wavespectra

Python library for ocean wave spectral data analysis and processing

Wavespectra is a powerful, open-source Python library built on top of xarray for working with ocean wave spectral data. It provides comprehensive tools for reading, analysing, manipulating, and visualising wave spectra from various sources including numerical models and buoy observations.

Key Features

Unified Data Model: Built on xarray with standardised conventions for wave spectral data
Extensive I/O Support: Read/write 15+ formats including WW3, SWAN, ERA5, NDBC, and more
Rich Analysis Tools: 60+ methods for wave parameter calculation and spectral transformations
Spectral Partitioning: Separate wind sea and swell using multiple algorithms (PTM1-PTM5, HP01, wave age)
Spectral Construction: Create synthetic spectra using parametric forms (JONSWAP, TMA, Gaussian, Pierson-Moskowitz)
Flexible Visualisation: Polar spectral plots with matplotlib integration
High Performance: Leverages dask for efficient processing of large datasets
Extensible: Plugin architecture for custom readers and analysis methods

Quick Start

Installation

Install from PyPI:

# Basic installation
$ pip install wavespectra

# Full installation with all optional dependencies
$ pip install wavespectra[extra]

Or from conda-forge:

$ conda install -c conda-forge wavespectra

Basic Usage

import xarray as xr
from wavespectra import read_swan

# Read wave spectra from various formats
dset = read_swan("spectra.spec")  # SWAN format
# dset = xr.open_dataset("era5.nc", engine="era5")  # ERA5 reanalysis
# dset = xr.open_dataset("ww3.nc", engine="ww3")    # WAVEWATCH III

# Calculate wave parameters
hs = dset.spec.hs()          # Significant wave height
tp = dset.spec.tp()          # Peak period
dm = dset.spec.dm()          # Mean direction
dspr = dset.spec.dspr()      # Directional spreading

# Multiple parameters at once
stats = dset.spec.stats(["hs", "tp", "dm", "dspr"])

# Spectral transformations
spectrum_1d = dset.spec.oned()                    # Convert to 1D
subset = dset.spec.split(fmin=0.05, fmax=0.5)     # Frequency subset
rotated = dset.spec.rotate(angle=15)              # Rotate directions
interpolated = dset.spec.interp(freq=new_freq)    # Interpolate

# Visualisation
dset.spec.plot(kind="contourf", figsize=(8, 6))   # Polar plot

Working with Different Data Sources

# Numerical model outputs
ww3_data = xr.open_dataset("ww3_output.nc", engine="ww3")
swan_data = read_swan("swan_output.swn")
era5_data = xr.open_dataset("era5_waves.nc", engine="era5")

# Buoy observations
ndbc_data = xr.open_dataset("ndbc_data.nc", engine="ndbc")
triaxys_data = xr.open_dataset("triaxys.nc", engine="triaxys")

# All use the same analysis interface
for dataset in [ww3_data, swan_data, era5_data]:
    hs = dataset.spec.hs()
    tp = dataset.spec.tp()

Advanced Analysis

Spectral Partitioning

Separate spectra into wind sea and swell components using various methods:

# PTM1: Watershed partitioning with wind sea identification
partitions = dset.spec.partition.ptm1(
    wspd=dset.wspd, wdir=dset.wdir, dpt=dset.dpt, swells=2
)

# PTM3: Simple ordering by wave height (no wind/depth needed)
partitions = dset.spec.partition.ptm3(parts=3)

# PTM4: Wave age criterion to separate wind sea from swell
partitions = dset.spec.partition.ptm4(
    wspd=dset.wspd, wdir=dset.wdir, dpt=dset.dpt, agefac=1.7
)

# PTM1_TRACK: Track partitions from unique wave systems over time
# Useful for following the evolution of individual swell events
partitions = dset.spec.partition.ptm1_track(
    wspd=dset.wspd, wdir=dset.wdir, dpt=dset.dpt, swells=2
)

Spectral Construction

Create synthetic spectra from parametric forms:

import numpy as np

from wavespectra.construct.frequency import jonswap, tma, gaussian
from wavespectra.construct.direction import cartwright
from wavespectra.construct import construct_partition

# Create JONSWAP spectrum for developing seas
freq = np.arange(0.03, 0.4, 0.01)
spectrum = jonswap(freq=freq, hs=2.5, fp=0.1, gamma=3.3)

# Create TMA spectrum for finite depth
spectrum_shallow = tma(freq=freq, hs=2.0, fp=0.1, dep=15)

# Create 2D spectrum by combining frequency and directional components
dir = np.arange(0, 360, 10)
spectrum_2d = jonswap(freq=freq, hs=2.5, fp=0.1) * cartwright(dir=dir, dm=270, dspr=30)

# Or use construct_partition for a complete 2D spectrum
spectrum_2d = construct_partition(
    freq_name="jonswap",
    dir_name="cartwright",
    freq_kwargs={"freq": freq, "hs": 2.5, "fp": 0.1, "gamma": 3.3},
    dir_kwargs={"dir": dir, "dm": 270, "dspr": 30}
)

Spectral Fitting

# Fit parametric forms to existing spectra
jonswap_params = dset.spec.fit_jonswap()          # Fit JONSWAP spectrum

Wave Physics

# Calculate wave physics parameters
celerity = dset.spec.celerity(depth=50)           # Wave speed
wavelength = dset.spec.wavelen(depth=50)          # Wavelength
stokes_drift = dset.spec.uss()                    # Stokes drift

Data Requirements

Wavespectra expects xarray objects with specific coordinate and variable naming:

Required coordinates:

freq: Wave frequency in Hz
dir: Wave direction in degrees (for 2D spectra)

Required variables:

efth: Wave energy density in m²/Hz/degree (2D) or m²/Hz (1D)

Optional variables:

wspd: Wind speed in m/s
wdir: Wind direction in degrees
dpt: Water depth in metres

Supported Formats

Input and Output Formats

Wave Models: WAVEWATCH III, SWAN, WWM, FUNWAVE, OrcaFlex
Reanalysis: ERA5, ERA-Interim, ECMWF
Observations: NDBC, TRIAXYS, Spotter, Datawell, Obscape, AWAC, Octopus
Generic: NetCDF, Zarr, JSON

Documentation

Full documentation is available at wavespectra.readthedocs.io

Development

Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Installation

$ git clone https://github.com/wavespectra/wavespectra.git
$ cd wavespectra
$ pip install -e .[extra,test,docs]

Running Tests

$ pytest tests

Building Documentation

$ make docs

Citation

If you use wavespectra in your research, please cite:

@software{wavespectra,
  author = {Guedes, Rafael and Durrant, Tom and de Bruin, Ruben and Perez, Jorge and Iannucci, Matthew and Delaux, Sebastien and Harrington, John and others},
  title = {wavespectra: Python library for ocean wave spectral data},
  url = {https://github.com/wavespectra/wavespectra},
  doi = {10.5281/zenodo.15238968}
}

Licence

This project is licenced under the MIT Licence - see the LICENSE file for details.

Support

Documentation: wavespectra.readthedocs.io
Issues: GitHub Issues
Discussions: GitHub Discussions

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Release history Release notifications | RSS feed

This release

4.8.0

Jul 22, 2026

4.7.0

Jul 9, 2026

4.6.0

Jul 8, 2026

4.5.1

Jun 27, 2026

4.5.0

Jun 16, 2026

4.4.3

Jun 2, 2026

4.4.2

Oct 30, 2025

4.4.1

Apr 18, 2025

4.4.0

Mar 25, 2025

4.3.0

Dec 20, 2024

4.2.1

Nov 25, 2024

4.2.0

Nov 13, 2024

4.1.1

Sep 18, 2024

4.1.0

Sep 9, 2024

4.0.3

Jun 19, 2024

4.0.2

Jun 18, 2024

4.0.1

Jun 14, 2024

4.0.0 yanked

Jun 14, 2024

Reason this release was yanked:

Wrong path for C data files

3.19.0

May 2, 2024

3.18.0

Jan 31, 2024

3.17.0

Jan 25, 2024

3.16.0

Dec 13, 2023

3.15.2

Dec 1, 2023

3.15.1

Aug 29, 2023

3.15.0

Aug 14, 2023

3.14.0

Jul 3, 2023

3.13.0

Jan 9, 2023

3.12.1

Oct 27, 2022

3.12.0

Aug 19, 2022

3.11.0

May 3, 2022

3.10.0

Aug 9, 2021

3.9.0

May 29, 2021

3.8.1

Apr 6, 2021

3.8.0

Mar 30, 2021

3.7.2

Feb 12, 2021

3.7.1

Aug 26, 2020

3.7.0

Jul 16, 2020

3.6.5

Jul 10, 2020

3.6.4

Jun 29, 2020

3.6.3

Jun 28, 2020

3.6.2

Jun 28, 2020

3.6.1

Jun 28, 2020

3.6.0

Jun 27, 2020

3.5.3

Apr 14, 2020

3.5.2

Mar 11, 2020

3.5.1

Dec 12, 2019

3.5.0

Dec 9, 2019

3.4

Mar 28, 2019

3.3.1

Mar 19, 2019

3.3.0

Feb 21, 2019

3.2.5

Jan 24, 2019

3.2.4

Jan 23, 2019

3.2.3

Dec 19, 2018

3.2.2

Dec 4, 2018

3.2.1

Nov 14, 2018

3.2.0

Nov 4, 2018

3.1.4

Aug 29, 2018

0.2.3

Jul 28, 2018

0.2.2

Jul 5, 2018

0.2.1

May 17, 2018

0.2.0

May 9, 2018

0.1.3

May 3, 2018

0.1.2

Apr 29, 2018

0.1.1

Apr 16, 2018

0.1.0

Apr 1, 2018

wavespectra 4.8.0

Key Features

Quick Start

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Working with Different Data Sources

Advanced Analysis

Spectral Partitioning

Spectral Construction

Spectral Fitting

Wave Physics

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