115 lines
4.1 KiB
ReStructuredText
115 lines
4.1 KiB
ReStructuredText
=============
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NumPy Roadmap
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=============
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This is a live snapshot of tasks and features we will be investing resources
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in. It may be used to encourage and inspire developers and to search for
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funding.
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Interoperability
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----------------
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We aim to make it easier to interoperate with NumPy. There are many NumPy-like
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packages that add interesting new capabilities to the Python ecosystem, as well
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as many libraries that extend NumPy's model in various ways. Work in NumPy to
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facilitate interoperability with all such packages, and the code that uses them,
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may include (among other things) interoperability protocols, better duck typing
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support and ndarray subclass handling.
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- The ``__array_function__`` protocol is currently experimental and needs to be
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matured. See `NEP 18`_ for details.
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- New protocols for overriding other functionality in NumPy may be needed.
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- Array duck typing, or handling "duck arrays", needs improvements. See
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`NEP 22`_ for details.
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Extensibility
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-------------
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We aim to make it much easier to extend NumPy. The primary topic here is to
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improve the dtype system.
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- Easier custom dtypes:
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- Simplify and/or wrap the current C-API
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- More consistent support for dtype metadata
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- Support for writing a dtype in Python
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- New string dtype(s):
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- Encoded strings with fixed-width storage (utf8, latin1, ...) and/or
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- Variable length strings (could share implementation with dtype=object,
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but are explicitly type-checked)
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- One of these should probably be the default for text data. The current
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behavior on Python 3 is neither efficient nor user friendly.
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- ``np.dtype(int)`` should not be platform dependent
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- Better coercion for string + number
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Performance
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-----------
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We want to further improve NumPy's performance, through:
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- Better use of SIMD instructions, also on platforms other than x86.
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- Reducing ufunc overhead.
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- Optimizations in individual functions.
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Furthermore we would like to improve the benchmarking system, in terms of coverage,
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easy of use, and publication of the results (now
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`here <https://pv.github.io/numpy-bench>`__) as part of the docs or website.
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Website and documentation
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-------------------------
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Our website (https://numpy.org) is in very poor shape and needs to be rewritten
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completely.
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The NumPy `documentation <https://www.numpy.org/devdocs/user/index.html>`__ is
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of varying quality - in particular the User Guide needs major improvements.
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Random number generation policy & rewrite
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-----------------------------------------
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A new random number generation framework with higher performance generators is
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close to completion, see `NEP 19`_ and `PR 13163`_.
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Indexing
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--------
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We intend to add new indexing modes for "vectorized indexing" and "outer indexing",
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see `NEP 21`_.
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Continuous Integration
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----------------------
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We depend on CI to discover problems as we continue to develop NumPy before the
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code reaches downstream users.
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- CI for more exotic platforms (if available as a service).
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- Multi-package testing
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- Add an official channel for numpy dev builds for CI usage by other projects so
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they may confirm new builds do not break their package.
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Other functionality
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-------------------
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- ``MaskedArray`` needs to be improved, ideas include:
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- Rewrite masked arrays to not be a ndarray subclass -- maybe in a separate project?
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- MaskedArray as a duck-array type, and/or
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- dtypes that support missing values
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- A backend system for ``numpy.fft`` (so that e.g. ``fft-mkl`` doesn't need to monkeypatch numpy)
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- Write a strategy on how to deal with overlap between NumPy and SciPy for ``linalg``
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and ``fft`` (and implement it).
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- Deprecate ``np.matrix`` (very slowly)
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.. _`NEP 19`: https://www.numpy.org/neps/nep-0019-rng-policy.html
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.. _`NEP 22`: http://www.numpy.org/neps/nep-0022-ndarray-duck-typing-overview.html
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.. _`NEP 18`: https://www.numpy.org/neps/nep-0018-array-function-protocol.html
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.. _implementation: https://gist.github.com/shoyer/1f0a308a06cd96df20879a1ddb8f0006
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.. _`reference implementation`: https://github.com/bashtage/randomgen
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.. _`NEP 21`: https://www.numpy.org/neps/nep-0021-advanced-indexing.html
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.. _`PR 13163`: https://github.com/numpy/numpy/pull/13163
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