diff --git a/architecture.py b/architecture.py index 836fa0e9beba90b96a6c10b85284230276c2ddfd..1d43ae0ba7bb6f8a19e8fc6ada92ccb6db873931 100644 --- a/architecture.py +++ b/architecture.py @@ -1,5 +1,5 @@ from Model_Library.dynamics import Dynamic, DynamicTree, TreeDynamic -from Model_Library.optimization_model import OptimizationBlock, OptimizationModel +import Model_Library import abc from dataclasses import dataclass @@ -998,8 +998,8 @@ class PeriodAggregation: self, variable, name: str, - start_block: OptimizationBlock, - end_blocks: List[OptimizationBlock], + start_block: "Model_Library.optimization_model.OptimizationBlock", + end_blocks: List["Model_Library.optimization_model.OptimizationBlock"], ): # variable: type hinting a np-array, type hinting a np-array aggregated_variables = np.empty(len(self.period_blocks), dtype=object) for i, end_block in enumerate(end_blocks): @@ -1317,7 +1317,7 @@ class ResamplingStep: v_map: Dict, name: str, block_prefix: str, - model: OptimizationModel, + model: "Model_Library.optimization_model.OptimizationModel", ): pass @@ -1344,7 +1344,7 @@ class CopyStep(ResamplingStep): v_map: Dict, name: str, block_prefix: str, - model: OptimizationModel, + model: "Model_Library.optimization_model.OptimizationModel", ): v_map[self.end_v] = v_map[self.start_v] @@ -1377,7 +1377,7 @@ class AssignmentStep(ResamplingStep): v_map: Dict, name: str, block_prefix: str, - model: OptimizationModel, + model: "Model_Library.optimization_model.OptimizationModel", ): start_dynamic = arcnitecture.underlying_dynamics[self.start_v.id] end_dynamic = arcnitecture.underlying_dynamics[self.end_v.id] @@ -1417,7 +1417,7 @@ class AggregationUpStep(ResamplingStep): v_map: Dict, name: str, block_prefix: str, - model: OptimizationModel, + model: "Model_Library.optimization_model.OptimizationModel", ): data = np.empty(len(self.start_vs), dtype=object) for i, start_v in enumerate(self.start_vs): @@ -1458,7 +1458,7 @@ class AggregationDownStep(ResamplingStep): v_map: Dict, name: str, block_prefix: str, - model: OptimizationModel, + model: "Model_Library.optimization_model.OptimizationModel", ): data = arcnitecture.all_period_aggregations[self.id].aggregate_variable_down( v_map[self.start_v], @@ -1778,7 +1778,7 @@ class Resampling: def execute_result( self, vars_iter, - block: OptimizationBlock, + block: "Model_Library.optimization_model.OptimizationBlock", dynamic: ArchitectureDynamic, target_values, target_dynamic: ArchitectureDynamic, @@ -1807,7 +1807,7 @@ class Resampling: def execute_fs_result( self, vars_iter, - block: OptimizationBlock, + block: "Model_Library.optimization_model.OptimizationBlock", dynamic: ArchitectureDynamic, target_values, target_dynamic: ArchitectureDynamic, @@ -1832,7 +1832,7 @@ class Resampling: self, name: str, block_prefix: str, - model: OptimizationModel, + model: "Model_Library.optimization_model.OptimizationModel", dynamic: ArchitectureDynamic, target_dynamic: ArchitectureDynamic, ): @@ -1888,7 +1888,7 @@ def resample( def resample_result( vars_iter, - block: OptimizationBlock, + block: "Model_Library.optimization_model.OptimizationBlock", dynamic: ArchitectureDynamic, target_values, target_dynamic: ArchitectureDynamic, @@ -1911,7 +1911,7 @@ def resample_result( def resample_first_state_result( vars_iter, - block: OptimizationBlock, + block: "Model_Library.optimization_model.OptimizationBlock", dynamic: ArchitectureDynamic, target_values, target_dynamic: ArchitectureDynamic, @@ -1933,7 +1933,7 @@ def resample_first_state_result( def resample_variable( name: str, block_prefix: str, - model: OptimizationModel, + model: "Model_Library.optimization_model.OptimizationModel", dynamic: ArchitectureDynamic, target_dynamic: ArchitectureDynamic, ):