Flexible one-zone open-box chemical evolution modeling. Abundance fitting and stellar feedback calculation
Oliver Philcox, in 2019 produced another Chempy paper focusing on multi-star inference.
Oliver Philcox, during his 2017 summer internship at MPIA coded a NeuralNet add-on of Chempy (together with a very nice jupyter tutorial), which is orders of magnitudes faster than the original version and used it to score different yield-tables from the literature which lead to this publication. He also included many more CC-SN yieldsets.
Kirsten Blancato in this paper used single zone models per star and got varying IMF high mass slopes across the chemical abundance space.
Nathan Sandford, produced an interactive and very instructive Widget which you can run in your browser to see the effect that the star formation history has on abundance patterns.
pip install git+https://github.com/jan-rybizki/Chempy.git
Chempy should run with the latest python 2 and python 3 version. Its dependencies are: Numpy, SciPy, matplotlib, multiprocessing and emcee (for the MCMC), and corner (for the MCMC plots). They are all pip installable and you can also get part of it with Anaconda.
You can install Chempy into a folder where you have write access:
pip install --install-option='--prefix=~/extra_package/' git+https://github.com/jan-rybizki/Chempy.git
Then you have to add the site-packages/ folder which will be one of the newly created subfolders in extra_package/ into the PYTHONPATH variable, e.g.:
export PYTHONPATH=~/extra_package/lib/python2.7/site-packages/:$PYTHONPATH
If you want this to be permanent, you can add the last line to your .bashrc.
- Jan Rybizki (MPIA, rybizki@mpia.de)
- Hans-Walter Rix (MPIA)
- Andreas Just (ZAH)
- Morgan Fouesneau (MPIA)
- An early version of Chempy is presented in chapter 4 of my phd thesis.
The jupyter tutorial illustrates the basic usage of Chempy and basic concepts of galactic chemical evolution modeling. It can be inspected in the github repository or you can run it interactively on your local machine.
To run it interactively first clone the repository with
git clone https://github.com/jan-rybizki/Chempy.git
Then you can jupyter notebook from within the tutorial folder (it will run if you have installed Chempy).
If you did not install Chempy you can still run the tutorial but need to point to the files in the Chempy folder. Basically you have to cd ../Chempy/ and then replace each from Chempy import ... with from . import ....
You can also read the automatically generated manual.
If you want to compare your abundance model/data to Chempy paper one results, look at the tutorial 7 where the stored abundance tracks are loaded and plotted for one element.
If you want to use the flexible framework of Chempy to produce IMF integrated metallicity dependent yield tables for your SPH or other Chemical Evolution model you can use tutorial 8. You can use net or gross yields and also look at individual processes contribution to the overall SSP yield table.
Please cite the paper when using the code in your research.
\bibitem[Rybizki et al.(2017)]{2017A&A...605A..59R} Rybizki, J., Just, A., & Rix, H.-W.\ 2017, \aap, 605, A59