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numpy

2.5.31.1B downloads/monumpy/numpy ↗New releases in your daily digest, changelog included.

Fundamental package for array computing in Python

Last release 22 days ago
06 Sep 2026
Ships fairly regularly
a new release about every 3 weeks
Most releases are documented
notes for 52 of the last 60 stable releases
1 version withdrawn
withdrawn after publishing
20 years old
151 releases · first in 2006

Release timeline

151 releases since 2006

20062008201020122014

Releases

1.18.022 Dec 2019Deprecated
…new random C-API, expires a large number of old deprecations, and improves the appearance of the documentation. The Python versions supported are 3.5-…
NumPy NumPy 1.18.0 Release Notes

In addition to the usual bug fixes, this NumPy release cleans up and documents the new random C-API, expires a large number of old deprecations, and improves the appearance of the documentation. The Python versions supported are 3.5-3.8. This is the last NumPy release series that will support Python 3.5.

Downstream developers should use Cython >= 0.29.14 for Python 3.8 support and OpenBLAS >= 3.7 to avoid problems on the Skylake architecture.

Highlights

The C-API for numpy.random has been defined and documented.

Basic infrastructure for linking with 64 bit BLAS and LAPACK libraries.

Many documentation improvements.

New functions

Multivariate hypergeometric distribution added to numpy.random

The method multivariate_hypergeometric has been added to the class [numpy.random.Generator]{.title-ref}. This method generates random variates from the multivariate hypergeometric probability distribution. (gh-13794)

Deprecations

np.fromfile and np.fromstring will error on bad data

In future numpy releases, the functions np.fromfile and np.fromstring will throw an error when parsing bad data. This will now give a DeprecationWarning where previously partial or even invalid data was silently returned. This deprecation also affects the C defined functions PyArray_FromString and PyArray_FromFile (gh-13605)

Deprecate non-scalar arrays as fill values in ma.fill_value

Setting a MaskedArray.fill_value to a non-scalar array is deprecated since the logic to broadcast the fill value to the array is fragile, especially when slicing. (gh-13698)

Deprecate PyArray_As1D, PyArray_As2D

PyArray_As1D, PyArray_As2D are deprecated, use PyArray_AsCArray instead (gh-14036)

Deprecate np.alen

np.alen was deprecated. Use len instead. (gh-14181)

Deprecate the financial functions

In accordance with NEP-32, the financial functions fv ipmt, irr, mirr, nper, npv, pmt, ppmt, pv and rate are deprecated, and will be removed from NumPy 1.20.The replacement for these functions is the Python package numpy-financial. (gh-14720)

The axis argument to numpy.ma.mask_cols and numpy.ma.mask_row is deprecated

This argument was always ignored. (gh-14996)

Expired deprecations

PyArray_As1D and PyArray_As2D have been removed in favor of PyArray_AsCArray (gh-14036)

np.rank has been removed. This was deprecated in NumPy 1.10 and has been replaced by np.ndim. (gh-14039)

The deprecation of expand_dims out-of-range axes in 1.13.0 has expired. (gh-14051)

PyArray_FromDimsAndDataAndDescr and PyArray_FromDims have been removed (they will always raise an error). Use PyArray_NewFromDescr and PyArray_SimpleNew instead. (gh-14100)

numeric.loads, numeric.load, np.ma.dump, np.ma.dumps, np.ma.load, np.ma.loads are removed, use pickle methods instead (gh-14256)

arrayprint.FloatFormat, arrayprint.LongFloatFormat has been removed, use FloatingFormat instead

arrayprint.ComplexFormat, arrayprint.LongComplexFormat has been removed, use ComplexFloatingFormat instead

arrayprint.StructureFormat has been removed, use StructureVoidFormat instead (gh-14259)

np.testing.rand has been removed. This was deprecated in NumPy 1.11 and has been replaced by np.random.rand. (gh-14325)

Class SafeEval in numpy/lib/utils.py has been removed. This was deprecated in NumPy 1.10. Use np.safe_eval instead. (gh-14335)

Remove deprecated support for boolean and empty condition lists in np.select (gh-14583)

Array order only accepts 'C', 'F', 'A', and 'K'. More permissive options were deprecated in NumPy 1.11. (gh-14596)

np.linspace parameter num must be an integer. Deprecated in NumPy 1.12. (gh-14620)

UFuncs with multiple outputs must use a tuple for the out kwarg. This finishes a deprecation started in NumPy 1.10. (gh-14682)

The files numpy/testing/decorators.py, numpy/testing/noseclasses.py and numpy/testing/nosetester.py have been removed. They were never meant to be public (all relevant objects are present in the numpy.testing namespace), and importing them has given a deprecation warning since NumPy 1.15.0 (gh-14567)

Compatibility notes

[numpy.lib.recfunctions.drop_fields]{.title-ref} can no longer return None

If drop_fields is used to drop all fields, previously the array would be completely discarded and None returned. Now it returns an array of the same shape as the input, but with no fields. The old behavior can be retained with:

dropped_arr = drop_fields(arr, ['a', 'b']) if dropped_arr.dtype.names == (): dropped_arr = None

converting the empty recarray to None (gh-14510)

numpy.argmin/argmax/min/max returns NaT if it exists in array

numpy.argmin, numpy.argmax, numpy.min, and numpy.max will return NaT if it exists in the array. (gh-14717)

np.can_cast(np.uint64, np.timedelta64, casting='safe') is now False

Previously this was True - however, this was inconsistent with uint64 not being safely castable to int64, and resulting in strange type resolution.

If this impacts your code, cast uint64 to int64 first. (gh-14718)

Changed random variate stream from numpy.random.Generator.integers

There was a bug in numpy.random.Generator.integers that caused biased sampling of 8 and 16 bit integer types. Fixing that bug has changed the output stream from what it was in previous releases. (gh-14777)

Add more ufunc loops for datetime64, timedelta64

np.datetime('NaT') should behave more like float('Nan'). Add needed infrastructure so np.isinf(a) and np.isnan(a) will run on datetime64 and timedelta64 dtypes. Also added specific loops for numpy.fmin and numpy.fmax that mask NaT. This may require adjustment to user- facing code. Specifically, code that either disallowed the calls to numpy.isinf or numpy.isnan or checked that they raised an exception will require adaptation, and code that mistakenly called numpy.fmax and numpy.fmin instead of numpy.maximum or numpy.minimum respectively will requre adjustment. This also affects numpy.nanmax and numpy.nanmin. (gh-14841)

C API changes

PyDataType_ISUNSIZED(descr) now returns False for structured datatypes

Previously this returned True for any datatype of itemsize 0, but now this returns false for the non-flexible datatype with itemsize 0, np.dtype([]). (gh-14393)

New Features

Add our own *.pxd cython import file

Added a numpy/__init__.pxd file. It will be used for cimport numpy (gh-12284)

A tuple of axes can now be input to expand_dims

The numpy.expand_dims axis keyword can now accept a tuple of axes. Previously, axis was required to be an integer. (gh-14051)

Support for 64-bit OpenBLAS

Added support for 64-bit (ILP64) OpenBLAS. See site.cfg.example for details. (gh-15012)

Add --f2cmap option to F2PY

Allow specifying a file to load Fortran-to-C type map customizations from. (gh-15113)

Improvements

Different C numeric types of the same size have unique names

On any given platform, two of np.intc, np.int_, and np.longlong would previously appear indistinguishable through their repr, despite their corresponding dtype having different properties. A similar problem existed for the unsigned counterparts to these types, and on some platforms for np.double and np.longdouble

These types now always print with a unique __name__. (gh-10151)

argwhere now produces a consistent result on 0d arrays

On N-d arrays, numpy.argwhere now always produces an array of shape (n_non_zero, arr.ndim), even when arr.ndim == 0. Previously, the last axis would have a dimension of 1 in this case. (gh-13610)

Add axis argument for random.permutation and random.shuffle

Previously the random.permutation and random.shuffle functions can only shuffle an array along the first axis; they now have a new argument axis which allows shuffle along a specified axis. (gh-13829)

method keyword argument for np.random.multivariate_normal

A method keyword argument is now available for np.random.multivariate_normal with possible values {'svd', 'eigh', 'cholesky'}. To use it, write np.random.multivariate_normal(..., method=<method>). (gh-14197)

Add complex number support for numpy.fromstring

Now numpy.fromstring can read complex numbers. (gh-14227)

numpy.unique has consistent axes order when axis is not None

Using moveaxis instead of swapaxes in numpy.unique, so that the ordering of axes except the axis in arguments will not be broken. (gh-14255)

numpy.matmul with boolean output now converts to boolean values

Calling numpy.matmul where the output is a boolean array would fill the array with uint8 equivalents of the result, rather than 0/1. Now it forces the output to 0 or 1 (NPY_TRUE or NPY_FALSE). (gh-14464)

numpy.random.randint produced incorrect value when the range was 2**32

The implementation introduced in 1.17.0 had an incorrect check when determining whether to use the 32-bit path or the full 64-bit path that incorrectly redirected random integer generation with a high - low range of 2**32 to the 64-bit generator. (gh-14501)

Add complex number support for numpy.fromfile

Now numpy.fromfile can read complex numbers. (gh-14730)

std=c99 added if compiler is named gcc

GCC before version 5 requires the -std=c99 command line argument. Newer compilers automatically turn on C99 mode. The compiler setup code will automatically add the code if the compiler name has gcc in it. (gh-14771)

Changes

NaT now sorts to the end of arrays

NaT is now effectively treated as the largest integer for sorting purposes, so that it sorts to the end of arrays. This change is for consistency with NaN sorting behavior. (gh-12658) (gh-15068)

Incorrect threshold in np.set_printoptions raises TypeError or ValueError

Previously an incorrect threshold raised ValueError; it now raises TypeError for non-numeric types and ValueError for nan values. (gh-13899)

Warn when saving a dtype with metadata

A UserWarning will be emitted when saving an array via numpy.save with metadata. Saving such an array may not preserve metadata, and if metadata is preserved, loading it will cause a ValueError. This shortcoming in save and load will be addressed in a future release. (gh-14142)

numpy.distutils append behavior changed for LDFLAGS and similar

[numpy.distutils]{.title-ref} has always overridden rather than appended to LDFLAGS and other similar such environment variables for compiling Fortran extensions. Now the default behavior has changed to appending - which is the expected behavior in most situations. To preserve the old (overwriting) behavior, set the NPY_DISTUTILS_APPEND_FLAGS environment variable to 0. This applies to: LDFLAGS, F77FLAGS, F90FLAGS, FREEFLAGS, FOPT, FDEBUG, and FFLAGS. NumPy 1.16 and 1.17 gave build warnings in situations where this change in behavior would have affected the compile flags used. (gh-14248)

Remove numpy.random.entropy without a deprecation

numpy.random.entropy was added to the numpy.random namespace in 1.17.0. It was meant to be a private c-extension module, but was exposed as public. It has been replaced by numpy.random.SeedSequence so the module was completely removed. (gh-14498)

Add options to quiet build configuration and build with -Werror

Added two new configuration options. During the build_src subcommand, as part of configuring NumPy, the files _numpyconfig.h and config.h are created by probing support for various runtime functions and routines. Previously, the very verbose compiler output during this stage clouded more important information. By default the output is silenced. Running runtests.py --debug-info will add --verbose-cfg to the build_src subcommand, which will restore the previous behaviour.

Adding CFLAGS=-Werror to turn warnings into errors would trigger errors during the configuration. Now runtests.py --warn-error will add --warn-error to the build subcommand, which will percolate to the build_ext and build_lib subcommands. This will add the compiler flag to those stages and turn compiler warnings into errors while actually building NumPy itself, avoiding the build_src subcommand compiler calls.

(gh-14527) (gh-14518)

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1.17.51 Jan 2020

This release contains fixes for bugs reported against NumPy 1.17.4 along with some build improvements. The Python versions supported in this release a

NumPy 1.17.5 Release Notes

This release contains fixes for bugs reported against NumPy 1.17.4 along with some build improvements. The Python versions supported in this release are 3.5-3.8.

Downstream developers should use Cython >= 0.29.14 for Python 3.8 support and OpenBLAS >= 3.7 to avoid errors on the Skylake architecture.

It is recommended that developers interested in the new random bit generators upgrade to the NumPy 1.18.x series, as it has updated documentation and many small improvements.

Contributors

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

Charles Harris
Eric Wieser
Ilhan Polat
Matti Picus
Michael Hudson-Doyle
Ralf Gommers

Pull requests merged

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

#14593: MAINT: backport Cython API cleanup to 1.17.x, remove docs
#14937: BUG: fix integer size confusion in handling array's ndmin argument
#14939: BUILD: remove SSE2 flag from numpy.random builds
#14993: MAINT: Added Python3.8 branch to dll lib discovery
#15038: BUG: Fix refcounting in ufunc object loops
#15067: BUG: Exceptions tracebacks are dropped
#15175: ENH: Backport improvements to testing functions.
#15213: REL: Prepare for the NumPy 1.17.5 release.

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fc56ec046a2cc3aba91fe29e482c145c17925db1b00eafa924d9e16020a3eb88  numpy-1.17.5-cp38-cp38-macosx_10_9_x86_64.whl
73d20aebe518997dce89da356d4b8e4cf60143151c22a0ec76cb00840bb09320  numpy-1.17.5-cp38-cp38-manylinux1_i686.whl
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16507ba6617f62ae3c6ab1725ae6f550331025d4d9a369b83f6d5a470446c342  numpy-1.17.5.zip

Open source →

1.17.411 Nov 2019

This release contains fixes for bugs reported against NumPy 1.17.3 along with some build improvements. The Python versions supported in this release a

NumPy 1.17.4 Release Notes

This release contains fixes for bugs reported against NumPy 1.17.3 along with some build improvements. The Python versions supported in this release are 3.5-3.8.

Downstream developers should use Cython >= 0.29.13 for Python 3.8 support and OpenBLAS >= 3.7 to avoid errors on the Skylake architecture.

Highlights

Fixed np.random.random_integers biased generation of 8 and 16 bit integers.
Fixed np.einsum regression on Power9 and z/Linux.
Fixed histogram problem with signed integer arrays.

Contributors

A total of 5 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Charles Harris
Chris Burr +
Matti Picus
Qiming Sun +
Warren Weckesser

Pull requests merged

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

#14758: BLD: declare support for python 3.8
#14781: BUG: random: biased samples from integers() with 8 or 16 bit...
#14851: BUG: Fix _ctypes class circular reference. (#13808)
#14852: BLD: add 'apt update' to shippable
#14855: BUG: Fix np.einsum errors on Power9 Linux and z/Linux
#14857: BUG: lib: Fix histogram problem with signed integer arrays.
#14858: BLD: Prevent -flto from optimising long double representation...
#14866: MAINT: move buffer.h -> npy_buffer.h to avoid conflicts

Checksums

MD5

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SHA256

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Open source →

1.17.317 Oct 2019

This release contains fixes for bugs reported against NumPy 1.17.2 along with a some documentation improvements. The Python versions supported in this

NumPy 1.17.3 Release Notes

This release contains fixes for bugs reported against NumPy 1.17.2 along with a some documentation improvements. The Python versions supported in this release are 3.5-3.8.

Downstream developers should use Cython >= 0.29.13 for Python 3.8 support and OpenBLAS >= 3.7 to avoid errors on the Skylake architecture.

Highlights

Wheels for Python 3.8
Boolean matmul fixed to use booleans instead of integers.

Compatibility notes

The seldom used PyArray_DescrCheck macro has been changed/fixed.

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
Kevin Sheppard
Matti Picus
Ralf Gommers
Sebastian Berg
Warren Weckesser

Pull requests merged

A total of 12 pull requests were merged for this release.

#14456: MAINT: clean up pocketfft modules inside numpy.fft namespace.
#14463: BUG: random.hypergeometic assumes npy_long is npy_int64, hung...
#14502: BUG: random: Revert gh-14458 and refix gh-14557.
#14504: BUG: add a specialized loop for boolean matmul.
#14506: MAINT: Update pytest version for Python 3.8
#14512: DOC: random: fix doc linking, was referencing private submodules.
#14513: BUG,MAINT: Some fixes and minor cleanup based on clang analysis
#14515: BUG: Fix randint when range is 2**32
#14519: MAINT: remove the entropy c-extension module
#14563: DOC: remove note about Pocketfft license file (non-existing here).
#14578: BUG: random: Create a legacy implementation of random.binomial.
#14687: BUG: properly define PyArray_DescrCheck

Checksums

MD5

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SHA256

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Open source →

1.17.27 Sept 2019

This release contains fixes for bugs reported against NumPy 1.17.1 along with a some documentation improvements. The most important fix is for lexsort

NumPy 1.17.2 Release Notes

This release contains fixes for bugs reported against NumPy 1.17.1 along with a some documentation improvements. The most important fix is for lexsort when the keys are of type (u)int8 or (u)int16. If you are currently using 1.17 you should upgrade.

The Python versions supported in this release are 3.5-3.7, Python 2.7 has been dropped. Python 3.8b4 should work with the released source packages, but there are no future guarantees.

Downstream developers should use Cython >= 0.29.13 for Python 3.8 support and OpenBLAS >= 3.7 to avoid errors on the Skylake architecture. The NumPy wheels on PyPI are built from the OpenBLAS development branch in order to avoid those errors.

Contributors

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

CakeWithSteak +
Charles Harris
Dan Allan
Hameer Abbasi
Lars Grueter
Matti Picus
Sebastian Berg

Pull requests merged

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

#14418: BUG: Fix aradixsort indirect indexing.
#14420: DOC: Fix a minor typo in dispatch documentation.
#14421: BUG: test, fix regression in converting to ctypes
#14430: BUG: Do not show Override module in private error classes.
#14432: BUG: Fixed maximum relative error reporting in assert_allclose.
#14433: BUG: Fix uint-overflow if padding with linear_ramp and negative...
#14436: BUG: Update 1.17.x with 1.18.0-dev pocketfft.py.
#14446: REL: Prepare for NumPy 1.17.2 release.

Checksums

MD5

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SHA256

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Open source →

1.17.127 Aug 2019

This release contains a number of fixes for bugs reported against NumPy 1.17.0 along with a few documentation and build improvements. The Python versi

NumPy 1.17.1 Release Notes

This release contains a number of fixes for bugs reported against NumPy 1.17.0 along with a few documentation and build improvements. The Python versions supported are 3.5-3.7, note that Python 2.7 has been dropped. Python 3.8b3 should work with the released source packages, but there are no future guarantees.

Downstream developers should use Cython >= 0.29.13 for Python 3.8 support and OpenBLAS >= 3.7 to avoid problems on the Skylake architecture. The NumPy wheels on PyPI are built from the OpenBLAS development branch in order to avoid those problems.

Contributors

A total of 17 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Alexander Jung +
Allan Haldane
Charles Harris
Eric Wieser
Giuseppe Cuccu +
Hiroyuki V. Yamazaki
Jérémie du Boisberranger
Kmol Yuan +
Matti Picus
Max Bolingbroke +
Maxwell Aladago +
Oleksandr Pavlyk
Peter Andreas Entschev
Sergei Lebedev
Seth Troisi +
Vladimir Pershin +
Warren Weckesser

Pull requests merged

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

#14156: TST: Allow fuss in testing strided/non-strided exp/log loops
#14157: BUG: avx2_scalef_ps must be static
#14158: BUG: Remove stray print that causes a SystemError on python 3.7.
#14159: BUG: Fix DeprecationWarning in python 3.8.
#14160: BLD: Add missing gcd/lcm definitions to npy_math.h
#14161: DOC, BUILD: cleanups and fix (again) 'build dist'
#14166: TST: Add 3.8-dev to travisCI testing.
#14194: BUG: Remove the broken clip wrapper (Backport)
#14198: DOC: Fix hermitian argument docs in svd.
#14199: MAINT: Workaround for Intel compiler bug leading to failing test
#14200: TST: Clean up of test_pocketfft.py
#14201: BUG: Make advanced indexing result on read-only subclass writeable...
#14236: BUG: Fixed default BitGenerator name
#14237: ENH: add c-imported modules for freeze analysis in np.random
#14296: TST: Pin pytest version to 5.0.1
#14301: BUG: Fix leak in the f2py-generated module init and PyMem_Del...
#14302: BUG: Fix formatting error in exception message
#14307: MAINT: random: Match type of SeedSequence.pool_size to DEFAULT_POOL_SIZE.
#14308: BUG: Fix numpy.random bug in platform detection
#14309: ENH: Enable huge pages in all Linux builds
#14330: BUG: Fix segfault in random.permutation(x) when x is a string.
#14338: BUG: don't fail when lexsorting some empty arrays (#14228)
#14339: BUG: Fix misuse of .names and .fields in various places (backport...
#14345: BUG: fix behavior of structured_to_unstructured on non-trivial...
#14350: REL: Prepare 1.17.1 release

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Open source →

1.17.026 Jul 2019Security

The functions load, and lib.format.read_array take an allow_pickle keyword which now defaults to False in response to CVE-2019-6446 _.

NumPy 1.17.0 Release Notes

This NumPy release contains a number of new features that should substantially improve its performance and usefulness, see Highlights below for a summary. The Python versions supported are 3.5-3.7, note that Python 2.7 has been dropped. Python 3.8b2 should work with the released source packages, but there are no future guarantees.

Downstream developers should use Cython >= 0.29.11 for Python 3.8 support and OpenBLAS >= 3.7 (not currently out) to avoid problems on the Skylake architecture. The NumPy wheels on PyPI are built from the OpenBLAS development branch in order to avoid those problems.

Highlights

A new extensible random module along with four selectable random number generators <random.BitGenerators> and improved seeding designed for use in parallel processes has been added. The currently available bit generators are MT19937 <random.mt19937.MT19937>, PCG64 <random.pcg64.PCG64>, Philox <random.philox.Philox>, and SFC64 <random.sfc64.SFC64>. See below under New Features.
NumPy's FFT <fft> implementation was changed from fftpack to pocketfft, resulting in faster, more accurate transforms and better handling of datasets of prime length. See below under Improvements.
New radix sort and timsort sorting methods. It is currently not possible to choose which will be used. They are hardwired to the datatype and used when either stable or mergesort is passed as the method. See below under Improvements.
Overriding numpy functions is now possible by default, see __array_function__ below.

New functions

numpy.errstate is now also a function decorator

Deprecations

`numpy.polynomial` functions warn when passed `float` in place of `int`

Previously functions in this module would accept float values provided they were integral (1.0, 2.0, etc). For consistency with the rest of numpy, doing so is now deprecated, and in future will raise a TypeError.

Similarly, passing a float like 0.5 in place of an integer will now raise a TypeError instead of the previous ValueError.

Deprecate `numpy.distutils.exec_command` and `temp_file_name`

The internal use of these functions has been refactored and there are better alternatives. Replace exec_command with subprocess.Popen and temp_file_name <numpy.distutils.exec_command> with tempfile.mkstemp.

Writeable flag of C-API wrapped arrays

When an array is created from the C-API to wrap a pointer to data, the only indication we have of the read-write nature of the data is the writeable flag set during creation. It is dangerous to force the flag to writeable. In the future it will not be possible to switch the writeable flag to True from python. This deprecation should not affect many users since arrays created in such a manner are very rare in practice and only available through the NumPy C-API.

`numpy.nonzero` should no longer be called on 0d arrays

The behavior of numpy.nonzero on 0d arrays was surprising, making uses of it almost always incorrect. If the old behavior was intended, it can be preserved without a warning by using nonzero(atleast_1d(arr)) instead of nonzero(arr). In a future release, it is most likely this will raise a ValueError.

Writing to the result of `numpy.broadcast_arrays` will warn

Commonly numpy.broadcast_arrays returns a writeable array with internal overlap, making it unsafe to write to. A future version will set the writeable flag to False, and require users to manually set it to True if they are sure that is what they want to do. Now writing to it will emit a deprecation warning with instructions to set the writeable flag True. Note that if one were to inspect the flag before setting it, one would find it would already be True. Explicitly setting it, though, as one will need to do in future versions, clears an internal flag that is used to produce the deprecation warning. To help alleviate confusion, an additional FutureWarning will be emitted when accessing the writeable flag state to clarify the contradiction.

Note that for the C-side buffer protocol such an array will return a readonly buffer immediately unless a writable buffer is requested. If a writeable buffer is requested a warning will be given. When using cython, the const qualifier should be used with such arrays to avoid the warning (e.g. cdef const double[::1] view).

Future Changes

Shape-1 fields in dtypes won't be collapsed to scalars in a future version

Currently, a field specified as [(name, dtype, 1)] or "1type" is interpreted as a scalar field (i.e., the same as [(name, dtype)] or [(name, dtype, ()]). This now raises a FutureWarning; in a future version, it will be interpreted as a shape-(1,) field, i.e. the same as [(name, dtype, (1,))] or "(1,)type" (consistently with [(name, dtype, n)] / "ntype" with n>1, which is already equivalent to [(name, dtype, (n,)] / "(n,)type").

Compatibility notes

`float16` subnormal rounding

Casting from a different floating point precision to float16 used incorrect rounding in some edge cases. This means in rare cases, subnormal results will now be rounded up instead of down, changing the last bit (ULP) of the result.

Signed zero when using divmod

Starting in version 1.12.0, numpy incorrectly returned a negatively signed zero when using the divmod and floor_divide functions when the result was zero. For example::

   >>> np.zeros(10)//1
   array([-0., -0., -0., -0., -0., -0., -0., -0., -0., -0.])

With this release, the result is correctly returned as a positively signed zero::

   >>> np.zeros(10)//1
   array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])

`MaskedArray.mask` now returns a view of the mask, not the mask itself

Returning the mask itself was unsafe, as it could be reshaped in place which would violate expectations of the masked array code. The behavior of mask <ma.MaskedArray.mask> is now consistent with data <ma.MaskedArray.data>, which also returns a view.

The underlying mask can still be accessed with ._mask if it is needed. Tests that contain assert x.mask is not y.mask or similar will need to be updated.

Do not lookup `buffer` attribute in `numpy.frombuffer`

Looking up __buffer__ attribute in numpy.frombuffer was undocumented and non-functional. This code was removed. If needed, use frombuffer(memoryview(obj), ...) instead.

`out` is buffered for memory overlaps in `take`, `choose`, `put`

If the out argument to these functions is provided and has memory overlap with the other arguments, it is now buffered to avoid order-dependent behavior.

Unpickling while loading requires explicit opt-in

The functions load, and lib.format.read_array take an allow_pickle keyword which now defaults to False in response to CVE-2019-6446 <https://nvd.nist.gov/vuln/detail/CVE-2019-6446>_.

Potential changes to the random stream in old random module

Due to bugs in the application of log to random floating point numbers, the stream may change when sampling from ~RandomState.beta, ~RandomState.binomial, ~RandomState.laplace, ~RandomState.logistic, ~RandomState.logseries or ~RandomState.multinomial if a 0 is generated in the underlying MT19937 <~numpy.random.mt11937.MT19937> random stream. There is a 1 in :math:10^{53} chance of this occurring, so the probability that the stream changes for any given seed is extremely small. If a 0 is encountered in the underlying generator, then the incorrect value produced (either numpy.inf or numpy.nan) is now dropped.

`i0` now always returns a result with the same shape as the input

Previously, the output was squeezed, such that, e.g., input with just a single element would lead to an array scalar being returned, and inputs with shapes such as (10, 1) would yield results that would not broadcast against the input.

Note that we generally recommend the SciPy implementation over the numpy one: it is a proper ufunc written in C, and more than an order of magnitude faster.

`can_cast` no longer assumes all unsafe casting is allowed

Previously, can_cast returned True for almost all inputs for casting='unsafe', even for cases where casting was not possible, such as from a structured dtype to a regular one. This has been fixed, making it more consistent with actual casting using, e.g., the .astype <ndarray.astype> method.

`ndarray.flags.writeable` can be switched to true slightly more often

In rare cases, it was not possible to switch an array from not writeable to writeable, although a base array is writeable. This can happen if an intermediate ndarray.base object is writeable. Previously, only the deepest base object was considered for this decision. However, in rare cases this object does not have the necessary information. In that case switching to writeable was never allowed. This has now been fixed.

C API changes

dimension or stride input arguments are now passed by `npy_intp const*`

Previously these function arguments were declared as the more strict npy_intp*, which prevented the caller passing constant data. This change is backwards compatible, but now allows code like::

npy_intp const fixed_dims[] = {1, 2, 3};
// no longer complains that the const-qualifier is discarded
npy_intp size = PyArray_MultiplyList(fixed_dims, 3);

New Features

New extensible `numpy.random` module with selectable random number generators

A new extensible numpy.random module along with four selectable random number generators and improved seeding designed for use in parallel processes has been added. The currently available :ref:Bit Generators <bit_generator> are ~mt19937.MT19937, ~pcg64.PCG64, ~philox.Philox, and ~sfc64.SFC64. PCG64 is the new default while MT19937 is retained for backwards compatibility. Note that the legacy random module is unchanged and is now frozen, your current results will not change. More information is available in the :ref:API change description <new-or-different> and in the top-level view <numpy.random> documentation.

libFLAME

Support for building NumPy with the libFLAME linear algebra package as the LAPACK, implementation, see libFLAME <https://www.cs.utexas.edu/~flame/web/libFLAME.html>_ for details.

User-defined BLAS detection order

distutils now uses an environment variable, comma-separated and case insensitive, to determine the detection order for BLAS libraries. By default NPY_BLAS_ORDER=mkl,blis,openblas,atlas,accelerate,blas. However, to force the use of OpenBLAS simply do::

NPY_BLAS_ORDER=openblas python setup.py build

which forces the use of OpenBLAS. This may be helpful for users which have a MKL installation but wishes to try out different implementations.

User-defined LAPACK detection order

numpy.distutils now uses an environment variable, comma-separated and case insensitive, to determine the detection order for LAPACK libraries. By default NPY_LAPACK_ORDER=mkl,openblas,flame,atlas,accelerate,lapack. However, to force the use of OpenBLAS simply do::

NPY_LAPACK_ORDER=openblas python setup.py build

which forces the use of OpenBLAS. This may be helpful for users which have a MKL installation but wishes to try out different implementations.

`ufunc.reduce` and related functions now accept a `where` mask

ufunc.reduce, sum, prod, min, max all now accept a where keyword argument, which can be used to tell which elements to include in the reduction. For reductions that do not have an identity, it is necessary to also pass in an initial value (e.g., initial=np.inf for min). For instance, the equivalent of nansum would be np.sum(a, where=~np.isnan(a)).

Timsort and radix sort have replaced mergesort for stable sorting

Both radix sort and timsort have been implemented and are now used in place of mergesort. Due to the need to maintain backward compatibility, the sorting kind options "stable" and "mergesort" have been made aliases of each other with the actual sort implementation depending on the array type. Radix sort is used for small integer types of 16 bits or less and timsort for the remaining types. Timsort features improved performace on data containing already or nearly sorted data and performs like mergesort on random data and requires :math:O(n/2) working space. Details of the timsort algorithm can be found at CPython listsort.txt <https://github.com/python/cpython/blob/3.7/Objects/listsort.txt>_.

`packbits` and `unpackbits` accept an `order` keyword

The order keyword defaults to big, and will order the bits accordingly. For 'order=big' 3 will become [0, 0, 0, 0, 0, 0, 1, 1], and [1, 1, 0, 0, 0, 0, 0, 0] for order=little

`unpackbits` now accepts a `count` parameter

count allows subsetting the number of bits that will be unpacked up-front, rather than reshaping and subsetting later, making the packbits operation invertible, and the unpacking less wasteful. Counts larger than the number of available bits add zero padding. Negative counts trim bits off the end instead of counting from the beginning. None counts implement the existing behavior of unpacking everything.

`linalg.svd` and `linalg.pinv` can be faster on hermitian inputs

These functions now accept a hermitian argument, matching the one added to linalg.matrix_rank in 1.14.0.

divmod operation is now supported for two `timedelta64` operands

The divmod operator now handles two timedelta64 operands, with type signature mm->qm.

`fromfile` now takes an `offset` argument

This function now takes an offset keyword argument for binary files, which specifics the offset (in bytes) from the file's current position. Defaults to 0.

New mode "empty" for `pad`

This mode pads an array to a desired shape without initializing the new entries.

`empty_like` and related functions now accept a `shape` argument

empty_like, full_like, ones_like and zeros_like now accept a shape keyword argument, which can be used to create a new array as the prototype, overriding its shape as well. This is particularly useful when combined with the __array_function__ protocol, allowing the creation of new arbitrary-shape arrays from NumPy-like libraries when such an array is used as the prototype.

Floating point scalars implement `as_integer_ratio` to match the builtin float

This returns a (numerator, denominator) pair, which can be used to construct a fractions.Fraction.

Structured `dtype` objects can be indexed with multiple fields names

arr.dtype[['a', 'b']] now returns a dtype that is equivalent to arr[['a', 'b']].dtype, for consistency with arr.dtype['a'] == arr['a'].dtype.

Like the dtype of structured arrays indexed with a list of fields, this dtype has the same itemsize as the original, but only keeps a subset of the fields.

This means that arr[['a', 'b']] and arr.view(arr.dtype[['a', 'b']]) are equivalent.

`.npy` files support unicode field names

A new format version of 3.0 has been introduced, which enables structured types with non-latin1 field names. This is used automatically when needed.

Improvements

Array comparison assertions include maximum differences

Error messages from array comparison tests such as testing.assert_allclose now include "max absolute difference" and "max relative difference," in addition to the previous "mismatch" percentage. This information makes it easier to update absolute and relative error tolerances.

Replacement of the fftpack based `fft` module by the pocketfft library

Both implementations have the same ancestor (Fortran77 FFTPACK by Paul N. Swarztrauber), but pocketfft contains additional modifications which improve both accuracy and performance in some circumstances. For FFT lengths containing large prime factors, pocketfft uses Bluestein's algorithm, which maintains :math:O(N log N) run time complexity instead of deteriorating towards :math:O(N*N) for prime lengths. Also, accuracy for real valued FFTs with near prime lengths has improved and is on par with complex valued FFTs.

Further improvements to `ctypes` support in `numpy.ctypeslib`

A new numpy.ctypeslib.as_ctypes_type function has been added, which can be used to converts a dtype into a best-guess ctypes type. Thanks to this new function, numpy.ctypeslib.as_ctypes now supports a much wider range of array types, including structures, booleans, and integers of non-native endianness.

`numpy.errstate` is now also a function decorator

Currently, if you have a function like::

def foo():
    pass

and you want to wrap the whole thing in errstate, you have to rewrite it like so::

def foo():
    with np.errstate(...):
        pass

but with this change, you can do::

@np.errstate(...)
def foo():
    pass

thereby saving a level of indentation

`numpy.exp` and `numpy.log` speed up for float32 implementation

float32 implementation of exp and log now benefit from AVX2/AVX512 instruction set which are detected during runtime. exp has a max ulp error of 2.52 and log has a max ulp error or 3.83.

Improve performance of `numpy.pad`

The performance of the function has been improved for most cases by filling in a preallocated array with the desired padded shape instead of using concatenation.

`numpy.interp` handles infinities more robustly

In some cases where interp would previously return nan, it now returns an appropriate infinity.

Pathlib support for `fromfile`, `tofile` and `ndarray.dump`

fromfile, ndarray.ndarray.tofile and ndarray.dump now support the pathlib.Path type for the file/fid parameter.

Specialized `isnan`, `isinf`, and `isfinite` ufuncs for bool and int types

The boolean and integer types are incapable of storing nan and inf values, which allows us to provide specialized ufuncs that are up to 250x faster than the previous approach.

`isfinite` supports `datetime64` and `timedelta64` types

Previously, isfinite used to raise a TypeError on being used on these two types.

New keywords added to `nan_to_num`

nan_to_num now accepts keywords nan, posinf and neginf allowing the user to define the value to replace the nan, positive and negative np.inf values respectively.

MemoryErrors caused by allocated overly large arrays are more descriptive

Often the cause of a MemoryError is incorrect broadcasting, which results in a very large and incorrect shape. The message of the error now includes this shape to help diagnose the cause of failure.

`floor`, `ceil`, and `trunc` now respect builtin magic methods

These ufuncs now call the __floor__, __ceil__, and __trunc__ methods when called on object arrays, making them compatible with decimal.Decimal and fractions.Fraction objects.

`quantile` now works on `fraction.Fraction` and `decimal.Decimal` objects

In general, this handles object arrays more gracefully, and avoids floating- point operations if exact arithmetic types are used.

Support of object arrays in `matmul`

It is now possible to use matmul (or the @ operator) with object arrays. For instance, it is now possible to do::

from fractions import Fraction
a = np.array([[Fraction(1, 2), Fraction(1, 3)], [Fraction(1, 3), Fraction(1, 2)]])
b = a @ a

Changes

`median` and `percentile` family of functions no longer warn about `nan`

numpy.median, numpy.percentile, and numpy.quantile used to emit a RuntimeWarning when encountering an nan. Since they return the nan value, the warning is redundant and has been removed.

`timedelta64 % 0` behavior adjusted to return `NaT`

The modulus operation with two np.timedelta64 operands now returns NaT in the case of division by zero, rather than returning zero

NumPy functions now always support overrides with `__array_function__`

NumPy now always checks the __array_function__ method to implement overrides of NumPy functions on non-NumPy arrays, as described in NEP 18_. The feature was available for testing with NumPy 1.16 if appropriate environment variables are set, but is now always enabled.

.. _NEP 18 : http://www.numpy.org/neps/nep-0018-array-function-protocol.html

`lib.recfunctions.structured_to_unstructured` does not squeeze single-field views

Previously structured_to_unstructured(arr[['a']]) would produce a squeezed result inconsistent with structured_to_unstructured(arr[['a', b']]). This was accidental. The old behavior can be retained with structured_to_unstructured(arr[['a']]).squeeze(axis=-1) or far more simply, arr['a'].

`clip` now uses a ufunc under the hood

This means that registering clip functions for custom dtypes in C via descr->f->fastclip is deprecated - they should use the ufunc registration mechanism instead, attaching to the np.core.umath.clip ufunc.

It also means that clip accepts where and casting arguments, and can be override with __array_ufunc__.

A consequence of this change is that some behaviors of the old clip have been deprecated:

Passing nan to mean "do not clip" as one or both bounds. This didn't work in all cases anyway, and can be better handled by passing infinities of the appropriate sign.
Using "unsafe" casting by default when an out argument is passed. Using casting="unsafe" explicitly will silence this warning.

Additionally, there are some corner cases with behavior changes:

Padding max < min has changed to be more consistent across dtypes, but should not be relied upon.
Scalar min and max take part in promotion rules like they do in all other ufuncs.

`__array_interface__` offset now works as documented

The interface may use an offset value that was mistakenly ignored.

Pickle protocol in `savez` set to 3 for `force zip64` flag

savez was not using the force_zip64 flag, which limited the size of the archive to 2GB. But using the flag requires us to use pickle protocol 3 to write object arrays. The protocol used was bumped to 3, meaning the archive will be unreadable by Python2.

Structured arrays indexed with non-existent fields raise `KeyError` not `ValueError`

arr['bad_field'] on a structured type raises KeyError, for consistency with dict['bad_field'].

Checksums

MD5

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SHA256

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312bb18e95218bedc3563f26fcc9c1c6bfaaf9d453d15942c0839acdd7e4c473  numpy-1.17.0-cp37-cp37m-manylinux1_i686.whl
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Open source →

1.16.629 Dec 2019

The NumPy 1.16.6 release fixes bugs reported against the 1.16.5 release, and also backports several enhancements from master that seem appropriate for

NumPy 1.16.6 Release Notes

The NumPy 1.16.6 release fixes bugs reported against the 1.16.5 release, and also backports several enhancements from master that seem appropriate for a release series that is the last to support Python 2.7. The wheels on PyPI are linked with OpenBLAS v0.3.7, which should fix errors on Skylake series cpus.

Downstream developers building this release should use Cython >= 0.29.2 and, if using OpenBLAS, OpenBLAS >= v0.3.7. The supported Python versions are 2.7 and 3.5-3.7.

Highlights

The np.testing.utils functions have been updated from 1.19.0-dev0. This improves the function documentation and error messages as well extending the assert_array_compare function to additional types.

New functions

Allow matmul (`@`) to work with object arrays.

This is an enhancement that was added in NumPy 1.17 and seems reasonable to include in the LTS 1.16 release series.

Compatibility notes

Fix regression in matmul (`@`) for boolean types

Booleans were being treated as integers rather than booleans, which was a regression from previous behavior.

Improvements

Array comparison assertions include maximum differences

Contributors

A total of 10 people contributed to this release.

CakeWithSteak
Charles Harris
Chris Burr
Eric Wieser
Fernando Saravia
Lars Grueter
Matti Picus
Maxwell Aladago
Qiming Sun
Warren Weckesser

Pull requests merged

A total of 14 pull requests were merged for this release.

#14211: BUG: Fix uint-overflow if padding with linear_ramp and negative...
#14275: BUG: fixing to allow unpickling of PY3 pickles from PY2
#14340: BUG: Fix misuse of .names and .fields in various places (backport...
#14423: BUG: test, fix regression in converting to ctypes.
#14434: BUG: Fixed maximum relative error reporting in assert_allclose
#14509: BUG: Fix regression in boolean matmul.
#14686: BUG: properly define PyArray_DescrCheck
#14853: BLD: add 'apt update' to shippable
#14854: BUG: Fix _ctypes class circular reference. (#13808)
#14856: BUG: Fix [np.einsum]{.title-ref} errors on Power9 Linux and z/Linux
#14863: BLD: Prevent -flto from optimising long double representation...
#14864: BUG: lib: Fix histogram problem with signed integer arrays.
#15172: ENH: Backport improvements to testing functions.
#15191: REL: Prepare for 1.16.6 release.

Checksums

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SHA256

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Open source →

1.16.528 Aug 2019

The NumPy 1.16.5 release fixes bugs reported against the 1.16.4 release, and also backports several enhancements from master that seem appropriate for

NumPy 1.16.5 Release Notes

The NumPy 1.16.5 release fixes bugs reported against the 1.16.4 release, and also backports several enhancements from master that seem appropriate for a release series that is the last to support Python 2.7. The wheels on PyPI are linked with OpenBLAS v0.3.7-dev, which should fix errors on Skylake series cpus.

Downstream developers building this release should use Cython >= 0.29.2 and, if using OpenBLAS, OpenBLAS >= v0.3.7. The supported Python versions are 2.7 and 3.5-3.7.

Contributors

A total of 18 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Alexander Shadchin
Allan Haldane
Bruce Merry +
Charles Harris
Colin Snyder +
Dan Allan +
Emile +
Eric Wieser
Grey Baker +
Maksim Shabunin +
Marten van Kerkwijk
Matti Picus
Peter Andreas Entschev +
Ralf Gommers
Richard Harris +
Sebastian Berg
Sergei Lebedev +
Stephan Hoyer

Pull requests merged

A total of 23 pull requests were merged for this release.

#13742: ENH: Add project URLs to setup.py
#13823: TEST, ENH: fix tests and ctypes code for PyPy
#13845: BUG: use npy_intp instead of int for indexing array
#13867: TST: Ignore DeprecationWarning during nose imports
#13905: BUG: Fix use-after-free in boolean indexing
#13933: MAINT/BUG/DOC: Fix errors in _add_newdocs
#13984: BUG: fix byte order reversal for datetime64[ns]
#13994: MAINT,BUG: Use nbytes to also catch empty descr during allocation
#14042: BUG: np.array cleared errors occured in PyMemoryView_FromObject
#14043: BUG: Fixes for Undefined Behavior Sanitizer (UBSan) errors.
#14044: BUG: ensure that casting to/from structured is properly checked.
#14045: MAINT: fix histogram*d dispatchers
#14046: BUG: further fixup to histogram2d dispatcher.
#14052: BUG: Replace contextlib.suppress for Python 2.7
#14056: BUG: fix compilation of 3rd party modules with Py_LIMITED_API...
#14057: BUG: Fix memory leak in dtype from dict contructor
#14058: DOC: Document array_function at a higher level.
#14084: BUG, DOC: add new recfunctions to __all__
#14162: BUG: Remove stray print that causes a SystemError on python 3.7
#14297: TST: Pin pytest version to 5.0.1.
#14322: ENH: Enable huge pages in all Linux builds
#14346: BUG: fix behavior of structured_to_unstructured on non-trivial...
#14382: REL: Prepare for the NumPy 1.16.5 release.

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SHA256

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Open source →

1.16.428 May 2019Deprecated

In the future it will not be possible to switch the writeable flag to True from python. This deprecation should not affect many users since arrays cre…

========================== NumPy 1.16.4 Release Notes

The NumPy 1.16.4 release fixes bugs reported against the 1.16.3 release, and also backports several enhancements from master that seem appropriate for a release series that is the last to support Python 2.7. The wheels on PyPI are linked with OpenBLAS v0.3.7-dev, which should fix issues on Skylake series cpus.

Downstream developers building this release should use Cython >= 0.29.2 and, if using OpenBLAS, OpenBLAS > v0.3.7. The supported Python versions are 2.7 and 3.5-3.7.

New deprecations

Writeable flag of C-API wrapped arrays

Compatibility notes

Potential changes to the random stream

Due to bugs in the application of log to random floating point numbers, the stream may change when sampling from np.random.beta, np.random.binomial, np.random.laplace, np.random.logistic, np.random.logseries or np.random.multinomial if a 0 is generated in the underlying MT19937 random stream. There is a 1 in :math:10^{53} chance of this occurring, and so the probability that the stream changes for any given seed is extremely small. If a 0 is encountered in the underlying generator, then the incorrect value produced (either np.inf or np.nan) is now dropped.

Changes

`numpy.lib.recfunctions.structured_to_unstructured` does not squeeze single-field views

Contributors

A total of 10 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Charles Harris
Eric Wieser
Dennis Zollo +
Hunter Damron +
Jingbei Li +
Kevin Sheppard
Matti Picus
Nicola Soranzo +
Sebastian Berg
Tyler Reddy

Pull requests merged

A total of 16 pull requests were merged for this release.

#13392: BUG: Some PyPy versions lack PyStructSequence_InitType2.
#13394: MAINT, DEP: Fix deprecated assertEquals()
#13396: BUG: Fix structured_to_unstructured on single-field types (backport)
#13549: BLD: Make CI pass again with pytest 4.5
#13552: TST: Register markers in conftest.py.
#13559: BUG: Removes ValueError for empty kwargs in arraymultiter_new
#13560: BUG: Add TypeError to accepted exceptions in crackfortran.
#13561: BUG: Handle subarrays in descr_to_dtype
#13562: BUG: Protect generators from log(0.0)
#13563: BUG: Always return views from structured_to_unstructured when...
#13564: BUG: Catch stderr when checking compiler version
#13565: BUG: longdouble(int) does not work
#13587: BUG: distutils/system_info.py fix missing subprocess import (#13523)
#13620: BUG,DEP: Fix writeable flag setting for arrays without base
#13641: MAINT: Prepare for the 1.16.4 release.
#13644: BUG: special case object arrays when printing rel-, abs-error

Checksums

MD5

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SHA256

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Open source →

1.16.322 Apr 2019Security

This backwards incompatible change was made in response to CVE-2019-6446 _.

========================== NumPy 1.16.3 Release Notes

The NumPy 1.16.3 release fixes bugs reported against the 1.16.2 release, and also backports several enhancements from master that seem appropriate for a release series that is the last to support Python 2.7. The wheels on PyPI are linked with OpenBLAS v0.3.4+, which should fix the known threading issues found in previous OpenBLAS versions.

Downstream developers building this release should use Cython >= 0.29.2 and, if using OpenBLAS, OpenBLAS > v0.3.4.

The most noticeable change in this release is that unpickling object arrays when loading *.npy or *.npz files now requires an explicit opt-in. This backwards incompatible change was made in response to CVE-2019-6446 <https://nvd.nist.gov/vuln/detail/CVE-2019-6446>_.

Compatibility notes

Unpickling while loading requires explicit opt-in

The functions np.load, and np.lib.format.read_array take an allow_pickle keyword which now defaults to False in response to CVE-2019-6446 <https://nvd.nist.gov/vuln/detail/CVE-2019-6446>_.

Improvements

Covariance in `random.mvnormal` cast to double

This should make the tolerance used when checking the singular values of the covariance matrix more meaningful.

Changes

`__array_interface__` offset now works as documented

The interface may use an offset value that was previously mistakenly ignored.

Checksums

MD5

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SHA256

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Open source →

1.16.226 Feb 2019

NumPy 1.16.2 is a quick release fixing several problems encountered on Windows. The Python versions supported are 2.7 and 3.5-3.7. The Windows problem

========================== NumPy 1.16.2 Release Notes

NumPy 1.16.2 is a quick release fixing several problems encountered on Windows. The Python versions supported are 2.7 and 3.5-3.7. The Windows problems addressed are:

DLL load problems for NumPy wheels on Windows,
distutils command line parsing on Windows.

There is also a regression fix correcting signed zeros produced by divmod, see below for details.

Downstream developers building this release should use Cython >= 0.29.2 and, if using OpenBLAS, OpenBLAS > v0.3.4.

If you are installing using pip, you may encounter a problem with older installed versions of NumPy that pip did not delete becoming mixed with the current version, resulting in an ImportError. That problem is particularly common on Debian derived distributions due to a modified pip. The fix is to make sure all previous NumPy versions installed by pip have been removed. See #12736 <https://github.com/numpy/numpy/issues/12736>__ for discussion of the issue.

Compatibility notes

Signed zero when using divmod

Starting in version 1.12.0, numpy incorrectly returned a negatively signed zero when using the divmod and floor_divide functions when the result was zero. For example:

   >>> np.zeros(10)//1
   array([-0., -0., -0., -0., -0., -0., -0., -0., -0., -0.])

With this release, the result is correctly returned as a positively signed zero:

   >>> np.zeros(10)//1
   array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])

Contributors

A total of 5 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Charles Harris
Eric Wieser
Matti Picus
Tyler Reddy
Tony LaTorre +

Pull requests merged

A total of 7 pull requests were merged for this release.

#12909: TST: fix vmImage dispatch in Azure
#12923: MAINT: remove complicated test of multiarray import failure mode
#13020: BUG: fix signed zero behavior in npy_divmod
#13026: MAINT: Add functions to parse shell-strings in the platform-native...
#13028: BUG: Fix regression in parsing of F90 and F77 environment variables
#13038: BUG: parse shell escaping in extra_compile_args and extra_link_args
#13041: BLD: Windows absolute path DLL loading

Checksums

MD5

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SHA256

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Open source →

1.16.131 Jan 2019

The NumPy 1.16.1 release fixes bugs reported against the 1.16.0 release, and also backports several enhancements from master that seem appropriate for

========================== NumPy 1.16.1 Release Notes

The NumPy 1.16.1 release fixes bugs reported against the 1.16.0 release, and also backports several enhancements from master that seem appropriate for a release series that is the last to support Python 2.7. The wheels on PyPI are linked with OpenBLAS v0.3.4+, which should fix the known threading issues found in previous OpenBLAS versions.

Downstream developers building this release should use Cython >= 0.29.2 and, if using OpenBLAS, OpenBLAS > v0.3.4.

Contributors

A total of 16 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Antoine Pitrou
Arcesio Castaneda Medina +
Charles Harris
Chris Markiewicz +
Christoph Gohlke
Christopher J. Markiewicz +
Daniel Hrisca +
EelcoPeacs +
Eric Wieser
Kevin Sheppard
Matti Picus
OBATA Akio +
Ralf Gommers
Sebastian Berg
Stephan Hoyer
Tyler Reddy

Enhancements

#12767: ENH: add mm->q floordiv
#12768: ENH: port np.core.overrides to C for speed
#12769: ENH: Add np.ctypeslib.as_ctypes_type(dtype), improve np.ctypeslib.as_ctypes
#12773: ENH: add "max difference" messages to np.testing.assert_array_equal...
#12820: ENH: Add mm->qm divmod
#12890: ENH: add _dtype_ctype to namespace for freeze analysis

Compatibility notes

The changed error message emited by array comparison testing functions may affect doctests. See below for detail.
Casting from double and single denormals to float16 has been corrected. In some rare cases, this may result in results being rounded up instead of down, changing the last bit (ULP) of the result.

New Features

divmod operation is now supported for two `timedelta64` operands

The divmod operator now handles two np.timedelta64 operands, with type signature mm->qm.

Improvements

Further improvements to `ctypes` support in `np.ctypeslib`

A new np.ctypeslib.as_ctypes_type function has been added, which can be used to converts a dtype into a best-guess ctypes type. Thanks to this new function, np.ctypeslib.as_ctypes now supports a much wider range of array types, including structures, booleans, and integers of non-native endianness.

Array comparison assertions include maximum differences

Error messages from array comparison tests such as np.testing.assert_allclose now include "max absolute difference" and "max relative difference," in addition to the previous "mismatch" percentage. This information makes it easier to update absolute and relative error tolerances.

Changes

`timedelta64 % 0` behavior adjusted to return `NaT`

The modulus operation with two np.timedelta64 operands now returns NaT in the case of division by zero, rather than returning zero

Checksums

MD5

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SHA256

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Open source →

1.16.014 Jan 2019Deprecated

The type dictionaries numpy.core.typeNA and numpy.core.sctypeNA are deprecated. They were buggy and not documented and will be removed in the 1.18 rel…

NumPy 1.16.0 Release Notes

This NumPy release is the last one to support Python 2.7 and will be maintained as a long term release with bug fixes until 2020. Support for Python 3.4 been dropped, the supported Python versions are 2.7 and 3.5-3.7. The wheels on PyPI are linked with OpenBLAS v0.3.4+, which should fix the known threading issues found in previous OpenBLAS versions.

Downstream developers building this release should use Cython >= 0.29 and, if using OpenBLAS, OpenBLAS > v0.3.4.

This release has seen a lot of refactoring and features many bug fixes, improved code organization, and better cross platform compatibility. Not all of these improvements will be visible to users, but they should help make maintenance easier going forward.

Highlights

Experimental support for overriding numpy functions, see __array_function__ below.
The matmul function is now a ufunc. This provides better performance and allows overriding with __array_ufunc__.
Improved support for the ARM and POWER architectures.
Improved support for AIX and PyPy.
Improved interop with ctypes.
Improved support for PEP 3118.

New functions

New functions added to the numpy.lib.recfuntions module to ease the structured assignment changes:
- assign_fields_by_name
- structured_to_unstructured
- unstructured_to_structured
- apply_along_fields
- require_fields
See the user guide at <https://docs.scipy.org/doc/numpy/user/basics.rec.html> for more info.

New deprecations

The type dictionaries numpy.core.typeNA and numpy.core.sctypeNA are deprecated. They were buggy and not documented and will be removed in the 1.18 release. Use`numpy.sctypeDict` instead.
The numpy.asscalar function is deprecated. It is an alias to the more powerful numpy.ndarray.item, not tested, and fails for scalars.
The numpy.set_array_ops and numpy.get_array_ops functions are deprecated. As part of NEP 15, they have been deprecated along with the C-API functions PyArray_SetNumericOps and PyArray_GetNumericOps. Users who wish to override the inner loop functions in built-in ufuncs should use PyUFunc_ReplaceLoopBySignature.
The numpy.unravel_index keyword argument dims is deprecated, use shape instead.
The numpy.histogram normed argument is deprecated. It was deprecated previously, but no warning was issued.
The positive operator (+) applied to non-numerical arrays is deprecated. See below for details.
Passing an iterator to the stack functions is deprecated

Expired deprecations

NaT comparisons now return False without a warning, finishing a deprecation cycle begun in NumPy 1.11.
np.lib.function_base.unique was removed, finishing a deprecation cycle begun in NumPy 1.4. Use numpy.unique instead.
multi-field indexing now returns views instead of copies, finishing a deprecation cycle begun in NumPy 1.7. The change was previously attempted in NumPy 1.14 but reverted until now.
np.PackageLoader and np.pkgload have been removed. These were deprecated in 1.10, had no tests, and seem to no longer work in 1.15.

Future changes

NumPy 1.17 will drop support for Python 2.7.

Compatibility notes

f2py script on Windows

On Windows, the installed script for running f2py is now an .exe file rather than a *.py file and should be run from the command line as f2py whenever the Scripts directory is in the path. Running f2py as a module python -m numpy.f2py [...] will work without path modification in any version of NumPy.

NaT comparisons

Consistent with the behavior of NaN, all comparisons other than inequality checks with datetime64 or timedelta64 NaT ("not-a-time") values now always return False, and inequality checks with NaT now always return True. This includes comparisons beteween NaT values. For compatibility with the old behavior, use np.isnat to explicitly check for NaT or convert datetime64/timedelta64 arrays with .astype(np.int64) before making comparisons.

complex64/128 alignment has changed

The memory alignment of complex types is now the same as a C-struct composed of two floating point values, while before it was equal to the size of the type. For many users (for instance on x64/unix/gcc) this means that complex64 is now 4-byte aligned instead of 8-byte aligned. An important consequence is that aligned structured dtypes may now have a different size. For instance, np.dtype('c8,u1', align=True) used to have an itemsize of 16 (on x64/gcc) but now it is 12.

More in detail, the complex64 type now has the same alignment as a C-struct struct {float r, i;}, according to the compiler used to compile numpy, and similarly for the complex128 and complex256 types.

nd_grid len removal

len(np.mgrid) and len(np.ogrid) are now considered nonsensical and raise a TypeError.

np.unravel_index now accepts shape keyword argument

Previously, only the dims keyword argument was accepted for specification of the shape of the array to be used for unraveling. dims remains supported, but is now deprecated.

multi-field views return a view instead of a copy

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 often have extra padding bytes corresponding to intervening fields in the original array, unlike before, which will affect code such as arr[['f1', 'f3']].view('float64'). This change has been planned since numpy 1.7. Operations hitting this path have emitted FutureWarnings since then. Additional FutureWarnings about this change were added in 1.12.

To help users update their code to account for these changes, a number of functions have been added to the numpy.lib.recfunctions module which safely allow such operations. For instance, the code above can be replaced with structured_to_unstructured(arr[['f1', 'f3']], dtype='float64'). See the "accessing multiple fields" section of the user guide.

C API changes

The NPY_API_VERSION was incremented to 0x0000D, due to the addition of:

PyUFuncObject.core_dim_flags
PyUFuncObject.core_dim_sizes
PyUFuncObject.identity_value
PyUFunc_FromFuncAndDataAndSignatureAndIdentity

New Features

Integrated squared error (ISE) estimator added to histogram

This method (bins='stone') for optimizing the bin number is a generalization of the Scott's rule. The Scott's rule assumes the distribution is approximately Normal, while the ISE is a non-parametric method based on cross-validation.

max_rows keyword added for np.loadtxt

New keyword max_rows in numpy.loadtxt sets the maximum rows of the content to be read after skiprows, as in numpy.genfromtxt.

modulus operator support added for np.timedelta64 operands

The modulus (remainder) operator is now supported for two operands of type np.timedelta64. The operands may have different units and the return value will match the type of the operands.

Improvements

no-copy pickling of numpy arrays

Up to protocol 4, numpy array pickling created 2 spurious copies of the data being serialized. With pickle protocol 5, and the PickleBuffer API, a large variety of numpy arrays can now be serialized without any copy using out-of-band buffers, and with one less copy using in-band buffers. This results, for large arrays, in an up to 66% drop in peak memory usage.

build shell independence

NumPy builds should no longer interact with the host machine shell directly. exec_command has been replaced with subprocess.check_output where appropriate.

np.polynomial.Polynomial classes render in LaTeX in Jupyter notebooks

When used in a front-end that supports it, Polynomial instances are now rendered through LaTeX. The current format is experimental, and is subject to change.

randint and choice now work on empty distributions

Even when no elements needed to be drawn, np.random.randint and np.random.choice raised an error when the arguments described an empty distribution. This has been fixed so that e.g. np.random.choice([], 0) == np.array([], dtype=float64).

linalg.lstsq, linalg.qr, and linalg.svd now work with empty arrays

Previously, a LinAlgError would be raised when an empty matrix/empty matrices (with zero rows and/or columns) is/are passed in. Now outputs of appropriate shapes are returned.

Chain exceptions to give better error messages for invalid PEP3118 format strings

This should help track down problems.

Einsum optimization path updates and efficiency improvements

Einsum was synchronized with the current upstream work.

numpy.angle and numpy.expand_dims now work on ndarray subclasses

In particular, they now work for masked arrays.

NPY_NO_DEPRECATED_API compiler warning suppression

Setting NPY_NO_DEPRECATED_API to a value of 0 will suppress the current compiler warnings when the deprecated numpy API is used.

np.diff Added kwargs prepend and append

New kwargs prepend and append, allow for values to be inserted on either end of the differences. Similar to options for ediff1d. Now the inverse of cumsum can be obtained easily via prepend=0.

ARM support updated

Support for ARM CPUs has been updated to accommodate 32 and 64 bit targets, and also big and little endian byte ordering. AARCH32 memory alignment issues have been addressed. CI testing has been expanded to include AARCH64 targets via the services of shippable.com.

Appending to build flags

numpy.distutils has always overridden rather than appended to LDFLAGS and other similar such environment variables for compiling Fortran extensions. Now, if the NPY_DISTUTILS_APPEND_FLAGS environment variable is set to 1, the behavior will be appending. This applied to: LDFLAGS, F77FLAGS, F90FLAGS, FREEFLAGS, FOPT, FDEBUG, and FFLAGS. See gh-11525 for more details.

Generalized ufunc signatures now allow fixed-size dimensions

By using a numerical value in the signature of a generalized ufunc, one can indicate that the given function requires input or output to have dimensions with the given size. E.g., the signature of a function that converts a polar angle to a two-dimensional cartesian unit vector would be ()->(2); that for one that converts two spherical angles to a three-dimensional unit vector would be (),()->(3); and that for the cross product of two three-dimensional vectors would be (3),(3)->(3).

Note that to the elementary function these dimensions are not treated any differently from variable ones indicated with a name starting with a letter; the loop still is passed the corresponding size, but it can now count on that size being equal to the fixed one given in the signature.

Generalized ufunc signatures now allow flexible dimensions

Some functions, in particular numpy's implementation of @ as matmul, are very similar to generalized ufuncs in that they operate over core dimensions, but one could not present them as such because they were able to deal with inputs in which a dimension is missing. To support this, it is now allowed to postfix a dimension name with a question mark to indicate that the dimension does not necessarily have to be present.

With this addition, the signature for matmul can be expressed as (m?,n),(n,p?)->(m?,p?). This indicates that if, e.g., the second operand has only one dimension, for the purposes of the elementary function it will be treated as if that input has core shape (n, 1), and the output has the corresponding core shape of (m, 1). The actual output array, however, has the flexible dimension removed, i.e., it will have shape (..., m). Similarly, if both arguments have only a single dimension, the inputs will be presented as having shapes (1, n) and (n, 1) to the elementary function, and the output as (1, 1), while the actual output array returned will have shape (). In this way, the signature allows one to use a single elementary function for four related but different signatures, (m,n),(n,p)->(m,p), (n),(n,p)->(p), (m,n),(n)->(m) and (n),(n)->().

np.clip and the clip method check for memory overlap

The out argument to these functions is now always tested for memory overlap to avoid corrupted results when memory overlap occurs.

New value unscaled for option cov in np.polyfit

A further possible value has been added to the cov parameter of the np.polyfit function. With cov='unscaled' the scaling of the covariance matrix is disabled completely (similar to setting absolute_sigma=True in scipy.optimize.curve_fit). This would be useful in occasions, where the weights are given by 1/sigma with sigma being the (known) standard errors of (Gaussian distributed) data points, in which case the unscaled matrix is already a correct estimate for the covariance matrix.

Detailed docstrings for scalar numeric types

The help function, when applied to numeric types such as numpy.intc, numpy.int_, and numpy.longlong, now lists all of the aliased names for that type, distinguishing between platform -dependent and -independent aliases.

module attribute now points to public modules

The __module__ attribute on most NumPy functions has been updated to refer to the preferred public module from which to access a function, rather than the module in which the function happens to be defined. This produces more informative displays for functions in tools such as IPython, e.g., instead of <function 'numpy.core.fromnumeric.sum'> you now see <function 'numpy.sum'>.

Large allocations marked as suitable for transparent hugepages

On systems that support transparent hugepages over the madvise system call numpy now marks that large memory allocations can be backed by hugepages which reduces page fault overhead and can in some fault heavy cases improve performance significantly. On Linux the setting for huge pages to be used, /sys/kernel/mm/transparent_hugepage/enabled, must be at least madvise. Systems which already have it set to always will not see much difference as the kernel will automatically use huge pages where appropriate.

Users of very old Linux kernels (~3.x and older) should make sure that /sys/kernel/mm/transparent_hugepage/defrag is not set to always to avoid performance problems due concurrency issues in the memory defragmentation.

Alpine Linux (and other musl c library distros) support

We now default to use fenv.h for floating point status error reporting. Previously we had a broken default that sometimes would not report underflow, overflow, and invalid floating point operations. Now we can support non-glibc distrubutions like Alpine Linux as long as they ship fenv.h.

Speedup np.block for large arrays

Large arrays (greater than 512 * 512) now use a blocking algorithm based on copying the data directly into the appropriate slice of the resulting array. This results in significant speedups for these large arrays, particularly for arrays being blocked along more than 2 dimensions.

arr.ctypes.data_as(...) holds a reference to arr

Previously the caller was responsible for keeping the array alive for the lifetime of the pointer.

Speedup np.take for read-only arrays

The implementation of np.take no longer makes an unnecessary copy of the source array when its writeable flag is set to False.

Support path-like objects for more functions

The np.core.records.fromfile function now supports pathlib.Path and other path-like objects in addition to a file object. Furthermore, the np.load function now also supports path-like objects when using memory mapping (mmap_mode keyword argument).

Better behaviour of ufunc identities during reductions

Universal functions have an .identity which is used when .reduce is called on an empty axis.

As of this release, the logical binary ufuncs, logical_and, logical_or, and logical_xor, now have identity s of type bool, where previously they were of type int. This restores the 1.14 behavior of getting bool s when reducing empty object arrays with these ufuncs, while also keeping the 1.15 behavior of getting int s when reducing empty object arrays with arithmetic ufuncs like add and multiply.

Additionally, logaddexp now has an identity of -inf, allowing it to be called on empty sequences, where previously it could not be.

This is possible thanks to the new PyUFunc_FromFuncAndDataAndSignatureAndIdentity, which allows arbitrary values to be used as identities now.

Improved conversion from ctypes objects

Numpy has always supported taking a value or type from ctypes and converting it into an array or dtype, but only behaved correctly for simpler types. As of this release, this caveat is lifted - now:

The _pack_ attribute of ctypes.Structure, used to emulate C's __attribute__((packed)), is respected.
Endianness of all ctypes objects is preserved
ctypes.Union is supported
Non-representable constructs raise exceptions, rather than producing dangerously incorrect results:
- Bitfields are no longer interpreted as sub-arrays
- Pointers are no longer replaced with the type that they point to

A new ndpointer.contents member

This matches the .contents member of normal ctypes arrays, and can be used to construct an np.array around the pointers contents. This replaces np.array(some_nd_pointer), which stopped working in 1.15. As a side effect of this change, ndpointer now supports dtypes with overlapping fields and padding.

matmul is now a ufunc

numpy.matmul is now a ufunc which means that both the function and the __matmul__ operator can now be overridden by __array_ufunc__. Its implementation has also changed. It uses the same BLAS routines as numpy.dot, ensuring its performance is similar for large matrices.

Start and stop arrays for linspace, logspace and geomspace

These functions used to be limited to scalar stop and start values, but can now take arrays, which will be properly broadcast and result in an output which has one axis prepended. This can be used, e.g., to obtain linearly interpolated points between sets of points.

CI extended with additional services

We now use additional free CI services, thanks to the companies that provide:

Codecoverage testing via codecov.io
Arm testing via shippable.com
Additional test runs on azure pipelines

These are in addition to our continued use of travis, appveyor (for wheels) and LGTM

Changes

Comparison ufuncs will now error rather than return NotImplemented

Previously, comparison ufuncs such as np.equal would return NotImplemented if their arguments had structured dtypes, to help comparison operators such as __eq__ deal with those. This is no longer needed, as the relevant logic has moved to the comparison operators proper (which thus do continue to return NotImplemented as needed). Hence, like all other ufuncs, the comparison ufuncs will now error on structured dtypes.

Positive will now raise a deprecation warning for non-numerical arrays

Previously, +array unconditionally returned a copy. Now, it will raise a DeprecationWarning if the array is not numerical (i.e., if np.positive(array) raises a TypeError. For ndarray subclasses that override the default __array_ufunc__ implementation, the TypeError is passed on.

NDArrayOperatorsMixin now implements matrix multiplication

Previously, np.lib.mixins.NDArrayOperatorsMixin did not implement the special methods for Python's matrix multiplication operator (@). This has changed now that matmul is a ufunc and can be overridden using __array_ufunc__.

The scaling of the covariance matrix in np.polyfit is different

So far, np.polyfit used a non-standard factor in the scaling of the the covariance matrix. Namely, rather than using the standard chisq/(M-N), it scaled it with chisq/(M-N-2) where M is the number of data points and N is the number of parameters. This scaling is inconsistent with other fitting programs such as e.g. scipy.optimize.curve_fit and was changed to chisq/(M-N).

maximum and minimum no longer emit warnings

As part of code introduced in 1.10, float32 and float64 set invalid float status when a Nan is encountered in numpy.maximum and numpy.minimum, when using SSE2 semantics. This caused a RuntimeWarning to sometimes be emitted. In 1.15 we fixed the inconsistencies which caused the warnings to become more conspicuous. Now no warnings will be emitted.

Umath and multiarray c-extension modules merged into a single module

The two modules were merged, according to NEP 15. Previously np.core.umath and np.core.multiarray were seperate c-extension modules. They are now python wrappers to the single np.core/_multiarray_math c-extension module.

getfield validity checks extended

numpy.ndarray.getfield now checks the dtype and offset arguments to prevent accessing invalid memory locations.

NumPy functions now support overrides with __array_function__

It is now possible to override the implementation of almost all NumPy functions on non-NumPy arrays by defining a __array_function__ method, as described in NEP 18. The sole exception are functions for explicitly casting to NumPy arrays such as np.array. As noted in the NEP, this feature remains experimental and the details of how to implement such overrides may change in the future.

Arrays based off readonly buffers cannot be set writeable

We now disallow setting the writeable flag True on arrays created from fromstring(readonly-buffer).

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SHA256

a80ecac5664f420556a725a5646f2d1c60a7c0489d68a38b5056393e949e27ac numpy-1.16.0-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 be43df2c563e264b38e3318574d80fc8f365df3fb745270934d2dbe54e006f41 numpy-1.16.0-cp27-cp27m-manylinux1_i686.whl f00a2c21f60284e024bba351875f3501c6d5817d64997a0afe4f4355161a8889 numpy-1.16.0-cp27-cp27m-manylinux1_x86_64.whl 5774d49516c37fd3fc1f232e033d2b152f3323ca4c7bfefd7277e4c67f3c08b4 numpy-1.16.0-cp27-cp27m-win32.whl 25600e8901012180a1b7cd1ac3e27e7793586ecd432383191929ac2edf37ff5d numpy-1.16.0-cp27-cp27m-win_amd64.whl 803b2af862dcad6c11231ea3cd1015d1293efd6c87088be33d713a9b23e9e419 numpy-1.16.0-cp27-cp27mu-manylinux1_i686.whl 24a9c287a4a1c427c2d45bf7c4fc6180c52a08fa0990d4c94e4c86a9b1e23ba5 numpy-1.16.0-cp27-cp27mu-manylinux1_x86_64.whl 96e49a0c82b4e3130093002f625545104037c2d25866fa2e0c90d6e54f5a1fbc numpy-1.16.0-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 a1dd8221f0e69038748f47b8bb3248d0b9ecdf13fe837440951c3d5ff72639bb numpy-1.16.0-cp35-cp35m-manylinux1_i686.whl 3e90a9fce378114b6c2fc01fff7423300515c7b54b7cc71b02a22bc0bd7dfdd8 numpy-1.16.0-cp35-cp35m-manylinux1_x86_64.whl 0470c5dc32212a08ebc2405f32e8ceb9a5b1c8ac61a2daf9835ec0856a220495 numpy-1.16.0-cp35-cp35m-win32.whl c40cb17188f6ae3c5b6efc6f0fd43a7ddd219b7807fe179e71027849a9b91afc numpy-1.16.0-cp35-cp35m-win_amd64.whl 00a458d6821b1e87be873f2126d5646b901047a7480e8ae9773ecf214f0e19f3 numpy-1.16.0-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 f1232f98a6bbd6d1678249f94028bccc541bbc306aa5c4e1471a881b0e5a3409 numpy-1.16.0-cp36-cp36m-manylinux1_i686.whl 2d279bd99329e72c30937bdef82b6dc7779c7607c5a379bab1bf76be1f4c1422 numpy-1.16.0-cp36-cp36m-manylinux1_x86_64.whl 95c830b09626508f7808ce7f1344fb98068e63143e6050e5dc3063142fc60007 numpy-1.16.0-cp36-cp36m-win32.whl ef4ae41add536cb825d8aa029c15ef510aead06ea5b68daea64f0b9ecbff17db numpy-1.16.0-cp36-cp36m-win_amd64.whl 32af2bcf4bb7631dac19736a6e092ec9715e770dcaa1f85fcd99dec5040b2a4d numpy-1.16.0-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 c6251e0f0ecac53ba2b99d9f0cc16fa9021914a78869c38213c436ba343641f0 numpy-1.16.0-cp37-cp37m-manylinux1_i686.whl b19a47ff1bd2fca0cacdfa830c967746764c32dca6a0c0328d9c893f4bfe2f6b numpy-1.16.0-cp37-cp37m-manylinux1_x86_64.whl fea682f6ddc09517df0e6d5caad9613c6d91a42232aeb082df67e4d205de19cc numpy-1.16.0-cp37-cp37m-win32.whl 64ff21aac30d40c20ba994c94a08d439b8ced3b9c704af897e9e4ba09d10e62c numpy-1.16.0-cp37-cp37m-win_amd64.whl 9b98898687b5dd05c4ac7bcf7e8669a50dd2bb19591b772e045e5806c23e0ca4 numpy-1.16.0.tar.gz cb189bd98b2e7ac02df389b6212846ab20661f4bafe16b5a70a6f1728c1cc7cb numpy-1.16.0.zip

Open source →

1.15.44 Nov 2018

This is a bugfix release for bugs and regressions reported following the 1.15.3 release. The Python versions supported by this release are 2.7, 3.4-3.

========================== NumPy 1.15.4 Release Notes

This is a bugfix release for bugs and regressions reported following the 1.15.3 release. 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 4 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Charles Harris
Matti Picus
Sebastian Berg
bbbbbbbbba +

Pull requests merged

A total of 4 pull requests were merged for this release.

#12296: BUG: Dealloc cached buffer info
#12297: BUG: Fix fill value in masked array '==' and '!=' ops.
#12307: DOC: Correct the default value of optimize in numpy.einsum
#12320: REL: Prepare for the NumPy 1.15.4 release

Checksums

MD5

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b3626fec2f39ab01cad8bbb63a103742  numpy-1.15.4.tar.gz
219ac537d12cf06ed14f478662096ebc  numpy-1.15.4.zip

SHA256

18e84323cdb8de3325e741a7a8dd4a82db74fde363dce32b625324c7b32aa6d7  numpy-1.15.4-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
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0df89ca13c25eaa1621a3f09af4c8ba20da849692dcae184cb55e80952c453fb  numpy-1.15.4-cp27-cp27mu-manylinux1_x86_64.whl
36e36b6868e4440760d4b9b44587ea1dc1f06532858d10abba98e851e154ca70  numpy-1.15.4-cp27-none-win32.whl
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df04f4bad8a359daa2ff74f8108ea051670cafbca533bb2636c58b16e962989e  numpy-1.15.4-cp34-none-win32.whl
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Open source →

1.15.322 Oct 2018

This is a bugfix release for bugs and regressions reported following the 1.15.2 release. The Python versions supported by this release are 2.7, 3.4-3.

========================== NumPy 1.15.3 Release Notes

This is a bugfix release for bugs and regressions reported following the 1.15.2 release. 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

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
Jeroen Demeyer
Kevin Sheppard
Matthew Bowden +
Matti Picus
Tyler Reddy

Pull requests merged

A total of 12 pull requests were merged for this release.

#12080: MAINT: Blacklist some MSVC complex functions.
#12083: TST: Add azure CI testing to 1.15.x branch.
#12084: BUG: test_path() now uses Path.resolve()
#12085: TST, MAINT: Fix some failing tests on azure-pipelines mac and...
#12187: BUG: Fix memory leak in mapping.c
#12188: BUG: Allow boolean subtract in histogram
#12189: BUG: Fix in-place permutation
#12190: BUG: limit default for get_num_build_jobs() to 8
#12191: BUG: OBJECT_to_* should check for errors
#12192: DOC: Prepare for NumPy 1.15.3 release.
#12237: BUG: Fix MaskedArray fill_value type conversion.
#12238: TST: Backport azure-pipeline testing fixes for Mac

Checksums

MD5

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SHA256

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63bca71691339d2d6f8a7c970821f2b12098a53afccc0190d4e1555e75e5223a  numpy-1.15.3-cp34-cp34m-manylinux1_x86_64.whl
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f1fd1a6f40a501ba4035f5ed2c1f4faa68245d1407bf97d2ee401e4f23d1720b  numpy-1.15.3-cp37-cp37m-manylinux1_x86_64.whl
094f8a83e5bd0a44a7557fa24a46db6ba7d5299c389ddbc9e0e18722f567fb63  numpy-1.15.3-cp37-none-win32.whl
a245464ddf6d90e2d6287e9cef6bcfda2a99467fdcf1b677b99cd0b6c7b43de2  numpy-1.15.3-cp37-none-win_amd64.whl
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1c0c80e74759fa4942298044274f2c11b08c86230b25b8b819e55e644f5ff2b6  numpy-1.15.3.zip

Open source →

1.15.223 Sept 2018Deprecated

The matrix PendingDeprecationWarning is now suppressed in pytest 3.8.

========================== NumPy 1.15.2 Release Notes

This is a bugfix release for bugs and regressions reported following the 1.15.1 release.

The matrix PendingDeprecationWarning is now suppressed in pytest 3.8.
The new cached allocations machinery has been fixed to be thread safe.
The boolean indexing of subclasses now works correctly.
A small memory leak in PyArray_AdaptFlexibleDType has been fixed.

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

Contributors

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

Charles Harris
Julian Taylor
Marten van Kerkwijk
Matti Picus

Pull requests merged

A total of 4 pull requests were merged for this release.

#11902: BUG: Fix matrix PendingDeprecationWarning suppression for pytest...
#11981: BUG: fix cached allocations without the GIL for 1.15.x
#11982: BUG: fix refcount leak in PyArray_AdaptFlexibleDType
#11992: BUG: Ensure boolean indexing of subclasses sets base correctly.

Checksums

MD5

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d40b15478148a48ec324327578de4583  numpy-1.15.2.tar.gz
5a55a994eca6095b1e82d44600217ece  numpy-1.15.2.zip

SHA256

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6a1e96568332fd8974b355a422b397288e214746715a7fa6abc10b34d06bad76  numpy-1.15.2.tar.gz
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Open source →

1.15.121 Aug 2018

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

========================== 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

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

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Open source →

1.15.023 Jul 2018Deprecated

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…

========================== 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 `histogram`and`histogramdd`` 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

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SHA256

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Open source →

1.14.623 Sept 2018

This is a bugfix release for bugs reported following the 1.14.5 release. The most significant fixes are:

========================== NumPy 1.14.6 Release Notes

This is a bugfix release for bugs reported following the 1.14.5 release. The most significant fixes are:

Fix for behavior change in ma.masked_values(shrink=True)
Fix the new cached allocations machinery to be thread safe.

The Python versions supported in this release are 2.7 and 3.4 - 3.7. The Python 3.6 wheels on PyPI should be compatible with all Python 3.6 versions.

Contributors

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

Charles Harris
Eric Wieser
Julian Taylor
Matti Picus

Pull requests merged

A total of 4 pull requests were merged for this release.

#11985: BUG: fix cached allocations without the GIL
#11986: BUG: Undo behavior change in ma.masked_values(shrink=True)
#11987: BUG: fix refcount leak in PyArray_AdaptFlexibleDType
#11995: TST: Add Python 3.7 testing to NumPy 1.14.

Checksums

MD5

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SHA256

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Open source →

1.14.512 Jun 2018

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

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

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

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SHA256

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Open source →

1.14.46 Jun 2018

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

========================== 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.

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

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SHA256

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Open source →

1.14.328 Apr 2018

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

========================== 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

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

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Open source →

1.14.212 Mar 2018

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

========================== 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

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Open source →

1.14.121 Feb 2018

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

========================== 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.

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.

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Open source →

1.14.06 Jan 2018Deprecated

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.T…

========================== 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_value`s 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 to`np.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])

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Open source →

1.13.329 Sept 2017Security

This is a bugfix release for some problems found since 1.13.1. The most important fixes are for CVE-2017-12852 and temporary elision. Users of earlier…

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1

========================== NumPy 1.13.3 Release Notes

This is a bugfix release for some problems found since 1.13.1. The most important fixes are for CVE-2017-12852 and temporary elision. Users of earlier versions of 1.13 should upgrade.

The Python versions supported 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. It was cythonized with Cython 0.26.1, which should be free of the bugs found in 0.27 while also being compatible with Python 3.7-dev. The Windows wheels were built with OpenBlas instead ATLAS, which should improve the performance of the linear algebra functions.

The NumPy 1.13.3 release is a re-release of 1.13.2, which suffered from a bug in Cython 0.27.0.

Contributors

A total of 12 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Allan Haldane
Brandon Carter
Charles Harris
Eric Wieser
Iryna Shcherbina +
James Bourbeau +
Jonathan Helmus
Julian Taylor
Matti Picus
Michael Lamparski +
Michael Seifert
Ralf Gommers

Pull requests merged

A total of 22 pull requests were merged for this release.

#9390 BUG: Return the poly1d coefficients array directly
#9555 BUG: Fix regression in 1.13.x in distutils.mingw32ccompiler.
#9556 BUG: Fix true_divide when dtype=np.float64 specified.
#9557 DOC: Fix some rst markup in numpy/doc/basics.py.
#9558 BLD: Remove -xhost flag from IntelFCompiler.
#9559 DOC: Removes broken docstring example (source code, png, pdf)...
#9580 BUG: Add hypot and cabs functions to WIN32 blacklist.
#9732 BUG: Make scalar function elision check if temp is writeable.
#9736 BUG: Various fixes to np.gradient
#9742 BUG: Fix np.pad for CVE-2017-12852
#9744 BUG: Check for exception in sort functions, add tests
#9745 DOC: Add whitespace after "versionadded::" directive so it actually...
#9746 BUG: Memory leak in np.dot of size 0
#9747 BUG: Adjust gfortran version search regex
#9757 BUG: Cython 0.27 breaks NumPy on Python 3.
#9764 BUG: Ensure _npy_scaled_cexp{,f,l} is defined when needed.
#9765 BUG: PyArray_CountNonzero does not check for exceptions
#9766 BUG: Fixes histogram monotonicity check for unsigned bin values
#9767 BUG: Ensure consistent result dtype of count_nonzero
#9771 BUG: MAINT: Fix mtrand for Cython 0.27.
#9772 DOC: Create the 1.13.2 release notes.
#9794 DOC: Create 1.13.3 release notes.

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Open source →

1.13.17 Jul 2017

This is a bugfix release for problems found in 1.13.0. The major changes are fixes for the new memory overlap detection and temporary elision as well

========================== NumPy 1.13.1 Release Notes

This is a bugfix release for problems found in 1.13.0. The major changes are fixes for the new memory overlap detection and temporary elision as well as reversion of the removal of the boolean binary - operator. Users of 1.13.0 should upgrade.

Thr Python versions supported are 2.7 and 3.4 - 3.6. Note that the Python 3.6 wheels available from PIP are built against 3.6.1, hence will not work when used with 3.6.0 due to Python bug 29943_. NumPy 1.13.2 will be released shortly after Python 3.6.2 is out to fix that problem. If you are using 3.6.0 the workaround is to upgrade to 3.6.1 or use an earlier Python version.

.. _#29943: https://bugs.python.org/issue29943

Pull requests merged

A total of 19 pull requests were merged for this release.

#9240 DOC: BLD: fix lots of Sphinx warnings/errors.
#9255 Revert "DEP: Raise TypeError for subtract(bool_, bool_)."
#9261 BUG: don't elide into readonly and updateifcopy temporaries for...
#9262 BUG: fix missing keyword rename for common block in numpy.f2py
#9263 BUG: handle resize of 0d array
#9267 DOC: update f2py front page and some doc build metadata.
#9299 BUG: Fix Intel compilation on Unix.
#9317 BUG: fix wrong ndim used in empty where check
#9319 BUG: Make extensions compilable with MinGW on Py2.7
#9339 BUG: Prevent crash if ufunc doc string is null
#9340 BUG: umath: un-break ufunc where= when no out= is given
#9371 DOC: Add isnat/positive ufunc to documentation
#9372 BUG: Fix error in fromstring function from numpy.core.records...
#9373 BUG: ')' is printed at the end pointer of the buffer in numpy.f2py.
#9374 DOC: Create NumPy 1.13.1 release notes.
#9376 BUG: Prevent hang traversing ufunc userloop linked list
#9377 DOC: Use x1 and x2 in the heaviside docstring.
#9378 DOC: Add $PARAMS to the isnat docstring
#9379 DOC: Update the 1.13.1 release notes

Contributors

A total of 12 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Andras Deak +
Bob Eldering +
Charles Harris
Daniel Hrisca +
Eric Wieser
Joshua Leahy +
Julian Taylor
Michael Seifert
Pauli Virtanen
Ralf Gommers
Roland Kaufmann
Warren Weckesser

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Open source →

1.13.07 Jun 2017Deprecated

Calling np.fix, np.isposinf, and np.isneginf with f(x, y=out) is deprecated - the argument should be passed as f(x, out=out), which matches other ufun…

========================== NumPy 1.13.0 Release Notes

This release supports Python 2.7 and 3.4 - 3.6.

Highlights

Operations like a + b + c will reuse temporaries on some platforms, resulting in less memory use and faster execution.
Inplace operations check if inputs overlap outputs and create temporaries to avoid problems.
New __array_ufunc__ attribute provides improved ability for classes to override default ufunc behavior.
New np.block function for creating blocked arrays.

New functions

New np.positive ufunc.
New np.divmod ufunc provides more efficient divmod.
New np.isnat ufunc tests for NaT special values.
New np.heaviside ufunc computes the Heaviside function.
New np.isin function, improves on in1d.
New np.block function for creating blocked arrays.
New PyArray_MapIterArrayCopyIfOverlap added to NumPy C-API.

See below for details.

Deprecations

Calling np.fix, np.isposinf, and np.isneginf with f(x, y=out) is deprecated - the argument should be passed as f(x, out=out), which matches other ufunc-like interfaces.
Use of the C-API NPY_CHAR type number deprecated since version 1.7 will now raise deprecation warnings at runtime. Extensions built with older f2py versions need to be recompiled to remove the warning.
np.ma.argsort, np.ma.minimum.reduce, and np.ma.maximum.reduce should be called with an explicit axis argument when applied to arrays with more than 2 dimensions, as the default value of this argument (None) is inconsistent with the rest of numpy (-1, 0, and 0, respectively).
np.ma.MaskedArray.mini is deprecated, as it almost duplicates the functionality of np.MaskedArray.min. Exactly equivalent behaviour can be obtained with np.ma.minimum.reduce.
The single-argument form of np.ma.minimum and np.ma.maximum is deprecated. np.maximum. np.ma.minimum(x) should now be spelt np.ma.minimum.reduce(x), which is consistent with how this would be done with np.minimum.
Calling ndarray.conjugate on non-numeric dtypes is deprecated (it should match the behavior of np.conjugate, which throws an error).
Calling expand_dims when the axis keyword does not satisfy -a.ndim - 1 <= axis <= a.ndim, where a is the array being reshaped, is deprecated.

Future Changes

Assignment between structured arrays with different field names will change in NumPy 1.14. Previously, fields in the dst would be set to the value of the identically-named field in the src. In numpy 1.14 fields will instead be assigned 'by position': The n-th field of the dst will be set to the n-th field of the src array. Note that the FutureWarning raised in NumPy 1.12 incorrectly reported this change as scheduled for NumPy 1.13 rather than NumPy 1.14.

Build System Changes

numpy.distutils now automatically determines C-file dependencies with GCC compatible compilers.

Compatibility notes

Error type changes

numpy.hstack() now throws ValueError instead of IndexError when input is empty.
Functions taking an axis argument, when that argument is out of range, now throw np.AxisError instead of a mixture of IndexError and ValueError. For backwards compatibility, AxisError subclasses both of these.

Tuple object dtypes

Support has been removed for certain obscure dtypes that were unintentionally allowed, of the form (old_dtype, new_dtype), where either of the dtypes is or contains the object dtype. As an exception, dtypes of the form (object, [('name', object)]) are still supported due to evidence of existing use.

DeprecationWarning to error

See Changes section for more detail.

partition, TypeError when non-integer partition index is used.
NpyIter_AdvancedNew, ValueError when oa_ndim == 0 and op_axes is NULL
negative(bool_), TypeError when negative applied to booleans.
subtract(bool_, bool_), TypeError when subtracting boolean from boolean.
np.equal, np.not_equal, object identity doesn't override failed comparison.
np.equal, np.not_equal, object identity doesn't override non-boolean comparison.
Deprecated boolean indexing behavior dropped. See Changes below for details.
Deprecated np.alterdot() and np.restoredot() removed.

FutureWarning to changed behavior

See Changes section for more detail.

numpy.average preserves subclasses
array == None and array != None do element-wise comparison.
np.equal, np.not_equal, object identity doesn't override comparison result.

dtypes are now always true

Previously bool(dtype) would fall back to the default python implementation, which checked if len(dtype) > 0. Since dtype objects implement __len__ as the number of record fields, bool of scalar dtypes would evaluate to False, which was unintuitive. Now bool(dtype) == True for all dtypes.

`getslice` and `setslice` are no longer needed in `ndarray` subclasses

When subclassing np.ndarray in Python 2.7, it is no longer necessary to implement __*slice__ on the derived class, as __*item__ will intercept these calls correctly.

Any code that did implement these will work exactly as before. Code that invokesndarray.__getslice__ (e.g. through super(...).__getslice__) will now issue a DeprecationWarning - .__getitem__(slice(start, end)) should be used instead.

Indexing MaskedArrays/Constants with `...` (ellipsis) now returns MaskedArray

This behavior mirrors that of np.ndarray, and accounts for nested arrays in MaskedArrays of object dtype, and ellipsis combined with other forms of indexing.

C API changes

GUfuncs on empty arrays and NpyIter axis removal

It is now allowed to remove a zero-sized axis from NpyIter. Which may mean that code removing axes from NpyIter has to add an additional check when accessing the removed dimensions later on.

The largest followup change is that gufuncs are now allowed to have zero-sized inner dimensions. This means that a gufunc now has to anticipate an empty inner dimension, while this was never possible and an error raised instead.

For most gufuncs no change should be necessary. However, it is now possible for gufuncs with a signature such as (..., N, M) -> (..., M) to return a valid result if N=0 without further wrapping code.

`PyArray_MapIterArrayCopyIfOverlap` added to NumPy C-API

Similar to PyArray_MapIterArray but with an additional copy_if_overlap argument. If copy_if_overlap != 0, checks if input has memory overlap with any of the other arrays and make copies as appropriate to avoid problems if the input is modified during the iteration. See the documentation for more complete documentation.

New Features

`__array_ufunc__` added

This is the renamed and redesigned __numpy_ufunc__. Any class, ndarray subclass or not, can define this method or set it to None in order to override the behavior of NumPy's ufuncs. This works quite similarly to Python's __mul__ and other binary operation routines. See the documentation for a more detailed description of the implementation and behavior of this new option. The API is provisional, we do not yet guarantee backward compatibility as modifications may be made pending feedback. See the NEP_ and documentation_ for more details.

.. _NEP: https://github.com/numpy/numpy/blob/master/doc/neps/ufunc-overrides.rst .. _documentation: https://github.com/charris/numpy/blob/master/doc/source/reference/arrays.classes.rst

New `positive` ufunc

This ufunc corresponds to unary +, but unlike + on an ndarray it will raise an error if array values do not support numeric operations.

New `divmod` ufunc

This ufunc corresponds to the Python builtin divmod, and is used to implement divmod when called on numpy arrays. np.divmod(x, y) calculates a result equivalent to (np.floor_divide(x, y), np.remainder(x, y)) but is approximately twice as fast as calling the functions separately.

`np.isnat` ufunc tests for NaT special datetime and timedelta values

The new ufunc np.isnat finds the positions of special NaT values within datetime and timedelta arrays. This is analogous to np.isnan.

`np.heaviside` ufunc computes the Heaviside function

The new function np.heaviside(x, h0) (a ufunc) computes the Heaviside function: .. code:: { 0 if x < 0, heaviside(x, h0) = { h0 if x == 0, { 1 if x > 0.

`np.block` function for creating blocked arrays

Add a new block function to the current stacking functions vstack, hstack, and stack. This allows concatenation across multiple axes simultaneously, with a similar syntax to array creation, but where elements can themselves be arrays. For instance::

>>> A = np.eye(2) * 2
>>> B = np.eye(3) * 3
>>> np.block([
...     [A,               np.zeros((2, 3))],
...     [np.ones((3, 2)), B               ]
... ])
array([[ 2.,  0.,  0.,  0.,  0.],
       [ 0.,  2.,  0.,  0.,  0.],
       [ 1.,  1.,  3.,  0.,  0.],
       [ 1.,  1.,  0.,  3.,  0.],
       [ 1.,  1.,  0.,  0.,  3.]])

While primarily useful for block matrices, this works for arbitrary dimensions of arrays.

It is similar to Matlab's square bracket notation for creating block matrices.

`isin` function, improving on `in1d`

The new function isin tests whether each element of an N-dimensonal array is present anywhere within a second array. It is an enhancement of in1d that preserves the shape of the first array.

Temporary elision

On platforms providing the backtrace function NumPy will try to avoid creating temporaries in expression involving basic numeric types. For example d = a + b + c is transformed to d = a + b; d += c which can improve performance for large arrays as less memory bandwidth is required to perform the operation.

`axes` argument for `unique`

In an N-dimensional array, the user can now choose the axis along which to look for duplicate N-1-dimensional elements using numpy.unique. The original behaviour is recovered if axis=None (default).

`np.gradient` now supports unevenly spaced data

Users can now specify a not-constant spacing for data. In particular np.gradient can now take:

A single scalar to specify a sample distance for all dimensions.
N scalars to specify a constant sample distance for each dimension. i.e. dx, dy, dz, ...
N arrays to specify the coordinates of the values along each dimension of F. The length of the array must match the size of the corresponding dimension
Any combination of N scalars/arrays with the meaning of 2. and 3.

This means that, e.g., it is now possible to do the following::

>>> f = np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float)
>>> dx = 2.
>>> y = [1., 1.5, 3.5]
>>> np.gradient(f, dx, y)
[array([[ 1. ,  1. , -0.5], [ 1. ,  1. , -0.5]]),
 array([[ 2. ,  2. ,  2. ], [ 2. ,  1.7,  0.5]])]

Support for returning arrays of arbitrary dimensions in `apply_along_axis`

Previously, only scalars or 1D arrays could be returned by the function passed to apply_along_axis. Now, it can return an array of any dimensionality (including 0D), and the shape of this array replaces the axis of the array being iterated over.

`.ndim` property added to `dtype` to complement `.shape`

For consistency with ndarray and broadcast, d.ndim is a shorthand for len(d.shape).

Support for tracemalloc in Python 3.6

NumPy now supports memory tracing with tracemalloc_ module of Python 3.6 or newer. Memory allocations from NumPy are placed into the domain defined by numpy.lib.tracemalloc_domain. Note that NumPy allocation will not show up in tracemalloc_ of earlier Python versions.

.. _tracemalloc: https://docs.python.org/3/library/tracemalloc.html

NumPy may be built with relaxed stride checking debugging

Setting NPY_RELAXED_STRIDES_DEBUG=1 in the environment when relaxed stride checking is enabled will cause NumPy to be compiled with the affected strides set to the maximum value of npy_intp in order to help detect invalid usage of the strides in downstream projects. When enabled, invalid usage often results in an error being raised, but the exact type of error depends on the details of the code. TypeError and OverflowError have been observed in the wild.

It was previously the case that this option was disabled for releases and enabled in master and changing between the two required editing the code. It is now disabled by default but can be enabled for test builds.

Improvements

Ufunc behavior for overlapping inputs

Operations where ufunc input and output operands have memory overlap produced undefined results in previous NumPy versions, due to data dependency issues. In NumPy 1.13.0, results from such operations are now defined to be the same as for equivalent operations where there is no memory overlap.

Operations affected now make temporary copies, as needed to eliminate data dependency. As detecting these cases is computationally expensive, a heuristic is used, which may in rare cases result to needless temporary copies. For operations where the data dependency is simple enough for the heuristic to analyze, temporary copies will not be made even if the arrays overlap, if it can be deduced copies are not necessary. As an example,np.add(a, b, out=a) will not involve copies.

To illustrate a previously undefined operation::

>>> x = np.arange(16).astype(float)
>>> np.add(x[1:], x[:-1], out=x[1:])

In NumPy 1.13.0 the last line is guaranteed to be equivalent to::

>>> np.add(x[1:].copy(), x[:-1].copy(), out=x[1:])

A similar operation with simple non-problematic data dependence is::

>>> x = np.arange(16).astype(float)
>>> np.add(x[1:], x[:-1], out=x[:-1])

It will continue to produce the same results as in previous NumPy versions, and will not involve unnecessary temporary copies.

The change applies also to in-place binary operations, for example::

>>> x = np.random.rand(500, 500)
>>> x += x.T

This statement is now guaranteed to be equivalent to x[...] = x + x.T, whereas in previous NumPy versions the results were undefined.

Partial support for 64-bit f2py extensions with MinGW

Extensions that incorporate Fortran libraries can now be built using the free MinGW_ toolset, also under Python 3.5. This works best for extensions that only do calculations and uses the runtime modestly (reading and writing from files, for instance). Note that this does not remove the need for Mingwpy; if you make extensive use of the runtime, you will most likely run into issues_. Instead, it should be regarded as a band-aid until Mingwpy is fully functional.

Extensions can also be compiled using the MinGW toolset using the runtime library from the (moveable) WinPython 3.4 distribution, which can be useful for programs with a PySide1/Qt4 front-end.

.. _MinGW: https://sf.net/projects/mingw-w64/files/Toolchains%20targetting%20Win64/Personal%20Builds/mingw-builds/6.2.0/threads-win32/seh/

.. _issues: https://mingwpy.github.io/issues.html

Performance improvements for `packbits` and `unpackbits`

The functions numpy.packbits with boolean input and numpy.unpackbits have been optimized to be a significantly faster for contiguous data.

Fix for PPC long double floating point information

In previous versions of NumPy, the finfo function returned invalid information about the double double_ format of the longdouble float type on Power PC (PPC). The invalid values resulted from the failure of the NumPy algorithm to deal with the variable number of digits in the significand that are a feature of PPC long doubles. This release by-passes the failing algorithm by using heuristics to detect the presence of the PPC double double format. A side-effect of using these heuristics is that the finfo function is faster than previous releases.

.. _PPC long doubles: https://www.ibm.com/support/knowledgecenter/en/ssw_aix_71/com.ibm.aix.genprogc/128bit_long_double_floating-point_datatype.htm

.. _double double: https://en.wikipedia.org/wiki/Quadruple-precision_floating-point_format#Double-double_arithmetic

Better default repr for `ndarray` subclasses

Subclasses of ndarray with no repr specialization now correctly indent their data and type lines.

More reliable comparisons of masked arrays

Comparisons of masked arrays were buggy for masked scalars and failed for structured arrays with dimension higher than one. Both problems are now solved. In the process, it was ensured that in getting the result for a structured array, masked fields are properly ignored, i.e., the result is equal if all fields that are non-masked in both are equal, thus making the behaviour identical to what one gets by comparing an unstructured masked array and then doing .all() over some axis.

np.matrix with booleans elements can now be created using the string syntax

np.matrix failed whenever one attempts to use it with booleans, e.g., np.matrix('True'). Now, this works as expected.

More `linalg` operations now accept empty vectors and matrices

All of the following functions in np.linalg now work when given input arrays with a 0 in the last two dimensions: det, slogdet, pinv, eigvals, eigvalsh, eig, eigh.

Bundled version of LAPACK is now 3.2.2

NumPy comes bundled with a minimal implementation of lapack for systems without a lapack library installed, under the name of lapack_lite. This has been upgraded from LAPACK 3.0.0 (June 30, 1999) to LAPACK 3.2.2 (June 30, 2010). See the LAPACK changelogs_ for details on the all the changes this entails.

While no new features are exposed through numpy, this fixes some bugs regarding "workspace" sizes, and in some places may use faster algorithms.

.. _LAPACK changelogs: http://www.netlib.org/lapack/release_notes.html#_4_history_of_lapack_releases

`reduce` of `np.hypot.reduce` and `np.logical_xor` allowed in more cases

This now works on empty arrays, returning 0, and can reduce over multiple axes. Previously, a ValueError was thrown in these cases.

Better `repr` of object arrays

Object arrays that contain themselves no longer cause a recursion error.

Object arrays that contain list objects are now printed in a way that makes clear the difference between a 2d object array, and a 1d object array of lists.

Changes

`argsort` on masked arrays takes the same default arguments as `sort`

By default, argsort now places the masked values at the end of the sorted array, in the same way that sort already did. Additionally, the end_with argument is added to argsort, for consistency with sort. Note that this argument is not added at the end, so breaks any code that passed fill_value as a positional argument.

`average` now preserves subclasses

For ndarray subclasses, numpy.average will now return an instance of the subclass, matching the behavior of most other NumPy functions such as mean. As a consequence, also calls that returned a scalar may now return a subclass array scalar.

`array == None` and `array != None` do element-wise comparison

Previously these operations returned scalars False and True respectively.

`np.equal, np.not_equal` for object arrays ignores object identity

Previously, these functions always treated identical objects as equal. This had the effect of overriding comparison failures, comparison of objects that did not return booleans, such as np.arrays, and comparison of objects where the results differed from object identity, such as NaNs.

Boolean indexing changes

Boolean array-likes (such as lists of python bools) are always treated as boolean indexes.
Boolean scalars (including python True) are legal boolean indexes and never treated as integers.
Boolean indexes must match the dimension of the axis that they index.
Boolean indexes used on the lhs of an assignment must match the dimensions of the rhs.
Boolean indexing into scalar arrays return a new 1-d array. This means that array(1)[array(True)] gives array([1]) and not the original array.

`np.random.multivariate_normal` behavior with bad covariance matrix

It is now possible to adjust the behavior the function will have when dealing with the covariance matrix by using two new keyword arguments:

tol can be used to specify a tolerance to use when checking that the covariance matrix is positive semidefinite.
check_valid can be used to configure what the function will do in the presence of a matrix that is not positive semidefinite. Valid options are ignore, warn and raise. The default value, warn keeps the the behavior used on previous releases.

`assert_array_less` compares `np.inf` and `-np.inf` now

Previously, np.testing.assert_array_less ignored all infinite values. This is not the expected behavior both according to documentation and intuitively. Now, -inf < x < inf is considered True for any real number x and all other cases fail.

`assert_array_` and masked arrays `assert_equal` hide less warnings

Some warnings that were previously hidden by the assert_array_ functions are not hidden anymore. In most cases the warnings should be correct and, should they occur, will require changes to the tests using these functions. For the masked array assert_equal version, warnings may occur when comparing NaT. The function presently does not handle NaT or NaN specifically and it may be best to avoid it at this time should a warning show up due to this change.

`offset` attribute value in `memmap` objects

The offset attribute in a memmap object is now set to the offset into the file. This is a behaviour change only for offsets greater than mmap.ALLOCATIONGRANULARITY.

`np.real` and `np.imag` return scalars for scalar inputs

Previously, np.real and np.imag used to return array objects when provided a scalar input, which was inconsistent with other functions like np.angle and np.conj.

The polynomial convenience classes cannot be passed to ufuncs

The ABCPolyBase class, from which the convenience classes are derived, sets __array_ufun__ = None in order of opt out of ufuncs. If a polynomial convenience class instance is passed as an argument to a ufunc, a TypeError will now be raised.

Output arguments to ufuncs can be tuples also for ufunc methods

For calls to ufuncs, it was already possible, and recommended, to use an out argument with a tuple for ufuncs with multiple outputs. This has now been extended to output arguments in the reduce, accumulate, and reduceat methods. This is mostly for compatibility with __array_ufunc; there are no ufuncs yet that have more than one output.

Checksums

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SHA256

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Open source →

1.12.118 Mar 2017

NumPy 1.12.1 supports Python 2.7 and 3.4 - 3.6 and fixes bugs and regressions found in NumPy 1.12.0. In particular, the regression in f2py constant pa

========================== NumPy 1.12.1 Release Notes

NumPy 1.12.1 supports Python 2.7 and 3.4 - 3.6 and fixes bugs and regressions found in NumPy 1.12.0. In particular, the regression in f2py constant parsing is fixed. Wheels for Linux, Windows, and OSX can be found on pypi,

Contributors

A total of 10 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Charles Harris
Eric Wieser
Greg Young
Joerg Behrmann +
John Kirkham
Julian Taylor
Marten van Kerkwijk
Matthew Brett
Shota Kawabuchi
Jean Utke +

Fixes Backported

#8483: BUG: Fix wrong future nat warning and equiv type logic error...
#8489: BUG: Fix wrong masked median for some special cases
#8490: DOC: Place np.average in inline code
#8491: TST: Work around isfinite inconsistency on i386
#8494: BUG: Guard against replacing constants without '_' spec in f2py.
#8524: BUG: Fix mean for float 16 non-array inputs for 1.12
#8571: BUG: Fix calling python api with error set and minor leaks for...
#8602: BUG: Make iscomplexobj compatible with custom dtypes again
#8618: BUG: Fix undefined behaviour induced by bad __array_wrap__
#8648: BUG: Fix MaskedArray.__setitem__
#8659: BUG: PPC64el machines are POWER for Fortran in f2py
#8665: BUG: Look up methods on MaskedArray in _frommethod
#8674: BUG: Remove extra digit in binary_repr at limit
#8704: BUG: Fix deepcopy regression for empty arrays.
#8707: BUG: Fix ma.median for empty ndarrays

Checksums

MD5

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SHA256

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727d6373355b00b96d9320254a676b878d6cd43ae409186bec27eec3e5e4e6e7  numpy-1.12.1-cp36-none-win32.whl
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d56d7fff81e844a407afa0503080c814e5d87678e338a73b8d8f98137713cfa9  numpy-1.12.1.tar.gz
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Open source →

1.12.015 Jan 2017Deprecated

np.linspace now raises DeprecationWarning when num cannot be safely interpreted as an integer.

NumPy 1.12.0 Release Notes

This release supports Python 2.7 and 3.4 - 3.6.

Highlights

The NumPy 1.12.0 release contains a large number of fixes and improvements, but few that stand out above all others. That makes picking out the highlights somewhat arbitrary but the following may be of particular interest or indicate areas likely to have future consequences.

Order of operations in np.einsum can now be optimized for large speed improvements.
New signature argument to np.vectorize for vectorizing with core dimensions.
The keepdims argument was added to many functions.
New context manager for testing warnings
Support for BLIS in numpy.distutils
Much improved support for PyPy (not yet finished)

Dropped Support

Support for Python 2.6, 3.2, and 3.3 has been dropped.

Added Support

Support for PyPy 2.7 v5.6.0 has been added. While not complete (nditer updateifcopy is not supported yet), this is a milestone for PyPy's C-API compatibility layer.

Build System Changes

Library order is preserved, instead of being reordered to match that of the directories.

Deprecations

Assignment of ndarray object's `data` attribute

Assigning the 'data' attribute is an inherently unsafe operation as pointed out in gh-7083. Such a capability will be removed in the future.

Unsafe int casting of the num attribute in `linspace`

np.linspace now raises DeprecationWarning when num cannot be safely interpreted as an integer.

Insufficient bit width parameter to `binary_repr`

If a 'width' parameter is passed into binary_repr that is insufficient to represent the number in base 2 (positive) or 2's complement (negative) form, the function used to silently ignore the parameter and return a representation using the minimal number of bits needed for the form in question. Such behavior is now considered unsafe from a user perspective and will raise an error in the future.

Future Changes

In 1.13 NAT will always compare False except for NAT != NAT, which will be True. In short, NAT will behave like NaN
In 1.13 np.average will preserve subclasses, to match the behavior of most other numpy functions such as np.mean. In particular, this means calls which returned a scalar may return a 0-d subclass object instead.

Multiple-field manipulation of structured arrays

In 1.13 the behavior of structured arrays involving multiple fields will change in two ways:

First, indexing a structured array with multiple fields (eg, arr[['f1', 'f3']]) will return a view into the original array in 1.13, instead of a copy. Note the returned view will have extra padding bytes corresponding to intervening fields in the original array, unlike the copy in 1.12, which will affect code such as arr[['f1', 'f3']].view(newdtype).

Second, for numpy versions 1.6 to 1.12 assignment between structured arrays occurs "by field name": Fields in the destination array are set to the identically-named field in the source array or to 0 if the source does not have a field::

>>> a = np.array([(1,2),(3,4)], dtype=[('x', 'i4'), ('y', 'i4')])
>>> b = np.ones(2, dtype=[('z', 'i4'), ('y', 'i4'), ('x', 'i4')])
>>> b[:] = a
>>> b
array([(0, 2, 1), (0, 4, 3)],
      dtype=[('z', '<i4'), ('y', '<i4'), ('x', '<i4')])

In 1.13 assignment will instead occur "by position": The Nth field of the destination will be set to the Nth field of the source regardless of field name. The old behavior can be obtained by using indexing to reorder the fields before assignment, e.g., b[['x', 'y']] = a[['y', 'x']].

Compatibility notes

DeprecationWarning to error

Indexing with floats raises IndexError, e.g., a[0, 0.0].
Indexing with non-integer array_like raises IndexError, e.g., a['1', '2']
Indexing with multiple ellipsis raises IndexError, e.g., a[..., ...].
Non-integers used as index values raise TypeError, e.g., in reshape, take, and specifying reduce axis.

FutureWarning to changed behavior

np.full now returns an array of the fill-value's dtype if no dtype is given, instead of defaulting to float.
np.average will emit a warning if the argument is a subclass of ndarray, as the subclass will be preserved starting in 1.13. (see Future Changes)

`power` and `**` raise errors for integer to negative integer powers

The previous behavior depended on whether numpy scalar integers or numpy integer arrays were involved.

For arrays

Zero to negative integer powers returned least integral value.
Both 1, -1 to negative integer powers returned correct values.
The remaining integers returned zero when raised to negative integer powers.

For scalars

Zero to negative integer powers returned least integral value.
Both 1, -1 to negative integer powers returned correct values.
The remaining integers sometimes returned zero, sometimes the correct float depending on the integer type combination.

All of these cases now raise a ValueError except for those integer combinations whose common type is float, for instance uint64 and int8. It was felt that a simple rule was the best way to go rather than have special exceptions for the integer units. If you need negative powers, use an inexact type.

Relaxed stride checking is the default

This will have some impact on code that assumed that F_CONTIGUOUS and C_CONTIGUOUS were mutually exclusive and could be set to determine the default order for arrays that are now both.

The `np.percentile` 'midpoint' interpolation method fixed for exact indices

The 'midpoint' interpolator now gives the same result as 'lower' and 'higher' when the two coincide. Previous behavior of 'lower' + 0.5 is fixed.

`keepdims` kwarg is passed through to user-class methods

numpy functions that take a keepdims kwarg now pass the value through to the corresponding methods on ndarray sub-classes. Previously the keepdims keyword would be silently dropped. These functions now have the following behavior:

If user does not provide keepdims, no keyword is passed to the underlying method.
Any user-provided value of keepdims is passed through as a keyword argument to the method.

This will raise in the case where the method does not support a keepdims kwarg and the user explicitly passes in keepdims.

The following functions are changed: sum, product, sometrue, alltrue, any, all, amax, amin, prod, mean, std, var, nanmin, nanmax, nansum, nanprod, nanmean, nanmedian, nanvar, nanstd

`bitwise_and` identity changed

The previous identity was 1, it is now -1. See entry in Improvements_ for more explanation.

ma.median warns and returns nan when unmasked invalid values are encountered

Similar to unmasked median the masked median ma.median now emits a Runtime warning and returns NaN in slices where an unmasked NaN is present.

Greater consistancy in `assert_almost_equal`

The precision check for scalars has been changed to match that for arrays. It is now::

abs(actual - desired) < 1.5 * 10**(-decimal)

Note that this is looser than previously documented, but agrees with the previous implementation used in assert_array_almost_equal. Due to the change in implementation some very delicate tests may fail that did not fail before.

`NoseTester` behaviour of warnings during testing

When raise_warnings="develop" is given, all uncaught warnings will now be considered a test failure. Previously only selected ones were raised. Warnings which are not caught or raised (mostly when in release mode) will be shown once during the test cycle similar to the default python settings.

`assert_warns` and `deprecated` decorator more specific

The assert_warns function and context manager are now more specific to the given warning category. This increased specificity leads to them being handled according to the outer warning settings. This means that no warning may be raised in cases where a wrong category warning is given and ignored outside the context. Alternatively the increased specificity may mean that warnings that were incorrectly ignored will now be shown or raised. See also the new suppress_warnings context manager. The same is true for the deprecated decorator.

C API

No changes.

New Features

Writeable keyword argument for `as_strided`

np.lib.stride_tricks.as_strided now has a writeable keyword argument. It can be set to False when no write operation to the returned array is expected to avoid accidental unpredictable writes.

`axes` keyword argument for `rot90`

The axes keyword argument in rot90 determines the plane in which the array is rotated. It defaults to axes=(0,1) as in the originial function.

Generalized `flip`

flipud and fliplr reverse the elements of an array along axis=0 and axis=1 respectively. The newly added flip function reverses the elements of an array along any given axis.

np.count_nonzero now has an axis parameter, allowing non-zero counts to be generated on more than just a flattened array object.

BLIS support in `numpy.distutils`

Building against the BLAS implementation provided by the BLIS library is now supported. See the [blis] section in site.cfg.example (in the root of the numpy repo or source distribution).

Hook in `numpy/init.py` to run distribution-specific checks

Binary distributions of numpy may need to run specific hardware checks or load specific libraries during numpy initialization. For example, if we are distributing numpy with a BLAS library that requires SSE2 instructions, we would like to check the machine on which numpy is running does have SSE2 in order to give an informative error.

Add a hook in numpy/__init__.py to import a numpy/_distributor_init.py file that will remain empty (bar a docstring) in the standard numpy source, but that can be overwritten by people making binary distributions of numpy.

New nanfunctions `nancumsum` and `nancumprod` added

Nan-functions nancumsum and nancumprod have been added to compute cumsum and cumprod by ignoring nans.

`np.interp` can now interpolate complex values

np.lib.interp(x, xp, fp) now allows the interpolated array fp to be complex and will interpolate at complex128 precision.

New polynomial evaluation function `polyvalfromroots` added

The new function polyvalfromroots evaluates a polynomial at given points from the roots of the polynomial. This is useful for higher order polynomials, where expansion into polynomial coefficients is inaccurate at machine precision.

New array creation function `geomspace` added

The new function geomspace generates a geometric sequence. It is similar to logspace, but with start and stop specified directly: geomspace(start, stop) behaves the same as logspace(log10(start), log10(stop)).

New context manager for testing warnings

A new context manager suppress_warnings has been added to the testing utils. This context manager is designed to help reliably test warnings. Specifically to reliably filter/ignore warnings. Ignoring warnings by using an "ignore" filter in Python versions before 3.4.x can quickly result in these (or similar) warnings not being tested reliably.

The context manager allows to filter (as well as record) warnings similar to the catch_warnings context, but allows for easier specificity. Also printing warnings that have not been filtered or nesting the context manager will work as expected. Additionally, it is possible to use the context manager as a decorator which can be useful when multiple tests give need to hide the same warning.

New masked array functions `ma.convolve` and `ma.correlate` added

These functions wrapped the non-masked versions, but propagate through masked values. There are two different propagation modes. The default causes masked values to contaminate the result with masks, but the other mode only outputs masks if there is no alternative.

New `float_power` ufunc

The new float_power ufunc is like the power function except all computation is done in a minimum precision of float64. There was a long discussion on the numpy mailing list of how to treat integers to negative integer powers and a popular proposal was that the __pow__ operator should always return results of at least float64 precision. The float_power function implements that option. Note that it does not support object arrays.

`np.loadtxt` now supports a single integer as `usecol` argument

Instead of using usecol=(n,) to read the nth column of a file it is now allowed to use usecol=n. Also the error message is more user friendly when a non-integer is passed as a column index.

Improved automated bin estimators for `histogram`

Added 'doane' and 'sqrt' estimators to histogram via the bins argument. Added support for range-restricted histograms with automated bin estimation.

`np.roll` can now roll multiple axes at the same time

The shift and axis arguments to roll are now broadcast against each other, and each specified axis is shifted accordingly.

The `complex` method has been implemented for the ndarrays

Calling complex() on a size 1 array will now cast to a python complex.

`pathlib.Path` objects now supported

The standard np.load, np.save, np.loadtxt, np.savez, and similar functions can now take pathlib.Path objects as an argument instead of a filename or open file object.

New `bits` attribute for `np.finfo`

This makes np.finfo consistent with np.iinfo which already has that attribute.

New `signature` argument to `np.vectorize`

This argument allows for vectorizing user defined functions with core dimensions, in the style of NumPy's :ref:generalized universal functions<c-api.generalized-ufuncs>. This allows for vectorizing a much broader class of functions. For example, an arbitrary distance metric that combines two vectors to produce a scalar could be vectorized with signature='(n),(n)->()'. See np.vectorize for full details.

Emit py3kwarnings for division of integer arrays

To help people migrate their code bases from Python 2 to Python 3, the python interpreter has a handy option -3, which issues warnings at runtime. One of its warnings is for integer division::

$ python -3 -c "2/3"

-c:1: DeprecationWarning: classic int division

In Python 3, the new integer division semantics also apply to numpy arrays. With this version, numpy will emit a similar warning::

$ python -3 -c "import numpy as np; np.array(2)/np.array(3)"

-c:1: DeprecationWarning: numpy: classic int division

numpy.sctypes now includes bytes on Python3 too

Previously, it included str (bytes) and unicode on Python2, but only str (unicode) on Python3.

Improvements

`bitwise_and` identity changed

The previous identity was 1 with the result that all bits except the LSB were masked out when the reduce method was used. The new identity is -1, which should work properly on twos complement machines as all bits will be set to one.

Generalized Ufuncs will now unlock the GIL

Generalized Ufuncs, including most of the linalg module, will now unlock the Python global interpreter lock.

Caches in `np.fft` are now bounded in total size and item count

The caches in np.fft that speed up successive FFTs of the same length can no longer grow without bounds. They have been replaced with LRU (least recently used) caches that automatically evict no longer needed items if either the memory size or item count limit has been reached.

Improved handling of zero-width string/unicode dtypes

Fixed several interfaces that explicitly disallowed arrays with zero-width string dtypes (i.e. dtype('S0') or dtype('U0'), and fixed several bugs where such dtypes were not handled properly. In particular, changed ndarray.__new__ to not implicitly convert dtype('S0') to dtype('S1') (and likewise for unicode) when creating new arrays.

Integer ufuncs vectorized with AVX2

If the cpu supports it at runtime the basic integer ufuncs now use AVX2 instructions. This feature is currently only available when compiled with GCC.

Order of operations optimization in `np.einsum`

np.einsum now supports the optimize argument which will optimize the order of contraction. For example, np.einsum would complete the chain dot example np.einsum(‘ij,jk,kl->il’, a, b, c) in a single pass which would scale like N^4; however, when optimize=True np.einsum will create an intermediate array to reduce this scaling to N^3 or effectively np.dot(a, b).dot(c). Usage of intermediate tensors to reduce scaling has been applied to the general einsum summation notation. See np.einsum_path for more details.

quicksort has been changed to an introsort

The quicksort kind of np.sort and np.argsort is now an introsort which is regular quicksort but changing to a heapsort when not enough progress is made. This retains the good quicksort performance while changing the worst case runtime from O(N^2) to O(N*log(N)).

`ediff1d` improved performance and subclass handling

The ediff1d function uses an array instead on a flat iterator for the subtraction. When to_begin or to_end is not None, the subtraction is performed in place to eliminate a copy operation. A side effect is that certain subclasses are handled better, namely astropy.Quantity, since the complete array is created, wrapped, and then begin and end values are set, instead of using concatenate.

Improved precision of `ndarray.mean` for float16 arrays

The computation of the mean of float16 arrays is now carried out in float32 for improved precision. This should be useful in packages such as Theano where the precision of float16 is adequate and its smaller footprint is desireable.

Changes

All array-like methods are now called with keyword arguments in fromnumeric.py

Internally, many array-like methods in fromnumeric.py were being called with positional arguments instead of keyword arguments as their external signatures were doing. This caused a complication in the downstream 'pandas' library that encountered an issue with 'numpy' compatibility. Now, all array-like methods in this module are called with keyword arguments instead.

Operations on np.memmap objects return numpy arrays in most cases

Previously operations on a memmap object would misleadingly return a memmap instance even if the result was actually not memmapped. For example, arr + 1 or arr + arr would return memmap instances, although no memory from the output array is memmaped. Version 1.12 returns ordinary numpy arrays from these operations.

Also, reduction of a memmap (e.g. .sum(axis=None) now returns a numpy scalar instead of a 0d memmap.

stacklevel of warnings increased

The stacklevel for python based warnings was increased so that most warnings will report the offending line of the user code instead of the line the warning itself is given. Passing of stacklevel is now tested to ensure that new warnings will receive the stacklevel argument.

This causes warnings with the "default" or "module" filter to be shown once for every offending user code line or user module instead of only once. On python versions before 3.4, this can cause warnings to appear that were falsely ignored before, which may be surprising especially in test suits.

Contributors

A total of 139 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Aditya Panchal +
Ales Erjavec +
Alex Griffing
Alexandr Shadchin +
Alistair Muldal
Allan Haldane
Amit Aronovitch +
Andrei Kucharavy +
Antony Lee
Antti Kaihola +
Arne de Laat +
Auke Wiggers +
AustereCuriosity +
Badhri Narayanan Krishnakumar +
Ben North +
Ben Rowland +
Bertrand Lefebvre
Boxiang Sun
CJ Carey
Charles Harris
Christoph Gohlke
Daniel Ching +
Daniel Rasmussen +
Daniel Smith +
David Schaich +
Denis Alevi +
Devin Jeanpierre +
Dmitry Odzerikho
Dongjoon Hyun +
Edward Richards +
Ekaterina Tuzova +
Emilien Kofman +
Endolith
Eren Sezener +
Eric Moore
Eric Quintero +
Eric Wieser +
Erik M. Bray
Frederic Bastien
Friedrich Dunne +
Gerrit Holl
Golnaz Irannejad +
Graham Markall +
Greg Knoll +
Greg Young
Gustavo Serra Scalet +
Ines Wichert +
Irvin Probst +
Jaime Fernandez
James Sanders +
Jan David Mol +
Jan Schlüter
Jeremy Tuloup +
John Kirkham
John Zwinck +
Jonathan Helmus
Joseph Fox-Rabinovitz
Josh Wilson +
Joshua Warner +
Julian Taylor
Ka Wo Chen +
Kamil Rytarowski +
Kelsey Jordahl +
Kevin Deldycke +
Khaled Ben Abdallah Okuda +
Lion Krischer +
Loïc Estève +
Luca Mussi +
Mads Ohm Larsen +
Manoj Kumar +
Mario Emmenlauer +
Marshall Bockrath-Vandegrift +
Marshall Ward +
Marten van Kerkwijk
Mathieu Lamarre +
Matthew Brett
Matthew Harrigan +
Matthias Geier
Matti Picus +
Meet Udeshi +
Michael Felt +
Michael Goerz +
Michael Martin +
Michael Seifert +
Mike Nolta +
Nathaniel Beaver +
Nathaniel J. Smith
Naveen Arunachalam +
Nick Papior
Nikola Forró +
Oleksandr Pavlyk +
Olivier Grisel
Oren Amsalem +
Pauli Virtanen
Pavel Potocek +
Pedro Lacerda +
Peter Creasey +
Phil Elson +
Philip Gura +
Phillip J. Wolfram +
Pierre de Buyl +
Raghav RV +
Ralf Gommers
Ray Donnelly +
Rehas Sachdeva
Rob Malouf +
Robert Kern
Samuel St-Jean
Sanchez Gonzalez Alvaro +
Saurabh Mehta +
Scott Sanderson +
Sebastian Berg
Shayan Pooya +
Shota Kawabuchi +
Simon Conseil
Simon Gibbons
Sorin Sbarnea +
Stefan van der Walt
Stephan Hoyer
Steven J Kern +
Stuart Archibald
Tadeu Manoel +
Takuya Akiba +
Thomas A Caswell
Tom Bird +
Tony Kelman +
Toshihiro Kamishima +
Valentin Valls +
Varun Nayyar
Victor Stinner +
Warren Weckesser
Wendell Smith
Wojtek Ruszczewski +
Xavier Abellan Ecija +
Yaroslav Halchenko
Yash Shah +
Yinon Ehrlich +
Yu Feng +
nevimov +

Pull requests merged

A total of 418 pull requests were merged for this release.

#4073: BUG: change real output checking to test if all imaginary parts...
#4619: BUG : np.sum silently drops keepdims for sub-classes of ndarray
#5488: ENH: add contract: optimizing numpy's einsum expression
#5706: ENH: make some masked array methods behave more like ndarray...
#5822: Allow many distributions to have a scale of 0.
#6054: WIP: MAINT: Add deprecation warning to views of multi-field indexes
#6298: Check lower base limit in base_repr.
#6430: Fix issues with zero-width string fields
#6656: ENH: usecols now accepts an int when only one column has to be...
#6660: Added pathlib support for several functions
#6872: ENH: linear interpolation of complex values in lib.interp
#6997: MAINT: Simplify mtrand.pyx helpers
#7003: BUG: Fix string copying for np.place
#7026: DOC: Clarify behavior in np.random.uniform
#7055: BUG: One Element Array Inputs Return Scalars in np.random
#7063: REL: Update master branch after 1.11.x branch has been made.
#7073: DOC: Update the 1.11.0 release notes.
#7076: MAINT: Update the git .mailmap file.
#7082: TST, DOC: Added Broadcasting Tests in test_random.py
#7087: BLD: fix compilation on non glibc-Linuxes
#7088: BUG: Have norm cast non-floating point arrays to 64-bit float...
#7090: ENH: Added 'doane' and 'sqrt' estimators to np.histogram in numpy.function_base
#7091: Revert "BLD: fix compilation on non glibc-Linuxes"
#7092: BLD: fix compilation on non glibc-Linuxes
#7099: TST: Suppressed warnings
#7102: MAINT: Removed conditionals that are always false in datetime_strings.c
#7105: DEP: Deprecate as_strided returning a writable array as default
#7109: DOC: update Python versions requirements in the install docs
#7114: MAINT: Fix typos in docs
#7116: TST: Fixed f2py test for win32 virtualenv
#7118: TST: Fixed f2py test for non-versioned python executables
#7119: BUG: Fixed mingw.lib error
#7125: DOC: Updated documentation wording and examples for np.percentile.
#7129: BUG: Fixed 'midpoint' interpolation of np.percentile in odd cases.
#7131: Fix setuptools sdist
#7133: ENH: savez: temporary file alongside with target file and improve...
#7134: MAINT: Fix some typos in a code string and comments
#7141: BUG: Unpickled void scalars should be contiguous
#7144: MAINT: Change call_fortran into callfortran in comments.
#7145: BUG: Fixed regressions in np.piecewise in ref to #5737 and #5729.
#7147: Temporarily disable __numpy_ufunc__
#7148: ENH,TST: Bump stacklevel and add tests for warnings
#7149: TST: Add missing suffix to temppath manager
#7152: BUG: mode kwargs passed as unicode to np.pad raises an exception
#7156: BUG: Reascertain that linspace respects ndarray subclasses in...
#7167: DOC: Update Wikipedia references for mtrand.pyx
#7171: TST: Fixed f2py test for Anaconda non-win32
#7174: DOC: Fix broken pandas link in release notes
#7177: ENH: added axis param for np.count_nonzero
#7178: BUG: Fix binary_repr for negative numbers
#7180: BUG: Fixed previous attempt to fix dimension mismatch in nanpercentile
#7181: DOC: Updated minor typos in function_base.py and test_function_base.py
#7191: DOC: add vstack, hstack, dstack reference to stack documentation.
#7193: MAINT: Removed supurious assert in histogram estimators
#7194: BUG: Raise a quieter MaskedArrayFutureWarning for mask changes.
#7195: STY: Drop some trailing spaces in numpy.ma.core.
#7196: Revert "DOC: add vstack, hstack, dstack reference to stack documentation."
#7197: TST: Pin virtualenv used on Travis CI.
#7198: ENH: Unlock the GIL for gufuncs
#7199: MAINT: Cleanup for histogram bin estimator selection
#7201: Raise IOError on not a file in python2
#7202: MAINT: Made iterable return a boolean
#7209: TST: Bump virtualenv to 14.0.6
#7211: DOC: Fix fmin examples
#7215: MAINT: Use PySlice_GetIndicesEx instead of custom reimplementation
#7229: ENH: implement __complex__
#7231: MRG: allow distributors to run custom init
#7232: BLD: Switch order of test for lapack_mkl and openblas_lapack
#7239: DOC: Removed residual merge markup from previous commit
#7240: Change 'pubic' to 'public'.
#7241: MAINT: update doc/sphinxext to numpydoc 0.6.0, and fix up some...
#7243: ENH: Adding support to the range keyword for estimation of the...
#7246: DOC: metion writeable keyword in as_strided in release notes
#7247: TST: Fail quickly on AppVeyor for superseded PR builds
#7248: DOC: remove link to documentation wiki editor from HOWTO_DOCUMENT.
#7250: DOC,REL: Update 1.11.0 notes.
#7251: BUG: only benchmark complex256 if it exists
#7252: Forward port a fix and enhancement from 1.11.x
#7253: DOC: note in h/v/dstack points users to stack/concatenate
#7254: BUG: Enforce dtype for randint singletons
#7256: MAINT: Use is None or is not None instead of == None or...
#7257: DOC: Fix mismatched variable names in docstrings.
#7258: ENH: Make numpy floor_divide and remainder agree with Python...
#7260: BUG/TST: Fix #7259, do not "force scalar" for already scalar...
#7261: Added self to mailmap
#7266: BUG: Segfault for classes with deceptive __len__
#7268: ENH: add geomspace function
#7274: BUG: Preserve array order in np.delete
#7275: DEP: Warn about assigning 'data' attribute of ndarray
#7276: DOC: apply_along_axis missing whitespace inserted (before colon)
#7278: BUG: Make returned unravel_index arrays writeable
#7279: TST: Fixed elements being shuffled
#7280: MAINT: Remove redundant trailing semicolons.
#7285: BUG: Make Randint Backwards Compatible with Pandas
#7286: MAINT: Fix typos in docs/comments of ma and polynomial modules.
#7292: Clarify error on repr failure in assert_equal.
#7294: ENH: add support for BLIS to numpy.distutils
#7295: DOC: understanding code and getting started section to dev doc
#7296: Revert part of #3907 which incorrectly propogated MaskedArray...
#7299: DOC: Fix mismatched variable names in docstrings.
#7300: DOC: dev: stop recommending keeping local master updated with...
#7301: DOC: Update release notes
#7305: BUG: Remove data race in mtrand: two threads could mutate the...
#7307: DOC: Missing some characters in link.
#7308: BUG: Incrementing the wrong reference on return
#7310: STY: Fix GitHub rendering of ordered lists >9
#7311: ENH: Make _pointer_type_cache functional
#7313: DOC: corrected grammatical error in quickstart doc
#7325: BUG, MAINT: Improve fromnumeric.py interface for downstream compatibility
#7328: DEP: Deprecated using a float index in linspace
#7331: Add comment, TST: fix MemoryError on win32
#7332: Check for no solution in np.irr Fixes #6744
#7338: TST: Install pytz in the CI.
#7340: DOC: Fixed math rendering in tensordot docs.
#7341: TST: Add test for #6469
#7344: DOC: Fix more typos in docs and comments.
#7346: Generalized flip
#7347: ENH Generalized rot90
#7348: Maint: Removed extra space from ureduce
#7349: MAINT: Hide nan warnings for masked internal MA computations
#7350: BUG: MA ufuncs should set mask to False, not array([False])
#7351: TST: Fix some MA tests to avoid looking at the .data attribute
#7358: BUG: pull request related to the issue #7353
#7359: Update 7314, DOC: Clarify valid integer range for random.seed...
#7361: MAINT: Fix copy and paste oversight.
#7363: ENH: Make no unshare mask future warnings less noisy
#7366: TST: fix #6542, add tests to check non-iterable argument raises...
#7373: ENH: Add bitwise_and identity
#7378: added NumPy logo and separator
#7382: MAINT: cleanup np.average
#7385: DOC: note about wheels / windows wheels for pypi
#7386: Added label icon to Travis status
#7397: BUG: incorrect type for objects whose __len__ fails
#7398: DOC: fix typo
#7404: Use PyMem_RawMalloc on Python 3.4 and newer
#7406: ENH ufunc called on memmap return a ndarray
#7407: BUG: Fix decref before incref for in-place accumulate
#7410: DOC: add nanprod to the list of math routines
#7414: Tweak corrcoef
#7415: DOC: Documention fixes
#7416: BUG: Incorrect handling of range in histogram with automatic...
#7418: DOC: Minor typo fix, hermefik -> hermefit.
#7421: ENH: adds np.nancumsum and np.nancumprod
#7423: BUG: Ongoing fixes to PR#7416
#7430: DOC: Update 1.11.0-notes.
#7433: MAINT: FutureWarning for changes to np.average subclass handling
#7437: np.full now defaults to the filling value's dtype.
#7438: Allow rolling multiple axes at the same time.
#7439: BUG: Do not try sequence repeat unless necessary
#7442: MANT: Simplify diagonal length calculation logic
#7445: BUG: reference count leak in bincount, fixes #6805
#7446: DOC: ndarray typo fix
#7447: BUG: scalar integer negative powers gave wrong results.
#7448: DOC: array "See also" link to full and full_like instead of fill
#7456: BUG: int overflow in reshape, fixes #7455, fixes #7293
#7463: BUG: fix array too big error for wide dtypes.
#7466: BUG: segfault inplace object reduceat, fixes #7465
#7468: BUG: more on inplace reductions, fixes #615
#7469: MAINT: Update git .mailmap
#7472: MAINT: Update .mailmap.
#7477: MAINT: Yet more .mailmap updates for recent contributors.
#7481: BUG: Fix segfault in PyArray_OrderConverter
#7482: BUG: Memory Leak in _GenericBinaryOutFunction
#7489: Faster real_if_close.
#7491: DOC: Update subclassing doc regarding downstream compatibility
#7496: BUG: don't use pow for integer power ufunc loops.
#7504: DOC: remove "arr" from keepdims docstrings
#7505: MAIN: fix to #7382, make scl in np.average writeable
#7507: MAINT: Remove nose.SkipTest import.
#7508: DOC: link frompyfunc and vectorize
#7511: numpy.power(0, 0) should return 1
#7515: BUG: MaskedArray.count treats negative axes incorrectly
#7518: BUG: Extend glibc complex trig functions blacklist to glibc <...
#7521: DOC: rephrase writeup of memmap changes
#7522: BUG: Fixed iteration over additional bad commands
#7526: DOC: Removed an extra :const:
#7529: BUG: Floating exception with invalid axis in np.lexsort
#7534: MAINT: Update setup.py to reflect supported python versions.
#7536: MAINT: Always use PyCapsule instead of PyCObject in mtrand.pyx
#7539: MAINT: Cleanup of random stuff
#7549: BUG: allow graceful recovery for no Liux compiler
#7562: BUG: Fix test_from_object_array_unicode (test_defchararray.TestBasic)…
#7565: BUG: Fix test_ctypeslib and test_indexing for debug interpreter
#7566: MAINT: use manylinux1 wheel for cython
#7568: Fix a false positive OverflowError in Python 3.x when value above...
#7579: DOC: clarify purpose of Attributes section
#7584: BUG: fixes #7572, percent in path
#7586: Make np.ma.take works on scalars
#7587: BUG: linalg.norm(): Don't convert object arrays to float
#7598: Cast array size to int64 when loading from archive
#7602: DOC: Remove isreal and iscomplex from ufunc list
#7605: DOC: fix incorrect Gamma distribution parameterization comments
#7609: BUG: Fix TypeError when raising TypeError
#7611: ENH: expose test runner raise_warnings option
#7614: BLD: Avoid using os.spawnve in favor of os.spawnv in exec_command
#7618: BUG: distance arg of np.gradient must be scalar, fix docstring
#7626: DOC: RST definition list fixes
#7627: MAINT: unify tup processing, move tup use to after all PyTuple_SetItem...
#7630: MAINT: add ifdef around PyDictProxy_Check macro
#7631: MAINT: linalg: fix comment, simplify math
#7634: BLD: correct C compiler customization in system_info.py Closes...
#7635: BUG: ma.median alternate fix for #7592
#7636: MAINT: clean up testing.assert_raises_regexp, 2.6-specific code...
#7637: MAINT: clearer exception message when importing multiarray fails.
#7639: TST: fix a set of test errors in master.
#7643: DOC : minor changes to linspace docstring
#7651: BUG: one to any power is still 1. Broken edgecase for int arrays
#7655: BLD: Remove Intel compiler flag -xSSE4.2
#7658: BUG: fix incorrect printing of 1D masked arrays
#7659: BUG: Temporary fix for str(mvoid) for object field types
#7664: BUG: Fix unicode with byte swap transfer and copyswap
#7667: Restore histogram consistency
#7668: ENH: Do not check the type of module.dict explicit in test.
#7669: BUG: boolean assignment no GIL release when transfer needs API
#7673: DOC: Create Numpy 1.11.1 release notes.
#7675: BUG: fix handling of right edge of final bin.
#7678: BUG: Fix np.clip bug NaN handling for Visual Studio 2015
#7679: MAINT: Fix up C++ comment in arraytypes.c.src.
#7681: DOC: Update 1.11.1 release notes.
#7686: ENH: Changing FFT cache to a bounded LRU cache
#7688: DOC: fix broken genfromtxt examples in user guide. Closes gh-7662.
#7689: BENCH: add correlate/convolve benchmarks.
#7696: DOC: update wheel build / upload instructions
#7699: BLD: preserve library order
#7704: ENH: Add bits attribute to np.finfo
#7712: BUG: Fix race condition with new FFT cache
#7715: BUG: Remove memory leak in np.place
#7719: BUG: Fix segfault in np.random.shuffle for arrays of different...
#7723: Change mkl_info.dir_env_var from MKL to MKLROOT
#7727: DOC: Corrections in Datetime Units-arrays.datetime.rst
#7729: DOC: fix typo in savetxt docstring (closes #7620)
#7733: Update 7525, DOC: Fix order='A' docs of np.array.
#7734: Update 7542, ENH: Add polyrootval to numpy.polynomial
#7735: BUG: fix issue on OS X with Python 3.x where npymath.ini was...
#7739: DOC: Mention the changes of #6430 in the release notes.
#7740: DOC: add reference to poisson rng
#7743: Update 7476, DEP: deprecate Numeric-style typecodes, closes #2148
#7744: DOC: Remove "ones_like" from ufuncs list (it is not)
#7746: DOC: Clarify the effect of rcond in numpy.linalg.lstsq.
#7747: Update 7672, BUG: Make sure we don't divide by zero
#7748: DOC: Update float32 mean example in docstring
#7754: Update 7612, ENH: Add broadcast.ndim to match code elsewhere.
#7757: Update 7175, BUG: Invalid read of size 4 in PyArray_FromFile
#7759: BUG: Fix numpy.i support for numpy API < 1.7.
#7760: ENH: Make assert_almost_equal & assert_array_almost_equal consistent.
#7766: fix an English typo
#7771: DOC: link geomspace from logspace
#7773: DOC: Remove a redundant the
#7777: DOC: Update Numpy 1.11.1 release notes.
#7785: DOC: update wheel building procedure for release
#7789: MRG: add note of 64-bit wheels on Windows
#7791: f2py.compile issues (#7683)
#7799: "lambda" is not allowed to use as keyword arguments in a sample...
#7803: BUG: interpret 'c' PEP3118/struct type as 'S1'.
#7807: DOC: Misplaced parens in formula
#7817: BUG: Make sure npy_mul_with_overflow_<type> detects overflow.
#7818: numpy/distutils/misc_util.py fix for #7809: check that _tmpdirs...
#7820: MAINT: Allocate fewer bytes for empty arrays.
#7823: BUG: Fixed masked array behavior for scalar inputs to np.ma.atleast_*d
#7834: DOC: Added an example
#7839: Pypy fixes
#7840: Fix ATLAS version detection
#7842: Fix versionadded tags
#7848: MAINT: Fix remaining uses of deprecated Python imp module.
#7853: BUG: Make sure numpy globals keep identity after reload.
#7863: ENH: turn quicksort into introsort
#7866: Document runtests extra argv
#7871: BUG: handle introsort depth limit properly
#7879: DOC: fix typo in documentation of loadtxt (closes #7878)
#7885: Handle NetBSD specific <sys/endian.h>
#7889: DOC: #7881. Fix link to record arrays
#7894: fixup-7790, BUG: construct ma.array from np.array which contains...
#7898: Spelling and grammar fix.
#7903: BUG: fix float16 type not being called due to wrong ordering
#7908: BLD: Fixed detection for recent MKL versions
#7911: BUG: fix for issue#7835 (ma.median of 1d)
#7912: ENH: skip or avoid gc/objectmodel differences btwn pypy and cpython
#7918: ENH: allow numpy.apply_along_axis() to work with ndarray subclasses
#7922: ENH: Add ma.convolve and ma.correlate for #6458
#7925: Monkey-patch _msvccompile.gen_lib_option like any other compilators
#7931: BUG: Check for HAVE_LDOUBLE_DOUBLE_DOUBLE_LE in npy_math_complex.
#7936: ENH: improve duck typing inside iscomplexobj
#7937: BUG: Guard against buggy comparisons in generic quicksort.
#7938: DOC: add cbrt to math summary page
#7941: BUG: Make sure numpy globals keep identity after reload.
#7943: DOC: #7927. Remove deprecated note for memmap relevant for Python...
#7952: BUG: Use keyword arguments to initialize Extension base class.
#7956: BLD: remove __NUMPY_SETUP__ from builtins at end of setup.py
#7963: BUG: MSVCCompiler grows 'lib' & 'include' env strings exponentially.
#7965: BUG: cannot modify tuple after use
#7976: DOC: Fixed documented dimension of return value
#7977: DOC: Create 1.11.2 release notes.
#7979: DOC: Corrected allowed keywords in add_(installed_)library
#7980: ENH: Add ability to runtime select ufunc loops, add AVX2 integer...
#7985: Rebase 7763, ENH: Add new warning suppression/filtering context
#7987: DOC: See also np.load and np.memmap in np.lib.format.open_memmap
#7988: DOC: Include docstring for cbrt, spacing and fabs in documentation
#7999: ENH: add inplace cases to fast ufunc loop macros
#8006: DOC: Update 1.11.2 release notes.
#8008: MAINT: Remove leftover imp module imports.
#8009: DOC: Fixed three typos in the c-info.ufunc-tutorial
#8011: DOC: Update 1.11.2 release notes.
#8014: BUG: Fix fid.close() to use os.close(fid)
#8016: BUG: Fix numpy.ma.median.
#8018: BUG: Fixes return for np.ma.count if keepdims is True and axis...
#8021: DOC: change all non-code instances of Numpy to NumPy
#8027: ENH: Add platform indepedent lib dir to PYTHONPATH
#8028: DOC: Update 1.11.2 release notes.
#8030: BUG: fix np.ma.median with only one non-masked value and an axis...
#8038: MAINT: Update error message in rollaxis.
#8040: Update add_newdocs.py
#8042: BUG: core: fix bug in NpyIter buffering with discontinuous arrays
#8045: DOC: Update 1.11.2 release notes.
#8050: remove refcount semantics, now a.resize() almost always requires...
#8051: Clear signaling NaN exceptions
#8054: ENH: add signature argument to vectorize for vectorizing like...
#8057: BUG: lib: Simplify (and fix) pad's handling of the pad_width
#8061: BUG : financial.pmt modifies input (issue #8055)
#8064: MAINT: Add PMIP files to .gitignore
#8065: BUG: Assert fromfile ending earlier in pyx_processing
#8066: BUG, TST: Fix python3-dbg bug in Travis script
#8071: MAINT: Add Tempita to randint helpers
#8075: DOC: Fix description of isinf in nan_to_num
#8080: BUG: non-integers can end up in dtype offsets
#8081: Update outdated Nose URL to nose.readthedocs.io
#8083: ENH: Deprecation warnings for / integer division when running...
#8084: DOC: Fix erroneous return type description for np.roots.
#8087: BUG: financial.pmt modifies input #8055
#8088: MAINT: Remove duplicate randint helpers code.
#8093: MAINT: fix assert_raises_regex when used as a context manager
#8096: ENH: Vendorize tempita.
#8098: DOC: Enhance description/usage for np.linalg.eig*h
#8103: ENH: Pypy fixes
#8104: Fix test code on cpuinfo's main function
#8107: BUG: Fix array printing with precision=0.
#8109: Fix bug in ravel_multi_index for big indices (Issue #7546)
#8110: BUG: distutils: fix issue with rpath in fcompiler/gnu.py
#8111: ENH: Add a tool for release authors and PRs.
#8112: DOC: Fix "See also" links in linalg.
#8114: BUG: core: add missing error check after PyLong_AsSsize_t
#8121: DOC: Improve histogram2d() example.
#8122: BUG: Fix broken pickle in MaskedArray when dtype is object (Return...
#8124: BUG: Fixed build break
#8125: Rebase, BUG: Fixed deepcopy of F-order object arrays.
#8127: BUG: integers to a negative integer powers should error.
#8141: improve configure checks for broken systems
#8142: BUG: np.ma.mean and var should return scalar if no mask
#8148: BUG: import full module path in npy_load_module
#8153: MAINT: Expose void-scalar "base" attribute in python
#8156: DOC: added example with empty indices for a scalar, #8138
#8160: BUG: fix _array2string for structured array (issue #5692)
#8164: MAINT: Update mailmap for NumPy 1.12.0
#8165: Fixup 8152, BUG: assert_allclose(..., equal_nan=False) doesn't...
#8167: Fixup 8146, DOC: Clarify when PyArray_{Max, Min, Ptp} return...
#8168: DOC: Minor spelling fix in genfromtxt() docstring.
#8173: BLD: Enable build on AIX
#8174: DOC: warn that dtype.descr is only for use in PEP3118
#8177: MAINT: Add python 3.6 support to suppress_warnings
#8178: MAINT: Fix ResourceWarning new in Python 3.6.
#8180: FIX: protect stolen ref by PyArray_NewFromDescr in array_empty
#8181: ENH: Improve announce to find github squash-merge commits.
#8182: MAINT: Update .mailmap
#8183: MAINT: Ediff1d performance
#8184: MAINT: make assert_allclose behavior on nans match pre 1.12
#8188: DOC: 'highest' is exclusive for randint()
#8189: BUG: setfield should raise if arr is not writeable
#8190: ENH: Add a float_power function with at least float64 precision.
#8197: DOC: Add missing arguments to np.ufunc.outer
#8198: DEP: Deprecate the keepdims argument to accumulate
#8199: MAINT: change path to env in distutils.system_info. Closes gh-8195.
#8200: BUG: Fix structured array format functions
#8202: ENH: specialize name of dev package by interpreter
#8205: DOC: change development instructions from SSH to HTTPS access.
#8216: DOC: Patch doc errors for atleast_nd and frombuffer
#8218: BUG: ediff1d should return subclasses
#8219: DOC: Turn SciPy references into links.
#8222: ENH: Make numpy.mean() do more precise computation
#8227: BUG: Better check for invalid bounds in np.random.uniform.
#8231: ENH: Refactor numpy ** operators for numpy scalar integer powers
#8234: DOC: Clarified when a copy is made in numpy.asarray
#8236: DOC: Fix documentation pull requests.
#8238: MAINT: Update pavement.py
#8239: ENH: Improve announce tool.
#8240: REL: Prepare for 1.12.x branch
#8243: BUG: Update operator ** tests for new behavior.
#8246: REL: Reset strides for RELAXED_STRIDE_CHECKING for 1.12 releases.
#8265: BUG: np.piecewise not working for scalars
#8272: TST: Path test should resolve symlinks when comparing
#8282: DOC: Update 1.12.0 release notes.
#8286: BUG: Fix pavement.py write_release_task.
#8296: BUG: Fix iteration over reversed subspaces in mapiter_@name@.
#8304: BUG: Fix PyPy crash in PyUFunc_GenericReduction.
#8319: BLD: blacklist powl (longdouble power function) on OS X.
#8320: BUG: do not link to Accelerate if OpenBLAS, MKL or BLIS are found.
#8322: BUG: fixed kind specifications for parameters
#8336: BUG: fix packbits and unpackbits to correctly handle empty arrays
#8338: BUG: fix test_api test that fails intermittently in python 3
#8339: BUG: Fix ndarray.tofile large file corruption in append mode.
#8359: BUG: Fix suppress_warnings (again) for Python 3.6.
#8372: BUG: Fixes for ma.median and nanpercentile.
#8373: BUG: correct letter case
#8379: DOC: Update 1.12.0-notes.rst.
#8390: ENH: retune apply_along_axis nanmedian cutoff in 1.12
#8391: DEP: Fix escaped string characters deprecated in Python 3.6.
#8394: DOC: create 1.11.3 release notes.
#8399: BUG: Fix author search in announce.py
#8402: DOC, MAINT: Update 1.12.0 notes and mailmap.
#8418: BUG: Fix ma.median even elements for 1.12
#8424: DOC: Fix tools and release notes to be more markdown compatible.
#8427: BUG: Add a lock to assert_equal and other testing functions
#8431: BUG: Fix apply_along_axis() for when func1d() returns a non-ndarray.
#8432: BUG: Let linspace accept input that has an array_interface.
#8437: TST: Update 3.6-dev tests to 3.6 after Python final release.
#8439: DOC: Update 1.12.0 release notes.
#8466: MAINT: Update mailmap entries.
#8467: DOC: Back-port the missing part of gh-8464.
#8476: DOC: Update 1.12.0 release notes.
#8477: DOC: Update 1.12.0 release notes.

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Open source →

1.11.319 Dec 2016

Numpy 1.11.3 fixes a bug that leads to file corruption when very large files opened in append mode are used in ndarray.tofile. It supports Python vers

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1

NumPy 1.11.3 Release Notes

Numpy 1.11.3 fixes a bug that leads to file corruption when very large files opened in append mode are used in ndarray.tofile. It supports Python versions 2.6 - 2.7 and 3.2 - 3.5. Wheels for Linux, Windows, and OS X can be found on PyPI.

Contributors to maintenance/1.11.3

A total of 2 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

- Charles Harris
- Pavel Potocek +

Pull Requests Merged

- #8341 <https://github.com/numpy/numpy/pull/8341>__: BUG: Fix ndarray.tofile large file corruption in append mode.
- #8346 <https://github.com/numpy/numpy/pull/8346>__: TST: Fix tests in PR #8341 for NumPy 1.11.x

Checksums

MD5


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SHA256

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Open source →

numpy

Release timeline

Releases

NumPy NumPy 1.18.0 Release Notes

Highlights

New functions

Multivariate hypergeometric distribution added to numpy.random

Deprecations

np.fromfile and np.fromstring will error on bad data

Deprecate non-scalar arrays as fill values in ma.fill_value

Deprecate PyArray_As1D, PyArray_As2D

Deprecate np.alen

Deprecate the financial functions

The axis argument to numpy.ma.mask_cols and numpy.ma.mask_row is deprecated

Expired deprecations

Compatibility notes

[numpy.lib.recfunctions.drop_fields]{.title-ref} can no longer return None

numpy.argmin/argmax/min/max returns NaT if it exists in array

np.can_cast(np.uint64, np.timedelta64, casting='safe') is now False

Changed random variate stream from numpy.random.Generator.integers

Add more ufunc loops for datetime64, timedelta64

C API changes

PyDataType_ISUNSIZED(descr) now returns False for structured datatypes

New Features

Add our own *.pxd cython import file

A tuple of axes can now be input to expand_dims

Support for 64-bit OpenBLAS

Add --f2cmap option to F2PY

Improvements

Different C numeric types of the same size have unique names

argwhere now produces a consistent result on 0d arrays

Add axis argument for random.permutation and random.shuffle

method keyword argument for np.random.multivariate_normal

Add complex number support for numpy.fromstring

numpy.unique has consistent axes order when axis is not None

numpy.matmul with boolean output now converts to boolean values

numpy.random.randint produced incorrect value when the range was 2**32

Add complex number support for numpy.fromfile

std=c99 added if compiler is named gcc

Changes

NaT now sorts to the end of arrays

Incorrect threshold in np.set_printoptions raises TypeError or ValueError

Warn when saving a dtype with metadata

numpy.distutils append behavior changed for LDFLAGS and similar

Remove numpy.random.entropy without a deprecation

Add options to quiet build configuration and build with -Werror

Checksums

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SHA256

NumPy 1.17.5 Release Notes

Contributors

Pull requests merged

Checksums

MD5

SHA256

NumPy 1.17.4 Release Notes

Highlights

Contributors

Pull requests merged

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SHA256

NumPy 1.17.3 Release Notes

Highlights

Compatibility notes

Contributors

Pull requests merged

Checksums

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SHA256

NumPy 1.17.2 Release Notes

Contributors

Pull requests merged

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NumPy 1.17.1 Release Notes

Contributors

Pull requests merged

Checksums

Multivariate hypergeometric distribution added to `numpy.random`

`np.fromfile` and `np.fromstring` will error on bad data

Deprecate non-scalar arrays as fill values in `ma.fill_value`

Deprecate `PyArray_As1D`, `PyArray_As2D`

Deprecate `np.alen`

The `axis` argument to `numpy.ma.mask_cols` and `numpy.ma.mask_row` is deprecated

`numpy.argmin/argmax/min/max` returns `NaT` if it exists in array

`np.can_cast(np.uint64, np.timedelta64, casting='safe')` is now `False`

Changed random variate stream from `numpy.random.Generator.integers`

Add more ufunc loops for `datetime64`, `timedelta64`

`PyDataType_ISUNSIZED(descr)` now returns False for structured datatypes

Add our own `*.pxd` cython import file

A tuple of axes can now be input to `expand_dims`

Add `--f2cmap` option to F2PY

`argwhere` now produces a consistent result on 0d arrays

Add `axis` argument for `random.permutation` and `random.shuffle`

`method` keyword argument for `np.random.multivariate_normal`

Add complex number support for `numpy.fromstring`

`numpy.unique` has consistent axes order when `axis` is not None

`numpy.matmul` with boolean output now converts to boolean values

`numpy.random.randint` produced incorrect value when the range was `2**32`

Add complex number support for `numpy.fromfile`

`std=c99` added if compiler is named `gcc`

`NaT` now sorts to the end of arrays

Incorrect `threshold` in `np.set_printoptions` raises `TypeError` or `ValueError`

`numpy.distutils` append behavior changed for LDFLAGS and similar

Remove `numpy.random.entropy` without a deprecation

Add options to quiet build configuration and build with `-Werror`

`numpy.polynomial` functions warn when passed `float` in place of `int`

Deprecate `numpy.distutils.exec_command` and `temp_file_name`

`numpy.nonzero` should no longer be called on 0d arrays

Writing to the result of `numpy.broadcast_arrays` will warn

`float16` subnormal rounding

`MaskedArray.mask` now returns a view of the mask, not the mask itself

Do not lookup `buffer` attribute in `numpy.frombuffer`

`out` is buffered for memory overlaps in `take`, `choose`, `put`

`i0` now always returns a result with the same shape as the input

`can_cast` no longer assumes all unsafe casting is allowed

`ndarray.flags.writeable` can be switched to true slightly more often

dimension or stride input arguments are now passed by `npy_intp const*`

New extensible `numpy.random` module with selectable random number generators

`ufunc.reduce` and related functions now accept a `where` mask

`packbits` and `unpackbits` accept an `order` keyword

`unpackbits` now accepts a `count` parameter

`linalg.svd` and `linalg.pinv` can be faster on hermitian inputs

divmod operation is now supported for two `timedelta64` operands

`fromfile` now takes an `offset` argument

New mode "empty" for `pad`

`empty_like` and related functions now accept a `shape` argument

Floating point scalars implement `as_integer_ratio` to match the builtin float

Structured `dtype` objects can be indexed with multiple fields names

`.npy` files support unicode field names

Replacement of the fftpack based `fft` module by the pocketfft library

Further improvements to `ctypes` support in `numpy.ctypeslib`

`numpy.errstate` is now also a function decorator

`numpy.exp` and `numpy.log` speed up for float32 implementation

Improve performance of `numpy.pad`

`numpy.interp` handles infinities more robustly

Pathlib support for `fromfile`, `tofile` and `ndarray.dump`

Specialized `isnan`, `isinf`, and `isfinite` ufuncs for bool and int types

`isfinite` supports `datetime64` and `timedelta64` types

New keywords added to `nan_to_num`

`floor`, `ceil`, and `trunc` now respect builtin magic methods

`quantile` now works on `fraction.Fraction` and `decimal.Decimal` objects

Support of object arrays in `matmul`

`median` and `percentile` family of functions no longer warn about `nan`

`timedelta64 % 0` behavior adjusted to return `NaT`

NumPy functions now always support overrides with `__array_function__`

`lib.recfunctions.structured_to_unstructured` does not squeeze single-field views

`clip` now uses a ufunc under the hood

`__array_interface__` offset now works as documented

Pickle protocol in `savez` set to 3 for `force zip64` flag

Structured arrays indexed with non-existent fields raise `KeyError` not `ValueError`