Investigating Stellar Isochrones in Astrophysics

This Jupyter notebook file appears to focus on astrophysics, specifically in plotting and analyzing stellar isochrones by temperature and gravity, utilizing various libraries such as matplotlib, numpy, pandas, and specific astrophysical packages like fidanka and pysep. It involves reading isochrone data, interpolating these data for specific ages, and visualizing the data in plots. Additionally, it demonstrates loading stellar model data and saving configurations, supporting research in stellar evolution or comparative analysis of stellar properties.

snippet.python

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from fidanka.isochrone.isochrone import interp_isochrone_age
from pysep.io.iso.read import read_iso
from fidanka.isochrone.MIST import read_iso as read_MIST_iso
import pathlib

snippet.python

def plot_Teff_logg(path):
    iso = read_MIST_iso(path)
    # print([x/1e9 for x in list(iso.keys())])
    header = iso[list(iso.keys())[0]].columns
    isoAtAge = interp_isochrone_age(iso, 14.174741629268077)
    df = pd.DataFrame(isoAtAge, columns=header)
    fig, ax = plt.subplots(1, 1, figsize=(10,7))
    ax.plot(10**df["log_Teff"].values, df["log_g"].values, 'o-', markersize=2)
    ax.invert_yaxis()
    ax.invert_xaxis()

snippet.python

path = "../outputs.disconTest/PopA+0.24/alpha-1.901/isochrones.txt"
assert os.path.exists(path), "File not found"
plot_Teff_logg(path)

snippet.python

fig, ax = plt.subplots(1,1,figsize=(10,7))
ax.invert_yaxis()
ax.invert_xaxis()
glob = pathlib.Path("../outputs.kttauTest/PopA+0.24/alpha-1.901/").rglob("*.iso")
for pID, path in enumerate(glob):
    iso, isoMeta = read_iso(path)
    print(isoMeta['M'])
    ageCut = iso['Age'].values < 12e9
    Log_T = iso['Log_T'][ageCut].values
    Log_g = iso['Log_g'][ageCut].values
    T = 10**Log_T
    ax.plot(T, Log_g)
ax.set_xlim(6100, 4000)

0.65
0.75
0.55
0.8
0.6
0.7
0.5
0.85





(6100.0, 4000.0)

snippet.python

from pysep.dsep import load_model

snippet.python

model68 = load_model("M0.680.dsep")

snippet.python

model70 = load_model("M0.700.dsep")

snippet.python

model68.control.save("68control.nml")

True

snippet.python

model70.physics.save("70phys.nml")
model70.control.save("70control.nml")

True

python