Functional approach to creating a DataGenerator

[mpvanderschelling]

To improve usability, we want to create a handy helper method that transfors this:

def f(x, s):
    return x + s

into this:

class MyDataGenerator(DataGenerator):
    def execute(self, s)
        x = self.experiment_sample.get(x)
        y = x + s
        self.experiment_sample.store('y', y, to_disk=False)

in the form:

data_generator: DataGenerator = convert_function(f, input=['x'], output=['y'], kwargs={'s': s})

[mpvanderschelling]

from typing import List, Optional, Dict, Any, Callable


def convert_function(f: Callable, input: List[str], output: Optional[List[str]]= None, 
                     kwargs: Optional[Dict[str, Any]] = None, 
                     to_disk: Optional[List[str]]= None) -> DataGenerator:
    
    kwargs = kwargs if kwargs is not None else {}
    to_disk = to_disk if to_disk is not None else []
    output = output if output is not None else []

    class TempDataGenerator(DataGenerator):
        def execute(self, **_kwargs) -> None:
            _input = {input_name: self.experiment_sample.get(input_name) for input_name in input}
            _output = f(**_input, **kwargs)

            # check if output is empty
            if output is None:
                return

            if len(output) == 1:
                _output = (_output,)
            
            for name, value in zip(output, _output):
                if name in to_disk:
                    self.experiment_sample.store(name=name, object=value, to_disk=True)
                else:
                    self.experiment_sample.store(name=name, object=value, to_disk=False)

    return TempDataGenerator()