{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "de48c1db-2a54-450d-a01f-a754ee24d65a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab ipympl \n",
    "\n",
    "import multizone_plot as mzp\n",
    "from multizone import mppnp_reader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e4b3a841-26a5-4d89-a49c-ce20238831c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "mixing_cases = []\n",
    "\n",
    "for case in [\"MLT\", \"PPM\", \"PPM3\", \"PPM10\", \"PPM50\"]:\n",
    "    for ing in [\"0.00E+00\",\"7.95E+01\",\"7.95E+02\",\"7.95E+03\"]:\n",
    "        res = mppnp_reader(initialpath = \"/data/niagara_project/projects/ocmerger_issa2025/CONDITIONS/initial_abund.dat\",\n",
    "                           surfpath = f\"/data/niagara_project/projects/ocmerger_issa2025/RUNS/{case}_RUNS/hif{ing}/H5_surf\",)\n",
    "        \n",
    "        mixing_cases.append(res)\n",
    "    \n",
    "for ing in [\"gosh\", \"gosh_stronger\", \"partial_merger\", \"partial_merger_stronger\"]:\n",
    "    res = mppnp_reader(initialpath = \"/data/niagara_project/projects/ocmerger_issa2025/CONDITIONS/initial_abund.dat\",\n",
    "                       surfpath = f\"/data/niagara_project/projects/ocmerger_issa2025/RUNS/GOSH_RUNS/{ing}/H5_surf\",)\n",
    "    mixing_cases.append(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "490de96e-6530-492c-8eb0-1f20ef046f8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def bar_OP(ifig, objs, isotopes, cycle):\n",
    "    '''Plot the mass fractions against the solar mass fractions.\n",
    "    INPUTS:\n",
    "        objs - the mppnp object you want to plot\n",
    "        isotopes - the isotopes you want to plot\n",
    "        cycle - the cycle you want to plot for\n",
    "        label - the label for the data\n",
    "        lines - true/False, whether to connect isotopes with the same Z with a line (use with separated = False)\n",
    "        separated - True/False, whether to plot the isotopes separate from each other.\n",
    "                  If True, shows shaded bands of max/min values instead of individual points.\n",
    "    '''\n",
    "    \n",
    "    plt.close(ifig); plt.figure(ifig, figsize=(15,6))\n",
    "    \n",
    "    if not isinstance(objs, list): objs = [objs]\n",
    "    \n",
    "    jmax = len(objs) - 1\n",
    "    ymin_glo, ymax_glo = 0, 0\n",
    "\n",
    "    xpos = np.linspace(1, len(isotopes), len(isotopes))\n",
    "    all_Xi_Xsol = []\n",
    "\n",
    "    # Collect data and display averages\n",
    "    for j, obj in enumerate(objs):\n",
    "        Xi_surf = obj.get('surf', cycle, isotopes)\n",
    "        Xi_solar = obj.get('initial', np.nan, isotopes)\n",
    "        Xi_Xsol = np.log10(Xi_surf/Xi_solar)\n",
    "        all_Xi_Xsol.append(Xi_Xsol)\n",
    "\n",
    "\n",
    "    # Process data for shaded regions\n",
    "    all_Xi_Xsol = np.array(all_Xi_Xsol)\n",
    "    min_Xi_Xsol = np.min(all_Xi_Xsol, axis=0)\n",
    "    max_Xi_Xsol = np.max(all_Xi_Xsol, axis=0)\n",
    "\n",
    "    # Create individual shaded regions\n",
    "    for i in range(len(isotopes)):\n",
    "        x_points = [xpos[i] - 0.4, xpos[i] + 0.4]\n",
    "        y_min = [min_Xi_Xsol[i], min_Xi_Xsol[i]]\n",
    "        y_max = [max_Xi_Xsol[i], max_Xi_Xsol[i]]\n",
    "\n",
    "        plt.fill_between(x_points, y_min, y_max, alpha=0.3, color='darkmagenta', edgecolor=None,zorder=5)\n",
    "        plt.plot(x_points, y_min, 'darkmagenta', linewidth=1.5,zorder=5)\n",
    "        plt.plot(x_points, y_max, 'darkmagenta', linewidth=1.5,zorder=5)\n",
    "        \n",
    "        print(isotopes[i], round(y_max[0] - y_min[0],2))\n",
    "        \n",
    "    # Set labels and limits\n",
    "    plt.ylabel(r'$\\mathrm{OP}=\\log_{10}(X_i / X_{i,\\mathrm{ini}})$', fontsize=14)\n",
    "\n",
    "    ymin, ymax = min(min_Xi_Xsol), max(max_Xi_Xsol)\n",
    "    ymin, ymax = objs[0].round_to_nearest(np.array([ymin, ymax]), 0.5)\n",
    "    xmin, xmax = -0.5, len(isotopes)+1\n",
    "\n",
    "    # Finalize plot\n",
    "    ax = plt.gca()\n",
    "    ax = mzp.plot_ticks(ax, ymin, ymax, xmin, xmax, 0.25, 0.5, noxlabel=True)\n",
    "    ax.tick_params(axis='y', labelsize=14)\n",
    "    \n",
    "    ymin, ymax = ax.get_ylim()\n",
    "    \n",
    "    # Add isotope labels\n",
    "    yshift = 0.3\n",
    "    for idx, isotope in enumerate(isotopes, 1):\n",
    "\n",
    "        ele, A = isotope.split('-')\n",
    "        iso = fr'$^{{{A}}}\\mathrm{{{ele}}}$'\n",
    "\n",
    "        text_y = ymax-yshift*1.2 if idx % 2 == 1 else ymin+yshift*0.4\n",
    "        if idx%2 == 1: plt.axvline(idx, color='lightgrey', lw=0.5,zorder=1)\n",
    "        \n",
    "        plt.text(idx-0.1, text_y, iso, ha='center', fontsize=14)\n",
    "    \n",
    "    if 0 < ymax and 0 > ymin: plt.axhline(0, color='grey', lw=3, zorder=1)\n",
    "\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2c9c520e-0eba-44d4-84df-2fa03a86b4d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "lightoddZ = ['P-31', 'Cl-35', 'Cl-37', 'K-39', 'K-40', 'K-41', 'Sc-45']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1d946688-28cf-4217-af77-6d03eb95305e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P-31 0.58\n",
      "Cl-35 1.76\n",
      "Cl-37 0.81\n",
      "K-39 2.42\n",
      "K-40 3.37\n",
      "K-41 2.13\n",
      "Sc-45 1.68\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c7e938182a034bd891d9596ea4b130bc",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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CIODu3bsqj1ld+BQhEbXYxo0bUVpaip07d6JPnz4YNmwYPD09YWBggNzcXHz99dcoKipqcM4kat/s7e2RlpYGX19fbN26FVVVVYonzajtS0pKwmuvvYbevXtj+PDh6NGjB0pKSnD58mWcPXsWUqkU+/btU8yD5ubmhl27diE0NBSurq6YMGEC7O3tcf/+faSnp2Ps2LHYtWuXZg9KJEywiKjFpFIpduzYgZkzZ+Kdd97BmTNnkJWVherqalhbWyMgIADBwcFKLwsm3WNra6tIsnbs2IGqqqp2cxHVdVu2bMHw4cNx6tQppKenK8ZW9ezZE3PmzMErr7yimKS2xuuvv47+/ftj+/btSEpKQnFxMaysrDB06FBMnTpVE4ehEhJBEARNB6Hr5HI5zM3NIZPJ6p1bRtvl5eUpzXVCuonnAQE8D+i/2vK50NprM8dgEREREYmMCRYRERGRyJhgEREREYlMpxOs0tJSLFu2DF5eXujRowc6dOiA7t27Y/jw4Th48CAqKiqaVE9qaiokEkm9PwkJCao9ECIiItIqOv0UYXFxMd555x0MGTIEY8eOhaWlJX7//XckJSVh7ty5+Ne//oWkpCRIpU3LQ729veHj41Nrvbu7u7iBExERkVbT6QTLwsICMpkMhoaGSusrKyvx4osvIjk5GUlJSRg7dmyT6vPx8eGEikRERKTbtwilUmmt5AoA9PX1MXHiRADAL7/8ou6wiIiIqI3T6R6s+lRXV+PEiRMAoJjevylycnKwa9cuPH78GDY2NvDz80PPnj1VFSYRERFpKSZYAMrLy7Fp0yYIgoCioiKcPn0a2dnZCA4OxqhRo5pcz9GjR3H06FHFsr6+PpYsWYKtW7dCT09PFaETERGRFmKChb8SrMjISMWyRCLB3//+d0RHRzdpf0tLS2zevBnjxo2Dg4MDSkpKcOHCBaxcuRI7d+6ERCJp0ru35HK50rKRkZHi/U1ERESkOmVlZSgrK1MsP31Nbi6dHoNVw9TUFIIgoKqqCvn5+YiNjUV8fDx8fHya9AG7urpixYoVcHV1hYmJCaysrDB+/HikpKTA0tISe/bswYMHDxqtx9bWFubm5oqfpiZ4RERE1DrR0dFK12BbW9tW1ccE6wlSqRQ2NjZYvHgx4uLikJGRgaioqBbX1717d4wfPx6VlZXIzMxstHx+fj5kMpniJzw8vMVtExERUdOFh4crXYPz8/NbVR9vEdbD398fwF+TiLZG165dAQAlJSWNljUzM2uzL3smIiJqy8QelsMerHrcvXsXAGBgYNCqemp6rhwcHFobEhEREbUROp1g/fDDD3j06FGt9Y8ePcKyZcsAAGPGjFGsLywsRHZ2NgoLC5XKX7p0qc76d+/ejZSUFDg5OcHT01PEyImIiEib6fQtwg8//BA7duzAiBEj4ODgADMzMxQUFCApKQlFRUUYOXIkli5dqigfExODyMhIREREKM3YPnnyZBgYGMDDwwM2NjYoKSnBxYsXcfnyZXTu3BmJiYmcpoGIiEiH6HSCNW7cONy9exfnz5/HhQsXUFxcDHNzcwwYMADTp0/H3Llzoa/f+Ee0ePFinDx5Eunp6SgqKoJUKoW9vT3efPNNhIWFwcbGRg1HQ0RERNpCIgiCoOkgdJ1cLoe5uTlkMlmbHeSel5cHe3t7TYdBGsbzgACeB/RfbflcaO21WafHYBERERGpAhMsIiIiIpExwSIiIiISGRMsIiIiIpExwSIiIiISGRMsIiIiIpExwSIiIiISGRMsIiIiIpExwSIiIiISGRMsLeLp6QkXFxfExsZqOhQiIiKdFBsbCxcXF3h6eraqHp1+F6G2ycrKarOvyiEi7SUIAioeVaitvcpHlSgvKVdbewYdDSCRSNTWHrVvISEhCAkJUbwqp6WYYBERtXMVjyqwx3SPpsNQmdDiUBiaGGo6DCIlvEVIREREJDL2YBERtXMGHQ0QWhyK3K9y1dLew4cPYWlpqZa2HMc4wqCjgVraImoOJlhERO2cRCKBoYkh9Duo50++npGe2trirUHSVrxFSERERCQyJlhEREREImOCRURERCQyJlhEREREImOCRURERCQynU6wSktLsWzZMnh5eaFHjx7o0KEDunfvjuHDh+PgwYOoqGj6zMfV1dXYu3cv3NzcYGxsDEtLS8yYMQM3b95U4REQERGRNtLpBKu4uBjvvPMOJBIJxo4di2XLlmHixIkoKCjA3LlzMW7cOFRXVzeprkWLFiE0NBSCICA0NBSjR4/Gxx9/DE9PT+Tk5Kj4SIiIiEib6PQ8WBYWFpDJZDA0VJ5HpbKyEi+++CKSk5ORlJSEsWPHNlhPSkoK4uPj4eXlhVOnTinqmzlzJsaMGYPXX38dJ0+eVNlxEBERkXbR6R4sqVRaK7kCAH19fUycOBEA8MsvvzRaz4EDBwAAGzZsUKovMDAQPj4+SE5Oxu3bt0WKmoiIiLSdTidY9amursaJEycAAP3792+0fGpqKkxMTDB8+PBa2wICAgAAaWlp4gZJREREWkunbxHWKC8vx6ZNmyAIAoqKinD69GlkZ2cjODgYo0aNanDfkpIS3Lt3D/3794eenl6t7U5OTgDAcVhEREQ6hAkW/kqwIiMjFcsSiQR///vfER0d3ei+MpkMAGBubl7ndjMzM6VyDZHL5UrLRkZGMDIyanQ/IiIiap2ysjKUlZUplp++JjcXbxECMDU1hSAIqKqqQn5+PmJjYxEfHw8fH59Wf8DNYWtrC3Nzc8VPUxI8IiIiar3o6Gila7CtrW2r6mOC9QSpVAobGxssXrwYcXFxyMjIQFRUVIP71PRc1ddDVZOg1dfD9aT8/HzIZDLFT3h4eDOPgIiIiFoiPDxc6Rqcn5/fqvp4i7Ae/v7+AP4awN4QExMTWFtbIzc3F1VVVbXGYdWMvaoZi9UQMzMzxS1FIiIiUh+xh+WwB6sed+/eBQAYGBg0Wtbb2xslJSXIyMiota1m/isvLy9xAyQiIiKtpdMJ1g8//IBHjx7VWv/o0SMsW7YMADBmzBjF+sLCQmRnZ6OwsFCp/MKFCwEAa9asQXl5uWJ9UlISUlNT4e/vD3t7e1UcAhEREWkhnb5F+OGHH2LHjh0YMWIEHBwcYGZmhoKCAiQlJaGoqAgjR47E0qVLFeVjYmIQGRmJiIgIrFu3TrHe19cX8+fPR3x8PAYNGoSxY8fi3r17OHbsGCwsLLB3714NHB0RERFpik4nWOPGjcPdu3dx/vx5XLhwAcXFxTA3N8eAAQMwffp0zJ07F/r6TfuI9u/fDzc3N8TFxWH37t0wNTXFxIkTERUVhV69eqn4SIiIiEibSARBEDQdhK6Ty+UwNzeHTCZrs4Pc8/LyeBuUeB5ouRuf31BLOw8ePICVlZVa2ur1Ev8HVpu15b8Jrb026/QYLCIiIiJVYIJFREREJDImWEREREQiY4JFREREJDImWEREREQiY4JFREREJDImWEREREQiY4JFREREJDImWEREREQiY4KlRTw9PeHi4oLY2FhNh0JERKSTYmNj4eLiAk9Pz1bVI/q7CFNSUnD69GlkZGTgzp07KCwsRMeOHWFpaQk3Nzd4e3tj3Lhx6N69u9hNt3lZWVlt9lU5RERE7UFISAhCQkIUr8ppKVESrJKSEuzZswcHDhxAXl4eal5v2KFDB1hYWODx48f4z3/+g2vXruH999+HgYEBXnrpJSxduhTDhw8XIwQiIiIirdHqW4TvvvsuevfujX/84x8wMzPDhg0bcPr0achkMjx69Ah37txBUVERKioqkJ2djUOHDmHq1KlITk6Gl5cXJk2ahNzcXDGOhYiIiEgrtDrBWrJkCV588UVcu3YNV65cwapVq+Dr64tOnToplZNIJHB2dsasWbNw5MgR/Prrr9i/fz+uXr2KI0eOtDYMIiIiIq3R6luE33//PZydnZu9n7GxMebPn4/g4GDcvn27tWEQERERaY1W92C1JLl6kp6eHhwdHVsbBhEREZHW4DQNRERERCITfZqGJy1fvhwREREwMTHB8uXLGyz79ttvqzIUIiLScYIgoOJRhabDUAmDjgaQSCSaDoOeoNIEKysrCxUVFYp/14cnBRERqVrFowrsMd2j6TBUIrQ4FIYmhpoOg56g0gQrJSWlzn8TERERtWcqTbC0XUFBAf73f/8XX331FbKzs3H//n1YWFhg+PDhWL58OYYOHdqkelJTU+Hr61vv9oMHDyIoKEikqImIqCUMOhogtDgUuV+1r7kXHcc4wqCjgabDoKeoNcG6cuUKzpw5gwcPHqC6ulppmybGYO3duxdbtmxBr1694O/vD0tLS+Tk5OCTTz7BJ598gqNHj2LatGlNrs/b2xs+Pj611ru7u4sXNBERtYhEIoGhiSH0O7SvvgXeGtROajvLdu/ejaVLl8LJyQnW1tZK4640NQZryJAhSE1Nhbe3t9L6s2fPYtSoUVi8eDEmTJgAIyOjJtXn4+ODdevWqSBSIiIiakvUlmBt3boVsbGxWLx4sbqabNSkSZPqXD9y5Ej4+voiOTkZ169fh4eHh5ojIyIiorZMbQlWSUkJ/P391dVcqxkY/HU/W1+/6R9RTk4Odu3ahcePH8PGxgZ+fn7o2bOnqkIkIiIiLaW2BCs4OBjHjx9vdD4sbXD79m18/fXXsLa2hpubW5P3O3r0KI4ePapY1tfXx5IlS7B161bo6empIlQiIiLSQmod6RcdHY3k5GS4ubkpeohqaMtEoxUVFZg1axbKysqwZcuWJiVGlpaW2Lx5M8aNGwcHBweUlJTgwoULWLlyJXbu3AmJRILt27c3Wo9cLldaNjIyavL4LyIiImq5srIylJWVKZafviY3l9oSrMuXL8Pd3R1VVVW4cuWK0jZtmWi0uroaQUFBSE9Px4IFCzBr1qwm7efq6gpXV1fFsomJCcaPH4+hQ4diwIAB2LNnD1asWAErK6sG67G1tVVajoiI4KB5IiIiNYiOjkZkZKRo9aktwdL2iUarq6sxd+5cHD16FK+++irefffdVtfZvXt3jB8/HvHx8cjMzMRLL73UYPn8/HyYmZkpltl7RUREpB7h4eFYtmyZYlkul9fq+GiO9jUZSAtVV1cjODgYhw8fxowZM5CQkACpVJz3YHft2hXAX4P8G2NmZqaUYBEREZF6iD0sR+df9vxkcjVt2jQcOXJE1AHpmZmZAAAHBwfR6iSipuMLfolIE3T6Zc81twUPHz6MKVOmIDExscHkqrCwEIWFhejatauiZwoALl26hMGDB9cqv3v3bqSkpMDJyQmenp4qOQYiahhf8EtEmqDTL3tev349Dh06BFNTUzg7O2Pjxo21ykyYMEHxqpuYmBhERkbWGnw+efJkGBgYwMPDAzY2NigpKcHFixdx+fJldO7cudHEjYiIiNoXnR6DdevWLQBAcXExoqKi6izj4ODQ6LsEFy9ejJMnTyI9PR1FRUWQSqWwt7fHm2++ibCwMNjY2IgcORE1FV/wS0SaIBEEQdB0ELpOLpfD3NwcMpmszQ5yz8vLg729vabDIA3T5vPgxuc3NB2CqHq91KvZ+6jrM3jw4EGj09KIRZs/B3VpyWegLtr8N6Exrb02i/OoHBEREREpMMEiIiIiEplWJFhSqRR+fn64dOmSpkMhIiIiajWtSLDee+89eHt7IzQ0VNOhEBEREbWaShOsJUuWoLS0FAAgk8nqLRcUFISIiAhkZGSoMhwiIiIitVDpNA0ymQylpaXo0KEDunTpAjs7OwwcOBADBgxQ/NfJyYkzERMREVG7otIE6/Dhw4p/Z2dn4+rVq7h27RquXr2KI0eO4Pbt2+jYsSNcXV0Vr5QhIiIiauvUNtGos7MznJ2dMWXKFMU6mUymSLqIiIiI2gtRxmAlJSW1aD9zc3N4eXnh9ddfFyMMIiIiIq0gSoI1fvx4xMfHi1GVTvP09ISLiwtiY2M1HQoREZFOio2NhYuLCzw9PVtVjyi3CHv27IlFixbh1q1bdb4wuS4///wznJ2dxWi+3cjKymqzr8ohIiJqD0JCQhASEqJ4VU5LidKDlZmZieeeew7R0dGYPXs2Kisr6y17/fp1TJ8+Ha6urmI0TURERKR1REmwrKyskJ6ejjFjxiAxMRGjR4+GXC5XKpOVlYXx48fD3d0dH374IQYNGiRG00RERERaR7SJRjt27IhPP/0Ur732Gs6cOYMRI0YgPz8f6enpCAgIwPPPP4/PP/8cw4cPx4kTJzgtAxEREbVbok7TIJVKsW/fPjg6OmLFihXo27cvSktLIQgCRo0ahTVr1sDLy0vMJomIiIi0jujzYH322Wf46KOPAACPHz+GRCJBVFQUwsPDxW6KiIiISCuJdovw2LFjGDhwICZOnIhLly5h6tSpOHz4MMzNzREZGYnExESxmiIiIiLSaqL0YPXt2xc5OTnQ09PDrFmzsGrVKsUUDIMGDUJgYCDmzJmD/Px89mQRERFRuydKD1Zubi7mz5+Pn376CQkJCUrzW7m4uODChQtwc3PD6tWrsXjxYgiCIEazRERERFpJlB6smzdvomfPnvVu79GjB86dO4dJkyZh//79KCgowLFjx2BsbCxG80RERERaRZQerIaSqxqmpqb46quvMGvWLHzxxRfw8fERo+lWKSgowK5du+Dv7w87OzsYGhqie/fumDx5crOnkaiursbevXvh5uYGY2NjWFpaYsaMGbh586aKoiciIiJtJUqCtW/fPhQUFDRaTl9fH4cOHUJ4eDi+/fZbMZpulb1792Lp0qW4efMm/P39ERYWhhEjRuDTTz/F3/72Nxw7dqzJdS1atAihoaEQBAGhoaEYPXo0Pv74Y3h6eiInJ0eFR0FERETaRpRbhK+//jqWLFmCQYMGYcKECXj55Zfh5uZWb/moqCg4ODiI0XSrDBkyBKmpqfD29lZaf/bsWYwaNQqLFy/GhAkTYGRk1GA9KSkpiI+Ph5eXF06dOgVDQ0MAwMyZMzFmzBi8/vrrOHnypMqOg4iIiLSLKD1YWVlZWLVqFcrLy7FmzRq4u7ujV69eCAsLQ1paGqqrq2vts2DBAjGabpVJkybVSq4AYOTIkfD19cXvv/+O69evN1rPgQMHAAAbNmxQJFcAEBgYCB8fHyQnJ+P27dviBU5ERERaTZQEa/DgwdiwYQOuXr2KmzdvYtu2bbCzs8OePXvg5+eHbt26ITg4GJ988gkePXokRpMqZ2BgAOCv25qNSU1NhYmJCYYPH15rW0BAAAAgLS1N3ACJiIhIa4k20WgNBwcHLF26FCkpKfj1119x8OBBjBw5Eh999BEmTZqErl274qWXXsJ7772HBw8eiN28KG7fvo2vv/4a1tbWDd7qBICSkhLcu3cPjo6O0NPTq7XdyckJADgOi4iISIeI/qqcJ1lYWGD27NmYPXs2ysrKcOrUKXz66af44osv8OWXX0IqleL555/HuXPnVBlGs1RUVGDWrFkoKyvDli1b6kyaniSTyQAA5ubmdW43MzNTKtcQuVyutGxkZNTo+C8iIiJqvbKyMpSVlSmWn74mN5foPVj1MTIywrhx43DgwAHcvXsXGRkZCAsLQ1FRkbpCaFR1dTWCgoKQnp6OBQsWYNasWWpt39bWFubm5oqf6OhotbZPRESkq6Kjo5Wuwba2tq2qT6U9WPWRSCQYNmwYhg0bhi1btmgihFqqq6sxd+5cHD16FK+++irefffdJu1X03NVXw9VTQZcXw/Xk/Lz8xU9XgDYe0VERKQm4eHhWLZsmWJZLpe3KslSaQ/WkiVLUFpaCqBpt8g0pbq6GsHBwTh06BBmzJiBhIQESKVN+2hMTExgbW2N3NxcVFVV1dpeM/aqZixWQ8zMzJR+mGARERGph5GRUa3rcGuoNMGSyWSKBKtLly5wcHDA+PHjsWbNGnz00Uf4+eefNf5ewprk6vDhw5g2bRqOHDnS6Lirp3l7e6OkpAQZGRm1ttXMf+Xl5SVKvERERKT9VJpgHT58GJ07dwYAZGdnY+vWrRgwYACuXr2Kv//97+jbty86deqEoUOHqjKMetXcFjx8+DCmTJmCxMTEBpOrwsJCZGdno7CwUGn9woULAQBr1qxBeXm5Yn1SUhJSU1Ph7+8Pe3t71RwEERERaR1RxmAlJSUhMDCwwTLOzs5wdnbGlClTFOtkMhmuXr2Ka9euiRFGs61fvx6HDh2CqakpnJ2dsXHjxlplJkyYAHd3dwBATEwMIiMjERERgXXr1inK+Pr6Yv78+YiPj8egQYMwduxY3Lt3D8eOHYOFhQX27t2rpiMiIiIibSBKgjV+/Hjs27cP8+fPb9Z+5ubm8PLy0tjts1u3bgEAiouLERUVVWcZBwcHRYLVkP3798PNzQ1xcXHYvXs3TE1NMXHiRERFRaFXr14iRk1ERETaTiKIMAjK0dERt2/fRnh4eJ29QHX5+eef4ezs3Nqm2wW5XA5zc3PIZLJWD6rTlLy8PN4GJa0+D258fkPTIYiq10vN/x83dX0GDx48gJWVlVra0ubPQV1a8hmoizb/TWhMa6/NoozByszMxHPPPYfo6GjMnj0blZWV9Za9fv06pk+fDldXVzGaJiIiItI6oiRYVlZWSE9Px5gxY5CYmIjRo0fXmgE1KysL48ePh7u7Oz788EMMGjRIjKaJiIiItI5oTxF27NgRn376KV577TWcOXMGI0aMQH5+PtLT0xEQEIDnn38en3/+OYYPH44TJ04gMzNTrKaJiIiItIqoM7lLpVLs27cPjo6OWLFiBfr27YvS0lIIgoBRo0ZhzZo1nA+KiIiI2j3RX5Xz2Wef4aOPPgIAPH78GBKJBFFRUQgPDxe7KSIiIiKtJNotwmPHjmHgwIGYOHEiLl26hKlTp+Lw4cMwNzdHZGQkEhMTxWqKiIiISKuJ0oPVt29f5OTkQE9PD7NmzcKqVasUUzAMGjQIgYGBmDNnDvLz89mTRURERO2eKD1Yubm5mD9/Pn766SckJCQozW/l4uKCCxcuwM3NDatXr8bixYs1/v5BIiIiIlUSpQfr5s2b6NmzZ73be/TogXPnzmHSpEnYv38/CgoKcOzYMRgbG4vRPBERETWBIAioeFShtvYqH1WivKS88YIiMOhoAIlEopa2mkKUBKuh5KqGqakpvvrqK8ybNw9HjhyBj48Pp2p4iqenJ/T09BASEoKQkBBNh0NERO1MxaMK7DHdo+kwVCK0OBSGJoatric2NhaxsbGoqqpqVT2iP0XYYGP6+jh06BBsbGywefNmdTbdJmRlZbXZV+UQERG1BzWdHDWvymkptSZYNaKiouDg4KCJpomIiHSWQUcDhBaHIverXLW09/DhQ1haWqq8HccxjjDoaKDydppDIwkWACxYsEBTTZOOUPdYA3XStrEGRNQ2SCQSGJoYQr+Dei7/ekZ6amlLjFuDYmv1UY8ePRobNmyAp6dns/ctKSnB3r170alTJ445ItFxrAEREWlKq6dpePjwIZ5//nn4+vri4MGDkMlkje5z8eJFvP7667C3t8eGDRvQrVu31oZBREREpDVa3YN16dIlHDp0CJGRkZg3bx4WLFiAPn36YPDgwejWrRs6d+6M0tJS/Pbbb/jpp5/w7bff4s8//4Senh6mT5+OjRs3ws7OToxjIVKi7rEG6qKNYw2IiEiZKDdG58yZg9mzZ+Orr77CwYMHkZqaWuercaRSKQYMGICJEydi/vz5sLa2FqN5ojqpe6yBuvDWIBGR9hPtyiORSDB27FiMHTsWAPDjjz/izp07KCoqgrGxMSwtLeHq6tqqRx6JiIiI2gKV/a99v3790K9fP1VVT0RERKS1RHkXIRERERH9l1oSrOLiYsTFxSE4OBiBgYEIDAxEcHAwDhw4gOLiYnWEUK/ExEQsWrQIHh4eMDIygkQiQUJCQrPqSE1NhUQiqfenufURERFR26by0b/Xrl1DQEAAKisr4ePjg2effRYA8ODBA6xatQoRERFITk5G//79VR1KnVavXo28vDx07doV1tbWyMvLa3Fd3t7e8PHxqbXe3d295QESERFRm6PyBCskJASjR49GfHw89PT0lLZVVlZi4cKFWLx4Mc6ePavqUOoUHx8PJycn2NvbY/PmzQgPD29xXT4+Pli3bp14wREREVGbpPIE69tvv0VcXFyt5Ar46+XPb731FgYNGqTqMOr1wgsvaKxtIiIiap9UnmB169YN3333Xb1PFH733XewsrJSdRhqkZOTg127duHx48ewsbGBn58fevbsqemwiIiISM1UnmAtXboU8+fPx3fffYdRo0YpXovz66+/4vTp03j33XexefNmVYehFkePHsXRo0cVy/r6+liyZAm2bt1aZw8eERERtU8qT7DeeOMNWFpaYteuXdizZw+qqqoAAHp6enjuuecQHx+PGTNmqDoMlbK0tMTmzZsxbtw4ODg4oKSkBBcuXMDKlSuxc+dOSCQSbN++vdF65HK50rKRkRGMjIxUFTYRERH9n7KyMpSVlSmWn74mN5dapmmYOXMmvvnmGzx69AgFBQUoKCjAo0eP8M0337T55AoAXF1dsWLFCri6usLExARWVlYYP348UlJSYGlpiT179uDBgweN1mNrawtzc3PFT3R0tBqiJyIioujoaKVrsK2tbavqU+tEowYGBrC2toa1tTUMDNr/y2q7d++O8ePHo7KyEpmZmY2Wz8/Ph0wmU/y05olGIiIiarrw8HCla3B+fn6r6tP4W3B///13fP7555g9e7amQ1GJrl27AgBKSkoaLWtmZgYzMzNVh0RERERPEXtYjsZflXP79m0EBwdrOgyVqem5cnBw0GwgREREpDYq78G6fft2g9vv3r2r6hBEU1hYiMLCQnTt2lXRMwUAly5dwuDBg2uV3717N1JSUuDk5ARPT091hkpEREQapPIEy8HBARKJpN7tgiA0uF3V4uPjce7cOQDA9evXFetSU1MBACNGjMD8+fMBADExMYiMjERERITSjO2TJ0+GgYEBPDw8YGNjg5KSEly8eBGXL19G586dkZiYyGkaiIiIdIjKE6wuXbpgw4YN8Pb2rnN7dnY2pk6dquow6nXu3DkcOnRIaV1GRgYyMjIUyzUJVn0WL16MkydPIj09HUVFRZBKpbC3t8ebb76JsLAw2NjYqCR2IiIi0k4qT7AGDx6M33//Ha6urnVur6yshCAIqg6jXgkJCUhISGhS2XXr1tX5rsEVK1ZgxYoV4gZGREREbZbKE6zFixc3+ASdnZ0dDh48qOowiIiIiNRG5QnWxIkTG9zepUsXzJkzR9VhEBEREamNxqdpICIiImpvRO/Bmjt3bqNlpFIpzMzM0KdPH4wbNw49e/YUOwwiIiIijRE9wUpISFBMu1DX4HWJRKK0fsmSJVi7di1Wr14tdihEREREGiH6LcIbN25g3LhxsLKywqZNm5CWlobs7GykpaVh06ZN6NatG15++WVkZmYiLi4OPXr0QEREBI4dOyZ2KEREREQaIXoP1rFjx5CZmYmrV6+iW7duivXOzs4YOXIkgoKC4O7ujpSUFCxfvhyBgYFwcXHBvn37MG3aNLHDISIiIlI70Xuw/vnPf2Lq1KlKydWTunfvjilTpuDAgQMAgJ49e2LcuHG4evWq2KEQERERaYToCdadO3cafRt1hw4dcOfOHcWynZ0dSktLxQ6FiIiISCNET7B69uyJTz75pN6EqbS0FJ988onSk4MPHjxAly5dxA6FiIiISCNET7DmzZuHGzduYMSIEfjss89QVFQEACgqKsJnn32GESNG4ObNm0rTOZw9exYDBw4UO5Q2x9PTEy4uLoiNjdV0KERERDopNjYWLi4u8PT0bFU9og9yX758OX788UckJiYqZnGXSqWorq4G8NfUDTNnzsTKlSsBAL/++ivGjh2L0aNHix1Km5OVlQUzMzNNh0FERKSzQkJCEBISArlcDnNz8xbXI3qCpaenh8OHDyMoKAhHjhzBtWvXIJfLYWZmhoEDB+L//b//h1GjRinKd+vWDTt37hQ7DCIiIiKNUdm7CP38/ODn56eq6omIiIi0Ft9FSERERCQylfVgZWRkICEhAVeuXFHcInR3d8ecOXMwYsQIVTVLREREpHEqSbCWLl2KPXv2KN45WPP+wUuXLuG9997DG2+8gR07dqiiaSIiIiKNE/0W4aFDh7B79244OTnh/fffx927d1FZWYl79+7h6NGjcHZ2xu7du3H48GGxmyYiIiLSCqInWO+88w5sbGyQmZmJGTNmoHv37pBIJOjWrRumT5+OixcvomfPnti3b5/YTRMRERFpBdETrO+//x6TJ0+ud+4Ic3NzTJ48Gd9//73YTRMRERFpBY08RSiRSDTRLBEREZFaiJ5gubq64vjx4yguLq5z+59//onjx4/D1dVV7KZbJDExEYsWLYKHhweMjIwgkUiQkJDQ7Hqqq6uxd+9euLm5wdjYGJaWlpgxYwZu3rwpftBERESk1URPsBYtWoQ7d+5g2LBhOH78OAoLCwEAhYWF+Oijj/C3v/0Nd+7cweLFi8VuukVWr16NuLg45OXlwdrausX1LFq0CKGhoRAEAaGhoRg9ejQ+/vhjeHp6IicnR8SIiYiISNuJPk1DcHAwLl++jJiYGEydOhVA7XcRLlmyBHPmzBG76RaJj4+Hk5MT7O3tsXnzZoSHhze7jpSUFMTHx8PLywunTp2CoaEhAGDmzJkYM2YMXn/9dZw8eVLs0ImIiEhLqWQerD179mDKlCm1Jhp97rnnMGfOHIwcOVIVzbbICy+80Oo6Dhw4AADYsGGDIrkCgMDAQPj4+CA5ORm3b9+GnZ1dq9siIiIi7aeymdxHjhypVYmUKqWmpsLExATDhw+vtS0gIACpqalIS0vDrFmzNBAdERERqZvKEixdUVJSgnv37qF///7Q09Ortd3JyQkAmjQOSy6XKy0bGRnByMhInECJiIioXmVlZSgrK1MsP31Nbq5WJ1hz585t0X4SiQT//Oc/W9u8xslkMgCod94vMzMzpXINsbW1VVqOiIjAunXrWhcgERERNSo6OhqRkZGi1dfqBKslUxoA7SfBElN+fr4iIQPA3isiIiI1CQ8Px7JlyxTLcrm8VsdHc7Q6wcrNzW1tFW1aTc9VfT1UNV2M9fVwPcnMzEwpwSIiIiL1EHtYTqsTLHt7ezHiaLNMTExgbW2N3NxcVFVV1RqHVTP2qmYsFhEREbV/GnlVTnvj7e2NkpISZGRk1NpWM/+Vl5eXusMiIiIiDWGC1QyFhYXIzs5WzE5fY+HChQCANWvWoLy8XLE+KSkJqamp8Pf31/mePiIiIl2i89M0xMfH49y5cwCA69evK9alpqYCAEaMGIH58+cDAGJiYhAZGVnr6T5fX1/Mnz8f8fHxGDRoEMaOHYt79+7h2LFjsLCwwN69e9V6TERERKRZOp9gnTt3DocOHVJal5GRoXS7rybBasj+/fvh5uaGuLg47N69G6amppg4cSKioqLQq1cv0eMmagpBEFDxqEJt7VU+qkR5SXnjBUVg0NEAEolELW0RETWXzidYCQkJTZ5qYt26dfXOSyWVShEaGorQ0FDxgiNqpYpHFdhjukfTYahEaHEoDE0MGy9IRKQBHINFREREJDKV9WBdu3YNV65cwezZs+tcJiLVM+hogNDiUOR+pZ756h4+fAhLS0uVt+M4xhEGHQ1U3g4RUUupLMH697//jfXr1ysSqqeXiUj1JBIJDE0Mod9BPaMB9Iz01NIWbw0SkbbjLUIiIiIikTHBIiIiIhIZEywiIiIikTHBIiIiIhIZEywiIiIikTHBIiIiIhIZEywt4unpCRcXF8TGxmo6FCIiIp0UGxsLFxcXeHp6tqoenX9VjjbJysqCmZmZpsMgIiLSWSEhIQgJCYFcLoe5uXmL62EPFhEREZHImGARERERiUxlCZa5uTns7OzqXSYiIiJqr1SWYL355pvIzc2td5mIiIioveItQiIiIiKRMcEiIiIiEhkTLCIiIiKRMcEiIiIiEhknGm2nBEFAxaMKtbVX+agS5SXlamnLoKMBJBKJWtoiIiJqCdESrAsXLuAf//gHsrKyIJFIMHToUERFRWHIkCFiNaEyWVlZiIiIwPnz51FRUQE3NzcsW7YMU6dObdL+CQkJCA4Ornd7SkoKfHx8RIq2aSoeVWCP6R61tqkuocWhMDQx1HQYRERE9RIlwbp+/TpGjRqF0tJSxbrTp0/j/Pnz+Oabb+Dq6ipGMyqRkpKCgIAAdOjQAdOnT0enTp1w/PhxTJs2Dfn5+QgLC2tyXePHj4e7u3ut9Q4ODuIFTERERFpPlARr8+bNKC0txT/+8Q8sWbIEwF8vS9ywYQO2bNmCw4cPi9GM6CorK7FgwQJIpVKkp6crkqO1a9diyJAhWLVqFV555RXY29s3qb4JEyYgKChIdQE3g0FHA4QWhyL3K/XMPfbw4UNYWlqqvB3HMY4w6Gig8naIiIhaQ5QE6+zZsxgxYgQ2bNigWBcZGYnU1FSkpaWJ0YRKnDlzBjdu3EBwcLBSz5O5uTlWrVqFoKAgHDp0CGvXrtVckC0kkUhgaGII/Q7qGWanZ6SnlrZ4a5CIiNoCUa6Iv/76K6ZPn15r/dChQ5GZmSlGEyqRmpoKAPD396+1LSAgAACalSBevnwZRUVFqKyshIODA1544QU888wzosRKREREbYcoCVZFRQVMTU1rrTcxMUFFhfqeZGuunJwcAICTk1Otbd27d4epqamiTFPs2aM8qNzY2BgRERFYsWJFk/aXy+VKy0ZGRjAyMmpy+0REVL+ap6srSys1HYqoykvK+XS1CMrKylBWVqZYfvqa3Fw6PU2DTCYD8NctwbqYmZkpyjTE0dERe/fuRUBAAGxsbPDbb7/hzJkzCA8Px8qVK9GxY0fF2LSG2NraKi1HRERg3bp1jR8IERE1ik9XU0Oio6MRGRkpWn2iJViJiYm4ePGi0rpffvkFADBmzJha5SUSCb788kuxmtcob29veHt7K5Z79uyJWbNmYdCgQfDw8MC6deuwePFi6Os3/HHn5+fDzMxMsczeKyIiIvUIDw/HsmXLFMtyubxWx0dziJZg/fLLL4qE6mknTpyotU4bujJreq7q66WSy+Xo0qVLi+t3dXXFiBEj8PXXX+PHH3+Em5tbg+XNzMyUEiwiIhJPzdPV7RGfrm49sYfliJJg5eaqZyoAsdWMvcrJycHgwYOVtt2/fx/FxcWtnii1a9euAICSkpJW1UNERK1T83Q1kTqIkmA1dZ4obePt7Y3o6GgkJyfXegry5MmTijItVVVVhW+//RZA2/2MiIiIqPlU9rLngoICfPvtt/j2229RUFCgqmZaZdSoUXj22Wdx9OhRXLlyRbFeJpNh06ZNMDQ0xOzZsxXr7927h+zs7Fq3FC9dulSr7qqqKqxcuRK//PILfH19YW1trbLjICIiIu0i6lOExcXF2LZtG957771aSVXPnj0xb948hIWF1Tmlgybo6+sjPj4eAQEB8PLyUnpVTl5eHrZt26b0mpvw8HAcOnQIBw8eVJqx3cPDAwMGDMCAAQPQs2dP/Pbbb0hLS8PPP/8MGxsbxMfHq//giIiISGNES7Bu3LiBwMBA3LhxA4IgoEePHorR9/n5+bhz5w7Wr1+Po0eP4sSJE3B0dBSr6Vbx9fXFuXPnEBERgWPHjile9rxlyxZMmzatSXWEhYXh4sWLOHXqFH777TcYGhqid+/eWL16NZYtW9aqgfJERETU9oiSYJWVlWHs2LH45ZdfMHPmTKxZswZ9+vRRKvPTTz9h48aNeP/99zFmzBhcuXJFa6YhGDJkCJKSkhotl5CQgISEhFrrt23bpoKoiIiIqK0SZQzWO++8g59//hkRERFITEyslVwBQJ8+fXDkyBFERkbip59+wrvvvitG00RERERaR5QE6/jx4+jdu3eTXoq8evVqODk54X//93/FaJqIiIhI64iSYP3www/w9/dv0uShEokE/v7++PHHH8VomoiIiEjriJJglZSU1Ps+v7qYmZlx4k0iIiJqt0RJsKysrOp9TU5dbty4AUtLSzGaJiIiItI6oiRYw4YNQ1JSEu7fv99o2fv37+PLL7/E8OHDxWiaiIiISOuIkmC99tprKC4uxsSJE1FYWFhvuaKiIkycOBGPHj3CwoULxWiaiIiISOuIMg+Wr68vFixYgAMHDqBfv35YtGgR/Pz8lCYaPX36NA4cOIDCwkLMmzcPfn5+YjRNREREpHVEm8l93759MDMzw86dOxEdHY3o6Gil7YIgQCqVYunSpXj77bfFapaIiIhI64iWYOnp6WHr1q1YuHAhEhIScOHCBcWYrO7du+Nvf/sbZs+eDWdnZ7GabHc8PT2hp6eHkJAQhISEaDocIiIinRMbG4vY2FhUVVW1qh5RX/YMAE5OToiKihK7Wp2QlZUFMzMzTYdBRESks2o6OeRyebOmoHqaKIPciYiIiOi/mGARERERiYwJFhEREZHImGARERERiYwJFhEREZHImGARERERiYwJFhEREZHImGARERERiUz0iUaJSHsIgoCKRxWoLK1US3tVZVVqaau8pBwGHQ0gkUhU3hYRUUswwcJfM6hHRETg/PnzqKiogJubG5YtW4apU6c2uY6ysjJs2bIFR44cQX5+PiwsLDBu3Dhs3LgRVlZWKoyeqH4Vjyqwx3SPpsNQidDiUBiaGGo6DCKiOul8gpWSkoKAgAB06NAB06dPR6dOnXD8+HFMmzYN+fn5CAsLa7SO6upqjB8/HidPnsTzzz+PyZMnIycnB/Hx8Th9+jQuXrwIS0tLNRwNERFR/dirrT46nWBVVlZiwYIFkEqlSE9Ph7u7OwBg7dq1GDJkCFatWoVXXnkF9vb2DdZz6NAhnDx5EjNmzMD777+v+ILfffddLF68GKtXr8b+/ftVfThEtRh0NEBocaja2su/nQ9bO1u1tGXQ0UAt7RC1J+zVVh+dTrDOnDmDGzduIDg4WJFcAYC5uTlWrVqFoKAgHDp0CGvXrm2wngMHDgAAoqOjlbLnRYsWYevWrXj//fexa9cuGBsbN1hPSUkJ9PT0Wn5AdSgtLRW1vvqUl5erpa2SkpJm76Ouz0BdWvIZqEultBIVqFBLWxWPmtcOz4P29/cA0O7fB21UUaKe309NeFTySNS/P609t3Q6wUpNTQUA+Pv719oWEBAAAEhLS2uwjtLSUmRmZqJPnz61erokEglefPFF7N+/H99++y1GjhzZYF179uxBhw4dmnEEjdP7SdyETdOqfqhq9j78DHScAKAC0MtpZ+fBlSrAAEAz7oi0t98FgL8PzSYAWKXpIFRj977dzfp9aExr/ydBpxOsnJwcAICTk1Otbd27d4epqamiTH1u3LiB6urqOut4su6cnJxGE6yysjKlZX19fejrt/Ar+r+LCtRzm119ytHsiwrpuArAZJOJpqNQiZJVJYD23BGhtkACnjP1qKysRGXlfy+aT1+Tm0unEyyZTAbgr1uCdTEzM1OUaU0dT5ZryM6dO5WWvb294evr2+h+deJFhYiIqMnOnj3b6F2r5tDpBEvbLF26FEZGRorlFvdeEdF/GfxfUt4ecZw/kWhGjhyJYcOGKZbLyspqdXw0h05fwWt6nerrXZLL5ejSpUur63iyXEPeeustRY9XawmCgMo31Hd/sKCgAD179lRLW/od9Zv1KO6tr26pLhgNcBjjoOkQ6nXnzh3Y2NhoOgyqh7p+FwoLC9G1a1e1tKXNvw/Utv8myOVyJlgt9eT4qMGDByttu3//PoqLizFkyJAG63j22WchlUrrHavV0Divp5mYmMDERMTbeqbiVdUY2WMZOlt1Vl+DzSD2gwOaJuo5IrIOHTpodXy6Tl2/C4aGhmpri+ebdmvLfxOqqlr3AIVOJ1je3t6Ijo5GcnIypk+frrTt5MmTijINMTY2xpAhQ3Dx4kXk5eUpPUkoCAJOnToFExMTeHh4iH8A1CB1T6inLto4oR4RESnT6QRr1KhRePbZZ3H06FGEhoYq5sKSyWTYtGkTDA0NMXv2bEX5e/fuQSaTwdraWumW38KFC3Hx4kWEh4crTTS6f/9+3Lx5EwsXLmx0DiwSHyfUIyIiTZFqOgBN0tfXR3x8PKqrq+Hl5YWFCxciLCwMAwcOxM8//4xNmzbBwcFBUT48PBz9+vXDv//9b6V65syZg4CAAHzwwQf429/+hpUrV+KVV17B//zP/8DR0REbN25U85ERERGRJul0DxYA+Pr64ty5c4iIiMCxY8cUL3vesmULpk2b1qQ6pFIpPv30U2zevBlHjhzBzp07YWFhgXnz5mHjxo18D6GGqPs1MerE18QQEWk3iSAIgqaD0HVyuRzm5uaQyWSiPUWobk+PPyPdxPNAu934/IZa2nnw4AGsrKzU0lavl3qppR1qmbb8N6G112advkVIREREpAo6f4uQiKi9U/cTtVVlVWpri0/VkrZigkVE1M615ydqAT5VS9qJtwiJiIiIRMYeLCKidk7dT9Tm386HrZ2t2trjU7WkjZhgERG1cxKJRK230PQ76vOWHek83iIkIiIiEhkTLCIiIiKRMcEiIiIiEhkTLC3i6ekJFxcXxMbGajoUIiIinRQbGwsXFxd4enq2qh4OctciWVlZbfZVOURERO1BSEgIQkJCFK/KaSn2YBERERGJjAkWERERkciYYBERERGJjAkWERERkciYYBERERGJjAkWERERkciYYBERERGJjAkWERERkciYYBERERGJTOcTLLlcjmXLlsHe3h5GRkZwcHDAW2+9heLi4mbVI5FI6v0JCgpSTfBERESklXT6VTklJSXw9vbGlStX4O/vjxkzZuDy5cvYtm0b0tLSkJ6ejg4dOjS5Pnt7+zqTKXd3d/GCJiIiIq2n0wnW22+/jStXrmDFihXYvHmzYv3KlSuxZcsW7Ny5E+Hh4U2uz8HBAevWrVNBpERERNSW6OwtQkEQEB8fD1NTU6xZs0Zp25o1a2Bqaor4+HgNRUdERERtmc72YOXk5ODu3bsICAiAiYmJ0jYTExMMHz4cJ0+eRH5+PmxtbZtU5x9//IG4uDgUFhbCwsICw4cPh5ubmyrCJyIiIi2m0wkWADg5OdW53cnJCSdPnkROTk6TE6yrV69i0aJFSutGjx6NQ4cOwcrKqtH95XK50rKRkRGMjIya1DYRERG1XFlZGcrKyhTLT1+Tm0tnbxHKZDIAgLm5eZ3bzczMlMo1JiwsDOfPn0dhYSHkcjnOnz+PwMBAnDhxAuPGjUNVVVWjddja2sLc3FzxEx0d3cSjISIiotaIjo5WugY3tXOlPm2+ByssLEwp42zMG2+8UW+vVWts27ZNaXnYsGH44osv4Ofnh7S0NHz66aeYNGlSg3Xk5+crEjsA7L0iIiJSk/DwcCxbtkyxLJfLW5VktfkEa//+/SgpKWly+VdeeQVOTk6Knqv6eqhqugbr6+FqCqlUigULFiAtLQ0ZGRmNJlhmZmZKCRYRERGph9jDctp8gtXcCUFr1PRi1YzFelpjY7SaqmvXrgDQrCSQiIiI2jadHYPl5OSEHj16ICMjo1byU1JSgoyMDDg6Orb6HmxmZiaAv+bIIiIiIt2gswmWRCLB/PnzUVxcjA0bNiht27BhA4qLi7FgwQKl9Y8ePUJ2djZu376ttP769euoqKio1cb58+exZcsWGBgYYMqUKeIfBBEREWmlNn+LsDWWL1+OTz/9FFu2bMHly5cxaNAgfPfdd0hOToanpyfefPNNpfLffPMNfH194e3tjdTUVMX67du348svv8SIESNga2sLAwMDfP/990hOToZEIkFsbCx69eql3oMjIiIijdHpBMvExARpaWlYt24djh8/jpSUFFhbWyMsLAwREREwNjZuUj3jx4/HH3/8gatXr+LUqVMoLy9H9+7dMX36dLz55psYMmSIio+EiIiItIlEEARB00HoOrlcDnNzc8hksjb7FGFeXh7s7e01HQZpGM8DAnge0H+15XOhtddmnR2DRbpp586dcHV1hampKTp37gw/Pz/FgwgAsG7dOkgkEqUfDw8PDUZMqtTY+SCXy/H666/D1tYWHTt2xKhRo5Cdna3BiEkdFi9eDIlEgpiYGMW6jz/+GC+++CIsLCwgkUhw69YtzQVIbQITLNIp9vb22LFjB65evYrz58+jd+/eCAgIQFFRkaLMwIEDce/ePcXPyZMnNRgxqVJj58P8+fNx7tw5HDt2DFeuXEHfvn3x4osvtnh6GNJ+X3zxBS5cuIAePXoorS8pKYGXlxfWr1+vociozRFI42QymQBAkMlkmg6lxW7duqXpEFqk5rNPTU0VBEEQIiIihMGDB6ukrdzcXAGAMGfOHJXUrw3a6nlQ48nz4dGjR4Kenp5w4sQJxfaqqirByspKiIuL02CU2q+tngf3798XbGxshGvXrgn29vbC3r17a5W5fv26AEDIzc1Vf4BtUFs9FwSh9ddm9mCRziovL0dcXBy6dOkCNzc3xfoff/wR1tbW6N27N4KDg3H//v0G67l06RLmzZsHJycnmJiYwNjYGL169cKsWbNw6tQpVR8GieTp86GyshJVVVVKD7tIpVIYGhoiIyNDad9bt25BIpFg9OjRdda9e/duSKVS2NnZ4aefflLpcVDLBQcHIzQ0VOnvQXPxXKAaTLBI55w9exampqYwNjbGzp07cerUKVhYWAAAhg4dioSEBCQnJyMmJgbff/89/Pz86nzfZXV1NZYtWwYPDw8cPnwYzz77LF577TW88cYbGDx4ML788kv4+/vXmmeNtEt950OnTp0wdOhQrF+/Hg8ePEBFRQW2bduGO3fu4N69e02uf+3atXjzzTfRp08fZGRkoE+fPio8GmqpmJgYlJSUICwsTGVt8FzQLTo9TQPpJg8PD1y5cgVFRUU4cOAApk6diszMTHTt2hWBgYGKcm5ubhg8eDDs7OzwxRdfYPLkyUr1rF69Gjt37oS7uzs++uijWnOdPX78GDExMUrju0j7NHQ+JCYmYs6cOejWrRv09PTg4+NTb8/E0wRBwJIlSxAbGwsPDw8kJSUpXp1F2iU7OxsbNmxAZmYmpFLx+x14Lugm9mCRzjE2Nkbv3r0xdOhQxMfHQyqV4uDBg3WWtbS0hIODA3Jzc5XW//LLL3j77bfxzDPP4MSJE3VOJGtsbIy33noLkZGRKjkOEkdD50Pv3r2RkZEBuVyOu3fv4uuvv8bvv/8OR0fHBuusqKjAq6++itjYWPj5+eHMmTO8oGqxixcv4uHDh+jduzf09fWhr6+PvLw8vPHGG3B3d29V3TwXdBd7sEjnCYJQ5y1AAPj999+Rl5dX612SCQkJqKqqwqJFi9CtW7cG6xfz7eykenWdD506dUKnTp1w8+ZNfPvtt1i3bl29+z9+/BivvPIKvvrqK0ycOBEffPABzwEtN2HChFrTsQQEBCAoKAjBwcEtrpfngm5jgkU6ZcWKFXj55ZdhY2OD3377Dfv27cOdO3cUt//eeustvPTSS7Czs8OdO3ewatUq9OzZE2PGjFGqp2aQs5+fn9qPgcTT2Plw4sQJSKVS9O7dGz/88APeeOMNjB07tt7bhHK5HP7+/jh37hzmzp2LuLg46OnpqfOQqAU6d+6Mzp07K60zMDBQPOwCAL/99htu376NGzduAAB++OEH/PHHH7Czs1OM4XwSzwVigqVFPD09oaenh5CQEISEhGg6nHbp7t27mD59Oh48eAALCwt4enri7Nmz6NevHwAgPz8f06dPR2FhIbp16wZvb28cOXIEHTt2VKqn5slCGxsbtR8Diaex8+H333/HqlWrUFBQACsrK7z66qsN9l5duHABADBs2DD885//VMchkJp89tlnSr1ZY8eOBQAcPHgQQUFBtcrzXGi7YmNjERsbi6qqqlbVw1flaAG+Kqft6devH7Kzs5Gdnd3kJ4Fu3boFR0dHzJkzBwkJCaoNUEN07TyoUfPduri44I8//sDdu3cRGRmJtWvXajo0jdDV8wDgufC0tnwu8FU5RBrQvXt3AEBBQYGGIyFtYmtri7S0NNjY2CAiIgIRERGaDok0hOcCMcEiaoHhw4cDAE6fPq3hSEjb9O7dG2lpabCzs8P69euxevVqTYdEGsJzQbcxwSJqgaCgIOjp6SEuLg4PHz5ssGx9TyhS+/Xss88iNTUV9vb2iIqKQnh4uKZDIg3huaC7mGARtUDv3r2xfPlyFBYWIjAwsNY8WQBQWlqKHTt2NDgomtovR0dHpKWlwdHREZs3b8by5cs1HRJpCM8F3cSnCIlaaOPGjSgtLcXOnTvRp08f+Pn5oX///jAwMEBubi6+/vprFBUVYePGjZoOlTTE3t4eaWlp8PX1xdatW1FVVYXt27drOizSAJ4LuocJFlELSaVS7NixAzNnzsQ777yD9PR0pKeno7q6GtbW1ggICEBwcDBeeOEFTYdKGlQz2NnX1xc7duxAVVUVdu3apemwSAN4LugWTtOgBThNA7UXPA8I4HlA/9WWzwVO00BERESkZZhgEREREYlMpxOsK1euYNWqVQgICIClpSUkEgl8fHxaXF9WVhbGjBmDzp07w8TEBM8//zw+/PBD8QImIiKiNkGnB7l/8skniI6OhqGhIZydnVFYWNjiulJSUhAQEIAOHTpg+vTp6NSpE44fP45p06YhPz8fYWFhIkZORERE2kyne7CmTJmCS5cuobi4GKdOnWpxPZWVlViwYAGkUinS09MRFxeH7du34+rVq3B2dsaqVauQl5cnYuRERESkzXQ6wXJ1dcWgQYNgYGDQqnrOnDmDGzduYObMmXB3d1esNzc3x6pVq1BeXo5Dhw61MloiIiJqK3Q6wRJLamoqAMDf37/WtoCAAABAWlqaOkMiIiIiDdLpMVhiycnJAQA4OTnV2ta9e3eYmpoqyjRELpcrLRsZGcHIyEicIImIiKheZWVlSu+Offqa3FxMsEQgk8kA/HVLsC5mZmaKMnWpmevV1tZWaf3KlSvbzItB//zzz1afjNT28TwggOcB/VdbOheio6OxefPmWutbOh97m0+wwsLClDLOxrzxxht19jRpo82bN9f5ZRMREZF2a/MJ1v79+1FSUtLk8q+88oroCVZNz1V9vVRyuRxdunSpd//GeriIiIhIMzp16tSi/dp8glVcXKzpEBQJW05ODgYPHqy07f79+yguLsaQIUPq3V8ikbTZdxASERFRbXyKUATe3t4AgOTk5FrbTp48qVSGiIiI2j8mWM1QUVGB7Oxs3LhxQ2n9qFGj8Oyzz+Lo0aO4cuWKYr1MJsOmTZtgaGiI2bNnqzlaIiIi0pQ2f4uwNbKzsxWDyB8/fqxYFxQUpCiTkJCg+HdBQQH69esHe3t73Lp1S7FeX18f8fHxCAgIgJeXl9KrcvLy8rBt2zY4ODio4YiIiIhIG0iElj5/2A6kpqbC19e3wTJPfjy3bt2Co6NjrQSrxjfffIOIiAicP38eFRUVcHNzw7JlyzBt2jSxQyciIiItptMJFhEREZEqcAwWERERkciYYBERERGJjAkWERERkciYYBERERGJjAkWERERkciYYBERERGJjAkWERERkciYYBERERGJjAkWERERkciYYBERERGJjAkWERERkciYYBERERGJjAkWERERkciYYBERERGJjAkWERERkciYYBERERGJjAkWERERkciYYBERERGJ7P8DscXWwCUV0XcAAAAASUVORK5CYII=",
      "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=600.0/>\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": [
    "bar_OP(1,mixing_cases, lightoddZ, 11000)\n",
    "\n",
    "plt.savefig(\"../figures/lightoddZ_barplot.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c21961ff-d5bf-4ecf-9bcd-0c83db740eaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.8214285714285714"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OPs = [0.58,1.76,0.81,2.42,3.37, 2.13, 1.68]\n",
    "\n",
    "np.average(OPs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "089e2183-c14e-47ee-a75b-af2a0c4b5ee7",
   "metadata": {},
   "outputs": [],
   "source": [
    "stable_isotopes = [\n",
    "    \"C-12\", \"C-13\",\n",
    "    \"N-14\", \"N-15\",\n",
    "    \"O-16\", \"O-17\", \"O-18\",\n",
    "    \"F-19\",\n",
    "    \"Ne-20\", \"Ne-21\", \"Ne-22\",\n",
    "    \"Na-23\",\n",
    "    \"Mg-24\", \"Mg-25\", \"Mg-26\",\n",
    "    \"Al-27\",\n",
    "    \"Si-28\", \"Si-29\", \"Si-30\",\n",
    "    \"P-31\",\n",
    "    \"S-32\", \"S-33\", \"S-34\", \"S-36\",\n",
    "    \"Cl-35\", \"Cl-37\",\n",
    "    \"Ar-36\", \"Ar-38\", \"Ar-40\",\n",
    "    \"K-39\", \"K-40\", \"K-41\",\n",
    "    \"Ca-40\", \"Ca-42\", \"Ca-43\", \"Ca-44\", \"Ca-46\", \"Ca-48\",\n",
    "    \"Sc-45\",\n",
    "    \"Ti-46\", \"Ti-47\", \"Ti-48\", \"Ti-49\", \"Ti-50\",\n",
    "    \"V-51\",\n",
    "    \"Cr-50\", \"Cr-52\", \"Cr-53\", \"Cr-54\",\n",
    "    \"Mn-55\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "65f61a4b-f05c-401f-ab4f-10f9e1138cd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C-12 2.94\n",
      "C-13 3.35\n",
      "N-14 4.57\n",
      "N-15 3.58\n",
      "O-16 0.55\n",
      "O-17 4.02\n",
      "O-18 7.38\n",
      "F-19 8.56\n",
      "Ne-20 5.43\n",
      "Ne-21 6.76\n",
      "Ne-22 4.71\n",
      "Na-23 4.66\n",
      "Mg-24 0.23\n",
      "Mg-25 3.1\n",
      "Mg-26 2.5\n",
      "Al-27 1.32\n",
      "Si-28 0.44\n",
      "Si-29 0.65\n",
      "Si-30 1.53\n",
      "P-31 0.58\n",
      "S-32 0.72\n",
      "S-33 0.46\n",
      "S-34 0.36\n",
      "S-36 1.56\n",
      "Cl-35 1.76\n",
      "Cl-37 0.81\n",
      "Ar-36 1.81\n",
      "Ar-38 0.53\n",
      "Ar-40 1.76\n",
      "K-39 2.42\n",
      "K-40 3.37\n",
      "K-41 2.13\n",
      "Ca-40 2.2\n",
      "Ca-42 1.82\n",
      "Ca-43 2.35\n",
      "Ca-44 0.5\n",
      "Ca-46 1.53\n",
      "Ca-48 5.32\n",
      "Sc-45 1.68\n",
      "Ti-46 2.18\n",
      "Ti-47 1.98\n",
      "Ti-48 0.38\n",
      "Ti-49 1.3\n",
      "Ti-50 0.77\n",
      "V-51 0.87\n",
      "Cr-50 2.25\n",
      "Cr-52 0.4\n",
      "Cr-53 1.11\n",
      "Cr-54 1.27\n",
      "Mn-55 0.23\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6766370f940547d380bdf51da712f54a",
       "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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a3Vev+M4IPKLmIlGbt27XL6eaWlpcbKlSuNiy66yGjSpInRqlUrY/z48cahQ4dqfB4F99+cPXvW+MMf/mDExsYaR48erbT+8OHDxsGDB43AwEDjnHPOMU6cOFHjts6cOWNW2FWqKpeXXnrJsFgsRmBgoP1PkhEQEGBccskl9nYVP1xMmjTJkGS89tprDtvwZsE9OjramDZtmsOyOXPmGAMGDLA/Pnz4sPHPf/7TCAgIMFq2bFnjazJx4kRDkvHAAw+YGn+ZqvIoLCw0rFarwzx8hmEYV155pZGUlOSw7KOPPvJoHtXt70VFRUZgYKDxl7/8xaH9mDFjjFtuucVhWXX7kLMF9+3btxsjR440zjnnHCM4ONjo0qWL8cc//tEoKCgwJRdn11eXS8ULEXfnUl2czh7XFfOYMWOGERQUZPz0008Or8nbb79tf62qKlTbbDZjyZIlRqdOnYyQkBCjc+fOxpIlS4xDhw45dT6o6d/bmeO6pjwM438fLtydR11ydOWYT0xMNCQZe/fudWqbnpj7vEuXLsYjjzzi0nngmmuu8bk8yivLqYwz7/Wu5uQpZbk4c04oO058KZeKr0VFF1xwgfHoo486LKvLdZe79rHy8Tt7Livvd7/7nREUFGScOHHCGDJkiBEQEFDl/KgZGRmGJCMlJcXYuXOncdVVVxmRkZF1KqTUtq+4cqx7Oxd35VZVwb24uNh+vTV48GAjLy+v3vGaGbNhVD42apprt+zYKCoqMlauXGkMHTrUaN++vREcHGxERUUZo0ePNv72t7+Zmkt1qjoPeDMXZ/Jw9niv6nxVXS7ueE2czacqVZ1/y79/uDKX8/bt241Ro0YZrVu3NoKDg4327dsbo0ePNj755BOX8ynjSl5///vfDUnVbs/VgruZ7ymu5JGXl2e/9r300kuNmTNn1piLJ/OoTVX7U21zuJd9wbNgwYIq+yx7XS+++GJ3hGwYRuW4nfkMWZfzlzvfGyvm4so+56u5NGbM4W6yv//970pJSVFYWJjLz50xY4aSk5NlGIaSk5N19dVX66233lJ8fLwOHjzohmj9h2EYSkpK0saNG/XRRx9V+3PR9PR0lZaWasaMGZV+7lmRN+5cf9111+mrr77Svn377H/nnXee7r//fr355pvVPu+hhx5SSEiI5s+fL5vN5sGIq3f55Zfru+++c1j27bffKiYmxmHZmjVrdPbsWd122201viZlP3l68cUXzQ+2GjabTTabTYGBgQ7LAwMDdfbsWYdlGzZs8FgeNe3vwcHB+t3vfqcPP/zQvqykpESZmZnq169ftX26ug89++yzSkxM1M6dOzVixAglJyerffv2Wrx4sa666ioVFxfXOxdn1teUy/Llyz2WS01x1vW4njp1qkpKSrR27VqH5S+++KJatmyp6667rtrnzZs3T5KUlJSkq6++Wk888YTuvvvueuUhOX9cezuPmtSUoyvHfIsWLST9lr+vMAxDRUVFLp0HzjnnHEm+lUd5ZTmV/b8z5wNfzaksF1fOCb6US/nXoqJ///vfOnLkSKV7MfjSdVf5+F09l/3jH//Q3r17NXToUJ177rm65ZZbdPbsWb300kvVbu/TTz9VYmKiLBaLbrvttjpN2VLbvlKX93xv5eKJ3CTp9OnTuu6667R+/XqNHj1amzZtUrNmzeodr9kx1+XY+OWXX3T33XerqKhIw4cP1z333KPExERt2rRJl19+ubKyskzLpTpVnQe8mYszeTh7vHv7NXE2n4qqO//W5f1jxYoVSkxM1LZt23TVVVdp9uzZGjx4sL788ssa9wsz83rxxRcVFxen/v3713l77uJKHs2aNVPr1q31/fff6/PPP69yakVfVN3+VJvJkycrICBA6enpMgyj0vqy95hp06aZEWYlFeP2RH3IHe+NFXNxZZ/zxVxQT/Wr9/uX4uJio1evXsall15q3HTTTS6NcP/oo4/sP78vKiqyL9+0aZMhqcafhTaGEe6nTp0y9u7da7z55puGJGPjxo3G3r17jVOnThmG8dtPNZs3b2588sknxo8//mj/K/9vefjwYfuIxA8++MBbqdSaS0W1TSlTNpqnbDqT8m3dOcK9tjx2795tBAYGGo899phx8OBB49lnnzWCgoKMnTt3OuRR9pps27at1m2ed955hqRqv6F2Rx4DBgwwLrnkEuOTTz4xDh06ZDzxxBNGQEBApRGwl112mcfyqG1/f+WVV4wmTZoYa9euNb755htjypQpxrnnnlvpHFHdPrRw4UJ7m6r2oW+++cYICgoyLrnkEuPkyZMOfaamphqSjMcee8yUXJw5tmvKpbbjwaxcnI2zTG1TypTlcdFFFxndu3e3j1D48ccfjaCgIOPOO+80DMOoNDL8gw8+sP+MsPzo/OPHjxvnnntureeD2vJw5riuKQ/D+O24d3ceNaktR2eP+RdeeMGQZDRr1syYPXu2sWXLlkr7UFX/Jmadj++77z5jx44dxuHDh42//e1vxvTp042QkBDjH//4h2EYzp8H3n33Xa/m4UpOzr7Xu5qTO9SWS0XVTSnjrVxqi//ee+81tm/fbmRnZxuffPKJMWDAAKNLly6VfhVUl+suM/ax2uJ39lxWpmwKqVdffdUwDMP49ddfjbCwMKNDhw5GaWmpQ9uy0WKSjBdffLHOOVSn4r7i7LHui7lUVJfcyl8T9+vXz+jfv78hyZg6dapRUlLikzEbRuVjw5kRyGfOnKk0ZaFh/DZyNDw83LjyyitNzcXZ85iv5VIxD2eP96rOV7Xl4u7XxDAq5+Ps+bf8+8ett95a6/vHvn37jICAAOO8886rNIr57NmzxrFjx+qdS015GcZvv25s3bq1sWTJkmqf580R7lWpmMfmzZuNLVu2GIcOHTL+8pe/GJ06dTKuvfbaKp/rCyPca9ufnKldlOVx9dVXV/meXzaNZEhISLU1D7PjdvazWV3OX2a/Nzp7TJep7nOkL+QCR0wpY6KUlBQjJCTE+Oabb+xTGjhbcJ8wYYIhqcp5lsoOnOrmw2oMBfeXXnrJfiIo//fSSy8ZhmFUuU6SkZGRYe/j8OHDxgUXXGBIMg4cOOCdRIzac6nI2YL7L7/8YjRv3txo3bq18euvvxqG4d6CuzN5vPXWW8aFF15oNGnSxLj44ouNt99+u1Ierrwml156qSHJ2LNnj8fyOHbsmHHTTTcZbdq0MZo2bWr06NHDePnllyv107lzZ4/l4cz+/uSTTxrR0dFGcHCwcfnllxv79u2r1E91+1CrVq1q3IeSk5MNScbHH39cqc/S0lIjKirK6N27tym5OJNrdblERETUejyYlYuzcZZxtuD++OOPG5Lsx07ZlGVl00tULFRPnjzZkGS89dZblfpesmRJrecDZ/Ko7biuKY/du3cbhw8fdnseNaktR2eP+cOHDxvLly83wsPDHfrp3LmzkZSUZHz77bdV/puYdT6+6aab7D9fb9OmjfH73/++0rzBzpwHDMMw5s+f77U8XMnJ2fd6wzBcem3cwZnXp7zqCu6G4Z1caot/3LhxRtu2bQ2r1Wq0b9/e+L//+78qP9jV5brLjH3MmX9/Z85lhvHbgJqoqCijWbNmxunTpx22IcnYsmWLQ/uyD6+9evWqc/w1qer9w9lj3ddyqaguuZW/Ji7769evn0firWvMhlH52HBlyo+q/P73vzeCg4ON4uLiOvdRMRdnz2O+lktVr4kzx3tV56v65GLGa2IYlfNx9vxrGK69f8ycOdOjRbeqXqd33nnHCAgIqPJLjDK+XnBfv369ERsba1itVqNdu3bGnDlzHM635flCwb22/cmZz/xl7f/85z8bkoyJEyc6bKNsGskbb7zRY3E7+9msLucvs98bXTmmDaP6z5G+kAscUXA3yRdffGEEBQXZv411teDetm1bIywsrMqRGGWjLKv6wG8YjaPgbgZfKbibobobRJUVsVJSUgzD8NwNL+vKFwruZvFkwd0sdd2Hym5488c//tFISUmp9HfeeecZYWFhXs9lzpw5DS6Xinn861//MqxWqzFhwgTDMAzj/PPPN373u9/Z21csVP/ud78zJFV5U6Dt27d77HxQXR633Xabcfjw4QaTR03KLmLz8vKMN954w7j77ruN/v37G1ar1ZBkNGnSxHj33Xft7T0997krDh8+7Bd5GIbjhwtnc/JFFT8kNdRcvFVwN1NZAWHcuHEOy7du3VplAaHsw+uMGTM8GaZT/CmXMuWvibt162b/FWH5X+v5oroWqffu3WtMmDDBiI6Otp8Hyv8dP37cnWFXyV9yqWvB3dfyqCgvL89IS0ur9f2jd+/ehqQai92+wNcK7vXhCwV3M5TlUfalbtOmTY3//Oc/9vW///3vq/xS1xfUp0jta++N/pSLv6hvrTZIUFFRkW655Rb17NlT9913n8vPLygo0I8//qiLLrqo0ryxkhQXFydJzONugjZt2ujAgQM6duyYzj//fG+HY7rk5GQ9/fTTWr58uW6//XZvh+OUstckJyen1tckJydHktS2bVtPhOaSqKgoHTp0qMHnkZycrBUrVtS4D/3yyy+SpMWLF3syNJdNmTJFr7zySoPOJSoqSr///e/1/vvv64MPPtA///lPPfXUU9W2z8vLU0BAgFq1alVpXW1z+blTWR6vvfaaBg4c2GDzqEqzZs00duxYjR07VpKUm5urefPm6ZlnntG0adN07NgxBQcHeznK2vlLHuX5U04NNRd/uO7605/+JEm6/vrrHZYPGTJE7dq107vvvqtffvlFLVu2dFjva+cqyb9yqUp0dLTeffddDRo0SCkpKSotLdXChQu9HVaV6nJsfPrppxo8eLAkaejQoYqLi1N4eLgsFoveeecdffnll9XeZ8Gd/CUXf8mjombNmmnEiBH2Oeure//Izc2VxWLxyc8naBisVqtuvvlmPf7441q/fr1mzpypEydOaPPmzerQoYOuvPJKb4dYSX2uU3ztvdGfcsFvuGmqpAcffFAHDx7USy+9V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width=1500.0/>\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": [
    "bar_OP(1,mixing_cases, stable_isotopes, 11000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23449f58-bac2-4c2a-8c11-6b4ece4e0650",
   "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
}