[Numpy-discussion] numpy.mean still broken for largefloat32arrays

[Eelco Hoogendoorn]

Arguably, the whole of floating point numbers and their related shenanigans is not very pythonic in the first place. The accuracy of the output WILL depend on the input, to some degree or another. At the risk of repeating myself: explicit is better than implicit

-----Original Message-----
From: "RayS" <rays at blue-cove.com>
Sent: ‎25-‎7-‎2014 19:56
To: "Discussion of Numerical Python" <numpy-discussion at scipy.org>
Subject: Re: [Numpy-discussion] numpy.mean still broken for largefloat32arrays

At 07:22 AM 7/25/2014, you wrote:

> We were talking on this in the office, as we
> realized it does affect a couple of lines dealing
> with large arrays, including complex64.
> As I expect Python modules to work uniformly
> cross platform unless documented otherwise, to me
> that includes 32 vs 64 bit platforms, implying
> that the modules should automatically use large
> enough accumulators for the data type input.

The 32/64-bitness of your platform has nothing to do with floating
point.

As a naive end user, I can, and do, download different binaries for different CPUs/Windows versions and will get different results
http://mail.scipy.org/pipermail/numpy-discussion/2014-July/070747.html


 Nothing discussed in this thread is platform-specific (modulo
some minor details about the hardware FPU, but that should be taken as
read).

And compilers, apparently.

The important point was that it would be best if all of the methods affected by summing 32 bit floats with 32 bit accumulators had the same Notes as numpy.mean(). We went through a lot of code yesterday, assuming that any numpy or Scipy.stats functions that use accumulators suffer the same issue, whether noted or not, and found it true.

"Depending on the input data, this can cause the results to be inaccurate, especially for float32 (see example below). Specifying a higher-precision accumulator using the dtype keyword can alleviate this issue." seems rather un-Pythonic.

- Ray
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