Turn warning into an error.

Also reorders the branches of the if statement to avoid excessive indentation.
ioannis-vm · Dec 3, 2024 · c7dd540 · c7dd540
1 parent 56da5f6
commit c7dd540
Showing 1 changed file with 34 additions and 32 deletions.
diff --git a/pelicun/model/damage_model.py b/pelicun/model/damage_model.py
@@ -1330,7 +1330,7 @@ def map_ds(values: np.ndarray, offset: int) -> np.ndarray:
 
         def parse_scaling_specification(scaling_specification: dict) -> dict:  # noqa: C901
             """
-            Parse and validate the scaling specification, used in the '_create_dmg_RVs' method.
+            Parse and validate the scaling specification, used in the'_create_dmg_RVs' method.
 
             Parameters
             ----------
@@ -1355,11 +1355,11 @@ def parse_scaling_specification(scaling_specification: dict) -> dict:  # noqa: C
 
             Raises
             ------
-            ValueError
-                If the scaling specification is invalid.
-            TypeError
-                If the type of an entry is invalid.
-            """
+            ValueError: If the scaling specification is invalid.
+            ValueError: If an unupported distribution is specified.
+            TypeError: If the type of an entry is invalid.
+
+            """  # noqa: DOC502
             # if there are contents, ensure they are valid.
             # See docstring for an example of what is expected.
             parsed_scaling_specification = defaultdict(dict)
@@ -1372,7 +1372,7 @@ def parse_scaling_specification(scaling_specification: dict) -> dict:  # noqa: C
                             f'Invalid entry in scaling_specification: '
                             f"{value}. No other entries are allowed for a component when 'ALL' is used."
                         )
-                        raise ValueError(msg)
+                        raise ValueError(msg)  # noqa: DOC501
                 for limit_state_id, specifics in value.items():
                     if not (
                         limit_state_id.startswith('LS') or limit_state_id == 'ALL'
@@ -1395,7 +1395,7 @@ def parse_scaling_specification(scaling_specification: dict) -> dict:  # noqa: C
                                 f'The specified scaling operation has to be a string.'
                                 f'See docstring of DamageModel._create_dmg_RVs.'
                             )
-                            raise TypeError(msg)
+                            raise TypeError(msg)  # noqa: DOC501
                         capacity_adjustment_operation = spec[0]
                         number = spec[1::]
                         if capacity_adjustment_operation not in {'+', '-', '*', '/'}:
@@ -1485,34 +1485,36 @@ def parse_scaling_specification(scaling_specification: dict) -> dict:  # noqa: C
                     )
 
                     if capacity_adjustment_operation:
-                        if family in {'normal', 'lognormal', 'deterministic'}:
-                            # Only scale the median value if ls_id is defined in capacity_adjustment_operation
-                            # Otherwise, use the original value
-                            new_theta_0 = None
-                            if 'ALL' in capacity_adjustment_operation:
-                                new_theta_0 = self._handle_operation_list(
-                                    theta[0],
-                                    capacity_adjustment_operation['ALL'],
-                                )
-                            elif f'LS{ls_id}' in capacity_adjustment_operation:
-                                new_theta_0 = self._handle_operation_list(
-                                    theta[0],
-                                    capacity_adjustment_operation[f'LS{ls_id}'],
-                                )
-                            if new_theta_0 is not None:
-                                if new_theta_0.size == 1:
-                                    theta[0] = new_theta_0[0]
-                                else:
-                                    # Repeat the theta values new_theta_0.size times along axis 0
-                                    # and 1 time along axis 1
-                                    theta = np.tile(theta, (new_theta_0.size, 1))
-                                    theta[:, 0] = new_theta_0
-                        else:
-                            self.log.warning(
+                        if family not in {'normal', 'lognormal', 'deterministic'}:
+                            msg = (
                                 f'Capacity adjustment is only supported '
                                 f'for `normal` or `lognormal` distributions. '
                                 f'Ignoring: `{cmp_loc_dir}`, which is `{family}`'
                             )
+                            raise ValueError(msg)  # noqa: DOC501
+                        # Only scale the median value if ls_id is
+                        # defined in capacity_adjustment_operation
+                        # Otherwise, use the original value
+                        new_theta_0 = None
+                        if 'ALL' in capacity_adjustment_operation:
+                            new_theta_0 = self._handle_operation_list(
+                                theta[0],
+                                capacity_adjustment_operation['ALL'],
+                            )
+                        elif f'LS{ls_id}' in capacity_adjustment_operation:
+                            new_theta_0 = self._handle_operation_list(
+                                theta[0],
+                                capacity_adjustment_operation[f'LS{ls_id}'],
+                            )
+                        if new_theta_0 is not None:
+                            if new_theta_0.size == 1:
+                                theta[0] = new_theta_0[0]
+                            else:
+                                # Repeat the theta values
+                                # new_theta_0.size times along
+                                # axis 0 and 1 time along axis 1
+                                theta = np.tile(theta, (new_theta_0.size, 1))
+                                theta[:, 0] = new_theta_0
 
                     tr_lims = np.array(
                         [