[Numpy-discussion] test results of 1.2.0rc1

Pauli Virtanen pav@iki...
Thu Sep 4 14:07:24 CDT 2008

Thu, 04 Sep 2008 11:51:14 -0700, Andrew Straw wrote:
> Hi, with numpy 1.2.0rc1 running 'python -c "import numpy; numpy.test()"'
> on my Ubuntu Hardy amd64 machine results in 1721 tests being run and 1
> skipped. So far, so good.
> However, if I run numpy.test(10,10,all=True), I get 1846 tests with: the
> message "FAILED (SKIP=1, errors=8, failures=68)" Furthermore, there are
> several matplotlib windows that pop up, many of which are
> non-reassuringly blank: Bartlett window frequency response (twice -- I
> guess the 2nd is actually for the Blackman window), Hamming window
> frequency response, Kaiser window, Kaiser window frequency response,
> sinc function. Additionally, the linspace and logspace tests each
> generate a plots with green and blue dots at Y values of 0.0 and 0.5,
> but it would be nice to have an axes title.
> Should I be concerned that there are so many errors and failures with
> the numpy test suite? Or am I just running it with unintended settings?
> If these tests should pass, I will attempt to find time to generate bug
> reports for them, although I don't think there's anything particularly
> weird about my setup.

I'd say that the settings are "unintended" in the sense that they run all 
examples in all docstrings. There are quite a few of these, and some 
indeed plot some graphs.

Ideally, all examples should run, but this is not assured at the moment. 
Some of the examples were added during the summer's documentation 
marathon, but others date way back.

There was some discussion about what to do with the plots, but as far as 
I know, no conclusions were reached about this, so we sticked writing 
them like all other examples.

Some alternatives offered were

1) Mark them with >>> and live with not being able to doctest docstrings.


2) Mark them with >>> and fake matplotlib so that the plots don't appear.


3) Mark them with :: and live no syntax highlighting in rendered
   documentation and not being able to test or render plots easily.


4) Steal or adapt the plot:: ReST directive from matplotlib, and use that,
   as Stéfan suggested at some point. Haven't got yet around to
   implementing this.

   Tentatively +1; depends a bit of how the implementation goes.

Pauli Virtanen

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