torchcell.metabolism.constraints.ThermoTable

class ThermoTable(*, met_delta_g: ~torch.Tensor, met_mask: ~torch.Tensor, rxn_delta_g: ~torch.Tensor, rxn_mask: ~torch.Tensor, met_coverage: ~torchcell.metabolism.constraints.TableCoverage, rxn_coverage: ~torchcell.metabolism.constraints.TableCoverage, source_paths: dict[str, str], sha256: dict[str, str], sentinel: float = 10000000.0, units: str = 'kJ/mol', temperature_k: float = 303.15, retrieved_at: ~datetime.datetime = <factory>, notes: str = "delta_f G'^circ values are the TRANSFORMED standard energies shipped with the GEM; no further pH / ionic-strength Legendre transform is applied here, and no component-contribution covariance is available from this source.")[source]

Bases: BaseModel

Standard transformed Gibbs energies for one GEM, with provenance.

The stored CSV plus its sha256 is canonical; source_path is retrieval metadata. sentinel is recorded explicitly so a future reader cannot mistake the convention.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].