On Mon, Dec 23, 2013 at 12:14 AM, Matti Picus <matti.picus@gmail.com> wrote:

Hi. I started to port the stdlib cmath C99 compatible complex number
tests to numpy, after noticing that numpy seems to have different
complex number routines than cmath. The work is available on a
"retest_complex" branch of numpy
https://github.com/mattip/numpy/tree/retest_complex
The tests can be run by pulling the branch (no need to rebuild numpy)
and running

python <path-to-branch>/numpy/core/tests/test_umath_complex.py >
test.log 2>&1

So far it is just a couple of  commits that run the tests on numpy, I
did not dive into modifying the math routines. If I did the work
correctly, failures point to some differences, most due to edge cases
with inf and nan, but there are a number of failures due to different
finite values (for some small definition of different).
I guess my first question is "did I do the tests properly".

They work fine, however you did it in a nonstandard way which makes the output hard to read. Some comments:

- the assert_* functions expect "actual" as first input and "desired" next, while you have them reversed.

- it would be good to split those tests into multiple cases, for example one per function to be tested.

- you shouldn't print anything, just let it fail. If you want to see each individual failure, use generator tests.

- the cmathtestcases.txt is a little nonstandard but should be OK to keep it like that.

Assuming I did, the next question is "are the inconsistencies
intentional" i.e. are they that way in order to be compatible with
Matlab  or some other non-C99 conformant library?

The implementation should conform to IEEE 754.

For instance, a comparison between the implementation of cmath's sqrt
and numpy's sqrt shows that numpy does not check for subnormals.

I suspect no handling for denormals was done on purpose, since that should have a significant performance penalty. I'm not sure about other differences, probably just following a different reference.

And I am probably mistaken since I am new to the generator methods of numpy,
but could it be that trigonometric functions like acos and acosh are
generated in umath/funcs.inc.src, using a very different algorithm than
cmathmodule.c?

You're not mistaken.
 

Would there be interest in a pull request that changed the routines to
be more compatible with results from cmath?

 

I don't think compatibility with cmath should be a goal, but if you find differences where cmath has a more accurate or faster implementation, then a PR to adopt the cmath algorithm would be very welcome.

Ralf