analise da taxa de energia por camadas no teste make o ne wd com alpha=2
In [1]:
import mesa as ms
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams["figure.figsize"] = (15., 10.)
In [2]:
folder = '/media/glauffer/Data/mesa/alpha2_r8845/LOGS'
# lendo os dados do history
s = ms.history_data(folder)
s.cols
Using old history.datasa file ...
reading ...100%
Out[2]:
{'burn_qtop_1': 156,
'burn_qtop_10': 174,
'burn_qtop_11': 176,
'burn_qtop_12': 178,
'burn_qtop_13': 180,
'burn_qtop_14': 182,
'burn_qtop_15': 184,
'burn_qtop_16': 186,
'burn_qtop_17': 188,
'burn_qtop_18': 190,
'burn_qtop_19': 192,
'burn_qtop_2': 158,
'burn_qtop_20': 194,
'burn_qtop_21': 196,
'burn_qtop_22': 198,
'burn_qtop_23': 200,
'burn_qtop_24': 202,
'burn_qtop_25': 204,
'burn_qtop_26': 206,
'burn_qtop_27': 208,
'burn_qtop_28': 210,
'burn_qtop_29': 212,
'burn_qtop_3': 160,
'burn_qtop_30': 214,
'burn_qtop_31': 216,
'burn_qtop_32': 218,
'burn_qtop_33': 220,
'burn_qtop_34': 222,
'burn_qtop_35': 224,
'burn_qtop_36': 226,
'burn_qtop_37': 228,
'burn_qtop_38': 230,
'burn_qtop_39': 232,
'burn_qtop_4': 162,
'burn_qtop_40': 234,
'burn_qtop_41': 236,
'burn_qtop_42': 238,
'burn_qtop_43': 240,
'burn_qtop_44': 242,
'burn_qtop_45': 244,
'burn_qtop_46': 246,
'burn_qtop_47': 248,
'burn_qtop_48': 250,
'burn_qtop_49': 252,
'burn_qtop_5': 164,
'burn_qtop_50': 254,
'burn_qtop_51': 256,
'burn_qtop_52': 258,
'burn_qtop_53': 260,
'burn_qtop_54': 262,
'burn_qtop_55': 264,
'burn_qtop_56': 266,
'burn_qtop_57': 268,
'burn_qtop_58': 270,
'burn_qtop_59': 272,
'burn_qtop_6': 166,
'burn_qtop_60': 274,
'burn_qtop_61': 276,
'burn_qtop_62': 278,
'burn_qtop_63': 280,
'burn_qtop_64': 282,
'burn_qtop_65': 284,
'burn_qtop_66': 286,
'burn_qtop_67': 288,
'burn_qtop_68': 290,
'burn_qtop_69': 292,
'burn_qtop_7': 168,
'burn_qtop_70': 294,
'burn_qtop_71': 296,
'burn_qtop_72': 298,
'burn_qtop_73': 300,
'burn_qtop_74': 302,
'burn_qtop_75': 304,
'burn_qtop_76': 306,
'burn_qtop_77': 308,
'burn_qtop_78': 310,
'burn_qtop_79': 312,
'burn_qtop_8': 170,
'burn_qtop_80': 314,
'burn_qtop_9': 172,
'burn_type_1': 155,
'burn_type_10': 173,
'burn_type_11': 175,
'burn_type_12': 177,
'burn_type_13': 179,
'burn_type_14': 181,
'burn_type_15': 183,
'burn_type_16': 185,
'burn_type_17': 187,
'burn_type_18': 189,
'burn_type_19': 191,
'burn_type_2': 157,
'burn_type_20': 193,
'burn_type_21': 195,
'burn_type_22': 197,
'burn_type_23': 199,
'burn_type_24': 201,
'burn_type_25': 203,
'burn_type_26': 205,
'burn_type_27': 207,
'burn_type_28': 209,
'burn_type_29': 211,
'burn_type_3': 159,
'burn_type_30': 213,
'burn_type_31': 215,
'burn_type_32': 217,
'burn_type_33': 219,
'burn_type_34': 221,
'burn_type_35': 223,
'burn_type_36': 225,
'burn_type_37': 227,
'burn_type_38': 229,
'burn_type_39': 231,
'burn_type_4': 161,
'burn_type_40': 233,
'burn_type_41': 235,
'burn_type_42': 237,
'burn_type_43': 239,
'burn_type_44': 241,
'burn_type_45': 243,
'burn_type_46': 245,
'burn_type_47': 247,
'burn_type_48': 249,
'burn_type_49': 251,
'burn_type_5': 163,
'burn_type_50': 253,
'burn_type_51': 255,
'burn_type_52': 257,
'burn_type_53': 259,
'burn_type_54': 261,
'burn_type_55': 263,
'burn_type_56': 265,
'burn_type_57': 267,
'burn_type_58': 269,
'burn_type_59': 271,
'burn_type_6': 165,
'burn_type_60': 273,
'burn_type_61': 275,
'burn_type_62': 277,
'burn_type_63': 279,
'burn_type_64': 281,
'burn_type_65': 283,
'burn_type_66': 285,
'burn_type_67': 287,
'burn_type_68': 289,
'burn_type_69': 291,
'burn_type_7': 167,
'burn_type_70': 293,
'burn_type_71': 295,
'burn_type_72': 297,
'burn_type_73': 299,
'burn_type_74': 301,
'burn_type_75': 303,
'burn_type_76': 305,
'burn_type_77': 307,
'burn_type_78': 309,
'burn_type_79': 311,
'burn_type_8': 169,
'burn_type_80': 313,
'burn_type_9': 171,
'c_core_mass': 26,
'center_ar36': 64,
'center_c12': 58,
'center_ca40': 65,
'center_cr56': 69,
'center_entropy': 51,
'center_fe54': 66,
'center_fe56': 67,
'center_h1': 56,
'center_he4': 57,
'center_n14': 59,
'center_ne20': 61,
'center_ni56': 68,
'center_o16': 60,
'center_omega': 48,
'center_omega_div_omega_crit': 49,
'center_s32': 63,
'center_si28': 62,
'center_ye': 50,
'conv_mx1_bot': 10,
'conv_mx1_top': 9,
'conv_mx2_bot': 12,
'conv_mx2_top': 11,
'epsnuc_M_1': 17,
'epsnuc_M_2': 18,
'epsnuc_M_3': 19,
'epsnuc_M_4': 20,
'epsnuc_M_5': 21,
'epsnuc_M_6': 22,
'epsnuc_M_7': 23,
'epsnuc_M_8': 24,
'fe_core_mass': 29,
'he_core_mass': 25,
'kh_timescale': 30,
'log_L': 33,
'log_LH': 31,
'log_LHe': 32,
'log_R': 35,
'log_Teff': 34,
'log_abs_mdot': 4,
'log_average_h1': 70,
'log_average_h2': 71,
'log_average_he3': 72,
'log_average_he4': 73,
'log_average_li7': 74,
'log_center_P': 47,
'log_center_Rho': 46,
'log_center_T': 45,
'log_dt': 6,
'log_g': 36,
'log_rotational_mdot_boost': 5,
'log_total_angular_momentum': 8,
'mix_qtop_1': 76,
'mix_qtop_10': 94,
'mix_qtop_11': 96,
'mix_qtop_12': 98,
'mix_qtop_13': 100,
'mix_qtop_14': 102,
'mix_qtop_15': 104,
'mix_qtop_16': 106,
'mix_qtop_17': 108,
'mix_qtop_18': 110,
'mix_qtop_19': 112,
'mix_qtop_2': 78,
'mix_qtop_20': 114,
'mix_qtop_21': 116,
'mix_qtop_22': 118,
'mix_qtop_23': 120,
'mix_qtop_24': 122,
'mix_qtop_25': 124,
'mix_qtop_26': 126,
'mix_qtop_27': 128,
'mix_qtop_28': 130,
'mix_qtop_29': 132,
'mix_qtop_3': 80,
'mix_qtop_30': 134,
'mix_qtop_31': 136,
'mix_qtop_32': 138,
'mix_qtop_33': 140,
'mix_qtop_34': 142,
'mix_qtop_35': 144,
'mix_qtop_36': 146,
'mix_qtop_37': 148,
'mix_qtop_38': 150,
'mix_qtop_39': 152,
'mix_qtop_4': 82,
'mix_qtop_40': 154,
'mix_qtop_5': 84,
'mix_qtop_6': 86,
'mix_qtop_7': 88,
'mix_qtop_8': 90,
'mix_qtop_9': 92,
'mix_type_1': 75,
'mix_type_10': 93,
'mix_type_11': 95,
'mix_type_12': 97,
'mix_type_13': 99,
'mix_type_14': 101,
'mix_type_15': 103,
'mix_type_16': 105,
'mix_type_17': 107,
'mix_type_18': 109,
'mix_type_19': 111,
'mix_type_2': 77,
'mix_type_20': 113,
'mix_type_21': 115,
'mix_type_22': 117,
'mix_type_23': 119,
'mix_type_24': 121,
'mix_type_25': 123,
'mix_type_26': 125,
'mix_type_27': 127,
'mix_type_28': 129,
'mix_type_29': 131,
'mix_type_3': 79,
'mix_type_30': 133,
'mix_type_31': 135,
'mix_type_32': 137,
'mix_type_33': 139,
'mix_type_34': 141,
'mix_type_35': 143,
'mix_type_36': 145,
'mix_type_37': 147,
'mix_type_38': 149,
'mix_type_39': 151,
'mix_type_4': 81,
'mix_type_40': 153,
'mix_type_5': 83,
'mix_type_6': 85,
'mix_type_7': 87,
'mix_type_8': 89,
'mix_type_9': 91,
'model_number': 1,
'mx1_bot': 14,
'mx1_top': 13,
'mx2_bot': 16,
'mx2_top': 15,
'num_backups': 317,
'num_retries': 316,
'num_zones': 7,
'o_core_mass': 27,
'si_core_mass': 28,
'star_age': 2,
'star_mass': 3,
'surf_avg_Lrad_div_Ledd': 44,
'surf_avg_j_rot': 37,
'surf_avg_omega': 38,
'surf_avg_omega_crit': 39,
'surf_avg_omega_div_omega_crit': 40,
'surf_avg_v_crit': 42,
'surf_avg_v_div_v_crit': 43,
'surf_avg_v_rot': 41,
'surface_c12': 53,
'surface_he4': 52,
'surface_n14': 54,
'surface_o16': 55,
'v_div_csound_surf': 315}
In [3]:
# dados por modelo
a = ms.mesa_profile(folder, 500, num_type='nearest_model')
a.cols
58 in profiles.index file ...
Found and load nearest profile for cycle 500
reading /media/glauffer/Data/mesa/alpha2_r8845/LOGS/profile16.data ...
reading ...100%
Out[3]:
{'abar': 29,
'am_log_D_DSI': 117,
'am_log_D_ES': 120,
'am_log_D_GSF': 121,
'am_log_D_SH': 118,
'am_log_D_SSI': 119,
'am_log_D_ST': 122,
'am_log_nu_ST': 123,
'am_log_sig': 116,
'burn_ar': 86,
'burn_c': 78,
'burn_ca': 87,
'burn_cr': 89,
'burn_fe': 90,
'burn_mg': 83,
'burn_n': 79,
'burn_na': 82,
'burn_ne': 81,
'burn_o': 80,
'burn_s': 85,
'burn_si': 84,
'burn_ti': 88,
'c12': 62,
'c12_c12': 91,
'c12_o16': 92,
'c13': 63,
'cno': 76,
'conv_vel_div_csound': 44,
'csound': 11,
'dlnT_dt': 55,
'dlnd_dt': 54,
'dq_ratio': 18,
'dynamo_log_B_phi': 125,
'dynamo_log_B_r': 124,
'entropy': 9,
'eps_grav': 34,
'eps_nuc': 32,
'eta': 15,
'f19': 70,
'fp_rot': 106,
'free_e': 28,
'ft_rot': 107,
'gradL': 51,
'gradT': 47,
'gradT_sub_grada': 37,
'grad_density': 50,
'grad_temperature': 49,
'grada': 27,
'gradr': 48,
'gradr_sub_grada': 126,
'h1': 59,
'he3': 60,
'he4': 61,
'i_rot': 104,
'j_rot': 105,
'ledoux_stable': 53,
'logL': 7,
'logP': 4,
'logPgas': 26,
'logR': 5,
'logRho': 3,
'logT': 2,
'log_D_conv': 38,
'log_D_mix': 42,
'log_D_ovr': 40,
'log_D_semi': 39,
'log_D_thrm': 41,
'log_J_div_M53': 100,
'log_J_inside': 101,
'log_abs_shear': 102,
'log_am_nu': 108,
'log_conv_vel': 43,
'log_j_rot': 99,
'log_mlt_D_mix': 45,
'log_omega': 98,
'log_opacity': 31,
'log_r_equatorial': 112,
'log_r_polar': 110,
'logdq': 17,
'logtau': 23,
'logxq': 58,
'luminosity': 6,
'mass': 56,
'mg24': 73,
'mixing_type': 10,
'mlt_mixing_length': 35,
'mlt_mixing_type': 36,
'mmid': 57,
'mu': 16,
'n13': 64,
'n14': 65,
'n15': 66,
'ne20': 71,
'ne22': 72,
'non_nuc_neu': 33,
'o16': 67,
'o16_o16': 93,
'o17': 68,
'o18': 69,
'omega': 97,
'omega_crit': 114,
'omega_div_omega_crit': 115,
'other': 96,
'pgas_div_ptotal': 25,
'photo': 95,
'pnhe4': 94,
'pp': 75,
'pressure': 24,
'pressure_scale_height': 46,
'q': 19,
'r_e_div_r_p': 113,
'r_equatorial': 111,
'r_polar': 109,
'radius': 20,
'sch_stable': 52,
'si28': 74,
'tau': 22,
'temperature': 21,
'total_energy': 13,
'tri_alfa': 77,
'v_div_csound': 12,
'v_div_r': 14,
'v_rot': 103,
'velocity': 8,
'ye': 30,
'zone': 1}
In [37]:
# Pelo arquivo profile_columns.list dentro da pasta defaults, o header eps_nuc seria a taxa de energia
# das reacoes nucleares menos a energia dos neutrinos ( ergs/g/s )
import numpy as np
e_nuc = a.get('eps_nuc')
zone = a.get('zone')
mass = a.get('mass')
q = -np.log(1 - a.get('q'))
-c:7: RuntimeWarning: divide by zero encountered in log
In [51]:
plt.plot(mass/9, e_nuc)
plt.title('modelo 500 - antes dos TP')
plt.xlim(0,1)
Out[51]:
(0, 1)
In [11]:
plt.plot(mass, e_nuc)
Out[11]:
[<matplotlib.lines.Line2D at 0x7fa6c8764990>]
In [8]:
print mass
[ 7.88791080e+00 7.88791080e+00 7.88791080e+00 7.88791080e+00
7.88791080e+00 7.88791080e+00 7.88791080e+00 7.88791080e+00
7.88791080e+00 7.88791080e+00 7.88791080e+00 7.88791079e+00
7.88791078e+00 7.88791077e+00 7.88791073e+00 7.88791068e+00
7.88791061e+00 7.88791054e+00 7.88791041e+00 7.88791023e+00
7.88790999e+00 7.88790971e+00 7.88790910e+00 7.88790798e+00
7.88790584e+00 7.88790221e+00 7.88789738e+00 7.88788977e+00
7.88787672e+00 7.88784809e+00 7.88783566e+00 7.88781784e+00
7.88779048e+00 7.88775952e+00 7.88772264e+00 7.88769817e+00
7.88768033e+00 7.88763862e+00 7.88760550e+00 7.88758459e+00
7.88753474e+00 7.88750434e+00 7.88747821e+00 7.88742358e+00
7.88737470e+00 7.88730949e+00 7.88726239e+00 7.88721807e+00
7.88717191e+00 7.88712333e+00 7.88708747e+00 7.88704058e+00
7.88699943e+00 7.88695763e+00 7.88691925e+00 7.88690006e+00
7.88688087e+00 7.88686099e+00 7.88684111e+00 7.88682124e+00
7.88680136e+00 7.88678077e+00 7.88676017e+00 7.88673958e+00
7.88671899e+00 7.88667633e+00 7.88663367e+00 7.88654529e+00
7.88645375e+00 7.88635893e+00 7.88626072e+00 7.88615900e+00
7.88605364e+00 7.88594453e+00 7.88583153e+00 7.88571451e+00
7.88559331e+00 7.88546781e+00 7.88533785e+00 7.88520327e+00
7.88506392e+00 7.88491963e+00 7.88477023e+00 7.88461554e+00
7.88437319e+00 7.88420447e+00 7.88393938e+00 7.88366176e+00
7.88346797e+00 7.88305970e+00 7.88273351e+00 7.88250712e+00
7.88227280e+00 7.88190694e+00 7.88138739e+00 7.88111393e+00
7.88082601e+00 7.88022978e+00 7.87954728e+00 7.87917420e+00
7.87873828e+00 7.87833913e+00 7.87788638e+00 7.87751789e+00
7.87723682e+00 7.87672007e+00 7.87643000e+00 7.87599491e+00
7.87525484e+00 7.87476403e+00 7.87449740e+00 7.87410330e+00
7.87370920e+00 7.87292101e+00 7.87233276e+00 7.87203864e+00
7.87174452e+00 7.87103657e+00 7.87032862e+00 7.86928264e+00
7.86823665e+00 7.86771366e+00 7.86693281e+00 7.86615196e+00
7.86537111e+00 7.86380941e+00 7.86302856e+00 7.86186720e+00
7.85945664e+00 7.85765661e+00 7.85585659e+00 7.85405657e+00
7.85225655e+00 7.84836087e+00 7.84641304e+00 7.84446520e+00
7.84047233e+00 7.83647947e+00 7.83248660e+00 7.83049017e+00
7.82849374e+00 7.82450087e+00 7.82050800e+00 7.81651514e+00
7.81252227e+00 7.80942801e+00 7.80633374e+00 7.80014521e+00
7.79395668e+00 7.78776815e+00 7.78157961e+00 7.77539108e+00
7.76920255e+00 7.76301402e+00 7.75662134e+00 7.75022867e+00
7.74383600e+00 7.73744332e+00 7.73105065e+00 7.72465798e+00
7.71187263e+00 7.69856202e+00 7.68525142e+00 7.67194082e+00
7.65863021e+00 7.64950467e+00 7.64037912e+00 7.63125357e+00
7.62212803e+00 7.60805389e+00 7.59397976e+00 7.57990562e+00
7.56583148e+00 7.54646523e+00 7.52709898e+00 7.50727191e+00
7.48744483e+00 7.45676189e+00 7.42607894e+00 7.40506252e+00
7.38404610e+00 7.35166309e+00 7.31928008e+00 7.29702425e+00
7.27476842e+00 7.22923506e+00 7.19404233e+00 7.15884961e+00
7.11068640e+00 7.08606456e+00 7.06144272e+00 7.02354885e+00
6.98565498e+00 6.93363901e+00 6.88048416e+00 6.82617388e+00
6.77069177e+00 6.71402154e+00 6.65614709e+00 6.59705250e+00
6.53672209e+00 6.50593125e+00 6.47514041e+00 6.41229232e+00
6.34816299e+00 6.31545046e+00 6.28273793e+00 6.21600309e+00
6.14794485e+00 6.11324747e+00 6.07855008e+00 6.00780618e+00
5.97192452e+00 5.93570110e+00 5.89913451e+00 5.82496638e+00
5.76838595e+00 5.71110752e+00 5.68211787e+00 5.65295230e+00
5.58416454e+00 5.53434792e+00 5.49410395e+00 5.44330940e+00
5.41254716e+00 5.37123288e+00 5.32956045e+00 5.27697667e+00
5.24513900e+00 5.20238905e+00 5.12672160e+00 5.07197851e+00
5.02778750e+00 4.94959714e+00 4.89305207e+00 4.84742014e+00
4.77834325e+00 4.73181361e+00 4.66130367e+00 4.62578109e+00
4.58999163e+00 4.54212438e+00 4.51796940e+00 4.46948314e+00
4.43289844e+00 4.39605080e+00 4.34677500e+00 4.30949184e+00
4.24700751e+00 4.19660727e+00 4.14586571e+00 4.09478528e+00
4.06907697e+00 4.01749366e+00 3.95255840e+00 3.91333460e+00
3.84758254e+00 3.80787419e+00 3.76800681e+00 3.73457427e+00
3.70114173e+00 3.66080536e+00 3.59317018e+00 3.55912714e+00
3.52508411e+00 3.48399652e+00 3.44958798e+00 3.41517945e+00
3.35283360e+00 3.30414201e+00 3.26221253e+00 3.24118125e+00
3.20608532e+00 3.17085974e+00 3.12847484e+00 3.10725087e+00
3.07171387e+00 3.05394538e+00 3.01477143e+00 2.99336599e+00
2.95753727e+00 2.93962290e+00 2.90640891e+00 2.89110928e+00
2.87580965e+00 2.84697298e+00 2.82055124e+00 2.79478183e+00
2.78189713e+00 2.76966476e+00 2.74699108e+00 2.72610845e+00
2.70200942e+00 2.67469398e+00 2.66103625e+00 2.64461684e+00
2.62819744e+00 2.61176232e+00 2.60078464e+00 2.58431813e+00
2.57334045e+00 2.56234304e+00 2.54676022e+00 2.53758938e+00
2.52841854e+00 2.51466229e+00 2.50731803e+00 2.49445684e+00
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In [12]:
# analizando para um modelo apos os "TP"
a2 = ms.mesa_profile(folder, 2090, num_type='nearest_model')
e_nuc2 = a2.get('eps_nuc')
zone2 = a2.get('zone')
mass2 = a2.get('mass')
58 in profiles.index file ...
Found and load nearest profile for cycle 2100
reading /media/glauffer/Data/mesa/alpha2_r8845/LOGS/profile48.data ...
reading ...100%
In [52]:
plt.plot(mass2/9, e_nuc2)
plt.title('modelo 2090 - depois dos TP')
plt.xlim(0,1)
Out[52]:
(0, 1)
In [28]:
# extraindo os dados de reacao pp, cno e 3_alpha do modelo apos os TP
cno2 = a2.get('cno')
tri_alfa2 = a2.get('tri_alfa')
pp2 = a2.get('pp')
In [35]:
import numpy as np
#plt.plot(zone2, tri_alfa2)
#plt.plot(zone2,pp2)
plt.plot(zone2, cno2)
#plt.plot(-np.log(1-a2.get('q')), cno2)
Out[35]:
[<matplotlib.lines.Line2D at 0x7fa6c78b36d0>]
In [27]:
# extraindo energia total
e2 = a2.get('total_energy')
plt.plot(mass2, e2)
Out[27]:
[<matplotlib.lines.Line2D at 0x7fa6c8202290>]
In [ ]:
Content source: glauffer/mesa
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