A Python utility script designed to read, parse, and extract metadata and data from stellar evolution files, specifically DSEP (Dartmouth Stellar Evolution Database) formatted '.trk' and '.iso' files. It allows for the extraction of detailed stellar properties and their evolution over time from simulation output files, with functionality to save this extracted data into a structured Python dictionary and optionally serialize it into a binary pickle file for later use. This utility encompasses functions for handling and parsing data, extracting metadata, and providing mechanisms to organize and access star evolution models based on specific stellar parameters.

import re
import pandas as pd
import numpy as np
import os
import itertools
import argparse
import pickle
 
def load_iso(path):
    header = ["Age", "Log_T", "Log_g", "Log_L", "Log_R", "Y_core", "Z_core", "(Z/X)_surf", "L_H", "L_He", "M_He_core", "M_CO_core"]
    iso = pd.read_csv(path, delim_whitespace=True, names=header, comment='#')
    with open(path, 'r') as f:
        metadata_str = f.readline()
    metadata_str = metadata_str.strip('#').lstrip().rstrip().split(' ')[:3]
    metadata = dict()
    for i, data_elem in enumerate(metadata_str):
        keyvalue = data_elem.split('=')
        metadata[keyvalue[0]] = float(keyvalue[1])
    return iso, metadata
 
def get_trk_metadata(line):
    mass_search = re.compile('(?<=Total mass =  ).*?(?=\s)')
    mixing_length_search = re.compile('(?<=Mixing length = ).*?(?=\s)')
    EOS_search = re.compile('(?<=EOS = ).*?(?=\s)')
    Atm_search = re.compile('(?<=Atm = ).*?(?=\s)')
    Low_T_opacities_search = re.compile('(?<=Low T opacities = ).*?(?=\s)')
 
    mass = mass_search.search(line)
    mixing_length = mixing_length_search.search(line)
    EOS = EOS_search.search(line)
    Atm = Atm_search.search(line)
    Low_T_opacities = Low_T_opacities_search.search(line)
    meta = {'Mass':float(mass.group()), 'Mixing Length': float(mixing_length.group()),
            'EOS': EOS.group(), 'Atm':Atm.group(), 'Low T Opacities': Low_T_opacities.group()}
    return meta
 
def load_trk(path):
    header = [ "Model_#", "shells", "AGE", "log_L", "log_R", "log_g",
              "log_Teff", "Mconv_core", "Mconv_env", "Rconv_env",
              "M_He_core", "Xenv", "Zenv", "L_ppI", "L_ppII", "L_ppIII",
              "L_CNO", "L_triple-alpha", "L_He-C", "L_gravity", "L_neutrinos_old",
              "L_%_Grav_eng", "L_Itot", "C_log_T", "C_log_RHO", "C_log_P",
              "C_BETA", "C_ETA", "C_X", "C_Z", "C_H" "C_shell_midpoint",
              "C_H_shell_mass", "C_T_at_base_of_cz", "C_rho_at_base_of_cz","CA_He3",
              "CA_C12", "CA_C13", "CA_N14", "CA_N15", "CA_O16",
              "CA_O17", "CA_O18", "SA_He3", "SA_C12", "SA_C13", "SA_N14",
              "SA_N15", "SA_O16", "SA_O17","SA_O18", "N_pp", "N_pep", "N_hep",
              "N_Be7", "N_B8", "N_N13", "N_O15", "N_F17", "Cl37_flux",
              "Ga71_flux"]
    trk = pd.DataFrame(columns=header)
    with open(path, 'r') as trk_file:
        all_lines = trk_file.readlines()
    metadata = get_trk_metadata(all_lines[4])
    lines = [x.lstrip().rstrip().split() for x in all_lines[14:]]
    merged_lines = [x+y+z+w+q for x, y, z, w, q in zip(lines[::6], lines[1::6], lines[2::6], lines[3::6], lines[4::6], lines[5::6])]
    numeric_lines = [[float(y) for y in x] for x in merged_lines]
    for index, row in enumerate(numeric_lines):
        trk.loc[index] = row
    return trk, metadata
 
def load_trk_models(path):
    trks = list()
    metas = list()
    for file in os.listdir(path):
        if file.endswith('.trk'):
            print(file)
            trk, meta = load_trk(os.path.join(path, file))
            trks.append(trk)
            metas.append(meta)
    trks, metas = sort_based_on_key(trks, metas, key='Mass')
    return trks, metas
 
def load_iso_models(path):
    isos = list()
    metas = list()
    for file in os.listdir(path):
        if file.endswith('.iso'):
            iso, metadata = load_iso(os.path.join(path, file))
            isos.append(iso)
            metas.append(metadata)
    isos, metas = sort_based_on_key(isos, metas, key='M')
    return isos, metas
 
def load(path):
    functions = {'track': load_trk, 'iso': load_iso}
    suffix = args.path.split('.')[-1]
    data, metadata = functions[suffix](args.path)
    return data, metadata
 
 
if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Load data from a dsep3 trk or iso file")
    parser.add_argument("path", help="path to file", type=str)
    parser.add_argument("-o", "--output", help="path to save as pickle", type=str)
 
    args = parser.parse_args()
 
    data, metadata = load(args.path)
 
    if args.output:
        data_package = {"data": data, "metadata": metadata}
        pickle.dump(data_package, open(args.output, "wb"))
    else:
        print("========= METADATA ===========")
        for key in metadata:
            print(f"{key}: {metadata[key]}")
 
        print("=========== DATA =============")
        for index, row in data.iterrows():
            print(row)