Add fill_new_category() to LeverNode

configs/dinspec.yaml

@@ -4,7 +4,7 @@ supported_languages: [de]
 site_url: https://dinspec.paths.kausal.tech
 dataset_repo:
   url: https://github.com/kausaltech/dvctest.git
-  commit: c27477d6486bcecbd9053abb360d1e00f2b7d431
+  commit: 4a4cd140f9022c9ab5ac0e75307945e22aa832e3
   dvc_remote: kausal-s3
 name: Greenhouse Gas Inventory according to DINSpec 91637
 owner: Kausal
@@ -15,7 +15,7 @@ minimum_historical_year: 2020
 maximum_historical_year: 2023
 emission_unit: kt/a
 emission_forecast_from: 2024
-emission_dimensions: [causal_step, sector]
+emission_dimensions: [transport_mode, causal_step, sector]
 features:
   baseline_visible_in_graphs: true
   show_significant_digits: 3

configs/modules/dinspec/car_to_bike_shift.yaml

@@ -23,8 +23,8 @@ actions:
   unit: dimensionless
   color: '#2ca25f' # FIXME Does not change color
   group: lever
-  input_dimensions: [causal_step, sector]
-  output_dimensions: [causal_step, sector]
+  input_dimensions: [causal_step, sector, transport_mode]
+  output_dimensions: [causal_step, sector, transport_mode]
   input_datasets:
   - id: gpc/dinspec
     filters:
@@ -51,6 +51,7 @@ actions:
     - id: sector
     - id: causal_step
       categories: [activity_shift]
+    - id: transport_mode
 
 # Actions
 
@@ -508,12 +509,19 @@ nodes:
   name_en: Shift from cars to bikes
   type: simple.LeverNode
   quantity: emissions # TODO Use fraction after fixing colors
-  unit: dimensionless
+  unit: '%'
   color: '#ccebc5'
-  input_dimensions: [sector, causal_step]
-  output_dimensions: [sector, causal_step]
+  input_dimensions: [transport_mode, sector, causal_step]
+  output_dimensions: [transport_mode, sector, causal_step]
   input_nodes:
   - id: fraction_of_car_trips
+    to_dimensions:
+    - id: sector
+    - id: causal_step
+    - id: transport_mode
+      categories: [cars]
+  params:
+    new_category: transport_mode:bicycles
 
 # Effect
 
@@ -524,8 +532,8 @@ nodes:
   quantity: emissions
   unit: kt/a
   color: '#decbe4'
-  input_dimensions: [causal_step, sector]
-  output_dimensions: [causal_step, sector]
+  input_dimensions: [transport_mode, sector, causal_step]
+  output_dimensions: [transport_mode, sector, causal_step]
   input_nodes:
   - id: shift_from_cars_to_bikes
     from_dimensions:
@@ -537,6 +545,7 @@ nodes:
     - id: sector
     - id: causal_step
       categories: [effect]
+    - id: transport_mode
   input_datasets:
   - id: gpc/dinspec
     filters:
@@ -552,8 +561,8 @@ nodes:
   quantity: emissions
   unit: t/a
   color: '#decbe4'
-  input_dimensions: [causal_step, sector]
-  output_dimensions: [causal_step, sector]
+  input_dimensions: [transport_mode, sector, causal_step]
+  output_dimensions: [transport_mode, sector, causal_step]
   input_nodes:
   - id: shift_from_cars_to_bikes
     from_dimensions:
@@ -565,6 +574,7 @@ nodes:
     - id: sector
     - id: causal_step
       categories: [effect]
+    - id: transport_mode
   input_datasets:
   - id: gpc/dinspec
     filters:
@@ -578,8 +588,8 @@ nodes:
   quantity: currency
   unit: MEUR/a
   color: '#decbe4'
-  input_dimensions: [causal_step, sector]
-  output_dimensions: [causal_step, sector]
+  input_dimensions: [transport_mode, sector, causal_step]
+  output_dimensions: [transport_mode, sector, causal_step]
   input_nodes:
   - id: shift_from_cars_to_bikes
     from_dimensions:
@@ -591,6 +601,7 @@ nodes:
     - id: sector
     - id: causal_step
       categories: [effect]
+    - id: transport_mode
   input_datasets:
   - id: gpc/dinspec
     filters:
@@ -604,8 +615,8 @@ nodes:
   quantity: emissions
   unit: t/a
   color: '#decbe4'
-  input_dimensions: [causal_step, sector]
-  output_dimensions: [causal_step, sector]
+  input_dimensions: [transport_mode, sector, causal_step]
+  output_dimensions: [transport_mode, sector, causal_step]
   input_nodes:
   - id: shift_from_cars_to_bikes
     from_dimensions:
@@ -617,6 +628,7 @@ nodes:
     - id: sector
     - id: causal_step
       categories: [effect]
+    - id: transport_mode
   input_datasets:
   - id: gpc/dinspec
     filters:
@@ -630,8 +642,8 @@ nodes:
   quantity: emissions # TODO Use exposure after fixing colors
   unit: Lden
   color: '#decbe4'
-  input_dimensions: [causal_step, sector]
-  output_dimensions: [causal_step, sector]
+  input_dimensions: [transport_mode, sector, causal_step]
+  output_dimensions: [transport_mode, sector, causal_step]
   input_nodes:
   - id: shift_from_cars_to_bikes
     from_dimensions:
@@ -643,6 +655,7 @@ nodes:
     - id: sector
     - id: causal_step
       categories: [effect]
+    - id: transport_mode
   input_datasets:
   - id: gpc/dinspec
     filters:

nodes/simple.py

@@ -986,11 +986,17 @@ class LeverNode(GenericNode):
     explanation = _(
         """LeverNode replaces the upstream computation completely, if the lever is enabled."""
     )
+    allowed_parameters = [
+        *GenericNode.allowed_parameters,
+        StringParameter(local_id='new_category'),
+    ]
 
     def compute(self) -> ppl.PathsDataFrame:
         df = super().compute()
         lever = self.get_input_node(tag='other_node', required=True)
-        return self.override_with_lever(df, lever)
+        df = self.override_with_lever(df, lever)
+        df = self.fill_new_category(df)
+        return df
 
     def override_with_lever(self, df: ppl.PathsDataFrame, lever: Node) -> ppl.PathsDataFrame:
         if not isinstance(lever, ActionNode):
@@ -1010,6 +1016,26 @@ def override_with_lever(self, df: ppl.PathsDataFrame, lever: Node) -> ppl.PathsD
             raise NodeError(self, f"Lever {lever.id} must result in the same structure {s}")
         return out
 
+    def fill_new_category(self, df: ppl.PathsDataFrame) -> ppl.PathsDataFrame:
+        category = self.get_parameter_value_str('new_category', required=True)
+        dim, cat = category.split(':')
+
+        df = df.ensure_unit(VALUE_COLUMN, 'dimensionless')
+
+        df2 = df.paths.sum_over_dims(dim)
+        df2 = df2.with_columns((pl.lit(1.0) - pl.col(VALUE_COLUMN)).alias(VALUE_COLUMN))
+        df2 = df2.with_columns(pl.lit(cat).cast(pl.Categorical).alias(dim))
+        df2 = df2.select(df.columns)
+
+        df = df.paths.concat_vertical(df2)
+        df = df.ensure_unit(VALUE_COLUMN, self.unit)
+        if self.get_parameter_value('drop_nans', required=False):  # FIXME Not consistent with the parameter name!
+            df = df.paths.to_wide()
+            for col in df.metric_cols:
+                df = df.filter(~pl.col(col).is_null())
+            df = df.paths.to_narrow()
+        return df
+
 
 class WeightedSumNode(GenericNode):
     explanation = _(