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
xarraydata (boxcar, Gaussian-weighted, Savitzky-Golay), via the.ufiltaccessor inwootils.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.
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