On Fri, May 8, 2009 at 9:17 AM, Anders Logg <[email protected]> wrote: > On Fri, May 08, 2009 at 09:06:16AM +0200, Johan Hake wrote: >> On Friday 08 May 2009 08:49:59 Garth N. Wells wrote: >> > Anders Logg wrote: >> > > On Fri, May 08, 2009 at 08:12:35AM +0200, Johan Hake wrote: >> > >> On Thursday 07 May 2009 23:16:54 Anders Logg wrote: >> > >>> On Thu, May 07, 2009 at 11:05:49PM +0200, Johan Hake wrote: >> > >>>> On Thursday 07 May 2009 18:54:04 Anders Logg wrote: >> > >>>>> I've added some of the requested features to the parameter system, >> > >>>>> some pushed and some sitting here in a local repository. But the >> > >>>>> current design makes it a pain to add new features. A single change >> > >>>>> will make it necessary to add a function in at least 5 different >> > >>>>> classes. >> > >>>>> >> > >>>>> So I'm thinking of reimplementing and simplifying the parameter >> > >>>>> system. I think I know how to make it simpler. >> > >>>>> >> > >>>>> But before I do that, does anyone have opinions on the >> > >>>>> design/implementation? Is there any third-party library that we >> > >>>>> could/should use (maybe something in boost)? >> > >>>> >> > >>>> It would be nice to have something that easely could be transferable >> > >>>> to Python. >> > >>>> >> > >>>> Having a base class let say Parameterized and then let all inherit >> > >>>> this to be able to define parameters will not work well for the >> > >>>> shared_ptr interface we have. We have problems with the Variable >> > >>>> class, which does not work for the derived shared_ptr classes e.g. >> > >>>> Function. I would rather have classes that have a parameter rather >> > >>>> than beeing. >> > >>> >> > >>> How would that work? Inheritance now provides get/set functions for >> > >>> subclasses making it possible to do >> > >>> >> > >>> solver.set("tolerance", 0.1); >> > >> >> > >> Not sure what you ask for here. I know of Parametrized and I agree that >> > >> the above syntax is nice. But I prefer to keep the parameters in its own >> > >> object and just operate on that. These can then be collected into one >> > >> "dict/map" and then form the parameters of an application. This is also >> > >> easier to wrap to python. >> > >> >> > >> The shared_ptr argument might not be so relevant as the potential >> > >> parametrized classes may not be declared as shared_ptr classes in the >> > >> swig interface anyway. However if that will be the case we must declare >> > >> Parametrized as a shared_ptr class in swig and then we must declare all >> > >> Parametrized sub classes as shared_ptr... >> > >> >> > >>>> Also by defining a parameter(list/dict) class which can be accessed as >> > >>>> a dict let us make the transition to python smoother. >> > >>>> >> > >>>> ParameterDict p = solver.default_params(); >> > >>>> p["abs_tol"] = 1e-9; >> > >>> >> > >>> It would need to be >> > >>> >> > >>> ParameterDict& p = solver.default_params(); >> > >> >> > >> Sure :P >> > >> >> > >>> and I'd suggest naming it Parameters: >> > >>> >> > >>> Parameters& p = solver.parameters(); >> > >> >> > >> Fine. >> > >> >> > >>>> By defining some templated check classes we could controll the >> > >>>> assignment. In the Solver: >> > >>>> ... >> > >>>> ParameterDict& default_params(){ >> > >>>> if (!_par) >> > >>>> { >> > >>>> _par = new ParameterDict(); >> > >>>> _par->add_param("abs_tol",new RangeCheck<double>(1e-15,0,1)); >> > >>>> vector<string> * allowed_prec = new Vector<string>(); >> > >>>> allowed_prec->push_back("ilu"); >> > >>>> allowed_prec->push_back("amg"); >> > >>>> allowed_prec->push_back("jacobi"); >> > >>>> _par->add_param("prec",new >> > >>>> OptionCheck<string>("ilu"),allowed_prec)); >> > >>>> _par->add_param("nonsense","jada"); // No checks >> > >>>> } >> > >>>> } >> > >>>> >> > >>>> Well, I admit that the above code is not beautiful, and others can >> > >>>> probably make it cleaner and spot errors. The point is that RangeCheck >> > >>>> and OptionCheck can be derived from a ParCheck class that overloads >> > >>>> the operator=(). This will just call a private set function which is >> > >>>> defined in the derived classes, and which do the check. >> > >>> >> > >>> I think we can also solve this without excessive templating... ;-) >> > >> >> > >> Good! >> > >> >> > >>>> The to and from file can be implemented in the ParameterDict body. The >> > >>>> checks do not have to be written or read, as a ParameterDict can only >> > >>>> read in allready predefined parameters, and the check will be done >> > >>>> when the file is read. >> > >>>> >> > >>>> The option parser ability can also be implemented in ParameterDict >> > >>>> using boost or other libraries, based on the registered parameters. >> > >>>> >> > >>>> I have implemented something like this in Python, and the above is a >> > >>>> try to scetch something similare in c++. >> > >>> >> > >>> What exactly is needed from the Python side? I think I can make a >> > >>> fairly simple implementation of this in C++ using a minimal amount of >> > >>> templates with simple syntax. >> > >> >> > >> Using operator[] to get and set parameters can straightforwardly be >> > >> mapped to python, and we can then also implement the map/dict protocol >> > >> on top of that. Other get and set methods can also be used, however set >> > >> is a built in type in Python and not a good alternative. >> > >> >> > >>> Is the main difference that instead of inheriting Parametrized, a >> > >>> subclass needs to implement a method named parameters() which returns >> > >>> the parameter "dictionary"? >> > >> >> > >> Yes. >> > > >> > > ok, I'll try this. I'll add a sketch of a new class using as much of >> > > po as seems reasonable and then you could have a look before I proceed. >> > >> > Will there be just one parameter dictionary, or will objects have their >> > own? I'm thinking of cases like when a program uses two Krylov solvers >> > but may use different tolerances for each one. >> >> You mean one parameter dictionary per class or one per instance? I have the >> same distinction in a Python application. Some places I need one per instance >> and other places it is more convinient to have one per class. > > One per instance. But there could be a default Parameter database for > "Krylov solver" which is used if an option is not set for a specific > instance.
Suggestions: const Parameters & a = FooBarType::default_parameters(); Parameters & b = foobar.parameters(); Parameters p = b.diff(a); // parameters in b that differs from a p.disp(); file << p.format(); Parameters par; par["beta"] = 1.0; foobar.set_parameters(par); I prefer the global/class defaults to be immutable. (Global state is _always_ evil!) Otherwise combining applications becomes a real mess. Application-wide defaults can still be handled manually using set_parameters. Martin _______________________________________________ DOLFIN-dev mailing list [email protected] http://www.fenics.org/mailman/listinfo/dolfin-dev
