Merge pull request #587 from Tjev/table-refactor

refactor(gooddata-sdk) - Switch up function definitions ordering

Reviewed-by: Lubo Slivka
             https://github.com/lupko

gooddata-sdk/gooddata_sdk/table.py

@@ -272,25 +272,6 @@ def create(cls, total: TotalDefinition, measures: list[VisualizationMetric]) ->
         )
 
 
-def _create_dimension(bucket: VisualizationBucket, measures_item_identifier: Optional[str] = None) -> TableDimension:
-    item_ids = [a.local_id for a in bucket.attributes]
-    if measures_item_identifier is not None:
-        item_ids.append(measures_item_identifier)
-    return TableDimension(
-        item_ids=item_ids,
-        idx=_GET_DIM_INDEX_OF_BUCKET_TYPE[bucket.type],
-    )
-
-
-def _get_dim_idx_for_predicate(
-    dims: list[TableDimension], predicate: Callable[[TableDimension], bool]
-) -> Optional[int]:
-    for dim_idx, dim in enumerate(dims):
-        if predicate(dim):
-            return dim_idx
-    return None
-
-
 @frozen
 class AttributeLocator:
     attribute_identifier: str
@@ -367,6 +348,73 @@ def _create_data_col_locators(locators: list[VisualizationSortLocator]) -> list[
     return converted_locators
 
 
+def _get_dim_idx_for_predicate(
+    dims: list[TableDimension], predicate: Callable[[TableDimension], bool]
+) -> Optional[int]:
+    for dim_idx, dim in enumerate(dims):
+        if predicate(dim):
+            return dim_idx
+    return None
+
+
+def _append_attribute_sort_key(
+    dims: list[TableDimension], sort_item: VisualizationSort, sorting: list[list[SortKey]]
+) -> None:
+    dim_idx = _get_dim_idx_for_predicate(dims, lambda x: sort_item.attribute_identifier in x.item_ids)
+    if dim_idx is None:
+        log_msg = (
+            f'attempting to sort by attribute with localId "{sort_item.attribute_identifier}" '
+            "but this attribute is not in any dimension."
+        )
+        logger.warning(log_msg)
+        return
+
+    sorting[dim_idx].append(
+        SortKeyAttribute(
+            sort_type=sort_item.type,
+            direction=sort_item.direction,
+            attribute_identifier=sort_item.attribute_identifier,
+            attribute_sort_type=sort_item.attribute_sort_type,
+        )
+    )
+
+
+def _append_measure_sort_key(
+    measure_dim: Optional[TableDimension],
+    non_measure_dim_idx: Optional[int],
+    sort_item: VisualizationSort,
+    sorting: list[list[SortKey]],
+) -> None:
+    if non_measure_dim_idx is None:
+        logger.warning(
+            "Trying to use measure sort in an execution that only contains dimension with MeasureGroup. "
+            "This is not valid sort. Measure sort is used to sort the non-measure dimension by values "
+            "from measure dimension. Skipping."
+        )
+        return
+
+    if not measure_dim:
+        logger.warning("Trying to use measure sort in an execution that does not contain MeasureGroup. Skipping.")
+        return
+
+    sorting[non_measure_dim_idx].append(
+        SortKeyValue(
+            sort_type=sort_item.type,
+            direction=sort_item.direction,
+            measure_dim_identifier=f"dim_{measure_dim.idx}",
+            data_column_locators=_create_data_col_locators(sort_item.locators),
+        )
+    )
+
+
+def _merge_dims_with_sorting(dims: list[TableDimension], sorting: list[list[SortKey]]) -> list[TableDimension]:
+    for dim in dims:
+        dim_sorting = sorting[dim.idx]
+        if dim_sorting:
+            dim.sorting = [ds.to_dict() for ds in dim_sorting]
+    return dims
+
+
 def _create_dims_with_sorts(dims: list[TableDimension], sorts: list[VisualizationSort]) -> list[TableDimension]:
     """
     Places sorting into dimensions. Returns the same dimensions objects but with modified sorting.
@@ -406,71 +454,23 @@ def _create_dims_with_sorts(dims: list[TableDimension], sorts: list[Visualizatio
     return _merge_dims_with_sorting(dims, sorting)
 
 
-def _merge_dims_with_sorting(dims: list[TableDimension], sorting: list[list[SortKey]]) -> list[TableDimension]:
-    for dim in dims:
-        dim_sorting = sorting[dim.idx]
-        if dim_sorting:
-            dim.sorting = [ds.to_dict() for ds in dim_sorting]
-    return dims
-
-
-def _append_measure_sort_key(
-    measure_dim: Optional[TableDimension],
-    non_measure_dim_idx: Optional[int],
-    sort_item: VisualizationSort,
-    sorting: list[list[SortKey]],
-) -> None:
-    if non_measure_dim_idx is None:
-        logger.warning(
-            "Trying to use measure sort in an execution that only contains dimension with MeasureGroup. "
-            "This is not valid sort. Measure sort is used to sort the non-measure dimension by values "
-            "from measure dimension. Skipping."
-        )
-        return
-
-    if not measure_dim:
-        logger.warning("Trying to use measure sort in an execution that does not contain MeasureGroup. Skipping.")
-        return
-
-    sorting[non_measure_dim_idx].append(
-        SortKeyValue(
-            sort_type=sort_item.type,
-            direction=sort_item.direction,
-            measure_dim_identifier=f"dim_{measure_dim.idx}",
-            data_column_locators=_create_data_col_locators(sort_item.locators),
-        )
-    )
-
-
-def _append_attribute_sort_key(
-    dims: list[TableDimension], sort_item: VisualizationSort, sorting: list[list[SortKey]]
-) -> None:
-    dim_idx = _get_dim_idx_for_predicate(dims, lambda x: sort_item.attribute_identifier in x.item_ids)
-    if dim_idx is None:
-        log_msg = (
-            f'attempting to sort by attribute with localId "{sort_item.attribute_identifier}" '
-            "but this attribute is not in any dimension."
-        )
-        logger.warning(log_msg)
-        return
-
-    sorting[dim_idx].append(
-        SortKeyAttribute(
-            sort_type=sort_item.type,
-            direction=sort_item.direction,
-            attribute_identifier=sort_item.attribute_identifier,
-            attribute_sort_type=sort_item.attribute_sort_type,
-        )
-    )
-
-
 def _vis_is_transposed(visualization: Visualization) -> bool:
     controls = visualization.properties.get("controls")
     if not controls:
         return False
     return controls.get("measureGroupDimension") == "rows"
 
 
+def _create_dimension(bucket: VisualizationBucket, measures_item_identifier: Optional[str] = None) -> TableDimension:
+    item_ids = [a.local_id for a in bucket.attributes]
+    if measures_item_identifier is not None:
+        item_ids.append(measures_item_identifier)
+    return TableDimension(
+        item_ids=item_ids,
+        idx=_GET_DIM_INDEX_OF_BUCKET_TYPE[bucket.type],
+    )
+
+
 def _create_dimensions(visualization: Visualization) -> list[TableDimension]:
     measures_item_identifier = _MEASURE_GROUP_IDENTIFIER if visualization.metrics else None
     row_bucket = visualization.get_bucket_of_type(BucketType.ROWS)
@@ -567,59 +567,22 @@ def update_compute_info(self, col_total_attr_id: str, row_total_attr_id: str) ->
             self.column_subtotal_dimension_index = self.col_attr_ids.index(col_total_attr_id)
 
 
-def _get_additional_totals(visualization: Visualization, dimensions: list[TableDimension]) -> list[TotalDefinition]:
-    """Construct special cases of pivot table totals.
-
-    These special cases are -
-      1. Grand totals - is the value obtained from row and column totals.
-      2. Marginal totals - is the value obtained within the subgroups from row and column subtotals.
-
-    For `Grand Totals`, in Tiger AFM, you specify that you want total of totals with total that have only
-    "measureGroup" present.
-    For `Marginal Total`, in Tiger AFM, would need to iterate through both dimensions and obtain the missing
-    totalDimensions items based on the attribute and column identifiers order in buckets.
-    """
-    totals: list[TotalDefinition] = []
-    row_bucket = visualization.get_bucket_of_type(BucketType.ROWS)
-    col_bucket = visualization.get_bucket_of_type(BucketType.COLS)
-
-    tci = TotalsComputeInfo(
-        row_attr_ids=[a.local_id for a in row_bucket.attributes],
-        col_attr_ids=[a.local_id for a in col_bucket.attributes],
-        measure_group_rows=[_MEASURE_GROUP_IDENTIFIER] if _MEASURE_GROUP_IDENTIFIER in dimensions[0].item_ids else [],
-        measure_group_cols=[_MEASURE_GROUP_IDENTIFIER] if _MEASURE_GROUP_IDENTIFIER in dimensions[1].item_ids else [],
-    )
-    for row_index, row_total in enumerate(row_bucket.totals):
-        tci.reset_to_defaults()
-        for col_index, col_total in enumerate(col_bucket.totals):
-            # Check for totals from same measure and type
-            if row_total.measure_id == col_total.measure_id and row_total.type == col_total.type:
-                tci.update_compute_info(col_total.attribute_id, row_total.attribute_id)
-
-            if tci.has_row_and_column_sub_totals:
-                totals.append(_extend_marginal_totals(col_index, row_total, tci))
-
-        if tci.has_row_subtotal_and_column_grand_total:
-            totals.append(_extend_marginal_totals_of_rows(row_index, row_total, tci))
-
-        if tci.has_column_subtotal_and_row_grand_total:
-            totals.append(_extend_marginal_totals_of_cols(row_index, row_total, tci))
-
-        if tci.has_row_and_column_grand_totals:
-            totals.append(_extend_grand_totals(row_index, row_total, tci))
-
-    return totals
-
-
-def _extend_grand_totals(row_index: int, row_total: VisualizationTotal, tci: TotalsComputeInfo) -> TotalDefinition:
-    # Extend grand totals payload
-    row_dim = [TotalDimension(idx=0, items=tci.measure_group_rows)] if tci.measure_group_rows else []
-    col_dim = [TotalDimension(idx=1, items=tci.measure_group_cols)] if tci.measure_group_cols else []
+def _extend_marginal_totals(col_index: int, row_total: VisualizationTotal, tci: TotalsComputeInfo) -> TotalDefinition:
+    # Extend marginal totals payload
     return TotalDefinition(
-        local_id=_grand_total_local_identifier(row_total, row_index),
+        local_id=_marginal_total_local_identifier(row_total, col_index),
         aggregation=row_total.type,
         metric_local_id=row_total.measure_id,
-        total_dims=row_dim + col_dim,
+        total_dims=[
+            TotalDimension(
+                idx=0,
+                items=tci.row_attr_ids[: tci.row_dimension_index] + tci.measure_group_rows,
+            ),
+            TotalDimension(
+                idx=1,
+                items=tci.col_attr_ids[: tci.column_dimension_index] + tci.measure_group_cols,
+            ),
+        ],
     )
 
 
@@ -663,23 +626,60 @@ def _extend_marginal_totals_of_rows(
     )
 
 
-def _extend_marginal_totals(col_index: int, row_total: VisualizationTotal, tci: TotalsComputeInfo) -> TotalDefinition:
-    # Extend marginal totals payload
+def _extend_grand_totals(row_index: int, row_total: VisualizationTotal, tci: TotalsComputeInfo) -> TotalDefinition:
+    # Extend grand totals payload
+    row_dim = [TotalDimension(idx=0, items=tci.measure_group_rows)] if tci.measure_group_rows else []
+    col_dim = [TotalDimension(idx=1, items=tci.measure_group_cols)] if tci.measure_group_cols else []
     return TotalDefinition(
-        local_id=_marginal_total_local_identifier(row_total, col_index),
+        local_id=_grand_total_local_identifier(row_total, row_index),
         aggregation=row_total.type,
         metric_local_id=row_total.measure_id,
-        total_dims=[
-            TotalDimension(
-                idx=0,
-                items=tci.row_attr_ids[: tci.row_dimension_index] + tci.measure_group_rows,
-            ),
-            TotalDimension(
-                idx=1,
-                items=tci.col_attr_ids[: tci.column_dimension_index] + tci.measure_group_cols,
-            ),
-        ],
+        total_dims=row_dim + col_dim,
+    )
+
+
+def _get_additional_totals(visualization: Visualization, dimensions: list[TableDimension]) -> list[TotalDefinition]:
+    """Construct special cases of pivot table totals.
+
+    These special cases are -
+      1. Grand totals - is the value obtained from row and column totals.
+      2. Marginal totals - is the value obtained within the subgroups from row and column subtotals.
+
+    For `Grand Totals`, in Tiger AFM, you specify that you want total of totals with total that have only
+    "measureGroup" present.
+    For `Marginal Total`, in Tiger AFM, would need to iterate through both dimensions and obtain the missing
+    totalDimensions items based on the attribute and column identifiers order in buckets.
+    """
+    totals: list[TotalDefinition] = []
+    row_bucket = visualization.get_bucket_of_type(BucketType.ROWS)
+    col_bucket = visualization.get_bucket_of_type(BucketType.COLS)
+
+    tci = TotalsComputeInfo(
+        row_attr_ids=[a.local_id for a in row_bucket.attributes],
+        col_attr_ids=[a.local_id for a in col_bucket.attributes],
+        measure_group_rows=[_MEASURE_GROUP_IDENTIFIER] if _MEASURE_GROUP_IDENTIFIER in dimensions[0].item_ids else [],
+        measure_group_cols=[_MEASURE_GROUP_IDENTIFIER] if _MEASURE_GROUP_IDENTIFIER in dimensions[1].item_ids else [],
     )
+    for row_index, row_total in enumerate(row_bucket.totals):
+        tci.reset_to_defaults()
+        for col_index, col_total in enumerate(col_bucket.totals):
+            # Check for totals from same measure and type
+            if row_total.measure_id == col_total.measure_id and row_total.type == col_total.type:
+                tci.update_compute_info(col_total.attribute_id, row_total.attribute_id)
+
+            if tci.has_row_and_column_sub_totals:
+                totals.append(_extend_marginal_totals(col_index, row_total, tci))
+
+        if tci.has_row_subtotal_and_column_grand_total:
+            totals.append(_extend_marginal_totals_of_rows(row_index, row_total, tci))
+
+        if tci.has_column_subtotal_and_row_grand_total:
+            totals.append(_extend_marginal_totals_of_cols(row_index, row_total, tci))
+
+        if tci.has_row_and_column_grand_totals:
+            totals.append(_extend_grand_totals(row_index, row_total, tci))
+
+    return totals
 
 
 def _get_computable_totals(visualization: Visualization, dimensions: list[TableDimension]) -> list[TotalDefinition]: