Hi Robert, so in a big data analysis framework, that is essentially written in C ++, exposed to python with SWIG, plus dedicated python modules, the user performs an analysis choosing some given modules by name,as in : myOpt="foo" my_analyse.perform(use_optimizer=myOpt)
The attribute use_optimizer is then checked to perform the right calls/instantiations of python but also C++ objects..... and the actual crunching number is in the C++ part. But then I realize that I need to tweak this optimizer's state, and the optimizer object is accessible from a library pyOpt that has been swigified from a C++ library. Then I access the object by calling optObject = eval("pyOpt.%s(some_args)"%myOpt) where myOpt would be "foo" in this particular analysis. This is because what the attribute use_optimizer expects is also the object name in the library, of course. It goes without saying that I could do : if myOpt=="foo": optObject=pyOpt.foo(some_args) else: .... and if you guys tell me it is way safer, I will do that instead of the use of eval that I liked because of the compactness.....
Well, optObject=getattr(pyOpt, myOpt) is a bit nicer and marginally more likely to return sane error messages than optObject=eval("pyOpt. %s"%myOpt). Then you could do result=optObject(some_args)... But why not have the user just pass in the relevant optObject from the pyOpt namespace (or some restricted namespace that just has the relevant functions exposed)? E.g. my_analysis.perform(optimizer=pyOpt.Amoeba) rather than my_analysis.perform(optimizer='Amoeba') This lets users do introspection on the pyOpt namespace to see what functions they can choose from, which is rather friendlier in an interactive environment. Zach