Pipeline Architecture: Lab vs Simulation

The package provides two distinct processing pipelines designed for different stages of the research workflow.

Lab Mode (Post-Facto)

High-Precision Analysis & Validation

The Lab Mode pipeline is designed for batch analysis where the complete dataset is available. It aims for parity with the legacy R implementation.

Characteristics

Memory: Full dataset loaded into memory.
Processing: Acausal algorithms (e.g., filtfilt, centered moving averages).
Calibration: Batch computation from the full dataset.
Depth: Acausal interpolation of missing values.

Configuration

To enable Lab Mode, set the following in your YAML config:

pipeline:
  mode: "LAB"
  calibration:
    attachment_angle: "batch_compute"
    magnetometer: "batch_compute"
  depth:
    mode: "interpolate"

Simulation Mode (Real-Time)

On-Tag Algorithm Development

The Simulation Mode pipeline is designed for real-time processing where data arrives sample-by-sample. It uses fully causal algorithms with no lookahead.

Characteristics

Memory: Fixed memory footprint (O(1)).
Processing: Causal algorithms (e.g., lfilter, EMA).
Calibration: Online adaptive calibration or fixed parameters.
Depth: Real-time estimation (hold last value or predict).

Configuration

To enable Simulation Mode, set the following in your YAML config:

pipeline:
  mode: "SIMULATION"
  calibration:
    attachment_angle: "fixed"  # or "progressive" (future)
    magnetometer: "fixed"
  depth:
    mode: "realtime"