PyAutoFit/test_autofit/graphical/test_combined_analysis.py at 7989fe97ecaa8abd0f15126280bf4a68dde6e224 · PyAutoLabs/PyAutoFit · GitHub

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import pytest

import autofit as af
from autofit import AbstractPaths, DirectoryPaths, AbstractPriorModel
from autofit.non_linear.paths.null import NullPaths
from autofit.non_linear.paths.sub_directory_paths import SubDirectoryPaths


def test_make_result():
    model = af.Model(af.ex.Gaussian)
    factor_graph_model = af.FactorGraphModel(
        af.AnalysisFactor(
            model,
            af.Analysis(),
        )
    )
    result = factor_graph_model.make_result(
        samples_summary=af.SamplesSummary(
            max_log_likelihood_sample=af.Sample(
                0,
                0,
                0,
                kwargs={
                    ("0", "centre"): 1.0,
                    ("0", "normalization"): 1.0,
                    ("0", "sigma"): 1.0,
                },
            ),
            model=af.Collection(model),
        ),
        paths=NullPaths(),
    )
    assert len(result.child_results) == 1
    assert isinstance(result.model, af.Collection)

    (child_result,) = result.child_results
    assert child_result.model == model


class MockAnalysis(af.Analysis):
    def __init__(self):
        super().__init__()
        calls = []
        self.calls = calls

        class Visualizer(af.Visualizer):
            @staticmethod
            def visualize_before_fit(
                analysis,
                paths: SubDirectoryPaths,
                model: AbstractPriorModel,
            ):
                calls.append(("visualize_before_fit", paths.analysis_name))

            @staticmethod
            def visualize(
                analysis,
                paths: SubDirectoryPaths,
                instance,
                during_analysis,
            ):
                calls.append(
                    (
                        "visualize",
                        paths.analysis_name,
                        during_analysis,
                    )
                )

        self.Visualizer = Visualizer

    def log_likelihood_function(self, instance):
        return 0.0

    def save_attributes(self, paths: SubDirectoryPaths):
        self.calls.append(("save_attributes", paths.analysis_name))

    def save_results(self, paths: SubDirectoryPaths, result):
        self.calls.append(("save_results", paths.analysis_name))


@pytest.fixture
def analysis():
    return MockAnalysis()


@pytest.fixture
def model():
    return af.Model(af.ex.Gaussian)


@pytest.fixture
def factor_graph(analysis, model):
    return af.FactorGraphModel(
        af.AnalysisFactor(
            prior_model=model,
            analysis=analysis,
        )
    )


@pytest.fixture
def instance(model):
    return af.Collection(model).instance_from_prior_medians()


@pytest.mark.parametrize(
    "during_analysis",
    [True, False],
)
def test_visualize(
    analysis,
    factor_graph,
    instance,
    during_analysis,
):
    factor_graph.visualize(
        DirectoryPaths(),
        instance,
        during_analysis,
    )

    assert analysis.calls == [
        (
            "visualize",
            "analyses/analysis_0",
            during_analysis,
        ),
    ]


def test_visualize_before_fit(
    analysis,
    factor_graph,
    instance,
    model,
):
    factor_graph.visualize_before_fit(
        DirectoryPaths(),
        af.Collection(model),
    )

    assert analysis.calls == [
        ("visualize_before_fit", "analyses/analysis_0"),
    ]


def test_save_attributes(
    analysis,
    factor_graph,
    instance,
):
    factor_graph.save_attributes(
        DirectoryPaths(),
    )

    assert analysis.calls == [
        ("save_attributes", "analyses/analysis_0"),
    ]


def test_save_results(
    analysis,
    factor_graph,
    instance,
):
    factor_graph.save_results(
        DirectoryPaths(),
        [None],
    )

    assert analysis.calls == [
        ("save_results", "analyses/analysis_0"),
    ]