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=============
NumPy Roadmap
=============
This is a live snapshot of tasks and features we will be investing resources
in. It may be used to encourage and inspire developers and to search for
funding.
Interoperability protocols & duck typing
----------------------------------------
- `__array_function__`
See `NEP 18`_ and a sample implementation_
- Array Duck-Typing
`NEP 22`_ `np.asduckarray()`
- Mixins like `NDArrayOperatorsMixin`:
- for mutable arrays
- for reduction methods implemented as ufuncs
Better dtypes
-------------
- Easier custom dtypes
- Simplify and/or wrap the current C-API
- More consistent support for dtype metadata
- Support for writing a dtype in Python
- New string dtype(s):
- Encoded strings with fixed-width storage (utf8, latin1, ...) and/or
- Variable length strings (could share implementation with dtype=object, but are explicitly type-checked)
- One of these should probably be the default for text data. The current behavior on Python 3 is neither efficient nor user friendly.
- `np.int` should not be platform dependent
- better coercion for string + number
Random number generation policy & rewrite
-----------------------------------------
`NEP 19`_ and a `reference implementation`_
Indexing
--------
vindex/oindex `NEP 21`_
Infrastructure
--------------
NumPy is much more than just the code base itself, we also maintain
docs, CI, benchmarks, etc.
- Rewrite numpy.org
- Benchmarking: improve the extent of the existing suite, and run & render
the results as part of the docs or website.
- Hardware: find a machine that can reliably run serial benchmarks
- ASV produces graphs, could we set up a site? Currently at
https://pv.github.io/numpy-bench/, should that become a community resource?
Functionality outside core
--------------------------
Some things inside NumPy do not actually match the `Scope of NumPy`.
- A backend system for `numpy.fft` (so that e.g. `fft-mkl` doesn't need to monkeypatch numpy)
- Rewrite masked arrays to not be a ndarray subclass -- maybe in a separate project?
- MaskedArray as a duck-array type, and/or
- dtypes that support missing values
- Write a strategy on how to deal with overlap between numpy and scipy for `linalg` and `fft` (and implement it).
- Deprecate `np.matrix`
Continuous Integration
----------------------
We depend on CI to discover problems as we continue to develop NumPy before the
code reaches downstream users.
- CI for more exotic platforms (e.g. ARM is now available from
http://www.shippable.com/, but it is not free).
- Multi-package testing
- Add an official channel for numpy dev builds for CI usage by other projects so
they may confirm new builds do not break their package.
Typing
------
Python type annotation syntax should support ndarrays and dtypes.
- Type annotations for NumPy: github.com/numpy/numpy-stubs
- Support for typing shape and dtype in multi-dimensional arrays in Python more generally
NumPy scalars
-------------
Numpy has both scalars and zero-dimensional arrays.
- The current implementation adds a large maintenance burden -- can we remove
scalars and/or simplify it internally?
- Zero dimensional arrays get converted into scalars by most NumPy
functions (i.e., output of `np.sin(x)` depends on whether `x` is
zero-dimensional or not). This inconsistency should be addressed,
so that one could, e.g., write sane type annotations.
.. _`NEP 19`: https://www.numpy.org/neps/nep-0019-rng-policy.html
.. _`NEP 22`: http://www.numpy.org/neps/nep-0022-ndarray-duck-typing-overview.html
.. _`NEP 18`: https://www.numpy.org/neps/nep-0018-array-function-protocol.html
.. _implementation: https://gist.github.com/shoyer/1f0a308a06cd96df20879a1ddb8f0006
.. _`reference implementation`: https://github.com/bashtage/randomgen
.. _`NEP 21`: https://www.numpy.org/neps/nep-0021-advanced-indexing.html
|
