Supplementary material for OMpy article

OMpy

ompy is the Oslo method implementation in python. It contains all the functionality needed to go from a raw coincidence matrix, via unfolding and the first-generation method, to fitting a level density and gamma-ray strength function. It also supports uncertainty propagation by Monte Carlo. The repository for ompy can be found here and is included as a submodule in this repository.

Note that you may have to run git submodule update --init --recursive to download ompy, and build it using python setup.py build_ext --inplace in the ompy directory. See ompy respository for more details.

Here we provide the notebooks used for the analysis in the article introducing OMpy. A list of the files follows below.

Link to article: [add when published]

If you want to try the package before installation, you may simply click here to launch it on Binder. Note the cpu limitations probably will restrict to what extend you can rerun the analysis online. However, the attached Dockerfile can be used to easily set up the analysis on any machine. Please note also that sometimes MyBinder takes a long time to start up or for some calculations. You may sometimes have to wait a little and/or restart the kernel and/or whole repo.

Original Runtime: approx 5 min on 51 vCPUs (type N1 from the google cloud)

Files:

• analysis.ipynb: Notebook used for the analysis
• Dockerfile: File that can be used by Docker to automatically build an images ("installation") with this notebook and ompy. Used eg. by MyBinder to run this notebook online. If you use this yourself, make sure to include also the hooks
• figs: Save folder for figures used in the article.
• hooks: Needed to this from Dockerfile due to the submodule ompy for MyBinder
• misc_data: External data for comparison (see Nyhus, H. T. et al. (2010). DOI: 10.1103/physrevc.81.024325 and is reanalyzed in Renstrøm, T. et al. (2018). DOI: 10.1103/physrevc.98.054310)
• myplots.py: small convenience script for plotting the ensemble
• ompy: OMpy as a submodule. Including it as a submodule ensures that we use a specific version, even if you might have another version installed on your machine, too.
• RAINIER_164Dy: Files to generate the synthetic data with RAINIER
• saved_run: Persistency folder. With the regernerate flag in the notebook we can adjust whether or not we read the saved files from disk. E.g., we might just want to change some plots, not rerun all calculations.