--- /dev/null
+
+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 <https://github.com/numpy/numpy/pull/13742>`__: ENH: Add project URLs to setup.py
+* `#13823 <https://github.com/numpy/numpy/pull/13823>`__: TEST, ENH: fix tests and ctypes code for PyPy
+* `#13845 <https://github.com/numpy/numpy/pull/13845>`__: BUG: use npy_intp instead of int for indexing array
+* `#13867 <https://github.com/numpy/numpy/pull/13867>`__: TST: Ignore DeprecationWarning during nose imports
+* `#13905 <https://github.com/numpy/numpy/pull/13905>`__: BUG: Fix use-after-free in boolean indexing
+* `#13933 <https://github.com/numpy/numpy/pull/13933>`__: MAINT/BUG/DOC: Fix errors in _add_newdocs
+* `#13984 <https://github.com/numpy/numpy/pull/13984>`__: BUG: fix byte order reversal for datetime64[ns]
+* `#13994 <https://github.com/numpy/numpy/pull/13994>`__: MAINT,BUG: Use nbytes to also catch empty descr during allocation
+* `#14042 <https://github.com/numpy/numpy/pull/14042>`__: BUG: np.array cleared errors occured in PyMemoryView_FromObject
+* `#14043 <https://github.com/numpy/numpy/pull/14043>`__: BUG: Fixes for Undefined Behavior Sanitizer (UBSan) errors.
+* `#14044 <https://github.com/numpy/numpy/pull/14044>`__: BUG: ensure that casting to/from structured is properly checked.
+* `#14045 <https://github.com/numpy/numpy/pull/14045>`__: MAINT: fix histogram*d dispatchers
+* `#14046 <https://github.com/numpy/numpy/pull/14046>`__: BUG: further fixup to histogram2d dispatcher.
+* `#14052 <https://github.com/numpy/numpy/pull/14052>`__: BUG: Replace contextlib.suppress for Python 2.7
+* `#14056 <https://github.com/numpy/numpy/pull/14056>`__: BUG: fix compilation of 3rd party modules with Py_LIMITED_API...
+* `#14057 <https://github.com/numpy/numpy/pull/14057>`__: BUG: Fix memory leak in dtype from dict contructor
+* `#14058 <https://github.com/numpy/numpy/pull/14058>`__: DOC: Document array_function at a higher level.
+* `#14084 <https://github.com/numpy/numpy/pull/14084>`__: BUG, DOC: add new recfunctions to `__all__`
+* `#14162 <https://github.com/numpy/numpy/pull/14162>`__: BUG: Remove stray print that causes a SystemError on python 3.7
+* `#14297 <https://github.com/numpy/numpy/pull/14297>`__: TST: Pin pytest version to 5.0.1.
+* `#14322 <https://github.com/numpy/numpy/pull/14322>`__: ENH: Enable huge pages in all Linux builds
+* `#14346 <https://github.com/numpy/numpy/pull/14346>`__: BUG: fix behavior of structured_to_unstructured on non-trivial...
+* `#14382 <https://github.com/numpy/numpy/pull/14382>`__: REL: Prepare for the NumPy 1.16.5 release.
--- /dev/null
+
+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 <https://github.com/numpy/numpy/pull/14211>`__: BUG: Fix uint-overflow if padding with linear_ramp and negative...
+* `#14275 <https://github.com/numpy/numpy/pull/14275>`__: BUG: fixing to allow unpickling of PY3 pickles from PY2
+* `#14340 <https://github.com/numpy/numpy/pull/14340>`__: BUG: Fix misuse of .names and .fields in various places (backport...
+* `#14423 <https://github.com/numpy/numpy/pull/14423>`__: BUG: test, fix regression in converting to ctypes.
+* `#14434 <https://github.com/numpy/numpy/pull/14434>`__: BUG: Fixed maximum relative error reporting in assert_allclose
+* `#14509 <https://github.com/numpy/numpy/pull/14509>`__: BUG: Fix regression in boolean matmul.
+* `#14686 <https://github.com/numpy/numpy/pull/14686>`__: BUG: properly define PyArray_DescrCheck
+* `#14853 <https://github.com/numpy/numpy/pull/14853>`__: BLD: add 'apt update' to shippable
+* `#14854 <https://github.com/numpy/numpy/pull/14854>`__: BUG: Fix _ctypes class circular reference. (#13808)
+* `#14856 <https://github.com/numpy/numpy/pull/14856>`__: BUG: Fix `np.einsum` errors on Power9 Linux and z/Linux
+* `#14863 <https://github.com/numpy/numpy/pull/14863>`__: BLD: Prevent -flto from optimising long double representation...
+* `#14864 <https://github.com/numpy/numpy/pull/14864>`__: BUG: lib: Fix histogram problem with signed integer arrays.
+* `#15172 <https://github.com/numpy/numpy/pull/15172>`__: ENH: Backport improvements to testing functions.
+* `#15191 <https://github.com/numpy/numpy/pull/15191>`__: REL: Prepare for 1.16.6 release.
--- /dev/null
+
+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 7 pull requests were merged for this release.
+
+* `#14593 <https://github.com/numpy/numpy/pull/14593>`__: MAINT: backport Cython API cleanup to 1.17.x, remove docs
+* `#14937 <https://github.com/numpy/numpy/pull/14937>`__: BUG: fix integer size confusion in handling array's ndmin argument
+* `#14939 <https://github.com/numpy/numpy/pull/14939>`__: BUILD: remove SSE2 flag from numpy.random builds
+* `#14993 <https://github.com/numpy/numpy/pull/14993>`__: MAINT: Added Python3.8 branch to dll lib discovery
+* `#15038 <https://github.com/numpy/numpy/pull/15038>`__: BUG: Fix refcounting in ufunc object loops
+* `#15067 <https://github.com/numpy/numpy/pull/15067>`__: BUG: Exceptions tracebacks are dropped
+* `#15175 <https://github.com/numpy/numpy/pull/15175>`__: ENH: Backport improvements to testing functions.
--- /dev/null
+.. currentmodule:: numpy
+
+==========================
+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 <https://github.com/numpy/numpy/pull/13742>`__: ENH: Add project URLs to setup.py
+* `#13823 <https://github.com/numpy/numpy/pull/13823>`__: TEST, ENH: fix tests and ctypes code for PyPy
+* `#13845 <https://github.com/numpy/numpy/pull/13845>`__: BUG: use npy_intp instead of int for indexing array
+* `#13867 <https://github.com/numpy/numpy/pull/13867>`__: TST: Ignore DeprecationWarning during nose imports
+* `#13905 <https://github.com/numpy/numpy/pull/13905>`__: BUG: Fix use-after-free in boolean indexing
+* `#13933 <https://github.com/numpy/numpy/pull/13933>`__: MAINT/BUG/DOC: Fix errors in _add_newdocs
+* `#13984 <https://github.com/numpy/numpy/pull/13984>`__: BUG: fix byte order reversal for datetime64[ns]
+* `#13994 <https://github.com/numpy/numpy/pull/13994>`__: MAINT,BUG: Use nbytes to also catch empty descr during allocation
+* `#14042 <https://github.com/numpy/numpy/pull/14042>`__: BUG: np.array cleared errors occured in PyMemoryView_FromObject
+* `#14043 <https://github.com/numpy/numpy/pull/14043>`__: BUG: Fixes for Undefined Behavior Sanitizer (UBSan) errors.
+* `#14044 <https://github.com/numpy/numpy/pull/14044>`__: BUG: ensure that casting to/from structured is properly checked.
+* `#14045 <https://github.com/numpy/numpy/pull/14045>`__: MAINT: fix histogram*d dispatchers
+* `#14046 <https://github.com/numpy/numpy/pull/14046>`__: BUG: further fixup to histogram2d dispatcher.
+* `#14052 <https://github.com/numpy/numpy/pull/14052>`__: BUG: Replace contextlib.suppress for Python 2.7
+* `#14056 <https://github.com/numpy/numpy/pull/14056>`__: BUG: fix compilation of 3rd party modules with Py_LIMITED_API...
+* `#14057 <https://github.com/numpy/numpy/pull/14057>`__: BUG: Fix memory leak in dtype from dict contructor
+* `#14058 <https://github.com/numpy/numpy/pull/14058>`__: DOC: Document array_function at a higher level.
+* `#14084 <https://github.com/numpy/numpy/pull/14084>`__: BUG, DOC: add new recfunctions to `__all__`
+* `#14162 <https://github.com/numpy/numpy/pull/14162>`__: BUG: Remove stray print that causes a SystemError on python 3.7
+* `#14297 <https://github.com/numpy/numpy/pull/14297>`__: TST: Pin pytest version to 5.0.1.
+* `#14322 <https://github.com/numpy/numpy/pull/14322>`__: ENH: Enable huge pages in all Linux builds
+* `#14346 <https://github.com/numpy/numpy/pull/14346>`__: BUG: fix behavior of structured_to_unstructured on non-trivial...
+* `#14382 <https://github.com/numpy/numpy/pull/14382>`__: REL: Prepare for the NumPy 1.16.5 release.
--- /dev/null
+.. currentmodule:: numpy
+
+==========================
+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 (`@` operator) 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 (`@` operator) 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 <https://github.com/numpy/numpy/pull/14211>`__: BUG: Fix uint-overflow if padding with linear_ramp and negative...
+* `#14275 <https://github.com/numpy/numpy/pull/14275>`__: BUG: fixing to allow unpickling of PY3 pickles from PY2
+* `#14340 <https://github.com/numpy/numpy/pull/14340>`__: BUG: Fix misuse of .names and .fields in various places (backport...
+* `#14423 <https://github.com/numpy/numpy/pull/14423>`__: BUG: test, fix regression in converting to ctypes.
+* `#14434 <https://github.com/numpy/numpy/pull/14434>`__: BUG: Fixed maximum relative error reporting in assert_allclose
+* `#14509 <https://github.com/numpy/numpy/pull/14509>`__: BUG: Fix regression in boolean matmul.
+* `#14686 <https://github.com/numpy/numpy/pull/14686>`__: BUG: properly define PyArray_DescrCheck
+* `#14853 <https://github.com/numpy/numpy/pull/14853>`__: BLD: add 'apt update' to shippable
+* `#14854 <https://github.com/numpy/numpy/pull/14854>`__: BUG: Fix _ctypes class circular reference. (#13808)
+* `#14856 <https://github.com/numpy/numpy/pull/14856>`__: BUG: Fix `np.einsum` errors on Power9 Linux and z/Linux
+* `#14863 <https://github.com/numpy/numpy/pull/14863>`__: BLD: Prevent -flto from optimising long double representation...
+* `#14864 <https://github.com/numpy/numpy/pull/14864>`__: BUG: lib: Fix histogram problem with signed integer arrays.
+* `#15172 <https://github.com/numpy/numpy/pull/15172>`__: ENH: Backport improvements to testing functions.
+* `#15191 <https://github.com/numpy/numpy/pull/15191>`__: REL: Prepare for 1.16.6 release.
===================
- The seldom used ``PyArray_DescrCheck`` macro has been changed/fixed.
+- The use of the new ``numpy.random`` features from Cython and Numba
+ was not well documented and parts have been removed or refactored.
+ We plan to finish the refactor and fully document it in 1.18.0
+
Contributors
--- /dev/null
+.. currentmodule:: numpy
+
+==========================
+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 >= 0.29.13 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 <https://github.com/numpy/numpy/pull/14593>`__: MAINT: backport Cython API cleanup to 1.17.x, remove docs
+* `#14937 <https://github.com/numpy/numpy/pull/14937>`__: BUG: fix integer size confusion in handling array's ndmin argument
+* `#14939 <https://github.com/numpy/numpy/pull/14939>`__: BUILD: remove SSE2 flag from numpy.random builds
+* `#14993 <https://github.com/numpy/numpy/pull/14993>`__: MAINT: Added Python3.8 branch to dll lib discovery
+* `#15038 <https://github.com/numpy/numpy/pull/15038>`__: BUG: Fix refcounting in ufunc object loops
+* `#15067 <https://github.com/numpy/numpy/pull/15067>`__: BUG: Exceptions tracebacks are dropped
+* `#15175 <https://github.com/numpy/numpy/pull/15175>`__: ENH: Backport improvements to testing functions.
+* `#15213 <https://github.com/numpy/numpy/pull/15213>`__: REL: Prepare for the NumPy 1.17.5 release.
+++ /dev/null
-.. currentmodule:: numpy.random
-
-Extending
----------
-The BitGenerators have been designed to be extendable using standard tools for
-high-performance Python -- numba and Cython. The `~Generator` object can also
-be used with user-provided BitGenerators as long as these export a small set of
-required functions.
-
-Numba
-=====
-Numba can be used with either CTypes or CFFI. The current iteration of the
-BitGenerators all export a small set of functions through both interfaces.
-
-This example shows how numba can be used to produce Box-Muller normals using
-a pure Python implementation which is then compiled. The random numbers are
-provided by ``ctypes.next_double``.
-
-.. code-block:: python
-
- from numpy.random import PCG64
- import numpy as np
- import numba as nb
-
- x = PCG64()
- f = x.ctypes.next_double
- s = x.ctypes.state
- state_addr = x.ctypes.state_address
-
- def normals(n, state):
- out = np.empty(n)
- for i in range((n+1)//2):
- x1 = 2.0*f(state) - 1.0
- x2 = 2.0*f(state) - 1.0
- r2 = x1*x1 + x2*x2
- while r2 >= 1.0 or r2 == 0.0:
- x1 = 2.0*f(state) - 1.0
- x2 = 2.0*f(state) - 1.0
- r2 = x1*x1 + x2*x2
- g = np.sqrt(-2.0*np.log(r2)/r2)
- out[2*i] = g*x1
- if 2*i+1 < n:
- out[2*i+1] = g*x2
- return out
-
- # Compile using Numba
- print(normals(10, s).var())
- # Warm up
- normalsj = nb.jit(normals, nopython=True)
- # Must use state address not state with numba
- normalsj(1, state_addr)
- %timeit normalsj(1000000, state_addr)
- print('1,000,000 Box-Muller (numba/PCG64) randoms')
- %timeit np.random.standard_normal(1000000)
- print('1,000,000 Box-Muller (NumPy) randoms')
-
-
-Both CTypes and CFFI allow the more complicated distributions to be used
-directly in Numba after compiling the file distributions.c into a DLL or so.
-An example showing the use of a more complicated distribution is in the
-examples folder.
-
-.. _randomgen_cython:
-
-Cython
-======
-
-Cython can be used to unpack the ``PyCapsule`` provided by a BitGenerator.
-This example uses `~pcg64.PCG64` and
-``random_gauss_zig``, the Ziggurat-based generator for normals, to fill an
-array. The usual caveats for writing high-performance code using Cython --
-removing bounds checks and wrap around, providing array alignment information
--- still apply.
-
-.. code-block:: cython
-
- import numpy as np
- cimport numpy as np
- cimport cython
- from cpython.pycapsule cimport PyCapsule_IsValid, PyCapsule_GetPointer
- from numpy.random.common cimport *
- from numpy.random.distributions cimport random_gauss_zig
- from numpy.random import PCG64
-
-
- @cython.boundscheck(False)
- @cython.wraparound(False)
- def normals_zig(Py_ssize_t n):
- cdef Py_ssize_t i
- cdef bitgen_t *rng
- cdef const char *capsule_name = "BitGenerator"
- cdef double[::1] random_values
-
- x = PCG64()
- capsule = x.capsule
- if not PyCapsule_IsValid(capsule, capsule_name):
- raise ValueError("Invalid pointer to anon_func_state")
- rng = <bitgen_t *> PyCapsule_GetPointer(capsule, capsule_name)
- random_values = np.empty(n)
- # Best practice is to release GIL and acquire the lock
- with x.lock, nogil:
- for i in range(n):
- random_values[i] = random_gauss_zig(rng)
- randoms = np.asarray(random_values)
- return randoms
-
-The BitGenerator can also be directly accessed using the members of the basic
-RNG structure.
-
-.. code-block:: cython
-
- @cython.boundscheck(False)
- @cython.wraparound(False)
- def uniforms(Py_ssize_t n):
- cdef Py_ssize_t i
- cdef bitgen_t *rng
- cdef const char *capsule_name = "BitGenerator"
- cdef double[::1] random_values
-
- x = PCG64()
- capsule = x.capsule
- # Optional check that the capsule if from a BitGenerator
- if not PyCapsule_IsValid(capsule, capsule_name):
- raise ValueError("Invalid pointer to anon_func_state")
- # Cast the pointer
- rng = <bitgen_t *> PyCapsule_GetPointer(capsule, capsule_name)
- random_values = np.empty(n)
- with x.lock, nogil:
- for i in range(n):
- # Call the function
- random_values[i] = rng.next_double(rng.state)
- randoms = np.asarray(random_values)
- return randoms
-
-These functions along with a minimal setup file are included in the
-examples folder.
-
-New Basic RNGs
-==============
-`~Generator` can be used with other user-provided BitGenerators. The simplest
-way to write a new BitGenerator is to examine the pyx file of one of the
-existing BitGenerators. The key structure that must be provided is the
-``capsule`` which contains a ``PyCapsule`` to a struct pointer of type
-``bitgen_t``,
-
-.. code-block:: c
-
- typedef struct bitgen {
- void *state;
- uint64_t (*next_uint64)(void *st);
- uint32_t (*next_uint32)(void *st);
- double (*next_double)(void *st);
- uint64_t (*next_raw)(void *st);
- } bitgen_t;
-
-which provides 5 pointers. The first is an opaque pointer to the data structure
-used by the BitGenerators. The next three are function pointers which return
-the next 64- and 32-bit unsigned integers, the next random double and the next
-raw value. This final function is used for testing and so can be set to
-the next 64-bit unsigned integer function if not needed. Functions inside
-``Generator`` use this structure as in
-
-.. code-block:: c
-
- bitgen_state->next_uint64(bitgen_state->state)
* Optional ``out`` argument that allows existing arrays to be filled for
select distributions
* All BitGenerators can produce doubles, uint64s and uint32s via CTypes
- (`~.PCG64.ctypes`) and CFFI (`~.PCG64.cffi`). This allows the bit generators
- to be used in numba.
-* The bit generators can be used in downstream projects via
- :ref:`Cython <randomgen_cython>`.
* `~.Generator.integers` is now the canonical way to generate integer
random numbers from a discrete uniform distribution. The ``rand`` and
``randn`` methods are only available through the legacy `~.RandomState`.
Multithreaded Generation <multithreading>
new-or-different
Comparing Performance <performance>
- extending
Original Source
~~~~~~~~~~~~~~~
``randn`` methods are only available through the legacy `~.RandomState`.
This replaces both ``randint`` and the deprecated ``random_integers``.
* The Box-Muller method used to produce NumPy's normals is no longer available.
-* All bit generators can produce doubles, uint64s and
- uint32s via CTypes (`~PCG64.ctypes`) and CFFI (`~PCG64.cffi`).
- This allows these bit generators to be used in numba.
+* All bit generators can produce doubles, uint64s and uint32s via CTypes
+ (`~PCG64.ctypes`) and CFFI (`~PCG64.cffi`). This allows these bit generators
+ to be used in numba.
* The bit generators can be used in downstream projects via
Cython.
-
.. ipython:: python
from numpy.random import Generator, PCG64
Release Notes
*************
+.. include:: ../release/1.17.5-notes.rst
.. include:: ../release/1.17.4-notes.rst
.. include:: ../release/1.17.3-notes.rst
.. include:: ../release/1.17.2-notes.rst
.. include:: ../release/1.17.1-notes.rst
.. include:: ../release/1.17.0-notes.rst
+.. include:: ../release/1.16.6-notes.rst
+.. include:: ../release/1.16.5-notes.rst
.. include:: ../release/1.16.4-notes.rst
.. include:: ../release/1.16.3-notes.rst
.. include:: ../release/1.16.2-notes.rst
+++ /dev/null
-#ifndef _RANDOM_BITGEN_H
-#define _RANDOM_BITGEN_H
-
-#pragma once
-#include <stddef.h>
-#include <stdbool.h>
-#include <stdint.h>
-
-/* Must match the declaration in numpy/random/common.pxd */
-
-typedef struct bitgen {
- void *state;
- uint64_t (*next_uint64)(void *st);
- uint32_t (*next_uint32)(void *st);
- double (*next_double)(void *st);
- uint64_t (*next_raw)(void *st);
-} bitgen_t;
-
-
-#endif
except Exception:
fielddict = ndarray.__getattribute__(self, 'dtype').fields or {}
if attr not in fielddict:
- exctype, value = sys.exc_info()[:2]
- raise exctype(value)
+ raise
else:
fielddict = ndarray.__getattribute__(self, 'dtype').fields or {}
if attr not in fielddict:
PyArrayObject *oparr = NULL, *ret = NULL;
npy_bool subok = NPY_FALSE;
npy_bool copy = NPY_TRUE;
- int nd;
- npy_intp ndmin = 0;
+ int ndmin = 0, nd;
PyArray_Descr *type = NULL;
PyArray_Descr *oldtype = NULL;
NPY_ORDER order = NPY_KEEPORDER;
ndmin_obj = PyDict_GetItem(kws, npy_ma_str_ndmin);
if (ndmin_obj) {
- ndmin = PyLong_AsLong(ndmin_obj);
- if (error_converting(ndmin)) {
+ long t = PyLong_AsLong(ndmin_obj);
+ if (error_converting(t)) {
goto clean_type;
}
- else if (ndmin > NPY_MAXDIMS) {
+ else if (t > NPY_MAXDIMS) {
goto full_path;
}
+ ndmin = t;
}
/* copy=False with default dtype, order (any is OK) and ndim */
PyObject **out = (PyObject **)op1;
PyObject *ret, *func;
func = PyObject_GetAttrString(in1 ? in1 : Py_None, meth);
- if (func == NULL || !PyCallable_Check(func)) {
+ if (func != NULL && !PyCallable_Check(func)) {
+ Py_DECREF(func);
+ func = NULL;
+ }
+ if (func == NULL) {
PyObject *exc, *val, *tb;
PyTypeObject *type = in1 ? Py_TYPE(in1) : Py_TYPE(Py_None);
PyErr_Fetch(&exc, &val, &tb);
return;
}
ret = PyObject_Call(func, tup, NULL);
+ Py_DECREF(func);
if (ret == NULL) {
Py_DECREF(tup);
return;
extra_dll_dir = os.path.join(os.path.dirname(__file__), '.libs')
if sys.platform == 'win32' and os.path.isdir(extra_dll_dir):
- os.environ.setdefault('PATH', '')
- os.environ['PATH'] += os.pathsep + extra_dll_dir
+ if sys.version_info >= (3, 8):
+ os.add_dll_directory(extra_dll_dir)
+ else:
+ os.environ.setdefault('PATH', '')
+ os.environ['PATH'] += os.pathsep + extra_dll_dir
"""))
fielddict = ndarray.__getattribute__(self, 'dtype').fields or {}
optinfo = ndarray.__getattribute__(self, '_optinfo') or {}
if not (attr in fielddict or attr in optinfo):
- exctype, value = sys.exc_info()[:2]
- raise exctype(value)
+ raise
else:
# Get the list of names
fielddict = ndarray.__getattribute__(self, 'dtype').fields or {}
uintptr_t)
from libc.math cimport sqrt
-cdef extern from "numpy/random/bitgen.h":
+cdef extern from "src/bitgen.h":
struct bitgen:
void *state
uint64_t (*next_uint64)(void *st) nogil
defs.append(('NPY_NO_DEPRECATED_API', 0))
config.add_data_dir('tests')
- config.add_data_files('common.pxd')
- config.add_data_files('bit_generator.pxd')
EXTRA_LINK_ARGS = []
# Math lib
elif not is_msvc:
# Some bit generators require c99
EXTRA_COMPILE_ARGS += ['-std=c99']
- INTEL_LIKE = any(arch in platform.machine()
- for arch in ('x86', 'i686', 'i386', 'amd64'))
- if INTEL_LIKE:
- # Assumes GCC or GCC-like compiler
- EXTRA_COMPILE_ARGS += ['-msse2']
# Use legacy integer variable sizes
LEGACY_DEFS = [('NP_RANDOM_LEGACY', '1')]
--- /dev/null
+#ifndef _RANDOM_BITGEN_H
+#define _RANDOM_BITGEN_H
+
+#pragma once
+#include <stddef.h>
+#include <stdbool.h>
+#include <stdint.h>
+
+/* Must match the declaration in numpy/random/common.pxd */
+
+typedef struct bitgen {
+ void *state;
+ uint64_t (*next_uint64)(void *st);
+ uint32_t (*next_uint32)(void *st);
+ double (*next_double)(void *st);
+ uint64_t (*next_raw)(void *st);
+} bitgen_t;
+
+
+#endif
#include "Python.h"
#include "numpy/npy_common.h"
#include "numpy/npy_math.h"
-#include "numpy/random/bitgen.h"
+#include "src/bitgen.h"
/*
* RAND_INT_TYPE is used to share integer generators with RandomState which
check that all elements of these objects are equal. An exception is raised
at the first conflicting values.
+ When one of `actual` and `desired` is a scalar and the other is array_like,
+ the function checks that each element of the array_like object is equal to
+ the scalar.
+
This function handles NaN comparisons as if NaN was a "normal" number.
That is, no assertion is raised if both objects have NaNs in the same
positions. This is in contrast to the IEEE standard on NaNs, which says
if isscalar(desired) != isscalar(actual):
raise AssertionError(msg)
- # Inf/nan/negative zero handling
- try:
- isdesnan = gisnan(desired)
- isactnan = gisnan(actual)
- if isdesnan and isactnan:
- return # both nan, so equal
-
- # handle signed zero specially for floats
- if desired == 0 and actual == 0:
- if not signbit(desired) == signbit(actual):
- raise AssertionError(msg)
-
- except (TypeError, ValueError, NotImplementedError):
- pass
-
try:
isdesnat = isnat(desired)
isactnat = isnat(actual)
except (TypeError, ValueError, NotImplementedError):
pass
+ # Inf/nan/negative zero handling
+ try:
+ isdesnan = gisnan(desired)
+ isactnan = gisnan(actual)
+ if isdesnan and isactnan:
+ return # both nan, so equal
+
+ # handle signed zero specially for floats
+ array_actual = array(actual)
+ array_desired = array(desired)
+ if (array_actual.dtype.char in 'Mm' or
+ array_desired.dtype.char in 'Mm'):
+ # version 1.18
+ # until this version, gisnan failed for datetime64 and timedelta64.
+ # Now it succeeds but comparison to scalar with a different type
+ # emits a DeprecationWarning.
+ # Avoid that by skipping the next check
+ raise NotImplementedError('cannot compare to a scalar '
+ 'with a different type')
+
+ if desired == 0 and actual == 0:
+ if not signbit(desired) == signbit(actual):
+ raise AssertionError(msg)
+
+ except (TypeError, ValueError, NotImplementedError):
+ pass
+
try:
# Explicitly use __eq__ for comparison, gh-2552
if not (desired == actual):
header='', precision=6, equal_nan=True,
equal_inf=True):
__tracebackhide__ = True # Hide traceback for py.test
- from numpy.core import array, array2string, isnan, inf, bool_, errstate, all
+ from numpy.core import array, array2string, isnan, inf, bool_, errstate, all, max, object_
x = array(x, copy=False, subok=True)
y = array(y, copy=False, subok=True)
# do not trigger a failure (np.ma.masked != True evaluates as
# np.ma.masked, which is falsy).
if cond != True:
- mismatch = 100. * (reduced.size - reduced.sum(dtype=intp)) / ox.size
- remarks = ['Mismatch: {:.3g}%'.format(mismatch)]
+ n_mismatch = reduced.size - reduced.sum(dtype=intp)
+ n_elements = flagged.size if flagged.ndim != 0 else reduced.size
+ percent_mismatch = 100 * n_mismatch / n_elements
+ remarks = [
+ 'Mismatched elements: {} / {} ({:.3g}%)'.format(
+ n_mismatch, n_elements, percent_mismatch)]
with errstate(invalid='ignore', divide='ignore'):
# ignore errors for non-numeric types
with contextlib.suppress(TypeError):
error = abs(x - y)
- max_abs_error = error.max()
- if error.dtype == 'object':
+ max_abs_error = max(error)
+ if getattr(error, 'dtype', object_) == object_:
remarks.append('Max absolute difference: '
+ str(max_abs_error))
else:
if all(~nonzero):
max_rel_error = array(inf)
else:
- max_rel_error = (error[nonzero] / abs(y[nonzero])).max()
- if error.dtype == 'object':
+ max_rel_error = max(error[nonzero] / abs(y[nonzero]))
+ if getattr(error, 'dtype', object_) == object_:
remarks.append('Max relative difference: '
+ str(max_rel_error))
else:
Raises an AssertionError if two array_like objects are not equal.
Given two array_like objects, check that the shape is equal and all
- elements of these objects are equal. An exception is raised at
- shape mismatch or conflicting values. In contrast to the standard usage
- in numpy, NaNs are compared like numbers, no assertion is raised if
- both objects have NaNs in the same positions.
+ elements of these objects are equal (but see the Notes for the special
+ handling of a scalar). An exception is raised at shape mismatch or
+ conflicting values. In contrast to the standard usage in numpy, NaNs
+ are compared like numbers, no assertion is raised if both objects have
+ NaNs in the same positions.
The usual caution for verifying equality with floating point numbers is
advised.
relative and/or absolute precision.
assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
+ Notes
+ -----
+ When one of `x` and `y` is a scalar and the other is array_like, the
+ function checks that each element of the array_like object is equal to
+ the scalar.
+
Examples
--------
The first assert does not raise an exception:
>>> np.testing.assert_array_equal([1.0,2.33333,np.nan],
... [np.exp(0),2.33333, np.nan])
- Assert fails with numerical inprecision with floats:
+ Assert fails with numerical imprecision with floats:
>>> np.testing.assert_array_equal([1.0,np.pi,np.nan],
... [1, np.sqrt(np.pi)**2, np.nan])
... [1, np.sqrt(np.pi)**2, np.nan],
... rtol=1e-10, atol=0)
+ As mentioned in the Notes section, `assert_array_equal` has special
+ handling for scalars. Here the test checks that each value in `x` is 3:
+
+ >>> x = np.full((2, 5), fill_value=3)
+ >>> np.testing.assert_array_equal(x, 3)
+
"""
__tracebackhide__ = True # Hide traceback for py.test
assert_array_compare(operator.__eq__, x, y, err_msg=err_msg,
if desired == actual:
return
- diff = list(difflib.Differ().compare(actual.splitlines(1), desired.splitlines(1)))
+ diff = list(difflib.Differ().compare(actual.splitlines(True), desired.splitlines(True)))
diff_list = []
while diff:
d1 = diff.pop(0)
for t in ['S1', 'U1']:
foo(t)
+ def test_0_ndim_array(self):
+ x = np.array(473963742225900817127911193656584771)
+ y = np.array(18535119325151578301457182298393896)
+ assert_raises(AssertionError, self._assert_func, x, y)
+
+ y = x
+ self._assert_func(x, y)
+
+ x = np.array(43)
+ y = np.array(10)
+ assert_raises(AssertionError, self._assert_func, x, y)
+
+ y = x
+ self._assert_func(x, y)
+
def test_generic_rank3(self):
"""Test rank 3 array for all dtypes."""
def foo(t):
with pytest.raises(AssertionError) as exc_info:
self._assert_func(x, y, decimal=12)
msgs = str(exc_info.value).split('\n')
- assert_equal(msgs[3], 'Mismatch: 100%')
+ assert_equal(msgs[3], 'Mismatched elements: 3 / 3 (100%)')
assert_equal(msgs[4], 'Max absolute difference: 1.e-05')
assert_equal(msgs[5], 'Max relative difference: 3.33328889e-06')
assert_equal(
with pytest.raises(AssertionError) as exc_info:
self._assert_func(x, y)
msgs = str(exc_info.value).split('\n')
- assert_equal(msgs[3], 'Mismatch: 33.3%')
+ assert_equal(msgs[3], 'Mismatched elements: 1 / 3 (33.3%)')
assert_equal(msgs[4], 'Max absolute difference: 1.e-05')
assert_equal(msgs[5], 'Max relative difference: 3.33328889e-06')
assert_equal(msgs[6], ' x: array([1. , 2. , 3.00003])')
with pytest.raises(AssertionError) as exc_info:
self._assert_func(x, y)
msgs = str(exc_info.value).split('\n')
- assert_equal(msgs[3], 'Mismatch: 50%')
+ assert_equal(msgs[3], 'Mismatched elements: 1 / 2 (50%)')
assert_equal(msgs[4], 'Max absolute difference: 1.')
assert_equal(msgs[5], 'Max relative difference: 1.')
assert_equal(msgs[6], ' x: array([inf, 0.])')
with pytest.raises(AssertionError) as exc_info:
self._assert_func(x, y)
msgs = str(exc_info.value).split('\n')
- assert_equal(msgs[3], 'Mismatch: 100%')
+ assert_equal(msgs[3], 'Mismatched elements: 2 / 2 (100%)')
assert_equal(msgs[4], 'Max absolute difference: 2')
assert_equal(msgs[5], 'Max relative difference: inf')
+ def test_error_message_2(self):
+ """Check the message is formatted correctly when either x or y is a scalar."""
+ x = 2
+ y = np.ones(20)
+ with pytest.raises(AssertionError) as exc_info:
+ self._assert_func(x, y)
+ msgs = str(exc_info.value).split('\n')
+ assert_equal(msgs[3], 'Mismatched elements: 20 / 20 (100%)')
+ assert_equal(msgs[4], 'Max absolute difference: 1.')
+ assert_equal(msgs[5], 'Max relative difference: 1.')
+
+ y = 2
+ x = np.ones(20)
+ with pytest.raises(AssertionError) as exc_info:
+ self._assert_func(x, y)
+ msgs = str(exc_info.value).split('\n')
+ assert_equal(msgs[3], 'Mismatched elements: 20 / 20 (100%)')
+ assert_equal(msgs[4], 'Max absolute difference: 1.')
+ assert_equal(msgs[5], 'Max relative difference: 0.5')
+
def test_subclass_that_cannot_be_bool(self):
# While we cannot guarantee testing functions will always work for
# subclasses, the tests should ideally rely only on subclasses having
def setup(self):
self._assert_func = assert_approx_equal
- def test_simple_arrays(self):
- x = np.array([1234.22])
- y = np.array([1234.23])
+ def test_simple_0d_arrays(self):
+ x = np.array(1234.22)
+ y = np.array(1234.23)
self._assert_func(x, y, significant=5)
self._assert_func(x, y, significant=6)
with pytest.raises(AssertionError) as exc_info:
assert_allclose(a, b)
msg = str(exc_info.value)
- assert_('Mismatch: 25%\nMax absolute difference: 1\n'
+ assert_('Mismatched elements: 1 / 4 (25%)\n'
+ 'Max absolute difference: 1\n'
'Max relative difference: 0.5' in msg)
def test_equal_nan(self):
#-----------------------------------
# Path to the release notes
-RELEASE_NOTES = 'doc/release/1.17.4-notes.rst'
+RELEASE_NOTES = 'doc/release/1.17.5-notes.rst'
#-------------------------------------------------------
MAJOR = 1
MINOR = 17
-MICRO = 4
+MICRO = 5
ISRELEASED = True
VERSION = '%d.%d.%d' % (MAJOR, MINOR, MICRO)
projects / platform / upstream / python3-numpy.git / commitdiff