def extract_theta_dot_xi(dataFiles):
""" return the polytroic index assosiated solution for every given binary file path
Positional Arguments:
dataFiles -- iterator of path to binary data files produced from c executable itegrate
Returns -> (N, STATE):
STATE -- List of data from binary file paths (parallel to N)
META -- Metadata extracted from header of dump files
"""
STATE = list()
META = list()
for dataFile in dataFiles:
state, metadata = load_C_output(dataFile)
META.append(metadata)
STATE.append(state)
return STATE, META
import numpy as np
from readCPython import load_C_output
if __name__ == "__main__":
state, meta = load_C_output(
"../data/laneEmdenDataFile_1.000000-nonDegenerate.dat")
ftheta = lambda xi: np.sin(xi) / xi
theta = ftheta(state[0])
print("Mean Differnce between exact and numeric solution: {}".format(
np.mean(theta - state[1])))
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Plot WD Mass vs Central Density")
parser.add_argument("files", nargs="+", type=str, help="Path to file")
parser.add_argument("-o", "--output", type=str, help="save path")
parser.add_argument('-t',
'--tex',
action='store_true',
help='Use the tex rendering engine when plotting')
args = parser.parse_args()
rho_c = list()
R = list()
for file in args.files:
state, meta = load_C_output(file)
# convert dimensionless values to physical equivilents
rho_c.append(3.789e6 * meta['theta_c'])
xi1, theta_xi1 = find_root(state[0], state[1])
R.append(alpha * xi1 * 100)
set_style(usetex=args.tex)
fig, ax = plt.subplots(1, 1, figsize=(10, 7))
ax.semilogx(rho_c, R, 'kx')
ax.set_ylabel(r'Radius [R$_{\odot}$/100]', fontsize=17)
ax.set_xlabel(r'Central Density [g cm$^{-3}$]', fontsize=17)
plt.savefig(args.output, bbox_inches='tight')
default=0.24)
parser.add_argument("-Z", type=float, help="Meltallicity", default=0)
parser.add_argument("-o",
"--output",
type=str,
help="Output Location",
default="NULL")
args = parser.parse_args()
# Exctract the polytropic index from the file name convention
n = float(args.path.split('/')[-1].split('_')[1][:-7])
# Load data from the c dump binary
state, metadata = load_C_output(args.path)
xi1, thetaXi1 = find_root(state[0], state[1])
dthetaXdxi1, _ = find_root(state[2], state[1])
# Select only the portion of the the solution less than the radius of the star
conditional = state[0] <= xi1
xi = state[0][conditional]
theta = state[1][conditional]
dtheta = state[2][conditional]
M = args.mass
# Select a-priori radius or given luminosity and temperature
if args.radius:
R = args.radius
parser.add_argument("-o", "--output", type=str, help="save path")
parser.add_argument('-t',
'--tex',
action='store_true',
help='Use the tex rendering engine when plotting')
parser.add_argument('-m',
'--mass',
nargs="+",
type=float,
help='Given mass')
args = parser.parse_args()
masses = list()
for file in args.files:
state, meta = load_C_output(file, oh=True)
# convert dimensionless values to physical equivilents
masses.append(0.09 * meta['m'])
masses = np.array(masses)
set_style(usetex=args.tex)
fig, ax = plt.subplots(1, 1, figsize=(10, 7))
for mass in args.mass:
# select the closest mass to the given mass
idx = (np.abs(masses - mass)).argmin()
state, meta = load_C_output(args.files[idx])
ax.plot(alpha * state[0] * 100,
3.789e6 * state[1],
label=r"M: {:0.2f} M$_{{\odot}}$".format(0.09 * meta['m']))
ax.legend(fontsize=17)