test: keep aggregator fixtures physically valid

scripts/misc/aggregator/ellipse.py

@@ -47,7 +47,10 @@ def aggregator_from(database_file, analysis, model, samples):
     result_path = path.join(conf.instance.output_path, "test_mode", database_file)
     clean(database_file)
     search = ag.m.MockSearch(
-        samples=samples, result=ag.m.MockResult(model=model, samples=samples)
+        samples=samples,
+        result=af.m.MockResult(
+            model=model, samples=samples, samples_summary=samples.summary()
+        ),
     )
     search.paths = af.DirectoryPaths(path_prefix=database_file)
     search.fit(model=model, analysis=analysis)
@@ -74,7 +77,19 @@ def aggregator_from(database_file, analysis, model, samples):
 
 model = af.Collection(ellipses=ellipse_list, multipoles=multipole_list_model)
 
-parameters = [model.prior_count * [1.0], model.prior_count * [10.0]]
+
+def parameter_list_with_physical_ell_comps(value):
+    parameter_list = model.prior_count * [value]
+    for index, path_tuple in enumerate(model.all_paths):
+        if "ell_comps" in path_tuple[0]:
+            parameter_list[index] = 0.1
+    return parameter_list
+
+
+parameters = [
+    parameter_list_with_physical_ell_comps(1.0),
+    parameter_list_with_physical_ell_comps(10.0),
+]
 sample_list = Sample.from_lists(
     model=model,
     parameter_lists=parameters,
@@ -84,7 +99,7 @@ def aggregator_from(database_file, analysis, model, samples):
 )
 samples = ag.m.MockSamples(
     model=model,
-    prior_means=[1.0] * model.prior_count,
+    prior_means=parameter_list_with_physical_ell_comps(1.0),
     sample_list=sample_list,
 )
 

scripts/misc/aggregator/fit_imaging.py

@@ -56,7 +56,10 @@ def aggregator_from(analysis, model, samples):
     result_path = path.join(conf.instance.output_path, "test_mode", database_file)
     clean()
     search = ag.m.MockSearch(
-        samples=samples, result=ag.m.MockResult(model=model, samples=samples)
+        samples=samples,
+        result=af.m.MockResult(
+            model=model, samples=samples, samples_summary=samples.summary()
+        ),
     )
     search.paths = af.DirectoryPaths(path_prefix=database_file)
     search.fit(model=model, analysis=analysis)
@@ -82,7 +85,17 @@ def make_model():
 
 
 def make_samples(model):
-    parameters = [model.prior_count * [1.0], model.prior_count * [10.0]]
+    def parameter_list_with_physical_ell_comps(value):
+        parameter_list = model.prior_count * [value]
+        for index, path_tuple in enumerate(model.all_paths):
+            if "ell_comps" in path_tuple[0]:
+                parameter_list[index] = 0.1
+        return parameter_list
+
+    parameters = [
+        parameter_list_with_physical_ell_comps(1.0),
+        parameter_list_with_physical_ell_comps(10.0),
+    ]
     sample_list = Sample.from_lists(
         model=model,
         parameter_lists=parameters,
@@ -92,7 +105,7 @@ def make_samples(model):
     )
     return ag.m.MockSamples(
         model=model,
-        prior_means=[1.0] * model.prior_count,
+        prior_means=parameter_list_with_physical_ell_comps(1.0),
         sample_list=sample_list,
     )
 
@@ -215,7 +228,10 @@ def make_samples(model):
 @with_config("general", "output", "samples_to_csv", value=True)
 def _agg_imaging():
     search = ag.m.MockSearch(
-        samples=samples, result=ag.m.MockResult(model=model, samples=samples)
+        samples=samples,
+        result=af.m.MockResult(
+            model=model, samples=samples, samples_summary=samples.summary()
+        ),
     )
     search.paths = af.DirectoryPaths(path_prefix=db_file_imaging)
     search.fit(model=model, analysis=analysis_oversampled)

scripts/misc/aggregator/fit_interferometer.py

@@ -56,7 +56,10 @@ def aggregator_from(analysis, model, samples):
     result_path = path.join(conf.instance.output_path, "test_mode", database_file)
     clean()
     search = ag.m.MockSearch(
-        samples=samples, result=ag.m.MockResult(model=model, samples=samples)
+        samples=samples,
+        result=af.m.MockResult(
+            model=model, samples=samples, samples_summary=samples.summary()
+        ),
     )
     search.paths = af.DirectoryPaths(path_prefix=database_file)
     search.fit(model=model, analysis=analysis)
@@ -82,7 +85,17 @@ def make_model():
 
 
 def make_samples(model):
-    parameters = [model.prior_count * [1.0], model.prior_count * [10.0]]
+    def parameter_list_with_physical_ell_comps(value):
+        parameter_list = model.prior_count * [value]
+        for index, path_tuple in enumerate(model.all_paths):
+            if "ell_comps" in path_tuple[0]:
+                parameter_list[index] = 0.1
+        return parameter_list
+
+    parameters = [
+        parameter_list_with_physical_ell_comps(1.0),
+        parameter_list_with_physical_ell_comps(10.0),
+    ]
     sample_list = Sample.from_lists(
         model=model,
         parameter_lists=parameters,
@@ -92,7 +105,7 @@ def make_samples(model):
     )
     return ag.m.MockSamples(
         model=model,
-        prior_means=[1.0] * model.prior_count,
+        prior_means=parameter_list_with_physical_ell_comps(1.0),
         sample_list=sample_list,
     )
 
@@ -208,7 +221,10 @@ def make_samples(model):
 @with_config("general", "output", "samples_to_csv", value=True)
 def _agg_inter():
     search = ag.m.MockSearch(
-        samples=samples, result=ag.m.MockResult(model=model, samples=samples)
+        samples=samples,
+        result=af.m.MockResult(
+            model=model, samples=samples, samples_summary=samples.summary()
+        ),
     )
     search.paths = af.DirectoryPaths(path_prefix=db_file_inter)
     search.fit(model=model, analysis=analysis_custom)

scripts/misc/aggregator/galaxies.py

@@ -49,7 +49,10 @@ def aggregator_from(analysis, model, samples):
     result_path = path.join(conf.instance.output_path, "test_mode", database_file)
     clean()
     search = ag.m.MockSearch(
-        samples=samples, result=ag.m.MockResult(model=model, samples=samples)
+        samples=samples,
+        result=af.m.MockResult(
+            model=model, samples=samples, samples_summary=samples.summary()
+        ),
     )
     search.paths = af.DirectoryPaths(path_prefix=database_file)
     search.fit(model=model, analysis=analysis)
@@ -75,7 +78,17 @@ def make_model():
 
 
 def make_samples(model):
-    parameters = [model.prior_count * [1.0], model.prior_count * [10.0]]
+    def parameter_list_with_physical_ell_comps(value):
+        parameter_list = model.prior_count * [value]
+        for index, path_tuple in enumerate(model.all_paths):
+            if "ell_comps" in path_tuple[0]:
+                parameter_list[index] = 0.1
+        return parameter_list
+
+    parameters = [
+        parameter_list_with_physical_ell_comps(1.0),
+        parameter_list_with_physical_ell_comps(10.0),
+    ]
     sample_list = Sample.from_lists(
         model=model,
         parameter_lists=parameters,
@@ -85,7 +98,7 @@ def make_samples(model):
     )
     return ag.m.MockSamples(
         model=model,
-        prior_means=[1.0] * model.prior_count,
+        prior_means=parameter_list_with_physical_ell_comps(1.0),
         sample_list=sample_list,
     )