utility

Shared dataclasses returned by all Runner classes when you call get_statistics() or get_modifications(). Import them only if you need to type-hint or inspect these objects programmatically.

from pyadps.processing.utility import QCCheckStats, DataModificationStats, QCPipelineReport

QCCheckStats

Returned in the dict from runner.get_statistics(), keyed by check name.

Attribute	Type	Description
`check_name`	str	Name of the check (e.g. `'Correlation Check'`)
`threshold`	float / list / tuple	Threshold value(s) used
`cells_pre_masked`	int	Cells already masked before this check
`cells_newly_masked`	int	Cells flagged by this check
`cells_total_masked`	int	Cumulative masked cells after this check
`total_cells`	int	Total cells in the dataset
`pre_masked_pct`	float	Percent masked before this check
`newly_masked_pct`	float	Percent flagged by this check (impact)
`total_masked_pct`	float	Cumulative percent masked
`valid_cells`	int	Cells still valid after this check
`valid_pct`	float	Percent still valid
`to_dict()`	dict	JSON-serializable representation

stats = runner.get_statistics()
for name, s in stats.items():
    print(f"{name}: {s.newly_masked_pct:.1f}% flagged, {s.valid_pct:.1f}% valid")

DataModificationStats

Returned in the dict from runner.get_modifications(), keyed by variable name.

Attribute	Type	Description
`operation`	str	Operation name (e.g. `'replace_data'`, `'correct_sound_speed'`)
`variable_name`	str	Dataset variable that was modified
`original_stats`	dict	`min`, `max`, `mean`, `std` of data before modification
`modified_stats`	dict	`min`, `max`, `mean`, `std` of data after modification
`mean_change`	float \| None	Absolute change in mean
`mean_change_pct`	float \| None	Percentage change in mean
`to_dict()`	dict	JSON-serializable representation

QCPipelineReport

Returned by runner.get_pipeline_report(). Aggregates all checks and modifications for one processing stage. Used internally by ProcessedDataset to build the full-pipeline summary.

Attribute	Type	Description
`module_name`	str	Processing stage name
`baseline_masked`	int	Cells masked before any checks in this stage
`total_cells`	int	Total cells in the dataset
`checks`	list[QCCheckStats]	Ordered list of per-check statistics
`modifications`	list[DataModificationStats]	Data modification records
`final_valid_pct`	float	Percent valid after all checks in this stage
`pipeline_impact_pct`	float	Additional masking caused by this stage
`to_dict()`	dict	JSON-serializable representation

See Also

sensor_health — get_statistics() and get_modifications()
signal_quality — get_statistics()
profile_operation — get_statistics()
velocity_check — get_statistics() and get_modifications()
core — ProcessedDataset.print_summary()