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v1.19.0rc1

18 May 19:20
@charris charris
v1.19.0rc1
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v1.19.0rc1 Pre-release
Pre-release

NumPy 1.19.0 Release Notes

This NumPy release is marked by the removal of much technical debt:
support for Python 2 has been removed, many deprecations have been
expired, and documentation has been improved. The polishing of the
random module continues apace with bug fixes and better usability from
Cython.

The Python versions supported for this release are 3.6-3.8. Downstream
developers should use Cython >= 0.29.16 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid problems on the Skylake architecture.

Highlights

  • Code compatibility with Python versions < 3.5 (including Python 2)
    was dropped from both the python and C code. The shims in
    numpy.compat will remain to support third-party packages, but they
    may be deprecated in a future release.

    (gh-15233)

Expired deprecations

numpy.insert and numpy.delete can no longer be passed an axis on 0d arrays

This concludes a deprecation from 1.9, where when an axis argument was
passed to a call to ~numpy.insert and ~numpy.delete on a 0d array,
the axis and obj argument and indices would be completely ignored.
In these cases, insert(arr, "nonsense", 42, axis=0) would actually
overwrite the entire array, while delete(arr, "nonsense", axis=0)
would be arr.copy()

Now passing axis on a 0d array raises ~numpy.AxisError.

(gh-15802)

numpy.delete no longer ignores out-of-bounds indices

This concludes deprecations from 1.8 and 1.9, where np.delete would
ignore both negative and out-of-bounds items in a sequence of indices.
This was at odds with its behavior when passed a single index.

Now out-of-bounds items throw IndexError, and negative items index
from the end.

(gh-15804)

numpy.insert and numpy.delete no longer accept non-integral indices

This concludes a deprecation from 1.9, where sequences of non-integers
indices were allowed and cast to integers. Now passing sequences of
non-integral indices raises IndexError, just like it does when passing
a single non-integral scalar.

(gh-15805)

numpy.delete no longer casts boolean indices to integers

This concludes a deprecation from 1.8, where np.delete would cast
boolean arrays and scalars passed as an index argument into integer
indices. The behavior now is to treat boolean arrays as a mask, and to
raise an error on boolean scalars.

(gh-15815)

Compatibility notes

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

A bug in the generation of random variates for the Dirichlet
distribution with small 'alpha' values was fixed by using a different
algorithm when max(alpha) < 0.1. Because of the change, the stream of
variates generated by dirichlet in this case will be different from
previous releases.

(gh-14924)

Scalar promotion in PyArray_ConvertToCommonType

The promotion of mixed scalars and arrays in
PyArray_ConvertToCommonType has been changed to adhere to those used
by np.result_type. This means that input such as
(1000, np.array([1], dtype=np.uint8))) will now return uint16
dtypes. In most cases the behaviour is unchanged. Note that the use of
this C-API function is generally discouraged. This also fixes
np.choose to behave the same way as the rest of NumPy in this respect.

(gh-14933)

Fasttake and fastputmask slots are deprecated and NULL'ed

The fasttake and fastputmask slots are now never used and must always be
set to NULL. This will result in no change in behaviour. However, if a
user dtype should set one of these a DeprecationWarning will be given.

(gh-14942)

np.ediff1d casting behaviour with to_end and to_begin

np.ediff1d now uses the "same_kind" casting rule for its additional
to_end and to_begin arguments. This ensures type safety except when
the input array has a smaller integer type than to_begin or to_end.
In rare cases, the behaviour will be more strict than it was previously
in 1.16 and 1.17. This is necessary to solve issues with floating point
NaN.

(gh-14981)

Converting of empty array-like objects to NumPy arrays

Objects with len(obj) == 0 which implement an "array-like"
interface, meaning an object implementing obj.__array__(),
obj.__array_interface__, obj.__array_struct__, or the python buffer
interface and which are also sequences (i.e. Pandas objects) will now
always retain there shape correctly when converted to an array. If such
an object has a shape of (0, 1) previously, it could be converted into
an array of shape (0,) (losing all dimensions after the first 0).

(gh-14995)

Removed multiarray.int_asbuffer

As part of the continued removal of Python 2 compatibility,
multiarray.int_asbuffer was removed. On Python 3, it threw a
NotImplementedError and was unused internally. It is expected that
there are no downstream use cases for this method with Python 3.

(gh-15229)

numpy.distutils.compat has been removed

This module contained only the function get_exception(), which was
used as:

try:
    ...
except Exception:
    e = get_exception()

Its purpose was to handle the change in syntax introduced in Python 2.6,
from except Exception, e: to except Exception as e:, meaning it was
only necessary for codebases supporting Python 2.5 and older.

(gh-15255)

issubdtype no longer interprets float as np.floating

numpy.issubdtype had a FutureWarning since NumPy 1.14 which has
expired now. This means that certain input where the second argument was
neither a datatype nor a NumPy scalar type (such as a string or a python
type like int or float) will now be consistent with passing in
np.dtype(arg2).type. This makes the result consistent with
expectations and leads to a false result in some cases which previously
returned true.

(gh-15773)

Change output of round on scalars to be consistent with Python

Output of the __round__ dunder method and consequently the Python
built-in round has been changed to be a Python int to be consistent
with calling it on Python float objects when called with no arguments.
Previously, it would return a scalar of the np.dtype that was passed
in.

(gh-15840)

The numpy.ndarray constructor no longer interprets strides=() as strides=None

The former has changed to have the expected meaning of setting
numpy.ndarray.strides to (), while the latter continues to result in
strides being chosen automatically.

(gh-15882)

C-Level string to datetime casts changed

The C-level casts from strings were simplified. This changed also fixes
string to datetime and timedelta casts to behave correctly (i.e. like
Python casts using string_arr.astype("M8") while previously the cast
would behave like string_arr.astype(np.int_).astype("M8"). This only
affects code using low-level C-API to do manual casts (not full array
casts) of single scalar values or using e.g. PyArray_GetCastFunc, and
should thus not affect the vast majority of users.

(gh-16068)

Deprecations

Deprecate automatic dtype=object for ragged input

Calling np.array([[1, [1, 2, 3]]) will issue a DeprecationWarning as
per NEP 34. Users should
explicitly use dtype=object to avoid the warning.

(gh-15119)

Passing shape=0 to factory functions in numpy.rec is deprecated

0 is treated as a special case and is aliased to None in the
functions:

  • numpy.core.records.fromarrays
  • numpy.core.records.fromrecords
  • numpy.core.records.fromstring
  • numpy.core.records.fromfile

In future, 0 will not be special cased, and will be treated as an
array length like any other integer.

(gh-15217)

Deprecation of probably unused C-API functions

The following C-API functions are probably unused and have been
deprecated:

  • PyArray_GetArrayParamsFromObject
  • PyUFunc_GenericFunction
  • PyUFunc_SetUsesArraysAsData

In most cases PyArray_GetArrayParamsFromObject should be replaced by
converting to an array, while PyUFunc_GenericFunction can be replaced
with PyObject_Call (see documentation for details).

(gh-15427)

Converting certain types to dtypes is Deprecated

The super classes of scalar types, such as np.integer, np.generic,
or np.inexact will now give a deprecation warning when converted to a
dtype (or used in a dtype keyword argument). The reason for this is that
np.integer is converted to np.int_, while it would be expected to
represent any integer (e.g. also int8, int16, etc. For example,
dtype=np.floating is currently identical to dtype=np.float64, even
though also np.float32 is a subclass of np.floating.

(gh-15534)

Deprecation of round for np.complexfloating scalars

Output of the __round__ dunder method and conseq...

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v1.18.4

03 May 15:52
@charris charris
v1.18.4
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v1.18.4

title: 'NumPy 1.18.4 Release Notes'

This is that last planned release in the 1.18.x series. It reverts the
bool("0") behavior introduced in 1.18.3 and fixes a bug in
Generator.integers. There is also improved help in the error message
emitted when numpy import fails due to a link to a new troubleshooting
section in the documentation that is now included.

The Python versions supported in this release are 3.5-3.8. Downstream
developers should use Cython >= 0.29.15 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid errors on the Skylake architecture.

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
  • Warren Weckesser

Pull requests merged

A total of 6 pull requests were merged for this release.

  • #16055 BLD: add i686 for 1.18 builds
  • #16090 BUG: random: Generator.integers(2**32) always returned 0.
  • #16091 BLD: fix path to libgfortran on macOS
  • #16109 REV: Reverts side-effect changes to casting
  • #16114 BLD: put openblas library in local directory on windows
  • #16132 DOC: Change import error "howto" to link to new troubleshooting...

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Assets 6

v1.18.3

19 Apr 20:28
@charris charris
v1.18.3
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charris Charles Harris
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v1.18.3

NumPy 1.18.3 Release Notes

This release contains various bug/regression fixes.

The Python versions supported in this release are 3.5-3.8. Downstream
developers should use Cython >= 0.29.15 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid errors on the Skylake architecture.

Highlights

  • Fix for the method='eigh' and method='cholesky' options in
    numpy.random.multivariate_normal. Those were producing samples
    from the wrong distribution.

Contributors

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

Pull requests merged

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

  • #15916: BUG: Fix eigh and cholesky methods of numpy.random.multivariate_normal
  • #15929: BUG,MAINT: Remove incorrect special case in string to number...
  • #15930: BUG: Guarantee array is in valid state after memory error occurs...
  • #15954: BUG: Check that [pvals]{.title-ref} is 1D in _generator.multinomial.
  • #16017: BUG: Alpha parameter must be 1D in _generator.dirichlet

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93ee59ec38f3bf8f9a42d5f4301f60e6825a4a6385a145f70badcd2bf2a11134  numpy-1.18.3.tar.gz
e46e2384209c91996d5ec16744234d1c906ab79a701ce1a26155c9ec890b8dc8  numpy-1.18.3.zip
Assets 6

v1.18.2

17 Mar 17:03
@charris charris
v1.18.2
This tag was signed with the committer’s verified signature.
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v1.18.2

NumPy 1.18.2 Release Notes

This small elease contains a fix for a performance regression in
numpy/random and several bug/maintenance updates.

The Python versions supported in this release are 3.5-3.8. Downstream
developers should use Cython >= 0.29.15 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid errors on the Skylake architecture.

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
  • Ganesh Kathiresan +
  • Matti Picus
  • Sebastian Berg
  • przemb +

Pull requests merged

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

  • #15675: TST: move _no_tracing to testing._private
  • #15676: MAINT: Large overhead in some random functions
  • #15677: TST: Do not create gfortran link in azure Mac testing.
  • #15679: BUG: Added missing error check in ndarray.__contains__
  • #15722: MAINT: use list-based APIs to call subprocesses
  • #15729: REL: Prepare for 1.18.2 release.
  • #15734: BUG: fix logic error when nm fails on 32-bit

Checksums

MD5

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511010c9fbd2516fe5a24aabcb76a56d  numpy-1.18.2.zip

SHA256

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Assets 6

v1.18.1

06 Jan 22:47
@charris charris
v1.18.1
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v1.18.1

NumPy 1.18.1 Release Notes

This release contains fixes for bugs reported against NumPy 1.18.0. Two
bugs in particular that caused widespread problems downstream were:

  • The cython random extension test was not using a temporary directory
    for building, resulting in a permission violation. Fixed.
  • Numpy distutils was appending [-std=c99]{.title-ref} to all C
    compiler runs, leading to changed behavior and compile problems
    downstream. That flag is now only applied when building numpy C
    code.

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.

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
  • Matti Picus
  • Maxwell Aladago
  • Pauli Virtanen
  • Ralf Gommers
  • Tyler Reddy
  • Warren Weckesser

Pull requests merged

A total of 13 pull requests were merged for this release.

  • #15158: MAINT: Update pavement.py for towncrier.
  • #15159: DOC: add moved modules to 1.18 release note
  • #15161: MAINT, DOC: Minor backports and updates for 1.18.x
  • #15176: TST: Add assert_array_equal test for big integer arrays
  • #15184: BUG: use tmp dir and check version for cython test (#15170)
  • #15220: BUG: distutils: fix msvc+gfortran openblas handling corner case
  • #15221: BUG: remove -std=c99 for c++ compilation (#15194)
  • #15222: MAINT: unskip test on win32
  • #15223: TST: add BLAS ILP64 run in Travis & Azure
  • #15245: MAINT: only add --std=c99 where needed
  • #15246: BUG: lib: Fix handling of integer arrays by gradient.
  • #15247: MAINT: Do not use private Python function in testing
  • #15250: REL: Prepare for the NumPy 1.18.1 release.

Checksums

MD5

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SHA256

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Assets 6

v1.17.5

01 Jan 17:37
@charris charris
v1.17.5
This tag was signed with the committer’s verified signature.
charris Charles Harris
GPG key ID: 679F228377C5247B
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v1.17.5

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.

Checksums

MD5

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SHA256

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Assets 6

v1.16.6

29 Dec 23:56
@charris charris
v1.16.6
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v1.16.6

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

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.

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.

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Assets 6

v1.18.0

22 Dec 16:42
@charris charris
v1.18.0
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v1.18.0

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 an...

Read more
Assets 6

v1.18.0rc1

06 Dec 02:14
@charris charris
v1.18.0rc1
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v1.18.0rc1 Pre-release
Pre-release

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. This method generates random variates from the
multivariate hypergeometric probability distribution.
(gh-13794 <https://github.com/numpy/numpy/pull/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 <https://github.com/numpy/numpy/pull/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 <https://github.com/numpy/numpy/pull/13698>__)

Deprecate PyArray_As1D, PyArray_As2D

PyArray_As1D, PyArray_As2D are deprecated, use PyArray_AsCArray
instead (gh-14036 <https://github.com/numpy/numpy/pull/14036>__)

Deprecate np.alen

np.alen was deprecated. Use len instead.
(gh-14181 <https://github.com/numpy/numpy/pull/14181>__)

Deprecate the financial functions

In accordance with
NEP-32 <https://numpy.org/neps/nep-0032-remove-financial-functions.html>,
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 <https://pypi.org/project/numpy-financial>*.
(gh-14720 <https://github.com/numpy/numpy/pull/14720>__)

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

This argument was always ignored.
(gh-14996 <https://github.com/numpy/numpy/pull/14996>__)

Expired deprecations

  • PyArray_As1D and PyArray_As2D have been removed in favor of
    PyArray_AsCArray
    (gh-14036 <https://github.com/numpy/numpy/pull/14036>__)

  • np.rank has been removed. This was deprecated in NumPy 1.10 and
    has been replaced by np.ndim.
    (gh-14039 <https://github.com/numpy/numpy/pull/14039>__)

  • The deprecation of expand_dims out-of-range axes in 1.13.0 has
    expired.
    (gh-14051 <https://github.com/numpy/numpy/pull/14051>__)

  • PyArray_FromDimsAndDataAndDescr and PyArray_FromDims have been
    removed (they will always raise an error). Use
    PyArray_NewFromDescr and PyArray_SimpleNew instead.
    (gh-14100 <https://github.com/numpy/numpy/pull/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 <https://github.com/numpy/numpy/pull/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 <https://github.com/numpy/numpy/pull/14259>__)

  • np.testing.rand has been removed. This was deprecated in NumPy
    1.11 and has been replaced by np.random.rand.
    (gh-14325 <https://github.com/numpy/numpy/pull/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 <https://github.com/numpy/numpy/pull/14335>__)

  • Remove deprecated support for boolean and empty condition lists in
    np.select
    (gh-14583 <https://github.com/numpy/numpy/pull/14583>__)

  • Array order only accepts 'C', 'F', 'A', and 'K'. More permissive
    options were deprecated in NumPy 1.11.
    (gh-14596 <https://github.com/numpy/numpy/pull/14596>__)

  • np.linspace parameter num must be an integer. Deprecated in NumPy
    1.12. (gh-14620 <https://github.com/numpy/numpy/pull/14620>__)

  • UFuncs with multiple outputs must use a tuple for the out kwarg.
    This finishes a deprecation started in NumPy 1.10.
    (gh-14682 <https://github.com/numpy/numpy/pull/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 <https://github.com/numpy/numpy/pull/14567>__)

Compatibility notes

numpy.lib.recfunctions.drop_fields 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 <https://github.com/numpy/numpy/pull/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 <https://github.com/numpy/numpy/pull/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 <https://github.com/numpy/numpy/pull/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 <https://github.com/numpy/numpy/pull/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 <https://github.com/numpy/numpy/pull/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 <https://github.com/numpy/numpy/pull/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 <https://github.com/numpy/numpy/pull/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 <https://github.com/numpy/numpy/pull/14051>__)

Support for 64-bit OpenBLAS with symbol suffix

Added support for 64-bit (ILP64) OpenBLAS compiled with
make INTERFACE64=1 SYMBOLSUFFIX=64_. See site.cfg.example for
details. (gh-15012 <https://github.com/numpy/numpy/pull/15012>__)

Improvements

Different C numeric types of the same size have ...

Read more
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v1.17.4

11 Nov 02:14
@charris charris
v1.17.4
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v1.17.4

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

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Assets 6