Merge pull request #761 from PowerGridModel/refactor/move-dataset-typ…

…e-to-internal-utils

move datasetype from dataset to _utils

src/power_grid_model/_utils.py

@@ -40,6 +40,7 @@
     SparseBatchData,
 )
 from power_grid_model.enum import ComponentAttributeFilterOptions
+from power_grid_model.errors import PowerGridError
 from power_grid_model.typing import ComponentAttributeMapping, _ComponentAttributeMappingDict
 
 
@@ -630,3 +631,48 @@ def _extract_row_based_data(data: ComponentData, is_batch: bool | None = None) -
 
 def _extract_data_from_component_data(data: ComponentData, is_batch: bool | None = None):
     return _extract_columnar_data(data, is_batch) if is_columnar(data) else _extract_row_based_data(data, is_batch)
+
+
+def get_dataset_type(data: Dataset) -> DatasetType:
+    """
+    Deduce the dataset type from the provided dataset.
+
+    Args:
+        data: the dataset
+
+    Raises:
+        ValueError
+            if the dataset type cannot be deduced because multiple dataset types match the format
+            (probably because the data contained no supported components, e.g. was empty)
+        PowerGridError
+            if no dataset type matches the format of the data
+            (probably because the data contained conflicting data formats)
+
+    Returns:
+        The dataset type.
+    """
+    candidates = set(power_grid_meta_data.keys())
+
+    if all(is_columnar(v) for v in data.values()):
+        raise ValueError("The dataset type could not be deduced. At least one component should have row based data.")
+
+    for dataset_type, dataset_metadatas in power_grid_meta_data.items():
+        for component, dataset_metadata in dataset_metadatas.items():
+            if component not in data or is_columnar(data[component]):
+                continue
+            component_data = data[component]
+
+            component_dtype = component_data["data"].dtype if is_sparse(component_data) else component_data.dtype
+            if component_dtype is not dataset_metadata.dtype:
+                candidates.discard(dataset_type)
+                break
+
+    if not candidates:
+        raise PowerGridError(
+            "The dataset type could not be deduced because no type matches the data. "
+            "This usually means inconsistent data was provided."
+        )
+    if len(candidates) > 1:
+        raise ValueError("The dataset type could not be deduced because multiple dataset types match the data.")
+
+    return next(iter(candidates))

src/power_grid_model/core/power_grid_dataset.py

@@ -8,7 +8,7 @@
 
 from typing import Any, Mapping, Optional
 
-from power_grid_model._utils import is_columnar, is_nan_or_equivalent, is_sparse, process_data_filter
+from power_grid_model._utils import get_dataset_type, is_columnar, is_nan_or_equivalent, process_data_filter
 from power_grid_model.core.buffer_handling import (
     BufferProperties,
     CAttributeBuffer,
@@ -29,7 +29,6 @@
 from power_grid_model.core.power_grid_meta import ComponentMetaData, DatasetMetaData, power_grid_meta_data
 from power_grid_model.data_types import AttributeType, ComponentData, Dataset
 from power_grid_model.enum import ComponentAttributeFilterOptions
-from power_grid_model.errors import PowerGridError
 from power_grid_model.typing import ComponentAttributeMapping, _ComponentAttributeMappingDict
 
 
@@ -126,51 +125,6 @@ def total_elements(self) -> Mapping[ComponentType, int]:
         }
 
 
-def get_dataset_type(data: Dataset) -> DatasetType:
-    """
-    Deduce the dataset type from the provided dataset.
-
-    Args:
-        data: the dataset
-
-    Raises:
-        ValueError
-            if the dataset type cannot be deduced because multiple dataset types match the format
-            (probably because the data contained no supported components, e.g. was empty)
-        PowerGridError
-            if no dataset type matches the format of the data
-            (probably because the data contained conflicting data formats)
-
-    Returns:
-        The dataset type.
-    """
-    candidates = set(power_grid_meta_data.keys())
-
-    if all(is_columnar(v) for v in data.values()):
-        raise ValueError("The dataset type could not be deduced. At least one component should have row based data.")
-
-    for dataset_type, dataset_metadatas in power_grid_meta_data.items():
-        for component, dataset_metadata in dataset_metadatas.items():
-            if component not in data or is_columnar(data[component]):
-                continue
-            component_data = data[component]
-
-            component_dtype = component_data["data"].dtype if is_sparse(component_data) else component_data.dtype
-            if component_dtype is not dataset_metadata.dtype:
-                candidates.discard(dataset_type)
-                break
-
-    if not candidates:
-        raise PowerGridError(
-            "The dataset type could not be deduced because no type matches the data. "
-            "This usually means inconsistent data was provided."
-        )
-    if len(candidates) > 1:
-        raise ValueError("The dataset type could not be deduced because multiple dataset types match the data.")
-
-    return next(iter(candidates))
-
-
 class CMutableDataset:
     """
     A view of a user-owned dataset.

src/power_grid_model/utils.py

@@ -21,11 +21,11 @@
     _extract_indptr,
     get_and_verify_batch_sizes as _get_and_verify_batch_sizes,
     get_batch_size as _get_batch_size,
+    get_dataset_type,
     is_columnar,
     is_sparse,
 )
 from power_grid_model.core.dataset_definitions import DatasetType, _map_to_component_types
-from power_grid_model.core.power_grid_dataset import get_dataset_type
 from power_grid_model.core.serialization import (  # pylint: disable=unused-import
     json_deserialize,
     json_serialize,

tests/unit/test_dataset.py

@@ -2,13 +2,11 @@
 #
 # SPDX-License-Identifier: MPL-2.0
 
-import itertools
-
 import numpy as np
 import pytest
 
 from power_grid_model.core.dataset_definitions import ComponentType, DatasetType
-from power_grid_model.core.power_grid_dataset import CConstDataset, get_dataset_type
+from power_grid_model.core.power_grid_dataset import CConstDataset
 from power_grid_model.core.power_grid_meta import power_grid_meta_data
 from power_grid_model.errors import PowerGridError
 
@@ -34,36 +32,6 @@ def dataset_type(request):
     return request.param
 
 
-def test_get_dataset_type(dataset_type):
-    assert (
-        get_dataset_type(
-            data={
-                ComponentType.node: np.zeros(1, dtype=power_grid_meta_data[dataset_type]["node"]),
-                ComponentType.sym_load: np.zeros(1, dtype=power_grid_meta_data[dataset_type]["sym_load"]),
-            }
-        )
-        == dataset_type
-    )
-
-
-def test_get_dataset_type__empty_data():
-    with pytest.raises(ValueError):
-        get_dataset_type(data={})
-
-
-def test_get_dataset_type__conflicting_data():
-    for first, second in itertools.product(all_dataset_types(), all_dataset_types()):
-        data = {
-            "node": np.zeros(1, dtype=power_grid_meta_data[first]["node"]),
-            "sym_load": np.zeros(1, dtype=power_grid_meta_data[second]["sym_load"]),
-        }
-        if first == second:
-            assert get_dataset_type(data=data) == first
-        else:
-            with pytest.raises(PowerGridError):
-                get_dataset_type(data=data)
-
-
 def test_const_dataset__empty_dataset(dataset_type):
     dataset = CConstDataset(data={}, dataset_type=dataset_type)
     info = dataset.get_info()

tests/unit/test_internal_utils.py

@@ -2,6 +2,7 @@
 #
 # SPDX-License-Identifier: MPL-2.0
 
+import itertools
 from unittest.mock import MagicMock, patch
 
 import numpy as np
@@ -14,14 +15,17 @@
     convert_dataset_to_python_dataset,
     get_and_verify_batch_sizes,
     get_batch_size,
+    get_dataset_type,
     is_nan,
     process_data_filter,
     split_dense_batch_data_in_batches,
     split_sparse_batch_data_in_batches,
 )
 from power_grid_model.core.dataset_definitions import ComponentType as CT, DatasetType as DT
+from power_grid_model.core.power_grid_meta import power_grid_meta_data
 from power_grid_model.data_types import BatchDataset, BatchList
 from power_grid_model.enum import ComponentAttributeFilterOptions
+from power_grid_model.errors import PowerGridError
 
 from .utils import convert_python_to_numpy
 
@@ -581,3 +585,37 @@ def test_get_batch_size(data, expected_size):
 def test_get_batch_size__single_dataset_is_not_supported(data):
     with pytest.raises(ValueError):
         get_batch_size(data)
+
+
+@pytest.mark.parametrize("dataset_type", [DT.input, DT.update, DT.sym_output, DT.asym_output, DT.sc_output])
+def test_get_dataset_type(dataset_type):
+    assert (
+        get_dataset_type(
+            data={
+                CT.node: np.zeros(1, dtype=power_grid_meta_data[dataset_type]["node"]),
+                CT.sym_load: np.zeros(1, dtype=power_grid_meta_data[dataset_type]["sym_load"]),
+            }
+        )
+        == dataset_type
+    )
+
+
+def test_get_dataset_type__empty_data():
+    with pytest.raises(ValueError):
+        get_dataset_type(data={})
+
+
+def test_get_dataset_type__conflicting_data():
+    for first, second in itertools.product(
+        [DT.input, DT.update, DT.sym_output, DT.asym_output, DT.sc_output],
+        [DT.input, DT.update, DT.sym_output, DT.asym_output, DT.sc_output],
+    ):
+        data = {
+            "node": np.zeros(1, dtype=power_grid_meta_data[first]["node"]),
+            "sym_load": np.zeros(1, dtype=power_grid_meta_data[second]["sym_load"]),
+        }
+        if first == second:
+            assert get_dataset_type(data=data) == first
+        else:
+            with pytest.raises(PowerGridError):
+                get_dataset_type(data=data)

tests/unit/test_serialization.py

@@ -10,9 +10,7 @@
 import pytest
 
 from power_grid_model import DatasetType
-from power_grid_model._utils import is_columnar, is_sparse
-from power_grid_model.core.dataset_definitions import ComponentType
-from power_grid_model.core.power_grid_dataset import get_dataset_type
+from power_grid_model._utils import get_dataset_type, is_columnar, is_sparse
 from power_grid_model.data_types import BatchDataset, Dataset, SingleDataset
 from power_grid_model.enum import ComponentAttributeFilterOptions
 from power_grid_model.utils import json_deserialize, json_serialize, msgpack_deserialize, msgpack_serialize