Merge pull request #282 from primap-community/remove-weights

primap2/_convert.py

@@ -17,8 +17,6 @@ def convert(
         dim: Hashable | str,
         *,
         conversion: climate_categories.Conversion,
-        input_weights: xr.DataArray | None = None,
-        output_weights: xr.DataArray | None = None,
         auxiliary_dimensions: dict[str, str] | None = None,
     ) -> xr.DataArray:
         """Convert the data along the given dimension into the new categorization.
@@ -40,26 +38,6 @@ def convert(
             The conversion rules that describe the conversion from the old to the new
             categorization. Contains ``climate_categories.Categorization``
             object for old and new categorization.
-        input_weights : xr.DataArray, optional
-            If data in input categories has to be summed up and the sum_rule is
-            ``intensive``, weights for the input categories are required.
-            The weights can be given in any shape compatible with the DataArray that
-            is converted, e.g. to give different weights for industrial sectors by
-            country. However, at least the ``dim`` that is converted needs to be in
-            ``input_weights.dims``.
-            If no weights are specified but a rule requiring weights is specified
-            in the conversion rules, a warning is issued and the respective rule is
-            skipped (probably resulting in more NaNs in the output).
-        output_weights : xr.DataArray, optional
-            If data has to be divided into several output categories and the sum_rule is
-            ``extensive``, weights for the output categories are required.
-            The weights can be given in any shape compatible with the DataArray that
-            is converted, e.g. to give different weights for industrial sectors by
-            country. However, at least the ``dim`` that is converted needs to be in
-            ``output_weights.dims``.
-            If no weights are specified but a rule requiring weights is specified
-            in the conversion rules, a warning is issued and the respective rule is
-            skipped (probably resulting in more NaNs in the output).
         auxiliary_dimensions : dict[str, str], optional
             Mapping of auxiliary categorizations to dimension names used in this
             DataArray. In conversions which contain rules which are valid only for
@@ -115,8 +93,6 @@ def convert(
                 category=category.item(),
                 conversion=conversion,
                 auxiliary_dimensions=auxiliary_dimensions,
-                input_weights=input_weights,
-                output_weights=output_weights,
             )
             converted_categories += newly_converted_categories
 
@@ -131,8 +107,6 @@ def _fill_category(
         category: climate_categories.Category,
         conversion: climate_categories.Conversion,
         auxiliary_dimensions: dict[climate_categories.Categorization, str] | None,
-        input_weights: xr.DataArray | None = None,
-        output_weights: xr.DataArray | None = None,
     ) -> tuple[list[climate_categories.Category], xr.DataArray]:
         """Return a copy of da with the given category filled by values converted
         using the given conversion.
@@ -155,10 +129,6 @@ def _fill_category(
             The conversion to use to compute the values for the given category.
         auxiliary_dimensions:
             See docstring of `convert`.
-        input_weights: xr.DataArray, optional
-            See docstring of `convert`.
-        output_weights: xr.DataArray, optional
-            See docstring of `convert`.
 
         Returns
         -------
@@ -211,16 +181,12 @@ def _fill_category(
                     category=category,
                     rule=rule,
                     operation_type="input",
-                    selection=input_selection,
-                    weights=input_weights,
                 )
                 effective_output_weights = derive_weights(
                     dim=new_dim,
                     category=category,
                     rule=rule,
                     operation_type="output",
-                    selection=output_selection,
-                    weights=output_weights,
                 )
             except WeightingInfoMissing as err:
                 logger.warning(str(err))
@@ -450,8 +416,6 @@ def derive_weights(
     category: climate_categories.Category,
     rule: climate_categories.ConversionRule,
     operation_type: str,
-    weights: xr.DataArray | None,
-    selection: dict[str, list[str]],
 ) -> xr.DataArray | float:
     """Derive the weights to use for applying a specific rule.
 
@@ -466,43 +430,29 @@ def derive_weights(
     operation_type: ``input`` or ``output``
         If weights for the source data (input) or the result data (output) should
         be derived.
-    weights: xr.DataArray, optional
-        Weights for the individual categories.
-    selection: dict[str, list[str]]
-        Selection derived from the rule.
 
     Returns
     -------
     factors: float or xr.DataArray
         Object which can be multiplied with the input or output DataArray to apply
         weights.
     """
+    # TODO this may change again in the next PR
     if operation_type == "input":
-        operation_verb = "sum up"
-        rule_cardinality = rule.cardinality_a
-    else:
-        operation_verb = "split"
-        rule_cardinality = rule.cardinality_b
-
-    # just one category or trivial sum rule, so no weights required
-    if rule_cardinality == "one" or operation_type == "input":
         return 1.0
-    if operation_type == "output":
-        if weights is None:
-            raise WeightingInfoMissing(
-                category=category,
-                rule=rule,
-                message=f"We need to {operation_verb} multiple categories with"
-                f" but no {operation_type}_weights are"
-                f" specified.",
+    elif operation_type == "output":
+        if rule.cardinality_b == "one":
+            return 1.0
+        else:
+            raise NotImplementedError(
+                "Splitting input categories into multiple"
+                " output categories is currently not supported. "
+                f"{rule.csv_original_text=}, {category=}"
             )
-        effective_weights = weights.loc[selection]
-        # normalize so it is actually a weight, not a factor
-        return effective_weights / effective_weights.sum(dim=dim)
-
-    raise NotImplementedError(
-        f"operation_type must be either input or output. Got {operation_type}"
-    )
+    else:
+        raise NotImplementedError(
+            f"operation_type must be either input or output. Got {operation_type}"
+        )
 
 
 def prepare_auxiliary_dimensions(

primap2/tests/test_convert.py

@@ -40,6 +40,7 @@ def test_conversion_source_does_not_match_dataset_dimension(empty_ds):
         )
 
 
+@pytest.mark.xfail
 def test_convert_ipcc(empty_ds: xr.Dataset):
     # build a DA categorized by IPCC1996 and with 1 everywhere so results are easy
     # to see