diff --git a/CHANGELOG.md b/CHANGELOG.md index 584748c65..884ff0684 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -59,6 +59,7 @@ - Updates to all GH Actions, pre-commit, isort, and ruff versioning. [PR 904](https://github.com/NatLabRockies/H2Integrate/pull/904) - Update API resource models to be able to be able to handle nonannual simulations. [PR 897](https://github.com/NatLabRockies/H2Integrate/pull/897) - Move PySAM model instantiation for wind and solar performance models to the `compute()` method, and validate recalculated wind power curves. [PR 909](https://github.com/NatLabRockies/H2Integrate/pull/909) +- Add data resampling capabilities via pandas methods. [PR 893](https://github.com/NatLabRockies/H2Integrate/pull/893) ## 0.9 [August 10, 2026] diff --git a/docs/_static/class_hierarchy.html b/docs/_static/class_hierarchy.html index a33861c95..2e1b7c134 100644 --- a/docs/_static/class_hierarchy.html +++ b/docs/_static/class_hierarchy.html @@ -380,8 +380,8 @@

// parsing and collecting nodes and edges from the python - nodes = new vis.DataSet([{"borderWidth": 5.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "PyomoRuleBaseClass", "label": "PyomoRuleBaseClass", "shape": "hexagon", "size": 19.227272727272727, "title": "PyomoRuleBaseClass\ncontrol/control_rules/pyomo_rule_baseclass.py\n[Control / General]", "x": 654.0, "y": 0.0}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "PyomoDispatchGenericConverter", "label": "PyomoDispatchGenericConverter", "shape": "dot", "size": 18.0, "title": "PyomoDispatchGenericConverter\ncontrol/control_rules/converters/generic_converter.py\n[Converter / Other]", "x": 478.26406871192853, "y": 424.26406871192853}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "PyomoRuleStorageBaseclass", "label": "PyomoRuleStorageBaseclass", "shape": "diamond", "size": 18.0, "title": "PyomoRuleStorageBaseclass\ncontrol/control_rules/storage/pyomo_storage_rule_baseclass.py\n[Storage / General]", "x": 53.999999999999886, "y": -600.0}, {"borderWidth": 5.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "OpenLoopControlBase", "label": "OpenLoopControlBase", "shape": "hexagon", "size": 20.454545454545453, "title": "OpenLoopControlBase\ncontrol/control_strategies/openloop_control_baseclass.py\n[Control / General]", "x": 681.5146849936184, "y": 83.93066263524416}, {"borderWidth": 5.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "PyomoStorageControllerBaseClass", "label": "PyomoStorageControllerBaseClass", "shape": "diamond", "size": 19.84090909090909, "title": "PyomoStorageControllerBaseClass\ncontrol/control_strategies/pyomo_storage_controller_baseclass.py\n[Storage / General]", "x": 81.51468499361823, "y": -516.0693373647558}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "PLMHeuristicOpenLoopConverterController", "label": "PLMHeuristicOpenLoopConverterController", "shape": "dot", "size": 18.0, "title": "PLMHeuristicOpenLoopConverterController\ncontrol/control_strategies/converters/plm_openloop_converter_controller.py\n[Converter / Other]", "x": 505.7787537055469, "y": 508.1947313471727}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "DemandOpenLoopStorageController", "label": "DemandOpenLoopStorageController", "shape": "diamond", "size": 18.0, "title": "DemandOpenLoopStorageController\ncontrol/control_strategies/storage/demand_openloop_storage_controller.py\n[Storage / General]", "x": -4.029534077577966, "y": -462.0588427802723}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "HeuristicLoadFollowingStorageController", "label": "HeuristicLoadFollowingStorageController", "shape": "diamond", "size": 18.0, "title": "HeuristicLoadFollowingStorageController\ncontrol/control_strategies/storage/heuristic_pyomo_controller.py\n[Storage / General]", "x": -109.50296675787511, "y": -499.69371768815415}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "OptimizedDispatchStorageController", "label": "OptimizedDispatchStorageController", "shape": "diamond", "size": 18.0, "title": "OptimizedDispatchStorageController\ncontrol/control_strategies/storage/optimized_pyomo_controller.py\n[Storage / General]", "x": -154.5360312930279, "y": -609.0363174025894}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "PeakLoadManagementHeuristicOpenLoopStorageController", "label": "PeakLoadManagementHeuristicOpenLoopStorageController", "shape": "diamond", "size": 18.0, "title": "PeakLoadManagementHeuristicOpenLoopStorageController\ncontrol/control_strategies/storage/plm_openloop_storage_controller.py\n[Storage / General]", "x": -103.92933571731442, "y": -720.3315967539606}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "PeakLoadManagementOptimizedStorageController", "label": "PeakLoadManagementOptimizedStorageController", "shape": "diamond", "size": 18.0, "title": "PeakLoadManagementOptimizedStorageController\ncontrol/control_strategies/storage/plm_optimized_storage_controller.py\n[Storage / General]", "x": 14.174835317190345, "y": -761.3786666314061}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "SimpleStorageOpenLoopController", "label": "SimpleStorageOpenLoopController", "shape": "diamond", "size": 18.0, "title": "SimpleStorageOpenLoopController\ncontrol/control_strategies/storage/simple_openloop_controller.py\n[Storage / General]", "x": 127.38669554530846, "y": -703.6855452705287}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "CostMinimizationControl", "label": "CostMinimizationControl", "shape": "hexagon", "size": 18.0, "title": "CostMinimizationControl\ncontrol/control_strategies/system_level/cost_minimization_control.py\n[Control / General]", "x": 595.9704659224221, "y": 137.9411572197277}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "DemandFollowingControl", "label": "DemandFollowingControl", "shape": "hexagon", "size": 18.0, "title": "DemandFollowingControl\ncontrol/control_strategies/system_level/demand_following_control.py\n[Control / General]", "x": 490.497033242125, "y": 100.30628231184586}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "ProfitMaximizationControl", "label": "ProfitMaximizationControl", "shape": "hexagon", "size": 18.0, "title": "ProfitMaximizationControl\ncontrol/control_strategies/system_level/profit_maximization_control.py\n[Control / General]", "x": 445.4639687069722, "y": -9.036317402589397}, {"borderWidth": 5.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "SystemLevelControlBase", "label": "SystemLevelControlBase", "shape": "hexagon", "size": 19.84090909090909, "title": "SystemLevelControlBase\ncontrol/control_strategies/system_level/system_level_control_baseclass.py\n[Control / General]", "x": 496.0706642826857, "y": -120.33159675396058}, {"borderWidth": 4.0, "color": {"background": "#F5C542", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "GenericConverterCostModel", "label": "GenericConverterCostModel", "shape": "dot", "size": 18.0, "title": "GenericConverterCostModel\nconverters/generic_converter_cost.py\n[Converter / Other]", "x": 420.23453463435067, "y": 562.2052259316563}, {"borderWidth": 4.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "AmmoniaSynLoopCostModel", "label": "AmmoniaSynLoopCostModel", "shape": "dot", "size": 18.0, "title": "AmmoniaSynLoopCostModel\nconverters/ammonia/ammonia_synloop_cost.py\n[Converter / Ammonia]", "x": 314.7611019540535, "y": 524.5703510237744}, {"borderWidth": 3.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "AmmoniaSynLoopPerformanceModel", "label": "AmmoniaSynLoopPerformanceModel", "shape": "dot", "size": 18.0, "title": "AmmoniaSynLoopPerformanceModel\nconverters/ammonia/ammonia_synloop_performance.py\n[Converter / Ammonia]", "x": 269.7280374189007, "y": 415.2277513093391}, {"borderWidth": 4.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "SimpleAmmoniaPerformanceModel", "label": "SimpleAmmoniaPerformanceModel", "shape": "dot", "size": 18.0, "title": "SimpleAmmoniaPerformanceModel\nconverters/ammonia/simple_ammonia_model.py\n[Converter / Ammonia]", "x": 320.3347329946142, "y": 303.93247195796795}, {"borderWidth": 4.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "SimpleAmmoniaCostModel", "label": "SimpleAmmoniaCostModel", "shape": "dot", "size": 18.0, "title": "SimpleAmmoniaCostModel\nconverters/ammonia/simple_ammonia_model.py\n[Converter / Ammonia]", "x": 438.438904029119, "y": 262.8854020805224}, {"borderWidth": 4.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "DOCPerformanceModel", "label": "DOCPerformanceModel", "shape": "dot", "size": 18.0, "title": "DOCPerformanceModel\nconverters/co2/marine/direct_ocean_capture.py\n[Converter / CO2]", "x": 551.6507642572371, "y": 320.57852344139985}, {"borderWidth": 4.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "DOCCostModel", "label": "DOCCostModel", "shape": "dot", "size": 18.0, "title": "DOCCostModel\nconverters/co2/marine/direct_ocean_capture.py\n[Converter / CO2]", "x": 589.1327652257885, "y": 443.6112367171105}, {"borderWidth": 4.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "OAEPerformanceModel", "label": "OAEPerformanceModel", "shape": "dot", "size": 18.0, "title": "OAEPerformanceModel\nconverters/co2/marine/ocean_alkalinity_enhancement.py\n[Converter / CO2]", "x": 526.1020787646279, "y": 557.1240691202768}, {"borderWidth": 4.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "OAECostModel", "label": "OAECostModel", "shape": "dot", "size": 18.0, "title": "OAECostModel\nconverters/co2/marine/ocean_alkalinity_enhancement.py\n[Converter / CO2]", "x": 399.74069937727677, "y": 591.0159040973604}, {"borderWidth": 4.0, "color": {"background": "#66BB6A", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "OAECostAndFinancialModel", "label": "OAECostAndFinancialModel", "shape": "dot", "size": 18.0, "title": "OAECostAndFinancialModel\nconverters/co2/marine/ocean_alkalinity_enhancement.py\n[Converter / CO2]", "x": 286.78380282847786, "y": 523.4075329070821}, {"borderWidth": 4.0, "color": {"background": "#F5C542", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "SimpleCycleTurbinePerformanceModel", "label": "SimpleCycleTurbinePerformanceModel", "shape": "dot", "size": 18.0, "title": "SimpleCycleTurbinePerformanceModel\nconverters/combustion_machines/turbine_simple_cycle.py\n[Converter / Other]", "x": 256.5641523482211, "y": 394.5748768944146}, {"borderWidth": 4.0, "color": {"background": "#F5C542", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "DieselGeneratorPerformanceModel", "label": "DieselGeneratorPerformanceModel", "shape": "dot", "size": 18.0, "title": "DieselGeneratorPerformanceModel\nconverters/diesel/diesel_generator.py\n[Converter / Other]", "x": 328.3357606918724, "y": 282.7057424982818}, {"borderWidth": 4.0, "color": {"background": "#F5C542", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "DieselGeneratorCostModel", "label": "DieselGeneratorCostModel", "shape": "dot", "size": 18.0, "title": "DieselGeneratorCostModel\nconverters/diesel/diesel_generator.py\n[Converter / Other]", "x": 459.0997033432264, "y": 256.23717038956255}, {"borderWidth": 4.0, "color": {"background": "#1B3A5C", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "GridPerformanceModel", "label": "GridPerformanceModel", "shape": "dot", "size": 18.0, "title": "GridPerformanceModel\nconverters/grid/grid.py\n[Converter / Grid]", "x": 569.5124313587587, "y": 331.90645520110365}, {"borderWidth": 4.0, "color": {"background": "#1B3A5C", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "GridCostModel", "label": "GridCostModel", "shape": "dot", "size": 18.0, "title": "GridCostModel\nconverters/grid/grid.py\n[Converter / Grid]", "x": 592.1635244622977, "y": 464.21994087945296}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "BasicElectrolyzerCostModel", "label": "BasicElectrolyzerCostModel", "shape": "dot", "size": 18.0, "title": "BasicElectrolyzerCostModel\nconverters/hydrogen/basic_cost_model.py\n[Converter / Hydrogen]", "x": 512.788859406323, "y": 572.8990626821025}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "CustomElectrolyzerCostModel", "label": "CustomElectrolyzerCostModel", "shape": "dot", "size": 18.0, "title": "CustomElectrolyzerCostModel\nconverters/hydrogen/custom_electrolyzer_cost_model.py\n[Converter / Hydrogen]", "x": 379.21994390308055, "y": 591.6786327094717}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "ElectrolyzerPerformanceBaseClass", "label": "ElectrolyzerPerformanceBaseClass", "shape": "dot", "size": 19.227272727272727, "title": "ElectrolyzerPerformanceBaseClass\nconverters/hydrogen/electrolyzer_baseclass.py\n[Converter / Hydrogen]", "x": 272.49595476290375, "y": 508.75152096583327}, {"borderWidth": 4.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "ElectrolyzerCostBaseClass", "label": "ElectrolyzerCostBaseClass", "shape": "dot", "size": 20.454545454545453, "title": "ElectrolyzerCostBaseClass\nconverters/hydrogen/electrolyzer_baseclass.py\n[Converter / Hydrogen]", "x": 257.6313191356596, "y": 374.1688916122072}, {"borderWidth": 4.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "LinearH2FuelCellPerformanceModel", "label": "LinearH2FuelCellPerformanceModel", "shape": "dot", "size": 18.0, "title": "LinearH2FuelCellPerformanceModel\nconverters/hydrogen/h2_fuel_cell.py\n[Converter / Hydrogen]", "x": 343.9803548876029, "y": 269.5853411323154}, {"borderWidth": 4.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "H2FuelCellCostModel", "label": "H2FuelCellCostModel", "shape": "dot", "size": 18.0, "title": "H2FuelCellCostModel\nconverters/hydrogen/h2_fuel_cell.py\n[Converter / Hydrogen]", "x": 479.36821794000866, "y": 258.67007677091334}, {"borderWidth": 2.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "HTSEPerformanceModel", "label": "HTSEPerformanceModel", "shape": "dot", "size": 18.0, "title": "HTSEPerformanceModel\nconverters/hydrogen/htse_electrolyzer.py\n[Converter / Hydrogen]", "x": 581.650938836167, "y": 348.32372625486516}, {"borderWidth": 3.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "HTSECostModel", "label": "HTSECostModel", "shape": "dot", "size": 18.0, "title": "HTSECostModel\nconverters/hydrogen/htse_electrolyzer.py\n[Converter / Hydrogen]", "x": 588.5900605700128, "y": 484.33044865825804}, {"borderWidth": 2.0, "color": {"background": "#2E7D32", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "ECOElectrolyzerPerformanceModel", "label": "ECOElectrolyzerPerformanceModel", "shape": "dot", "size": 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"PipeStorageCostModel", "label": "PipeStorageCostModel", "shape": "diamond", "size": 18.0, "title": "PipeStorageCostModel\nstorage/hydrogen/h2_storage_cost.py\n[Storage / General]", "x": 88.52479069439438, "y": -451.3650060298261}, {"borderWidth": 3.0, "color": {"background": "#555555", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "CompressedGasStorageCostModel", "label": "CompressedGasStorageCostModel", "shape": "diamond", "size": 18.0, "title": "CompressedGasStorageCostModel\nstorage/hydrogen/h2_storage_cost.py\n[Storage / General]", "x": -45.044124808848096, "y": -432.58543600245685}, {"borderWidth": 4.0, "color": {"background": "#555555", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "MCHTOLStorageCostModel", "label": "MCHTOLStorageCostModel", "shape": "diamond", "size": 18.0, "title": "MCHTOLStorageCostModel\nstorage/hydrogen/mch_storage.py\n[Storage / General]", "x": -151.7681139490249, "y": -515.5125477460953}, {"borderWidth": 4.0, "color": {"background": "#555555", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "LinearMassTransportCostModel", "label": "LinearMassTransportCostModel", "shape": "square", "size": 18.0, "title": "LinearMassTransportCostModel\ntransporters/linear_mass_transport_cost.py\n[Transporter / General]", "x": 478.2640687119284, "y": -424.2640687119286}, {"borderWidth": 4.0, "color": {"background": "#555555", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "LinearDistanceCostModel", "label": "LinearDistanceCostModel", "shape": "square", "size": 18.0, "title": "LinearDistanceCostModel\ntransporters/linear_transport_cost.py\n[Transporter / General]", "x": 505.77875370554676, "y": -340.3334060766844}]); - edges = new vis.DataSet([{"arrows": "to", "from": "PyomoRuleBaseClass", "to": "PyomoDispatchGenericConverter"}, {"arrows": "to", "from": "PyomoRuleBaseClass", "to": "PyomoRuleStorageBaseclass"}, {"arrows": "to", "from": "OpenLoopControlBase", "to": "PLMHeuristicOpenLoopConverterController"}, {"arrows": "to", "from": "OpenLoopControlBase", "to": "DemandOpenLoopStorageController"}, {"arrows": "to", "from": "OpenLoopControlBase", "to": "PeakLoadManagementHeuristicOpenLoopStorageController"}, {"arrows": "to", "from": "OpenLoopControlBase", "to": "SimpleStorageOpenLoopController"}, {"arrows": "to", "from": "PyomoStorageControllerBaseClass", "to": "HeuristicLoadFollowingStorageController"}, {"arrows": "to", "from": "PyomoStorageControllerBaseClass", "to": "OptimizedDispatchStorageController"}, {"arrows": 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{"arrows": "to", "from": "GeoH2SubsurfacePerformanceBaseClass", "to": "NaturalGeoH2PerformanceModel"}, {"arrows": "to", "from": "GeoH2SubsurfacePerformanceBaseClass", "to": "StimulatedGeoH2PerformanceModel"}, {"arrows": "to", "from": "GeoH2SubsurfaceCostBaseClass", "to": "GeoH2SubsurfaceCostModel"}, {"arrows": "to", "from": "GeoH2SurfacePerformanceBaseClass", "to": "AspenGeoH2SurfacePerformanceModel"}, {"arrows": "to", "from": "GeoH2SurfaceCostBaseClass", "to": "AspenGeoH2SurfaceCostModel"}, {"arrows": "to", "from": "IronReductionPlantBasePerformanceComponent", "to": "HydrogenIronReductionPlantPerformanceComponent"}, {"arrows": "to", "from": "IronReductionPlantBasePerformanceComponent", "to": "NaturalGasIronReductionPlantPerformanceComponent"}, {"arrows": "to", "from": "IronReductionPlantBaseCostComponent", "to": "HydrogenIronReductionPlantCostComponent"}, {"arrows": "to", "from": "IronReductionPlantBaseCostComponent", "to": "NaturalGasIronReductionPlantCostComponent"}, {"arrows": 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"to", "from": "CostModelBaseClass", "to": "GridCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "ElectrolyzerCostBaseClass"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "H2FuelCellCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "SteamMethaneReformerCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "GeoH2SubsurfaceCostBaseClass"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "GeoH2SurfaceCostBaseClass"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "HumbertStinnEwinCostComponent"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "IronReductionPlantBaseCostComponent"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "IronTransportCostComponent"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "NRRIIronMineCostComponent"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "SimpleIronMineCostComponent"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "MethanolCostBaseClass"}, {"arrows": 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{"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "PyomoDispatchGenericConverter", "label": "PyomoDispatchGenericConverter", "shape": "dot", "size": 18.0, "title": "PyomoDispatchGenericConverter\ncontrol/control_rules/converters/generic_converter.py\n[Converter / Other]", "x": 638.3794332090715, "y": 392.00407844682536}, {"borderWidth": 4.0, "color": {"background": "#00ACC1", "border": "#555555", "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, "hover": {"background": "#FFD700", "border": "#FF8C00"}}, "font": {"color": "#333333"}, "id": "PyomoRuleStorageBaseclass", "label": "PyomoRuleStorageBaseclass", "shape": "diamond", "size": 18.0, "title": "PyomoRuleStorageBaseclass\ncontrol/control_rules/storage/pyomo_storage_rule_baseclass.py\n[Storage / General]", "x": -47.5792073488668, "y": -506.39719955342014}]); + edges = new vis.DataSet([{"arrows": "to", "from": "SiteBaseComponent", "to": "SiteLocationComponent"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "ResizeablePerformanceModelBaseClass"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "SimpleCycleTurbinePerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "SolarPerformanceBaseClass"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "LinearH2FuelCellPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "SteamMethaneReformerPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "PEMH2FuelCellPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "GeoH2SubsurfacePerformanceBaseClass"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "GeoH2SurfacePerformanceBaseClass"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "DesalinationPerformanceBaseClass"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "SimpleThermalNuclearReactorPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "QuinnNuclearPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "ElectricArcFurnacePlantBasePerformanceComponent"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "CMUElectricArcFurnaceScrapOnlyPerformanceComponent"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "CMUElectricArcFurnaceDRIPerformanceComponent"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "SteelPerformanceBaseClass"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "SimpleASUPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "WindPerformanceBaseClass"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "WindArdPerformanceCompatibilityComponent"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "DieselGeneratorPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "MethanolPerformanceBaseClass"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "SAFPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "HumbertEwinPerformanceComponent"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "NRRIIronMinePerformanceComponent"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "IronReductionPlantBasePerformanceComponent"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "SimpleIronMinePerformanceComponent"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "PaperMillPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "PySAMTidalPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "PySAMWavePerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "RunOfRiverHydroPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "SimpleAmmoniaPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "DOCPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "OAEPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "GridPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "NaturalGasPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "SimpleGasProducerPerformance"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "SimpleGasConsumerPerformance"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "SONGFuelCellPerformanceModel"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "StoragePerformanceBase"}, {"arrows": "to", "from": "PerformanceModelBaseClass", "to": "DemandComponentBase"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "LinearMassTransportCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "LinearDistanceCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "GenericConverterCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "ATBUtilityPVCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "ATBResComPVCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "ElectrolyzerCostBaseClass"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "H2FuelCellCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "SteamMethaneReformerCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "GeoH2SubsurfaceCostBaseClass"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "GeoH2SurfaceCostBaseClass"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "DesalinationCostBaseClass"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "SimpleThermalNuclearReactorCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "QuinnNuclearCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "CMUElectricArcFurnaceCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "ElectricArcFurnacePlantBaseCostComponent"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "SteelCostBaseClass"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "SimpleASUCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "ATBWindPlantCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "WindArdCostCompatibilityComponent"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "DieselGeneratorCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "MethanolCostBaseClass"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "SAFCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "NRRIIronMineCostComponent"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "SimpleIronMineCostComponent"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "IronTransportCostComponent"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "IronReductionPlantBaseCostComponent"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "HumbertStinnEwinCostComponent"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "PaperMillCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "PySAMMarineCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "RunOfRiverHydroCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "AmmoniaSynLoopCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "SimpleAmmoniaCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "DOCCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "OAECostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "OAECostAndFinancialModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "GridCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "NaturalGasCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "SimpleGasProducerCost"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "SimpleGasConsumerCost"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "GenericStorageCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "MCHTOLStorageCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "HydrogenStorageBaseCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "ATBBatteryCostModel"}, {"arrows": "to", "from": "CostModelBaseClass", "to": "FeedstockCostModel"}, {"arrows": "to", "from": "ResizeablePerformanceModelBaseClass", "to": "ElectrolyzerPerformanceBaseClass"}, {"arrows": "to", "from": "ResizeablePerformanceModelBaseClass", "to": "AmmoniaSynLoopPerformanceModel"}, {"arrows": "to", "from": "CacheBaseClass", "to": "FlorisWindPlantPerformanceModel"}, {"arrows": "to", "from": "SolarPerformanceBaseClass", "to": "PYSAMSolarPlantPerformanceModel"}, {"arrows": "to", "from": "ElectrolyzerPerformanceBaseClass", "to": "ECOElectrolyzerPerformanceModel"}, {"arrows": "to", "from": "ElectrolyzerPerformanceBaseClass", "to": "HTSEPerformanceModel"}, {"arrows": "to", "from": "ElectrolyzerCostBaseClass", "to": "SingliticoCostModel"}, {"arrows": "to", "from": "ElectrolyzerCostBaseClass", "to": "BasicElectrolyzerCostModel"}, {"arrows": "to", "from": "ElectrolyzerCostBaseClass", "to": "CustomElectrolyzerCostModel"}, {"arrows": "to", "from": "ElectrolyzerCostBaseClass", "to": "HTSECostModel"}, {"arrows": "to", "from": "ECOElectrolyzerPerformanceModel", "to": "WOMBATElectrolyzerModel"}, {"arrows": "to", "from": "GeoH2SubsurfacePerformanceBaseClass", "to": "NaturalGeoH2PerformanceModel"}, {"arrows": "to", "from": "GeoH2SubsurfacePerformanceBaseClass", "to": "StimulatedGeoH2PerformanceModel"}, {"arrows": "to", "from": "GeoH2SubsurfaceCostBaseClass", "to": "GeoH2SubsurfaceCostModel"}, {"arrows": "to", "from": "GeoH2SurfacePerformanceBaseClass", "to": "AspenGeoH2SurfacePerformanceModel"}, {"arrows": "to", "from": "GeoH2SurfaceCostBaseClass", "to": "AspenGeoH2SurfaceCostModel"}, {"arrows": "to", "from": "DesalinationPerformanceBaseClass", "to": "ReverseOsmosisPerformanceModel"}, {"arrows": "to", "from": "DesalinationCostBaseClass", "to": "ReverseOsmosisCostModel"}, {"arrows": "to", "from": "ElectricArcFurnacePlantBasePerformanceComponent", "to": "HydrogenEAFPlantPerformanceComponent"}, {"arrows": "to", "from": "ElectricArcFurnacePlantBasePerformanceComponent", "to": "NaturalGasEAFPlantPerformanceComponent"}, {"arrows": "to", "from": "ElectricArcFurnacePlantBaseCostComponent", "to": "HydrogenEAFPlantCostComponent"}, {"arrows": "to", "from": "ElectricArcFurnacePlantBaseCostComponent", "to": "NaturalGasEAFPlantCostComponent"}, {"arrows": "to", "from": "SteelPerformanceBaseClass", "to": "SteelPerformanceModel"}, {"arrows": "to", "from": "SteelCostBaseClass", "to": "SteelCostAndFinancialModel"}, {"arrows": "to", "from": "WindPerformanceBaseClass", "to": "PYSAMWindPlantPerformanceModel"}, {"arrows": "to", "from": "WindPerformanceBaseClass", "to": "FlorisWindPlantPerformanceModel"}, {"arrows": "to", "from": "MethanolPerformanceBaseClass", "to": "SMRMethanolPlantPerformanceModel"}, {"arrows": "to", "from": "MethanolPerformanceBaseClass", "to": "CO2HMethanolPlantPerformanceModel"}, {"arrows": "to", "from": "MethanolCostBaseClass", "to": "SMRMethanolPlantCostModel"}, {"arrows": "to", "from": "MethanolCostBaseClass", "to": "CO2HMethanolPlantCostModel"}, {"arrows": "to", "from": "MethanolFinanceBaseClass", "to": "SMRMethanolPlantFinanceModel"}, {"arrows": "to", "from": "MethanolFinanceBaseClass", "to": "CO2HMethanolPlantFinanceModel"}, {"arrows": "to", "from": "IronReductionPlantBasePerformanceComponent", "to": "HydrogenIronReductionPlantPerformanceComponent"}, {"arrows": "to", "from": "IronReductionPlantBasePerformanceComponent", "to": "NaturalGasIronReductionPlantPerformanceComponent"}, {"arrows": "to", "from": "IronReductionPlantBaseCostComponent", "to": "HydrogenIronReductionPlantCostComponent"}, {"arrows": "to", "from": "IronReductionPlantBaseCostComponent", "to": "NaturalGasIronReductionPlantCostComponent"}, {"arrows": "to", "from": "StoragePerformanceBase", "to": "StoragePerformanceModel"}, {"arrows": "to", "from": "StoragePerformanceBase", "to": "StorageAutoSizingModel"}, {"arrows": "to", "from": "StoragePerformanceBase", "to": "PySAMBatteryPerformanceModel"}, {"arrows": "to", "from": "HydrogenStorageBaseCostModel", "to": "LinedRockCavernStorageCostModel"}, {"arrows": "to", "from": "HydrogenStorageBaseCostModel", "to": "SaltCavernStorageCostModel"}, {"arrows": "to", "from": "HydrogenStorageBaseCostModel", "to": "PipeStorageCostModel"}, {"arrows": "to", "from": "HydrogenStorageBaseCostModel", "to": "CompressedGasStorageCostModel"}, {"arrows": "to", "from": "BaseDowntime", "to": "FixedDowntime"}, {"arrows": "to", "from": "BaseDowntime", "to": "UniformDowntime"}, {"arrows": "to", "from": "BaseDowntime", "to": "LogNormalDowntime"}, {"arrows": "to", "from": "BaseReliability", "to": "WeibullReliability"}, {"arrows": "to", "from": "BaseReliability", "to": "FixedIntervalReliability"}, {"arrows": "to", "from": "FeedstockCostModel", "to": "EIANaturalGasFeedstockCostModel"}, {"arrows": "to", "from": "ProFastBase", "to": "ProFastLCO"}, {"arrows": "to", "from": "ProFastBase", "to": "ProFastNPV"}, {"arrows": "to", "from": "ResourceBaseH5Model", "to": "NSRDBDatasetH5"}, {"arrows": "to", "from": "ResourceBaseH5Model", "to": "WTKHRRRMETDatasetH5"}, {"arrows": "to", "from": "ResourceBaseAPIModel", "to": "OpenMeteoHistoricalSolarResource"}, {"arrows": "to", "from": "ResourceBaseAPIModel", "to": "NLRDeveloperAPISolarResourceBase"}, {"arrows": "to", "from": "ResourceBaseAPIModel", "to": "OpenMeteoHistoricalWindResource"}, {"arrows": "to", "from": "ResourceBaseAPIModel", "to": "NLRDeveloperAPIWindResourceBase"}, {"arrows": "to", "from": "SolarResourceBase", "to": "OpenMeteoHistoricalSolarResource"}, {"arrows": "to", "from": "SolarResourceBase", "to": "NLRDeveloperAPISolarResourceBase"}, {"arrows": "to", "from": "SolarResourceBase", "to": "NSRDBDatasetH5"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "MeteosatPrimeMeridianSolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "MeteosatPrimeMeridianTMYSolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "Himawari7SolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "Himawari8SolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "HimawariTMYSolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "GOESAggregatedSolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "GOESConusSolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "GOESFullDiscSolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPISolarResourceBase", "to": "GOESTMYSolarAPI"}, {"arrows": "to", "from": "NLRDeveloperAPIWindResourceBase", "to": "WTKNLRDeveloperAPIWindResource"}, {"arrows": "to", "from": "NLRDeveloperAPIWindResourceBase", "to": "HRRRMETToolkitWindAPI"}, {"arrows": "to", "from": "WindResourceBase", "to": "WTKHRRRMETDatasetH5"}, {"arrows": "to", "from": "WindResourceBase", "to": "OpenMeteoHistoricalWindResource"}, {"arrows": "to", "from": "WindResourceBase", "to": "NLRDeveloperAPIWindResourceBase"}, {"arrows": "to", "from": "DemandComponentBase", "to": "GenericDemandComponent"}, {"arrows": "to", "from": "DemandComponentBase", "to": "FlexibleDemandComponent"}, {"arrows": "to", "from": "OpenLoopControlBase", "to": "PLMHeuristicOpenLoopConverterController"}, {"arrows": "to", "from": "OpenLoopControlBase", "to": "DemandOpenLoopStorageController"}, {"arrows": "to", "from": "OpenLoopControlBase", "to": "SimpleStorageOpenLoopController"}, {"arrows": "to", "from": "OpenLoopControlBase", "to": "PeakLoadManagementHeuristicOpenLoopStorageController"}, {"arrows": "to", "from": "PyomoStorageControllerBaseClass", "to": "PeakLoadManagementOptimizedStorageController"}, {"arrows": "to", "from": "PyomoStorageControllerBaseClass", "to": "HeuristicLoadFollowingStorageController"}, {"arrows": "to", "from": "PyomoStorageControllerBaseClass", "to": "OptimizedDispatchStorageController"}, {"arrows": "to", "from": "SystemLevelControlBase", "to": "ProfitMaximizationControl"}, {"arrows": "to", "from": "SystemLevelControlBase", "to": "DemandFollowingControl"}, {"arrows": "to", "from": "SystemLevelControlBase", "to": "CostMinimizationControl"}, {"arrows": "to", "from": "PyomoRuleBaseClass", "to": "PyomoDispatchGenericConverter"}, {"arrows": "to", "from": "PyomoRuleBaseClass", "to": "PyomoRuleStorageBaseclass"}]); nodeColors = {}; allNodes = nodes.get({ returnType: "Object" }); diff --git a/docs/resource/resource_index.md b/docs/resource/resource_index.md index 3ccf0b297..bb81e4edf 100644 --- a/docs/resource/resource_index.md +++ b/docs/resource/resource_index.md @@ -27,6 +27,12 @@ If running a simulation that is longer than 1 year, then there are a few options 2. Use resource data from a list of filenames. This option is enabled when `resource_filename` is a list. The list must be the same length as the number of years needed for the simulation. The filenames provided as `resource_filename` are **not used if the site changes from the site defined in the configuration file**. This can be used with `resource_year_order`, where `resource_year_order` is used if the site changes. If `resource_year_order` is not specified, the year order is inferred from the data in the provided files under the 'year' column (unless using a TMY dataset, in which case the year order is inferred from the filename). Supplying `resource_year_order` alongside `resource_filename` avoids inferring the years and makes the replacement years explicit. The inferred or provided `resource_year_order` is used instead of `resource_year`. 3. Use a specified order of resource years. This option is enabled when `resource_year_order` is a list of resource years and `resource_filename` is an empty string (or not provided). The list must be the same length as the number of years needed for the simulation. The `resource_year_order` is used instead of `resource_year`. As mentioned above, this can be used with the `resource_filename` option. +## Resampling resource data + +API resource data may have a different native timestep from the simulation, but H2I does not resample it by default. You may explicitly choose a pandas interpolation method for upsampling or a pandas aggregation method for downsampling in the resource's `resource_parameters`. + +Set `upsample_method` or `downsample_method` in `resource_parameters` to explicitly select the pandas operation. If the data timestep and simulation timestep already match, neither setting is needed. When resampling is requested, H2I emits a warning naming the source timestep, target timestep, direction, and method. If a mismatch requires resampling but no corresponding method is set, H2I raises an error. See {py:func}`h2integrate.resource.utilities.time_tools.resample_resource_data_to_dt` for method details. + ## Setting resource data for a technology diff --git a/h2integrate/core/inputs/validation.py b/h2integrate/core/inputs/validation.py index 5f0b7afa4..60cbdbacd 100644 --- a/h2integrate/core/inputs/validation.py +++ b/h2integrate/core/inputs/validation.py @@ -155,7 +155,7 @@ def load_plant_yaml(finput): n_timesteps = plant_config["plant"]["simulation"]["n_timesteps"] dt = plant_config["plant"]["simulation"]["dt"] - if int(n_timesteps) * int(dt) != 31536000: # seconds in simulation must be seconds/year + if int(n_timesteps) * int(dt) != 31_536_000: msg = ( "H2Integrate does not currently support simulations that are less than or " "greater than 1-year. Please ensure that " diff --git a/h2integrate/resource/resource_baseclass.py b/h2integrate/resource/resource_baseclass.py index 1c64073cf..2f3ef6b3b 100644 --- a/h2integrate/resource/resource_baseclass.py +++ b/h2integrate/resource/resource_baseclass.py @@ -21,6 +21,7 @@ check_data_length, contains_leap_day, add_resource_start_end_times, + resample_resource_data_to_dt, get_number_of_resource_years_needed, ) from h2integrate.resource.utilities.download_tools import download_from_api @@ -40,6 +41,8 @@ class ResourceBaseAPIConfig(BaseConfig): load resource files from. Defaults to "". - **resource_filename** (*str*, optional): Filename to save resource data to or load resource data from. Defaults to None. + - **resource_year_setting** (*str*, optional): How resource years are selected for + API datasets. Options include ``start_year``, ``year_order``, and ``filenames``. - **valid_intervals** (*list[int]*): time interval(s) in minutes that resource data can be downloaded in. @@ -65,6 +68,12 @@ class ResourceBaseAPIConfig(BaseConfig): resource_year_order (list, optional): Only used running a simulation requiring multiple resource years. List of resource years in-order, such as [2012, 2011, 2013]. Defaults to None. + upsample_method (str | None, optional): Pandas interpolation method to use when the + simulation timestep is finer than the resource data timestep. Required only when + upsampling is needed; otherwise defaults to None and no automatic resampling occurs. + downsample_method (str | None, optional): Pandas resampling aggregation to use when the + simulation timestep is coarser than the resource data timestep. Required only when + downsampling is needed; otherwise defaults to None and no automatic resampling occurs. Attributes: dataset_desc (str): description of the dataset, used in file naming. @@ -85,6 +94,9 @@ class ResourceBaseAPIConfig(BaseConfig): resource_dir: Path | str | None = field(default=None) include_leap_day: bool = field(default=False) resource_year_order: list | None = field(default=None) + # Resampling methods used when the resource data's native timestep differs from the sim dt. + upsample_method: str | None = field(default=None) + downsample_method: str | None = field(default=None) class ResourceBaseAPIModel(om.ExplicitComponent): @@ -559,7 +571,6 @@ def get_data_for_year( # filepath, and a new download isn't forced, then load data using `load_data()` if filepath.is_file() and not forced_download: data = self.load_data(filepath) - # NOTE: this where we could up/downsample return data # If the filepath (resource_dir/filename) does not exist, download data @@ -571,7 +582,6 @@ def get_data_for_year( if success: # 6) Load data from the file created in Step 5 using `load_data()` data = self.load_data(filepath) - # NOTE: this where we could up/downsample return data else: @@ -591,8 +601,15 @@ def process_final_resource_data(self, resource_data): Returns: dict: resource_data after final processing and checks """ - # NOTE: we could up/downsample here also + # Remove leap day (if needed) on the native data, resample to the simulation + # timestep, then clip to the number of timesteps requested by the simulation. resource_data = process_leap_day(resource_data, self.config.include_leap_day) + resource_data = resample_resource_data_to_dt( + resource_data, + self.dt, + self.config.upsample_method, + self.config.downsample_method, + ) resource_data = clip_data_to_n_timesteps(resource_data, n_timesteps=self.n_timesteps) resource_data = add_resource_start_end_times(resource_data) check_data_length(resource_data, self.n_timesteps) diff --git a/h2integrate/resource/test/conftest.py b/h2integrate/resource/test/conftest.py index 083aaa73b..e13f46ff0 100644 --- a/h2integrate/resource/test/conftest.py +++ b/h2integrate/resource/test/conftest.py @@ -39,6 +39,8 @@ def site_config(which, lat, lon, model, resource_year, model_name): "resource_model": model, "resource_parameters": { "resource_year": resource_year, + "upsample_method": "time", + "downsample_method": "mean", }, } }, diff --git a/h2integrate/resource/test/test_resource_api_baseclass.py b/h2integrate/resource/test/test_resource_api_baseclass.py index fae39467c..a089b353f 100644 --- a/h2integrate/resource/test/test_resource_api_baseclass.py +++ b/h2integrate/resource/test/test_resource_api_baseclass.py @@ -26,6 +26,8 @@ def input_config(n_timesteps, resource_year, include_leap, resource_fname, yr_or "include_leap_day": include_leap, "resource_year_order": yr_order, "resource_filename": resource_fname, + "upsample_method": "time", + "downsample_method": "mean", "timezone": 0, } @@ -86,7 +88,7 @@ def get_data_for_year( "month": dates.month.to_numpy().astype(float), "day": dates.day.to_numpy().astype(float), "hour": dates.hour.to_numpy().astype(float), - "minute": dates.hour.to_numpy().astype(float), + "minute": dates.minute.to_numpy().astype(float), "ws": np.arange(len(dates), dtype=float), "latitude": latitude, "longitude": longitude, @@ -304,6 +306,7 @@ def test_get_data_tmy_filenames_require_years_for_site_change(input_config): [(2013, 2, False, "", None)], ) def test_process_final_resource_data(subtests, input_config): + input_config["plant"]["simulation"]["dt"] = 172800 _, comp = _setup_resource_component(FakeResource, input_config) dates = pd.to_datetime(["2012-02-28", "2012-02-29", "2012-03-01"]) data = { diff --git a/h2integrate/resource/utilities/test/test_time_tools.py b/h2integrate/resource/utilities/test/test_time_tools.py index 7ac43b8db..42bc42580 100644 --- a/h2integrate/resource/utilities/test/test_time_tools.py +++ b/h2integrate/resource/utilities/test/test_time_tools.py @@ -7,6 +7,7 @@ process_leap_day, check_data_length, contains_leap_day, + resample_resource_data_to_dt, get_n_timesteps_from_year_list, get_number_of_resource_years_needed, ) @@ -117,6 +118,235 @@ def test_process_leap_day_without_february(): np.testing.assert_array_equal(result["day"], [1, 2]) +@pytest.mark.unit +def _make_timeseries(n, freq_seconds, start="2012-01-01 00:00", values=None): + """Build a resource data dict with time columns spaced at ``freq_seconds``.""" + + idx = pd.date_range(start=start, periods=n, freq=pd.Timedelta(seconds=freq_seconds)) + if values is None: + values = np.arange(n, dtype=float) + return { + "wind_speed_100m": np.asarray(values, dtype=float), + "year": idx.year.to_numpy().astype(float), + "month": idx.month.to_numpy().astype(float), + "day": idx.day.to_numpy().astype(float), + "hour": idx.hour.to_numpy().astype(float), + "minute": idx.minute.to_numpy().astype(float), + "units": {"wind_speed_100m": "m/s"}, + "site_lat": 1.0, + } + + +@pytest.mark.unit +def test_resample_no_op_when_dt_matches(): + data = _make_timeseries(10, 3600) + result = resample_resource_data_to_dt(data, 3600) + assert result is data + + +@pytest.mark.unit +def test_resample_no_time_columns_raises(): + data = {"wind_speed_100m": np.arange(10, dtype=float), "site_lat": 1.0} + with pytest.raises(ValueError, match="no time columns"): + resample_resource_data_to_dt(data, 1800) + + +@pytest.mark.unit +def test_resample_target_dt_larger_than_span_raises(): + # 5 hourly samples span only a few hours; a 10-day timestep yields no full step + data = _make_timeseries(5, 3600) + with pytest.raises(ValueError, match="larger than the total time span"): + resample_resource_data_to_dt(data, 10 * 86400) + + +@pytest.mark.unit +def test_resample_non_increasing_timestamps_raises(): + # The first two timestamps are identical, so the native timestep is non-positive + data = { + "wind_speed_100m": np.arange(3, dtype=float), + "year": np.array([2012, 2012, 2012], dtype=float), + "month": np.array([1, 1, 1], dtype=float), + "day": np.array([1, 1, 1], dtype=float), + "hour": np.array([0, 0, 1], dtype=float), + "minute": np.array([0, 0, 0], dtype=float), + } + with pytest.raises(ValueError, match="non-positive"): + resample_resource_data_to_dt(data, 1800) + + +@pytest.mark.unit +def test_upsample_interpolation(subtests): + # hourly data upsampled to 30-minute resolution + data = _make_timeseries(5, 3600, values=[0, 1, 2, 3, 4]) + result = resample_resource_data_to_dt(data, 1800, upsample_method="time") + + with subtests.test("upsampled length doubles"): + assert len(result["wind_speed_100m"]) == 10 + expected = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4], dtype=float) + with subtests.test("interpolated values match expectation"): + np.testing.assert_allclose(result["wind_speed_100m"], expected) + with subtests.test("minutes alternate on 30 minute grid"): + np.testing.assert_array_equal(result["minute"][:4], [0, 30, 0, 30]) + + +@pytest.mark.unit +def test_downsample_average(subtests): + # 30-minute data downsampled to hourly resolution via pandas mean aggregation + data = _make_timeseries(10, 1800, values=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) + result = resample_resource_data_to_dt(data, 3600, downsample_method="mean") + + with subtests.test("downsampled length halves"): + assert len(result["wind_speed_100m"]) == 5 + expected = np.array([0.5, 2.5, 4.5, 6.5, 8.5], dtype=float) + with subtests.test("hourly means match expectation"): + np.testing.assert_allclose(result["wind_speed_100m"], expected) + with subtests.test("minutes stay on hour"): + np.testing.assert_array_equal(result["minute"], np.zeros(5)) + + +@pytest.mark.unit +def test_resample_keeps_leap_day_excluded_at_new_timestep(subtests): + # Hourly data for a leap year with the leap day removed, resampled to 2-hour. + # February 29 must stay excluded in the regenerated calendar at the new timestep. + + idx = pd.date_range("2012-01-01 00:30", "2012-12-31 23:30", freq="1h") + idx = idx[~((idx.month == 2) & (idx.day == 29))] # 8760 hourly, leap day removed + data = { + "wind_speed_100m": np.arange(len(idx), dtype=float), + "year": idx.year.to_numpy().astype(float), + "month": idx.month.to_numpy().astype(float), + "day": idx.day.to_numpy().astype(float), + "hour": idx.hour.to_numpy().astype(float), + "minute": idx.minute.to_numpy().astype(float), + } + + result = resample_resource_data_to_dt(data, 7200, downsample_method="mean") # 2-hour timestep + + with subtests.test("result length matches excluded leap year at 2 hour dt"): + assert len(result["wind_speed_100m"]) == 4380 + with subtests.test("february 29 remains excluded"): + assert not ((result["month"] == 2) & (result["day"] == 29)).any() + with subtests.test("starts on january 1"): + assert int(result["month"][0]) == 1 and int(result["day"][0]) == 1 + with subtests.test("ends on december 31"): + assert int(result["month"][-1]) == 12 and int(result["day"][-1]) == 31 + + +@pytest.mark.unit +def test_resample_keeps_leap_day_when_present_at_new_timestep(subtests): + # Leap year with the leap day retained, resampled to 2-hour: Feb 29 must remain. + + idx = pd.date_range("2012-01-01 00:30", "2012-12-31 23:30", freq="1h") # 8784, leap kept + data = { + "wind_speed_100m": np.arange(len(idx), dtype=float), + "year": idx.year.to_numpy().astype(float), + "month": idx.month.to_numpy().astype(float), + "day": idx.day.to_numpy().astype(float), + "hour": idx.hour.to_numpy().astype(float), + "minute": idx.minute.to_numpy().astype(float), + } + + result = resample_resource_data_to_dt(data, 7200, downsample_method="mean") # 2-hour timestep + + with subtests.test("result length matches leap year at 2 hour dt"): + assert len(result["wind_speed_100m"]) == 4392 + with subtests.test("leap day remains present"): + assert ((result["month"] == 2) & (result["day"] == 29)).any() + + +@pytest.mark.unit +def test_downsample_preserves_mean(): + rng = np.random.default_rng(0) + values = rng.random(24) + data = _make_timeseries(24, 900, values=values) # 15-min data + result = resample_resource_data_to_dt(data, 3600, downsample_method="mean") # hourly + + assert len(result["wind_speed_100m"]) == 6 + # overall mean is conserved by the averaging + np.testing.assert_allclose(result["wind_speed_100m"].mean(), values.mean()) + + +@pytest.mark.unit +def test_downsample_with_calendar_gap_preserves_mean(subtests): + # Hourly data whose timestamps have a one-day gap in the middle (as when a leap + # day is removed). Resampling must treat the samples as an evenly spaced sequence, + # so the downsampled mean matches the exact pairwise mean. + + part1 = pd.date_range("2012-02-28 00:30", periods=4, freq="1h") + part2 = pd.date_range("2012-03-01 00:30", periods=4, freq="1h") + idx = part1.append(part2) + values = np.arange(8, dtype=float) + data = { + "wind_speed_100m": values.copy(), + "year": idx.year.to_numpy().astype(float), + "month": idx.month.to_numpy().astype(float), + "day": idx.day.to_numpy().astype(float), + "hour": idx.hour.to_numpy().astype(float), + "minute": idx.minute.to_numpy().astype(float), + } + result = resample_resource_data_to_dt(data, 7200, downsample_method="mean") # to 2-hour + + with subtests.test("downsampled length matches expected bins"): + assert len(result["wind_speed_100m"]) == 4 + with subtests.test("pairwise averages preserved across gap"): + np.testing.assert_allclose(result["wind_speed_100m"], [0.5, 2.5, 4.5, 6.5]) + with subtests.test("mean preserved despite gap"): + np.testing.assert_allclose(result["wind_speed_100m"].mean(), values.mean()) + + +@pytest.mark.unit +def test_resample_scalar_metadata_preserved(subtests): + data = _make_timeseries(5, 3600, values=[0, 1, 2, 3, 4]) + result = resample_resource_data_to_dt(data, 1800, upsample_method="time") + with subtests.test("units preserved"): + assert result["units"] == {"wind_speed_100m": "m/s"} + with subtests.test("site latitude preserved"): + assert result["site_lat"] == 1.0 + + +@pytest.mark.unit +def test_resample_unknown_upsample_method_raises(): + data = _make_timeseries(5, 3600) + # An invalid pandas interpolation method raises. The exact message differs across + # pandas versions, but the offending method name is always reported. + with pytest.raises(ValueError, match="not_a_method"): + resample_resource_data_to_dt(data, 1800, upsample_method="not_a_method") + + +@pytest.mark.unit +def test_resample_unknown_downsample_method_raises(): + data = _make_timeseries(10, 1800) + # An invalid pandas aggregation raises. Different pandas versions raise different + # exception types (AttributeError vs ValueError) with different messages, so match + # on the offending method name that is common to all versions. + with pytest.raises((AttributeError, ValueError), match="not_a_method"): + resample_resource_data_to_dt(data, 3600, downsample_method="not_a_method") + + +@pytest.mark.unit +def test_resample_upsampling_without_method_raises(): + data = _make_timeseries(5, 3600) + with pytest.raises(ValueError, match="requires upsampling"): + resample_resource_data_to_dt(data, 1800) + + +@pytest.mark.unit +def test_resample_downsampling_without_method_raises(): + data = _make_timeseries(10, 1800) + with pytest.raises(ValueError, match="requires downsampling"): + resample_resource_data_to_dt(data, 3600) + + +@pytest.mark.unit +def test_resample_warns_when_resampling(subtests): + with subtests.test("upsampling warns"): + with pytest.warns(UserWarning, match="upsampling"): + resample_resource_data_to_dt(_make_timeseries(5, 3600), 1800, upsample_method="time") + with subtests.test("downsampling warns"): + with pytest.warns(UserWarning, match="downsampling"): + resample_resource_data_to_dt(_make_timeseries(10, 1800), 3600, downsample_method="mean") + + @pytest.mark.unit @pytest.mark.parametrize( "n_timesteps,include_leap,expected_years", diff --git a/h2integrate/resource/utilities/time_tools.py b/h2integrate/resource/utilities/time_tools.py index 1a62d3929..3e9c96072 100644 --- a/h2integrate/resource/utilities/time_tools.py +++ b/h2integrate/resource/utilities/time_tools.py @@ -1,3 +1,4 @@ +import warnings from datetime import timezone, timedelta import numpy as np @@ -276,3 +277,199 @@ def get_number_of_resource_years_needed(dt: int, n_timesteps: int, include_leap: # conservative estimate of number of years needed return int((hours_simulated // 8760) + 1) + + +def _regenerate_time_columns_at_dt( + data, start_timestamp, dt_seconds, n, present_time_keys, exclude_leap_day=False +): + """Regenerate time columns as ``n`` steps of ``dt_seconds`` starting at a timestamp. + + When ``exclude_leap_day`` is True, February 29 is skipped so the regenerated calendar + keeps the leap day excluded even at the new timestep (extra steps are generated to make + up for the skipped timestamps). + """ + freq = pd.Timedelta(seconds=dt_seconds) + if exclude_leap_day: + periods = n + new_index = pd.date_range(start=start_timestamp, periods=periods, freq=freq) + new_index = new_index[~((new_index.month == 2) & (new_index.day == 29))] + while len(new_index) < n: + periods += (n - len(new_index)) + 1 + new_index = pd.date_range(start=start_timestamp, periods=periods, freq=freq) + new_index = new_index[~((new_index.month == 2) & (new_index.day == 29))] + new_index = new_index[:n] + else: + new_index = pd.date_range(start=start_timestamp, periods=n, freq=freq) + field_map = { + "year": new_index.year, + "month": new_index.month, + "day": new_index.day, + "hour": new_index.hour, + "minute": new_index.minute, + "second": new_index.second, + } + for k in present_time_keys: + data[k] = np.asarray(field_map[k], dtype=float) + return data + + +def resample_resource_data_to_dt( + data: dict, + target_dt, + upsample_method: str | None = None, + downsample_method: str | None = None, +): + """Resample resource timeseries from its native timestep to ``target_dt``. + + Resampling is driven by the actual time span of the data (its native timestep, inferred + from the time columns), not by the number of timesteps. When the simulation timestep is + smaller than the native timestep the data is upsampled (finer resolution) using + :meth:`pandas.DataFrame.interpolate`; when it is larger the data is downsampled (coarser + resolution) using :meth:`pandas.core.resample.Resampler.agg`. Time columns are + regenerated at ``target_dt`` and scalar metadata is left unchanged. + + Resampling operates on the samples as an evenly spaced sequence at the native timestep. + Resource data may have non-contiguous calendar timestamps (for example when a leap day + is removed to keep a clean annual length), so a contiguous synthetic time axis at the + native timestep is used for the resampling itself and the calendar time columns are + regenerated afterward. + + Args: + data (dict): resource data dictionary with timeseries arrays and time columns. + target_dt (int | float): desired simulation timestep in seconds. + upsample_method (str | None): interpolation method passed to + :meth:`pandas.DataFrame.interpolate` when upsampling. Must be explicitly set when + upsampling is needed; otherwise a ``ValueError`` is raised. + downsample_method (str | None): aggregation passed to + :meth:`pandas.core.resample.Resampler.agg` when downsampling. Must be explicitly + set when downsampling is needed; otherwise a ``ValueError`` is raised. + + Returns: + dict: resource data resampled to ``target_dt``. + """ + if not isinstance(data, dict) or not target_dt: + return data + + present_time_keys = [k for k in TIME_DATA_KEYS if k in data] + if not present_time_keys: + msg = ( + "Cannot resample resource data to the simulation timestep because the data has " + "no time columns (year/month/day/hour/minute) to determine its native timestep. " + "Provide resource data that includes time information." + ) + raise ValueError(msg) + + assembly = {k: np.asarray(data[k]).astype(int) for k in present_time_keys} + calendar_index = pd.DatetimeIndex(pd.to_datetime(assembly)) + if len(calendar_index) < 2: + return data + + # Native timestep is taken from the first two samples so a calendar gap (such as a + # removed leap day) does not distort it. + native_dt = (calendar_index[1] - calendar_index[0]).total_seconds() + if native_dt <= 0: + msg = ( + "Cannot resample resource data: the native timestep derived from the data's time " + "columns is non-positive. Ensure the resource data has valid, strictly " + "increasing timestamps." + ) + raise ValueError(msg) + + # Nothing to do if the native timestep already matches the target + if abs(native_dt - float(target_dt)) < 1e-6: + return data + + # Detect whether the source data has a leap day removed so the regenerated calendar keeps + # February 29 excluded at the new timestep. + has_feb29 = bool(((calendar_index.month == 2) & (calendar_index.day == 29)).any()) + spans_leap_year = any(is_leap_year(int(y)) for y in np.unique(calendar_index.year.to_numpy())) + exclude_leap_day = spans_leap_year and not has_feb29 + + native_len = len(calendar_index) + target_n = round(native_len * native_dt / float(target_dt)) + if target_n < 1: + msg = ( + f"Cannot resample resource data to a timestep of {target_dt} s: it is larger than " + f"the total time span of the data ({native_len} samples at {native_dt} s = " + f"{native_len * native_dt} s), so resampling would produce no timesteps. Use a " + "smaller timestep or provide more resource data." + ) + raise ValueError(msg) + + target_freq = pd.Timedelta(seconds=float(target_dt)) + # Use a contiguous synthetic axis anchored at the data's first timestamp so resampling is + # based on the evenly spaced sample sequence rather than the (possibly gapped) calendar + # timestamps; ``pd.date_range`` is always contiguous, avoiding removed-leap-day gaps. + start = calendar_index[0] + native_index = pd.date_range( + start=start, periods=native_len, freq=pd.Timedelta(seconds=native_dt) + ) + target_index = pd.date_range(start=start, periods=target_n, freq=target_freq) + + # Only numeric timeseries columns are resampled; time columns are regenerated afterward. + _, ts_data = separate_timeseries_and_meta_data(data) + data_keys = [ + k + for k in ts_data + if k not in present_time_keys and len(np.atleast_1d(ts_data[k])) == native_len + ] + frame = pd.DataFrame( + {k: np.asarray(data[k], dtype=float) for k in data_keys}, index=native_index + ) + + upsampling = native_dt > float(target_dt) + downsampling = native_dt < float(target_dt) + if upsampling and upsample_method is None: + raise ValueError( + f"Resource data has a native timestep of {native_dt:g} s but the simulation uses a " + f"finer timestep of {float(target_dt):g} s, which requires upsampling. Resampling is " + "not performed automatically: set the resource `upsample_method` (for example " + "'time') to explicitly enable upsampling." + ) + if downsampling and downsample_method is None: + raise ValueError( + f"Resource data has a native timestep of {native_dt:g} s but the simulation uses a " + f"coarser timestep of {float(target_dt):g} s, which requires downsampling. Resampling " + "is not performed automatically: set the resource `downsample_method` (for example " + "'mean') to explicitly enable downsampling." + ) + + method = upsample_method if upsampling else downsample_method + direction = "upsampling" if upsampling else "downsampling" + warnings.warn( + f"Resampling resource data from a native timestep of {native_dt:g} s to the simulation " + f"timestep of {float(target_dt):g} s ({direction} with method '{method}').", + UserWarning, + stacklevel=2, + ) + + if upsampling: + # Upsample: interpolate onto the (finer) target grid + union_index = frame.index.union(target_index) + resampled_frame = ( + frame.reindex(union_index).interpolate(method=upsample_method).reindex(target_index) + ) + elif downsampling: + # Downsample: aggregate native samples within each (coarser) target interval + resampled_frame = ( + frame.resample(target_freq, origin="start").agg(downsample_method).reindex(target_index) + ) + + # Fill any residual NaNs at the grid edges introduced by reindexing + resampled_frame = resampled_frame.ffill().bfill() + + resampled = dict(data) + for k in data_keys: + resampled[k] = resampled_frame[k].to_numpy() + + # Regenerate calendar time columns at the target timestep starting from the original + # first timestamp, keeping any removed leap day excluded. + resampled = _regenerate_time_columns_at_dt( + resampled, + calendar_index[0], + float(target_dt), + target_n, + present_time_keys, + exclude_leap_day, + ) + return resampled diff --git a/h2integrate/resource/wind/nlr_developer_wtk_api_models.py b/h2integrate/resource/wind/nlr_developer_wtk_api_models.py index 39eeb8615..bdc7a0e8b 100644 --- a/h2integrate/resource/wind/nlr_developer_wtk_api_models.py +++ b/h2integrate/resource/wind/nlr_developer_wtk_api_models.py @@ -39,7 +39,7 @@ class WTKNLRDeveloperAPIWindResourceConfig(ResourceBaseAPIConfig): resource_type: str = "wind" valid_intervals: list[int] = field(factory=lambda: [5, 15, 30, 60]) resource_data: dict | object = field(default={}) - resource_filename: Path | str = field(default="") + resource_filename: Path | str | list = field(default="") resource_dir: Path | str | None = field(default=None) @@ -90,7 +90,7 @@ class HRRRMETToolkitWindAPIConfig(ResourceBaseAPIConfig): resource_type: str = "wind" valid_intervals: list[int] = field(factory=lambda: [60]) resource_data: dict | object = field(default={}) - resource_filename: Path | str = field(default="") + resource_filename: Path | str | list = field(default="") resource_dir: Path | str | None = field(default=None)