final check

Signed-off-by: zhen0427 <[email protected]>

Author: zhen0427

src/power_grid_model_io/converters/pandapower_converter.py

@@ -397,7 +397,7 @@ def _create_pgm_input_lines(self):
         Returns:
             a power-grid-model structured array for the Line component
         """
-        pp_lines = self.pp_input_data[ComponentType.line]
+        pp_lines = self.pp_input_data["line"]
 
         if pp_lines.empty:
             return
@@ -491,7 +491,7 @@ def _create_pgm_input_shunts(self):
         Returns:
             a power-grid-model structured array for the Shunt component
         """
-        pp_shunts = self.pp_input_data[ComponentType.shunt]
+        pp_shunts = self.pp_input_data["shunt"]
 
         if pp_shunts.empty:
             return
@@ -1900,7 +1900,7 @@ def _pp_lines_output_3ph(self):
                 "loading_b_percent",
                 "loading_c_percent",
             ],
-            index=self._get_pp_ids(ComponentType.line, pgm_output_lines["id"]),
+            index=self._get_pp_ids("line", pgm_output_lines["id"]),
         )
 
         pp_output_lines_3ph["p_a_from_mw"] = pgm_output_lines["p_from"][:, 0] * 1e-6

src/power_grid_model_io/converters/pgm_json_converter.py

@@ -8,6 +8,7 @@
 import json
 import logging
 import warnings
+from enum import Enum
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Union
 
@@ -92,7 +93,7 @@ def _parse_data(self, data: StructuredData, data_type: str, extra_info: Optional
         return result
 
     def _parse_dataset(
-        self, data: SinglePythonDataset, data_type: str, extra_info: Optional[ExtraInfo]
+        self, data: SinglePythonDataset, data_type: Enum, extra_info: Optional[ExtraInfo]
     ) -> SingleDataset:
         """This function parses a single Python dataset and returns a power-grid-model input or update dictionary
 
@@ -119,7 +120,7 @@ def _parse_dataset(
 
     @staticmethod
     def _parse_component(
-        objects: ComponentList, component: str, data_type: str, extra_info: Optional[ExtraInfo]
+        objects: ComponentList, component: Enum, data_type: Enum, extra_info: Optional[ExtraInfo]
     ) -> np.ndarray:
         """This function generates a structured numpy array (power-grid-model native) from a structured dataset
 
@@ -152,13 +153,15 @@ def _parse_component(
                     try:
                         array[i][attribute] = value
                     except ValueError as ex:
-                        raise ValueError(f"Invalid '{attribute}' value for {component} {data_type} data: {ex}") from ex
+                        raise ValueError(
+                            f"Invalid '{attribute}' value for {component.value} {data_type.value} data: {ex}"
+                        ) from ex
 
                 # If an attribute doesn't exist, it is added to the extra_info lookup table
                 elif extra_info is not None:
                     obj_id = obj["id"]
                     if not isinstance(obj_id, int):
-                        raise ValueError(f"Invalid 'id' value for {component} {data_type} data")
+                        raise ValueError(f"Invalid 'id' value for {component.value} {data_type.value} data")
                     if obj_id not in extra_info:
                         extra_info[obj_id] = {}
                     extra_info[obj_id][attribute] = value
@@ -215,8 +218,9 @@ def _is_batch(data: Dataset) -> bool:
             is_sparse_batch = isinstance(array, dict) and "indptr" in array and "data" in array
             if is_batch is not None and is_batch != (is_dense_batch or is_sparse_batch):
                 raise ValueError(
-                    f"Mixed {'' if is_batch else 'non-'}batch data "
-                    f"with {'non-' if is_batch else ''}batch data ({component})."
+                    f"Mixed {'' if is_batch else 'non-'}batch data with "
+                    f"{'non-' if is_batch else ''}batch data ("
+                    f"{component.value if isinstance(component, Enum) else component})."
                 )
             is_batch = is_dense_batch or is_sparse_batch
         return bool(is_batch)

src/power_grid_model_io/converters/tabular_converter.py

@@ -7,12 +7,12 @@
 
 import inspect
 import logging
+from enum import Enum
 from pathlib import Path
 from typing import Any, Collection, Dict, List, Mapping, Optional, Union, cast
 
 import numpy as np
 import pandas as pd
-from enum import Enum
 import yaml
 from power_grid_model import initialize_array
 from power_grid_model.data_types import Dataset

tests/unit/converters/test_pandapower_converter_input.py

@@ -197,15 +197,15 @@ def test_fill_pgm_extra_info():
 def test_fill_pp_extra_info():
     # Arrange
     converter = PandaPowerConverter()
-    converter.idx_lookup[(ComponentType.line, None)] = pd.Series([102, 103], index=[1, 2])
+    converter.idx_lookup[("line", None)] = pd.Series([102, 103], index=[1, 2])
     converter.idx_lookup[("trafo", None)] = pd.Series([201, 202, 203], index=[3, 4, 5])
-    converter.idx[(ComponentType.line, None)] = pd.Series([1, 2], index=[102, 103])
+    converter.idx[("line", None)] = pd.Series([1, 2], index=[102, 103])
     converter.idx[("trafo", None)] = pd.Series([3, 4, 5], index=[201, 202, 203])
 
     converter.pp_input_data["trafo"] = pd.DataFrame(
         {"df": [0.1, 0.2, 0.3], "other": [0.1, 0.2, 0.3]}, index=[201, 202, 203]
     )
-    converter.pp_input_data[ComponentType.line] = pd.DataFrame([10, 11, 12], columns=["df"], index=[201, 202, 203])
+    converter.pp_input_data["line"] = pd.DataFrame([10, 11, 12], columns=["df"], index=[201, 202, 203])
 
     # Act
     extra_info = {}
@@ -500,16 +500,16 @@ def test_create_pgm_input_nodes(mock_init_array: MagicMock, two_pp_objs: MockDf,
 @patch("power_grid_model_io.converters.pandapower_converter.initialize_array")
 def test_create_pgm_input_lines(mock_init_array: MagicMock, two_pp_objs, converter):
     # Arrange
-    converter.pp_input_data[ComponentType.line] = two_pp_objs
+    converter.pp_input_data["line"] = two_pp_objs
 
     # Act
     converter._create_pgm_input_lines()
 
     # Assert
 
     # administration
-    converter.get_switch_states.assert_called_once_with(ComponentType.line)
-    converter._generate_ids.assert_called_once_with(ComponentType.line, two_pp_objs.index)
+    converter.get_switch_states.assert_called_once_with("line")
+    converter._generate_ids.assert_called_once_with("line", two_pp_objs.index)
     converter._get_pgm_ids.assert_any_call("bus", _get_pp_attr("line", "from_bus", expected_type="u4"))
     converter._get_pgm_ids.assert_any_call("bus", _get_pp_attr("line", "to_bus", expected_type="u4"))
 
@@ -671,7 +671,9 @@ def test_create_pgm_input_sym_loads(mock_init_array: MagicMock, two_pp_objs, con
     # administration:
 
     # initialization
-    mock_init_array.assert_called_once_with(data_type=DatasetType.input, component_type=ComponentType.sym_load, shape=3 * 2)
+    mock_init_array.assert_called_once_with(
+        data_type=DatasetType.input, component_type=ComponentType.sym_load, shape=3 * 2
+    )
 
     # retrieval:
     converter._get_pp_attr.assert_any_call("load", "bus", expected_type="u4")
@@ -708,7 +710,9 @@ def test_create_pgm_input_asym_loads(mock_init_array: MagicMock, two_pp_objs, co
     converter._generate_ids.assert_called_once_with("asymmetric_load", two_pp_objs.index)
 
     # initialization
-    mock_init_array.assert_called_once_with(data_type=DatasetType.input, component_type=ComponentType.asym_load, shape=2)
+    mock_init_array.assert_called_once_with(
+        data_type=DatasetType.input, component_type=ComponentType.asym_load, shape=2
+    )
 
     # retrieval:
     converter._get_pp_attr.assert_any_call("asymmetric_load", "bus", expected_type="u4")
@@ -790,7 +794,7 @@ def test_create_pgm_input_transformers__tap_dependent_impedance() -> None:
 @patch("power_grid_model_io.converters.pandapower_converter.initialize_array")
 def test_create_pgm_input_shunts(mock_init_array: MagicMock, two_pp_objs, converter):
     # Arrange
-    converter.pp_input_data[ComponentType.shunt] = two_pp_objs
+    converter.pp_input_data["shunt"] = two_pp_objs
 
     # Act
     converter._create_pgm_input_shunts()
@@ -804,12 +808,12 @@ def test_create_pgm_input_shunts(mock_init_array: MagicMock, two_pp_objs, conver
     mock_init_array.assert_called_once_with(data_type=DatasetType.input, component_type=ComponentType.shunt, shape=2)
 
     # retrieval:
-    converter._get_pp_attr.assert_any_call(ComponentType.shunt, "bus", expected_type="u4")
-    converter._get_pp_attr.assert_any_call(ComponentType.shunt, "p_mw", expected_type="f8")
-    converter._get_pp_attr.assert_any_call(ComponentType.shunt, "q_mvar", expected_type="f8")
-    converter._get_pp_attr.assert_any_call(ComponentType.shunt, "vn_kv", expected_type="f8")
-    converter._get_pp_attr.assert_any_call(ComponentType.shunt, "step", expected_type="u4", default=1)
-    converter._get_pp_attr.assert_any_call(ComponentType.shunt, "in_service", expected_type="bool", default=True)
+    converter._get_pp_attr.assert_any_call("shunt", "bus", expected_type="u4")
+    converter._get_pp_attr.assert_any_call("shunt", "p_mw", expected_type="f8")
+    converter._get_pp_attr.assert_any_call("shunt", "q_mvar", expected_type="f8")
+    converter._get_pp_attr.assert_any_call("shunt", "vn_kv", expected_type="f8")
+    converter._get_pp_attr.assert_any_call("shunt", "step", expected_type="u4", default=1)
+    converter._get_pp_attr.assert_any_call("shunt", "in_service", expected_type="bool", default=True)
 
     assert len(converter._get_pp_attr.call_args_list) == 6
 
@@ -868,7 +872,9 @@ def test_create_pgm_input_transformers(mock_init_array: MagicMock, two_pp_objs,
     )
 
     # initialization
-    mock_init_array.assert_called_once_with(data_type=DatasetType.input, component_type=ComponentType.transformer, shape=2)
+    mock_init_array.assert_called_once_with(
+        data_type=DatasetType.input, component_type=ComponentType.transformer, shape=2
+    )
 
     # retrieval:
     converter._get_pp_attr.assert_any_call("trafo", "hv_bus", expected_type="u4")
@@ -1581,7 +1587,9 @@ def test_create_pgm_input_wards(mock_init_array: MagicMock, two_pp_objs, convert
     # administration:
 
     # initialization
-    mock_init_array.assert_called_once_with(data_type=DatasetType.input, component_type=ComponentType.sym_load, shape=2 * 2)
+    mock_init_array.assert_called_once_with(
+        data_type=DatasetType.input, component_type=ComponentType.sym_load, shape=2 * 2
+    )
 
     # retrieval:
     converter._get_pp_attr.assert_any_call("ward", "bus", expected_type="u4")
@@ -2034,7 +2042,7 @@ def test_get_switch_states_lines(mock_get_individual_switch_states: MagicMock):
     # Arrange
     converter = PandaPowerConverter()
     converter.pp_input_data = {
-        ComponentType.line: pd.DataFrame(columns=["from_bus", "to_bus"], data=[[101, 102]], index=[1]),
+        "line": pd.DataFrame(columns=["from_bus", "to_bus"], data=[[101, 102]], index=[1]),
         "switch": pd.DataFrame(
             columns=["bus", "et", "element", "closed"],
             data=[[101, "l", 1, False], [102, "x", 1, False]],
@@ -2108,7 +2116,7 @@ def test_get_switch_states__exception():
 
     # Act / Assert
     with pytest.raises(KeyError, match=r"link"):
-        converter.get_switch_states(ComponentType.link)
+        converter.get_switch_states("link")
 
 
 @patch("power_grid_model_io.converters.pandapower_converter.PandaPowerConverter.get_individual_switch_states")
@@ -2138,11 +2146,11 @@ def test_lookup_id():
     # Arrange
     converter = PandaPowerConverter()
     converter.idx_lookup = {
-        (ComponentType.line, None): pd.Series([0, 1, 2, 3, 4], index=[21, 345, 0, 3, 15]),
+        ("line", None): pd.Series([0, 1, 2, 3, 4], index=[21, 345, 0, 3, 15]),
         ("load", "const_current"): pd.Series([5, 6, 7, 8, 9], index=[543, 14, 34, 48, 4]),
     }
 
-    expected_line = {"table": ComponentType.line, "index": 4}
+    expected_line = {"table": "line", "index": 4}
     expected_load = {"table": "load", "name": "const_current", "index": 8}
 
     # Act
@@ -2157,7 +2165,7 @@ def test_lookup_id():
 def test_lookup_id__value_error():
     # Arrange
     converter = PandaPowerConverter()
-    converter.idx_lookup = {(ComponentType.line, None): pd.Series([0, 1, 2, 3, 4], index=[21, 345, 0, 3, 15])}
+    converter.idx_lookup = {("line", None): pd.Series([0, 1, 2, 3, 4], index=[21, 345, 0, 3, 15])}
 
     # Act / Assert
     with pytest.raises(KeyError):

tests/unit/converters/test_pgm_json_converter.py

@@ -27,7 +27,7 @@ def converter():
 @pytest.fixture
 def structured_input_data():
     input_data = {
-        ComponentType.node: [
+        "node": [
             {"id": 1, "u_rated": 400.0},
             {"id": 2, "u_rated": 400.0, "some_extra_info": 2.1},
         ]
@@ -38,8 +38,8 @@ def structured_input_data():
 @pytest.fixture
 def structured_batch_data():
     batch_data = [
-        {ComponentType.sym_load: [{"id": 3, "p_specified": 1.0}]},
-        {ComponentType.sym_load: [{"id": 3, "p_specified": 2.0}, {"id": 4, "p_specified": 3.0}]},
+        {"sym_load": [{"id": 3, "p_specified": 1.0}]},
+        {"sym_load": [{"id": 3, "p_specified": 2.0}, {"id": 4, "p_specified": 3.0}]},
     ]
     return batch_data
 
@@ -111,9 +111,12 @@ def test_parse_component(converter: PgmJsonConverter, structured_input_data):
     node_with_wrong_attr_val = {"id": 3, "u_rated": "fault"}
     objects[0].append(node_with_wrong_attr_val)  # type: ignore
     with pytest.raises(
-        ValueError, match="Invalid 'u_rated' value for ComponentType.node DatasetType.input data: could not convert string to float: 'fault'"
+        ValueError,
+        match="Invalid 'u_rated' value for node input data: could not convert string to float: 'fault'",
     ):
-        converter._parse_component(objects=objects[0], component=component, data_type=DatasetType.input, extra_info=None)
+        converter._parse_component(
+            objects=objects[0], component=component, data_type=DatasetType.input, extra_info=None
+        )
 
 
 def test_serialize_data(converter: PgmJsonConverter, pgm_input_data: SingleDataset, pgm_batch_data: BatchDataset):
@@ -122,9 +125,9 @@ def test_serialize_data(converter: PgmJsonConverter, pgm_input_data: SingleDatas
     with capture_logs() as cap_log:
         structured_batch_data = converter._serialize_data(data=pgm_batch_data, extra_info={})
     assert structured_batch_data == [
-        {ComponentType.line: [{}, {}]},
-        {ComponentType.line: [{}, {}]},
-        {ComponentType.line: [{}, {}]},
+        {"line": [{}, {}]},
+        {"line": [{}, {}]},
+        {"line": [{}, {}]},
     ]
     assert_log_match(cap_log[0], "warning", "Extra info is not supported for batch data export")