refactor!: migrate clustering from tsam to tsam_xarray

flixopt/clustering/__init__.py

@@ -1,13 +1,11 @@
 """
 Time Series Aggregation Module for flixopt.
 
-This module provides wrapper classes around tsam's clustering functionality:
-- Clustering: Top-level class stored on FlowSystem after clustering
-- ClusteringResults: Manages collection of tsam ClusteringResult objects (for IO)
+This module provides the Clustering class stored on FlowSystem after clustering,
+wrapping tsam_xarray's ClusteringResult.
 
 Example usage:
 
-    # Cluster a FlowSystem to reduce timesteps
     from tsam import ExtremeConfig
 
     fs_clustered = flow_system.transform.cluster(
@@ -16,36 +14,21 @@
         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'Dims: {clustering.dims}')  # e.g., ('period', 'scenario')
-    print(f'Coords: {clustering.coords}')  # e.g., {'period': [2024, 2025]}
+    print(f'Clustering result: {clustering.clustering_result}')
 
-    # Access tsam AggregationResult for detailed analysis
-    # NOTE: Only available BEFORE saving/loading. Lost after IO.
-    result = clustering.sel(period=2024, scenario='high')
-    result.cluster_representatives  # DataFrame with aggregated time series
-    result.accuracy  # AccuracyMetrics (rmse, mae)
-    result.plot.compare()  # tsam's built-in comparison plot
-
-    # Iterate over all results (only before IO)
-    for key, result in clustering.items():
-        print(f'{key}: {result.n_clusters} clusters')
-
-    # 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)
+    # Access tsam_xarray AggregationResult (only before saving/loading)
+    result = clustering.aggregation_result
+    result.cluster_representatives  # DataArray
+    result.accuracy  # AccuracyMetrics
 
     # Expand back to full resolution
     fs_expanded = fs_clustered.transform.expand()
 """
 
-from .base import AggregationResults, Clustering, ClusteringResults
+from .base import Clustering
 
 __all__ = [
-    'ClusteringResults',
-    'AggregationResults',
     'Clustering',
 ]