Metadata-Version: 2.1
Name: turbx
Version: 0.1.2
Summary: Tools for analysis of turbulent flow datasets.
Home-page: https://github.com/iagappel/turbx
Author: Jason Appelbaum
Maintainer: Jason Appelbaum
License: MIT
Keywords: scientific computing,statistics,simulation,turbulence,turbulent flows,direct numerical simulation,DNS,parallel,visualization
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Visualization
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE

# turbx
[![PyPI version](https://badge.fury.io/py/turbx.svg)](https://badge.fury.io/py/turbx)
[![Downloads](https://pepy.tech/badge/turbx)](https://pepy.tech/project/turbx)

Tools for analysis of turbulent flow datasets.

Install with `pip`:

```
pip install --upgrade --user turbx
```

Documentation available at: https://iagappel.github.io/turbx

`turbx` runs in `python3` and uses parallel `HDF5` (wrapped by `h5py`) for high-performance collective MPI-IO with `mpi4py`. This requires:

- A `python3` installation (3.8+ recommended)
- An MPI implementation such as `OpenMPI`
- A parallel `HDF5` installation (must be compiled with `--enable-parallel`) 
- `mpi4py` (optionally compiled from source)
- `h5py` compiled with parallel configuration

An environment configuration guide can be found here: https://iagappel.github.io/turbx/env

Visualization of `HDF5` datasets is possible using `Paraview` with the use of `xdmf` data descriptor files, which are written automatically by calling `.make_xdmf()` on `turbx` data class (such as `rgd`) class instances.
