test: skip jax-backed unit tests when jax is absent (Python matrix)

test_autolens/interferometer/model/test_analysis_interferometer.py

@@ -1,176 +1,189 @@
-from pathlib import Path
-import pytest
-
-import autofit as af
-import autoarray as aa
-import autolens as al
-from autolens import exc
-
-from autolens.interferometer.model.result import ResultInterferometer
-
-directory = Path(__file__).resolve().parent
-
-
-def test__make_result__result_interferometer_is_returned(interferometer_7):
-    model = af.Collection(galaxies=af.Collection(galaxy_0=al.Galaxy(redshift=0.5)))
-
-    analysis = al.AnalysisInterferometer(dataset=interferometer_7, use_jax=False)
-
-    search = al.m.MockSearch(name="test_search")
-
-    result = search.fit(model=model, analysis=analysis)
-
-    assert isinstance(result, ResultInterferometer)
-
-
-def test__figure_of_merit__matches_correct_fit_given_galaxy_profiles(interferometer_7):
-    lens_galaxy = al.Galaxy(redshift=0.5, light=al.lp.Sersic(intensity=0.1))
-
-    model = af.Collection(galaxies=af.Collection(lens=lens_galaxy))
-
-    analysis = al.AnalysisInterferometer(dataset=interferometer_7, use_jax=False)
-
-    instance = model.instance_from_unit_vector([])
-    analysis_log_likelihood = analysis.log_likelihood_function(instance=instance)
-
-    tracer = analysis.tracer_via_instance_from(instance=instance)
-
-    fit = al.FitInterferometer(dataset=interferometer_7, tracer=tracer)
-
-    assert fit.log_likelihood == analysis_log_likelihood
-
-
-def test__positions__likelihood_overwrite__changes_likelihood(
-    interferometer_7, mask_2d_7x7
-):
-    lens = al.Galaxy(redshift=0.5, mass=al.mp.IsothermalSph(centre=(0.05, 0.05)))
-    source = al.Galaxy(redshift=1.0, light=al.lp.SersicSph(centre=(0.05, 0.05)))
-
-    model = af.Collection(galaxies=af.Collection(lens=lens, source=source))
-
-    analysis = al.AnalysisInterferometer(dataset=interferometer_7, use_jax=False)
-
-    instance = model.instance_from_unit_vector([])
-    analysis_log_likelihood = analysis.log_likelihood_function(instance=instance)
-
-    tracer = analysis.tracer_via_instance_from(instance=instance)
-
-    fit = al.FitInterferometer(dataset=interferometer_7, tracer=tracer)
-
-    assert fit.log_likelihood == analysis_log_likelihood
-    assert analysis_log_likelihood == pytest.approx(-62.463179940, 1.0e-4)
-
-    positions_likelihood = al.PositionsLH(
-        positions=al.Grid2DIrregular([(1.0, 100.0), (200.0, 2.0)]), threshold=0.01
-    )
-
-    analysis = al.AnalysisInterferometer(
-        dataset=interferometer_7,
-        positions_likelihood_list=[positions_likelihood],
-        use_jax=False,
-    )
-    analysis_log_likelihood = analysis.log_likelihood_function(instance=instance)
-
-    assert analysis_log_likelihood == pytest.approx(-44097289569.2342, 1.0e-4)
-
-
-def _pixelization_model():
-    pixelization = al.Pixelization(
-        mesh=al.mesh.RectangularUniform(shape=(3, 3)),
-        regularization=al.reg.Constant(coefficient=0.01),
-    )
-    return af.Collection(
-        galaxies=af.Collection(
-            galaxy_0=al.Galaxy(redshift=0.5),
-            galaxy_1=al.Galaxy(redshift=0.5, pixelization=pixelization),
-        )
-    )
-
-
-def test__shared_state_from__preloads_curvature_reused__figure_of_merit_unchanged(
-    interferometer_7,
-):
-    dataset = interferometer_7.apply_sparse_operator(use_jax=False)
-
-    model = _pixelization_model()
-    instance = model.instance_from_unit_vector([])
-
-    analysis = al.AnalysisInterferometer(
-        dataset=dataset, use_jax=False, shared_preloads=True
-    )
-
-    # `shared_state_from` builds a `PreloadsInterferometer` carrying the curvature matrix `F` and
-    # the mapper (the channel-invariant inversion-setup quantities).
-    shared = analysis.shared_state_from(instance=instance)
-    assert isinstance(shared, aa.PreloadsInterferometer)
-    assert shared.curvature_matrix is not None
-    assert shared.mapper_galaxy_dict is not None
-
-    # The preloaded `F` and mapper are reused by the fit (identity) and leave the figure of merit
-    # unchanged. Reusing the mapper means the Delaunay triangulation is not rebuilt per channel.
-    fit_unshared = analysis.fit_from(instance=instance)
-    fit_shared = analysis.fit_from(instance=instance, preloads=shared)
-
-    assert fit_shared.inversion.curvature_matrix is shared.curvature_matrix
-    assert fit_shared.tracer_to_inversion.mapper_galaxy_dict is shared.mapper_galaxy_dict
-    assert fit_shared.figure_of_merit == pytest.approx(fit_unshared.figure_of_merit)
-
-    # The full `log_likelihood_function` with the shared object matches the unshared call.
-    assert analysis.log_likelihood_function(
-        instance=instance, shared=shared
-    ) == pytest.approx(analysis.log_likelihood_function(instance=instance))
-
-
-def test__shared_state_from__returns_none_when_not_opted_in(interferometer_7):
-    dataset = interferometer_7.apply_sparse_operator(use_jax=False)
-
-    model = _pixelization_model()
-    instance = model.instance_from_unit_vector([])
-
-    analysis = al.AnalysisInterferometer(dataset=dataset, use_jax=False)
-
-    assert analysis.shared_state_from(instance=instance) is None
-
-
-def test__preloads_scoped__cross_type_preloads_reduced_to_mesh_view(interferometer_7):
-    lens = al.Galaxy(redshift=0.5, light=al.lp.Sersic(intensity=0.1))
-    tracer = al.Tracer(galaxies=[lens])
-
-    # Cross-dataset-type preloads (e.g. from an imaging lead factor in a joint graph): only
-    # the mesh-geometry view is valid for an interferometer fit.
-    cross_type = aa.PreloadsImaging(
-        source_plane_mesh_grid=[["mesh"]], image_plane_mesh_grid=[["image-mesh"]]
-    )
-
-    fit = al.FitInterferometer(
-        dataset=interferometer_7, tracer=tracer, preloads=cross_type
-    )
-
-    scoped = fit._preloads_scoped
-    assert isinstance(scoped, aa.PreloadsInterferometer)
-    assert scoped.source_plane_mesh_grid == [["mesh"]]
-    assert scoped.curvature_matrix is None
-    assert scoped.mapper_galaxy_dict is None
-
-    same_type = aa.PreloadsInterferometer(curvature_matrix="F")
-    fit = al.FitInterferometer(
-        dataset=interferometer_7, tracer=tracer, preloads=same_type
-    )
-    assert fit._preloads_scoped is same_type
-
-
-def test__shared_state_from__populates_mesh_geometry_fields(interferometer_7):
-    dataset = interferometer_7.apply_sparse_operator(use_jax=False)
-
-    model = _pixelization_model()
-    instance = model.instance_from_unit_vector([])
-
-    analysis = al.AnalysisInterferometer(
-        dataset=dataset, use_jax=False, shared_preloads=True
-    )
-
-    # The mesh-geometry fields ride alongside the curvature matrix + mapper so that
-    # cross-dataset-type factors of a joint graph can consume the shared mesh.
-    shared = analysis.shared_state_from(instance=instance)
-    assert shared.source_plane_mesh_grid is not None
-    assert shared.image_plane_mesh_grid is not None
+import importlib.util
+from pathlib import Path
+import pytest
+
+import autofit as af
+import autoarray as aa
+import autolens as al
+from autolens import exc
+
+# The interferometer shared-state preload builds the jax-backed NUFFT sparse
+# operator, so these tests need jax installed to run (it ships via the
+# `[optional]` extras). The NumPy-only Python-version matrix has no jax, so skip
+# there rather than fail.
+requires_jax = pytest.mark.skipif(
+    importlib.util.find_spec("jax") is None,
+    reason="requires jax (installed via the [optional] extras; absent on the NumPy-only matrix env)",
+)
+
+from autolens.interferometer.model.result import ResultInterferometer
+
+directory = Path(__file__).resolve().parent
+
+
+def test__make_result__result_interferometer_is_returned(interferometer_7):
+    model = af.Collection(galaxies=af.Collection(galaxy_0=al.Galaxy(redshift=0.5)))
+
+    analysis = al.AnalysisInterferometer(dataset=interferometer_7, use_jax=False)
+
+    search = al.m.MockSearch(name="test_search")
+
+    result = search.fit(model=model, analysis=analysis)
+
+    assert isinstance(result, ResultInterferometer)
+
+
+def test__figure_of_merit__matches_correct_fit_given_galaxy_profiles(interferometer_7):
+    lens_galaxy = al.Galaxy(redshift=0.5, light=al.lp.Sersic(intensity=0.1))
+
+    model = af.Collection(galaxies=af.Collection(lens=lens_galaxy))
+
+    analysis = al.AnalysisInterferometer(dataset=interferometer_7, use_jax=False)
+
+    instance = model.instance_from_unit_vector([])
+    analysis_log_likelihood = analysis.log_likelihood_function(instance=instance)
+
+    tracer = analysis.tracer_via_instance_from(instance=instance)
+
+    fit = al.FitInterferometer(dataset=interferometer_7, tracer=tracer)
+
+    assert fit.log_likelihood == analysis_log_likelihood
+
+
+def test__positions__likelihood_overwrite__changes_likelihood(
+    interferometer_7, mask_2d_7x7
+):
+    lens = al.Galaxy(redshift=0.5, mass=al.mp.IsothermalSph(centre=(0.05, 0.05)))
+    source = al.Galaxy(redshift=1.0, light=al.lp.SersicSph(centre=(0.05, 0.05)))
+
+    model = af.Collection(galaxies=af.Collection(lens=lens, source=source))
+
+    analysis = al.AnalysisInterferometer(dataset=interferometer_7, use_jax=False)
+
+    instance = model.instance_from_unit_vector([])
+    analysis_log_likelihood = analysis.log_likelihood_function(instance=instance)
+
+    tracer = analysis.tracer_via_instance_from(instance=instance)
+
+    fit = al.FitInterferometer(dataset=interferometer_7, tracer=tracer)
+
+    assert fit.log_likelihood == analysis_log_likelihood
+    assert analysis_log_likelihood == pytest.approx(-62.463179940, 1.0e-4)
+
+    positions_likelihood = al.PositionsLH(
+        positions=al.Grid2DIrregular([(1.0, 100.0), (200.0, 2.0)]), threshold=0.01
+    )
+
+    analysis = al.AnalysisInterferometer(
+        dataset=interferometer_7,
+        positions_likelihood_list=[positions_likelihood],
+        use_jax=False,
+    )
+    analysis_log_likelihood = analysis.log_likelihood_function(instance=instance)
+
+    assert analysis_log_likelihood == pytest.approx(-44097289569.2342, 1.0e-4)
+
+
+def _pixelization_model():
+    pixelization = al.Pixelization(
+        mesh=al.mesh.RectangularUniform(shape=(3, 3)),
+        regularization=al.reg.Constant(coefficient=0.01),
+    )
+    return af.Collection(
+        galaxies=af.Collection(
+            galaxy_0=al.Galaxy(redshift=0.5),
+            galaxy_1=al.Galaxy(redshift=0.5, pixelization=pixelization),
+        )
+    )
+
+
+@requires_jax
+def test__shared_state_from__preloads_curvature_reused__figure_of_merit_unchanged(
+    interferometer_7,
+):
+    dataset = interferometer_7.apply_sparse_operator(use_jax=False)
+
+    model = _pixelization_model()
+    instance = model.instance_from_unit_vector([])
+
+    analysis = al.AnalysisInterferometer(
+        dataset=dataset, use_jax=False, shared_preloads=True
+    )
+
+    # `shared_state_from` builds a `PreloadsInterferometer` carrying the curvature matrix `F` and
+    # the mapper (the channel-invariant inversion-setup quantities).
+    shared = analysis.shared_state_from(instance=instance)
+    assert isinstance(shared, aa.PreloadsInterferometer)
+    assert shared.curvature_matrix is not None
+    assert shared.mapper_galaxy_dict is not None
+
+    # The preloaded `F` and mapper are reused by the fit (identity) and leave the figure of merit
+    # unchanged. Reusing the mapper means the Delaunay triangulation is not rebuilt per channel.
+    fit_unshared = analysis.fit_from(instance=instance)
+    fit_shared = analysis.fit_from(instance=instance, preloads=shared)
+
+    assert fit_shared.inversion.curvature_matrix is shared.curvature_matrix
+    assert fit_shared.tracer_to_inversion.mapper_galaxy_dict is shared.mapper_galaxy_dict
+    assert fit_shared.figure_of_merit == pytest.approx(fit_unshared.figure_of_merit)
+
+    # The full `log_likelihood_function` with the shared object matches the unshared call.
+    assert analysis.log_likelihood_function(
+        instance=instance, shared=shared
+    ) == pytest.approx(analysis.log_likelihood_function(instance=instance))
+
+
+@requires_jax
+def test__shared_state_from__returns_none_when_not_opted_in(interferometer_7):
+    dataset = interferometer_7.apply_sparse_operator(use_jax=False)
+
+    model = _pixelization_model()
+    instance = model.instance_from_unit_vector([])
+
+    analysis = al.AnalysisInterferometer(dataset=dataset, use_jax=False)
+
+    assert analysis.shared_state_from(instance=instance) is None
+
+
+def test__preloads_scoped__cross_type_preloads_reduced_to_mesh_view(interferometer_7):
+    lens = al.Galaxy(redshift=0.5, light=al.lp.Sersic(intensity=0.1))
+    tracer = al.Tracer(galaxies=[lens])
+
+    # Cross-dataset-type preloads (e.g. from an imaging lead factor in a joint graph): only
+    # the mesh-geometry view is valid for an interferometer fit.
+    cross_type = aa.PreloadsImaging(
+        source_plane_mesh_grid=[["mesh"]], image_plane_mesh_grid=[["image-mesh"]]
+    )
+
+    fit = al.FitInterferometer(
+        dataset=interferometer_7, tracer=tracer, preloads=cross_type
+    )
+
+    scoped = fit._preloads_scoped
+    assert isinstance(scoped, aa.PreloadsInterferometer)
+    assert scoped.source_plane_mesh_grid == [["mesh"]]
+    assert scoped.curvature_matrix is None
+    assert scoped.mapper_galaxy_dict is None
+
+    same_type = aa.PreloadsInterferometer(curvature_matrix="F")
+    fit = al.FitInterferometer(
+        dataset=interferometer_7, tracer=tracer, preloads=same_type
+    )
+    assert fit._preloads_scoped is same_type
+
+
+@requires_jax
+def test__shared_state_from__populates_mesh_geometry_fields(interferometer_7):
+    dataset = interferometer_7.apply_sparse_operator(use_jax=False)
+
+    model = _pixelization_model()
+    instance = model.instance_from_unit_vector([])
+
+    analysis = al.AnalysisInterferometer(
+        dataset=dataset, use_jax=False, shared_preloads=True
+    )
+
+    # The mesh-geometry fields ride alongside the curvature matrix + mapper so that
+    # cross-dataset-type factors of a joint graph can consume the shared mesh.
+    shared = analysis.shared_state_from(instance=instance)
+    assert shared.source_plane_mesh_grid is not None
+    assert shared.image_plane_mesh_grid is not None