refactor!: remove ClusteringPlotAccessor, metrics, original_data, include_original_data (#655)

BREAKING CHANGES:
- Remove ClusteringPlotAccessor (compare, heatmap, clusters methods)
- Remove original_data and aggregated_data from Clustering
- Remove _metrics from Clustering (use aggregation_result.accuracy instead)
- Remove include_original_data parameter from to_dataset/to_netcdf
- Simplify _create_reference_structure to just serialize ClusteringResult dict
- Remove build_metrics from _ReducedFlowSystemBuilder

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: FBumann

flixopt/clustering/__init__.py

@@ -6,7 +6,6 @@
 
 Example usage:
 
-    # Cluster a FlowSystem to reduce timesteps
     from tsam import ExtremeConfig
 
     fs_clustered = flow_system.transform.cluster(
@@ -15,21 +14,14 @@
         extremes=ExtremeConfig(method='new_cluster', max_value=['Demand|fixed_relative_profile']),
     )
 
-    # Access clustering structure (available before AND after IO)
     clustering = fs_clustered.clustering
     print(f'Number of clusters: {clustering.n_clusters}')
-    print(f'Clustering info: {clustering.clustering_result}')  # tsam_xarray ClusteringResult
+    print(f'Clustering result: {clustering.clustering_result}')
 
-    # Access tsam_xarray AggregationResult for detailed analysis
-    # NOTE: Only available BEFORE saving/loading. Lost after IO.
+    # Access tsam_xarray AggregationResult (only before saving/loading)
     result = clustering.aggregation_result
-    result.cluster_representatives  # DataArray with aggregated time series
-    result.accuracy  # AccuracyMetrics (rmse, mae)
-
-    # Save and load - structure preserved, AggregationResult access lost
-    fs_clustered.to_netcdf('system.nc')
-    # Use include_original_data=False for smaller files (~38% reduction)
-    fs_clustered.to_netcdf('system.nc', include_original_data=False)
+    result.cluster_representatives  # DataArray
+    result.accuracy  # AccuracyMetrics
 
     # Expand back to full resolution
     fs_expanded = fs_clustered.transform.expand()