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and Currents","monthly_downloads":2274,"total_dependent_repos":6,"total_dependent_packages":3,"readme":"===========\nwavespectra\n===========\n\n.. image:: https://deepwiki.com/badge.svg\n   :target: https://deepwiki.com/wavespectra/wavespectra\n   :alt: Ask DeepWiki\n\n.. image:: https://zenodo.org/badge/205463939.svg\n   :target: https://zenodo.org/badge/latestdoi/205463939\n   :alt: DOI\n\n.. image:: https://img.shields.io/github/actions/workflow/status/wavespectra/wavespectra/python-publish.yml\n   :target: https://github.com/wavespectra/wavespectra/actions\n   :alt: Build Status\n\n.. image:: https://coveralls.io/repos/github/wavespectra/wavespectra/badge.svg?branch=master\n   :target: https://coveralls.io/github/wavespectra/wavespectra?branch=master\n   :alt: Coverage\n\n.. image:: https://readthedocs.org/projects/wavespectra/badge/?version=latest\n   :target: https://wavespectra.readthedocs.io/en/latest/\n   :alt: Documentation\n\n.. image:: https://img.shields.io/pypi/v/wavespectra.svg\n   :target: https://pypi.org/project/wavespectra/\n   :alt: PyPI\n\n.. image:: https://img.shields.io/pypi/dm/wavespectra\n   :target: https://pypistats.org/packages/wavespectra\n   :alt: Downloads\n\n.. image:: https://anaconda.org/conda-forge/wavespectra/badges/version.svg\n   :target: https://anaconda.org/conda-forge/wavespectra\n   :alt: Conda\n\n.. image:: https://img.shields.io/pypi/pyversions/wavespectra\n   :target: https://pypi.org/project/wavespectra/\n   :alt: Python\n\n**Python library for ocean wave spectral data analysis and processing**\n\nWavespectra 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.\n\n.. _xarray: https://xarray.pydata.org/\n\nKey Features\n============\n\n- **Unified Data Model**: Built on xarray with standardised conventions for wave spectral data\n- **Extensive I/O Support**: Read/write 15+ formats including WW3, SWAN, ERA5, NDBC, and more\n- **Rich Analysis Tools**: 60+ methods for wave parameter calculation and spectral transformations\n- **Spectral Partitioning**: Separate wind sea and swell using multiple algorithms (PTM1-5, watershed, wave age)\n- **Spectral Construction**: Create synthetic spectra using parametric forms (JONSWAP, TMA, Gaussian, Pierson-Moskowitz)\n- **Flexible Visualisation**: Polar spectral plots with matplotlib integration\n- **High Performance**: Leverages dask for efficient processing of large datasets\n- **Extensible**: Plugin architecture for custom readers and analysis methods\n\nQuick Start\n===========\n\nInstallation\n------------\n\nInstall from PyPI:\n\n.. code-block:: console\n\n   # Basic installation\n   $ pip install wavespectra\n\n   # Full installation with all optional dependencies\n   $ pip install wavespectra[extra]\n\nOr from conda-forge:\n\n.. code-block:: console\n\n   $ conda install -c conda-forge wavespectra\n\nBasic Usage\n-----------\n\n.. code-block:: python\n\n   import xarray as xr\n   from wavespectra import read_swan\n\n   # Read wave spectra from various formats\n   dset = read_swan(\"spectra.swn\")  # SWAN format\n   # dset = xr.open_dataset(\"era5.nc\", engine=\"era5\")  # ERA5 reanalysis\n   # dset = xr.open_dataset(\"ww3.nc\", engine=\"ww3\")    # WAVEWATCH III\n\n   # Calculate wave parameters\n   hs = dset.spec.hs()          # Significant wave height\n   tp = dset.spec.tp()          # Peak period\n   dm = dset.spec.dm()          # Mean direction\n   dspr = dset.spec.dspr()      # Directional spreading\n\n   # Multiple parameters at once\n   stats = dset.spec.stats([\"hs\", \"tp\", \"dm\", \"dspr\"])\n\n   # Spectral transformations\n   spectrum_1d = dset.spec.oned()                    # Convert to 1D\n   subset = dset.spec.split(fmin=0.05, fmax=0.5)     # Frequency subset\n   rotated = dset.spec.rotate(angle=15)              # Rotate directions\n   interpolated = dset.spec.interp(freq=new_freq)    # Interpolate\n\n   # Visualisation\n   dset.spec.plot(kind=\"contourf\", figsize=(8, 6))   # Polar plot\n\nWorking with Different Data Sources\n-----------------------------------\n\n.. code-block:: python\n\n   # Numerical model outputs\n   ww3_data = xr.open_dataset(\"ww3_output.nc\", engine=\"ww3\")\n   swan_data = read_swan(\"swan_output.swn\")\n   era5_data = xr.open_dataset(\"era5_waves.nc\", engine=\"era5\")\n\n   # Buoy observations\n   ndbc_data = xr.open_dataset(\"ndbc_data.nc\", engine=\"ndbc\")\n   triaxys_data = xr.open_dataset(\"triaxys.nc\", engine=\"triaxys\")\n\n   # All use the same analysis interface\n   for dataset in [ww3_data, swan_data, era5_data]:\n       hs = dataset.spec.hs()\n       tp = dataset.spec.tp()\n\nAdvanced Analysis\n-----------------\n\nSpectral Partitioning\n~~~~~~~~~~~~~~~~~~~~~\n\nSeparate spectra into wind sea and swell components using various methods:\n\n.. code-block:: python\n\n   # PTM1: Watershed partitioning with wind sea identification\n   partitions = dset.spec.partition.ptm1(\n       wspd=dset.wspd, wdir=dset.wdir, dpt=dset.dpt, swells=2\n   )\n   \n   # PTM3: Simple ordering by wave height (no wind/depth needed)\n   partitions = dset.spec.partition.ptm3(parts=3)\n   \n   # PTM4: Wave age criterion to separate wind sea from swell\n   partitions = dset.spec.partition.ptm4(\n       wspd=dset.wspd, wdir=dset.wdir, dpt=dset.dpt, agefac=1.7\n   )\n   \n   # PTM1_TRACK: Track partitions from unique wave systems over time\n   # Useful for following the evolution of individual swell events\n   partitions = dset.spec.partition.ptm1_track(\n       wspd=dset.wspd, wdir=dset.wdir, dpt=dset.dpt, swells=2\n   )\n\nSpectral Construction\n~~~~~~~~~~~~~~~~~~~~~\n\nCreate synthetic spectra from parametric forms:\n\n.. code-block:: python\n\n   from wavespectra.construct.frequency import jonswap, tma, gaussian\n   from wavespectra.construct.direction import cartwright\n   from wavespectra.construct import construct_partition\n   \n   # Create JONSWAP spectrum for developing seas\n   freq = np.arange(0.03, 0.4, 0.01)\n   spectrum = jonswap(freq=freq, hs=2.5, fp=0.1, gamma=3.3)\n   \n   # Create TMA spectrum for finite depth\n   spectrum_shallow = tma(freq=freq, hs=2.0, fp=0.1, dep=15)\n   \n   # Create 2D spectrum by combining frequency and directional components\n   dir = np.arange(0, 360, 10)\n   spectrum_2d = jonswap(freq=freq, hs=2.5, fp=0.1) * cartwright(dir=dir, dm=270, dspr=30)\n   \n   # Or use construct_partition for a complete 2D spectrum\n   spectrum_2d = construct_partition(\n       freq_name=\"jonswap\",\n       dir_name=\"cartwright\",\n       freq_kwargs={\"freq\": freq, \"hs\": 2.5, \"fp\": 0.1, \"gamma\": 3.3},\n       dir_kwargs={\"dir\": dir, \"dm\": 270, \"dspr\": 30}\n   )\n\nSpectral Fitting\n~~~~~~~~~~~~~~~~\n\n.. code-block:: python\n\n   # Fit parametric forms to existing spectra\n   jonswap_params = dset.spec.fit_jonswap()          # Fit JONSWAP spectrum\n\nWave Physics\n~~~~~~~~~~~~\n\n.. code-block:: python\n\n   # Calculate wave physics parameters\n   celerity = dset.spec.celerity(depth=50)           # Wave speed\n   wavelength = dset.spec.wavelen(depth=50)          # Wavelength\n   stokes_drift = dset.spec.uss()                    # Stokes drift\n\n\nData Requirements\n=================\n\nWavespectra expects xarray objects with specific coordinate and variable naming:\n\n**Required coordinates:**\n\n- ``freq``: Wave frequency in Hz\n- ``dir``: Wave direction in degrees (for 2D spectra)\n\n**Required variables:**\n\n- ``efth``: Wave energy density in m²/Hz/degree (2D) or m²/Hz (1D)\n\n**Optional variables:**\n\n- ``wspd``: Wind speed in m/s\n- ``wdir``: Wind direction in degrees\n- ``dpt``: Water depth in metres\n\nSupported Formats\n=================\n\nInput and Output Formats\n------------------------\n\n- **Wave Models**: WAVEWATCH III, SWAN, WWM, FUNWAVE, OrcaFlex\n- **Reanalysis**: ERA5, ERA-Interim, ECMWF\n- **Observations**: NDBC, TRIAXYS, Spotter, Octopus, AWAC\n- **Generic**: NetCDF, JSON, CSV\n\nDocumentation\n=============\n\nFull documentation is available at `wavespectra.readthedocs.io`_\n\n- `Installation Guide`_\n- `Quick Start Tutorial`_\n- `Spectral Construction`_\n- `API Reference`_\n- `Example Gallery`_\n\n.. _wavespectra.readthedocs.io: https://wavespectra.readthedocs.io/en/latest/\n.. _Installation Guide: https://wavespectra.readthedocs.io/en/latest/install.html\n.. _Quick Start Tutorial: https://wavespectra.readthedocs.io/en/latest/quickstart.html\n.. _Spectral Construction: https://wavespectra.readthedocs.io/en/latest/construction.html\n.. _API Reference: https://wavespectra.readthedocs.io/en/latest/api.html\n.. _Example Gallery: https://wavespectra.readthedocs.io/en/latest/gallery.html\n\nDevelopment\n===========\n\nContributing\n------------\n\nWe welcome contributions! Please see our `Contributing Guide`_ for details.\n\n.. _Contributing Guide: https://wavespectra.readthedocs.io/en/latest/contributing.html\n\nDevelopment Installation\n------------------------\n\n.. code-block:: console\n\n   $ git clone https://github.com/wavespectra/wavespectra.git\n   $ cd wavespectra\n   $ pip install -e .[extra,test,docs]\n\nRunning Tests\n-------------\n\n.. code-block:: console\n\n   $ pytest tests\n\nBuilding Documentation\n----------------------\n\n.. code-block:: console\n\n   $ make docs\n\nCitation\n========\n\nIf you use wavespectra in your research, please cite:\n\n.. code-block:: bibtex\n\n   @software{wavespectra,\n     author = {Guedes, Rafael and Durrant, Tom and de Bruin, Ruben and Perez, Jorge and Iannucci, Matthew and Delaux, Sebastien and Harrington, John and others},\n     title = {wavespectra: Python library for ocean wave spectral data},\n     url = {https://github.com/wavespectra/wavespectra},\n     doi = {10.5281/zenodo.15238968}\n   }\n\nLicence\n=======\n\nThis project is licenced under the MIT Licence - see the `LICENSE`_ file for details.\n\n.. _LICENSE: LICENSE.txt\n\nSupport\n=======\n\n- **Documentation**: `wavespectra.readthedocs.io`_\n- **Issues**: `GitHub Issues`_\n- **Discussions**: `GitHub Discussions`_\n\n.. _GitHub Issues: https://github.com/wavespectra/wavespectra/issues\n.. _GitHub Discussions: https://github.com/wavespectra/wavespectra/discussions\n","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["mesh","vtk"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/16884","html_url":"https://ost.ecosyste.ms/projects/16884"}