Analyzing Brown Dwarf Models with MESA Data in Python
InvestigateMESABrownDwarfModel.ipynb
This file appears to be a Jupyter notebook (.ipynb) focused on analyzing brown dwarf models using data processed through the Modules for Experiments in Stellar Astrophysics (MESA). It includes Python code to import necessary libraries (matplotlib for plotting, numpy for numerical calculations, mesa_reader for reading MESA data, pandas for data manipulation), define a function to parse MESA model files, load and process data from a specific model file, and plot the results to visualize the properties (like radius and density) of brown dwarfs. The notebook likely serves as an educational or research tool to investigate the physical characteristics of brown dwarfs using computational astrophysics methods.
- snippet.python
import matplotlib.pyplot as plt import numpy as np import mesa_reader as mr import pandas as pd from io import StringIO import re plt.style.use(pubStyle)
- snippet.python
def parse_mesa_MOD_file(filepath): with open(filepath, 'r') as f: content = f.read() metadataSection = '\n'.join(content.split('\n')[:23]) metadata = re.finditer(r"(([A-Za-z_\/]+)\s+(.+)\n)[^\n]", metadataSection) dataSection = '\n'.join(content.split('\n')[23:-9]) dataSection = dataSection.replace("D", "E") df = pd.read_fwf(StringIO(dataSection), colspecs='infer') return df
- snippet.python
df = parse_mesa_MOD_file("../../BrownDwarfMESA/BD_TEST.mod") print(df)
Unnamed: 0 lnd lnT lnR L dq \
0 1 -12.054305 6.527047 22.453983 4.802104e+27 5.000000e-13
1 2 -11.980301 6.538191 22.453980 4.802104e+27 2.500000e-13
2 3 -11.934523 6.545397 22.453979 4.802104e+27 2.500000e-13
3 4 -11.899147 6.551094 22.453977 4.802104e+27 1.562500e-13
4 5 -11.872967 6.555398 22.453976 4.802104e+27 1.562500e-13
.. ... ... ... ... ... ...
784 85 6.282040 13.137382 17.382338 1.268326e+22 6.250582e-07
785 86 6.282077 13.137392 17.151280 6.341716e+21 3.125291e-07
786 87 6.282099 13.137398 16.920226 3.170886e+21 1.562646e-07
787 88 6.282114 13.137402 16.689173 1.585451e+21 7.813228e-08
788 89 6.282126 13.137405 16.458122 7.927282e+20 7.813228e-08
mlt_vc h1 he3 he4 c12 n14 o16 ne20 \
0 0.0 0.7 0.00003 0.27997 0.003447 0.00101 0.009381 0.002085
1 0.0 0.7 0.00003 0.27997 0.003447 0.00101 0.009381 0.002085
2 0.0 0.7 0.00003 0.27997 0.003447 0.00101 0.009381 0.002085
3 0.0 0.7 0.00003 0.27997 0.003447 0.00101 0.009381 0.002085
4 0.0 0.7 0.00003 0.27997 0.003447 0.00101 0.009381 0.002085
.. ... ... ... ... ... ... ... ...
784 0.0 0.7 0.00003 0.27997 0.003447 0.00101 0.009381 0.002085
785 0.0 0.7 0.00003 0.27997 0.003447 0.00101 0.009381 0.002085
786 0.0 0.7 0.00003 0.27997 0.003447 0.00101 0.009381 0.002085
787 0.0 0.7 0.00003 0.27997 0.003447 0.00101 0.009381 0.002085
788 0.0 0.7 0.00003 0.27997 0.003447 0.00101 0.009381 0.002085
mg24
0 0.004077
1 0.004077
2 0.004077
3 0.004077
4 0.004077
.. ...
784 0.004077
785 0.004077
786 0.004077
787 0.004077
788 0.004077
[789 rows x 15 columns]
- snippet.python
fig, ax = plt.subplots(1, 1, figsize=(10, 7)) ax.plot(np.exp(df.lnR.values), np.exp(df.lnd.values))
[<matplotlib.lines.Line2D at 0x7f18a3815310>]
- snippet.python
df.lnd.values
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python