autoemx.config.schema_models.acquisition module

class autoemx.config.schema_models.acquisition.Coordinate2D(*, x: float, y: float)

Bases: BaseModel

Simple 2D coordinate pair.

x: float

y: float

model_config = {'extra': 'forbid'}

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

classmethod validate_coordinate(v: float) → float

class autoemx.config.schema_models.acquisition.SpotCoordinates(*, machine_coordinates: Coordinate2D | None = None, pixel_coordinates: Tuple[int, int] | None = None)

Bases: BaseModel

Acquisition spot coordinates within a frame image.

machine_coordinates are normalized frame-relative coordinates (as used by the EM API), not absolute stage positions in mm. They are derived from pixel_coordinates via frame_pixel_to_rel_coords.

machine_coordinates: Coordinate2D | None

pixel_coordinates: Tuple[int, int] | None

model_config = {'extra': 'forbid'}

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

classmethod validate_pixel_coordinates(v: Tuple[int, int] | None) → Tuple[int, int] | None

class autoemx.config.schema_models.acquisition.AcquisitionDetails(*, frame_id: str | None = None, particle_id: int | None = None, spot_coordinates: SpotCoordinates | None = None)

Bases: BaseModel

Structured acquisition context for one spectrum.

frame_id: str | None

particle_id: int | None

spot_coordinates: SpotCoordinates | None

model_config = {'extra': 'forbid'}

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

classmethod normalize_frame_id(v: str | None) → str | None

classmethod validate_particle_id(v: int | None) → int | None

class autoemx.config.schema_models.acquisition.ParticleInfo(*, id: int, area_um: float | None = None, eq_diameter_um: float | None = None, coordinates: Coordinate2D | None = None, frame_id: str | None = None, image_pixel_size_um: float | None = None, clusters: List[int] = <factory>, composition: str | None = None, selection: Literal['size_stats', 'auto', 'manual', 'list'] | None=None, edge_correction: float | None = None)

Bases: BaseModel

Geometry and analysis summary for one segmented particle.

coordinates are absolute stage positions in mm of the particle center. They are not the frame-relative SpotCoordinates.machine_coordinates. image_pixel_size_um is the pixel size of the particle image saved with the spectrum spots (SEM images/<sample>_par<id>_fr<frame>_xyspots), in which the SpotCoordinates.pixel_coordinates of its spectra are given. selection is how the particle was chosen: found by a particle size statistics measurement (‘size_stats’), or measured by EDS with particle selection mode ‘auto’, ‘manual’ or ‘list’ (None in ledgers saved before AutoEMX recorded it). edge_correction is the Miles-Lantuéjoul correction of a particle found by particle size statistics: the inverse of the fraction of frame positions where it would not touch the frame edge (particles touching it are left out), by which its weight is multiplied so that large particles are not under-counted.

id: int

area_um: float | None

eq_diameter_um: float | None

coordinates: Coordinate2D | None

frame_id: str | None

image_pixel_size_um: float | None

clusters: List[int]

composition: str | None

selection: Literal['size_stats', 'auto', 'manual', 'list'] | None

edge_correction: float | None

model_config = {'extra': 'forbid'}

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

classmethod validate_id(v: int) → int

classmethod validate_positive_finite(v: float | None) → float | None

classmethod normalize_frame_id(v: str | None) → str | None

classmethod validate_edge_correction(v: float | None) → float | None

class autoemx.config.schema_models.acquisition.SpectrumEntry(*, spectrum_id: str | None = None, total_counts: int | None = None, live_acquisition_time: float, acquisition_details: AcquisitionDetails | None = None, spectrum_relpath: str | None = None, instrument_background_relpath: str | None = None, quantification_results: List[QuantificationResult] = <factory>)

Bases: BaseModel

Minimal per-spectrum record inside a sample ledger.

spectrum_id: str | None

total_counts: int | None

live_acquisition_time: float

acquisition_details: AcquisitionDetails | None

spectrum_relpath: str | None

instrument_background_relpath: str | None

quantification_results: List[QuantificationResult]

model_config = {'extra': 'forbid'}

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

classmethod drop_legacy_fields(data: Any) → Any

Drop removed legacy fields so old ledger JSON remains loadable.

classmethod validate_live_acquisition_time(v: float) → float

classmethod validate_total_counts(v: int | None) → int | None

classmethod validate_spectrum_relpath(v: str | None) → str | None

classmethod validate_instrument_background_relpath(v: str | None) → str | None

validate_total_and_background() → SpectrumEntry