snapred.backend.dao.normalization package

Submodules

snapred.backend.dao.normalization.Normalization module

class snapred.backend.dao.normalization.Normalization.Normalization(*, version: int | ~snapred.backend.dao.indexing.Versioning.VersionState, indexEntry: ~snapred.backend.dao.indexing.IndexEntry.IndexEntry, instrumentState: ~snapred.backend.dao.state.InstrumentState.InstrumentState, seedRun: str, useLiteMode: bool | ~numpy.bool, creationDate: float = <factory>, name: str, **extra_data: ~typing.Any)

Bases: CalculationParameters

This class represents a normalization opject with essential attributes to track its origin, application, and metadata. It is designed to work within a system that requires understanding of the instrument state, facilitating data normalization processes in a structured and version-controlled manner.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'allow', 'strict': True, 'validate_assignment': True}

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

snapred.backend.dao.normalization.NormalizationRecord module

class snapred.backend.dao.normalization.NormalizationRecord.NormalizationRecord(*, version: int | ~snapred.backend.dao.indexing.Versioning.VersionState, indexEntry: ~snapred.backend.dao.indexing.IndexEntry.IndexEntry, runNumber: str, useLiteMode: bool, calculationParameters: ~snapred.backend.dao.normalization.Normalization.Normalization, hooks: ~typing.Dict[str, ~typing.List[~snapred.backend.dao.Hook.Hook]] | None = None, snapredVersion: str = <factory>, snapwrapVersion: str | None = <factory>, backgroundRunNumber: str, smoothingParameter: float, workspaceNames: ~typing.List[~snapred.meta.mantid.WorkspaceNameGenerator.WorkspaceName] = [], calibrationVersionUsed: int | ~snapred.backend.dao.indexing.Versioning.VersionState = <factory>, crystalDBounds: ~snapred.backend.dao.Limit.Limit[float], normalizationCalibrantSamplePath: str)

Bases: Record

This class is crucial for tracking the specifics of each normalization step, facilitating reproducibility and data management within scientific workflows. It serves as a comprehensive record of the parameters and context of normalization operations, contributing to the integrity and utility of the resulting data.

backgroundRunNumber: str

calculationParameters: Normalization

calibrationVersionUsed: int | VersionState

crystalDBounds: Limit[float]

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'ignore', 'strict': True, 'validate_assignment': True}

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

normalizationCalibrantSamplePath: str

smoothingParameter: float

classmethod validate_backgroundRunNumber(v: Any) → Any

classmethod version_is_integer(v: Any) → Any

workspaceNames: List[WorkspaceName]

Module contents