Fix Validation Errors

Signed-off-by: furqan463 <[email protected]>

Author: furqan463

src/power_grid_model_io/converters/pandapower_converter.py

@@ -40,6 +40,14 @@
 pp_ref_version = Version("3.1.2")
 
 
+def get_loss_params_3ph():
+    if pp_curr_version <= pp_ref_version:
+        loss_params = ["p_a_l_mw", "q_a_l_mvar", "p_b_l_mw", "q_b_l_mvar", "p_c_l_mw", "q_c_l_mvar"]
+    else:
+        loss_params = ["pl_a_mw", "ql_a_mvar", "pl_b_mw", "ql_b_mvar", "pl_c_mw", "ql_c_mvar"]
+    return loss_params
+
+
 # pylint: disable=too-many-instance-attributes
 class PandaPowerConverter(BaseConverter[PandaPowerData]):
     """
@@ -1844,13 +1852,6 @@ def _pp_buses_output_3ph__accumulate_power(self, pp_output_buses_3ph: pd.DataFra
         # Finally apply the unit conversion (W -> MW and VAR -> MVAR)
         pp_output_buses_3ph[power_columns] /= 1e6
 
-    def get_loss_params_3ph(self):
-        if pp_curr_version <= pp_ref_version:
-            loss_params = ["p_a_l_mw", "q_a_l_mvar", "p_b_l_mw", "q_b_l_mvar", "p_c_l_mw", "q_c_l_mvar"]
-        else:
-            loss_params = ["pl_a_mw", "ql_a_mvar", "pl_b_mw", "ql_b_mvar", "pl_c_mw", "ql_c_mvar"]
-        return loss_params
-
     def _pp_lines_output_3ph(self):
         """
         This function converts a power-grid-model Line output array to a Line Dataframe of PandaPower.
@@ -1877,7 +1878,7 @@ def _pp_lines_output_3ph(self):
         i_from = (pgm_output_lines["p_from"] + 1j * pgm_output_lines["q_from"]) / u_complex.iloc[from_nodes, :]
         i_to = (pgm_output_lines["p_to"] + 1j * pgm_output_lines["q_to"]) / u_complex.iloc[to_nodes, :]
 
-        loss_params = self.get_loss_params_3ph()
+        loss_params = get_loss_params_3ph()
         pp_output_lines_3ph = pd.DataFrame(
             columns=[
                 "p_a_from_mw",
@@ -2058,7 +2059,7 @@ def _pp_trafos_output_3ph(self):  # pylint: disable=too-many-statements
 
         loading = np.maximum(np.maximum(loading_a_percent, loading_b_percent), loading_c_percent)
 
-        loss_params = self.get_loss_params_3ph()
+        loss_params = get_loss_params_3ph()
         pp_output_trafos_3ph = pd.DataFrame(
             columns=[
                 "p_a_hv_mw",

tests/validation/converters/test_pandapower_converter_output.py

@@ -17,7 +17,7 @@
 from power_grid_model.validation import assert_valid_input_data
 
 from power_grid_model_io.converters import PandaPowerConverter
-from power_grid_model_io.converters.pandapower_converter import PandaPowerData
+from power_grid_model_io.converters.pandapower_converter import PandaPowerData, get_loss_params_3ph
 
 from ...data.pandapower.pp_validation import pp_net, pp_net_3ph, pp_net_3ph_minimal_trafo
 from ..utils import component_attributes_df, load_json_single_dataset
@@ -324,19 +324,19 @@ def _get_total_powers_3ph(net):
         s_shunt = np.complex128()
 
     s_load = s_load_sym + s_load_asym + s_motor + s_ward + s_shunt
-
+    loss_params = get_loss_params_3ph()
     if "res_line_3ph" in net:
         s_loss_line = (
-            net.res_line_3ph.loc[:, ["p_a_l_mw", "p_b_l_mw", "p_c_l_mw"]].sum().sum()
-            + 1j * net.res_line_3ph.loc[:, ["q_a_l_mvar", "q_b_l_mvar", "q_c_l_mvar"]].sum().sum()
+            net.res_line_3ph.loc[:, [loss_params[0], loss_params[2], loss_params[4]]].sum().sum()
+            + 1j * net.res_line_3ph.loc[:, [loss_params[1], loss_params[3], loss_params[5]]].sum().sum()
         )
     else:
         s_loss_line = np.complex128()
 
     if "res_trafo_3ph" in net:
         s_loss_trafo = (
-            net.res_trafo_3ph.loc[:, ["p_a_l_mw", "p_b_l_mw", "p_c_l_mw"]].sum().sum()
-            + 1j * net.res_trafo_3ph.loc[:, ["q_a_l_mvar", "q_b_l_mvar", "q_c_l_mvar"]].sum().sum()
+            net.res_trafo_3ph.loc[:, [loss_params[0], loss_params[2], loss_params[4]]].sum().sum()
+            + 1j * net.res_trafo_3ph.loc[:, [loss_params[1], loss_params[3], loss_params[5]]].sum().sum()
         )
     else:
         s_loss_trafo = np.complex128()