSE only warns if inside SSE loop
  • #11203: BUG: Bytes delimiter/comments in genfromtxt should be decoded
  • #11211: BUG: Fix reference count/memory leak exposed by better testing
  • #11219: BUG: Fixes einsum broadcasting bug when optimize=True
  • #11251: DOC: Document 1.14.4 release.

Checksums

MD5

118e010f76fba91f05111e775d08b9d2  numpy-1.14.4-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
a08af11af72e8393d61f1724e2a42258  numpy-1.14.4-cp27-cp27m-manylinux1_i686.whl
bbf56f4de32bb2c4215e01ea4f1b9445  numpy-1.14.4-cp27-cp27m-manylinux1_x86_64.whl
b5e17dcc08205a278ffd33c6baeb7562  numpy-1.14.4-cp27-cp27mu-manylinux1_i686.whl
e6844d6134fed4f79b52cd89d66edb76  numpy-1.14.4-cp27-cp27mu-manylinux1_x86_64.whl
e9d4ab30ffee0f57da2292ed2c42bdcb  numpy-1.14.4-cp27-none-win32.whl
ff04e3451a90fdf9ae8b6db8b3e8c2d6  numpy-1.14.4-cp27-none-win_amd64.whl
fbe6a5a9a0de9f85bcb729702a132769  numpy-1.14.4-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
33a177cf9d60fa26d30dc80b7163a374  numpy-1.14.4-cp34-cp34m-manylinux1_i686.whl
6335ee571648d8db7561a619328b69c7  numpy-1.14.4-cp34-cp34m-manylinux1_x86_64.whl
e53dd3796a0cdec43037b18c5c54d1a3  numpy-1.14.4-cp34-none-win32.whl
aab911c898c58073b47a2d1f28228a41  numpy-1.14.4-cp34-none-win_amd64.whl
a05e215d9443c838a531119eb5c1eadc  numpy-1.14.4-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
7c5f7ff2cccb13c22b87f768ac1cc6e2  numpy-1.14.4-cp35-cp35m-manylinux1_i686.whl
d22105d03a15c9fd6ec4ecffa4b1f764  numpy-1.14.4-cp35-cp35m-manylinux1_x86_64.whl
7a5d4c66c7f6e430eb73b5683d99cacb  numpy-1.14.4-cp35-none-win32.whl
cf0c074d9243f8bf6eff8291ac12a003  numpy-1.14.4-cp35-none-win_amd64.whl
79233bdad30a65beb515c86a4612102d  numpy-1.14.4-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
135139bd2ec26e2b52bdd2d36be94c44  numpy-1.14.4-cp36-cp36m-manylinux1_i686.whl
9c56d525cf6da2b8489e723d72ccc9a2  numpy-1.14.4-cp36-cp36m-manylinux1_x86_64.whl
ec9af9e19aac597e1a245ada9c333e2d  numpy-1.14.4-cp36-none-win32.whl
f8ec9c6167f2b0d08066ec78c3a01a4c  numpy-1.14.4-cp36-none-win_amd64.whl
7de00fc3be91a3ab913d4efe206b3928  numpy-1.14.4.tar.gz
a8a23723342a561e579757553e9db73a  numpy-1.14.4.zip

SHA256

c0c4bdcb771a147cb14286e3aeb72267e1664652d4150b0df255f0c210166a62  numpy-1.14.4-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
939376b3b8d9bd42529a2713534c9bae7f11c774614d4d2f7f2a38cae96101f1  numpy-1.14.4-cp27-cp27m-manylinux1_i686.whl
6105d909e56c4f3f173a7294154eee5da80853104e9c3ebcf9e523fb3bb6cf70  numpy-1.14.4-cp27-cp27m-manylinux1_x86_64.whl
3ed68b8ef0635e12b06c216d3ed33572d9c15b05a5a5d6ab870d073190c3eef3  numpy-1.14.4-cp27-cp27mu-manylinux1_i686.whl
1dc831683f18c11e6b5b7ad3610b9f00417b8d3fc63a8adcdbe68844d9dd6f62  numpy-1.14.4-cp27-cp27mu-manylinux1_x86_64.whl
8d87ac65d830ee3087e6bd02b0201e68aed4c715ff2e227e3640e7ded38d8a2e  numpy-1.14.4-cp27-none-win32.whl
7fbceea93b6877419d84516705a265dfc4626939a29107a4d04db599bf6cdf8d  numpy-1.14.4-cp27-none-win_amd64.whl
a1b4a80d59658fc438716095deb1971c6315482b461d976f760d920b6509fd5d  numpy-1.14.4-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
ef7a07f6a77658a1038e6d22e53458129c04a95b5770f080b5741320d9491e32  numpy-1.14.4-cp34-cp34m-manylinux1_i686.whl
c5065b3aec37cd1b7ec2882b3ab86e200d15219a0fb96fea65a16c6b59d3c0f0  numpy-1.14.4-cp34-cp34m-manylinux1_x86_64.whl
b2b2741da83b1e016094b2fef2cadec1abd3ccd3d97428634ec6afe1dcb699b8  numpy-1.14.4-cp34-none-win32.whl
419dfe9bcb09d2e87ecf296c5ebf2b047c568419c89588acc9dbce6d2d761bea  numpy-1.14.4-cp34-none-win_amd64.whl
be4664fe153ca6dbd961fb06f99b9b88b114ab44649376253b540aafbf42e469  numpy-1.14.4-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
0d6d7bbcb54babaf39fe658bcc6f79641c9c62813c6d477802d783c7ba1a437c  numpy-1.14.4-cp35-cp35m-manylinux1_i686.whl
f54114395aabe13c7c4e4b425145cfd998eaf0781e87a9e9b2e77426f1ec8a82  numpy-1.14.4-cp35-cp35m-manylinux1_x86_64.whl
eb6ccd2b47d43199ec9a7c39bd45e399ccb5756e7367aaf92ced3c46fa67b16b  numpy-1.14.4-cp35-none-win32.whl
f6a4ae8d5e1126bf4d8520a9aa6a82d067ab3ce7d21f58f0d50ead2aebda7bfb  numpy-1.14.4-cp35-none-win_amd64.whl
b037993dfb1175a68b6a2bfc6b1c2af57c09031d1332fea3ab25a539b43bd475  numpy-1.14.4-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
e6c24c83ca64d447a18f041bd53cbe96c74405f59939b6006755105583b62629  numpy-1.14.4-cp36-cp36m-manylinux1_i686.whl
f29a9c5607b0fded7a9f0871dbd06918a88cb0a465acfac5c67f92d1a4115d48  numpy-1.14.4-cp36-cp36m-manylinux1_x86_64.whl
d9ceb6c680ffbe55ef6cf9d93558e0ddb72d616b885d77c536920f3da2112703  numpy-1.14.4-cp36-none-win32.whl
9e6694912f13afd8b1e15aa8002e9c951a377c94080c5442de154d743a69b3ff  numpy-1.14.4-cp36-none-win_amd64.whl
c9a83644685edf8b5383b7632daa37df115b41aa20ca6ec3139e707d88f7c903  numpy-1.14.4.tar.gz
2185a0f31ecaa0792264fa968c8e0ba6d96acf144b26e2e1d1cd5b77fc11a691  numpy-1.14.4.zip

@ahaldane ahaldane released this Apr 28, 2018 · 1583 commits to master since this release

Assets 6

==========================
NumPy 1.14.3 Release Notes

This is a bugfix release for a few bugs reported following the 1.14.2 release:

  • np.lib.recfunctions.fromrecords accepts a list-of-lists, until 1.15
  • In python2, float types use the new print style when printing to a file
  • style arg in "legacy" print mode now works for 0d arrays

The Python versions supported in this release are 2.7 and 3.4 - 3.6. The Python
3.6 wheels available from PIP are built with Python 3.6.2 and should be
compatible with all previous versions of Python 3.6. The source releases were
cythonized with Cython 0.28.2.

Contributors

A total of 6 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

  • Allan Haldane
  • Charles Harris
  • Jonathan March +
  • Malcolm Smith +
  • Matti Picus
  • Pauli Virtanen

Pull requests merged

A total of 8 pull requests were merged for this release.

  • #10862: BUG: floating types should override tp_print (1.14 backport)
  • #10905: BUG: for 1.14 back-compat, accept list-of-lists in fromrecords
  • #10947: BUG: 'style' arg to array2string broken in legacy mode (1.14...
  • #10959: BUG: test, fix for missing flags['WRITEBACKIFCOPY'] key
  • #10960: BUG: Add missing underscore to prototype in check_embedded_lapack
  • #10961: BUG: Fix encoding regression in ma/bench.py (Issue #10868)
  • #10962: BUG: core: fix NPY_TITLE_KEY macro on pypy
  • #10974: BUG: test, fix PyArray_DiscardWritebackIfCopy...

Checksums

MD5

14b675b1f5c0e33dea22735df8ecf5d1  numpy-1.14.3-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
501b9237037beee4c1262180c317f527  numpy-1.14.3-cp27-cp27m-manylinux1_i686.whl
51f3c8de7bac77ce864a8a28dc0c3f10  numpy-1.14.3-cp27-cp27m-manylinux1_x86_64.whl
37bfe26b655464a77356ee053deafad2  numpy-1.14.3-cp27-cp27mu-manylinux1_i686.whl
c8243f0d6a77c88acf48235aaedf1497  numpy-1.14.3-cp27-cp27mu-manylinux1_x86_64.whl
9c616eb6134c92ca42cca5883e7861b7  numpy-1.14.3-cp27-none-win32.whl
fa3f732464bc83eb08fc6748aeb01ba0  numpy-1.14.3-cp27-none-win_amd64.whl
711dd188cf3269e092adb4240742731b  numpy-1.14.3-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
0450e19513ff2406055bdffcdfef8d82  numpy-1.14.3-cp34-cp34m-manylinux1_i686.whl
1a0fc864b3b1aea403b426eb2e83276c  numpy-1.14.3-cp34-cp34m-manylinux1_x86_64.whl
13fa200925025289dbd120078c54377f  numpy-1.14.3-cp34-none-win32.whl
fc74d7d13da26e2ffc8bf39d5c24d171  numpy-1.14.3-cp34-none-win_amd64.whl
faee14118dea28c6e2be5aadaa1613ca  numpy-1.14.3-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
e93edc38b9e31d774af60b45ad25d3d7  numpy-1.14.3-cp35-cp35m-manylinux1_i686.whl
6d7ced18705cdd82030472b7a0b106c9  numpy-1.14.3-cp35-cp35m-manylinux1_x86_64.whl
42000f9cfef06906e25c0020a9c92366  numpy-1.14.3-cp35-none-win32.whl
b7cd0a630d24ef8ed245cde71e50c46e  numpy-1.14.3-cp35-none-win_amd64.whl
d728ee343c54c8b9b1186747bae6800b  numpy-1.14.3-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
0f8ed907b7c37d7e8c0508ee30ac5e0b  numpy-1.14.3-cp36-cp36m-manylinux1_i686.whl
04428f5a071531dd463504250c194de3  numpy-1.14.3-cp36-cp36m-manylinux1_x86_64.whl
a376953ac6bfca04371899d70126ebd4  numpy-1.14.3-cp36-none-win32.whl
955959dbc1a743308bfcafb4d867da29  numpy-1.14.3-cp36-none-win_amd64.whl
7c3c806ae27196c92d2fb3fbd4991e81  numpy-1.14.3.tar.gz
97416212c0a172db4bc6b905e9c4634b  numpy-1.14.3.zip

SHA256

a8dbab311d4259de5eeaa5b4e83f5f8545e4808f9144e84c0f424a6ee55a7b98  numpy-1.14.3-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
034717bfef517858abc79324820a702dc6cd063effb9baab86533e8a78670689  numpy-1.14.3-cp27-cp27m-manylinux1_i686.whl
f39afab5769b3aaa786634b94b4a23ef3c150bdda044e8a32a3fc16ddafe803b  numpy-1.14.3-cp27-cp27m-manylinux1_x86_64.whl
8670067685051b49d1f2f66e396488064299fefca199c7c80b6ba0c639fedc98  numpy-1.14.3-cp27-cp27mu-manylinux1_i686.whl
0db6301324d0568089663ef2701ad90ebac0e975742c97460e89366692bd0563  numpy-1.14.3-cp27-cp27mu-manylinux1_x86_64.whl
98ff275f1b5907490d26b30b6ff111ecf2de0254f0ab08833d8fe61aa2068a00  numpy-1.14.3-cp27-none-win32.whl
aaef1bea636b6e552bbc5dae0ada87d4f6046359daaa97a05a013b0169620f27  numpy-1.14.3-cp27-none-win_amd64.whl
760550fdf9d8ec7da9c4402a4afe6e25c0f184ae132011676298a6b636660b45  numpy-1.14.3-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
e8578a62a8eaf552b95d62f630bb5dd071243ba1302bbff3e55ac48588508736  numpy-1.14.3-cp34-cp34m-manylinux1_i686.whl
e33baf50f2f6b7153ddb973601a11df852697fba4c08b34a5e0f39f66f8120e1  numpy-1.14.3-cp34-cp34m-manylinux1_x86_64.whl
0074d42e2cc333800bd09996223d40ec52e3b1ec0a5cab05dacc09b662c4c1ae  numpy-1.14.3-cp34-none-win32.whl
c3fe23df6fe0898e788581753da453f877350058c5982e85a8972feeecb15309  numpy-1.14.3-cp34-none-win_amd64.whl
1864d005b2eb7598063e35c320787d87730d864f40d6410f768fe4ea20672016  numpy-1.14.3-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
f22b3206f1c561dd9110b93d144c6aaa4a9a354e3b07ad36030df3ea92c5bb5b  numpy-1.14.3-cp35-cp35m-manylinux1_i686.whl
c80fcf9b38c7f4df666150069b04abbd2fe42ae640703a6e1f128cda83b552b7  numpy-1.14.3-cp35-cp35m-manylinux1_x86_64.whl
510863d606c932b41d2209e4de6157ab3fdf52001d3e4ad351103176d33c4b8b  numpy-1.14.3-cp35-none-win32.whl
c5eb7254cfc4bd7a4330ad7e1f65b98343836865338c57b0e25c661e41d5cfd9  numpy-1.14.3-cp35-none-win_amd64.whl
b8987e30d9a0eb6635df9705a75cf8c4a2835590244baecf210163343bc65176  numpy-1.14.3-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
57dc6c22d59054542600fce6fae2d1189b9c50bafc1aab32e55f7efcc84a6c46  numpy-1.14.3-cp36-cp36m-manylinux1_i686.whl
46ce8323ca9384814c7645298b8b627b7d04ce97d6948ef02da357b2389d6972  numpy-1.14.3-cp36-cp36m-manylinux1_x86_64.whl
9ccf4d5c9139b1e985db915039baa0610a7e4a45090580065f8d8cb801b7422f  numpy-1.14.3-cp36-none-win32.whl
560e23a12e7599be8e8b67621396c5bc687fd54b48b890adbc71bc5a67333f86  numpy-1.14.3-cp36-none-win_amd64.whl
cfcfc7a9a8ba4275c60a815c683d59ac5e7aa9362d76573b6cc4324ffb1235fa  numpy-1.14.3.tar.gz
9016692c7d390f9d378fc88b7a799dc9caa7eb938163dda5276d3f3d6f75debf  numpy-1.14.3.zip

@charris charris released this Mar 12, 2018 · 1583 commits to master since this release

Assets 6

==========================
NumPy 1.14.2 Release Notes

This is a bugfix release for some bugs reported following the 1.14.1 release. The major
problems dealt with are as follows.

  • Residual bugs in the new array printing functionality.
  • Regression resulting in a relocation problem with shared library.
  • Improved PyPy compatibility.

The Python versions supported in this release are 2.7 and 3.4 - 3.6. The Python
3.6 wheels available from PIP are built with Python 3.6.2 and should be
compatible with all previous versions of Python 3.6. The source releases were
cythonized with Cython 0.26.1, which is known to not support the upcoming
Python 3.7 release. People who wish to run Python 3.7 should check out the
NumPy repo and try building with the, as yet, unreleased master branch of
Cython.

Contributors

A total of 4 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

  • Allan Haldane
  • Charles Harris
  • Eric Wieser
  • Pauli Virtanen

Pull requests merged

A total of 5 pull requests were merged for this release.

  • #10674: BUG: Further back-compat fix for subclassed array repr
  • #10725: BUG: dragon4 fractional output mode adds too many trailing zeros
  • #10726: BUG: Fix f2py generated code to work on PyPy
  • #10727: BUG: Fix missing NPY_VISIBILITY_HIDDEN on npy_longdouble_to_PyLong
  • #10729: DOC: Create 1.14.2 notes and changelog.

Checksums

MD5

9bb06966218d0f3d0a25a6155c7d2439  numpy-1.14.2-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
b8a260b915d44475f4385fed4c6a7ec8  numpy-1.14.2-cp27-cp27m-manylinux1_i686.whl
7733aa702cebb5b0469b820ea9cfc293  numpy-1.14.2-cp27-cp27m-manylinux1_x86_64.whl
ef1065f3ecd08054eca9c6c14a2e3518  numpy-1.14.2-cp27-cp27mu-manylinux1_i686.whl
1227a63fcc8ce91a75d2ab006d406df7  numpy-1.14.2-cp27-cp27mu-manylinux1_x86_64.whl
6ac633c46c13dd2af93761460d63436e  numpy-1.14.2-cp27-none-win32.whl
187a94722b84d65cc3a9ecfce27ee3b2  numpy-1.14.2-cp27-none-win_amd64.whl
580340cfe4a14f8a9e1d781d7b42955b  numpy-1.14.2-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
7f38fb83008ed4bb8217840ac27aeba4  numpy-1.14.2-cp34-cp34m-manylinux1_i686.whl
cbe383ad27db21767b6ffdd943e3df9c  numpy-1.14.2-cp34-cp34m-manylinux1_x86_64.whl
350a1e0f0c825ffa1de264108c648482  numpy-1.14.2-cp34-none-win32.whl
ececd9b8891d801d4a968c2ec5eac7bb  numpy-1.14.2-cp34-none-win_amd64.whl
8a74bb1f94ad8c1ad8f37e73f967b850  numpy-1.14.2-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
c1231d7e7fc52c09dff9a529ad228818  numpy-1.14.2-cp35-cp35m-manylinux1_i686.whl
ef57856bf6dade82922ab58922756dd0  numpy-1.14.2-cp35-cp35m-manylinux1_x86_64.whl
8c98ab081112832e3a7faca624598119  numpy-1.14.2-cp35-none-win32.whl
2652e9660be5d074224d14436504f008  numpy-1.14.2-cp35-none-win_amd64.whl
1cdb6cf8d60dfbe99f60639dac38471e  numpy-1.14.2-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
b11c80344b84853b7a24acc51bbe4945  numpy-1.14.2-cp36-cp36m-manylinux1_i686.whl
65c3802c0f25f2d26aa784433643f655  numpy-1.14.2-cp36-cp36m-manylinux1_x86_64.whl
8f9986b323d4215925d6cfa1cd1bc14d  numpy-1.14.2-cp36-none-win32.whl
9d78ceef101313f49fd0b8fed25d889c  numpy-1.14.2-cp36-none-win_amd64.whl
e39878fafb11828983aeec583dda4a06  numpy-1.14.2.tar.gz
080f01a19707cf467393e426382c7619  numpy-1.14.2.zip

SHA256

719d914f564f35cce4dc103808f8297c807c9f0297ac183ed81ae8b5650e698e  numpy-1.14.2-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
0f6a5ed0cd7ab1da11f5c07a8ecada73fc55a70ef7bb6311a4109891341d7277  numpy-1.14.2-cp27-cp27m-manylinux1_i686.whl
d0928076d9bd8a98de44e79b1abe50c1456e7abbb40af7ef58092086f1a6c729  numpy-1.14.2-cp27-cp27m-manylinux1_x86_64.whl
d858423f5ed444d494b15c4cc90a206e1b8c31354c781ac7584da0d21c09c1c3  numpy-1.14.2-cp27-cp27mu-manylinux1_i686.whl
20cac3123d791e4bf8482a580d98d6b5969ba348b9d5364df791ba3a666b660d  numpy-1.14.2-cp27-cp27mu-manylinux1_x86_64.whl
528ce59ded2008f9e8543e0146acb3a98a9890da00adf8904b1e18c82099418b  numpy-1.14.2-cp27-none-win32.whl
56e392b7c738bd70e6f46cf48c8194d3d1dd4c5a59fae4b30c58bb6ef86e5233  numpy-1.14.2-cp27-none-win_amd64.whl
99051e03b445117b26028623f1a487112ddf61a09a27e2d25e6bc07d37d94f25  numpy-1.14.2-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
768e777cc1ffdbf97c507f65975c8686ebafe0f3dc8925d02ac117acc4669ce9  numpy-1.14.2-cp34-cp34m-manylinux1_i686.whl
675e0f23967ce71067d12b6944add505d5f0a251f819cfb44bdf8ee7072c090d  numpy-1.14.2-cp34-cp34m-manylinux1_x86_64.whl
a958bf9d4834c72dee4f91a0476e7837b8a2966dc6fcfc42c421405f98d0da51  numpy-1.14.2-cp34-none-win32.whl
bb370120de6d26004358611441e07acda26840e41dfedc259d7f8cc613f96495  numpy-1.14.2-cp34-none-win_amd64.whl
f2b1378b63bdb581d5d7af2ec0373c8d40d651941d283a2afd7fc71184b3f570  numpy-1.14.2-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
a1413d06abfa942ca0553bf3bccaff5fdb36d55b84f2248e36228db871147dab  numpy-1.14.2-cp35-cp35m-manylinux1_i686.whl
7f76d406c6b998d6410198dcb82688dcdaec7d846aa87e263ccf52efdcfeba30  numpy-1.14.2-cp35-cp35m-manylinux1_x86_64.whl
a7157c9ac6bddd2908c35ef099e4b643bc0e0ebb4d653deb54891d29258dd329  numpy-1.14.2-cp35-none-win32.whl
0fd65cbbfdbf76bbf80c445d923b3accefea0fe2c2082049e0ce947c81fe1d3f  numpy-1.14.2-cp35-none-win_amd64.whl
8c18ee4dddd5c6a811930c0a7c7947bf16387da3b394725f6063f1366311187d  numpy-1.14.2-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
0739146eaf4985962f07c62f7133aca89f3a600faac891ce6c7f3a1e2afe5272  numpy-1.14.2-cp36-cp36m-manylinux1_i686.whl
07e21f14490324cc1160db101e9b6c1233c33985af4cb1d301dd02650fea1d7f  numpy-1.14.2-cp36-cp36m-manylinux1_x86_64.whl
e6120d63b50e2248219f53302af7ec6fa2a42ed1f37e9cda2c76dbaca65036a7  numpy-1.14.2-cp36-none-win32.whl
6be6b0ca705321c178c9858e5ad5611af664bbdfae1df1541f938a840a103888  numpy-1.14.2-cp36-none-win_amd64.whl
ddbcda194f49e0cf0663fa8131cb9d7a3b876d14dea0047d3c5fdfaf20adbb40  numpy-1.14.2.tar.gz
facc6f925c3099ac01a1f03758100772560a0b020fb9d70f210404be08006bcb  numpy-1.14.2.zip

@charris charris released this Feb 21, 2018 · 1583 commits to master since this release

Assets 6

==========================
NumPy 1.14.1 Release Notes

This is a bugfix release for some problems reported following the 1.14.0 release. The major
problems fixed are the following.

  • Problems with the new array printing, particularly the printing of complex
    values, Please report any additional problems that may turn up.
  • Problems with np.einsum due to the new optimized=True default. Some
    fixes for optimization have been applied and optimize=False is now the
    default.
  • The sort order in np.unique when axis=<some-number> will now always
    be lexicographic in the subarray elements. In previous NumPy versions there
    was an optimization that could result in sorting the subarrays as unsigned
    byte strings.
  • The change in 1.14.0 that multi-field indexing of structured arrays returns a
    view instead of a copy has been reverted but remains on track for NumPy 1.15.
    Affected users should read the 1.14.1 Numpy User Guide section
    "basics/structured arrays/accessing multiple fields" for advice on how to
    manage this transition.

The Python versions supported in this release are 2.7 and 3.4 - 3.6. The Python
3.6 wheels available from PIP are built with Python 3.6.2 and should be
compatible with all previous versions of Python 3.6. The source releases were
cythonized with Cython 0.26.1, which is known to not support the upcoming
Python 3.7 release. People who wish to run Python 3.7 should check out the
NumPy repo and try building with the, as yet, unreleased master branch of
Cython.

Contributors

A total of 14 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

  • Allan Haldane
  • Charles Harris
  • Daniel Smith
  • Dennis Weyland +
  • Eric Larson
  • Eric Wieser
  • Jarrod Millman
  • Kenichi Maehashi +
  • Marten van Kerkwijk
  • Mathieu Lamarre
  • Sebastian Berg
  • Simon Conseil
  • Simon Gibbons
  • xoviat

Pull requests merged

A total of 36 pull requests were merged for this release.

  • #10339: BUG: restrict the config modifications to win32
  • #10368: MAINT: Adjust type promotion in linalg.norm
  • #10375: BUG: add missing paren and remove quotes from repr of fieldless...
  • #10395: MAINT: Update download URL in setup.py.
  • #10396: BUG: fix einsum issue with unicode input and py2
  • #10397: BUG: fix error message not formatted in einsum
  • #10398: DOC: add documentation about how to handle new array printing
  • #10403: BUG: Set einsum optimize parameter default to False.
  • #10424: ENH: Fix repr of np.record objects to match np.void types #10412
  • #10425: MAINT: Update zesty to artful for i386 testing
  • #10431: REL: Add 1.14.1 release notes template
  • #10435: MAINT: Use ValueError for duplicate field names in lookup (backport)
  • #10534: BUG: Provide a better error message for out-of-order fields
  • #10536: BUG: Resize bytes_ columns in genfromtxt (backport of #10401)
  • #10537: BUG: multifield-indexing adds padding bytes: revert for 1.14.1
  • #10539: BUG: fix np.save issue with python 2.7.5
  • #10540: BUG: Add missing DECREF in Py2 int() cast
  • #10541: TST: Add circleci document testing to maintenance/1.14.x
  • #10542: BUG: complex repr has extra spaces, missing + (1.14 backport)
  • #10550: BUG: Set missing exception after malloc
  • #10557: BUG: In numpy.i, clear CARRAY flag if wrapped buffer is not C_CONTIGUOUS.
  • #10558: DEP: Issue FutureWarning when malformed records detected.
  • #10559: BUG: Fix einsum optimize logic for singleton dimensions
  • #10560: BUG: Fix calling ufuncs with a positional output argument.
  • #10561: BUG: Fix various Big-Endian test failures (ppc64)
  • #10562: BUG: Make dtype.descr error for out-of-order fields.
  • #10563: BUG: arrays not being flattened in union1d
  • #10607: MAINT: Update sphinxext submodule hash.
  • #10608: BUG: Revert sort optimization in np.unique.
  • #10609: BUG: infinite recursion in str of 0d subclasses
  • #10610: BUG: Align type definition with generated lapack
  • #10612: BUG/ENH: Improve output for structured non-void types
  • #10622: BUG: deallocate recursive closure in arrayprint.py (1.14 backport)
  • #10624: BUG: Correctly identify comma seperated dtype strings
  • #10629: BUG: deallocate recursive closure in arrayprint.py (backport...
  • #10630: REL: Prepare for 1.14.1 release.

Checksums

MD5

8a56c4b06e859ccad60a85d3486b214a  numpy-1.14.1-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
94189ecffbc1032df54f570bb6ff490d  numpy-1.14.1-cp27-cp27m-manylinux1_i686.whl
61473860888d024caa1261274620352e  numpy-1.14.1-cp27-cp27m-manylinux1_x86_64.whl
f9f6ada0f110230569cea9d8d2f5416a  numpy-1.14.1-cp27-cp27mu-manylinux1_i686.whl
0c2c6637c5c8ca639e1b7b3fa4ac64cc  numpy-1.14.1-cp27-cp27mu-manylinux1_x86_64.whl
dbae0fec3c033b42695d9df9636ba9a5  numpy-1.14.1-cp27-none-win32.whl
c7ee8517a1a52b90f08651c1f17b6e39  numpy-1.14.1-cp27-none-win_amd64.whl
bb051505823a3f990ea22750a08cd40b  numpy-1.14.1-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
655f4c67598dfe583fce3075e0152b06  numpy-1.14.1-cp34-cp34m-manylinux1_i686.whl
94cdf22837fdec46d03709fe0338ee09  numpy-1.14.1-cp34-cp34m-manylinux1_x86_64.whl
5b7fc9eb18463356ed8d018a3b486d53  numpy-1.14.1-cp34-none-win32.whl
b261be176aa57dce8a64f4fac169c74b  numpy-1.14.1-cp34-none-win_amd64.whl
196639515a2084dc5b4b86a5ea0247ce  numpy-1.14.1-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
d897ae36d1487a101714deeb8782b7c5  numpy-1.14.1-cp35-cp35m-manylinux1_i686.whl
12f2c45cc7501dc5a5e670042300f1e6  numpy-1.14.1-cp35-cp35m-manylinux1_x86_64.whl
e94355704fe2f6b3d1bcf6c8f6189df4  numpy-1.14.1-cp35-none-win32.whl
13b79737d10e857ee808a1dfdd2ff01e  numpy-1.14.1-cp35-none-win_amd64.whl
8819860639f492ddf6045a95227624d0  numpy-1.14.1-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
2b3d5774779e808cef193872dd4f6dbe  numpy-1.14.1-cp36-cp36m-manylinux1_i686.whl
dd2321ea4590ec05d825d8c9a64fd64b  numpy-1.14.1-cp36-cp36m-manylinux1_x86_64.whl
a5803be2b83c1ec5f36ed9f58a0f848c  numpy-1.14.1-cp36-none-win32.whl
299c92352d2c08baa6a8142971b39295  numpy-1.14.1-cp36-none-win_amd64.whl
0e09f20f62ab9f8a02cb7bd3fd023482  numpy-1.14.1.tar.gz
b8324ef90ac9064cd0eac46b8b388674  numpy-1.14.1.zip

SHA256

e2335d56d2fd9fc4e3a3f2d3148aafec4962682375f429f05c45a64dacf19436  numpy-1.14.1-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
9b762e78739b6e021124adbea07611682db99cd3fca7f3c3a8b98b8f74ea5699  numpy-1.14.1-cp27-cp27m-manylinux1_i686.whl
7d4c549e41507db4f04ec7cfab5597de8acf7871b16c9cf64cebcb9d39031ca6  numpy-1.14.1-cp27-cp27m-manylinux1_x86_64.whl
b803306c4c201e7dcda0ce1b9a9c87f61a7c7ce43de2c60c8e56147b76849a1a  numpy-1.14.1-cp27-cp27mu-manylinux1_i686.whl
2da8dff91d489fea3e20155d41f4cd680de7d01d9a89fdd0ebb1bee6e72d3800  numpy-1.14.1-cp27-cp27mu-manylinux1_x86_64.whl
6b8c2daacbbffc83b4a2ba83a61aa3ce60c66340b07b962bd27b6c6bb175bee1  numpy-1.14.1-cp27-none-win32.whl
89b9419019c47ec87cf4cfca77d85da4611cc0be636ec87b5290346490b98450  numpy-1.14.1-cp27-none-win_amd64.whl
49880b47d7272f902946dd995f346842c95fe275e2deb3082ef0495f0c718a69  numpy-1.14.1-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
3d7ddd5bdfb12ec9668edf1aa49a4a3eddb0db4661b57ea431477eb9a2468894  numpy-1.14.1-cp34-cp34m-manylinux1_i686.whl
788e1757f8e409cd805a7cd82993cd9252fa19e334758a4c6eb5a8b334abb084  numpy-1.14.1-cp34-cp34m-manylinux1_x86_64.whl
377def0873bbb1fbdedb14b3275b10a29b1b55619a3f7f775c4e7f9ce2461b9c  numpy-1.14.1-cp34-none-win32.whl
9501c9ccd081977ca5579a3ec4009d6baff6bacb04bf07214aade3324734195a  numpy-1.14.1-cp34-none-win_amd64.whl
a1f5173df8190ef9c6235d260d70ca70c6fb029683ceb66e244c5cc6e335947a  numpy-1.14.1-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
12cf4b27039b88e407ad66894d99a957ef60fea0eeb442026af325add2ab264d  numpy-1.14.1-cp35-cp35m-manylinux1_i686.whl
4e2fc841c8c642f7fd44591ef856ca409cedba6aea27928df34004c533839eee  numpy-1.14.1-cp35-cp35m-manylinux1_x86_64.whl
e5ade7a69dccbd99c4fdbb95b6d091d941e62ffa588b0ed8fb0a2854118fef3f  numpy-1.14.1-cp35-none-win32.whl
6b1011ffc87d7e2b1b7bcc6dc21bdf177163658746ef778dcd21bf0516b9126c  numpy-1.14.1-cp35-none-win_amd64.whl
a8bc80f69570e11967763636db9b24c1e3e3689881d10ae793cec74cf7a627b6  numpy-1.14.1-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
81b9d8f6450e752bd82e7d9618fa053df8db1725747880e76fb09710b57f78d0  numpy-1.14.1-cp36-cp36m-manylinux1_i686.whl
e8522cad377cc2ef20fe13aae742cc265172910c98e8a0d6014b1a8d564019e2  numpy-1.14.1-cp36-cp36m-manylinux1_x86_64.whl
a3d5dd437112292c707e54f47141be2f1100221242f07eda7bd8477f3ddc2252  numpy-1.14.1-cp36-none-win32.whl
c8000a6cbc5140629be8c038c9c9cdb3a1c85ff90bd4180ec99f0f0c73050b5e  numpy-1.14.1-cp36-none-win_amd64.whl
8708a775be9a9a457b80a49193c57bd9d51a8a195ed1f1c4b8e89eaf3aa646ee  numpy-1.14.1.tar.gz
fa0944650d5d3fb95869eaacd8eedbd2d83610c85e271bd9d3495ffa9bc4dc9c  numpy-1.14.1.zip

@charris charris released this Jan 7, 2018 · 1594 commits to master since this release

Assets 6

==========================
NumPy 1.14.0 Release Notes

Numpy 1.14.0 is the result of seven months of work and contains a large number
of bug fixes and new features, along with several changes with potential
compatibility issues. The major change that users will notice are the
stylistic changes in the way numpy arrays and scalars are printed, a change
that will affect doctests. See below for details on how to preserve the
old style printing when needed.

A major decision affecting future development concerns the schedule for
dropping Python 2.7 support in the runup to 2020. The decision has been made to
support 2.7 for all releases made in 2018, with the last release being
designated a long term release with support for bug fixes extending through
2019. In 2019 support for 2.7 will be dropped in all new releases. More details
can be found in the relevant NEP_.

This release supports Python 2.7 and 3.4 - 3.6.

.. _NEP: https://github.com/numpy/numpy/blob/master/doc/neps/dropping-python2.7-proposal.rst

Highlights

  • The np.einsum function uses BLAS when possible

  • genfromtxt, loadtxt, fromregex and savetxt can now handle
    files with arbitrary Python supported encoding.

  • Major improvements to printing of NumPy arrays and scalars.

New functions

  • parametrize: decorator added to numpy.testing

  • chebinterpolate: Interpolate function at Chebyshev points.

  • format_float_positional and format_float_scientific : format
    floating-point scalars unambiguously with control of rounding and padding.

  • PyArray_ResolveWritebackIfCopy and PyArray_SetWritebackIfCopyBase,
    new C-API functions useful in achieving PyPy compatibity.

Deprecations

  • Using np.bool_ objects in place of integers is deprecated. Previously
    operator.index(np.bool_) was legal and allowed constructs such as
    [1, 2, 3][np.True_]. That was misleading, as it behaved differently from
    np.array([1, 2, 3])[np.True_].

  • Truth testing of an empty array is deprecated. To check if an array is not
    empty, use array.size > 0.

  • Calling np.bincount with minlength=None is deprecated.
    minlength=0 should be used instead.

  • Calling np.fromstring with the default value of the sep argument is
    deprecated. When that argument is not provided, a broken version of
    np.frombuffer is used that silently accepts unicode strings and -- after
    encoding them as either utf-8 (python 3) or the default encoding
    (python 2) -- treats them as binary data. If reading binary data is
    desired, np.frombuffer should be used directly.

  • The style option of array2string is deprecated in non-legacy printing mode.

  • PyArray_SetUpdateIfCopyBase has been deprecated. For NumPy versions >= 1.14
    use PyArray_SetWritebackIfCopyBase instead, see C API changes below for
    more details.

  • The use of UPDATEIFCOPY arrays is deprecated, see C API changes below
    for details. We will not be dropping support for those arrays, but they are
    not compatible with PyPy.

Future Changes

  • np.issubdtype will stop downcasting dtype-like arguments.
    It might be expected that issubdtype(np.float32, 'float64') and
    issubdtype(np.float32, np.float64) mean the same thing - however, there
    was an undocumented special case that translated the former into
    issubdtype(np.float32, np.floating), giving the surprising result of True.

    This translation now gives a warning that explains what translation is
    occurring. In the future, the translation will be disabled, and the first
    example will be made equivalent to the second.

  • np.linalg.lstsq default for rcond will be changed. The rcond
    parameter to np.linalg.lstsq will change its default to machine precision
    times the largest of the input array dimensions. A FutureWarning is issued
    when rcond is not passed explicitly.

  • a.flat.__array__() will return a writeable copy of a when a is
    non-contiguous. Previously it returned an UPDATEIFCOPY array when a was
    writeable. Currently it returns a non-writeable copy. See gh-7054 for a
    discussion of the issue.

  • Unstructured void array's .item method will return a bytes object. In the
    future, calling .item() on arrays or scalars of np.void datatype will
    return a bytes object instead of a buffer or int array, the same as
    returned by bytes(void_scalar). This may affect code which assumed the
    return value was mutable, which will no longer be the case. A
    FutureWarning is now issued when this would occur.

Compatibility notes

The mask of a masked array view is also a view rather than a copy

There was a FutureWarning about this change in NumPy 1.11.x. In short, it is
now the case that, when changing a view of a masked array, changes to the mask
are propagated to the original. That was not previously the case. This change
affects slices in particular. Note that this does not yet work properly if the
mask of the original array is nomask and the mask of the view is changed.
See gh-5580 for an extended discussion. The original behavior of having a copy
of the mask can be obtained by calling the unshare_mask method of the view.

np.ma.masked is no longer writeable

Attempts to mutate the masked constant now error, as the underlying arrays
are marked readonly. In the past, it was possible to get away with::

# emulating a function that sometimes returns np.ma.masked
val = random.choice([np.ma.masked, 10])
var_arr = np.asarray(val)
val_arr += 1  # now errors, previously changed np.ma.masked.data

np.ma functions producing fill_values have changed

Previously, np.ma.default_fill_value would return a 0d array, but
np.ma.minimum_fill_value and np.ma.maximum_fill_value would return a
tuple of the fields. Instead, all three methods return a structured np.void
object, which is what you would already find in the .fill_value attribute.

Additionally, the dtype guessing now matches that of np.array - so when
passing a python scalar x, maximum_fill_value(x) is always the same as
maximum_fill_value(np.array(x)). Previously x = long(1) on Python 2
violated this assumption.

a.flat.__array__() returns non-writeable arrays when a is non-contiguous

The intent is that the UPDATEIFCOPY array previously returned when a was
non-contiguous will be replaced by a writeable copy in the future. This
temporary measure is aimed to notify folks who expect the underlying array be
modified in this situation that that will no longer be the case. The most
likely places for this to be noticed is when expressions of the form
np.asarray(a.flat) are used, or when a.flat is passed as the out
parameter to a ufunc.

np.tensordot now returns zero array when contracting over 0-length dimension

Previously np.tensordot raised a ValueError when contracting over 0-length
dimension. Now it returns a zero array, which is consistent with the behaviour
of np.dot and np.einsum.

numpy.testing reorganized

This is not expected to cause problems, but possibly something has been left
out. If you experience an unexpected import problem using numpy.testing
let us know.

np.asfarray no longer accepts non-dtypes through the dtype argument

This previously would accept dtype=some_array, with the implied semantics
of dtype=some_array.dtype. This was undocumented, unique across the numpy
functions, and if used would likely correspond to a typo.

1D np.linalg.norm preserves float input types, even for arbitrary orders

Previously, this would promote to float64 when arbitrary orders were
passed, despite not doing so under the simple cases::

>>> f32 = np.float32([1, 2])
>>> np.linalg.norm(f32, 2.0).dtype
dtype('float32')
>>> np.linalg.norm(f32, 2.0001).dtype
dtype('float64')  # numpy 1.13
dtype('float32')  # numpy 1.14

This change affects only float32 and float16 arrays.

count_nonzero(arr, axis=()) now counts over no axes, not all axes

Elsewhere, axis==() is always understood as "no axes", but
count_nonzero had a special case to treat this as "all axes". This was
inconsistent and surprising. The correct way to count over all axes has always
been to pass axis == None.

__init__.py files added to test directories

This is for pytest compatibility in the case of duplicate test file names in
the different directories. As a result, run_module_suite no longer works,
i.e., python <path-to-test-file> results in an error.

.astype(bool) on unstructured void arrays now calls bool on each element

On Python 2, void_array.astype(bool) would always return an array of
True, unless the dtype is V0. On Python 3, this operation would usually
crash. Going forwards, astype matches the behavior of bool(np.void),
considering a buffer of all zeros as false, and anything else as true.
Checks for V0 can still be done with arr.dtype.itemsize == 0.

MaskedArray.squeeze never returns np.ma.masked

np.squeeze is documented as returning a view, but the masked variant would
sometimes return masked, which is not a view. This has been fixed, so that
the result is always a view on the original masked array.
This breaks any code that used masked_arr.squeeze() is np.ma.masked, but
fixes code that writes to the result of .squeeze().

Renamed first parameter of can_cast from from to from_

The previous parameter name from is a reserved keyword in Python, which made
it difficult to pass the argument by name. This has been fixed by renaming
the parameter to from_.

isnat raises TypeError when passed wrong type

The ufunc isnat used to raise a ValueError when it was not passed
variables of type datetime or timedelta. This has been changed to
raising a TypeError.

dtype.__getitem__ raises TypeError when passed wrong type

When indexed with a float, the dtype object used to raise ValueError.

User-defined types now need to implement __str__ and __repr__

Previously, user-defined types could fall back to a default implementation of
__str__ and __repr__ implemented in numpy, but this has now been
removed. Now user-defined types will fall back to the python default
object.__str__ and object.__repr__.

Many changes to array printing, disableable with the new "legacy" printing mode

The str and repr of ndarrays and numpy scalars have been changed in
a variety of ways. These changes are likely to break downstream user's
doctests.

These new behaviors can be disabled to mostly reproduce numpy 1.13 behavior by
enabling the new 1.13 "legacy" printing mode. This is enabled by calling
np.set_printoptions(legacy="1.13"), or using the new legacy argument to
np.array2string, as np.array2string(arr, legacy='1.13').

In summary, the major changes are:

  • For floating-point types:

    • The repr of float arrays often omits a space previously printed
      in the sign position. See the new sign option to np.set_printoptions.
    • Floating-point arrays and scalars use a new algorithm for decimal
      representations, giving the shortest unique representation. This will
      usually shorten float16 fractional output, and sometimes float32 and
      float128 output. float64 should be unaffected. See the new
      floatmode option to np.set_printoptions.
    • Float arrays printed in scientific notation no longer use fixed-precision,
      and now instead show the shortest unique representation.
    • The str of floating-point scalars is no longer truncated in python2.
  • For other data types:

    • Non-finite complex scalars print like nanj instead of nan*j.
    • NaT values in datetime arrays are now properly aligned.
    • Arrays and scalars of np.void datatype are now printed using hex
      notation.
  • For line-wrapping:

    • The "dtype" part of ndarray reprs will now be printed on the next line
      if there isn't space on the last line of array output.
    • The linewidth format option is now always respected.
      The repr or str of an array will never exceed this, unless a single
      element is too wide.
    • The last line of an array string will never have more elements than earlier
      lines.
    • An extra space is no longer inserted on the first line if the elements are
      too wide.
  • For summarization (the use of ... to shorten long arrays):

    • A trailing comma is no longer inserted for str.
      Previously, str(np.arange(1001)) gave
      '[ 0 1 2 ..., 998 999 1000]', which has an extra comma.
    • For arrays of 2-D and beyond, when ... is printed on its own line in
      order to summarize any but the last axis, newlines are now appended to that
      line to match its leading newlines and a trailing space character is
      removed.
  • MaskedArray arrays now separate printed elements with commas, always
    print the dtype, and correctly wrap the elements of long arrays to multiple
    lines. If there is more than 1 dimension, the array attributes are now
    printed in a new "left-justified" printing style.

  • recarray arrays no longer print a trailing space before their dtype, and
    wrap to the right number of columns.

  • 0d arrays no longer have their own idiosyncratic implementations of str
    and repr. The style argument to np.array2string is deprecated.

  • Arrays of bool datatype will omit the datatype in the repr.

  • User-defined dtypes (subclasses of np.generic) now need to
    implement __str__ and __repr__.

Some of these changes are described in more detail below. If you need to retain
the previous behavior for doctests or other reasons, you may want to do
something like::

# FIXME: We need the str/repr formatting used in Numpy < 1.14.
try:
    np.set_printoptions(legacy='1.13')
except TypeError:
    pass

C API changes

PyPy compatible alternative to UPDATEIFCOPY arrays

UPDATEIFCOPY arrays are contiguous copies of existing arrays, possibly with
different dimensions, whose contents are copied back to the original array when
their refcount goes to zero and they are deallocated. Because PyPy does not use
refcounts, they do not function correctly with PyPy. NumPy is in the process of
eliminating their use internally and two new C-API functions,

  • PyArray_SetWritebackIfCopyBase
  • PyArray_ResolveWritebackIfCopy,

have been added together with a complimentary flag,
NPY_ARRAY_WRITEBACKIFCOPY. Using the new functionality also requires that
some flags be changed when new arrays are created, to wit:
NPY_ARRAY_INOUT_ARRAY should be replaced by NPY_ARRAY_INOUT_ARRAY2 and
NPY_ARRAY_INOUT_FARRAY should be replaced by NPY_ARRAY_INOUT_FARRAY2.
Arrays created with these new flags will then have the WRITEBACKIFCOPY
semantics.

If PyPy compatibility is not a concern, these new functions can be ignored,
although there will be a DeprecationWarning. If you do wish to pursue PyPy
compatibility, more information on these functions and their use may be found
in the c-api_ documentation and the example in how-to-extend_.

.. _c-api: https://github.com/numpy/numpy/blob/master/doc/source/reference/c-api.array.rst
.. _how-to-extend: https://github.com/numpy/numpy/blob/master/doc/source/user/c-info.how-to-extend.rst

New Features

Encoding argument for text IO functions

genfromtxt, loadtxt, fromregex and savetxt can now handle files
with arbitrary encoding supported by Python via the encoding argument.
For backward compatibility the argument defaults to the special bytes value
which continues to treat text as raw byte values and continues to pass latin1
encoded bytes to custom converters.
Using any other value (including None for system default) will switch the
functions to real text IO so one receives unicode strings instead of bytes in
the resulting arrays.

External nose plugins are usable by numpy.testing.Tester

numpy.testing.Tester is now aware of nose plugins that are outside the
nose built-in ones. This allows using, for example, nose-timer like
so: np.test(extra_argv=['--with-timer', '--timer-top-n', '20']) to
obtain the runtime of the 20 slowest tests. An extra keyword timer was
also added to Tester.test, so np.test(timer=20) will also report the 20
slowest tests.

parametrize decorator added to numpy.testing

A basic parametrize decorator is now available in numpy.testing. It is
intended to allow rewriting yield based tests that have been deprecated in
pytest so as to facilitate the transition to pytest in the future. The nose
testing framework has not been supported for several years and looks like
abandonware.

The new parametrize decorator does not have the full functionality of the
one in pytest. It doesn't work for classes, doesn't support nesting, and does
not substitute variable names. Even so, it should be adequate to rewrite the
NumPy tests.

chebinterpolate function added to numpy.polynomial.chebyshev

The new chebinterpolate function interpolates a given function at the
Chebyshev points of the first kind. A new Chebyshev.interpolate class
method adds support for interpolation over arbitrary intervals using the scaled
and shifted Chebyshev points of the first kind.

Support for reading lzma compressed text files in Python 3

With Python versions containing the lzma module the text IO functions can
now transparently read from files with xz or lzma extension.

sign option added to np.setprintoptions and np.array2string

This option controls printing of the sign of floating-point types, and may be
one of the characters '-', '+' or ' '. With '+' numpy always prints the sign of
positive values, with ' ' it always prints a space (whitespace character) in
the sign position of positive values, and with '-' it will omit the sign
character for positive values. The new default is '-'.

This new default changes the float output relative to numpy 1.13. The old
behavior can be obtained in 1.13 "legacy" printing mode, see compatibility
notes above.

hermitian option added tonp.linalg.matrix_rank

The new hermitian option allows choosing between standard SVD based matrix
rank calculation and the more efficient eigenvalue based method for
symmetric/hermitian matrices.

threshold and edgeitems options added to np.array2string

These options could previously be controlled using np.set_printoptions, but
now can be changed on a per-call basis as arguments to np.array2string.

concatenate and stack gained an out argument

A preallocated buffer of the desired dtype can now be used for the output of
these functions.

Support for PGI flang compiler on Windows

The PGI flang compiler is a Fortran front end for LLVM released by NVIDIA under
the Apache 2 license. It can be invoked by ::

python setup.py config --compiler=clang --fcompiler=flang install

There is little experience with this new compiler, so any feedback from people
using it will be appreciated.

Improvements

Numerator degrees of freedom in random.noncentral_f need only be positive.

Prior to NumPy 1.14.0, the numerator degrees of freedom needed to be > 1, but
the distribution is valid for values > 0, which is the new requirement.

The GIL is released for all np.einsum variations

Some specific loop structures which have an accelerated loop version
did not release the GIL prior to NumPy 1.14.0. This oversight has been
fixed.

The np.einsum function will use BLAS when possible and optimize by default

The np.einsum function will now call np.tensordot when appropriate.
Because np.tensordot uses BLAS when possible, that will speed up execution.
By default, np.einsum will also attempt optimization as the overhead is
small relative to the potential improvement in speed.

f2py now handles arrays of dimension 0

f2py now allows for the allocation of arrays of dimension 0. This allows
for more consistent handling of corner cases downstream.

numpy.distutils supports using MSVC and mingw64-gfortran together

Numpy distutils now supports using Mingw64 gfortran and MSVC compilers
together. This enables the production of Python extension modules on Windows
containing Fortran code while retaining compatibility with the
binaries distributed by Python.org. Not all use cases are supported,
but most common ways to wrap Fortran for Python are functional.

Compilation in this mode is usually enabled automatically, and can be
selected via the --fcompiler and --compiler options to
setup.py. Moreover, linking Fortran codes to static OpenBLAS is
supported; by default a gfortran compatible static archive
openblas.a is looked for.

np.linalg.pinv now works on stacked matrices

Previously it was limited to a single 2d array.

numpy.save aligns data to 64 bytes instead of 16

Saving NumPy arrays in the npy format with numpy.save inserts
padding before the array data to align it at 64 bytes. Previously
this was only 16 bytes (and sometimes less due to a bug in the code
for version 2). Now the alignment is 64 bytes, which matches the
widest SIMD instruction set commonly available, and is also the most
common cache line size. This makes npy files easier to use in
programs which open them with mmap, especially on Linux where an
mmap offset must be a multiple of the page size.

NPZ files now can be written without using temporary files

In Python 3.6+ numpy.savez and numpy.savez_compressed now write
directly to a ZIP file, without creating intermediate temporary files.

Better support for empty structured and string types

Structured types can contain zero fields, and string dtypes can contain zero
characters. Zero-length strings still cannot be created directly, and must be
constructed through structured dtypes::

str0 = np.empty(10, np.dtype([('v', str, N)]))['v']
void0 = np.empty(10, np.void)

It was always possible to work with these, but the following operations are
now supported for these arrays:

  • arr.sort()
  • arr.view(bytes)
  • arr.resize(...)
  • pickle.dumps(arr)

Support for decimal.Decimal in np.lib.financial

Unless otherwise stated all functions within the financial package now
support using the decimal.Decimal built-in type.

Float printing now uses "dragon4" algorithm for shortest decimal representation

The str and repr of floating-point values (16, 32, 64 and 128 bit) are
now printed to give the shortest decimal representation which uniquely
identifies the value from others of the same type. Previously this was only
true for float64 values. The remaining float types will now often be shorter
than in numpy 1.13. Arrays printed in scientific notation now also use the
shortest scientific representation, instead of fixed precision as before.

Additionally, the str of float scalars scalars will no longer be truncated
in python2, unlike python2 floats. np.double scalars now have a str
and repr identical to that of a python3 float.

New functions np.format_float_scientific and np.format_float_positional
are provided to generate these decimal representations.

A new option floatmode has been added to np.set_printoptions and
np.array2string, which gives control over uniqueness and rounding of
printed elements in an array. The new default is floatmode='maxprec' with
precision=8, which will print at most 8 fractional digits, or fewer if an
element can be uniquely represented with fewer. A useful new mode is
floatmode="unique", which will output enough digits to specify the array
elements uniquely.

Numpy complex-floating-scalars with values like inf*j or nan*j now
print as infj and nanj, like the pure-python complex type.

The FloatFormat and LongFloatFormat classes are deprecated and should
both be replaced by FloatingFormat. Similarly ComplexFormat and
LongComplexFormat should be replaced by ComplexFloatingFormat.

void datatype elements are now printed in hex notation

A hex representation compatible with the python bytes type is now printed
for unstructured np.void elements, e.g., V4 datatype. Previously, in
python2 the raw void data of the element was printed to stdout, or in python3
the integer byte values were shown.

printing style for void datatypes is now independently customizable

The printing style of np.void arrays is now independently customizable
using the formatter argument to np.set_printoptions, using the
'void' key, instead of the catch-all numpystr key as before.

Reduced memory usage of np.loadtxt

np.loadtxt now reads files in chunks instead of all at once which decreases
its memory usage significantly for large files.

Changes

Multiple-field indexing/assignment of structured arrays

The indexing and assignment of structured arrays with multiple fields has
changed in a number of ways, as warned about in previous releases.

First, indexing a structured array with multiple fields, e.g.,
arr[['f1', 'f3']], returns a view into the original array instead of a
copy. The returned view will have extra padding bytes corresponding to
intervening fields in the original array, unlike the copy in 1.13, which will
affect code such as arr[['f1', 'f3']].view(newdtype).

Second, assignment between structured arrays will now occur "by position"
instead of "by field name". The Nth field of the destination will be set to the
Nth field of the source regardless of field name, unlike in numpy versions 1.6
to 1.13 in which fields in the destination array were set to the
identically-named field in the source array or to 0 if the source did not have
a field.

Correspondingly, the order of fields in a structured dtypes now matters when
computing dtype equality. For example, with the dtypes ::

x = dtype({'names': ['A', 'B'], 'formats': ['i4', 'f4'], 'offsets': [0, 4]})
y = dtype({'names': ['B', 'A'], 'formats': ['f4', 'i4'], 'offsets': [4, 0]})

the expression x == y will now return False, unlike before.
This makes dictionary based dtype specifications like
dtype({'a': ('i4', 0), 'b': ('f4', 4)}) dangerous in python < 3.6
since dict key order is not preserved in those versions.

Assignment from a structured array to a boolean array now raises a ValueError,
unlike in 1.13, where it always set the destination elements to True.

Assignment from structured array with more than one field to a non-structured
array now raises a ValueError. In 1.13 this copied just the first field of the
source to the destination.

Using field "titles" in multiple-field indexing is now disallowed, as is
repeating a field name in a multiple-field index.

The documentation for structured arrays in the user guide has been
significantly updated to reflect these changes.

Integer and Void scalars are now unaffected by np.set_string_function

Previously, unlike most other numpy scalars, the str and repr of
integer and void scalars could be controlled by np.set_string_function.
This is no longer possible.

0d array printing changed, style arg of array2string deprecated

Previously the str and repr of 0d arrays had idiosyncratic
implementations which returned str(a.item()) and 'array(' + repr(a.item()) + ')' respectively for 0d array a, unlike both numpy
scalars and higher dimension ndarrays.

Now, the str of a 0d array acts like a numpy scalar using str(a[()])
and the repr acts like higher dimension arrays using formatter(a[()]),
where formatter can be specified using np.set_printoptions. The
style argument of np.array2string is deprecated.

This new behavior is disabled in 1.13 legacy printing mode, see compatibility
notes above.

Seeding RandomState using an array requires a 1-d array

RandomState previously would accept empty arrays or arrays with 2 or more
dimensions, which resulted in either a failure to seed (empty arrays) or for
some of the passed values to be ignored when setting the seed.

MaskedArray objects show a more useful repr

The repr of a MaskedArray is now closer to the python code that would
produce it, with arrays now being shown with commas and dtypes. Like the other
formatting changes, this can be disabled with the 1.13 legacy printing mode in
order to help transition doctests.

The repr of np.polynomial classes is more explicit

It now shows the domain and window parameters as keyword arguments to make
them more clear::

>>> np.polynomial.Polynomial(range(4))
Polynomial([0.,  1.,  2.,  3.], domain=[-1,  1], window=[-1,  1])

Checksums

MD5

dddfd1effddd4b73120bfa0f31a27f30  numpy-1.14.0-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
ab7e3518c7fe2a0d8e3c1e90c32ab57b  numpy-1.14.0-cp27-cp27m-manylinux1_i686.whl
8ba61f88afea560cc93ffddc9ea717d8  numpy-1.14.0-cp27-cp27m-manylinux1_x86_64.whl
edea7b57d4d924173c9c6d8125affe4e  numpy-1.14.0-cp27-cp27mu-manylinux1_i686.whl
bb048ccc49572e5e661ec7ead183272d  numpy-1.14.0-cp27-cp27mu-manylinux1_x86_64.whl
23248896d89fd09ef06aecbdc1e74eb7  numpy-1.14.0-cp27-none-win32.whl
09bef789f8d9352cafe98a6773c53f3a  numpy-1.14.0-cp27-none-win_amd64.whl
8bc2cc282df9597fe4c5fda72d0ff851  numpy-1.14.0-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
c9ce8e4de0293585ad8455a829cdbdff  numpy-1.14.0-cp34-cp34m-manylinux1_i686.whl
8179996e99adfbe7c66c5a9f7ad37139  numpy-1.14.0-cp34-cp34m-manylinux1_x86_64.whl
bf839d99514ecc03da1a2dc42808c1c7  numpy-1.14.0-cp34-none-win32.whl
80ead56627e40a00d0832793f5798ce8  numpy-1.14.0-cp34-none-win_amd64.whl
1a58fbaea199e425b71b3bfb1276d957  numpy-1.14.0-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
00cbdb6d9aa1eca97ecdb73a2705730b  numpy-1.14.0-cp35-cp35m-manylinux1_i686.whl
47de646ff0d4591431030ee93412f9f3  numpy-1.14.0-cp35-cp35m-manylinux1_x86_64.whl
f42aab8c9d6a21f60b051d6ea0b33425  numpy-1.14.0-cp35-none-win32.whl
b6d917bb34760e0f659c671efc08c602  numpy-1.14.0-cp35-none-win_amd64.whl
5a6456f4471b2f7d03fec5759e929544  numpy-1.14.0-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
3c72836ee01e7b88d7cebee0a1c94c6e  numpy-1.14.0-cp36-cp36m-manylinux1_i686.whl
34f763b99cc39ca3224c158ec82e2b39  numpy-1.14.0-cp36-cp36m-manylinux1_x86_64.whl
3bee8e2e4414a9df909d6510bd803aa1  numpy-1.14.0-cp36-none-win32.whl
f3db47f66b406f2803b681051f452f6e  numpy-1.14.0-cp36-none-win_amd64.whl
c573e2d2f26a5786d5198a660f87d8e4  numpy-1.14.0.tar.gz
c12d4bf380ac925fcdc8a59ada6c3298  numpy-1.14.0.zip

SHA256

428cd3c0b197cf857671353d8c85833193921af9fafcc169a1f29c7185833d50  numpy-1.14.0-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
a476e437d73e5754aa66e1e75840d0163119c3911b7361f4cd06985212a3c3fb  numpy-1.14.0-cp27-cp27m-manylinux1_i686.whl
289ff717138cd9aa133adcbd3c3e284458b9c8230db4d42b39083a3407370317  numpy-1.14.0-cp27-cp27m-manylinux1_x86_64.whl
c5eccb4bf96dbb2436c61bb3c2658139e779679b6ae0d04c5e268e6608b58053  numpy-1.14.0-cp27-cp27mu-manylinux1_i686.whl
75471acf298d455b035226cc609a92aee42c4bb6aa71def85f77fa2c2b646b61  numpy-1.14.0-cp27-cp27mu-manylinux1_x86_64.whl
5c54fb98ecf42da59ed93736d1c071842482b18657eb16ba6e466bd873e1b923  numpy-1.14.0-cp27-none-win32.whl
9ddf384ac3aacb72e122a8207775cc29727cbd9c531ee1a4b95754f24f42f7f3  numpy-1.14.0-cp27-none-win_amd64.whl
781d3197da49c421a07f250750de70a52c42af08ca02a2f7bdb571c0625ae7eb  numpy-1.14.0-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
93b26d6c06a22e64d56aaca32aaaffd27a4143db0ac2f21a048f0b571f2bfc55  numpy-1.14.0-cp34-cp34m-manylinux1_i686.whl
b2547f57d05ba59df4289493254f29f4c9082d255f1f97b7e286f40f453e33a1  numpy-1.14.0-cp34-cp34m-manylinux1_x86_64.whl
eef6af1c752eef538a96018ef9bdf8e37bbf28aab50a1436501a4aa47a6467df  numpy-1.14.0-cp34-none-win32.whl
ff8a4b2c3ac831964f529a2da506c28d002562b230261ae5c16885f5f53d2e75  numpy-1.14.0-cp34-none-win_amd64.whl
194074058c22a4066e1b6a4ea432486ee468d24ab16f13630c1030409e6b8666  numpy-1.14.0-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
4e13f1a848fde960dea33702770265837c72b796a6a3eaac7528cfe75ddefadd  numpy-1.14.0-cp35-cp35m-manylinux1_i686.whl
91101216d72749df63968d86611b549438fb18af2c63849c01f9a897516133c7  numpy-1.14.0-cp35-cp35m-manylinux1_x86_64.whl
97507349abb7d1f6b76b877258defe8720833881dc7e7fd052bac90c88587387  numpy-1.14.0-cp35-none-win32.whl
1479b46b6040b5c689831496354c8859c456b152d37315673a0c18720b41223b  numpy-1.14.0-cp35-none-win_amd64.whl
98b1ac79c160e36093d7914244e40ee1e7164223e795aa2c71dcce367554e646  numpy-1.14.0-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
24bbec9a199f938eab75de8390f410969bc33c218e5430fa1ae9401b00865255  numpy-1.14.0-cp36-cp36m-manylinux1_i686.whl
7880f412543e96548374a4bb1d75e4cdb8cad80f3a101ed0f8d0e0428f719c1c  numpy-1.14.0-cp36-cp36m-manylinux1_x86_64.whl
6112f152b76a28c450bbf665da11757078a724a90330112f5b7ea2d6b6cefd67  numpy-1.14.0-cp36-none-win32.whl
7c5276763646480143d5f3a6c2acb2885460c765051a1baf4d5070f63d05010f  numpy-1.14.0-cp36-none-win_amd64.whl
c45d99134bb07600e916537c51c22e87f9e0c44b05d71018ab4907e195f007ce  numpy-1.14.0.tar.gz
3de643935b212307b420248018323a44ec51987a336d1d747c1322afc3c099fb  numpy-1.14.0.zip