feature/sub analysis aggregator

[rhayes777]

Resolves #705.

Values from child analyses can be obtained using the child_values method

aggregator.child_values('data')

Fits which are not top-level are filtered out during aggregation. These fits can be included by setting a flag.

aggregator = af.Aggregator.from_database('filename', top_level_only=False)

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Merging #708 (e1d0e18) into main (bf059ae) will increase coverage by 0.10%.
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+ Coverage   81.78%   81.89%   +0.10%     
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+ Hits        10788    10826      +38     
+ Misses       2402     2393       -9     

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[Jammy2211]

This seems to work:

def _data_from(fit: af.Fit):

    data = fit.value(name="data")

    return data

data_gen = agg.child_values(name="data")

data = [data for data in data_gen]

Is there any way to do the child_values in the generator, so something like:

def _data_from(fit: af.Fit):

    data = fit.child_values(name="data")

    return data

data_gen = agg.values(name="data")

data = [data for data in data_gen]

I probably doesn't matter but would make it easier to maintain a consistent API in places.

[Jammy2211]

The reason is that generators may combine the two types of fits:

def _dataset_1d_from(fit: af.Fit, settings_dataset: Optional[SettingsDataset1D] = None):
    """
    Returns a `Dataset1D` object from an aggregator's `SearchOutput` class, which we call an 'agg_obj' to describe
    that it acts as the aggregator object for one result in the `Aggregator`. This uses the aggregator's generator
    outputs such that the function can use the `Aggregator`'s map function to create a `Dataset1D` generator.

     The `Dataset1D` is created following the same method as the PyAutoCTI `Search` classes, including using the
    `SettingsDataset1D` instance output by the Search to load inputs of the `Dataset1D`.

    Parameters
    ----------
    fit
        A PyAutoFit aggregator's SearchOutput object containing the generators of the results of model-fits.
    """

    dataset_list = fit.child_values(name="dataset")

    settings_dataset = settings_dataset or fit.value(name="settings_dataset")

    for dataset in dataset_list:

        dataset.apply_settings(settings=settings_dataset)

    return dataset

[rhayes777]

Done