torchcell.metabolism.parameters.ParameterTable

class ParameterTable(*, values: Tensor, provenance: list[ParameterProvenance], known_mask: Tensor, experimental_mask: Tensor, unit: str, coverage: TableCoverage, experimental_coverage: TableCoverage, notes: str = '')

Bases: BaseModel

One resolved parameter per entity, with a per-entity provenance tag.

The provenance array is the point of this class. A capacity constraint where 95 % of turnover numbers are an organism default is a uniform rescaling of the flux box, not an enzyme constraint, and the two are indistinguishable from a loss curve.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod build(values: Tensor, provenance: list[ParameterProvenance], unit: str, notes: str = '') → ParameterTable

Derive both masks and both coverages from the provenance list.