Adapted PySCeS Thin Layer to BaseThinLayer

[JR-1991]

The PySCeS Thin Layer has been adapted to the BaseThinLayer class and utilizes the __init__-method given in the class.

• Incorporates the abstract methods optimize and write to perform parameter estimation and writing the gathered parameters to a new EnzymeMLDocument instance.
• Initial values are applied using the enzmldoc.applyModelInitialization-method which takes the values from a YAML file.
• When writing back, the initial values are kept as such and will only be written to the value-attribute

Example usage:

tl = ThinLayerPysces("Path/To/OMEX", "init_file="Path/To/inifile.yaml")
tl.optimize(model_dir="ModelDir")

enzmldoc = tl.write()
enzmldoc.toFile()

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This change is 

[fbergmann]

ideally the same things would apply to the COPASI thin layer as well, could we find a way to have the same methods be present on the base class, so that things just work for every modelling tool?

[JR-1991]

ideally the same things would apply to the COPASI thin layer as well, could we find a way to have the same methods be present on the base class, so that things just work for every modelling tool?

I agree, that would be great to have a similar structure in the COPASI thin layer. Are you thinking about to adapt the optimization preparation or complete process in terms of methods? Should we implement this into the base class as well?

[JR-1991]

We should also think about to allow an EnzymeMLDocument object as an alternative input. Currently some tests are failing due to files not found and providing a document object as fixture would help.

[fbergmann]

currently the method you introduced here into the base class write, is in the COPASI TL as def update_enzymeml_doc(self): there i also did not return the enzymeml document in optimize and the update method, as it is available from the class at any point.

And yes, having a constructor that takes an enzyme ml document, instead to always have to read from archive would be a good idea.