[SciPy-dev] are masked array statistical function hidden intentionally?
Wed Nov 19 09:35:22 CST 2008
> This is actually my fault -- I left sorting out the functions under
> submodules last when I added most other parts of numpy, and I haven't
> still finished numpy.ma. Also numpy.emath, numpy.rec, numpy.numarray,
> numpy.oldnumeric, numpy.ctypeslib, numpy.matlib would need work (but
> less important than MA).
Pauli, no problem. I agree that there should be at least one specific
page for numpy.ma functions/methods, organized by topics. Where should
I create it (them) ?
> Sphinx stuff will work in the docstrings, but also `numpy.foo` should
> IIRC generate reference links (this comes from the numpy Sphinx
> extension). I don't remember if Sphinx markup was discussed when the
> docstring format was agreed on, but I remember people being worried
> making the docstrings more difficult to read on the terminal. If the
> markup doesn't compromise this, at least I don't see problems with
Mmh, my question was more about links to other functions/methods
inside the docstring, using for eample :func:, :meth:, :attr: fields...
> I think a useful way forward could be:
> 1. Editing numpy-docs/source/routines.ma.rst and adding any missing
> utility functions inside the autosummary:: directives.
Can you remind me where I can find numpy-docs ? It's not on the numpy
SVN, right ? What's the address of the repository ? Do I have write
access to it ?
> Including the documentation using the other auto*:: directives is
> but personally I find this a bit distracting. Numpy's docstrings
> to become very long and detailed, so that a page with more than
> one on
> it is difficult to read.
Agreed. I guess I'll find templates on the numpy-docs site, right ?
> Alternatively, split the MA documentation to a separate page, for
> example arrays.ma.rst. I'm not sure what is the best organization
> here or if it makes sense to split the MA docs in two places.
Well, there are 2 different aspects: the actual implementation
(functions docstring), and some kind of tutorial. This latter may find
its place in numpy/docs, actually...
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