SpecificRangeInitializer -> InitializerParamBounds

autofit/__init__.py

@@ -66,7 +66,7 @@
 from .non_linear.grid.sensitivity import Sensitivity
 from .non_linear.initializer import InitializerBall
 from .non_linear.initializer import InitializerPrior
-from .non_linear.initializer import SpecificRangeInitializer
+from .non_linear.initializer import InitializerParamBounds
 from .non_linear.search.mcmc.auto_correlations import AutoCorrelationsSettings
 from .non_linear.search.mcmc.emcee.search import Emcee
 from .non_linear.search.mcmc.zeus.search import Zeus

autofit/mapper/prior_model/abstract.py

@@ -1465,6 +1465,14 @@ def paths(self) -> List[Path]:
         """
         return [path for path, _ in self.path_priors_tuples]
 
+    @property
+    def paths_formatted(self) -> List[Path]:
+        """
+        A list of paths to all the priors in the model, ordered by their
+        ids
+        """
+        return [path for path, _ in self.path_priors_tuples]
+
     @property
     def composition(self):
         return [".".join(path) for path in self.paths]

autofit/non_linear/fitness.py

@@ -154,6 +154,8 @@ def __call__(self, parameters, *kwargs):
             instance = self.model.instance_from_vector(vector=parameters)
             log_likelihood = self.log_likelihood_function(instance=instance)
 
+            print(log_likelihood)
+
             if np.isnan(log_likelihood):
                 return self.resample_figure_of_merit
 

autofit/non_linear/initializer.py

@@ -175,7 +175,7 @@ def samples_in_test_mode(self, total_points: int, model: AbstractPriorModel):
         return unit_parameter_lists, parameter_lists, figure_of_merit_list
 
 
-class SpecificRangeInitializer(AbstractInitializer):
+class InitializerParamBounds(AbstractInitializer):
     def __init__(
         self,
         parameter_dict: Dict[Prior, Tuple[float, float]],
@@ -226,7 +226,7 @@ def _generate_unit_parameter_list(self, model: AbstractPriorModel) -> List[float
                 key = ".".join(model.path_for_prior(prior))
                 if key not in self._generated_warnings:
                     logger.warning(
-                        f"Range for {key} not set in the SpecificRangeInitializer. "
+                        f"Range for {key} not set in the InitializerParamBounds. "
                         f"Using defaults."
                     )
                     self._generated_warnings.add(key)

autofit/non_linear/search/nest/abstract_nest.py

@@ -8,7 +8,7 @@
 from autofit.non_linear.initializer import (
     InitializerPrior,
     AbstractInitializer,
-    SpecificRangeInitializer,
+    InitializerParamBounds,
 )
 from autofit.non_linear.samples import SamplesNest
 from autofit.non_linear.plot.nest_plotters import NestPlotter
@@ -44,9 +44,9 @@ def __init__(
         session
             An SQLAlchemy session instance so the results of the model-fit are written to an SQLite database.
         """
-        if isinstance(initializer, SpecificRangeInitializer):
+        if isinstance(initializer, InitializerParamBounds):
             raise ValueError(
-                "SpecificRangeInitializer cannot be used for nested sampling"
+                "InitializerParamBounds cannot be used for nested sampling"
             )
 
         super().__init__(

docs/cookbooks/search.rst

@@ -241,7 +241,7 @@ We now define the start point of certain parameters in the model as follows.
 
 .. code-block:: python
 
-    initializer = af.SpecificRangeInitializer(
+    initializer = af.InitializerParamBounds(
         {
             model.centre: (49.0, 51.0),
             model.normalization: (4.0, 6.0),

test_autofit/non_linear/test_initializer.py

@@ -235,7 +235,7 @@ def make_model():
 
 
 def test_starting_point_initializer(model):
-    initializer = af.SpecificRangeInitializer(
+    initializer = af.InitializerParamBounds(
         {
             model.centre: (1.0, 2.0),
             model.normalization: (2.0, 3.0),
@@ -250,7 +250,7 @@ def test_starting_point_initializer(model):
 
 
 def test_offset(model):
-    initializer = af.SpecificRangeInitializer(
+    initializer = af.InitializerParamBounds(
         {
             model.centre: (1.5, 2.0),
             model.normalization: (2.5, 3.0),
@@ -265,7 +265,7 @@ def test_offset(model):
 
 
 def test_missing_parameter(model):
-    initializer = af.SpecificRangeInitializer(
+    initializer = af.InitializerParamBounds(
         {
             model.centre: (1.5, 2.0),
             model.normalization: (2.5, 3.0),