{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Import the numpy module to provide numerical functionality\n", "import numpy as np\n", "\n", "# Import the matplotlib.pyplot module to provide plotting functionality\n", "import matplotlib.pyplot as plt\n", "\n", "# Tell matplotlib.pyplot to do inline plots\n", "%matplotlib inline\n", "\n", "# Import the mesa-web module to simplify reading MESA-Web files\n", "\n", "import mesa_web as mw" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "dict_keys(['version_number', 'compiler', 'build', 'MESA_SDK_version', 'date', 'burn_min1', 'burn_min2', 'model_number', 'star_age', 'star_mass', 'log_L', 'log_R', 'log_Teff', 'log_center_T', 'log_center_Rho', 'log_center_P', 'center_h1', 'center_he3', 'center_he4', 'center_c12', 'center_n14', 'center_o16', 'center_ne20', 'center_mg24', 'center_si28', 'center_s32', 'center_ar36', 'center_ca40', 'center_ti44', 'center_cr48', 'center_fe52', 'center_fe54', 'center_fe56', 'center_ni56', 'center_degeneracy', 'center_ye', 'center_entropy', 'compactness_parameter', 'dynamic_timescale', 'kh_timescale', 'nuc_timescale', 'pp', 'cno', 'tri_alfa', 'log_LH', 'log_LHe', 'log_LZ', 'log_Lneu', 'he_core_mass', 'c_core_mass', 'o_core_mass', 'si_core_mass', 'fe_core_mass', 'he_core_radius', 'c_core_radius', 'o_core_radius', 'si_core_radius', 'fe_core_radius', 'max_abs_v_velocity', 'surf_avg_omega_div_omega_crit', 'log_total_angular_momentum', 'surf_avg_omega', 'surf_avg_v_rot', 'star_mdot'])\n" ] } ], "source": [ "# Read history data. Be sure to replace the MMDDNNNNNN with the\n", "# specific digits of your folder\n", "\n", "hist_data = mw.read_history('MESA-Web_Job_1008205501/trimmed_history.data')\n", "\n", "# Inspect the hist_data variable\n", "\n", "print(type(hist_data))\n", "print(hist_data.keys())" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function read_history in module mesa_web:\n", "\n", "read_history(filename)\n", " Read data from a MESA-Web history file\n", " \n", " Parameters\n", " ----------\n", " \n", " filename : string giving name of history file\n", " \n", " Returns\n", " -------\n", " \n", " hist_data: dict containing header and history data (see below for\n", " details)\n", " \n", " Header Data \n", " -----------\n", " \n", " The following keys/value pairs in the data dict contain header\n", " data -- i.e., scalars describing time-independent properties of\n", " the star. Where applicable, units are given in square brackets [].\n", " \n", " version_number -- version number of MESA\n", " initial_mass -- initial mass [Msun]\n", " initial_z -- initial metal mass fraction\n", " burn_min1 -- 1st limit for reported burning [erg/g/s]\n", " burn_min2 -- 2nd limit for reported burning [erg/g/s]\n", " \n", " History Data\n", " ------------\n", " \n", " The following keys/value pairs in the data dict contain history\n", " data -- i.e., arrays describing global properties of the star over\n", " a sequence of time-steps. Where applicable, units are given in\n", " square brackets [].\n", " \n", " model_number -- model number\n", " star_age -- stellar age [years]\n", " star_mass -- stellar mass [Msun]\n", " log_L -- log10(stellar luminosity [Lsun])\n", " log_R -- log10(stellar radius [Rsun])\n", " log_Teff -- log10(effective temperature [K])\n", " log_center_T -- log10(center temperature [K])\n", " log_center_Rho -- log10(center density [g/cm^3])\n", " log_center_P -- log10(center pressure [dyn/cm^2])\n", " center_h1 -- center 1H mass fraction\n", " center_he3 -- center 3He mass fraction\n", " center_he4 -- center 4He mass fraction\n", " center_c12 -- center 12C mass fraction\n", " center_n14 -- center 14N mass fraction\n", " center_o16 -- center 16O mass fraction\n", " center_ne20 -- center 20Ne mass fraction\n", " center_mg24 -- center 24Mg mass fraction\n", " center_si28 -- center 28Si mass fraction\n", " center_s32 -- center 32S mass fraction\n", " center_ar36 -- center 36Ar mass fraction\n", " center_ca40 -- center 40Ca mass fraction\n", " center_ti44 -- center 44Ti mass fraction\n", " center_cr48 -- center 48Cr mass fraction\n", " center_fe52 -- center 52Fe mass fraction\n", " center_fe54 -- center 54Fe mass fraction\n", " center_fe56 -- center 56Fe mass fraction\n", " center_ni56 -- center 56Ni mass fraction\n", " center_degeneracy -- center electron chemical potential [kB*T]\n", " center_ye -- center average charge per baryon [e]\n", " center_entropy -- center entropy [kB]\n", " compactness_parameter -- (m/Msun)/(R(m)/1000km) for m = 2.5 Msun\n", " dynamic_timescale -- dynamical timescale [s]\n", " kh_timescale -- Kelvin-Helmholtz timescale [s]\n", " nuc_timescale -- nuclear timescale [s]\n", " pp -- log10(total pp luminosity [Lsun])\n", " cno -- log10(total CNO luminosity [Lsun])\n", " tri_alfa -- log10(total triple-alpha luminosity [Lsun])\n", " log_LH -- log10(total H-burning luminosity, excluding neutrinos [Lsun])\n", " log_LHe -- log10(total He-burning luminosity, excluding neutrinos [Lsun])\n", " log_LZ -- log10(total metal-burning luminosity, excluding neutrinos [Lsun])\n", " log_Lneu -- log10(total neutrino luminosity [Lsun])\n", " he_core_mass -- mass of helium core [Msun]\n", " c_core_mass -- mass of carbon core [Msun]\n", " o_core_mass -- mass of oxygen core [Msun]\n", " si_core_mass -- mass of silicon core [Msun]\n", " fe_core_mass -- mass of iron core [Msun]\n", " he_core_radius -- radius of helium core [Rsun]\n", " c_core_radius -- radius of carbon core [Rsun]\n", " o_core_radius -- radius of oxygen core [Rsun]\n", " si_core_radius -- radius of silicon core [Rsun]\n", " fe_core_radius -- radius of iron core [Rsun]\n", " max_abs_v_velocity -- maximum absolute velocity \n", " surf_avg_omega_div_omega_crit -- surface average rotation angular frequency [Omega_crit]\n", " log_total_angular_momentum -- log10(total angular momentum [cm^2 g/s]\n", " surf_avg_omega -- surface average rotation angular frequency [rad/s]\n", " surf_avg_v_rot -- surface average rotation velocity [km/s]\n", " star_mdot -- mass-loss rate [Msun/year]\n", "\n" ] } ], "source": [ "# Print out documentation for the read_history function\n", "\n", "help(mw.read_history)\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Hertzsprung-Russel Diagram for Sun')" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Extract data from hist_data, using dict indexing\n", "\n", "log_Teff = hist_data['log_Teff'] # log(Teff/K)\n", "log_L = hist_data['log_L'] # log(L/Lsun)\n", "\n", "# Create the HR diagram\n", "\n", "plt.figure()\n", "\n", "plt.plot(log_Teff, log_L)\n", "\n", "plt.xlim(4.0, 3.5)\n", "plt.ylim(-1,4)\n", "\n", "plt.xlabel('log (T/K)')\n", "plt.ylabel('log (L/Lsun)')\n", "\n", "plt.title('Hertzsprung-Russel Diagram for Sun')" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "297\n", "log(L_H) at present : 0.0010016883770744924\n", "log(L) at present : 0.0009110072324429323\n", "log(Teff) at present : 3.760364770917345\n", "log(R) at present : 0.0029847982341584044\n", "Model number at present : 298\n" ] } ], "source": [ "# Extract log(LH) from hist_data\n", "\n", "log_LH = hist_data['log_LH']\n", "\n", "# Find where log(LH) is closest to zero (i.e., LH closest to 1 Lsun),\n", "# as representative of the present-day Sun. The np.abs() function \n", "# returns the absolute value. The np.argmin() function returns the \n", "# index of the smallest element\n", "\n", "i_pres = np.argmin(np.abs(log_LH))\n", "\n", "print(i_pres)\n", "\n", "# Print out values at this index\n", "\n", "print('log(L_H) at present :', log_LH[i_pres])\n", "print('log(L) at present :', log_L[i_pres])\n", "print('log(Teff) at present :', log_Teff[i_pres])\n", "print('log(R) at present :', hist_data['log_R'][i_pres])\n", "print('Model number at present :', hist_data['model_number'][i_pres])" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Create the HR diagram with the present-day Sun\n", "\n", "plt.figure()\n", "\n", "plt.plot(log_Teff, log_L)\n", "\n", "plt.scatter(log_Teff[i_pres], log_L[i_pres], color='r', label='Present Day')\n", "\n", "plt.xlim(4.0, 3.5)\n", "plt.ylim(-1,4)\n", "\n", "plt.xlabel('log (T/K')\n", "plt.ylabel('log (L/Lsun)')\n", "\n", "plt.title('Hertzsprung-Russel Diagram for Sun')\n", "\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'Age (Gyr)')" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot central hydrogen abundance versus age (measured since the\n", "# start of the calculation)\n", "\n", "X_c = hist_data['center_h1']\n", "Y_c = hist_data['center_he4']\n", "age = hist_data['star_age']\n", "\n", "plt.figure()\n", "\n", "plt.plot(age/1E9, X_c, color='b', label='X')\n", "plt.plot(age/1E9, Y_c, color='r', label='Y')\n", "\n", "plt.xlabel('Age (Gyr)')\n" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot central hydrogen abundance versus model number\n", "\n", "X_c = hist_data['center_h1']\n", "Y_c = hist_data['center_he4']\n", "mod_num = hist_data['model_number']\n", "\n", "plt.figure()\n", "\n", "plt.plot(mod_num, X_c, color='b', label='X')\n", "plt.plot(mod_num, Y_c, color='r', label='Y')\n", "\n", "plt.xlabel('Model Number')\n", "\n", "plt.legend()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }