MPPNPy/plot_AGB.py at master · Bigash94/MPPNPy · GitHub

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import pandas as pd
import matplotlib.pyplot as plt
import pylab as pb
import numpy as np
from nugridpy import nugridse as mp
from nugridpy import mesa as ms

#LOGS = '../mppnp/z1m2/m1p65z1m2' #13
#LOGS = '../mppnp/z2m2/m1p65z2m2' #18
#LOGS= '../mppnp/z1m2/m2z1m2' #20
#LOGS = '../mppnp/z2m2/m2z2m2' #30
LOGS = '../mppnp/z3m2/m2z3m2_mk2' #27
#LOGS = '../mppnp/z1m2/m3z1m2' #13
#LOGS = '../mppnp/z2m2/m3z2m2' #18
#LOGS = '../mppnp/z3m2/m3z3m2' #16
#LOGS = '../mppnp/z6m3/m2z6m3' #21
#LOGS = '../mppnp/z2m2/m1p85z2m2_hCBM' #22
#LOGS = '../mppnp/z1m2/m2z1m2_hCBM' #17
#LOGS = '../mppnp/z2m2/m2z2m2_hCBM' #20
#LOGS = '../mppnp/z2m2/m3z2m2_hCBM' #17
#LOGS = '../mppnp/z6m3/m2z6m3_hCBM' #15
#LOGS = '../mppnp/z6m3/m3z6m3_hCBM' #

LOGS1= LOGS.replace('_','\_')
MODEL = LOGS1[14:]
st1=ms.star_log(LOGS)
cutting=150000
shift_t0=0
t0_model=st1.find_first_TP()+shift_t0
t0_idx=int(t0_model-st1.get("model_number")[0])
first_TP_he_lum=10**(st1.get("log_LHe")[t0_idx])
he_lum=10**(st1.get("log_LHe")[t0_idx:])
h_lum=10**(st1.get("log_LH")[t0_idx:])
model=st1.get("model_number")[t0_idx:]
h1_bndry=st1.get("h1_boundary_mass")[t0_idx:]
##define label
z=st1.header_attr["initial_z"]
mass=st1.header_attr["initial_mass"]
leg=str(mass)+"M$_{\odot}$ Z= "+str(z)
peak_lum_model=[]
h1_mass_tp=[]
h1_mass_min_DUP_model=[]
##TP identification with he lum if within 1% of first TP luminosity
perc=0.01
min_TP_distance=300 #model
lum_1=[]
model_1=[]
h1_mass_model=[]
TP_counter=0
new_TP=True
TP_interpulse=False
interpulse_counter=0
for i in range(len(he_lum)):
	if (h_lum[i]>he_lum[i]):
		TP_interpulse=True
		interpulse_counter+=1
	if (h_lum[i]<he_lum[i]):
		interpulse_counter=0
		new_TP=True
		TP_interpulse=False
		if i > 0:
			h1_mass_1.append(h1_bndry[i])
			h1_mass_model.append(model[i])
	if i ==0:
		lum_1.append(first_TP_he_lum)
		model_1.append(t0_model)
		h1_mass_1=[h1_bndry[0]]
		h1_mass_model=[t0_model]
	else:
		lum_1.append(he_lum[i])
		model_1.append(model[i])

	if (new_TP == True and TP_interpulse==True and interpulse_counter> 80):
		max_value=np.array(lum_1).max()
		max_index = lum_1.index(max_value)
		peak_lum_model.append(model_1[max_index])
		max_lum_idx=h1_mass_model.index(model_1[max_index])
		min_value=np.array(h1_mass_1[max_lum_idx:]).min()
		min_index = h1_mass_1.index(min_value)
		h1_mass_min_DUP_model.append(h1_mass_model[min_index])
		new_TP=False
modeln=[]
modeln1=[]
interp_md=[]
for i in range(len(peak_lum_model)):
	modeln.append(int(peak_lum_model[i]))
	modeln1.append(int(h1_mass_min_DUP_model[i]))
	if i < (len(peak_lum_model)-1):
		if ((peak_lum_model[i]+h1_mass_min_DUP_model[i])/2. < cutting):
			interp_md.append(int((peak_lum_model[i]+h1_mass_min_DUP_model[i])/2.))
modeln2=[]
for i in modeln1:
    modeln2.append(i-100)

run=mp.se(LOGS+'/H5_restart','restart.h5')
targets=np.unique(modeln2)
He4=[]
C12=[]
O16=[]
N14=[]

for cycle in targets:
    He4.append(run.se.get(cycle,'iso_massf','He-4'))
    C12.append(run.se.get(cycle,'iso_massf','C-12'))
    O16.append(run.se.get(cycle,'iso_massf','O-16'))
    N14.append(run.se.get(cycle,'iso_massf','N-14'))
    print(cycle)

c125=[]
he45=[]
o165=[]

for j, item in enumerate(N14):
    for i, line in enumerate(item):
        if (N14[j][::-1][i] < 0.000000000001 and
            He4[j][::-1][i] > 0.1 and
            C12[j][::-1][i] > 0.1 and
            O16[j][::-1][i] + C12[j][::-1][i] + He4[j][::-1][i] > 0.9):
                he45.append(He4[j][::-1][i])
                c125.append(C12[j][::-1][i])
                o165.append(O16[j][::-1][i])
                break

tp7=np.linspace(1,len(o165),len(o165))

plt.rcParams['text.usetex'] = True
plt.rcParams['axes.linewidth'] = 2
plt.rcParams['xtick.major.size'] = 20
plt.rcParams['xtick.major.width'] = 2
plt.rcParams['ytick.major.size'] = 20
plt.rcParams['ytick.major.width'] = 2
plt.rcParams['ytick.minor.size'] = 10
plt.rcParams['ytick.minor.width'] = 2
plt.tick_params(axis='both',which='both',pad=10,labelsize=30,direction='in')
plt.semilogy(tp7,o165,'ro-',markersize=15,label='$O^{16}$'+' '+'$'+MODEL+'$',lw=3)
plt.semilogy(tp7,c125,'g^-',markersize=15,label='$C^{12}$'+' '+'$'+MODEL+'$',lw=3)
plt.semilogy(tp7,he45,'bs-',markersize=15,label='$He^4$'+' '+'$'+MODEL+'$',lw=3)
plt.ylabel('$Mass$ $Fraction$ $(X)$', fontsize=40)
plt.xlabel('$TP$ $number$', fontsize=40)
plt.legend(prop={'size':25})
plt.show()

df = pd.read_csv('Werner,Herwig_2006_Table1.txt', sep='\t',usecols=[0,4,5,7],skiprows=[1,2,3,5,7,8,9,10,12])
x1 = df['He'].values
x2 = df['C'].values
x3 = df['O'].values
y1 = df['Spectral Class Star'].values
y=[1,2,3,4,5]

y2=[]
for i in y1:
    y2.append('$'+i+'$')

plt.rcParams['text.usetex'] = True
plt.rcParams['xtick.major.size'] = 20
plt.rcParams['xtick.major.width'] = 2
plt.rcParams['ytick.major.size'] = 20
plt.rcParams['ytick.major.width'] = 2
plt.rcParams['ytick.minor.size'] = 10
plt.rcParams['ytick.minor.width'] = 2
plt.rcParams['xtick.labelsize'] = 30
plt.rcParams['ytick.labelsize'] = 30
plt.tick_params(axis='both',which='both',pad=5,direction='in')

plt.semilogy(y,x1,'bo',markersize=30,lw=2,linestyle='None')
plt.semilogy(y,x2,'gs',markersize=30,lw=2,linestyle='None')
plt.semilogy(y,x3,'r^',markersize=30,lw=2,linestyle='None')
plt.errorbar(y,x1,yerr=x1/2,marker='o',color='b',markersize=30,lw=2,linestyle='None',label='$He$')
plt.errorbar(y,x2,yerr=x2/2,marker='s',color='g',markersize=30,lw=2,linestyle='None',label='$C$')
plt.errorbar(y,x3,yerr=x3/2,marker='^',color='r',markersize=30,lw=2,linestyle='None',label='$O$')

#Model
plt.axhline(y=he45[-1:],color='b',lw=2.2, label='$He$'+' '+'$'+MODEL+'$')
plt.axhline(y=c125[-1:],color='g',lw=2.2,label='$C$'+' '+'$'+MODEL+'$')
plt.axhline(y=o165[-1:],color='r',lw=2.2,label='$O$'+' '+'$'+MODEL+'$')

plt.ylabel('$Mass$ $Fraction$ $(X)$', fontsize=40)
pb.xticks(y, y2)
plt.legend(loc='best',prop={'size':25})
plt.xlim(0.5,5.5)
plt.show()