pytoast-core 0.1.0b2

Python Toolkit for Ocean, Atmospheric, and Surface-wave Turbulence

pyTOAST: Python Toolkit for Ocean, Atmospheric, and Surface-wave Turbulence

A pure-Python toolkit for analyzing observations of ocean and atmospheric turbulence and related bulk variables.

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Overview

pytoast is a library for physical oceanographers and meteorologists processing field observations, with a focus on turbulence statistics. It provides classes for common ocean and atmospheric sensors -- Acoustic Doppler Velocimeters (ADV), Acoustic Doppler Current Profilers (ADCP), sonic anemometers, CTDs, and bulk meteorological instruments -- along with a shared preprocessing pipeline (despiking, coordinate rotations) and derived calculations. These include TKE dissipation, Reynolds stresses with wave-turbulence decomposition, directional wave statistics, seawater and air thermodynamics (TEOS-10), and boundary-layer flux parameterizations (both air-sea and bottom boundary layer).

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Installation

pip install pytoast-core

For development:

git clone [e-mail]:galenegan/pytoast.git
cd pytoast
pip install -e ".[dev]"

Requirements

• Python >= 3.11
• Core dependencies (numpy, scipy, pandas, xarray, matplotlib, h5py, netCDF4, mat73) are installed automatically.

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Quick start

The notebooks/ folder contains Jupyter notebooks demonstrating initialization of each main instrument class, along with example calculations. Each notebook shows slight variations on initialization and processing procedures:

• adcp.ipynb: Traversing nested keys in source files, coordinate transformations, plotting output of two different stress estimation methods
• adv.ipynb: Specifying instrument deployment depths, rotating and despiking during preprocessing, adding turbulent stresses, directional wave statistics, and dissipation to an xarray dataset for netCDF export
• ctd.ipynb: Loading a single data file with (multidimensional) data from two deployment depths, calling CTD.derive for seawater thermodynamics calculations
• met.ipynb: Loading a single data file with instruments at different heights stored under different keys, calling Met.derive for atmospheric thermodynamics calculations
• sonic.ipynb: Forwarding arguments to pd.read_csv for non-standard data loading, alternate despiking method, calculation of dissipation and buoyancy flux.

In the simplest cases, these will look something like:

from pytoast.ocean.adv import ADV

name_map = {
    "u1": "u", "u2": "v", "u3": "w",
    "p": "pressure", "time": "time",
}

adv = ADV(files="burst.mat", name_map=name_map, fs=16, z=[1.0])

adv.set_preprocess_opts({
    "despike": {"method": "goring_nikora"},
    "rotate":  {"flow_rotation": "align_streamwise"},
})

burst = adv.load_burst(0)
diss  = adv.dissipation(burst, f_low=1.0, f_high=4.0)
print(diss["eps"])     # TKE dissipation rate (m^2/s^3) at each height

See the documentation for the full API reference.

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Running tests

pytest
pytest --cov=src    # with coverage report

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Contributing

1. Fork & clone the repo.
2. Create a feature branch: git checkout -b feature/my-improvement
3. Install dev dependencies: pip install -e ".[dev]"
4. Run tests before pushing: pytest
5. Open a pull request.

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Citation

If you use this software in published work, please cite:

@software{pytoast,
  author  = {Galen Egan},
  title   = {pyTOAST: Python Toolkit for Ocean, Atmospheric, and Surface-wave Turbulence},
  year    = {2026},
  url     = {https://github.com/galenegan/pytoast},
}

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License

This project is licensed under the MIT License -- see LICENSE for details.