Processing Module

The pyadps.processing module provides a six-step quality control pipeline for ADCP data. The primary interface is the ProcessedDataset class, which orchestrates the full pipeline while keeping the original dataset untouched.

Processing Workflow

Steps should be applied in this order. Any step can be skipped.

Step	Method	Purpose
1	`apply_time_axis()`	Snap drifted timestamps; fill time gaps
2	`apply_sensor_health()`	Check tilt, correct sound speed
3	`apply_signal_quality()`	Correlation, echo, error velocity, percent good
4	`apply_profile_operation()`	Trim ends, cut bins, regrid
5	`apply_velocity_check()`	Thresholds, magnetic correction, despike
6	`finalize()`	Return processed `xarray.Dataset` with metadata

Note

Profile operations (Step 4) must come after signal quality checks (Step 3). Regridding changes the cell structure and invalidates cell-based masks from earlier steps.

Quick Example

import pyadps
from pyadps.processing import ProcessedDataset

ds = pyadps.read('deployment.000')

result = (
    ProcessedDataset(ds)
    .apply_time_axis(snap=True, snap_freq='h')
    .apply_sensor_health(roll=True, roll_threshold=15.0)
    .apply_signal_quality(correlation=64, echo_intensity=40)
    .apply_profile_operation(cut_bins_side_lobe=True, water_depth=50.0)
    .apply_velocity_check(cutoff_u=2500, cutoff_v=2500, cutoff_w=500)
    .finalize()
)

result.to_netcdf('processed.nc')

Module Overview

Core

Module	Description
`core`	`ProcessedDataset` — orchestrates the full pipeline
`config`	`ProcessingConfig` — load/save processing settings as `config.ini`

Runner Classes

Each processing step delegates to a dedicated Runner class. You can access them directly via ProcessedDataset for advanced control, or use them standalone.

Module	Runner Class	Handles
`sensor_health`	`SensorHealthRunner`	Roll, pitch, sound speed correction
`signal_quality`	`SignalQualityRunner`	Correlation, echo, error velocity, percent good, false target
`profile_operation`	`ProfileOperationRunner`	Trim, side-lobe cut, manual cut, regrid
`velocity_check`	`VelocityCheckRunner`	Thresholds, magnetic declination, despike, flatline
`time_axis`	—	Time snapping and gap filling utilities

Automation and Batch Processing

Module	Key Function	Description
`autoprocess`	`autoprocess()`	Reprocess a file using a saved `config.ini`
`multifile`	`combine_file_list()`	Merge multiple binary files into one

Utilities

Module	Description
`utility`	`QCCheckStats`, `DataModificationStats`, `QCPipelineReport` — statistics dataclasses

Config-Based Processing

Processing settings can be saved and replayed via config.ini:

proc = ProcessedDataset(ds)
proc.apply_signal_quality(correlation=64, echo_intensity=40)

# Export settings after processing
proc.export_config('config.ini')

# Replay later
from pyadps.processing.autoprocess import autoprocess
result = autoprocess('config.ini', binary_file_path='deployment.000')