 |
|
AI generated song about WinNet (Download)
WinNet is a single zone nuclear reaction network, capable of calculating many different nucleosynthesis processes (i.e., r-process, nup-process, explosive nucleosynthesis, and many more). Please read and cite Reichert et al. 2023 when you use this network. A brief overview of the code is also given at the How it works documentation page. When using the code check your parameter file if you used any other input file within your calculation that require you to cite. A list of input files with the respective literature can be found at the Input files documentation page. We tried our best to produce a reliable code, but nevertheless we stress that this code comes with no warranty!
To run WinNet you need a Fortran compiler and the Math Kernel Library (MKL). The procedure of installing both can be different for Linux, Mac, and Windows. Therefore, in order to install WinNet on Linux follow the instruction here, for Mac here, and for Windows here. You can also follow the instructions here to directly download a Docker image from GitHub packages and run WinNet inside a docker container.
The WinNet base folder includes the file Makefile.example that you can change in order to fit your needs. MAC user for example have to include additional compiler flags that are included as a comment in the Makefile. If you are satisfied with the Makefile.example you can rename the file to Makefile. Afterwards you can run the command "make", it should work without complaining. If you have installed python, you can run "make tests" and all tests should pass.
The interface between the user and the code is given in the form of parameter files. Examples of those files can be found in the par/ folder. Within these files, you can change all the input to the code. There also exist a blank template file with all parameters and a short description in par/template.par. An overview of all valid parameters is given here.
WinNet comes with a variety of example cases. To access them you can rename the makerun.py.example into makerun.py. Afterwards, the list of examples is accessible by typing the command:
python makerun.py --example
One example is executed by running
python makerun.py -p Example_BigBang.par -r Example_BigBang
to calculate the primordial nucleosynthesis with WinNet.
If you used makerun.py to launch your simulation, the script will report the process ID (PID) in case you decide to pause or terminate the run. Standard output and standard error of the run are written to OUT and ERR files in the run output directory. The makerun.py script will also ask you if you want to monitor the simulation. Otherwise, you can monitor the progress using the "tail -f" command, for instance:
tail -f $WINNET/runs/Example_BigBang/OUT
WinNet includes a convenient script for generating movies, which can be found at bin/movie_script/winnet_movie.py. To use this script, you need to install the required Python packages listed in bin/movie_script/requirements.txt. For a comprehensive guide on available parameters, you can refer to the Readme of the same folder or access the script's help menu by running:
Once you have completed a simulation with the appropriate parameters, you can create a movie by executing the following command:
The resulting movie may resemble the following example:
If you have doxygen installed (see https://www.doxygen.nl/index.html) you can use it to create a local version of the documentation by executing
make doc
in the terminal. The compilation of the documentation was tested with doxygen version 1.8.17.
Here we try to list publications that direclty use a close version of the here presented Code:
Vonlanthen et al. (2009), Winteler et al. (2012), Korobkin et al. (2012), Rosswog et al. (2014), Grossman et al. (2014), Perego et al. (2014), Hansen et al. (2014), Perego et al. (2015), Eichler et al. (2015), Martin et al. (2015), Martin et al. (2016), Bliss et al. (2017), Rosswog et al. (2017), Tanvir et al. (2017), Lippuner & Roberts (2017), Bovard et al. (2017), Eichler et al. (2018), Martin et al. (2018), Bliss et al. (2018), Rosswog et al. (2018), Wollaeger et al. (2018), Koch et al. (2019), Côté et al. (2019), Eichler et al. (2019), Bliss et al. (2020), Côté et al. (2021), Kiss et al. (2021), Reichert et al. (2021a), OConnor et al. (2021), Korobkin et al. (2021), Reichert et al. (2021b), Molero et al. (2021), Wolleger et al. (2021), Szegedi et al. (2021), Witt et al. (2021), Ristic et al. (2022a), Chase et al. (2022), Psaltis et al. (2022), Ristic et al. (2022b). Setzer et al. (2023), Navó et al. (2023), Reichert et al. (2023a), Reichert et al. (2023b)
The list above is certainly not complete. It nevertheless shows the vast variety of usecases and the scientific relevance of WinNet.