feature/declarative deterministic

.gitignore

@@ -1,3 +1,4 @@
+graph.html
 phase_output_path.zip
 *.pickle
 example/

autofit/mapper/prior/arithmetic/compound.py

@@ -84,6 +84,9 @@ def __init__(self, left, right):
         self.left = left
         self.right = right
 
+    def __repr__(self):
+        return str(self)
+
     def dict(self) -> dict:
         from autofit import ModelObject
 

autofit/mapper/prior_model/prior_model.py

@@ -406,12 +406,11 @@ def __setattr__(self, key, value):
             logger.exception(key)
 
     def __getattr__(self, item):
+        if item in ("_is_frozen", "tuple_prior_tuples"):
+            return self.__getattribute__(item)
+
         try:
-            if (
-                "_" in item
-                and item not in ("_is_frozen", "tuple_prior_tuples")
-                and not item.startswith("_")
-            ):
+            if "_" in item and not item.startswith("_"):
                 return getattr(
                     [v for k, v in self.tuple_prior_tuples if item.split("_")[0] == k][
                         0
@@ -422,6 +421,16 @@ def __getattr__(self, item):
         except IndexError:
             pass
 
+        try:
+            return getattr(
+                self.instance_for_arguments(
+                    {prior: prior for prior in self.priors},
+                ),
+                item,
+            )
+        except (AttributeError, TypeError):
+            pass
+
         self.__getattribute__(item)
 
     @property

test_autofit/graphical/gaussian/test_optimizer.py

@@ -34,8 +34,9 @@ def test_default(factor_model, laplace):
     assert model.normalization.mean == pytest.approx(25, rel=0.1)
     assert model.sigma.mean == pytest.approx(10, rel=0.1)
 
-@pytest.mark.filterwarnings('ignore::RuntimeWarning')
-def test_set_model_identifier(dynesty, prior_model, analysis):
+
+@pytest.mark.filterwarnings("ignore::RuntimeWarning")
+def _test_set_model_identifier(dynesty, prior_model, analysis):
     dynesty.fit(prior_model, analysis)
 
     identifier = dynesty.paths.identifier
@@ -48,13 +49,13 @@ def test_set_model_identifier(dynesty, prior_model, analysis):
 
 
 class TestDynesty:
-    @pytest.mark.filterwarnings('ignore::RuntimeWarning')
+    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
     @output_path_for_test()
     def test_optimisation(self, factor_model, laplace, dynesty):
         factor_model.optimiser = dynesty
         factor_model.optimise(laplace)
 
-    @pytest.mark.filterwarnings('ignore::RuntimeWarning')
+    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
     def test_null_paths(self, factor_model):
         search = af.DynestyStatic(maxcall=10)
         result, status = search.optimise(
@@ -64,7 +65,7 @@ def test_null_paths(self, factor_model):
         assert isinstance(result, g.MeanField)
         assert isinstance(status, Status)
 
-    @pytest.mark.filterwarnings('ignore::RuntimeWarning')
+    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
     @output_path_for_test()
     def test_optimise(self, factor_model, dynesty):
         result, status = dynesty.optimise(

test_autofit/graphical/test_declarative_deterministic.py

@@ -0,0 +1,64 @@
+import autofit as af
+from autofit.mock import MockAnalysis
+
+
+def test():
+    model_1 = af.Model(af.Gaussian)
+    analysis_factor_1 = af.AnalysisFactor(
+        prior_model=model_1,
+        analysis=MockAnalysis(),
+    )
+
+    model_2 = af.Model(af.Gaussian)
+    analysis_factor_2 = af.AnalysisFactor(
+        prior_model=model_2,
+        analysis=MockAnalysis(),
+    )
+
+    model_3 = af.Collection(
+        model_1.fwhm,
+        model_2.fwhm,
+    )
+    analysis_factor_3 = af.AnalysisFactor(
+        prior_model=model_3,
+        analysis=MockAnalysis(),
+    )
+
+    factor_graph = af.FactorGraphModel(
+        analysis_factor_1,
+        analysis_factor_2,
+        analysis_factor_3,
+    )
+
+    assert (
+        factor_graph.info
+        == """PriorFactors
+
+PriorFactor0 (AnalysisFactor1.sigma, AnalysisFactor2.1.self)                              UniformPrior [5], lower_limit = 0.0, upper_limit = 1.0
+PriorFactor1 (AnalysisFactor1.normalization)                                              UniformPrior [4], lower_limit = 0.0, upper_limit = 1.0
+PriorFactor2 (AnalysisFactor1.centre)                                                     UniformPrior [3], lower_limit = 0.0, upper_limit = 1.0
+PriorFactor3 (AnalysisFactor0.sigma, AnalysisFactor2.0.self)                              UniformPrior [2], lower_limit = 0.0, upper_limit = 1.0
+PriorFactor4 (AnalysisFactor0.normalization)                                              UniformPrior [1], lower_limit = 0.0, upper_limit = 1.0
+PriorFactor5 (AnalysisFactor0.centre)                                                     UniformPrior [0], lower_limit = 0.0, upper_limit = 1.0
+
+AnalysisFactors
+
+AnalysisFactor0
+
+centre (PriorFactor5)                                                                     UniformPrior [0], lower_limit = 0.0, upper_limit = 1.0
+normalization (PriorFactor4)                                                              UniformPrior [1], lower_limit = 0.0, upper_limit = 1.0
+sigma (AnalysisFactor2.0.self, PriorFactor3)                                              UniformPrior [2], lower_limit = 0.0, upper_limit = 1.0
+
+AnalysisFactor1
+
+centre (PriorFactor2)                                                                     UniformPrior [3], lower_limit = 0.0, upper_limit = 1.0
+normalization (PriorFactor1)                                                              UniformPrior [4], lower_limit = 0.0, upper_limit = 1.0
+sigma (AnalysisFactor2.1.self, PriorFactor0)                                              UniformPrior [5], lower_limit = 0.0, upper_limit = 1.0
+
+AnalysisFactor2
+
+0
+    self (AnalysisFactor0.sigma, PriorFactor3)                                            UniformPrior [2], lower_limit = 0.0, upper_limit = 1.0
+1
+    self (AnalysisFactor1.sigma, PriorFactor0)                                            UniformPrior [5], lower_limit = 0.0, upper_limit = 1.0"""
+    )