wootils

A grab-bag of Python utilities for oceanographic/scientific data analysis: harmonic-model fitting, rolling-window filtering of unevenly spaced data, spectral analysis, geodesic/UTM coordinate conversion, time-coordinate helpers, and plotting convenience functions built on numpy, scipy, and xarray.

Please see the examples for a few worked snippets.

Documentation

Documentation is available on read the docs.

Installation

pip

pip install wootils

To install a local development version

pip install -e ./

To install latest from github

pip install git+https://github.com/williamedge/wootils.git

Features

• Harmonic-model fitting: design-matrix construction and least-squares fit/predict for annual (and other) harmonics, in wootils.linalg.
• Rolling-window filtering of unevenly spaced xarray data (boxcar, Gaussian-weighted, Savitzky-Golay), via the .ufilt accessor in wootils.ufilters.
• Spectral analysis: Nyquist/fundamental frequency estimation, chunked/overlapped PSD estimation, and quadratic barotropic-component removal, in wootils.ofreqs/fft_utils.
• Geodesic distance and lon/lat → UTM coordinate conversion, in wootils.spatial.
• Time-coordinate helpers (regularity/gap checks, season labeling) in wootils.time_wutils.
• Bivariate (u/v velocity) colormap plotting and other matplotlib convenience helpers, in wootils.plotcolours/plotnice/plottimeseries.

Quick Usage

import numpy as np
from wootils.linalg import fit_harmonic_model, predict_harmonic_model

# Fit the first two annual harmonics to a time series and evaluate the fit
params = fit_harmonic_model(time_sec, data, n_harmonics=2, mean=True)
fitted = predict_harmonic_model(time_sec, params, mean=True)

See the examples page for rolling filters, UTM conversion, and frequency estimation.