README.rst

============
NTRfC README
============

Numerical Test Rig for Cascades.


* Free software: MIT license
* Documentation: https://ntrfc.readthedocs.io.
* NTRfC is the base of the (NTRFlows)[https://gitlab.uni-hannover.de/tfd_public/tools/NTRFlows] repository, a workflow for cfd parameter studies



Features

Easy geometry and post-processing visualization and manipulation with pyvista.
Tested methods and functions for math, time-series, and mesh quality analysis.

Dependencies

NTRfC v0.1.0 and any following version is based on Python 3.10. Only older versions can be used with older versions of Python. Library requirements will be installed with the package itself.
Installation

NTRfC is utilizing powerful and complex dependencies like pyvista and gmsh. We strongly recommend using virtual or conda environments for installation.

For more information, see:

    virtualenv: https://pypi.org/project/virtualenv/
    miniconda: https://docs.conda.io/en/latest/miniconda.html
    anaconda: https://docs.anaconda.com/anaconda/install/index.html
    mamba: https://mamba.readthedocs.io/en/latest/installation.html



### Installation from gitlab with pip

```
pip install git+https://gitlab.uni-hannover.de/tfd_public/tools/NTRfC.git
```

### Installation from source

After cloning the repository, go to the project root dir and type

```
python setup.py install
```


### Editable installation from source with pip

After cloning the repository, go to the project root dir and type

```
pip install -e .
```

This way you have NTRfC installed but the code is not installed, but linked to the source-code.
You don't have to reinstall the package after your edits.
This speeds up testing and will lead to less debugging time.

### Singularity releases

use a singularity container from ntrfc singularity releases:  https://cloud.sylabs.io/library/nyhuma/ntrflows/ntr.sif].
The containers will come with a virtual graphics card and a xvfb display-server, enabling you to render on hpc-systems and any other unprepared system with limited graphics capability.

## Credits

This package was created with Cookiecutter and the `audreyr/cookiecutter-pypackage` project template. It uses the following libraries:

- [pyvista](https://github.com/pyvista)
- [gmsh](http://gmsh.info/)
- [Cookiecutter](https://github.com/audreyr/cookiecutter)
- [audreyr/cookiecutter-pypackage](https://github.com/audreyr/cookiecutter-pypackage)