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

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import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from nugridpy import isotopes as iso

def ppn(file):
    df = pd.read_csv(file, skiprows=[1,2,3,4,5,6],sep='\s+')
    df = df.replace(np.nan,' ').replace('nan','').replace(' ','')
    A = df['ABUNDANCE_MF'].values
    iso_num = df['ISOTP'].values
    iso=[]
    for i,j in zip(A,iso_num):
        iso.append(i+str(j).replace('.0',''))
    mf = df['ISOM'].values
    Z = df['Z'].values
    return(iso,mf,Z)

A1, MF1, Z1 = ppn('/media/ashley/Seagate Backup Plus Drive/slant/scratch/NuPPN-modular2/frames/ppn/RUN_RH5_rot_csh_mod2_z4r4/iso_massf02837.DAT')
A2, MF2, Z2 = ppn('/media/ashley/Seagate Backup Plus Drive/slant/scratch/NuPPN-modular2/frames/ppn/RUN_RH5_rot_csh_mod2_z4r4_newNe22/iso_massf02837.DAT')
A1i, MF1i, Z1i = ppn('/media/ashley/Seagate Backup Plus Drive/slant/scratch/NuPPN-modular2/frames/ppn/RUN_RH5_rot_csh_mod2_z4r4/iso_massf00000.DAT')
A2i, MF2i, Z2i = ppn('/media/ashley/Seagate Backup Plus Drive/slant/scratch/NuPPN-modular2/frames/ppn/RUN_RH5_rot_csh_mod2_z4r4_newNe22/iso_massf00000.DAT')

iso_stable=[]
for i in iso.Isotope.all_stable_isotopes:
    iso_stable.append(iso.Isotope.number_to_ppn_name(i[0],i[1]).replace(' ',''))

mf1=[]
mf2=[]
mf1i=[]
mf2i=[]
a=[]
for i,j in enumerate(A1):
    for k in iso_stable:
        if k==j:
            mf1.append(MF1[i])
            mf2.append(MF2[i])
            mf1i.append(MF1i[i])
            mf2i.append(MF2i[i])
            a.append(Z1[i])

params = {'text.usetex': True,
        'xtick.direction': 'in',
        'ytick.direction': 'in',
        #'axes.linewidth' : 5,
        'xtick.major.size': 15,
        'ytick.major.size': 15,
        'xtick.labelsize': 30,
        'ytick.labelsize': 30,
        'ytick.major.pad': 5,
        'ytick.major.width': 2,#3,
        'xtick.major.pad': 5,
        'xtick.major.width': 2}#3}
plt.rcParams.update(params)
plt.tick_params(axis='both', pad=5)

plt.semilogy(a, np.array(mf1)/np.array(mf1i), 'rv',markersize=10,label='Longland')
plt.semilogy(a, np.array(mf2)/np.array(mf2i), 'ys',markersize=10,label='This work')
plt.ylim(0.01,1000)
plt.xlim(55,120)
plt.xlabel('$Mass$ $number$', fontsize=40)
plt.ylabel('$X_{i}/X_{\odot}$', fontsize=40)
plt.rcParams['xtick.labelsize'] = 30
plt.rcParams['ytick.labelsize'] = 30
plt.legend(numpoints=1,loc='lower left',prop={'size':30})
plt.subplots_adjust(left= 0.09, bottom=0.105, right=0.975, top=0.97)
plt.show()

plt.semilogy(a, np.array(mf1)/np.array(mf1i), 'rv',markersize=10,label='Longland')
plt.semilogy(a, np.array(mf2)/np.array(mf2i), 'ys',markersize=10,label='This work')
plt.ylim(1,1000)
plt.xlim(69,97)
plt.xlabel('$Mass$ $number$', fontsize=40)
plt.ylabel('$X_{i}/X_{\odot}$', fontsize=40)
plt.rcParams['xtick.labelsize'] = 30
plt.rcParams['ytick.labelsize'] = 30
plt.legend(numpoints=1,loc='lower left',prop={'size':30})
plt.subplots_adjust(left= 0.09, bottom=0.105, right=0.975, top=0.97)
plt.show()

plt.semilogy(Z1, MF1/MF1i, 'rv',markersize=10,label='Longland')
plt.semilogy(Z2, MF2/MF2i, 'ys',markersize=10,label='This work')
plt.ylim(0.01,1000)
plt.xlim(55,120)
plt.xlabel('$Mass$ $number$', fontsize=40)
plt.ylabel('$X_{i}/X_{\odot}$', fontsize=40)
plt.rcParams['xtick.labelsize'] = 30
plt.rcParams['ytick.labelsize'] = 30
plt.legend(numpoints=1,loc='best',prop={'size':30})
plt.show()