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PyTurbo_SF
What PyTurbo_SF is
PyTurbo_SF is a free Python package for calculating structure functions on one-, two-, and three-dimensional data, including turbulent-flow analysis. It supports longitudinal, transverse, scalar, and combined structure functions, plus isotropic averaging for 2D and 3D data. Adaptive bootstrap resampling provides uncertainty estimates and checks convergence. Workflows accept xarray Datasets and return structure-function results as xarray Datasets, with data export available. Parallel execution can use loky, threading, or multiprocessing. The package runs on Linux, macOS, and Windows and requires Python 3.12 or newer. NumPy, xarray, SciPy, pandas, and joblib are required; matplotlib, Dask, Zarr, and netCDF4 are optional. Installation is available through pip or the GitHub source repository. The guide suggests tuning bootstrap blocks and using Dask when data exceeds memory.
Who it suits
PyTurbo_SF suits researchers in oceanography, atmospheric science, and fluid dynamics analyzing observations or simulations. It fits users working with xarray data and modern Python environments.
What it does well
- Handles 1D, 2D, and 3D data
- Adaptive bootstrap uncertainty estimates
- Isotropic averaging for 2D and 3D
- Multiple parallel-processing backends
- Free with no stated usage limits
What to know first
- Requires Python 3.12 or newer
- Large datasets may need Dask
- Several optional packages are separate installs
Plans
| Plan | Billed and limits | Price |
|---|---|---|
| PyTurbo_SF | No paid tiers or usage limits stated | Free |
As pyturbo-sf.readthedocs.io lists them, read 11 Oct 2026. The maker's own page has the final word.
Verdict
PyTurbo_SF provides specialized structure-function analysis with uncertainty handling and parallel options. Its Python requirement and scientific data workflow define the main setup considerations.
Facts from PyTurbo_SF's own pages
- Data export
- Yes · github.com, 23 Sept 2026
- Delivery mode
- desktop · github.com, 23 Sept 2026
- Free plan
- Yes · github.com, 23 Sept 2026
- Input and results
- The example workflow accepts xarray Datasets and returns structure-function results as xarray Datasets. · pyturbo-sf.readthedocs.io, 11 Oct 2026
- Installation
- The package can be installed with pip from PyPI or installed from its GitHub source repository. · pyturbo-sf.readthedocs.io, 11 Oct 2026
- Integrations
- Required dependencies include NumPy, xarray, SciPy, pandas, and joblib; optional packages include matplotlib, Dask, Zarr, and netCDF4. · pyturbo-sf.readthedocs.io, 11 Oct 2026
- Isotropic analysis
- It provides isotropic averaging for 2D and 3D data. · pyturbo-sf.readthedocs.io, 11 Oct 2026
- Large-data considerations
- The guide recommends tuning bootstrap block sizes and suggests Dask for datasets larger than available memory. · pyturbo-sf.readthedocs.io, 11 Oct 2026
- Parallel processing
- The quick start demonstrates loky and threading backends, and the guide also lists multiprocessing as an option. · pyturbo-sf.readthedocs.io, 11 Oct 2026
- Purpose
- PyTurbo_SF is a Python package for efficient structure-function calculations on 1D, 2D, and 3D data, including turbulent-flow analysis. · pyturbo-sf.readthedocs.io, 11 Oct 2026
- Python requirement
- The installation guide states that Python 3.12 or higher is required. · pyturbo-sf.readthedocs.io, 11 Oct 2026
- Security and compliance
- The documentation pages reviewed do not state security certifications or compliance programs. · pyturbo-sf.readthedocs.io, 11 Oct 2026
- Structure-function types
- It supports longitudinal, transverse, scalar, and combined structure functions. · pyturbo-sf.readthedocs.io, 11 Oct 2026
- Support
- For installation issues, the guide directs users to GitHub Issues and community discussions. · pyturbo-sf.readthedocs.io, 11 Oct 2026
- Target users
- The documentation describes use by researchers in oceanography, atmospheric science, and fluid dynamics analyzing observational data and simulations. · pyturbo-sf.readthedocs.io, 11 Oct 2026
- Uncertainty analysis
- It uses adaptive bootstrap resampling to provide statistical uncertainty estimates and detect convergence. · pyturbo-sf.readthedocs.io, 11 Oct 2026
Where PyTurbo_SF stands on Freedom251
- Physics Simulation Software#41 of 89