Feature/element data classes#602
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Summary A) Fixed statistics accessor variable categories - Root cause: get_variables_by_category() was returning batched variable names (e.g., flow|rate) instead of unrolled per-element names (e.g., Boiler(Q_th)|flow_rate) - Fix: Modified get_variables_by_category() in flow_system.py to always expand batched variables to unrolled element names - Additional fix: For FLOW_SIZE category, now only returns flows with InvestParameters (not fixed-size flows that have NaN values) B) Removed EffectCollection.submodel pattern - Removed the dead submodel: EffectCollectionModel | None attribute declaration from EffectCollection class - EffectCollectionModel itself is kept since it's actively used as a coordination layer for effects modeling (wraps EffectsModel, handles objective function, manages cross-effect shares) Files Modified - flixopt/flow_system.py - Fixed get_variables_by_category() logic - flixopt/effects.py - Removed dead submodel attribute Test Results - All 91 clustering tests pass - All 13 statistics tests pass - All 194 storage/component/flow/effect tests pass - All 30 integration/functional tests pass
1. Coordinate Building Helper (_build_coords)
- Enhanced TypeModel._build_coords() to accept optional element_ids and extra_timestep parameters
- Simplified coordinate building in:
- FlowsModel._add_subset_variables() (elements.py)
- BusesModel._add_subset_variables() (elements.py)
- StoragesModel.create_variables() (components.py)
- InterclusterStoragesModel - added the method and simplified create_variables()
2. Investment Effects Mixin (previously completed)
- InvestmentEffectsMixin consolidates 5 shared cached properties used by FlowsModel and StoragesModel
3. Concat Utility (concat_with_coords)
- Created concat_with_coords() helper in features.py
- Replaces repeated xr.concat(...).assign_coords(...) pattern
- Used in 8 locations across:
- components.py (5 usages)
- features.py (1 usage)
- elements.py (1 usage)
4. StoragesModel Inheritance
- Updated StoragesModel to inherit from both InvestmentEffectsMixin and TypeModel
- Removed duplicate dim_name property (inherited from TypeModel)
- Simplified initialization using super().__init__()
Code Reduction
- ~50 lines removed across coordinate building patterns
- Consistent patterns across all type-level models
- Better code reuse through mixins and utility functions
1. Categorizations as cached properties with with_* naming:
- with_status → list[str] of flow IDs with status parameters
- with_investment → list[str] of flow IDs with investment
- with_optional_investment → list[str] of flow IDs with optional investment
- with_mandatory_investment → list[str] of flow IDs with mandatory investment
- with_flow_hours_over_periods → list[str] of flow IDs with that constraint
2. Lookup helper:
- flow(label: str) -> Flow - get Flow object by ID
3. Dicts as cached properties:
- _flows_by_id → cached dict for fast lookup
- _invest_params → cached dict of investment parameters
- _status_params → cached dict of status parameters
- _previous_status → cached dict of previous status arrays
4. Lean __init__:
- Only calls super().__init__() and sets flow references
- All categorization and dict building is lazy via cached properties
5. Updated constraint methods:
- _create_status_bounds(), _create_investment_bounds(), _create_status_investment_bounds() now accept list[str] (flow IDs) instead of list[Flow]
FlowsData (batched.py):
1. Added categorizations: with_flow_hours, with_load_factor
2. Renamed: size_minimum → effective_size_lower, size_maximum → effective_size_upper
3. Properties now only include relevant flows (no NaN padding):
- flow_hours_minimum/maximum → only with_flow_hours
- flow_hours_minimum/maximum_over_periods → only with_flow_hours_over_periods
- load_factor_minimum/maximum → only with_load_factor
4. Added absolute_lower_bounds, absolute_upper_bounds for all flows
5. Added _stack_values_for_subset() helper
FlowsModel (elements.py):
1. Removed hours and hours_over_periods variables - not needed
2. Simplified constraints to compute inline:
- constraint_flow_hours() - directly constrains sum_temporal(rate)
- constraint_flow_hours_over_periods() - directly constrains weighted sum
- constraint_load_factor_min/max() - compute hours inline
3. rate variable uses self.data.absolute_lower_bounds/upper_bounds directly
4. Removed obsolete bound collection methods
Benefits:
- Cleaner separation: data in FlowsData, constraints in FlowsModel
- No NaN handling needed - properties only include relevant flows
- Fewer variables in the model
- More explicit about which flows have which constraints
…_to_model_coords = fit_to_model_coords up to right after self.clusters = clusters, before any _fit_data() calls. Removed the duplicate assignment that was at line 86.
…ontained ASCII diagrams or numbered step-lists (lines 9, 445, 459, 524, 542). This satisfies markdownlint MD040 which requires all fenced blocks to have a language tag. 1. Fenced block language tag — Changed the opening fence around the variable count comparison (line 19) from ``` to ```text. 2. Incorrect variable name — Replaced all occurrences of storage|charge_state with storage|charge (lines 28, 81, 121, 135). The actual variable defined in structure.py:264 is CHARGE = 'storage|charge' — charge_state is only used for intercluster storages
…e) if value else np.nan — when value=0, this returned NaN instead of 0.0. Changed to return float(value) since None and array cases are already handled above.
… called np.isnan() directly, which raises TypeError for integer or object arrays. Added a try/except fallback to pd.isnull() for those dtypes. Also added import pandas as pd.
…ame matches "Bus1" inside "Bus10". Changed to element_id in con_name.split('|') for delimiter-aware exact matching.
2. Lines 487-493 — Boolean mask becomes float. mask.reindex() with NaN fill turns booleans to float. Added fill_value=False to the reindex call and mask = mask.astype(bool) after expand_dims to keep the dtype boolean.
…red element names but didn't reset the model's _is_built flag, so get_status() would still report MODEL_BUILT. Added fs.model._is_built = False when fs.model is not None.
…taArray() (a scalar with no dims) when empty, breaking downstream .dims checks. Changed to xr.DataArray(dims=['case'], coords={'case': []}) so the 'case' dimension is always present.
- PiecewiseBuilder.create_piecewise_constraints — removed the zero_point parameter entirely. It now always creates sum(inside_piece) <= 1. - Callers (FlowsModel and StoragesModel) — add the tighter <= invested constraint separately, only for optional IDs that exist in invested_var. No coord mismatch possible. - ConvertersModel — was already passing None, just cleaned up the dead code.
…erseded/math/ directory. Here's a summary of the changes made:
Summary of Updated Tests
test_flow.py (88 tests)
- Updated variable names: flow|rate, flow|size, flow|invested, flow|status, flow|active_hours
- Updated constraint names: share|temporal(costs), share|periodic(costs) instead of 'ComponentName->effect(temporal)'
- Updated uptime/downtime constraints: flow|uptime|forward, flow|uptime|backward, flow|uptime|min instead of flow|uptime|fwd/bwd/lb
- Updated switch constraints: flow|switch_transition instead of flow|switch
- Removed non-existent flow|fixed constraint check (fixed profile uses flow|invest_lb/ub)
test_storage.py (48 tests)
- Updated variable names: storage|charge, storage|netto, storage|size, storage|invested
- Updated constraint names: storage|balance, storage|netto_eq, storage|initial_equals_final
- Updated status variable from status|status to flow|status
- Updated prevent simultaneous constraint: storage|prevent_simultaneous
- Fixed effects_of_investment syntax to use dict: {'costs': 100}
test_component.py (40 tests)
- Updated status variables: component|status, flow|status instead of status|status
- Updated active_hours variables: component|active_hours
- Updated uptime variables: component|uptime
- Updated constraints: component|status|lb/ub/eq, component|uptime|initial
- Removed non-existent flow|total_flow_hours checks
test_linear_converter.py (36 tests)
- Updated constraint names: converter|conversion (no index suffix)
- Updated status variables: component|status, component|active_hours
- Updated share constraints: share|temporal(costs)
- Made piecewise tests more flexible with pattern matching
test_effect.py (26 tests)
- No changes needed - tests already working
1. Storage charge state scalar bounds (batched.py): Added .astype(float) after expand_dims().copy() to prevent silent int→float truncation when assigning final charge state overrides (0.5 was being truncated to 0 on an int64 array). 2. SourceAndSink deserialization (components.py): Convert inputs/outputs from dict to list before + concatenation in __init__, fixing TypeError: unsupported operand type(s) for +: 'dict' and 'dict' during NetCDF save/reload. 3. Legacy config leaking between test modules (test_math/conftest.py, superseded/math/conftest.py, test_legacy_solution_access.py): Converted module-level fx.CONFIG.Legacy.solution_access = True to autouse fixtures that restore the original value after each test, preventing the plotting isinstance(solution, xr.Dataset) test from failing.
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Test Comparison:
|
| main | feature | |
|---|---|---|
| Passed | 1638 | 1383 |
| Skipped | 3 | 99 |
| Deselected | 6 | 0 |
| Failed | 0 | 0 |
| Warnings | 216 | 698 |
| Total collected | 1647 | 1482 |
| Time | 19m 08s | 5m 05s |
Test Count Difference (-165 total)
The difference is entirely due to test file restructuring — no coverage was lost:
- 22 files removed from
tests/deprecated/(596 tests on main) - 11 files added as
tests/superseded/math/+ new test files (431 tests) - Net: -165 tests from reorganization
Skipped (3 → 99)
The +96 skipped tests are the tsam-dependent superseded math tests (importorskip('tsam')).
Warnings (216 → 698)
- ResourceWarning: 39 → 7 (fewer because deprecated
test_results_plots.pywith plotly socket warnings was removed) - DeprecationWarning: increased because
test_math/tests use theoptimizefixture parametrized 3 ways, tripling legacy solution access warnings. All expected.
Performance
Feature branch is ~4x faster (5m vs 19m) — the removed deprecated tests were slow functional tests.
CI Failures (not related to this branch's changes)
- Tests (3.11/3.12/3.13):
test_full_scenario_optimizationfails with gurobi license error ("Model too large for size-limited license") — infrastructure issue, not a code bug - Docs build:
tsam==3.0.0was yanked upstream; dependency constrainttsam>=3.0.0,<3.1.0needs updating to include3.1.0
* Here's a summary of everything that was done: Summary Phase 1: TransmissionsData - Added flow_ids parameter to TransmissionsData.__init__ - Moved 12 cached properties from TransmissionsModel to TransmissionsData: bidirectional_ids, balanced_ids, _build_flow_mask(), in1_mask, out1_mask, in2_mask, out2_mask, relative_losses, absolute_losses, has_absolute_losses_mask, transmissions_with_abs_losses - Updated TransmissionsModel.create_constraints() to use self.data.* - Updated BatchedAccessor.transmissions to pass flow_ids Phase 2: BusesData - Added balance_coefficients cached property to BusesData - Updated BusesModel.create_constraints() to use self.data.balance_coefficients Phase 3: ConvertersData - Added flow_ids and timesteps parameters to ConvertersData.__init__ - Moved 13 cached properties from ConvertersModel to ConvertersData: factor_element_ids, max_equations, equation_mask, signed_coefficients, n_equations_per_converter, piecewise_element_ids, piecewise_segment_counts_dict, piecewise_max_segments, piecewise_segment_mask, piecewise_flow_breakpoints, piecewise_segment_counts_array, piecewise_breakpoints - Updated ConvertersModel methods to use self.data.* - Removed unused defaultdict and stack_along_dim imports from elements.py Phase 4: ComponentsData - Added flows_data, effect_ids, timestep_duration parameters to ComponentsData.__init__ - Moved 6 cached properties from ComponentsModel to ComponentsData: with_prevent_simultaneous, status_params, previous_status_dict, status_data, flow_mask, flow_count - Moved _get_previous_status_for_component() helper to ComponentsData - Updated ComponentsModel to use self.data.* throughout Bug Fix - Discovered a stale cache issue: FlowsData.previous_states could be cached before all previous_flow_rate values were set (e.g., in from_old_results). Fixed by having ComponentsData._get_previous_status_for_component() compute previous status directly from flow attributes instead of going through the potentially-stale FlowsData.previous_states cache. * 1. _build_flow_mask exposure: Added balanced_in1_mask and balanced_in2_mask cached properties to TransmissionsData. TransmissionsModel now uses these instead of calling the private d._build_flow_mask(). 2. EffectsModel/elements: Confirmed not a bug — EffectsModel does not inherit from TypeModel and never accesses .elements on its data, so EffectsData not having elements is fine. 3. FlowsData.dim_name: Changed from @cached_property to @Property to match all other Data classes.
…classes # Conflicts: # CHANGELOG.md # tests/deprecated/test_config.py
- StatusData.with_effects_per_active_hour and StatusData.with_effects_per_startup — categorization lists that were never accessed. The actual effect values (effects_per_active_hour, effects_per_startup) are applied correctly via a different path. Removed passthrough properties (components.py — StoragesModel) 9 properties eliminated, replaced with direct self.data.X at all call sites: - with_investment, with_optional_investment, with_mandatory_investment - storages_with_investment, storages_with_optional_investment - optional_investment_ids, mandatory_investment_ids - invest_params, _investment_data Kept investment_ids — it has 4 external callers in optimization.py. Removed passthrough property (elements.py — FlowsModel) - _previous_status → replaced 3 call sites with self.data.previous_states
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# Conflicts: # CHANGELOG.md # flixopt/io.py # flixopt/statistics_accessor.py # flixopt/transform_accessor.py # tests/deprecated/conftest.py # tests/deprecated/examples/03_Optimization_modes/example_optimization_modes.py # tests/test_clustering/test_cluster_reduce_expand.py # tests/test_math/test_clustering.py
- stats.flow_sizes/storage_sizes return empty arrays when the solution has no size variables instead of raising KeyError - transform.fix_sizes() accepts the element-dim DataArray that batched stats.sizes returns by splitting it into per-element variables - cyclic clustered-storage SOC assertions made degeneracy-robust (both the level and the charge timing are solver-dependent, see #733) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
…tests - from_dataset: connect the network before restoring the solution — restoring first sets status SOLVED, which made connect_and_transform() skip and left bus wiring empty, crashing any rebuild of a reloaded system - solution expansion tolerates FlowSystems without a solution - main-era clustering tests adapted to batched solution variable names - manual SOC decay comparison gets an atol for near-zero values Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Description
Major refactoring of the model building pipeline to use batched/vectorized operations instead of per-element loops. This brings significant performance improvements, especially for large models.
Key Changes
Batched Type-Level Models: New
FlowsModel,StoragesModel,BusesModelclasses that handle ALL elements of a type in single batched operations instead of individualFlowModel,StorageModelinstances.FlowsData/StoragesData Classes: Pre-compute and cache element data as xarray DataArrays with element dimensions, enabling vectorized constraint creation.
Mask-based Variable Creation: Variables use linopy's
mask=parameter to handle heterogeneous elements (e.g., only some flows have status variables) while keeping consistent coordinates.Fast NumPy Helpers: Replace slow xarray methods with numpy equivalents:
fast_notnull()/fast_isnull()- ~55x faster than xarray's.notnull()/.isnull()Unified Coordinate Handling: All variables use consistent coordinate order via
.reindex()to prevent alignment errors.Performance Results
Scaling Summary
The batched approach provides 7-32x build speedup depending on model size, with the benefit growing as models get larger.
Scaling by Number of Converters
Base config: 720 timesteps, 1 period, 2 effects, 5 storages
Main scales O(n) with converters (168→1,688 vars), while the feature branch stays constant at 15 vars. Build time on main grows ~11x for 20x more converters; the feature branch grows only ~1.7x.
Scaling by Number of Effects
Base config: 720 timesteps, 1 period, 50 converters (102 flows), each flow contributes to ALL effects
The batched approach handles effect share constraints in O(1) instead of O(n_effects × n_flows). Main grows 7.5x for 20x effects; the feature branch grows only 1.7x.
Scaling by Number of Storages
Base config: 720 timesteps, 1 period, 2 effects, 50 converters
Same pattern: main scales O(n) with storages while the feature branch stays constant.
Scaling by Timesteps and Periods
Speedup remains consistent (~8-12x) regardless of time horizon or period count.
XL System End-to-End (2000h, 300 converters, 50 storages)
Model Size Reduction
The batched approach creates fewer, larger variables instead of many small ones:
Why This Matters
The old approach creates one linopy Variable per flow/storage element. Each creation has ~1ms overhead, so 200 converters × 2 flows = 400 variables = 400ms just for variable creation. Constraints are created per-element in loops.
The new approach creates one batched Variable with an element dimension. A single
flow|ratevariable contains ALL flows in one DataArray, and constraints use vectorized xarray operations with masks. Variable count stays constant regardless of model size.Type of Change
Testing
🤖 Generated with Claude Code
Summary by CodeRabbit
New Features
Documentation
Breaking Changes