OutlierDetect

OutlierDetect is a Python package for local CTD-SRDL-style quality control, outlier detection, and constrained reconstruction of sparse ocean profiles.

It combines:

• TEOS-10 / GSW density calculations for the actual inference path,
• a Gaussian nuisance prior/posterior for local T/S correction,
• a small transformer model for profile-level and point-level QC,
• synthetic training data built from clean Argo or EN4 profiles,
• reconstruction plots and JSON sidecars for inspection.

The full documentation lives in Sphinx/MyST under docs/.

Install

pip install -e .

For training, prediction, Argo NetCDF I/O, and docs:

pip install -e ".[train,io,docs]"

Quickstart

Run the heuristic predictor on a profile you already have in memory:

import numpy as np
from outlierdetect import Heuristic, ProfileInput

profile = ProfileInput(
    pressure=np.array([5, 20, 45, 80, 130, 210, 330, 500], dtype=float),
    temperature=np.array([5.6, 5.4, 5.1, 4.8, 4.4, 3.8, 3.0, 2.3]),
    salinity=np.array([34.10, 34.12, 34.15, 34.20, 34.28, 34.40, 34.56, 34.70]),
    residual_t=np.array([0.12, 0.10, 0.11, 0.09, 0.12, 0.08, 0.10, 0.09]),
    residual_s=np.array([0.08, 0.08, 0.09, 0.08, 0.09, 0.08, 0.09, 0.08]),
    sigma_t=np.full(8, 0.25),
    sigma_s=np.full(8, 0.04),
    sigma_vert=np.full(8, 40.0),
    day_of_year=220,
    profile_id="example_profile",
)

result = Heuristic().predict(profile)
print(result.summary())

Train from Argo NetCDF data:

outlierdetect-train --config outlierdetect.toml --train-root C:\data\argo --test-root C:\data\argo_test

If you omit --test-root, the command uses --val-fraction to split the training root into train and validation subsets. When you do pass --test-root, the held-out side is built from raw TEMP/PSAL values where available and bypasses synthetic augmentation unless you also pass --test-augment. With --test-augment, the held-out side uses adjusted/corrected values and the same synthetic corruption pipeline as training.

Train from EN4 monthly NetCDF data:

outlierdetect-train --data-source en4 --train-root C:\data\en4 --config outlierdetect.toml

Predict on a dataset:

outlierdetect-predict --config outlierdetect.toml

Documentation

The Sphinx tree is the authoritative reference for the codebase:

• Project overview
• Physical model
• API reference

Build it locally with:

sphinx-build -b html docs docs/_build/html

Configuration

The starter config file is outlierdetect.toml. It covers data roots, checkpoint paths, sigma-vert loading for heave lookup, and the main training and prediction toggles.

CLI flags override TOML values, and each run writes the resolved configuration into its output directory.

You can manage a user-owned config file with:

outlierdetect config init
outlierdetect config show
outlierdetect config validate --config path\to\outlierdetect.toml