Log message:
py-numba: updated to 0.50.0

Version 0.50.0:

This is a more usual release in comparison to the others that have been made in \ 
the last six months. It comprises the result of a number of maintenance tasks \ 
along with some new features and a lot of bug fixes.

Highlights of core feature changes include:

The compilation chain is now based on LLVM 9.
The error handling and reporting system has been improved to reduce the size of \ 
error messages, and also improve quality and specificity.
The CUDA target has more stream constructors available and a new function for \ 
compiling to PTX without linking and loading the code to a device. Further, the \ 
macro-based system for describing CUDA threads and blocks has been replaced with \ 
standard typing and lowering implementations, for improved debugging and \ 
extensibility.

Log message:
Don't use non-portable alloca.h

Log message:
py-numba: updated to 0.49.1

Version 0.49.1:

This is a bugfix release for 0.49.0, it fixes some residual issues with SSA \ 
form, a critical bug in the branch pruning logic and a number of other smaller \ 
issues:

* Fixed Threading Implementation Typos
* Fixes Remove references to cffi_support from docs and examples
* Fix invalid type in resolve for comparison expr in parfors.
* Fix erroneous rewrite of predicate to bit const on prune.
* Fixes SSA local def scan based on invalid equality assumption.
* Fixes naming error in array_exprs
* Fix. Incorrect race variable detection due to SSA naming.
* Make literal_unroll function work as a freevar.
* Unset the memory manager after EMM Plugin tests
* Fix some SSA issues
* Pin to sphinx=2.4.4 to avoid problem with C declaration
* Fix unifying undefined first class function types issue
* Update example in 5m guide WRT SSA type stability.
* Restore numba.types as public API

Log message:
py-numba: remove patch that was removed from distinfo

Log message:
py-numba: updated to 0.49.0

Version 0.49.0:

This release is very large in terms of code changes. Large scale removal of \ 
unsupported Python and NumPy versions has taken place along with a significant \ 
amount of refactoring to simplify the Numba code base to make it easier for \ 
contributors. Numba’s intermediate representation has also undergone some \ 
important changes to solve a number of long standing issues. In addition some \ 
new features have been added and a large number of bugs have been fixed!

IMPORTANT: In this release Numba’s internals have moved about a lot. A \ 
backwards compatibility “shim” is provided for this release so as to not \ 
immediately break projects using Numba’s internals. If a module is imported \ 
from a moved location the shim will issue a deprecation warning and suggest how \ 
to update the import statement for the new location. The shim will be removed in \ 
0.50.0!

Highlights of core feature changes include:

Removal of all Python 2 related code and also updating the minimum supported \ 
Python version to 3.6, the minimum supported NumPy version to 1.15 and the \ 
minimum supported SciPy version to 1.0.
Refactoring of the Numba code base. The code is now organised into submodules by \ 
functionality. This cleans up Numba’s top level namespace.
Introduction of an ir.Del free static single assignment form for Numba’s \ 
intermediate representation
An OpenMP-like thread masking API has been added for use with code using the \ 
parallel CPU backends
For the CUDA target, all kernel launches now require a configuration, this \ 
preventing accidental launches of kernels with the old default of a single \ 
thread in a single block. The hard-coded autotuner is also now removed, such \ 
tuning is deferred to CUDA API calls that provide the same functionality
The CUDA target also gained an External Memory Management plugin interface to \ 
allow Numba to use another CUDA-aware library for all memory allocations and \ 
deallocations
The Numba Typed List container gained support for construction from iterables
Experimental support was added for first-class function types

Log message:
py-numba: updated to 0.48.0

Version 0.48.0

This release is particularly small as it was present to catch anything that \ 
missed the 0.47.0 deadline (the deadline deliberately coincided with the end of \ 
support for Python 2.7). The next release will be considerably larger.

The core changes in this release are dominated by the start of the clean up \ 
needed for the end of Python 2.7 support, improvements to the CUDA target and \ 
support for numerous additional unicode string methods.

Enhancements from user contributed PRs (with thanks!):

Brian Wignall fixed more spelling typos in.
Denis Smirnov added support for string methods capitalize, casefold, swapcase , \ 
rsplit , partition  and splitlines .
Elena Totmenina extended support for string methods startswith  and added endswith .
Eric Wieser made type_callable return the decorated function itself in
Ethan Pronovost added support for np.argwhere in
Graham Markall contributed a large number of CUDA enhancements and fixes, namely:
* Remove Python 3.4 backports from utils
* Make device_array_like create contiguous arrays
* Don’t launch ForAll kernels with 0 elements
* Fix various issues in CUDA library search
* Enable use of records and bools for shared memory, remove ddt, add additional \ 
transpose tests
* Fix: Add more appropriate typing for CUDA device arrays
* test_consuming_strides: Keep dev array alive
* State that CUDA Toolkit 8.0 required in docs
James Bourbeau added the Python 3.8 classifier to setup.py in.
John Kirkham added a clarification to the __cuda_array_interface__ documentation in.
Leo Fang Fixed an indexing problem in dummyarray in.
Marcel Bargull fixed a build and test issue for Python 3.8 in.
Maria Rubtsov added support for string methods isdecimal , isdigit , isnumeric  \ 
and replace .

General Enhancements:
* Make type_callable return the decorated function
* merge string prs

This merge PR included the following:
* Implement str.capitalize() based on CPython
* Implement str.casefold() based on CPython
* Implement str.swapcase() based on CPython
* Implement str.rsplit() based on CPython
* Implement str.isdecimal
* Implement str.isdigit
* Implement str.isnumeric
* Implement str.partition() based on CPython
* Implement str.splitlines() based on CPython
* Implement str.replace
* Functionality extension str.startswith() based on CPython
* Add functionality for str.endswith()
* Disable help messages.
* Add coverage for np.argwhere

Fixes:
* Only use lives (and not aliases) to create post parfor live set.
* Fix more spelling typos
* Propagate semantic constants ahead of static rewrites.
* Add Python 3.8 classifier to setup.py
* Update setup.py and buildscripts for dependency requirements
* Convert from arrays to names in define() and don’t invalidate for multiple \ 
consistent defines.
* Permit mixed int types in wrap_index
* Catch the use of global typed-list in JITed functions
* Fix, bug in bytecode analysis.

CUDA Enhancements/Fixes:
* Fix: Add more appropriate typing for CUDA device arrays
* Make device_array_like create contiguous arrays
* State that CUDA Toolkit 8.0 required in docs
* test_consuming_strides: Keep dev array alive
* Fix IndexError when accessing the “-1” element of dummyarray
* Enable use of records and bools for shared memory, remove ddt, add additional \ 
transpose tests
* Fix various issues in CUDA library search
* Don’t launch ForAll kernels with 0 elements
* Remove Python 3.4 backports from utils
Documentation Updates:

* Clarify what dictionary means
* Update docs for updated version requirements
* Update deprecation notices for 0.48.0
CI updates:

* Install optional dependencies for Python 3.8 tests
* Drop Py2.7 and Py3.5 from public CI
* Fix CI py38

Log message:
all: migrate homepages from http to https

pkglint -r --network --only "migrate"

As a side-effect of migrating the homepages, pkglint also fixed a few
indentations in unrelated lines. These and the new homepages have been
checked manually.

Log message:
py-numba: updated to 0.47.0

Version 0.47.0
--------------

This release expands the capability of Numba in a number of important areas and
is also significant as it is the last major point release with support for
Python 2 and Python 3.5 included. The next release (0.48.0) will be for Python
3.6+ only!  (This follows NumPy's deprecation schedule as specified in
`NEP 29 <https://numpy.org/neps/nep-0029-deprecation_policy.html>`_.)

Highlights of core feature changes include:

* Full support for Python 3.8 (Siu Kwan Lam)
* Opt-in bounds checking (Aaron Meurer)
* Support for ``map``, ``filter`` and ``reduce`` (Stuart Archibald)

Intel also kindly sponsored research and development that lead to some exciting
new features:

* Initial support for basic ``try``/``except`` use (Siu Kwan Lam)
* The ability to pass functions created from closures/lambdas as arguments
  (Stuart Archibald)
* ``sorted`` and ``list.sort()`` now accept the ``key`` argument (Stuart
  Archibald and Siu Kwan Lam)
* A new compiler pass triggered through the use of the function
  ``numba.literal_unroll`` which permits iteration over heterogeneous tuples
  and constant lists of constants. (Stuart Archibald)

Enhancements from user contributed PRs (with thanks!):

* Ankit Mahato added a reference to a new talk on Numba at PyCon India 2019
* Brian Wignall kindly fixed some spelling mistakes and typos
* Denis Smirnov wrote numerous methods to considerable enhance string support
  including:

  * ``str.rindex()``
  * ``str.isprintable()``
  * ``str.index()``
  * ``start/end`` parameters for ``str.find()``
  * ``str.isspace()``
  * ``str.isidentifier()``
  * ``str.rpartition()``
  * ``str.lower()`` and ``str.islower()``

* Elena Totmenina implemented both ``str.isalnum()``, ``str.isalpha()`` and
  ``str.isascii``
* Eric Larson fixed a bug in literal comparison
* Ethan Pronovost updated the ``np.arange`` implementation to allow
  the use of the ``dtype`` key word argument and also added ``bool``
  implementations for several types.
* Graham Markall fixed some issues with the CUDA target, namely:

  * Added physical limits for CC 7.0 / 7.5 to CUDA autotune
  * Fixed bugs in TestCudaWarpOperations
  * Improved errors / warnings for the CUDA vectorize decorator

* Guilherme Leobas fixed a typo in the ``urem`` implementation
* Isaac Virshup contributed a number of patches that fixed bugs, added support
  for more NumPy functions and enhanced Python feature support. These
  contributions included:

  * Allow array construction with mixed type shape tuples
  * Implementing ``np.lcm``
  * Implement np.gcd and math.gcd
  * Make slice constructor more similar to python.
  * Added support for slice.indices
  * Clarify numba ufunc supported features

* James Bourbeau fixed some issues with tooling, add ``setuptools`` as a
  dependency and add pre-commit hooks for ``flake8`` compliance.
* Leo Fang made ``numba.dummyarray.Array`` iterable
* Marc Garcia fixed the ``numba.jit`` parameter name signature_or_function
* Marcelo Duarte Trevisani patched the llvmlite requirement to ``>=0.30.0``
* Matt Cooper fixed a long standing CI problem by remove maxParallel
* Matti Picus fixed an issue with ``collections.abc``
  from Azure Pipelines.
* Rob Ennis patched a bug in ``np.interp`` ``float32`` handling
* VDimir fixed a bug in array transposition layouts and re-enabled and
  fixed some idle tests.
* Vyacheslav Smirnov Enable support for `str.istitle()``

General Enhancements:

* Bounds checking
* Add pre-commit hooks
* Handle kw args in inliner when callee is a function
* Permits closures to become functions, enables map(), filter()
* Implement method title() for unicode based on Cpython
* Enable support for istitle() method for unicode string
* Implement str.lower() and str.islower()
* Implement str.rfind()
* Refactor `overload*` and support `jit_options` and `inline`
* Added support for slice.indices
* Add `bool` overload for several types
* Allow array construction with mixed type shape tuples
* Python3.8 support
* Add parfor support for ndarray.fill.
* Update typeconv error message to ask for sys.executable.
* Update `np.arange` implementation with `@overload`
* Make slice constructor more similar to python.
* Implement np.gcd and math.gcd
* Add setuptools as a dependency
* put git hash into build string
* Better compiler error messages for improperly used reduction
  variables.
* Typed list implement and expose allocation
* Typed list faster copy
* Implement str.isspace() based on CPython
* Implement str.isprintable() based on CPython
* Implement str.isidentifier() based on CPython
* Implement str.isalnum() based on CPython
* Implement str.isalpha() based on CPython
* Implement str.rpartition() based on CPython
* Implement str.isascii() based on CPython
* Add graphviz output for FunctionIR
* Python3.8 looplifting
* Implement str.expandtabs() based on CPython
* Implement str.index() based on CPython
* Implement str.rindex() based on CPython
* Support params start/end for str.find()
* Bump to llvmlite 0.31
* Specialise arange dtype on arch + python version.
* basic support for try except
* Implement np.lcm
* loop canonicalisation and type aware tuple unroller/loop body
  versioning passes
* Update hash(tuple) for Python 3.8.
* Implement sort/sorted with key.
* Add `is_internal` property to all Type classes.

Fixes:

* Update to LLVM8 memset/memcpy intrinsic
* Convert sub to add and div to mul when doing the reduction across
  the per-thread reduction array.
* Handle 0 correctly as slice parameter.
* Remove multiply defined variables from all blocks' equivalence sets.
* Fix pickling of dufunc
* BUG: Comparison for literal
* Change get_call_table to support intermediate Vars.
* Requires  llvmlite >=0.30.0
* prefer to import from collections.abc
* fix flake8 errors
* Fix and enable idle tests from test_array_manipulation
* Fix transpose output array layout
* Fix issue with SVML (and knock-on function resolution effects).
* Treat 0d arrays like scalars.
* fix missing incref on flags
* fix typos in numba/targets/base.py
* fix typos
* fix spelling in now-failing tests
* windowing test should check equality only up to double precision
  errors
* fix refining list by using extend on an iterator
* Fix return type in arange and zero step size handling.
* suppress spurious RuntimeWarning about ufunc sizes
* skip the xfail test for now.  Py3.8 CFG refactor seems to have
  changed the test case
* regex needs to accept singular form of "argument"
* fix typed list equals
* Fix some spelling typos
* np.interp bugfix for float32 handling
* fix creating list with JIT disabled
* fix creating dict with JIT disabled
* Better handling of prange with multiple reductions on the same
  variable.
* Improve the error message for `raise <string>`.
* Move overload of literal_unroll to avoid circular dependency that
  breaks Python 2.7
* Fix test error on windows
* Fixes a bug in the relabelling logic in literal_unroll.
* Fix overload_method problem with stararg
* Add ind_to_const to enable fewer equivalence classes.
* Remove xfail for test which has since had underlying issue fixed.
* skip pycc test on Python 3.8 + macOS because of distutils issue

Log message:
py-numba: updated to 0.46.0

Version 0.46.0

This release significantly reworked one of the main parts of Numba, the compiler
pipeline, to make it more extensible and easier to use. The purpose of this was
to continue enhancing Numba's ability for use as a compiler toolkit. In a
similar vein, Numba now has an extension registration mechanism to allow other
Numba-using projects to automatically have their Numba JIT compilable functions
discovered. There were also a number of other related compiler toolkit
enhancement added along with some more NumPy features and a lot of bug fixes.

This release has updated the CUDA Array Interface specification to version 2,
which clarifies the `strides` attribute for C-contiguous arrays and specifies
the treatment for zero-size arrays. The implementation in Numba has been
changed and may affect downstream packages relying on the old behavior

General Enhancements:

* Add rewrite for semantic constants.
* Add np.cross support
* Make IR comparable and legalize it.
* R&D inlining, jitted and overloaded.
* Automatic JIT of called functions
* Inspection tool to check what numba supports
* Implement np.count_nonzero
* Unicode array support
* Entrypoints for numba extensions
* Literal dispatch
* Allow dtype input argument in np.sum
* New compiler.
* add support for np.append
* Refactor NRT C-API
* 0.46 scheduled deprecations
* Add env var to disable performance warnings.
* add np.array_equal support
* Implement numba.cross2d
* Add triangular indices functions
* Enable support for count() method for unicode string

Fixes:

* Fix inplace operator error for arrays
* Detect and raise unsupported on generator expressions
* Don't allow the allocation of mutable objects written into a
  container to be hoisted.
* Avoid deprecated use of inspect.getargspec
*  Replace GC macro with function call
* Loosen up typed container casting checks
* Fix some coding lines at the top of some files (utf8 -> utf-8)
* Replace "import \*" with explicit imports in numba/types
* Fix incorrect alg in isupper for ascii strings.
* test using jitclass in typed-list
* Add allocation hoisting info to LICM section at diagnostic L4
* Offset search box to avoid wrapping on some pages with Safari.
* Replace all "except BaseException" with "except Exception".
* Restore the "free" conda channel for NumPy 1.10 support.
* Add lowering for constant bytes.
* Add exception chaining for better error context
* Name of type should not contain user facing description for debug.
* Limit the number of return types for recursive functions
* Fixed two module teardown races in py2.
* Fix and test numpy.random.random_sample(n) for np117
* NamedTuple - Raises an error on non-iterable elements
* Add a newline in patched errors
* Fix liveness for remove dead of parfors (and other IR extensions)
* Make List.__getitem__ accept unsigned parameters
* Raise specific error at typing time for iteration on >1D array.
* Fix static_getitem with Literal type as index
* Update to inliner cost model information.
* Use specific random number seed when generating arbitrary test data
* Adjust test timeouts
* Skip unicode array tests on ppc64le that trigger an LLVM bug
* Fix packaging issue due to missing numba/cext
* Fix issue 4520 due to storage model mismatch
* Updates for llvmlite 0.30.0

CUDA Enhancements/Fixes:

* cudasim mishandling recarray
* Replace use of `np.prod` with `functools.reduce` for computing size
  from shape
* Prevent taking the GIL in ForAll
* Just pass NULL for b2d_func for constant dynamic
  sharedmem
* Update CUDA Array Interface & Enforce Numba compliance
* Implement math.{degrees, radians} for the CUDA target.
* Bump cuda array interface to version 2

Documentation Updates:

* Add docs for ARMv8/AArch64
* Add supported platforms to the docs.
* Add docstrings to inspect methods
* Update Python 2.7 EOL statement
* Add note about np.sum
* Minor parallel performance tips edits
* Clarify docs for typed dict with regard to arrays
* Fix example in guvectorize docstring.
* fix two typos in architecture.rst
* Document numba.extending.intrinsic and inlining.
* Fix typo in jit-compilation docs
* add dependency list to docs
* Add documentation for implementing new compiler passes.

Log message:
py-numba: updated to 0.45.1

Version 0.45.1
This patch release addresses some regressions reported in the 0.45.0 release and \ 
adds support for NumPy 1.17:

* accept scalar/0d-arrays
* Fix. Parfors reduction vars not deleted.
* Use process level locks for fork() only.
* Try to fix.
* Fix np1.17 isnan, isinf, isfinite ufuncs
* Fix np.interp for np1.17 nan handling
* Fix nump1.17 random function non-aliasing

Version 0.45.0
In this release, Numba gained an experimental numba.typed.List container as a \ 
future replacement of the reflected list. In addition, functions decorated with \ 
parallel=True can now be cached to reduce compilation overhead associated with \ 
the auto-parallelization.

Enhancements from user contributed PRs (with thanks!):

James Bourbeau added the Numba version to reportable error messages, added the \ 
signature parameter to inspect_types, improved the docstring of \ 
normalize_signatur, and fixed by adding reference counting to \ 
register_dispatcher
Guilherme Leobas implemented the dominator tree and dominance frontier algorithms
Nick White fixed the issue with round in the CUDA target.
Joshua Adelman added support for determining if a value is in a range (i.e. x in \ 
range(...)), and added windowing functions (np.bartlett, np.hamming, \ 
np.blackman, np.hanning, np.kaiser) from NumPy.
Lucio Fernandez-Arjona added support for np.select
Rob Ennis added support for np.flatnonzero
Keith Kraus extended the __cuda_array_interface__ with an optional mask attribute.
Gregory R. Lee replaced deprecated use of inspect.getargspec