autoemx.config.schema_models.particle_stats module

Particle size statistics stored in the sample ledger: the measurements (SampleLedger.size_measurements) and the statistics computed from frames under a segmentation (SampleLedger.size_results), with the working records of size bands and frames used to compute them (built from SampleLedger.frames, not stored).

autoemx.config.schema_models.particle_stats.ParticleSelection

found by a particle size statistics measurement (‘size_stats’, ledgers before frames were recorded), or measured by EDS with particle selection mode ‘auto’, ‘manual’ or ‘list’ (PowderMeasurementConfig.par_selection_mode).

Type:: How a particle was chosen

alias of Literal[‘size_stats’, ‘auto’, ‘manual’, ‘list’]

autoemx.config.schema_models.particle_stats.ParticleKey

(frame_id, index of the particle in the frame result)

Type:: A particle found by a segmentation of a frame

alias of Tuple[str, int]

class autoemx.config.schema_models.particle_stats.ParticleSizeStats(*, n_par_analysed: int, mean: float, stdev: float, median: float, max: float, min: float, D10: float, D25: float, D75: float, D90: float, D43: float | None = None, Dv10: float | None = None, Dv50: float | None = None, Dv90: float | None = None)[source]

Bases: BaseModel

Summary statistics of particle equivalent diameters, in µm (median is the D50).

D43 (volume-weighted mean, D[4,3]) and the volume-weighted percentiles Dv10, Dv50, Dv90 weight each particle by the volume of a sphere of its equivalent diameter, for comparison with laser diffraction (not computed before AutoEMX 1.6.0).

n_par_analysed: int

mean: float

stdev: float

median: float

max: float

min: float

D10: float

D25: float

D75: float

D90: float

D43: float | None

Dv10: float | None

Dv50: float | None

Dv90: float | None

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

classmethod validate_n_par(v: int) → int[source]

classmethod validate_finite(v: float | None) → float | None[source]

class autoemx.config.schema_models.particle_stats.ParticleStatsFrame(*, frame_id: str, center_mm: Coordinate2D | None = None, frame_width_um: float, pixel_size_um: float, area_um2: float, n_particles: int, particle_areas_um2: List[float] | None = None, particle_edge_corrections: List[float] | None = None, image_file: str | None = None, particle_marks_px: List[Tuple[int, int, int, int | None]] | None = None)[source]

Bases: BaseModel

Working record of a frame under one segmentation (built from SampleLedger.frames): center_mm (stage position of its centre, if known), frame width and pixel size, area_um2 (total area of the frame), the area and frame-edge correction of each particle valid for the statistics, image_file (in the SEM images folder of the sample) and particle_marks_px (x, y, radius in pixels, and index of the particle in the frame).

frame_id: str

center_mm: Coordinate2D | None

frame_width_um: float

pixel_size_um: float

area_um2: float

n_particles: int

particle_areas_um2: List[float] | None

particle_edge_corrections: List[float] | None

image_file: str | None

particle_marks_px: List[Tuple[int, int, int, int | None]] | None

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

validate_particle_lists() → ParticleStatsFrame[source]

classmethod validate_positive(v: float) → float[source]

classmethod validate_n_particles(v: int) → int[source]

class autoemx.config.schema_models.particle_stats.ParticleSizeBand(*, d_min_um: float, d_max_um: float, frame_width_um: float | None = None, n_frames: int, scanned_area_um2: float, frames: List[ParticleStatsFrame] = <factory>, number_density_per_mm2: float | None = None, n_eds_particles: int = 0)[source]

Bases: BaseModel

Working record of the frames of a size band of a measurement, or of frames searched by EDS with the same size range and frame width, under one segmentation: diameters searched, frame width, frames and scanned area, and (once weighted) number_density_per_mm2 (particles counted per mm²) and n_eds_particles (particles measured by EDS sharing the weight of the band, ledgers without frames searched by EDS).

d_min_um: float

d_max_um: float

frame_width_um: float | None

n_frames: int

scanned_area_um2: float

frames: List[ParticleStatsFrame]

number_density_per_mm2: float | None

n_eds_particles: int

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

classmethod validate_non_negative(v: float) → float[source]

classmethod validate_frame_width(v: float | None) → float | None[source]

validate_frames() → ParticleSizeBand[source]

property n_particles: int: Particles counted in the frames of the band.

add_frame(frame: ParticleStatsFrame) → None[source]: Record a frame analysed in the band.

frame_area_um2(frame_ids) → float[source]: Total area of the recorded frames of the band with the given ids.

class autoemx.config.schema_models.particle_stats.SizeBand(*, d_min_um: float, d_max_um: float, frame_width_um: float | None = None, frame_ids: List[str] = <factory>)[source]

Bases: BaseModel

Size band of a particle size statistics measurement: diameters searched, frame width, and its frames.

d_min_um: float

d_max_um: float

frame_width_um: float | None

frame_ids: List[str]

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

class autoemx.config.schema_models.particle_stats.SizeMeasurement(*, measurement_id: int, autoemx_version: str = '1.8.0', datetime: str = <factory>, status: Literal['in_progress', 'completed', 'interrupted']='in_progress', n_par_target: int, powder_meas_cfg: PowderMeasurementConfig, segmentation_id: int, count_eds_frames: bool = False, bands: List[SizeBand] = <factory>, precision_reached: bool | None = None)[source]

Bases: BaseModel

A particle size statistics measurement of the sample: its target, settings, size bands (largest particles first, with the frames acquired in each) and the segmentation used while acquiring (which decided when each band reached its target). status is ‘in_progress’ while acquiring (an interrupted measurement with the same target and settings is resumed), then ‘completed’, or ‘interrupted’ if a new measurement started instead of resuming it (its frames are kept). count_eds_frames: it continued an EDS acquisition, counting the frames searched by EDS.

measurement_id: int

autoemx_version: str

datetime: str

status: Literal['in_progress', 'completed', 'interrupted']

n_par_target: int

powder_meas_cfg: PowderMeasurementConfig

segmentation_id: int

count_eds_frames: bool

bands: List[SizeBand]

precision_reached: bool | None

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

property frame_ids: List[str]

class autoemx.config.schema_models.particle_stats.BandSummary(*, d_min_um: float, d_max_um: float, frame_width_um: float | None = None, n_frames: int, scanned_area_um2: float, n_particles: int, number_density_per_mm2: float | None = None, n_eds_particles: int = 0, frame_ids: List[str] = <factory>)[source]

Bases: BaseModel

Size band (or frames searched by EDS) as counted in a statistics result.

d_min_um: float

d_max_um: float

frame_width_um: float | None

n_frames: int

scanned_area_um2: float

n_particles: int

number_density_per_mm2: float | None

n_eds_particles: int

frame_ids: List[str]

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

class autoemx.config.schema_models.particle_stats.ParticleSizeStatsResult(*, result_id: int, autoemx_version: str = '1.8.0', datetime: str = <factory>, measurement_id: int | None = None, segmentation_id: int | None = None, eds_segmentation_id: int | None = None, n_par_target: int | None = None, powder_meas_cfg: PowderMeasurementConfig, n_par_by_selection: Dict[str, int]=<factory>, size_stats: ParticleSizeStats, unweighted_size_stats: ParticleSizeStats | None = None, n_par_without_weight: int = 0, confidence_intervals_95: Dict[str, ~typing.Tuple[float, float]]=<factory>, precision_reached: bool | None = None, bands: List[BandSummary] = <factory>, eds_scans: List[BandSummary] = <factory>, excluded_frame_ids: List[str] = <factory>, excluded_particles: Tuple[str, int]]=<factory>, overlapping_frame_ids: List[str] = <factory>, missing_frame_ids: List[str] = <factory>)[source]

Bases: BaseModel

Particle size statistics of the sample computed from frames under a segmentation: the frames of measurement measurement_id segmented with segmentation_id and, if eds_segmentation_id is set, the frames searched by EDS segmented with it (usually the same segmentation); frames without result of the segmentation are left out. For samples measured before frames searched by EDS were recorded, the particles measured by EDS with automatic selection (n_par_by_selection['auto']) share the weight of the band of their diameter.

size_stats are number-weighted: each particle counts its frame-edge correction over the area of the frames able to observe its size, so that each size counts in proportion to its number of particles per unit area of the sample; unweighted_size_stats treat all particles equally. confidence_intervals_95 are bootstrapped by frame. Frames and particles left out (excluded_frame_ids, excluded_particles as (frame_id, index)) are recorded; overlapping_frame_ids are left out because a larger frame counts the same sizes there. powder_meas_cfg are the settings of the measurement (of the EDS acquisition, without measurement).

result_id: int

autoemx_version: str

datetime: str

measurement_id: int | None

segmentation_id: int | None

eds_segmentation_id: int | None

n_par_target: int | None

powder_meas_cfg: PowderMeasurementConfig

n_par_by_selection: Dict[str, int]

size_stats: ParticleSizeStats

unweighted_size_stats: ParticleSizeStats | None

n_par_without_weight: int

confidence_intervals_95: Dict[str, Tuple[float, float]]

precision_reached: bool | None

bands: List[BandSummary]

eds_scans: List[BandSummary]

excluded_frame_ids: List[str]

excluded_particles: List[Tuple[str, int]]

overlapping_frame_ids: List[str]

missing_frame_ids: List[str]

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

inputs_key() → tuple[source]: What the result was computed from: a new result with the same inputs replaces it.