snapred.backend.dao.response package

Submodules

snapred.backend.dao.response.CalibrationAssessmentResponse module

class snapred.backend.dao.response.CalibrationAssessmentResponse.CalibrationAssessmentResponse(*, version: int | VersionState = VersionState.NEXT, calculationParameters: Calibration, crystalInfo: CrystallographicInfo, pixelGroups: List[PixelGroup] | None = None, focusGroupCalibrationMetrics: FocusGroupMetric, workspaces: WorkspaceName]], metricWorkspaces: List[str])

Bases: BaseModel

The CalibrationAssessmentResponse class serves as a response model specifically designed for summarizing the outcomes of calibration assessments. It incorporates a CalibrationRecord to detail the calibration performed and includes a list of metricWorkspaces, which are strings identifying the workspaces where the calibration metrics are stored.

calculationParameters: Calibration

crystalInfo: CrystallographicInfo

focusGroupCalibrationMetrics: FocusGroupMetric

metricWorkspaces: List[str]

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

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

pixelGroups: List[PixelGroup] | None

version: int | VersionState

workspaces: WorkspaceName]]

snapred.backend.dao.response.NormalizationResponse module

class snapred.backend.dao.response.NormalizationResponse.NormalizationResponse(*, correctedVanadium: str, focusedVanadium: str, smoothedVanadium: str, detectorPeaks: List[GroupPeakList], calibrationRunNumber: str | None = None)

Bases: BaseModel

This class serves as a structured representation of the outcomes from a vanadium-based normalization procedure, encapsulating the various states of vanadium data (corrected, focused, and smoothed) alongside detected peaks across detectors. It provides a comprehensive overview of the results, facilitating further analysis or reporting within scientific workflows. The detailed encapsulation of each state of vanadium data and the collected peak lists make this class an invaluable asset for post-normalization process evaluation and decision-making.

calibrationRunNumber: str | None

correctedVanadium: str

detectorPeaks: List[GroupPeakList]

focusedVanadium: str

model_config: ClassVar[ConfigDict] = {}

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

smoothedVanadium: str

Module contents