af.Gaussian -> af.ex.Gaussian

Author: Jammy2211

autofit/fixtures.py

@@ -7,7 +7,7 @@
 def make_model_gaussian_x1():
 
     return af.Model(
-        af.Gaussian
+        af.ex.Gaussian
     )
 
 

test_autofit/aggregator/test_reference.py

@@ -19,7 +19,7 @@ def test_without(directory):
     aggregator = Aggregator.from_directory(directory)
     model_list = [agg.model for agg in aggregator]
 
-    assert any([getattr(model, "cls", False) is af.Gaussian for model in model_list])
+    assert any([getattr(model, "cls", False) is af.ex.Gaussian for model in model_list])
 
 
 def test_with():

test_autofit/analysis/conftest.py

@@ -9,7 +9,7 @@ class Result(af.mock.MockResult):
 
 class Analysis(af.Analysis):
     def log_likelihood_function(self, instance):
-        return 1.0 if isinstance(instance, af.Gaussian) else 0.0
+        return 1.0 if isinstance(instance, af.ex.Gaussian) else 0.0
 
     def make_result(self, samples):
         return Result(
@@ -29,4 +29,4 @@ def analysis_class():
 
 @pytest.fixture(name="model")
 def make_model():
-    return af.Model(af.Gaussian)
+    return af.Model(af.ex.Gaussian)

test_autofit/analysis/test_latent_variables.py

@@ -55,7 +55,7 @@ def make_latent_samples():
     analysis = Analysis()
     return analysis.compute_latent_samples(
         SamplesPDF(
-            model=af.Model(af.Gaussian),
+            model=af.Model(af.ex.Gaussian),
             sample_list=[
                 af.Sample(
                     log_likelihood=1.0,
@@ -123,7 +123,7 @@ def test_complex_model():
     analysis = ComplexAnalysis()
     latent_samples = analysis.compute_latent_samples(
         SamplesPDF(
-            model=af.Model(af.Gaussian),
+            model=af.Model(af.ex.Gaussian),
             sample_list=[
                 af.Sample(
                     log_likelihood=1.0,

test_autofit/analysis/test_regression.py

@@ -33,7 +33,7 @@ def modify_after_fit(self, paths, model, result):
 
 
 def test_result_type():
-    model = af.Model(af.Gaussian)
+    model = af.Model(af.ex.Gaussian)
 
     analysis = MyAnalysis().with_model(model)
 

test_autofit/database/identifier/test_identifiers.py

@@ -106,10 +106,10 @@ def test_missing_field():
 
 def test_change_class():
     gaussian_0 = af.Model(
-        af.Gaussian, normalization=af.UniformPrior(lower_limit=1e-6, upper_limit=1e6)
+        af.ex.Gaussian, normalization=af.UniformPrior(lower_limit=1e-6, upper_limit=1e6)
     )
     gaussian_1 = af.Model(
-        af.Gaussian, normalization=af.LogUniformPrior(lower_limit=1e-6, upper_limit=1e6)
+        af.ex.Gaussian, normalization=af.LogUniformPrior(lower_limit=1e-6, upper_limit=1e6)
     )
 
     assert Identifier(gaussian_0) != Identifier(gaussian_1)
@@ -143,14 +143,14 @@ def test_identifier_fields():
 def test_unique_tag():
     search = af.m.MockSearch()
 
-    search.fit(model=af.Model(af.Gaussian), analysis=af.m.MockAnalysis())
+    search.fit(model=af.Model(af.ex.Gaussian), analysis=af.m.MockAnalysis())
 
     identifier = search.paths.identifier
 
     search = af.m.MockSearch(unique_tag="dataset")
 
     search.fit(
-        model=af.Model(af.Gaussian),
+        model=af.Model(af.ex.Gaussian),
         analysis=af.m.MockAnalysis(),
     )
 
@@ -165,42 +165,42 @@ def test_prior():
 
 
 def test_model():
-    identifier = af.Model(af.Gaussian, centre=af.UniformPrior()).identifier
-    assert identifier == af.Model(af.Gaussian, centre=af.UniformPrior()).identifier
+    identifier = af.Model(af.ex.Gaussian, centre=af.UniformPrior()).identifier
+    assert identifier == af.Model(af.ex.Gaussian, centre=af.UniformPrior()).identifier
     assert (
         identifier
-        != af.Model(af.Gaussian, centre=af.UniformPrior(upper_limit=0.5)).identifier
+        != af.Model(af.ex.Gaussian, centre=af.UniformPrior(upper_limit=0.5)).identifier
     )
 
 
 def test_collection():
     identifier = af.Collection(
-        gaussian=af.Model(af.Gaussian, centre=af.UniformPrior())
+        gaussian=af.Model(af.ex.Gaussian, centre=af.UniformPrior())
     ).identifier
     assert (
         identifier
         == af.Collection(
-            gaussian=af.Model(af.Gaussian, centre=af.UniformPrior())
+            gaussian=af.Model(af.ex.Gaussian, centre=af.UniformPrior())
         ).identifier
     )
     assert (
         identifier
         != af.Collection(
-            gaussian=af.Model(af.Gaussian, centre=af.UniformPrior(upper_limit=0.5))
+            gaussian=af.Model(af.ex.Gaussian, centre=af.UniformPrior(upper_limit=0.5))
         ).identifier
     )
 
 
 def test_instance():
-    identifier = af.Collection(gaussian=af.Gaussian()).identifier
-    assert identifier == af.Collection(gaussian=af.Gaussian()).identifier
-    assert identifier != af.Collection(gaussian=af.Gaussian(centre=0.5)).identifier
+    identifier = af.Collection(gaussian=af.ex.Gaussian()).identifier
+    assert identifier == af.Collection(gaussian=af.ex.Gaussian()).identifier
+    assert identifier != af.Collection(gaussian=af.ex.Gaussian(centre=0.5)).identifier
 
 
 def test__identifier_description():
     model = af.Collection(
         gaussian=af.Model(
-            af.Gaussian,
+            af.ex.Gaussian,
             centre=af.UniformPrior(lower_limit=0.0, upper_limit=1.0),
             normalization=af.LogUniformPrior(lower_limit=0.001, upper_limit=0.01),
             sigma=af.GaussianPrior(
@@ -270,7 +270,7 @@ def test__identifier_description():
 def test__identifier_description__after_model_and_instance():
     model = af.Collection(
         gaussian=af.Model(
-            af.Gaussian,
+            af.ex.Gaussian,
             centre=af.UniformPrior(lower_limit=0.0, upper_limit=1.0),
             normalization=af.LogUniformPrior(lower_limit=0.001, upper_limit=0.01),
             sigma=af.GaussianPrior(
@@ -315,7 +315,7 @@ def test__identifier_description__after_model_and_instance():
 def test__identifier_description__after_take_attributes():
     model = af.Collection(
         gaussian=af.Model(
-            af.Gaussian,
+            af.ex.Gaussian,
             centre=af.UniformPrior(lower_limit=0.0, upper_limit=1.0),
             normalization=af.LogUniformPrior(lower_limit=0.001, upper_limit=0.01),
             sigma=af.GaussianPrior(

test_autofit/database/paths/conftest.py

@@ -11,7 +11,7 @@ def make_paths(session):
         session
     )
     paths.model = af.Model(
-        af.Gaussian
+        af.ex.Gaussian
     )
     assert paths.is_complete is False
     return paths

test_autofit/database/paths/test_paths.py

@@ -80,7 +80,7 @@ def test_completion(
 
 
 def test_object(paths):
-    gaussian = af.Gaussian(
+    gaussian = af.ex.Gaussian(
         normalization=2.1
     )
 

test_autofit/database/paths/test_samples.py

@@ -14,7 +14,7 @@ def save_samples(
 ):
     samples =af.m.MockSamples(
         model=af.Model(
-            af.Gaussian
+            af.ex.Gaussian
         ),
 
     )
@@ -57,7 +57,7 @@ def test_load_samples(
 ):
     samples = paths._load_samples()
 
-    assert samples.model.cls is af.Gaussian
+    assert samples.model.cls is af.ex.Gaussian
 
     sample, = samples.sample_list
     assert sample.weight == 0.5

test_autofit/database/paths/test_switch.py

@@ -23,7 +23,7 @@ def make_search(session):
 )
 def make_model():
     return af.Model(
-        af.Gaussian
+        af.ex.Gaussian
     )