progs

Python modules for handling core-collapse supernova progenitors.

The main purpose is to provide a generalised method of loading data from different sets of existing progenitor models. These models are provided in a variety of formats, so I'd like to be able to easily load data from them without needing to think about the underlying data structure.

Supported progenitor sets:

• sukhbold_2016 - Sukhbold et al. (2016)
• sukhbold_2018 - Sukhbold et al. (2018)
• sn1987a - Menon & Heger (2017)

Python Dependencies

• python 3.8
• astropy
• matplotlib
• numpy
• pandas
• xarray

Use the included environment.yml file to set up a working conda environment:

conda env create -f environment.yml
conda activate progs

Getting Started

Download the Sukhbold 2016 set linked above and extract the contents into the data directory under a folder named sukhbold_2016:

data
│
└───sukhbold_2016
|   |   s9.0_presn
|   │   s9.5_presn
|   │   ...

Single model

Load a specific progenitor model

from progs import ProgModel
model = ProgModel(zams='12.0', progset_name='sukhbold_2016')

The radial stellar profile is stored in model.profile:

           mass        radius      velocity       density  ...  compactness    luminosity  
0      0.002183  4.310247e+06 -4.049142e+06  1.293904e+10  ...     0.050639  3.504802e+49  
1      0.004335  5.434843e+06 -5.104893e+06  1.270032e+10  ...     0.079767  5.616596e+49  
2      0.006957  6.383686e+06 -5.994816e+06  1.249277e+10  ...     0.108977  7.807254e+49  
3      0.010149  7.265809e+06 -6.820940e+06  1.227866e+10  ...     0.139688  1.019597e+50  
...         ...           ...           ...           ...  ...          ...           ...  
1142  10.905083  4.412274e+13 -3.062704e+02  8.553002e-11  ...     0.000025  5.750828e+37  
1143  10.905101  4.414139e+13 -3.726215e+02  7.828117e-11  ...     0.000025  5.662965e+37  
1144  10.905120  4.416261e+13 -4.243568e+02  7.152728e-11  ...     0.000025  5.556802e+37  
1145  10.905131  4.417666e+13 -4.431164e+02  6.614074e-11  ...     0.000025  5.405292e+37  

[1146 rows x 36 columns]

See config/sukhbold_2016.ini for column definitions.

Scalar quantities are stored in model.scalars:

{'presn_mass': 10.905131314018288,
 'presn_radius': 44176664349173.93,
 'presn_temperature': 2496.9102763233523,
 'presn_luminosity': 5.405292355608813e+37,
 'xi_1.75': 0.2113883617902056,
 'xi_2.5': 0.022063351890412038,
 'coremass_He': 3.1217002675140373,
 'coremass_CO': 2.0916700214852075,
 'coremass_Fe': 1.4056675104203924}

Create quick profile plots of composition and physical quantities

model.plot('entropy')
model.plot_composition()

Full model set

Or the full progenitor set:

from progs import ProgSet
pset = ProgSet(progset_name='sukhbold_2016')

Which stores a table of scalars from all models in pset.scalars:

       zams  presn_mass  presn_radius  presn_temperature  ...  coremass_CO  coremass_Fe
0      9.00    8.748467  2.865175e+13       2.577388e+03  ...     1.402958     1.320369
1      9.25    8.980991  2.810216e+13       2.588700e+03  ...     1.453464     1.292710
2      9.50    9.210556  2.865834e+13       2.596303e+03  ...     1.503286     1.304485
3      9.75    9.448257  3.094173e+13       2.577251e+03  ...     1.558010     1.303106
..      ...         ...           ...                ...  ...          ...          ...
196   70.00    6.410148  4.222433e+10       7.581580e+06  ...     6.410148     1.507171
197   80.00    6.370163  4.186143e+10       7.538151e+06  ...     6.370163     1.485098
198  100.00    6.037972  3.800036e+10       8.453200e+06  ...     6.037972     1.541309
199  120.00    6.161807  3.570717e+11       9.803012e+05  ...     6.161807     1.568219

[200 rows x 10 columns]

which you can also plot

pset.plot_scalars('coremass_Fe')