{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "207c46b1-46c4-4b1c-b6e3-c83abee753cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab ipympl \n",
    "\n",
    "from nugridpy import mesa as ms\n",
    "from nugridpy import nugridse as nuse\n",
    "from nugridpy import utils as ut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7601072a-302b-467a-aee9-02a283fa24ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "stars = {\n",
    "         'M15Z02': {'mesa_dir': \"/data/nugrid/data/set1ext/set1.2/see_wind/M15.0Z2.0e-02/LOGS\",\n",
    "                    'mppnp_dir': \"/data/nugrid/data/set1ext/set1.2/ppd_wind/M15.0Z2.0e-02/H5_out\"},\n",
    "\n",
    "         'M12Z01': {'mesa_dir': \"/data/nugrid/data/set1ext/set1.1/see_wind/M12.0Z1.0e-02/LOGS\",\n",
    "                    'mppnp_dir': \"/data/nugrid/data/set1ext/set1.1/ppd_wind/M12.0Z1.0e-02/H5_out\"},\n",
    "         'M15Z01': {'mesa_dir': \"/data/nugrid/data/set1ext/set1.1/see_wind/M15.0Z1.0e-02/LOGS\",\n",
    "                    'mppnp_dir': \"/data/nugrid/data/set1ext/set1.1/ppd_wind/M15.0Z1.0e-02/H5_out\"},\n",
    "         'M20Z01': {'mesa_dir': \"/data/nugrid/data/set1ext/set1.1/see_wind/M20.0Z1.0e-02/LOGS\",\n",
    "                    'mppnp_dir': \"/data/nugrid/data/set1ext/set1.1/ppd_wind/M20.0Z1.0e-02/H5_out\"}\n",
    "        }\n",
    "\n",
    "star_keys = stars.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7e7deba6-2583-4f87-9ddf-2c435fc4f6d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using old star.logsa file ...\n",
      " reading ...100% \n",
      "\n",
      "Searching files, please wait.......\n",
      "Reading preprocessor files\n",
      "File search complete.\n",
      "Using old star.logsa file ...\n",
      " reading ...100% \n",
      "\n",
      "Searching files, please wait.......\n",
      "Reading preprocessor files\n",
      "File search complete.\n",
      "Using old star.logsa file ...\n",
      " reading ...100% \n",
      "\n",
      "Searching files, please wait.......\n",
      "Reading preprocessor files\n",
      "File search complete.\n",
      "Using old star.logsa file ...\n",
      " reading ...100% \n",
      "\n",
      "Searching files, please wait.......\n",
      "Reading preprocessor files\n",
      "File search complete.\n"
     ]
    }
   ],
   "source": [
    "for star in star_keys:\n",
    "    stars[star]['mesa']   = ms.star_log(stars[star]['mesa_dir'])\n",
    "    stars[star]['nugrid'] = nuse.se(stars[star]['mppnp_dir'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2be9c6ba-9230-4740-a5b6-def26412cb88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "423 in profiles.index file ...\n",
      "Found and load nearest profile for cycle 9200\n",
      "reading profile/data/nugrid/data/set1ext/set1.2/see_wind/M15.0Z2.0e-02/LOGS/log193.data ...\n",
      " reading ...100% \n",
      "\n"
     ]
    }
   ],
   "source": [
    "prof = ms.mesa_profile(stars[\"M15Z02\"][\"mesa_dir\"], num=9200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4a3263d8-7f18-4cf1-a46c-abf5f65c1a53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 200.0)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5ea26171a0984a65980d1d64b08a3d24",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "l = prof.get('mlt_mixing_length')\n",
    "vconv = np.power(10,prof.get('log_conv_vel'))\n",
    "mass = prof.get('mass')\n",
    "\n",
    "ifig=1;plt.close(ifig);plt.figure(ifig);\n",
    "\n",
    "plt.plot(mass, l/(vconv))\n",
    "plt.xlim(1.5,3)\n",
    "plt.ylim(0,200)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "90d866da-cd43-4a1d-8d86-0935d9d9c4a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotting(key, cycle, ifig, mass_range):\n",
    "\n",
    "    pt   = stars[key]['nugrid']\n",
    "    prof = ms.mesa_profile(stars[key]['mesa_dir'], num=cycle)\n",
    "    \n",
    "    params = ['mass', 'dcoeff', 'C-12', 'O-16', 'Ne-20', 'Si-28', 'P-31', 'Cl-35', 'K-39', 'Sc-45', 'Mg-24', 'S-32', 'radius']\n",
    "    loaded = {param: pt.get(cycle, param) for param in params}\n",
    "\n",
    "    close(ifig)\n",
    "\n",
    "    fig = plt.figure(num=ifig, figsize=(12,6))\n",
    "    fig.suptitle(f'cycle {cycle}')\n",
    "    gs = fig.add_gridspec(2, 2, height_ratios=[1, 0.4])\n",
    "\n",
    "    ax0 = fig.add_subplot(gs[0, 0])\n",
    "    ax1 = fig.add_subplot(gs[0, 1], sharex=ax0)\n",
    "    ax2 = fig.add_subplot(gs[1, :], sharex=ax0)\n",
    "    \n",
    "    ax0.set_xlim(mass_range[0], mass_range[1])\n",
    "    ax0.set_ylabel(r'$X$ (mass fraction)')\n",
    "    ax2.set_xlabel(r'$m/M_\\odot$')\n",
    "    \n",
    "    #i = np.argmin(np.abs(1.55-loaded['mass']))\n",
    "    #k = np.argmin(np.abs(1.78-loaded['mass']))\n",
    "    #q = np.argmin(np.abs(1.955-loaded['mass']))\n",
    "    #j = np.argmin(np.abs(2.1-loaded['mass']))\n",
    "    \n",
    "    #oshell = round((loaded['radius'][k] - loaded['radius'][i])*700*50,)\n",
    "    #intershell = round((loaded['radius'][q] - loaded['radius'][k])*700*50,)\n",
    "    #cshell = round((loaded['radius'][j] - loaded['radius'][q])*700*50,)\n",
    "    #print(\"\\nAssuming a 896^3 grid with 9 Mm radius\")\n",
    "    #print(f\"O-shell: {oshell} cells\\nInter-shell: {intershell} cells\\nC-shell: {cshell} cells\")\n",
    "\n",
    "    # the main isotopes   \n",
    "    for c, iso in enumerate(['C-12', 'O-16', 'Ne-20', 'Si-28'], 0):\n",
    "        ax0.semilogy(loaded['mass'], loaded[iso], color=ut.linestylecb(c)[2], linestyle=ut.linestylecb(c)[0], label=iso)\n",
    "        \n",
    "    ax0.set_ylim(1e-4, 1.5)\n",
    "    ax0.legend(loc='lower right', ncol=2, fontsize='xx-small')\n",
    "\n",
    "    # the odd-Z isotopes\n",
    "    for c, iso in enumerate(['P-31', 'Cl-35', 'K-39', 'Sc-45'], 4): \n",
    "        ax1.semilogy(loaded['mass'], loaded[iso], color=ut.linestylecb(c)[2], linestyle=ut.linestylecb(c)[0], label=iso)\n",
    "\n",
    "    ax1.set_ylim(1e-9, 1.5)\n",
    "    ax1.legend(loc='lower right', ncol=2, fontsize='xx-small')\n",
    "\n",
    "    # stellar structure\n",
    "    ax2.semilogy(loaded['mass'], loaded['dcoeff'], linestyle='dotted', color='black', label=r'$D_{\\mathrm{mix}}$')\n",
    "    \n",
    "    mmass, meps_si = prof.get('mass'), prof.get('burn_si')\n",
    "    ax2.semilogy(mmass, meps_si, linestyle='dotted', color='red', label=r'$\\epsilon_{\\mathrm{Si}}$')\n",
    "    ax2.legend(loc='upper left', ncol=2, fontsize='xx-small')\n",
    "    ax2.set_ylim(1e8, 1e25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a57fcfb4-c5fc-4469-9f99-2057c19af78e",
   "metadata": {},
   "outputs": [],
   "source": [
    "stars['M12Z01'][\"properties\"] = {\"size\": [1.53,2.13], \"model_num\": [11420,15000]}\n",
    "stars['M15Z01'][\"properties\"] = {\"size\": [1.61,3.13], \"model_num\": [8900,11440]}\n",
    "stars['M20Z01'][\"properties\"] = {\"size\": [1.35,4.95],  \"model_num\": [11900,14860]}\n",
    "stars['M15Z02'][\"properties\"] = {\"size\": [1.55,2.98], \"model_num\": [9160,11480]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0a783569-b339-4b53-bfe8-c34b0097627d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def close_idx(arr, data): return np.argmin(np.abs(arr-data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "af1d1be2-79c5-4cd5-b70a-5b4ae8f63531",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " reading ['iso_massf']...100%"
     ]
    }
   ],
   "source": [
    "for star in star_keys:\n",
    "    analysis = stars[star]['mesa']\n",
    "    prop = stars[star][\"properties\"]\n",
    "    \n",
    "    \n",
    "    mn = analysis.get('model_number')\n",
    "    age = analysis.get('star_age')\n",
    "    \n",
    "    i,j = close_idx(mn,prop[\"model_num\"][0]), close_idx(mn,prop[\"model_num\"][1])\n",
    "        \n",
    "    tau_OC = ((age[j]-age[i])*8760)\n",
    "    size_OC = prop[\"size\"][1] - prop[\"size\"][0]\n",
    "    \n",
    "    massini, massfin = stars[star]['nugrid'].get(prop[\"model_num\"][0], \"mass\"), stars[star]['nugrid'].get(prop[\"model_num\"][1], \"mass\")\n",
    "    k39ini, k39fin = stars[star]['nugrid'].get(prop[\"model_num\"][0], \"K-39\"), stars[star]['nugrid'].get(prop[\"model_num\"][1], \"K-39\")\n",
    "    k40ini, k40fin = stars[star]['nugrid'].get(prop[\"model_num\"][0], \"K-40\"), stars[star]['nugrid'].get(prop[\"model_num\"][1], \"K-40\")\n",
    "    k41ini, k41fin = stars[star]['nugrid'].get(prop[\"model_num\"][0], \"K-41\"), stars[star]['nugrid'].get(prop[\"model_num\"][1], \"K-41\")\n",
    "\n",
    "    res = []\n",
    "    for m,k in zip([massini, massini, massini, massfin, massfin, massfin], [k39ini, k40ini, k41ini, k39fin, k40fin, k41fin]):\n",
    "        \n",
    "        i,j = close_idx(m, prop[\"size\"][0]), close_idx(m, prop[\"size\"][1])\n",
    "        \n",
    "        avgk = np.average(k[i:j])\n",
    "        res.append(avgk)\n",
    "        \n",
    "    stars[star][\"properties\"][\"tau_OC\"] = tau_OC\n",
    "    stars[star][\"properties\"][\"size_OC\"] = size_OC\n",
    "    stars[star][\"properties\"][\"k39ini\"] = res[0]\n",
    "    stars[star][\"properties\"][\"k39fin\"] = res[3]\n",
    "    stars[star][\"properties\"][\"k39OP\"] = np.log10(res[3]/res[0])\n",
    "    stars[star][\"properties\"][\"k40ini\"] = res[1]\n",
    "    stars[star][\"properties\"][\"k40fin\"] = res[4]\n",
    "    stars[star][\"properties\"][\"k40OP\"] = np.log10(res[4]/res[1])\n",
    "    stars[star][\"properties\"][\"k41ini\"] = res[2]\n",
    "    stars[star][\"properties\"][\"k41fin\"] = res[5]\n",
    "    stars[star][\"properties\"][\"k41OP\"] = np.log10(res[5]/res[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "416ed36d-43ff-4c3f-ad06-e8390d2b4f2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NuGrid O-C Properties\n",
      "---------------------\n",
      "M15Z02\n",
      "\tDuration: 1.252 hr\n",
      "\tSize: 1.43 Msun\n",
      "\tK-39: 8.85e-06 9.43e-04 2.028\n",
      "\tK-40: 6.34e-07 5.85e-05 1.965\n",
      "\tK-41: 1.18e-06 2.38e-05 1.305\n",
      "M12Z01\n",
      "\tDuration: 0.029 hr\n",
      "\tSize: 0.6 Msun\n",
      "\tK-39: 7.31e-06 2.48e-04 1.531\n",
      "\tK-40: 2.92e-07 6.72e-06 1.362\n",
      "\tK-41: 4.85e-07 1.49e-06 0.487\n",
      "M15Z01\n",
      "\tDuration: 0.74 hr\n",
      "\tSize: 1.52 Msun\n",
      "\tK-39: 3.16e-06 3.01e-04 1.98\n",
      "\tK-40: 3.78e-07 3.24e-05 1.934\n",
      "\tK-41: 6.34e-07 2.71e-05 1.63\n",
      "M20Z01\n",
      "\tDuration: 5.43 hr\n",
      "\tSize: 3.6 Msun\n",
      "\tK-39: 3.29e-06 2.28e-04 1.841\n",
      "\tK-40: 4.54e-07 3.56e-06 0.895\n",
      "\tK-41: 6.95e-07 7.01e-07 0.004\n"
     ]
    }
   ],
   "source": [
    "print(\"NuGrid O-C Properties\")\n",
    "print(\"---------------------\")\n",
    "\n",
    "for star in star_keys:\n",
    "    print(star)\n",
    "    \n",
    "    _,_,tau_OC,size_OC,k39ini,k39fin,k39OP,k40ini,k40fin,k40OP,k41ini,k41fin,k41OP=stars[star][\"properties\"].values()\n",
    "    print('\\tDuration:',round(tau_OC,3),'hr')\n",
    "    print('\\tSize:',round(size_OC,2),'Msun')\n",
    "    print('\\tK-39:', f'{k39ini:.2e}', f'{k39fin:.2e}', round(k39OP,3))\n",
    "    print('\\tK-40:', f'{k40ini:.2e}', f'{k40fin:.2e}', round(k40OP,3))\n",
    "    print('\\tK-41:', f'{k41ini:.2e}', f'{k41fin:.2e}', round(k41OP,3))\n",
    "    \n",
    "    \"\"\"\n",
    "    mzams, Z = star[1:3], '0.'+star[4:]\n",
    "    \n",
    "    frontk39, backk39 = f\"{k39fin:.2e}\".split('e')\n",
    "    frontk40, backk40 = f\"{k40fin:.2e}\".split('e')\n",
    "    frontk41, backk41 = f\"{k41fin:.2e}\".split('e')\n",
    "\n",
    "    print(\n",
    "        f\"{mzams} & {Z} & {round(size_OC, 2)} & {round(tau_OC, 3)} & \"\n",
    "        f\"\\\\natlog{{{frontk39}}}{{{int(backk39)}}} & {round(k39OP, 3)} & \"\n",
    "        f\"\\\\natlog{{{frontk40}}}{{{int(backk40)}}} & {round(k40OP, 3)} & \"\n",
    "        f\"\\\\natlog{{{frontk41}}}{{{int(backk41)}}} & {round(k41OP, 3)} \"\n",
    "    )\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bc43e26c-be29-4ba8-88e8-57f64a87d793",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "352181d857404872be8cf3a1ca4dc0a8",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=500.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ifig=1; plt.close(ifig); plt.figure(ifig, figsize=(5,3))\n",
    "\n",
    "for star in list(star_keys)[1:] + [list(star_keys)[0]]:\n",
    "    \n",
    "    _,_,tau_OC,size_OC,k39ini,k39fin,k39OP,k40ini,k40fin,k40OP,k41ini,k41fin,k41OP=stars[star][\"properties\"].values()\n",
    "\n",
    "    mzams, Z = float(star[1:3]), float('0.'+star[4:])\n",
    "    agefin = stars[star]['mesa'].get('star_age')[-1] * 8760\n",
    "\n",
    "    plt.scatter(star, Z/0.02*k39fin*(size_OC/mzams)*(tau_OC/agefin),c='tab:blue')\n",
    "    plt.scatter(star, Z/0.02*k40fin*(size_OC/mzams)*(tau_OC/agefin),c='tab:orange')\n",
    "    plt.scatter(star, Z/0.02*k41fin*(size_OC/mzams)*(tau_OC/agefin),c='tab:red')\n",
    "    \n",
    "    \n",
    "plt.ylabel(r\"$Z/0.02\\cdot X \\cdot (\\Delta m/\\mathrm{M_{ZAMS}}) \\cdot (\\tau/\\mathrm{age})$\")\n",
    "\n",
    "plt.yscale('log')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1d3e6312-a758-4e06-8766-1aeec902e211",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=500.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ifig=2; plt.close(ifig); plt.figure(ifig, figsize=(5,3))\n",
    "\n",
    "for star in list(star_keys)[1:] + [list(star_keys)[0]]:\n",
    "    \n",
    "    _,_,tau_OC,size_OC,k39ini,k39fin,k39OP,k40ini,k40fin,k40OP,k41ini,k41fin,k41OP=stars[star][\"properties\"].values()\n",
    "\n",
    "    mzams, Z = int(star[1:3]), float('0.'+star[4:])\n",
    "    agefin = stars[star]['mesa'].get('star_age')[-1] * 8760\n",
    "\n",
    "    plt.scatter(star, Z/0.02*k39OP*(size_OC/mzams)*(tau_OC/agefin),c='tab:blue')\n",
    "    plt.scatter(star, Z/0.02*k40OP*(size_OC/mzams)*(tau_OC/agefin),c='tab:orange')\n",
    "    plt.scatter(star, Z/0.02*k41OP*(size_OC/mzams)*(tau_OC/agefin),c='tab:red')\n",
    "    \n",
    "    \n",
    "plt.ylabel(r\"$Z/0.02\\cdot X \\cdot (\\Delta m/\\mathrm{M_{ZAMS}}) \\cdot (\\tau/\\mathrm{age})$\")\n",
    "\n",
    "plt.yscale('log')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abb853a1-d6b8-425b-aedb-df8c98400060",
   "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}