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NumPy Status Update - December/January

Work continued on the NumPy + PyPy front steadily in December and more lightly in January. The continued focus was compatibility, targeting incorrect or unimplemented features that appeared in multiple NumPy test suite failures. We now pass ~2/3 of the NumPy test suite. The biggest improvements were made in these areas:

- Bugs in conversions of arrays/scalars to/from native types
- Fix cases where we would choose incorrect dtypes when initializing or computing results
- Improve handling of subclasses of ndarray through computations
- Support some optional arguments for array methods that are used in the pure-python part of NumPy
- Support additional attributes in arrays, array.flags, and dtypes
- Fix some indexing corner cases that arise in NumPy testing
- Implemented part of numpy.fft (cffti and cfftf)

Looking forward, we plan to continue improving the correctness of the existing implemented NumPy functionality, while also beginning to look at performance. The initial focus for performance will be to look at areas where we are significantly worse than CPython+NumPy. Those interested in trying these improvements out will need a PyPy nightly, and an install of the PyPy NumPy fork. Thanks again to the NumPy on PyPy donors for funding this work.


Anatoly Vostryakov wrote on 2014-02-06 21:38:

Many thanks for your work! Looking forward to support a full functionality of numpy in pypy!

Anonymous wrote on 2014-02-06 22:21:

> We now pass ~2/3 of the NumPy test suite.

Is the test coverage of numpy high enough so that a 100% green numpypy can be considered a full port? (Honest question, I have no background information suggesting the opposite.)

Anonymous wrote on 2014-02-08 18:58:

Great news that you are making progress on numpy. I can't wait!

Anonymous wrote on 2014-02-13 14:57:

I can't wait to use Numpypy to speed up scientific analysis.

Are there any updates on using numpypy with a plotting package such as matplotlib?

Armin Rigo wrote on 2014-02-18 13:43: