Mailman 3 October 2020 - NumPy-Discussion

Multinomial random sampling
by Currurant Oct. 29, 2020

Oct. 29, 2020

I wonder if there is an efficient way to draw multinomial random samples. For univariate sampling, we can do that for different parameters and desired shape, but neither of rng nor multinomial function in numpy.random is able to achieve that. To be specific, can we draw multinomial samples for different probabilities and different n? I asked the question on Stackoverflow here: https://stackoverflow.com/questions/64529620/is-there-an-efficient-way-to-g…. Seems like there isn't a function to do that. Thank you and appreciate numpy, Daniel -- Sent from: http://numpy-discussion.10968.n7.nabble.com/

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Should we transfer MacPython/numpy-wheels to the NumPy org?
by Charles R Harris Oct. 29, 2020

Oct. 29, 2020

Hi All, Seems that is pretty easy to transfer a github repo to another owner. Should we do this for the numpy-wheels repo? That would put all the management in one place and now might be a good time to do it before the 1.20.x branch. Chuck

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NumPy 1.19.3 release
by Charles R Harris Oct. 29, 2020

Oct. 29, 2020

Hi All, On behalf of the NumPy team I am pleased to announce that NumPy 1.19.3 has been released. NumPy 1.19.3 is a small maintenance release with two major improvements: - Python 3.9 binary wheels on all supported platforms, - OpenBLAS fixes for Windows 10 version 2004 fmod bug. This release supports Python 3.6-3.9 and is linked with OpenBLAS 3.12 to avoid some of the fmod problems on Windows 10 version 2004. Microsoft is aware of the problem and users should upgrade when the fix becomes available, the fix here is limited in scope. NumPy Wheels for this release can be downloaded from the PyPI <https://pypi.org/project/numpy/1.19.3/>, source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.19.3>. Linux users will need pip >= 0.19.3 in order to install manylinux2010 and manylinux2014 wheels. *Contributors* A total of 8 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Charles Harris - Chris Brown + - Daniel Vanzo + - E. Madison Bray + - Hugo van Kemenade + - Ralf Gommers - Sebastian Berg - @danbeibei + *Pull requests merged* A total of 10 pull requests were merged for this release. - #17298: BLD: set upper versions for build dependencies - #17336: BUG: Set deprecated fields to null in PyArray_InitArrFuncs - #17446: ENH: Warn on unsupported Python 3.10+ - #17450: MAINT: Update test_requirements.txt. - #17522: ENH: Support for the NVIDIA HPC SDK nvfortran compiler - #17568: BUG: Cygwin Workaround for #14787 on affected platforms - #17647: BUG: Fix memory leak of buffer-info cache due to relaxed strides - #17652: MAINT: Backport openblas_support from master. - #17653: TST: Add Python 3.9 to the CI testing on Windows, Mac. - #17660: TST: Simplify source path names in test_extending. Cheers, Charles Harris

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NumPy Community Meeting Wednesday (1h difference for Europe due to daylight saving ending)
by Sebastian Berg Oct. 28, 2020

Oct. 28, 2020

Hi all, There will be a NumPy Community meeting Wednesday October 27th at 1pm Pacific Time (20:00 UTC). Everyone is invited and encouraged to join in and edit the work-in-progress meeting topics and notes at: https://hackmd.io/76o-IxCjQX2mOXO_wwkcpg?both Best wishes Sebastian

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Accepting NEP 42 — New and extensible DTypes
by Sebastian Berg Oct. 27, 2020

Oct. 27, 2020

Hi all, after another thorough revision of NEP 42 (much thanks to Ben!), I propose accepting the NEP, with the note that details are expected change. I am always happy to clarify and review the document based on feedback, but I feel the important technical points should be very clear and settled. Exposing all of the proposed API may need additional detailed API discussion. My focus is still a bit on the big picture design choices that the NEP makes need to move forward and settle the implementation internal to NumPy, although I am happy to discuss the details! The title of the NEP is: NEP 42 — New and extensible DTypes And available at: https://numpy.org/neps/nep-0042-new-dtypes.html While enabling new user-defined DTypes is the main goal, the main work is the internal restructure of NumPy's own DTypes necessary to allow that. I have pasted the "Abstract" and "Motivation and scope" section below, which give a good overview of the issues and we are trying to address. It is followed by the "Usage and impact" section which gives a big- picture overview of the design. I will refer to the full NEP for more detailed technical decisions and explanations. Cheers, Sebastian PS: In some places NEP 42 references NEP 43, for which I hope to merge the draft soon, the current status is here: https://github.com/numpy/numpy/pull/16723 However, this should be mainly interested for those wishing to go into more technical details. *********************************************************************** ******* Abstract *********************************************************************** ******* NumPy's dtype architecture is monolithic -- each dtype is an instance of a single class. There's no principled way to expand it for new dtypes, and the code is difficult to read and maintain. As :ref:`NEP 41 <NEP41>` explains, we are proposing a new architecture that is modular and open to user additions. dtypes will derive from a new ``DType`` class serving as the extension point for new types. ``np.dtype("float64")`` will return an instance of a ``Float64`` class, a subclass of root class ``np.dtype``. This NEP is one of two that lay out the design and API of this new architecture. This NEP addresses dtype implementation; NEP 43 addresses universal functions. .. note:: Details of the private and external APIs may change to reflect user comments and implementation constraints. The underlying principles and choices should not change significantly. *********************************************************************** ******* Motivation and scope *********************************************************************** ******* Our goal is to allow user code to create fully featured dtypes for a broad variety of uses, from physical units (such as meters) to domain- specific representations of geometric objects. :ref:`NEP 41 <NEP41>` describes a number of these new dtypes and their benefits. Any design supporting dtypes must consider: - How shape and dtype are determined when an array is created - How array elements are stored and accessed - The rules for casting dtypes to other dtypes In addition: - We want dtypes to comprise a class hierarchy open to new types and to subhierarchies, as motivated in :ref:`NEP 41 <NEP41>`. And to provide this, - We need to define a user API. All these are the subjects of this NEP. - The class hierarchy, its relation to the Python scalar types, and its important attributes are described in `nep42_DType class`_. - The functionality that will support dtype casting is described in `Casting`_. - The implementation of item access and storage, and the way shape and dtype are determined when creating an array, are described in :ref:`nep42_array_coercion`. - The functionality for users to define their own DTypes is described in `Public C-API`_. The API here and in NEP 43 is entirely on the C side. A Python-side version will be proposed in a future NEP. A future Python API is expected to be similar, but provide a more convenient API to reuse the functionality of existing DTypes. It could also provide shorthands to create structured DTypes similar to Python's `dataclasses <https://docs.python.org/3.8/library/dataclasses.html>`_. *********************************************************************** ******* Usage and impact *********************************************************************** ******* We believe the few structures in this section are sufficient to consolidate NumPy's present functionality and also to support complex user-defined DTypes. The rest of the NEP fills in details and provides support for the claim. Again, though Python is used for illustration, the implementation is a C API only; a future NEP will tackle the Python API. After implementing this NEP, creating a DType will be possible by implementing the following outlined DType base class, that is further described in `nep42_DType class`_: class DType(np.dtype): type : type # Python scalar type parametric : bool # (may be indicated by superclass) @property def canonical(self) -> bool: raise NotImplementedError def ensure_canonical(self : DType) -> DType: raise NotImplementedError For casting, a large part of the functionality is provided by the "methods" stored in ``_castingimpl`` @classmethod def common_dtype(cls : DTypeMeta, other : DTypeMeta) -> DTypeMeta: raise NotImplementedError def common_instance(self : DType, other : DType) -> DType: raise NotImplementedError # A mapping of "methods" each detailing how to cast to another DType # (further specified at the end of the section) _castingimpl = {} For array-coercion, also part of casting: def __dtype_setitem__(self, item_pointer, value): raise NotImplementedError def __dtype_getitem__(self, item_pointer, base_obj) -> object: raise NotImplementedError @classmethod def __discover_descr_from_pyobject__(cls, obj : object) -> DType: raise NotImplementedError # initially private: @classmethod def _known_scalar_type(cls, obj : object) -> bool: raise NotImplementedError Other elements of the casting implementation is the ``CastingImpl``: casting = Union["safe", "same_kind", "unsafe"] class CastingImpl: # Object describing and performing the cast casting : casting def resolve_descriptors(self, Tuple[DType] : input) -> (casting, Tuple[DType]): raise NotImplementedError # initially private: def _get_loop(...) -> lowlevel_C_loop: raise NotImplementedError which describes the casting from one DType to another. In NEP 43 this ``CastingImpl`` object is used unchanged to support universal functions.

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Proposal: split numpy.distutils into it's own package
by Dustin Spicuzza Oct. 26, 2020

Oct. 26, 2020

Hey all, [copied from https://github.com/numpy/numpy/issues/17620, as requested by the feature request guidelines] Cross-compiling scipy and other projects that depend on numpy's distutils is a huge pain right now, because to do it [in addition to lots of other details that you have to get right] you have to have both a native and cross-compiled version of numpy installed. It seems pretty unreasonable that I need a native version of numpy installed to compile scipy. One might ask, why is this needed? Well, scipy's setup.py uses numpy's distutils fork for the fortran support (I think). Because numpy.distutils is a subpackage of numpy, if you're working with a cross-compiled version of numpy, it eventually tries to import some .so that wasn't compiled for your host and everything dies -- thus you have to have a native numpy installed to allow numpy.distutils to import correctly. As far as I can tell, numpy's distutils fork is pure python and doesn't actually use anything else in numpy itself, and so is completely separable from numpy. If it were its own top-level package that scipy et al could use, then cross-compiling would be significantly less tricky. The mechanics of this would work like so: * contents of numpy.distutils would be moved to 'numpy_distutils' package * importing numpy.distutils would raise a deprecation warning and redirect to numpy_distutils, probably using import hook magic * scipy and other packages would now utilize numpy_distutils instead of numpy.distutils at build time Initially, I considered proposing creating a separate pypi package for numpy_distutils, but I expect that would be more trouble than it's worth. One could also propose creating a new PEP 517 build backend, or move to cmake, or other huge changes, but those also seem more trouble than they're worth. Thanks for your consideration. Dustin

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help needed
by Amardeep Singh Oct. 24, 2020

Oct. 24, 2020

Hi All I am a new joiner.Using macbook. Can someone please guide me how to debug numpy using clion? I am able to build not sure about debugging the c code. ProductName: Mac OS X ProductVersion: 10.15.7 BuildVersion: 19H2 thx

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ENH: Changed repr of np.bool_ #17592 (requesting confirmation)
by Ganesh Kathiresan Oct. 24, 2020

Oct. 24, 2020

Hi All, I have raised a PR <https://github.com/numpy/numpy/pull/17592>to change the np.bool_ string representation to avoid confusion with python's True and False as mentioned in this issue <https://github.com/numpy/numpy/issues/12950>. This change makes string representation of np.bool_ into numpy.True_ and numpy.False_ As Eric mentioned, I wanted your feedback on the change and if we can go ahead with this idea. Regards, Ganesh

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NumPy Development Meeting Wednesday - Triage Focus
by Sebastian Berg Oct. 20, 2020

Oct. 20, 2020

Hi all, Our bi-weekly triage-focused NumPy development meeting is today (Wednesday, October 21st) at 11 am Pacific Time (18:00 UTC). Everyone is invited to join in and edit the work-in-progress meeting topics and notes: https://hackmd.io/68i_JvOYQfy9ERiHgXMPvg I encourage everyone to notify us of issues or PRs that you feel should be prioritized, discussed, or reviewed. Best regards Sebastian

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ANN: SciPy 1.5.3
by Tyler Reddy Oct. 19, 2020

Oct. 19, 2020

Hi all, On behalf of the SciPy development team I'm pleased to announce the release of SciPy 1.5.3, which is a bug fix release that includes Linux ARM64 wheels for the first time. Sources and binary wheels can be found at: https://pypi.org/project/scipy/ and at: https://github.com/scipy/scipy/releases/tag/v1.5.3 One of a few ways to install this release with pip: pip install scipy==1.5.3 ========================== SciPy 1.5.3 Release Notes ========================== SciPy 1.5.3 is a bug-fix release with no new features compared to 1.5.2. In particular, Linux ARM64 wheels are now available and a compatibility issue with XCode 12 has been fixed. Authors ====== * Peter Bell * CJ Carey * Thomas Duvernay + * Gregory Lee * Eric Moore * odidev * Dima Pasechnik * Tyler Reddy * Simon Segerblom Rex + * Daniel B. Smith * Will Tirone + * Warren Weckesser A total of 12 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete. Issues closed for 1.5.3 ------------------------------ * `#9611 <https://github.com/scipy/scipy/issues/9611>`__: Overflow error with new way of p-value calculation in kendall... * `#10069 <https://github.com/scipy/scipy/issues/10069>`__: scipy.ndimage.watershed_ift regression in 1.0.0 * `#11260 <https://github.com/scipy/scipy/issues/11260>`__: BUG: DOP853 with complex data computes complex error norm, causing... * `#11479 <https://github.com/scipy/scipy/issues/11479>`__: RuntimeError: dictionary changed size during iteration on loading... * `#11972 <https://github.com/scipy/scipy/issues/11972>`__: BUG (solved): Error estimation in DOP853 ODE solver fails for... * `#12543 <https://github.com/scipy/scipy/issues/12543>`__: BUG: Picture rotated 180 degrees and rotated -180 degrees should... * `#12613 <https://github.com/scipy/scipy/issues/12613>`__: Travis X.4 and X.7 failures in master * `#12654 <https://github.com/scipy/scipy/issues/12654>`__: scipy.stats.combine_pvalues produces wrong results with method='mudholkar_george' * `#12819 <https://github.com/scipy/scipy/issues/12819>`__: BUG: Scipy Sparse slice indexing assignment Bug with zeros * `#12834 <https://github.com/scipy/scipy/issues/12834>`__: BUG: ValueError upon calling Scipy Interpolator objects * `#12836 <https://github.com/scipy/scipy/issues/12836>`__: ndimage.median can return incorrect values for integer inputs * `#12860 <https://github.com/scipy/scipy/issues/12860>`__: Build failure with Xcode 12 Pull requests for 1.5.3 ----------------------------- * `#12611 <https://github.com/scipy/scipy/pull/12611>`__: MAINT: prepare for SciPy 1.5.3 * `#12614 <https://github.com/scipy/scipy/pull/12614>`__: MAINT: prevent reverse broadcasting * `#12617 <https://github.com/scipy/scipy/pull/12617>`__: MAINT: optimize: Handle nonscalar size 1 arrays in fmin_slsqp... * `#12623 <https://github.com/scipy/scipy/pull/12623>`__: MAINT: stats: Loosen some test tolerances. * `#12638 <https://github.com/scipy/scipy/pull/12638>`__: CI, MAINT: pin pytest for Azure win * `#12668 <https://github.com/scipy/scipy/pull/12668>`__: BUG: Ensure factorial is not too large in mstats.kendalltau * `#12705 <https://github.com/scipy/scipy/pull/12705>`__: MAINT: \`openblas_support\` added sha256 hash * `#12706 <https://github.com/scipy/scipy/pull/12706>`__: BUG: fix incorrect 1d case of the fourier_ellipsoid filter * `#12721 <https://github.com/scipy/scipy/pull/12721>`__: BUG: use special.sindg in ndimage.rotate * `#12724 <https://github.com/scipy/scipy/pull/12724>`__: BUG: per #12654 adjusted mudholkar_george method to combine p... * `#12726 <https://github.com/scipy/scipy/pull/12726>`__: BUG: Fix DOP853 error norm for complex problems * `#12730 <https://github.com/scipy/scipy/pull/12730>`__: CI: pin xdist for Azure windows * `#12786 <https://github.com/scipy/scipy/pull/12786>`__: BUG: stats: Fix formula in the \`stats\` method of the ARGUS... * `#12795 <https://github.com/scipy/scipy/pull/12795>`__: CI: Pin setuptools on windows CI * `#12830 <https://github.com/scipy/scipy/pull/12830>`__: [BUG] sparse: Avoid using size attribute in LIL __setitem__ * `#12833 <https://github.com/scipy/scipy/pull/12833>`__: BUG: change list of globals items to list of a copy * `#12842 <https://github.com/scipy/scipy/pull/12842>`__: BUG: Use uint16 for cost in NI_WatershedElement * `#12845 <https://github.com/scipy/scipy/pull/12845>`__: BUG: avoid boolean or integer addition error in ndimage.measurements.median * `#12864 <https://github.com/scipy/scipy/pull/12864>`__: BLD: replace the #include of libqull_r.h with with this of qhull_ra.h... * `#12867 <https://github.com/scipy/scipy/pull/12867>`__: BUG: Fixes a ValueError yielded upon calling Scipy Interpolator... * `#12902 <https://github.com/scipy/scipy/pull/12902>`__: CI: Remove 'env' from pytest.ini * `#12913 <https://github.com/scipy/scipy/pull/12913>`__: MAINT: Ignore pytest's PytestConfigWarning 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