model: Remove no longer useful build_dtree wrapper

1 files changed, 24 insertions, 39 deletions

diff --git a/lib/model.py b/lib/model.py
index cb08fce..9266153 100644
--- a/lib/model.py
+++ b/lib/model.py
@@ -293,16 +293,9 @@ class AnalyticModel:
 
         return lut_median_getter
 
-    def get_fitted(self, use_mean=False, safe_functions_enabled=False):
-        """
-        Get parameter-aware model function and model information function.
+    def build_fitted(self, safe_functions_enabled=False):
 
-        Returns two functions:
-        model_function(name, attribute, param=parameter values) -> model value.
-        model_info(name, attribute) -> {'fit_result' : ..., 'function' : ... } or None
-        """
-
-        if self.force_tree and not self.fit_done:
+        if self.force_tree:
             for name in self.names:
                 for attr in self.by_name[name]["attributes"]:
                     if (
@@ -316,14 +309,12 @@ class AnalyticModel:
                     else:
                         threshold = 0
                     logger.debug(f"build_dtree({name}, {attr}, threshold={threshold})")
-                    self.build_dtree(
-                        name,
-                        attr,
+                    self.attr_by_name[name][attr].build_dtree(
+                        self.by_name[name]["param"],
+                        self.by_name[name][attr],
                         threshold=threshold,
                     )
-            self.fit_done = True
-
-        if not self.fit_done:
+        else:
             paramfit = ParamFit()
             tree_allowed = bool(int(os.getenv("DFATOOL_DTREE_ENABLED", "1")))
             use_fol = bool(int(os.getenv("DFATOOL_FIT_FOL", "0")))
@@ -368,11 +359,27 @@ class AnalyticModel:
                         logger.debug(
                             f"build_dtree({name}, {attr}, threshold={threshold})"
                         )
-                        self.build_dtree(name, attr, threshold=threshold)
+                        self.attr_by_name[name][attr].build_dtree(
+                            self.by_name[name]["param"],
+                            self.by_name[name][attr],
+                            threshold=threshold,
+                        )
                     else:
                         self.attr_by_name[name][attr].set_data_from_paramfit(paramfit)
 
-            self.fit_done = True
+        self.fit_done = True
+
+    def get_fitted(self, use_mean=False, safe_functions_enabled=False):
+        """
+        Get parameter-aware model function and model information function.
+
+        Returns two functions:
+        model_function(name, attribute, param=parameter values) -> model value.
+        model_info(name, attribute) -> {'fit_result' : ..., 'function' : ... } or None
+        """
+
+        if not self.fit_done:
+            self.build_fitted(safe_functions_enabled=safe_functions_enabled)
 
         static_model = dict()
         for name, attr in self.attr_by_name.items():
@@ -464,28 +471,6 @@ class AnalyticModel:
             return detailed_results, raw_results
         return detailed_results
 
-    def build_dtree(self, name, attribute, threshold=100, **kwargs):
-        if name not in self.attr_by_name:
-            self.attr_by_name[name] = dict()
-
-        if attribute not in self.attr_by_name[name]:
-            self.attr_by_name[name][attribute] = ModelAttribute(
-                name,
-                attribute,
-                self.by_name[name][attribute],
-                self.by_name[name]["param"],
-                self.parameters,
-                self._num_args.get(name, 0),
-                param_type=ParamType(self.by_name[name]["param"]),
-            )
-
-        self.attr_by_name[name][attribute].build_dtree(
-            self.by_name[name]["param"],
-            self.by_name[name][attribute],
-            threshold=threshold,
-            **kwargs,
-        )
-
     def to_dref(
         self, static_quality, lut_quality, model_quality, xv_models=None
     ) -> dict: