Recreate Appendix D from "Understanding Stellar Evolution" by Henry Lamers and Emily Levesque

create_appendix_d.py

import urllib
import pandas as pd
import numpy as np

def create_appendix_d(Zs=[0.014, 0.002], verbose=False,
                      masses=[0.8, 0.9, 1, 2, 4, 7, 12, 15, 20, 25, 32, 40, 60, 85, 120]):
    """Recreate Appendix D from "Understanding Stellar Evolution" by Henry Lamers and Emily Levesque.
    Parameters
    ----------
    Zs : `list`, optional
        List of metallicities to get, by default [0.014, 0.002]
    verbose : `bool`, optional
        Whether to print out progress messages, by default False
    masses : `list`, optional
        List of masses to get, by default [0.8, 0.9, 1, 2, 4, 7, 12, 15, 20, 25, 32, 40, 60, 85, 120]
    Returns
    -------
    dfs : `list of DataFrames`
        List of Pandas DataFrames corresponding to each metallicity
    """
    # constants for which lines refer to ZAMS and TAMS as defined by the textbook
    ZAMS = 1
    TAMS = 86
    
    # holder for the dataframes
    dfs = []
    
    for Z in Zs:
        if verbose:
            print(f"Creating table for Z={Z}")
        data = []
        for mass in masses:
            if verbose:
                print(f"  Getting m={mass}")
            mass_str = f"{mass:03}".replace(".", "p")
            Z_str = str(Z).split(".")[1]
            
            # get the data from the Ekstrom+2012 files
            url = f"https://obswww.unige.ch/Recherche/evol/tables_grids2011/Z{Z_str}/M{mass_str}Z{Z_str[1:]}V0.dat"
            response = urllib.request.urlopen(url)
            
            # read the files into a Pandas DataFrame
            df = pd.read_csv(response, delim_whitespace=True, skiprows=[1], header=0)
            df.set_index("line", inplace=True)
            
            # grab the relevant data from the dataframe
            data.append([df.loc[ZAMS]["lg(L)"].round(2), df.loc[ZAMS]["lg(Teff)"].round(2),
                         np.log10(df.loc[TAMS]["time"]).round(2), df.loc[TAMS]["mass"].round(1),
                         df.loc[TAMS]["lg(L)"].round(2), df.loc[TAMS]["lg(Teff)"].round(2)])
        
        # convert the data into a dataframe
        df = pd.DataFrame(data, columns=["lg(L_ZAMS)", "lg(Teff_ZAMS)", "lg(t_TAMS)",
                                         "m_TAMS", "lg(L_TAMS)", "lg(Teff_TAMS)"],
                          index=pd.Index(masses, name="m_ZAMS"))
        
        # save the file
        df.to_csv(f"appendix_d_Z_{Z}.csv")
        
        # add the dataframe to the list to be returned
        dfs.append(df)
    return dfs