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FitValenti_2.py
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FitValenti_2.py
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#!/usr/bin/env python
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx #
# xxxxxxxxxxxxx-------------FITTING ARNETT-VALENTI MODEL FOR A STRIPPED ENVELOPE SUPERNOVA--------------xxxxxxxxxxxxx #
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx #
# ------------------------------------------------------------------------------------------------------------------- #
# Import Required Libraries
# ------------------------------------------------------------------------------------------------------------------- #
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.integrate import quad
from lmfit import Minimizer, Parameters
from matplotlib.ticker import MultipleLocator
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Global Variables
# ------------------------------------------------------------------------------------------------------------------- #
file_name = 'BolLC_2017iro.dat'
fit_epoch = 60
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Details Of The SNe In Study (2017iro)
# ------------------------------------------------------------------------------------------------------------------- #
name_SN = '2017iro'
date_explosion = 2458084.0
guess_mni = 0.06
guess_taum = 1.5e6
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Constants
# ------------------------------------------------------------------------------------------------------------------- #
eni = 3.90e10 # Energy Production in 1s by 1g of 56Ni
eco = 6.78e9 # Energy Production in 1s by 1g of 56Co
tauni = 757728.0 # e-folding time of 56Ni
tauco = 9616320.0 # e-folding time of 56Co
mass_solar = 1.989e33 # Solar Mass
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Estimating The Value of 'taum'
# ------------------------------------------------------------------------------------------------------------------- #
kappa = 0.06
beta = 13.8
c = 2.99792458e10
msolar = 1.99e33
vph = 9.5e8
mej = 4.0 * msolar
ek = 0.3 * (vph ** 2) * mej
est_taum = ((2 * kappa * mej) / (beta * c * vph)) ** 0.5
print("Estimated Value of Taum: {0:>5.2f}".format(est_taum))
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Calculating Date Of Explosion From R-Band light Curve (Dessart et al. 2016)
# ------------------------------------------------------------------------------------------------------------------- #
datar = pd.read_csv('OUTPUT_InterpSNMag_R', sep='\s+')
datar['Phase'] = datar['JD'] - datar['JD'].min()
rmagmax = datar['R'].min()
jdmax = datar.loc[datar['R'] == datar['R'].min(), 'JD'].item()
rmag15 = datar.loc[datar['JD'] == jdmax + 15, 'R'].item()
delm = (rmag15 - rmagmax)
trise = 57.08 - 71.17 * delm + 32.98 * (delm ** 2)
est_dateexp = jdmax - trise
print("Epoch of R-Band Maximum: {0:>10.2f}".format(jdmax))
print("Estimated JD of Explosion: {0:>10.2f}".format(est_dateexp))
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Create A Set Of Parameters
# ------------------------------------------------------------------------------------------------------------------- #
params = Parameters()
params.add('mni', value=guess_mni)
params.add('taum', value=guess_taum)
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Function For Calculating Luminosity From Valenti Model Based On Initial Values
# ------------------------------------------------------------------------------------------------------------------- #
def calc_lum(param, list_time):
mni = param['mni'].value
taum = param['taum'].value
time_int = [float(time / taum) for time in list_time]
y = float(taum / (2 * tauni))
s = float(((taum * (tauco - tauni))/(2 * tauco * tauni)))
def a(z, y):
return 2 * z * np.exp((-2 * z * y) + (z ** 2))
def b(z, y, s):
return 2 * z * np.exp((-2 * z * y)+(2 * z * s) + (z ** 2))
lph = np.zeros(len(list_time))
lph_err = np.zeros(len(list_time))
for index, time in enumerate(time_int):
lph[index] = mass_solar * mni * np.exp(-time ** 2)*((eni - eco) * quad(a, 0, time, args=y)[0]
+ eco*quad(b, 0, time, args=(y, s))[0])
lph_err[index] = mass_solar * mni * np.exp(-time ** 2)*((eni - eco) * quad(a, 0, time, args=y)[1]
+ eco*quad(b, 0, time, args=(y, s))[1])
return [lph, lph_err]
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Defines Objective Function For Best Fit & Returns Numpy Array To Be Minimized
# ------------------------------------------------------------------------------------------------------------------- #
def func_min(param, list_time, list_flux):
lph = calc_lum(param, list_time)[0]
delta = np.zeros(len(list_time))
delta_err = np.zeros(len(list_time))
for index, flux in enumerate(list_flux):
delta[index] = float(lph[index]) - float(flux)
delta_err[index] = (float(lph[index]) - float(flux)) / float(flux)
return delta
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Read Observational Bolometric Light Curve Data
# ------------------------------------------------------------------------------------------------------------------- #
data_df = pd.read_csv(file_name, sep='\s+', engine='python')
data_df['Phase'] = (data_df['JD'] - date_explosion).round(2)
data_df['Time'] = data_df['Phase'].apply(lambda x: 86400 * x).round(2)
data_df = data_df[data_df['JD'] <= fit_epoch + date_explosion]
data_df = data_df[['JD', 'Phase', 'Time', 'Lum']].dropna()
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Minimize Using 'Least Squares' Technique
# ------------------------------------------------------------------------------------------------------------------- #
minner_object = Minimizer(func_min, params, fcn_args=(data_df['Time'], data_df['Lum']), nan_policy='omit')
initial_fit = minner_object.minimize(method='Nelder')
result_fit = minner_object.minimize(method='leastsq', params=initial_fit.params, **{'maxfev': 100})
# data_df['FitLum'] = calc_lum(result_fit.params, data_df['Time'])[0]
# data_df['FitLum'] = data_df['Lum'] + result_fit.residual
xaxis = np.linspace(data_df['Time'].min(), data_df['Time'].max(), 1000)
fit_lum, fit_lumerr = calc_lum(result_fit.params, xaxis)
datemax = xaxis[fit_lum.argmax()] / 86400.
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Plot Observational_Data & Best_Fit From The Valenti Model
# ------------------------------------------------------------------------------------------------------------------- #
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(111)
ax.plot(data_df['Phase'], data_df['Lum'].apply(lambda x: x * 1e-42), marker='*', markersize=8, linestyle='-',
color='k', label='Observed Data')
ax.plot([time / 86400 for time in xaxis], fit_lum * 1e-42, linestyle='--', color='r', label='Best Fit')
ax.axvline(datemax, linestyle='--', color='k')
ax.text(datemax + 1, 1.5, r'Epoch of Maximum', rotation=90, fontsize=12)
ax.set_xlim(0, 60)
ax.legend(markerscale=2, fontsize=12)
ax.yaxis.set_ticks_position('both')
ax.xaxis.set_ticks_position('both')
ax.yaxis.set_major_locator(MultipleLocator(0.5))
ax.yaxis.set_minor_locator(MultipleLocator(0.1))
ax.xaxis.set_major_locator(MultipleLocator(10))
ax.xaxis.set_minor_locator(MultipleLocator(2.5))
ax.tick_params(which='major', direction='in', length=6, width=1, labelsize=14)
ax.tick_params(which='minor', direction='in', length=3, width=1, labelsize=14)
ax.set_xlabel('Time Since Explosion [Days]', fontsize=16)
ax.set_ylabel(r'Bolometric Luminosity [x$\rm 10^{42}\ erg\ s^{-1}$]', fontsize=16)
fig.savefig('PLOT_FitValenti.eps', format='eps', dpi=1000, bbox_inches='tight')
plt.show()
plt.close(fig)
# ------------------------------------------------------------------------------------------------------------------- #