On 7 September 2010 19:19, Johan Hake <[email protected]> wrote: > On Tuesday September 7 2010 10:08:34 Kristian Ølgaard wrote: >> On 7 September 2010 18:51, Johan Hake <[email protected]> wrote: >> > On Tuesday September 7 2010 09:24:40 Kristian Ølgaard wrote: >> >> On 7 September 2010 17:59, Johan Hake <[email protected]> wrote: >> >> > [snip] >> >> > >> >> >> > But how do we extract the different arguments? I suppose this is >> >> >> > collected by Doxygen, and we just need to parse these and output >> >> >> > them in a correct way? >> >> >> >> >> >> I don't think we need to parse the arguments and output them. We just >> >> >> get the function name and if we have more than one set of arguments >> >> >> i.e., a different signature we know that we have an overloaded method >> >> >> and how to handle it. >> >> > >> >> > And I guess the almighty generate_cpp_documentation.py script are able >> >> > to extract the argument information? >> >> >> >> No, but we should be able to figure this out from the signature (split >> >> ',' in '()'). >> > >> > Ok! Anders mentioned this too. >> > >> >> >> The arguments should be described in the *Arguments* section of the >> >> >> individual docstring with links to classes formatted like >> >> >> _MeshEntity_, which we will substitute with :py:class:`MeshEntity` or >> >> >> >> >> >> :cpp:class:`MeshEntity` depending on which interface we document. >> >> > >> >> > Ok, but we only want that once for each method in python, even if it >> >> > is overloaded? >> >> >> >> No, I think we need to document the argument list for every overloaded >> >> version like it is done in docstrings.dolfin.cpp.Mesh. >> > >> > Agree, I think I misunderstood you. >> > >> >> >> Although I just realized that standard C++ stuff like double* which >> >> >> end up as numpy.array etc. should probably be handled. >> >> > >> >> > Yes this part I am a little worried about... But maybe a god >> >> > handwritten lookup table will do the trick? At least for 99% of the >> >> > cases ;) >> >> >> >> I share your concern, but if, as you suggest, we'll be able to get God >> >> to hand write our documentation I think we should be OK. :) >> > >> > Good to have God on our side! >> > >> >> (a lookup table would be my best bet at the moment) >> > >> > Ok! >> > >> >> >> On a related note: >> >> >> int some_func() >> >> >> and >> >> >> const int some_func() const >> >> >> are different in C++, but in Python we don't have const right? >> >> >> This will simplify the documentation a lot. >> >> > >> >> > Yes, we tend to %ignore all const versions of different methods. >> >> > >> >> > [snap] >> >> > >> >> >> >> > * Extended methods needs to be handled in one of three ways: >> >> >> >> > 1) Write the docstring directly into the foo_post.i file >> >> >> >> >> >> I like this option, if this is where we have the code for a function, >> >> >> then this is where the docstring should be as it increases the >> >> >> probability of the docstring being up to date. >> >> > >> >> > Ok, lets settle on this one. We also need to make sure that all >> >> > %extended methods in the C++ layer gets a proper docstring. However I >> >> > am not really sure how this can be done :P >> >> >> >> I'm not sure I follow this, which %extended methods do you mean? >> > >> > There are two ways to extend a class. >> > >> > 1) The C++ layer >> > 2) The Python layer >> > >> > often we use 1) to create a protected helper method which is called using >> > an extended method in the Python layer, 2). The latter can be properly >> > documented directly. >> > >> > But some cases excists where we just extend the C++ layer, see for >> > example the IMPLEMENT_VARIABLE_INTERFACE macro in shared_ptr_classes.i. >> > These methods gets no docstrings and I am not sure it is possible to add >> > them later. >> >> OK, docstrings for 2) should go in the code as we agreed, and I guess >> 1) will fall under the 1% category which we may/may not be able to >> handle in a clever way later. > > Ok. > >> >> > [snup] >> >> > >> >> >> > Why do we need to assign to these methods? They already get their >> >> >> > docstrings from the docstrings.i file. However if we want to get >> >> >> > rid of the new_instancemethod assignment above, we can just remove >> >> >> > the >> >> >> >> >> >> Some history. >> >> >> Initially, we wanted to have all docstrings separated from the DOLFIN >> >> >> code and collected in the fenics-doc module. The easiest way to get >> >> >> the >>> help(dolfin) docstring correct is to assign to __doc__ >> >> >> dynamically. >> >> >> If we could do this we wouldn't even need the docstrings.i file and >> >> >> things would be simple. >> >> >> However, we discovered that this was not possible, and because of >> >> >> that we still need to generate the docstrings.i file. >> >> >> Then, still assuming we wanted to separate docs from code and keeping >> >> >> docstrings in fenics-doc, I thought it would be easier to generate >> >> >> the docstrings.i file from the handwritten docstrings module in >> >> >> fenics-doc. >> >> >> Some methods don't get their docstrings from the docstrings.i file >> >> >> though, so we still need to assign to __doc__ which is the easiest >> >> >> thing to do. >> >> >> Just recently we decided to extract the docstrings from the C++ >> >> >> implementation thus moving the docs back into DOLFIN. This makes the >> >> >> docstrings module almost superfluous with the only practical usage is >> >> >> to have documentation for the extended methods defined in the _post.i >> >> >> files but if we put the docstrings directly in the _post.i files we >> >> >> no longer need it. >> >> > >> >> > Ok, then I do not see any reason for a separate docstring module, >> >> > makes life a lite bit easier... >> >> >> >> Agree. >> > >> > Ok. >> > >> >> > [snep] >> >> > >> >> >> > I am confused. Do you suggest that we just document the extended >> >> >> > Python layer directly in the python module as it is today? Why >> >> >> > should we then dumpt the docstrings in a separate docstring >> >> >> > module? So autodoc can have something to shew on? Couldn't autodoc >> >> >> > just shew on the dolfin module directly? >> >> >> >> >> >> I'm confused too. :) I guess my head has not been properly reset >> >> >> between the changes in documentation strategies. >> >> >> The Sphinx autodoc can only handle one dolfin module, so we need to >> >> >> either import the 'real' one or the docstrings dolfin module. >> >> >> If we can completely remove the need for the docstrings module, then >> >> >> we should of course include the 'real' one. >> >> > >> >> > Ok! >> >> > >> >> >> >> Then programmer's writing the Python >> >> >> >> layer just need to document while they're coding, where they are >> >> >> >> coding just like they do (or should anyways) for the C++ part. >> >> >> > >> >> >> > Still confused why we need a certain docstring module. >> >> >> >> >> >> Maybe we don't. >> >> >> >> >> >> >> > 2) for the extended Python layer in the cpp.py >> >> >> >> > >> >> >> >> > For the rest, and this will be the main part, we rely on parsed >> >> >> >> > docstrings from the headers. >> >> >> >> > >> >> >> >> > The python programmers reference will then be generated based on >> >> >> >> > the actual dolfin module using sphinx and autodoc. >> >> >> >> >> >> >> >> We could/should probably use either the dolfin module or the >> >> >> >> generated docstring module to generate the relevant reST files. >> >> >> >> Although we might need to run some cross-checks with the Doxygen >> >> >> >> xml to get the correct file names where the classes are defined >> >> >> >> in DOLFIN such that we retain the original DOLFIN source tree >> >> >> >> structure. Otherwise all our documentation will end up in cpp.rst >> >> >> >> which I would hate to navigate through as a user. >> >> >> > >> >> >> > This one got to technical for me. Do you say that there is no way >> >> >> > to split the documentation into smaller parts without relying on >> >> >> > the c++ module/file structure? >> >> >> >> >> >> But how would you split it? >> >> > >> >> > I do not know. But then I do not know what the generation step can >> >> > take as different inputs. >> >> >> >> The write_python_documentation step should probably take the dolfin >> >> module and the intermediate representation. >> > >> > What is the intermediate representation? >> >> It is whatever output we get from the extract_documentation script >> which we'll add to the dolfin module. How it will look depends a bit >> on what we need in the write_cpp_documentation and >> write_python_documentation functions in fenics-doc. > > Ok. > >> >> >> It makes sense to keep the classes Mesh >> >> >> and MeshEntity in the mesh/ part of the documentation. Unfortunately, >> >> >> Swig doesn't add info to the classes in the cpp.py module about where >> >> >> they were originally defined. This is why we need to pair it with >> >> >> info from the xml output. >> >> > >> >> > Ok, but say we keep all documentation in one module. If you are able >> >> > to pair the different classes or functions with a module name, or >> >> > file name you are able to create documentation which is structured >> >> > after this hierarchy? >> >> >> >> We need to figure out something, having everything in the cpp.py >> >> module would create one big mess and it makes sense to follow the >> >> DOLFIN C++ structure even for the Python interface. >> > >> > Ok, but we do not have everything in a big cpp file. Types get imported >> > into the Dolfin namespace in __init__ mostly from cpp.py. >> >> My point is, there's no telling where the cpp.Mesh class was >> originally defined. Everything from la to mesh to fem is dumped in the >> cpp.py module. > > Ok, but don't you just need a way to associate the classes to different > modules? I thought this was what you used the doxygen output to.
Yes, this is the plan. I thought I could use doxygen to tell me that the class Mesh is defined in dolfin/mesh/Mesh.h, and then associate the dolfin.cpp.Mesh class with the dolfin/mesh directory to generate prog-ref/python/mesh/Mesh.rst. But come to think of it, the potential renaming will screw this up arrrrgh. If we instead > use the module representation we should be able to do this association > directly with just the dolfin tree as the assosiated types should reside in: > > dolfin.submodule.__dict__ This has some potential, we just need to be careful what the submodules import. The big drawback as I see it is that if someone adds a class to say dolfin/mesh/Foo.h, we'll need to manually import it in dolfin/mesh/__init__.py. In the long run, I think that the doxygen approach will yield the most stable result, but we'll most likely have to look in the *.i files for %rename. >> > Would it help to add the cpp imports to submodules instead of the main >> > __init__ file? We already have the submodules: >> > >> > mesh, common, compilemodules, fem, adaptivity and function >> > >> > We could add: >> > >> > io, log, nls, ode, parameter, ale >> > >> > and drag in stuff from cpp.py in these modules instead. In this way we >> > structure the main __init__ file better, and you might be able to >> > structure the pythons reference manual better? >> >> I'm quite sure I tried something like this, and the problem was that >> even if you do: >> >> from dolfin.cpp import Mesh >> >> in dolfin/mesh/__init__.py >> >> the Mesh class will still point to dolfin.cpp when you inspect it --> >> difficult to create the proper structure for the *.rst files. > > Would something that I sceted above work? > >> I'll need to double check though that this is really the case. >> And even if we can use the approach you outline above it means that we >> have more stuff we need to manually maintain. > > Sure, but now everything is throwed into dolfin/__init__.py which is really a > mess now. No disagreeing you there. Kristian > Johan > _______________________________________________ Mailing list: https://launchpad.net/~fenics Post to : [email protected] Unsubscribe : https://launchpad.net/~fenics More help : https://help.launchpad.net/ListHelp
