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New _ReducedFlowSystemBuilder class (lines 82-381): - #593
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_ReducedFlowSystemBuilder ├── __init__(fs, aggregation_results, timesteps_per_cluster, dt, dim_names) │ └── Pre-computes: n_clusters, is_segmented, cluster_coords, time_coords, base_coords ├── _expand_and_combine() # Shared pattern: expand_dims → combine_slices ├── build_cluster_weights() # Extract weights from results ├── build_typical_periods() # Extract and reshape cluster representatives ├── build_segment_durations() # Extract segment durations (if segmented) ├── build_metrics() # Extract RMSE/MAE metrics ├── build_reduced_dataset() # Assemble variables into Dataset └── build() # Main entry: orchestrates all builders → FlowSystem Call sites updated: - cluster() → _ReducedFlowSystemBuilder(fs, ...).build(ds) - apply_clustering() → _ReducedFlowSystemBuilder(fs, ...).build(ds) Deleted from TransformAccessor (~360 lines): - _accuracy_to_dataframe() (moved to module-level) - _build_cluster_weight_da() - _build_typical_das() - _build_segment_durations_da() - _build_clustering_metrics() - _build_reduced_flow_system() - _build_reduced_dataset() Benefits: - Coordinates computed once in __init__, not passed to every method - Common _expand_and_combine() pattern extracted (was repeated 4x) - Clear single entry point: builder.build(ds) - Each build method independently testable - TransformAccessor significantly simplified
📝 WalkthroughWalkthroughThis PR refactors the reduction logic in Changes
Estimated code review effort🎯 4 (Complex) | ⏱️ ~45 minutes Possibly related PRs
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🤖 Fix all issues with AI agents
In `@flixopt/transform_accessor.py`:
- Around line 121-132: The segmented time-axis initialization is off-by-one
versus the expectations in _interpolate_charge_state_segmented: when
self._is_segmented is True, set self._n_time_points to self._n_segments + 1 and
initialize self._time_coords with n_segments+1 entries (e.g.,
pd.RangeIndex(self._n_segments + 1, name='time')) so seg_indices + 1 stays
in-bounds; update any related consumers if needed and verify against tsam's
segmentation semantics / cluster_representatives to ensure segment boundary
values are present.
| if self._is_segmented: | ||
| self._n_time_points = self._n_segments | ||
| self._time_coords = pd.RangeIndex(self._n_segments, name='time') | ||
| else: | ||
| self._n_time_points = timesteps_per_cluster | ||
| self._time_coords = pd.date_range( | ||
| start='2000-01-01', | ||
| periods=timesteps_per_cluster, | ||
| freq=pd.Timedelta(hours=dt), | ||
| name='time', | ||
| ) | ||
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🌐 Web query:
tsam segmentation cluster_representatives n_segments structure documentation
💡 Result:
In tsam, the “segmentation” concept is inner-period clustering (splitting each typical period into a smaller number of variable-length segments).
Where n_segments fits in tsam
tsam names this parameter noSegments (not n_segments). It is “the number of inner-period clusters/segments” inside each typical period. You pass it either via the main class (TimeSeriesAggregation(..., noSegments=..., segmentation=True)) or directly to the function segmentation.segmentation(..., noSegments=...). [2][3]
What segmentation returns (structure)
segmentation.segmentation() returns two DataFrames: [1][2]
segmentedNormalizedTypicalPeriods: like the original typical-period DataFrame, but time steps are replaced by segments and the index is expanded.
From the implementation, each typical period gets a MultiIndex where the segment rows carry three inner index levels: [1]
Segment Step(segment id within the period)Segment Duration(how many original timesteps are covered by that segment)Original Start Step(the original timestep index where that segment starts)
predictedSegmentedNormalizedTypicalPeriods: same shape as the original per-timestep typical periods, but each timestep’s values are overwritten by its segment representative (useful for reconstruction / accuracy evaluation). [1][2]
About cluster_representatives
tsam’s docs/code do not use the name cluster_representatives. For typical-period clustering, tsam exposes results as clusterCenters, clusterCenterIndices, and a cluster assignment/order (_clusterOrder). [3] Segment/period “representatives” are computed via tsam’s representations(...) functions (mean/medoid/maxoid/etc.). [1][4]
Sources: segmentation implementation and return/index structure [1][2], TimeSeriesAggregation parameters and stored clustering outputs [3], representation methods used to compute representatives [4].
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# Let's verify what _n_reduced_timesteps actually is for segmented systemscd flixopt && python3 << 'EOF'# Check the logic: if cluster_representatives has shape (n_clusters * n_segments) rows# Then _n_reduced_timesteps = n_clusters * n_segments# And when reshaped to (n_clusters, n_time_points, -1) where n_time_points = n_segments,# we get (n_clusters, n_segments, n_features)# This confirms cluster_representatives has n_clusters * n_segments rowsn_clusters = 2n_segments = 6n_features = 100 # hypothetical# Length of cluster_representatives before reshapetotal_rows = n_clusters * n_segmentsprint(f"cluster_representatives rows: {total_rows}")# After reshapereshaped_shape = (n_clusters, n_segments, n_features)print(f"Reshaped to: {reshaped_shape}")print(f"Total elements in reshaped: {n_clusters * n_segments * n_features}")print(f"Match: {total_rows * n_features == n_clusters * n_segments * n_features}")EOFRepository: flixOpt/flixopt
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# Verify the reshape logic will actually work or failcd flixopt && python3 << 'EOF'import numpy as np# Simulate segmented clusteringn_clusters = 2n_segments = 6n_features = 5# This is what cluster_representatives would be for segmented:# n_clusters * n_segments rows (not n_clusters * (n_segments+1))total_rows = n_clusters * n_segmentscluster_reps = np.arange(total_rows * n_features).reshape(total_rows, n_features)print(f"cluster_representatives shape: {cluster_reps.shape}")print(f"Trying to reshape to (n_clusters={n_clusters}, n_segments={n_segments}, features={n_features})")reshaped = cluster_reps.reshape(n_clusters, n_segments, n_features)print(f"Reshaped successfully: {reshaped.shape}")# Now simulate interpolation with seg_indicesseg_indices = np.array([0, 1, 2, 3, 4, 5]) # Max is n_segments-1=5print(f"\nseg_indices: {seg_indices}")print(f"seg_indices + 1: {seg_indices + 1}")print(f"Max index value: {(seg_indices + 1).max()}")print(f"Valid time indices: 0 to {n_segments-1}")print(f"IndexError risk: {(seg_indices + 1).max() >= n_segments}")EOFRepository: flixOpt/flixopt
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# Let me manually verify the reshape logiccd flixopt && python3 << 'EOF'# Manual calculation without numpyn_clusters = 2n_segments = 6n_features = 5# Total rows in cluster_representatives (assuming n_clusters * n_segments)total_rows = n_clusters * n_segmentsprint(f"cluster_representatives total rows: {total_rows}")# After reshape to (n_clusters, n_segments, n_features)reshaped_rows = n_clustersreshaped_cols = n_segmentsreshaped_features = n_featurestotal_elements = reshaped_rows * reshaped_cols * reshaped_featuresoriginal_elements = total_rows * n_featuresprint(f"Reshaped dimensions: ({reshaped_rows}, {reshaped_cols}, {reshaped_features})")print(f"Total elements match: {total_elements == original_elements}")# Interpolation indexingprint(f"\nInterpolation indexing:")print(f"seg_indices ranges from 0 to {n_segments-1}")print(f"seg_indices + 1 ranges from 1 to {n_segments}")print(f"Time dimension indices: 0 to {n_segments-1}")print(f"Out of bounds access: time index {n_segments} when max valid is {n_segments-1}")EOFRepository: flixOpt/flixopt
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# Check if maybe xarray's isel is lenient with out-of-bounds indexingcd flixopt && python3 << 'EOF'import xarray as xrimport pandas as pd# Create a test DataArray with n_segments=6 entriesn_segments = 6time_coord = pd.RangeIndex(n_segments, name='time')da = xr.DataArray([1, 2, 3, 4, 5, 6], dims=['time'], coords={'time': time_coord})print(f"DataArray shape: {da.shape}")print(f"Time indices available: 0 to {n_segments-1}")# Try to access index n_segments (out of bounds)try: result = da.isel(time=n_segments) print(f"Access time={n_segments} succeeded: {result.values}")except Exception as e: print(f"Access time={n_segments} failed: {type(e).__name__}: {e}")# Try with an array of indices including out-of-boundsimport numpy as npindices = np.array([0, 1, 2, 3, 4, 5, 6]) # Last one is out of boundstry: result = da.isel(time=indices) print(f"Access with indices {list(indices)}: Success, shape {result.shape}")except Exception as e: print(f"Access with indices including {n_segments}: {type(e).__name__}: {e}")EOFRepository: flixOpt/flixopt
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Fix the segmented time axis length for charge_state interpolation.
Lines 121–132 initialize the time coordinate with n_segments entries (indices 0 to n_segments-1), but _interpolate_charge_state_segmented (lines 556–557) indexes seg_indices + 1, expecting access up to index n_segments. This causes an out-of-bounds index when seg_indices reaches its maximum value. The docstring at line 517 correctly documents the intent ("time has n_segments+1 entries"), but the initialization is inconsistent with this requirement.
Either: (1) initialize time_coords with n_segments+1 entries (e.g., pd.RangeIndex(self._n_segments + 1)), or (2) adjust the interpolation to not require the +1 offset. Verify against tsam's segmentation semantics to confirm whether cluster_representatives actually provides segment boundary values.
🤖 Prompt for AI Agents
In `@flixopt/transform_accessor.py` around lines 121 - 132, The segmented
time-axis initialization is off-by-one versus the expectations in
_interpolate_charge_state_segmented: when self._is_segmented is True, set
self._n_time_points to self._n_segments + 1 and initialize self._time_coords
with n_segments+1 entries (e.g., pd.RangeIndex(self._n_segments + 1,
name='time')) so seg_indices + 1 stays in-bounds; update any related consumers
if needed and verify against tsam's segmentation semantics /
cluster_representatives to ensure segment boundary values are present.
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_ReducedFlowSystemBuilder
├── init(fs, aggregation_results, timesteps_per_cluster, dt, dim_names)
│ └── Pre-computes: n_clusters, is_segmented, cluster_coords, time_coords, base_coords
├── _expand_and_combine() # Shared pattern: expand_dims → combine_slices
├── build_cluster_weights() # Extract weights from results
├── build_typical_periods() # Extract and reshape cluster representatives
├── build_segment_durations() # Extract segment durations (if segmented)
├── build_metrics() # Extract RMSE/MAE metrics
├── build_reduced_dataset() # Assemble variables into Dataset
└── build() # Main entry: orchestrates all builders → FlowSystem
Call sites updated:
Deleted from TransformAccessor (~360 lines):
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