Make EnsembleDistribtuion.distribution_map public

Author: stevebachmeier

CHANGELOG.rst

@@ -1,3 +1,7 @@
+**2.2.2 - 12/30/25**
+
+ - Make EnsembleDistribution.distribution_map a public attribute
+
 **2.2.1 - 11/20/25**
 
  - Improve 'make build-env': better handle args and make the env name optional

src/risk_distributions/risk_distributions.py

@@ -535,7 +535,7 @@ def target_function(guess, m, s):
 class EnsembleDistribution:
     """Represents an arbitrary distribution as a weighted sum of several concrete distribution types."""
 
-    _distribution_map = {
+    distribution_map = {
         "betasr": Beta,
         "exp": Exponential,
         "gamma": Gamma,
@@ -567,13 +567,13 @@ def get_parameters(
         mean: Parameter = None,
         sd: Parameter = None,
     ) -> tuple[pd.DataFrame, dict[str, pd.DataFrame]]:
-        expected_columns = list(cls._distribution_map.keys())
+        expected_columns = list(cls.distribution_map.keys())
 
         weights = cls.fill_missing_weights(weights, expected_columns)
         weights = format_data(weights, expected_columns, "weights")
 
         params = {}
-        for name, dist in cls._distribution_map.items():
+        for name, dist in cls.distribution_map.items():
             try:
                 param = parameters[name] if parameters else None
                 params[name] = dist.get_parameters(param, mean, sd)
@@ -631,9 +631,7 @@ def pdf(self, x: pd.Series | np.ndarray | float | int) -> pd.Series | np.ndarray
             for name, parameters in self.parameters.items():
                 w = weights.loc[computable, name]
                 params = parameters.loc[computable] if len(parameters) > 1 else parameters
-                p += w * self._distribution_map[name](parameters=params).pdf(
-                    x.loc[computable]
-                )
+                p += w * self.distribution_map[name](parameters=params).pdf(x.loc[computable])
 
         if single_val:
             p = p.iloc[0]
@@ -682,7 +680,7 @@ def ppf(
                         if len(parameters) > 1
                         else parameters
                     )
-                    x[idx] = self._distribution_map[name](parameters=params).ppf(q[idx])
+                    x[idx] = self.distribution_map[name](parameters=params).ppf(q[idx])
 
         if single_val:
             x = x.iloc[0]
@@ -706,9 +704,7 @@ def cdf(self, x: pd.Series | np.ndarray | float | int) -> pd.Series | np.ndarray
             for name, parameters in self.parameters.items():
                 w = weights.loc[computable, name]
                 params = parameters.loc[computable] if len(parameters) > 1 else parameters
-                c += w * self._distribution_map[name](parameters=params).cdf(
-                    x.loc[computable]
-                )
+                c += w * self.distribution_map[name](parameters=params).cdf(x.loc[computable])
 
         if single_val:
             c = c.iloc[0]