Feature/build fixes

autofit/__init__.py

@@ -94,7 +94,6 @@
 from .non_linear.result import ResultsCollection
 from .non_linear.settings import SettingsSearch
 from .non_linear.samples.pdf import marginalize
-from .example.model import Gaussian, Exponential
 from .text import formatter
 from .text import samples_text
 from .visualise import VisualiseGraph
@@ -129,6 +128,7 @@
     "LogUniform",
     "Gaussian",
     "LogGaussian",
+    "TruncatedGaussian",
     "compound",
     "Constant",
 ):

autofit/fixtures.py

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

autofit/mapper/prior/abstract.py

@@ -233,6 +233,7 @@ def from_dict(
         )
         if id_ is not None:
             loaded_ids[id_] = prior
+
         return prior
 
     def dict(self) -> dict:

autofit/messages/truncated_normal.py

@@ -74,14 +74,11 @@ def __init__(
         ----------
         mean
             The mean (μ) of the normal distribution.
-
         sigma
             The standard deviation (σ) of the distribution. Must be non-negative.
-
         log_norm
             An additive constant to the log probability of the message. Used internally for message-passing normalization.
             Default is 0.0.
-
         id_
             An optional unique identifier used to track the message in larger probabilistic graphs or models.
         """
@@ -96,6 +93,7 @@ def __init__(
             log_norm=log_norm,
             id_=id_,
         )
+
         self.mean, self.sigma, self.lower_limit, self.upper_limit = self.parameters
 
     def cdf(self, x: Union[float, np.ndarray]) -> Union[float, np.ndarray]:

autofit/non_linear/plot/__init__.py

@@ -1,2 +1,5 @@
 from autofit.non_linear.plot.samples_plotters import SamplesPlotter
+from autofit.non_linear.plot.mcmc_plotters import MCMCPlotter
+from autofit.non_linear.plot.mle_plotters import MLEPlotter
+from autofit.non_linear.plot.nest_plotters import NestPlotter
 from autofit.non_linear.plot.output import Output

autofit/non_linear/search/abstract_search.py

@@ -965,7 +965,14 @@ def perform_update(
 
                     logger.info(f"Creating latent samples by drawing {total_draws} from the PDF.")
 
-                    latent_samples = samples.samples_drawn_randomly_via_pdf_from(total_draws=total_draws)
+                    try:
+                        latent_samples = samples.samples_drawn_randomly_via_pdf_from(total_draws=total_draws)
+                    except AttributeError:
+                        latent_samples = samples_save
+                        logger.info(
+                            "Drawing via PDF not available for this search, "
+                            "using all samples above the samples weight threshold instead."
+                            "")
 
                 else:
 

autofit/plot/__init__.py

@@ -0,0 +1,5 @@
+from autofit.non_linear.plot.samples_plotters import SamplesPlotter
+from autofit.non_linear.plot.mcmc_plotters import MCMCPlotter
+from autofit.non_linear.plot.mle_plotters import MLEPlotter
+from autofit.non_linear.plot.nest_plotters import NestPlotter
+from autofit.non_linear.plot.output import Output

test_autofit/aggregator/test_reference.py

@@ -19,18 +19,18 @@ 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():
 
     aggregator = Aggregator.from_directory(
         Path(__file__).parent,
-        reference={"": get_class_path(af.Exponential)},
+        reference={"": get_class_path(af.ex.Exponential)},
     )
     output_list = list(aggregator)
     model_list = [output.model for output in output_list]
-    assert any([getattr(model, "cls", False) is af.Exponential for model in model_list])
+    assert any([getattr(model, "cls", False) is af.ex.Exponential for model in model_list])
 
 @pytest.fixture(name="database_path")
 def database_path(output_directory):
@@ -49,7 +49,7 @@ def database_aggregator(
     )
     aggregator.add_directory(
         directory,
-        reference={"": get_class_path(af.Exponential)},
+        reference={"": get_class_path(af.ex.Exponential)},
         completed_only=True,
     )
 
@@ -59,7 +59,7 @@ def database_aggregator(
 def test_database(database_aggregator):
     fit = list(database_aggregator)[0]
     model = fit.model
-    assert model.cls is af.Exponential
+    assert model.cls is af.ex.Exponential
 
 
 @pytest.fixture(name="info")

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
     )
 
 

test_autofit/database/query/internal/test_construction.py

@@ -7,11 +7,11 @@ def test_simple(aggregator):
 
 
 def test_and(aggregator):
-    construction = ((aggregator.model.centre == af.Gaussian) & (aggregator.model.centre.x == 0))
+    construction = ((aggregator.model.centre == af.ex.Gaussian) & (aggregator.model.centre.x == 0))
     assert construction.query == q.Q(
         "centre",
         q.And(
-            q.T(af.Gaussian),
+            q.T(af.ex.Gaussian),
             q.Q(
                 "x",
                 q.V(
@@ -23,11 +23,11 @@ def test_and(aggregator):
 
 
 def test_or(aggregator):
-    construction = ((aggregator.model.centre == af.Gaussian) | (aggregator.model.centre.x == 0))
+    construction = ((aggregator.model.centre == af.ex.Gaussian) | (aggregator.model.centre.x == 0))
     assert construction.query == q.Q(
         "centre",
         q.Or(
-            q.T(af.Gaussian),
+            q.T(af.ex.Gaussian),
             q.Q(
                 "x",
                 q.V(
@@ -51,7 +51,7 @@ def test_third_level(aggregator):
 
 
 def test_with_type(aggregator):
-    assert (aggregator.model.centre == af.Gaussian).query == q.Q("centre", q.T(af.Gaussian)).query
+    assert (aggregator.model.centre == af.ex.Gaussian).query == q.Q("centre", q.T(af.ex.Gaussian)).query
 
 
 def test_with_string(aggregator):

test_autofit/database/query/query/conftest.py

@@ -11,7 +11,7 @@
 def make_gaussian_1():
     return db.Fit(
         id="gaussian_1",
-        instance=af.Gaussian(
+        instance=af.ex.Gaussian(
             centre=1
         ),
         info={"info": 1},
@@ -26,7 +26,7 @@ def make_gaussian_1():
 def make_gaussian_2():
     return db.Fit(
         id="gaussian_2",
-        instance=af.Gaussian(
+        instance=af.ex.Gaussian(
             centre=2
         ),
         info={"info": 2},

test_autofit/database/query/query/test_model.py

@@ -9,7 +9,7 @@ def test_embedded_query(
     model_1 = db.Fit(
         id="model_1",
         instance=af.Collection(
-            gaussian=af.Gaussian(
+            gaussian=af.ex.Gaussian(
                 centre=1
             )
         ),
@@ -18,7 +18,7 @@ def test_embedded_query(
     model_2 = db.Fit(
         id="model_2",
         instance=af.Collection(
-            gaussian=af.Gaussian(
+            gaussian=af.ex.Gaussian(
                 centre=2
             )
         ),

test_autofit/database/query/query/test_order_by.py

@@ -12,7 +12,7 @@ def make_gaussian_0(
 ):
     gaussian_0 = db.Fit(
         id="gaussian_0",
-        instance=af.Gaussian(
+        instance=af.ex.Gaussian(
             centre=1
         ),
         info={"info": 1},