merge

test_autolens/aggregator/conftest.py

@@ -66,10 +66,6 @@ def make_model():
 
 @pytest.fixture(name="samples")
 def make_samples(model):
-    galaxy_0 = al.Galaxy(redshift=0.5, light=al.lp.Sersic(centre=(0.0, 1.0)))
-    galaxy_1 = al.Galaxy(redshift=1.0, light=al.lp.Sersic())
-
-    tracer = al.Tracer(galaxies=[galaxy_0, galaxy_1])
 
     parameters = [model.prior_count * [1.0], model.prior_count * [10.0]]
 
@@ -84,6 +80,5 @@ def make_samples(model):
     return al.m.MockSamples(
         model=model,
         sample_list=sample_list,
-        max_log_likelihood_instance=tracer,
         prior_means=[1.0] * model.prior_count,
     )

test_autolens/analysis/test_result.py

@@ -23,13 +23,7 @@ def test__max_log_likelihood_tracer(
         )
     )
 
-    samples = al.m.MockSamples(
-        model=model,
-        max_log_likelihood_instance=tracer_x2_plane_7x7,
-        prior_means=[1.0] * model.prior_count,
-    )
-
-    search = al.m.MockSearch(name="test_search_2", samples=samples)
+    search = al.m.MockSearch(name="test_search_2")
 
     result = search.fit(model=model, analysis=analysis_imaging_7x7)
 
@@ -58,9 +52,9 @@ def test__max_log_likelihood_positions_threshold(masked_imaging_7x7):
         ]
     )
 
-    samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
+    samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = res.Result(samples=samples, analysis=analysis)
+    result = res.Result(samples_summary=samples_summary, analysis=analysis)
 
     assert result.max_log_likelihood_positions_threshold == pytest.approx(
         0.8309561230, 1.0e-4
@@ -76,9 +70,9 @@ def test__source_plane_light_profile_centre(analysis_imaging_7x7):
 
     tracer = al.Tracer(galaxies=[lens, source])
 
-    samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
+    samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = res.Result(samples=samples, analysis=analysis_imaging_7x7)
+    result = res.Result(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
 
     assert result.source_plane_light_profile_centre.in_list == [(1.0, 2.0)]
 
@@ -94,9 +88,9 @@ def test__source_plane_light_profile_centre(analysis_imaging_7x7):
 
     tracer = al.Tracer(galaxies=[lens, source_0, source_1])
 
-    samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
+    samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = res.Result(samples=samples, analysis=analysis_imaging_7x7)
+    result = res.Result(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
 
     assert result.source_plane_light_profile_centre.in_list == [(1.0, 2.0)]
 
@@ -110,17 +104,17 @@ def test__source_plane_light_profile_centre(analysis_imaging_7x7):
 
     tracer = al.Tracer(galaxies=[lens, source_0, source_1])
 
-    samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
+    samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = res.Result(samples=samples, analysis=analysis_imaging_7x7)
+    result = res.Result(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
 
     assert result.source_plane_light_profile_centre.in_list == [(5.0, 6.0)]
 
     tracer = al.Tracer(galaxies=[al.Galaxy(redshift=0.5)])
 
-    samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
+    samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = res.Result(samples=samples, analysis=analysis_imaging_7x7)
+    result = res.Result(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
 
     assert result.source_plane_light_profile_centre == None
 
@@ -137,9 +131,9 @@ def test__source_plane_inversion_centre(analysis_imaging_7x7):
 
     tracer = al.Tracer(galaxies=[lens, source])
 
-    samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
+    samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = ResultImaging(samples=samples, analysis=analysis_imaging_7x7)
+    result = ResultImaging(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
 
     assert (
         result.source_plane_inversion_centre.in_list[0]
@@ -155,9 +149,9 @@ def test__source_plane_inversion_centre(analysis_imaging_7x7):
 
     tracer = al.Tracer(galaxies=[lens, source])
 
-    samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
+    samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = ResultImaging(samples=samples, analysis=analysis_imaging_7x7)
+    result = ResultImaging(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
 
     assert result.source_plane_inversion_centre == None
 
@@ -166,9 +160,9 @@ def test__source_plane_inversion_centre(analysis_imaging_7x7):
 
     tracer = al.Tracer(galaxies=[lens, source])
 
-    samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
+    samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = ResultImaging(samples=samples, analysis=analysis_imaging_7x7)
+    result = ResultImaging(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
 
     assert result.source_plane_inversion_centre == None
 
@@ -189,9 +183,9 @@ def test__source_plane_centre(analysis_imaging_7x7):
 
     tracer = al.Tracer(galaxies=[lens, source])
 
-    samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
+    samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = ResultImaging(samples=samples, analysis=analysis_imaging_7x7)
+    result = ResultImaging(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
 
     assert result.source_plane_centre.in_list[0] == pytest.approx(
         (-0.916666, -0.916666), 1.0e-4
@@ -220,9 +214,9 @@ def test__image_plane_multiple_image_positions(analysis_imaging_7x7):
 
     tracer = al.Tracer(galaxies=[lens, source])
 
-    samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
+    samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = ResultImaging(samples=samples, analysis=analysis_imaging_7x7)
+    result = ResultImaging(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
 
     multiple_images = result.image_plane_multiple_image_positions
 
@@ -243,9 +237,9 @@ def test__image_plane_multiple_image_positions(analysis_imaging_7x7):
         ]
     )
 
-    samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
+    samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = res.Result(samples=samples, analysis=analysis_imaging_7x7)
+    result = res.Result(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
 
     assert result.image_plane_multiple_image_positions.in_list[0][0] == pytest.approx(
         1.0004, 1.0e-2
@@ -268,9 +262,9 @@ def test__positions_threshold_from(analysis_imaging_7x7):
         ]
     )
 
-    samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
+    samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = res.Result(samples=samples, analysis=analysis_imaging_7x7)
+    result = res.Result(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
 
     assert result.positions_threshold_from() == pytest.approx(0.000973519, 1.0e-4)
     assert result.positions_threshold_from(factor=5.0) == pytest.approx(
@@ -297,9 +291,9 @@ def test__positions_likelihood_from(analysis_imaging_7x7):
         ]
     )
 
-    samples = al.m.MockSamples(max_log_likelihood_instance=tracer)
+    samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = res.Result(samples=samples, analysis=analysis_imaging_7x7)
+    result = res.Result(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
 
     positions_likelihood = result.positions_likelihood_from(
         factor=0.1, minimum_threshold=0.2
@@ -320,7 +314,7 @@ def test__results_include_mask__available_as_property(
     analysis_imaging_7x7, masked_imaging_7x7, samples_summary_with_result
 ):
     result = res.ResultDataset(
-        samples=samples_summary_with_result,
+        samples_summary=samples_summary_with_result,
         analysis=analysis_imaging_7x7,
     )
 
@@ -331,7 +325,7 @@ def test__results_include_positions__available_as_property(
     analysis_imaging_7x7, masked_imaging_7x7, samples_summary_with_result
 ):
     result = res.ResultDataset(
-        samples=samples_summary_with_result,
+        samples_summary=samples_summary_with_result,
         analysis=analysis_imaging_7x7,
     )
 
@@ -346,7 +340,7 @@ def test__results_include_positions__available_as_property(
     )
 
     result = res.ResultDataset(
-        samples=samples_summary_with_result,
+        samples_summary=samples_summary_with_result,
         analysis=analysis,
     )
 
@@ -362,7 +356,7 @@ def test___image_dict(analysis_imaging_7x7):
     instance.galaxies = galaxies
 
     result = ResultImaging(
-        samples=al.m.MockSamples(max_log_likelihood_instance=instance),
+        samples_summary=al.m.MockSamplesSummary(max_log_likelihood_instance=instance),
         analysis=analysis_imaging_7x7,
     )
 

test_autolens/config/general.yaml

@@ -36,7 +36,7 @@ profiling:
   perform: true
   repeats: 1
 test:
-  check_likelihood_function: true   # if True, when a search is resumed the likelihood of a previous sample is recalculated to ensure it is consistent with the previous run.
+  check_likelihood_function: false  # if True, when a search is resumed the likelihood of a previous sample is recalculated to ensure it is consistent with the previous run.
   check_preloads: false
   exception_override: false
   preloads_check_threshold: 1.0     # If the figure of merit of a fit with and without preloads is greater than this threshold, the check preload test fails and an exception raised for a model-fit. 

test_autolens/imaging/model/test_analysis_imaging.py

@@ -1,4 +1,3 @@
-import numpy as np
 from os import path
 import pytest
 
@@ -15,17 +14,8 @@
 
 
 def test__make_result__result_imaging_is_returned(masked_imaging_7x7):
-    model = af.Collection(galaxies=af.Collection(galaxy_0=al.Galaxy(redshift=0.5)))
-
-    instance = model.instance_from_prior_medians()
-
-    samples = al.m.MockSamples(
-        model=model,
-        max_log_likelihood_instance=instance,
-        prior_means=[1.0] * model.prior_count
-    )
 
-    search = al.m.MockSearch(name="test_search", samples=samples)
+    model = af.Collection(galaxies=af.Collection(galaxy_0=al.Galaxy(redshift=0.5)))
 
     analysis = al.AnalysisImaging(dataset=masked_imaging_7x7)
 
@@ -36,6 +26,8 @@ def modify_after_fit(
 
     analysis.modify_after_fit = modify_after_fit
 
+    search = al.m.MockSearch(name="test_search")
+
     result = search.fit(model=model, analysis=analysis)
 
     assert isinstance(result, ResultImaging)

test_autolens/imaging/model/test_result_imaging.py

@@ -1,4 +1,3 @@
-import numpy as np
 import pytest
 
 import autofit as af
@@ -15,9 +14,9 @@ def test___linear_light_profiles_in_result(analysis_imaging_7x7):
     instance = af.ModelInstance()
     instance.galaxies = galaxies
 
-    samples = al.m.MockSamples(max_log_likelihood_instance=instance)
+    samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=instance)
 
-    result = ResultImaging(samples=samples, analysis=analysis_imaging_7x7)
+    result = ResultImaging(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
 
     assert not isinstance(
         result.max_log_likelihood_tracer.galaxies[0].bulge,

test_autolens/interferometer/model/test_analysis_interferometer.py

@@ -1,5 +1,4 @@
 from os import path
-import numpy as np
 import pytest
 
 import autofit as af
@@ -14,12 +13,6 @@
 def test__make_result__result_interferometer_is_returned(interferometer_7):
     model = af.Collection(galaxies=af.Collection(galaxy_0=al.Galaxy(redshift=0.5)))
 
-    instance = model.instance_from_prior_medians()
-
-    samples = al.m.MockSamples(max_log_likelihood_instance=instance)
-
-    search = al.m.MockSearch(name="test_search", samples=samples)
-
     analysis = al.AnalysisInterferometer(dataset=interferometer_7)
 
     def modify_after_fit(
@@ -29,6 +22,8 @@ def modify_after_fit(
 
     analysis.modify_after_fit = modify_after_fit
 
+    search = al.m.MockSearch(name="test_search")
+
     result = search.fit(model=model, analysis=analysis)
 
     assert isinstance(result, ResultInterferometer)

test_autolens/quantity/model/test_analysis_quantity.py

@@ -7,51 +7,49 @@
 
 directory = path.dirname(path.realpath(__file__))
 
+def test__make_result__result_quantity_is_returned(
+    dataset_quantity_7x7_array_2d
+):
+    model = af.Collection(galaxies=af.Collection(galaxy_0=al.Galaxy(redshift=0.5)))
 
-class TestAnalysisQuantity:
-    def test__make_result__result_quantity_is_returned(
-        self, dataset_quantity_7x7_array_2d
-    ):
-        model = af.Collection(galaxies=af.Collection(galaxy_0=al.Galaxy(redshift=0.5)))
+    analysis = al.AnalysisQuantity(
+        dataset=dataset_quantity_7x7_array_2d, func_str="convergence_2d_from"
+    )
 
-        analysis = al.AnalysisQuantity(
-            dataset=dataset_quantity_7x7_array_2d, func_str="convergence_2d_from"
-        )
+    search = al.m.MockSearch(name="test_search")
 
-        search = al.m.MockSearch(name="test_search")
+    result = search.fit(model=model, analysis=analysis)
 
-        result = search.fit(model=model, analysis=analysis)
+    assert isinstance(result, ResultQuantity)
 
-        assert isinstance(result, ResultQuantity)
+def test__figure_of_merit__matches_correct_fit_given_galaxy_profiles(
+    dataset_quantity_7x7_array_2d
+):
+    galaxy = al.Galaxy(redshift=0.5, light=al.mp.Isothermal(einstein_radius=1.0))
 
-    def test__figure_of_merit__matches_correct_fit_given_galaxy_profiles(
-        self, dataset_quantity_7x7_array_2d
-    ):
-        galaxy = al.Galaxy(redshift=0.5, light=al.mp.Isothermal(einstein_radius=1.0))
+    model = af.Collection(galaxies=af.Collection(galaxy=galaxy))
 
-        model = af.Collection(galaxies=af.Collection(galaxy=galaxy))
+    analysis = al.AnalysisQuantity(
+        dataset=dataset_quantity_7x7_array_2d, func_str="convergence_2d_from"
+    )
 
-        analysis = al.AnalysisQuantity(
-            dataset=dataset_quantity_7x7_array_2d, func_str="convergence_2d_from"
-        )
+    instance = model.instance_from_unit_vector([])
+    fit_figure_of_merit = analysis.log_likelihood_function(instance=instance)
 
-        instance = model.instance_from_unit_vector([])
-        fit_figure_of_merit = analysis.log_likelihood_function(instance=instance)
+    tracer = analysis.tracer_via_instance_from(instance=instance)
 
-        tracer = analysis.tracer_via_instance_from(instance=instance)
+    fit = al.FitQuantity(
+        dataset=dataset_quantity_7x7_array_2d,
+        tracer=tracer,
+        func_str="convergence_2d_from",
+    )
 
-        fit = al.FitQuantity(
-            dataset=dataset_quantity_7x7_array_2d,
-            tracer=tracer,
-            func_str="convergence_2d_from",
-        )
+    assert fit.log_likelihood == fit_figure_of_merit
 
-        assert fit.log_likelihood == fit_figure_of_merit
+    fit = al.FitQuantity(
+        dataset=dataset_quantity_7x7_array_2d,
+        tracer=tracer,
+        func_str="potential_2d_from",
+    )
 
-        fit = al.FitQuantity(
-            dataset=dataset_quantity_7x7_array_2d,
-            tracer=tracer,
-            func_str="potential_2d_from",
-        )
-
-        assert fit.log_likelihood != fit_figure_of_merit
+    assert fit.log_likelihood != fit_figure_of_merit