Notebook criado para analisar o teste make_o_ne_wd utilizando os seguintes parametro: (teste make_o_ne_wd do test_suite do MESA)

Rotação desligada (parametros ..rotation_flag e ...v_flag comentados)
Difusão de elementos ligada (do_element_diffusion = .true.)
$\alpha_{MLT} = 1$ (o parametro gradT_excesslambda1 que define $\alpha{MLT}=1$ para toda a estrela)
Massa inicial $9 M_\odot$

Lendo os dados do MESA



In [1]:

    
# os dados estao no HD de dados dentro da pasta /media/glauffer/Data/mesa/o_ne_wd_rotation_off
import mesa as ms
import matplotlib.pyplot as plt
%matplotlib inline

folder = '/media/glauffer/Data/mesa/o_ne_wd_rotation_off/LOGS'
s = ms.history_data(folder)









    



Using old history.datasa file ...
 reading ...100%

Analisando a perda de massa



In [2]:

    
mass = s.get('star_mass')
age = s.get('star_age')
model = s.get('model_number')



In [10]:

    
fig = plt.figure(figsize=(15., 10.))
plt.plot(model, mass)
plt.title('Mass vs Model', fontsize=18)
plt.xlabel('model', fontsize=16)
plt.xlabel('Mass', fontsize=16)
plt.ylim(1,9.2)









    Out[10]:





(1, 9.2)

Identificando alguns pontos no HRD



In [40]:

    
t = s.get('log_Teff')
L = s.get('log_L')
fig = plt.figure(figsize=(15., 10.))
plt.plot(t, L)
plt.plot(t[799], L[799], 'ro')
plt.plot(t[1705], L[1705], 'ro')
plt.plot(t[1750], L[1750], 'go')
plt.gca().invert_xaxis()
plt.title(r'HRD $\alpha_{MLT}=1$', fontsize=18)
plt.xlabel(r'$\log T_{Eff}$', fontsize=16)
plt.ylabel(r'$\log L$', fontsize=16)
#plt.savefig('hrd_alpha2_dot.png')









    Out[40]:





<matplotlib.text.Text at 0x7f62c272ec90>



In [42]:

    
#identificando os modelos 800 e 1706 na perda de massa
fig = plt.figure(figsize=(15., 10.))
plt.plot(model, mass)
plt.plot(model[799], mass[799], 'ro')
plt.plot(model[1705], mass[1705], 'ro')
plt.plot(model[1750], mass[1750], 'go')
plt.title('Mass vs Model', fontsize=18)
plt.xlabel('model', fontsize=16)
plt.xlabel('Mass', fontsize=16)
plt.ylim(1,9.2)
plt.savefig('perda_massa_alpha1.png')



In [43]:

    
# Fazendo o HRD com informacoes no grafico

fig = plt.figure(figsize=(15., 10.))
plt.plot(t, L)
plt.plot(t[799], L[799], 'ro')
plt.plot(t[1705], L[1705], 'go')
plt.plot(t[1750], L[1750], 'yo')
plt.gca().invert_xaxis()
plt.title(r'HRD $\alpha_{MLT}=1$ , $M_f = $' + str(mass[-1]), fontsize=18)
plt.xlabel(r'$\log T_{Eff}$', fontsize=16)
plt.ylabel(r'$\log L$', fontsize=16)
plt.text(4.5, 3.0, 'model 800 (ponto vermelho): '+ str(mass[799]) + r'$M_\odot$')
plt.text(4.5, 2.9, 'model 1706 (ponto verde): '+ str(mass[1705]) + r'$M_\odot$')
plt.text(4.5, 2.8, 'model 1706 - model 800: '+ str(abs(mass[1705] - mass[799])) + r'$M_\odot$')

plt.text(4.5, 2.4, 'model 1750 (ponto amarelo): '+ str(mass[1750]) + r'$M_\odot$')
plt.text(4.5, 2.3, 'model 1750 - model 1706: '+ str(abs(mass[1750] - mass[1705])) + r'$M_\odot$')

#plt.plot(t[2174], L[2174], 'kx')
#plt.annotate('Perde H (modelo 2175)', xy=(t[2174], L[2174]), xytext=(t[2174], L[2174]+0.2),
#            arrowprops=dict(facecolor='black', width = 1.5, headwidth = 10.0, shrink=0.15),)
            #)
plt.savefig('hrd_alpha1_anotacoes_perda_massa.png')



In [45]:

    
# analisando a luminosidade de He
fig = plt.figure(figsize=(15., 10.))
plt.plot(model, s.get('log_LHe'))
plt.title(r'$\log L_{He}$ vs model $n$', fontsize=18)
plt.xlabel('model', fontsize=16)
plt.ylabel(r'$\log L_{He}$', fontsize=16)
plt.plot(model[799], s.get('log_LHe')[799], 'ro')
plt.plot(model[1705], s.get('log_LHe')[1705], 'go')
#plt.plot(models[349], s.get('log_LHe')[349], 'ko')
#plt.xlim(0,800)
plt.savefig('alpha1_log_L_he.png')



In [ ]: