|
| 1 | +from typing import TYPE_CHECKING, Generic, TypeVar |
| 2 | + |
| 3 | +if TYPE_CHECKING: |
| 4 | + from . import InitKwargs |
| 5 | + |
| 6 | + |
| 7 | +import pandas as pd |
| 8 | + |
| 9 | +# TODO Import this from typing when dropping Python 3.11 |
| 10 | +from typing_extensions import TypedDict, Unpack |
| 11 | + |
| 12 | +from ixmp4.core.base import BaseFacade, BaseModelFacade |
| 13 | +from ixmp4.data import abstract |
| 14 | + |
| 15 | +FacadeOptimizationModelType = TypeVar( |
| 16 | + "FacadeOptimizationModelType", bound=BaseModelFacade |
| 17 | +) |
| 18 | +AbstractOptimizationModelType = TypeVar( |
| 19 | + "AbstractOptimizationModelType", bound=abstract.BaseModel |
| 20 | +) |
| 21 | + |
| 22 | + |
| 23 | +class OptimizationBaseRepository( |
| 24 | + BaseFacade, Generic[FacadeOptimizationModelType, AbstractOptimizationModelType] |
| 25 | +): |
| 26 | + _run: abstract.Run |
| 27 | + _backend_repository: abstract.BackendBaseRepository[AbstractOptimizationModelType] |
| 28 | + _model_type: type[FacadeOptimizationModelType] |
| 29 | + |
| 30 | + def __init__(self, _run: abstract.Run, **kwargs: Unpack["InitKwargs"]) -> None: |
| 31 | + super().__init__(**kwargs) |
| 32 | + self._run = _run |
| 33 | + |
| 34 | + |
| 35 | +class Creator( |
| 36 | + OptimizationBaseRepository[ |
| 37 | + FacadeOptimizationModelType, AbstractOptimizationModelType |
| 38 | + ], |
| 39 | + abstract.Creator, |
| 40 | +): |
| 41 | + def create( |
| 42 | + self, name: str, **kwargs: Unpack["abstract.optimization.base.CreateKwargs"] |
| 43 | + ) -> FacadeOptimizationModelType: |
| 44 | + model = self._backend_repository.create( |
| 45 | + run_id=self._run.id, name=name, **kwargs |
| 46 | + ) |
| 47 | + return self._model_type(_backend=self.backend, _model=model) |
| 48 | + |
| 49 | + |
| 50 | +class Retriever( |
| 51 | + OptimizationBaseRepository[ |
| 52 | + FacadeOptimizationModelType, AbstractOptimizationModelType |
| 53 | + ], |
| 54 | + abstract.Retriever, |
| 55 | +): |
| 56 | + def get(self, name: str) -> FacadeOptimizationModelType: |
| 57 | + model = self._backend_repository.get(run_id=self._run.id, name=name) |
| 58 | + return self._model_type(_backend=self.backend, _model=model) |
| 59 | + |
| 60 | + |
| 61 | +class Lister( |
| 62 | + OptimizationBaseRepository[ |
| 63 | + FacadeOptimizationModelType, AbstractOptimizationModelType |
| 64 | + ], |
| 65 | + abstract.Lister, |
| 66 | +): |
| 67 | + def list(self, name: str | None = None) -> list[FacadeOptimizationModelType]: |
| 68 | + models = self._backend_repository.list(run_id=self._run.id, name=name) |
| 69 | + return [self._model_type(_backend=self.backend, _model=m) for m in models] |
| 70 | + |
| 71 | + |
| 72 | +class Tabulator( |
| 73 | + OptimizationBaseRepository[ |
| 74 | + FacadeOptimizationModelType, AbstractOptimizationModelType |
| 75 | + ], |
| 76 | + abstract.Tabulator, |
| 77 | +): |
| 78 | + def tabulate(self, name: str | None = None) -> pd.DataFrame: |
| 79 | + return self._backend_repository.tabulate(run_id=self._run.id, name=name) |
| 80 | + |
| 81 | + |
| 82 | +class EnumerateKwargs(TypedDict, total=False): |
| 83 | + name: str | None |
| 84 | + |
| 85 | + |
| 86 | +class Enumerator( |
| 87 | + Lister[FacadeOptimizationModelType, AbstractOptimizationModelType], |
| 88 | + Tabulator[FacadeOptimizationModelType, AbstractOptimizationModelType], |
| 89 | + abstract.Enumerator, |
| 90 | +): |
| 91 | + def enumerate( |
| 92 | + self, table: bool = False, **kwargs: Unpack[EnumerateKwargs] |
| 93 | + ) -> list[FacadeOptimizationModelType] | pd.DataFrame: |
| 94 | + return self.tabulate(**kwargs) if table else self.list(**kwargs) |
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