Notebook criado para analisar a influencia do parametro diffusion_cgs_solver
no teste make_o_ne_wd
In [21]:
%pylab
%matplotlib inline
import mesa as ms
import numpy as np
import matplotlib
matplotlib.rcParams['figure.figsize'] = (14.0, 7.0) #(14.0, 10.0)
import matplotlib.pyplot as plt
In [24]:
# folder com o teste make_o_ne_wd
#folder = '/media/glauffer/Data/mesa/create_wd/test_suite/make_o_ne_wd/LOGS15/history_editado.data'
folder = '/media/glauffer/Data/mesa/create_wd/test_suite/make_o_ne_wd/LOGS15/'
# lendo os dados
hist = ms.history_data(folder)
teff = hist.get('log_Teff')
lum = hist.get('log_L')
#from astropy.table import Table
#tbl = Table.read(folder, format='ascii.basic', guess=False, header_start=5, data_start=6)
plot(teff, lum, '.-')
#plot(teff[2518], lum[2518], 'ko')
gca().invert_xaxis()
ylabel(r'$\log L$')
xlabel(r'$\log Teff$')
Out[24]:
Devido ao parametro diffusin_cgs_solver
o arquivo history.data ficou todo bagunçado. Eu tive que editar o arquivo, fazendo com que eu perdesse os dados de alguns modelos
In [26]:
models = hist.get('model_number')
prof = ms.mesa_profile(folder, models[-1], num_type='nearest_model')
q = prof.get('q')
logq = - prof.get('logxq')
h1 = prof.get('h1')
#he3 = prof.get('he3')
he4 = prof.get('he4')
c12 = prof.get('c12')
#c13 = prof.get('c13')
#n13 = prof.get('n13')
n14 = prof.get('n14')
#n15 = prof.get('n15')
o16 = prof.get('o16')
#o18 = prof.get('o18')
ne20 = prof.get('ne20')
#ne22 = prof.get('ne22')
mg24 = prof.get('mg24')
plt.plot(logq, he4, color='darkolivegreen', lw=1.5, label=r'$He_4$')
plt.plot(logq, c12, color='k', lw=1.5, label=r'$C_{12}$')
plt.plot(logq, n14, color='r', lw=1.5, label=r'$N_{14}$')
plt.plot(logq, o16, color='g', lw=1.5, label=r'$O_{16}$')
plt.plot(logq, ne20, color='y', lw=1.5, label=r'$Ne_{20}$')
plt.plot(logq, mg24, color='m', lw=1.5, label=r'$Mg_{24}$')
plt.plot(logq, h1, color='b', lw=1.5, label=r'$H_1$')
plt.xlabel(r'$- \log (1 - q)$')
plt.ylabel('mass fraction')
plt.title('Perfil Quimico ' + ' - model n ' + str(model[-1]))
plt.legend(bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.)
plt.xlim(0, 20)
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