feature/sensitivity mask

autofit/example/analysis.py

@@ -132,7 +132,7 @@ def save_attributes(self, paths: af.DirectoryPaths):
         Parameters
         ----------
         paths
-            The PyAutoFit paths object which manages all paths, e.g. where the non-linear search outputs are stored,
+            The paths object which manages all paths, e.g. where the non-linear search outputs are stored,
             visualization, and the pickled objects used by the aggregator output by this function.
         """
         paths.save_json(name="data", object_dict=self.data.tolist(), prefix="dataset")

autofit/example/visualize.py

@@ -33,7 +33,7 @@ def visualize_before_fit(
         analysis
             The analysis class used to perform the model-fit whose quantities are being visualized.
         paths
-            The PyAutoFit paths object which manages all paths, e.g. where the non-linear search outputs are stored,
+            The paths object which manages all paths, e.g. where the non-linear search outputs are stored,
             visualization, and the pickled objects used by the aggregator output by this function.
         model
             The model which is fitted to the data, which may be used to customize the visualization.
@@ -84,7 +84,7 @@ def visualize(
         analysis
             The analysis class used to perform the model-fit whose quantities are being visualized.
         paths
-            The PyAutoFit paths object which manages all paths, e.g. where the non-linear search outputs are stored,
+            The paths object which manages all paths, e.g. where the non-linear search outputs are stored,
             visualization, and the pickled objects used by the aggregator output by this function.
         instance
             An instance of the model that is being fitted to the data by this analysis (whose parameters have been set
@@ -163,7 +163,7 @@ def visualize_before_fit_combined(
         analyses
             A list of the analysis classes used to perform the model-fit whose quantities are being visualized.
         paths
-            The PyAutoFit paths object which manages all paths, e.g. where the non-linear search outputs are stored,
+            The paths object which manages all paths, e.g. where the non-linear search outputs are stored,
             visualization, and the pickled objects used by the aggregator output by this function.
         model
             The model which is fitted to the data, which may be used to customize the visualization.
@@ -202,7 +202,7 @@ def visualize_combined(
         analyses
             A list of the analysis classes used to perform the model-fit whose quantities are being visualized.
         paths
-            The PyAutoFit paths object which manages all paths, e.g. where the non-linear search outputs are stored,
+            The paths object which manages all paths, e.g. where the non-linear search outputs are stored,
             visualization, and the pickled objects used by the aggregator output by this function.
         model
             The model which is fitted to the data, which may be used to customize the visualization.

autofit/mapper/prior/abstract.py

@@ -95,16 +95,11 @@ def new(self):
         new.id = next(self._ids)
         return new
 
+    @abstractmethod
     def with_limits(self, lower_limit: float, upper_limit: float) -> "Prior":
         """
         Create a new instance of the same prior class with the passed limits.
         """
-        new = self.__class__(
-            lower_limit=max(lower_limit, self.lower_limit),
-            upper_limit=min(upper_limit, self.upper_limit),
-        )
-        new.message = self.message
-        return new
 
     @property
     def factor(self):

autofit/mapper/prior/log_gaussian.py

@@ -71,6 +71,37 @@ def __init__(
             id_=id_,
         )
 
+    @classmethod
+    def with_limits(cls, lower_limit: float, upper_limit: float) -> "LogGaussianPrior":
+        """
+        Create a new gaussian prior centred between two limits
+        with sigma distance between this limits.
+
+        Note that these limits are not strict so exceptions will not
+        be raised for values outside of the limits.
+
+        This function is typically used in prior passing, where the
+        result of a model-fit are used to create new Gaussian priors
+        centred on the previously estimated median PDF model.
+
+        Parameters
+        ----------
+        lower_limit
+            The lower limit of the new Gaussian prior.
+        upper_limit
+            The upper limit of the new Gaussian Prior.
+
+        Returns
+        -------
+        A new GaussianPrior
+        """
+        return cls(
+            mean=(lower_limit + upper_limit) / 2,
+            sigma=upper_limit - lower_limit,
+            lower_limit=lower_limit,
+            upper_limit=upper_limit,
+        )
+
     def _new_for_base_message(self, message):
         """
         Create a new instance of this wrapper but change the parameters used

autofit/mapper/prior/uniform.py

@@ -64,6 +64,16 @@ def __init__(
     def tree_flatten(self):
         return (self.lower_limit, self.upper_limit), (self.id,)
 
+    def with_limits(
+        self,
+        lower_limit: float,
+        upper_limit: float,
+    ) -> "Prior":
+        return UniformPrior(
+            lower_limit=lower_limit,
+            upper_limit=upper_limit,
+        )
+
     def logpdf(self, x):
         # TODO: handle x as a numpy array
         if x == self.lower_limit:
@@ -97,9 +107,9 @@ def value_for(self, unit: float, ignore_prior_limits: bool = False) -> float:
 
         physical_value = prior.value_for(unit=0.2)
         """
-        return float(round(
-            super().value_for(unit, ignore_prior_limits=ignore_prior_limits), 14
-        ))
+        return float(
+            round(super().value_for(unit, ignore_prior_limits=ignore_prior_limits), 14)
+        )
 
     def log_prior_from_value(self, value):
         """

autofit/non_linear/analysis/visualize.py

@@ -32,7 +32,7 @@ def should_visualize(
         Parameters
         ----------
         paths
-            The PyAutoFit paths object which manages all paths, e.g. where the non-linear search outputs are stored,
+            The paths object which manages all paths, e.g. where the non-linear search outputs are stored,
             visualization and the pickled objects used by the aggregator output by this function.
 
         Returns