{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "77993ae8-0310-4930-a21f-4346b58e570d",
   "metadata": {},
   "source": [
    "# ExoComp\n",
    "\n",
    "ExoComp is a set of tools for comparing/converting/interpreting measurements of the atmospheric composition of exoplanets. Here, we run a few test and example cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fdba5a23-bd8a-48fb-bba3-c61c2b052ac7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from exocomp import Abund\n",
    "from IPython.display import display\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00216351-0e3e-4eb4-82dc-d56ba8188461",
   "metadata": {},
   "source": [
    "## Estimate [M/H] and C/O from species abundances\n",
    "\n",
    "ExoComp can fit a metallicity, C/O, and refractory-to-volatile ratio to measurements of species abundances. By species abundances, I mean measurements of the abundance of individual atomic or molecular species. Below, we show that given abundances of H$_2$O, CO, and CO$_2$ consistent with 10x solar metallicity and C/O = 0.5, we indeed get the expected values back.\n",
    "\n",
    "p.s. For now, we're also assuming we're measuring an atmosphere at T=1000 K and at 1 bar... We'll come back to that..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ddef19de-d9ad-462c-bbe9-9059994414b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H2O: -2.1935948210641625\n",
      "CO: -3.740758551917497\n",
      "CO2: -5.704870644717015\n",
      "Consider adding errors/uncertainty to the provided measurements!\n",
      "---\n",
      "Best fit [M/H]: 1.000  +/- 0.376\n",
      "Best fit C/O: 0.100  +/- 0.042\n",
      "---\n",
      "Best Fit Abundances\n",
      "---\n",
      "H2O: -2.1935948210641496\n",
      "CO: -3.7407585519174846\n",
      "CO2: -5.704870644716999\n",
      "Updating bulk abundances with mean/median fit\n"
     ]
    },
    {
     "data": {
      "image/png": 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LS0t06dJFpofA09MT1apVQ3R0NEaNGgVPT08YGBhg+PDhGD16NLy9vVGjRg2Fa3mz90FXVxeS17qt58+fj3bt2sm0UaaHRt08PT2lYQZ4dZuwVq1auH37tnTb/v374evrC3t7e5nX98EHH2Dy5Mk4fvy4TPAJCAiQhh4A0p9N586dZa5dsP3OnTvw8PBQqX557ycA2NraAgCioqIAoFDI6d27N4KCglS6Jr3C4KNGpZ2HZ0nEdQYfIi1St27dQoObb9++jalTp2LAgAGwtLTEw4cP8ezZMxgYGMg9R0pKCoBXt5KOHj2KBQsWYMyYMUhPT0eNGjUwduxYjBs3rtg6EhISCvW4REVFyTz5VaNGDZla1cXa2homJiaIj49X6riCJ8peZ2hoiMzMTOn3Dx8+xL59+wqNmSpQ8N4VsLKykvm+4D0vantWVpZSNb+upPfz8ePHAIBq1arJbNfT05P72klxDD5qNMG/lso9PgXHE5F2a9iwIY4cOYLr16+jWbNmsLa2RpUqVYp8qsnMzEz6v729veHt7Y28vDycPXsWP/74I8aPHw9bW1v07du3yGva29sjNjZWZlvt2rXV84JKoKurCz8/Pxw6dAj37t2TeSy+tKytrdGwYUPMnj1b7n57e3u1XUvdCsJNUlISqlevLt2em5srDUWkGgYfNSruUXZTQz28yM6V/vumiVylnYgAnDt3DsD/DzoODAzEr7/+iry8PDRv3lyhc+jq6qJ58+aoU6cONm3ahH/++Qd9+/aVPjH1eq8I8KoHQ529OUVdpyhffPEFDh48iGHDhmHPnj2FerdycnJw+PBhpR/xDwwMxMGDB+Hm5obKlSsrdWxZK+ht27Rpk3RsFvBqzJe8QdGkOAYfNZMXfib518KmM3ekwWf4+zVk9jP0EGmnixcvSj/EHj9+jF27diEiIgLdu3eX3nrq27cvNm3ahICAAIwbNw7NmjWDvr4+7t27h6ioKHz44Yfo3r07Vq1ahWPHjqFz585wcnJCVlaW9PHu9u3bA3jVO+Ts7Iw9e/bAz88PVlZWsLa2VniSQUUVjHtZvXo1zMzMYGRkBFdX1yJv0bRs2RIrV67E6NGj0aRJE4waNQr169dHTk4O/v33X6xevRoeHh5KB59vvvkGERERaNWqFcaOHYvatWsjKysLCQkJOHjwIFatWqW2HqahQ4di/fr1iIuLg7Ozc4ntb9y4gZiYmELbHRwc4ODggLp162LAgAFYtmwZ9PX10b59e1y8eBGLFi0qNKD69u3bcHNzQ1BQEEJDQ9XyeioyBh8NKAgxSyOuY+L/JifcdOaO3P0TGHqItNbgwYOl/9vCwgKurq5YsmQJRo8eLd2uq6uLvXv34vvvv8fGjRsxd+5c6OnpwcHBAW3btkWDBg0AvBrs+/vvv2PmzJlISkqCqakpPDw8sHfvXnTo0EF6vtDQUEyZMgVdu3ZFdnY2goKC1L5MgqurK5YtW4bvv/8ePj4+yMvLQ1hYWLFzzwwbNgzNmjXD0qVLMX/+fCQlJUFfXx+1atVC//79pYO9lWFnZ4ezZ8/i22+/xcKFC3Hv3j2YmZnB1dVVOlmkuuTl5SEvL6/E+dsKTJ8+Xe72L7/8Et999x2AVz8rW1tbhIeH44cffoCnpyd27txZ6LalEEJ6fSqZRCj6U9ISaWlpsLCwQGpqqsqPKcrTYk4kktKyUM3cCDHT/dR2XqKKKCsrC/Hx8dI1m4jo/7m4uMDHx0fr1vUq6e+Cop/fnLmZiIiItAaDDxEREWkNjvEhIiJ6hyQkJJR1Ce80Bh8i0jprT97C86xcmBnpIdhb8dmNiejdx1tdRFTh/RB5A67TDuDHyBsAgLUn4/F95A2sPRkvs/+H/+0nooqLwYeIKrSCNfQEXq2J9+Mb4eb1/Usirr/V8PPNN9+gXr16yM/PBwBER0dDIpFIv86ePSttGxISAolEAh0dHdy6davQudLT02Fubg6JRFLkY+M9evTAhx9+KHO+N5dtKODh4SGzZAXwahHPgtpUebz8Tffu3cP48ePRtm1b6bmVfVLp1q1b6NGjBywtLWFqagp/f3/8888/ctv++uuv8PT0hJGREezt7TF+/Hi8ePGiVK8hPz8fGzduRPv27WFtbQ19fX1UrVoVgYGB2Ldvn/Rn+7rXfw4FDh8+jM6dO8PGxgaGhoZwdHREUFAQLl++XKr63qTM+/WmU6dOITg4GE2aNIGhoSEkEkmxt91+/PFH1KlTB4aGhnB1dcWsWbOQk5Mj02bGjBlo3Lix3PdJUxh8iKjCkrdw8OKI69LZ019k5xba/7bCz/3797FgwQJ88803MgtjAsDy5ctx+vRp6WKYrzM1NUVYWFih7du3b0dOTk6R61Klp6fj8OHD+Oijj1Su+ejRozh9+rTKx7/p5s2b2LRpEwwMDBAQEKD08Y8ePYK3tzeuX7+OdevWYdu2bcjKyoKPjw+uXbsm03bTpk3o168fmjZtikOHDmHmzJkIDw+XWfFeWVlZWQgICEBQUBCqVq2KlStX4tixY1i1ahXs7e3Rq1cv7Nu3T+YYeT+HqVOn4oMPPkB+fj5WrFiBiIgIzJw5E7GxsWjcuDF27dqlco2vU+b9kicyMhJHjx6Fk5MTWrVqVWzb2bNnY9y4cejRoweOHDmC0aNHY86cORgzZoxMu8mTJyM+Ph7r168v1WtTiiAZqampAoBITU1V63mbzz4qnD/fL5rPPqrW8xJVRJmZmeLy5csiMzNT5XN8f/S6cP58v8pf3x+9rsZXVNjUqVNF9erVRV5ennRbVFSUACCioqIKtZ85c6YAIIKDg4Wjo6PMcUII0aZNG9GvXz9RqVIlERQUVOj4bdu2CX19ffHkyROZ8z169EhuffXr1xdt27aVuw+AGDNmjGIvtBivv4bY2FgBQISFhSl8/JQpU4S+vr5ISEiQbktNTRXW1taid+/e0m25ubnCzs5OdOjQQeb4TZs2CQDi4MGDKtU/atQoAUCsX79e7v7r16+L//77T2bbmz+HzZs3CwBi1KhRhY5/8eKFaNKkiTAxMRFxcXEq1fg6Rd+vorz+81q4cKEAIOLj4wu1S0lJEUZGRmL48OEy22fPni0kEom4dOmSzPZPP/1U1KpVS+Tn5xd7/ZL+Lij6+c0eHyKqkJaWYsFgdRxfnJcvXyI0NBT9+/cv1NtTkiFDhuDu3buIiIiQbrt+/TpOnTqFIUOGFHnczp070a5du3K1ZpWyr/1Nv/32G9q1ayezRIS5uTl69OiBffv2SZcDiYmJwYMHD2RmygaAXr16wdTUFL/99pvS105KSsLatWvRsWNHDBw4UG4bd3d3NGzYUGbbmz+H2bNno3Llyli0aFGh4ytVqoQff/wRGRkZWLp0qdI1vknR96soiv68Dh8+jKysrELv9+DBgyGEwO7du2W2f/LJJ7h+/TqioqIUeyGlxOBDRBXSBP9apTp+YimPL86ZM2fw+PFj+Pr6Kn2su7s7vL29petwAcC6devg4uICPz/5s8JnZWXhwIEDcm9z5eXlITc3t9CXMoQQcs9R2vMWJzMzE3FxcYWCBfBqhfvMzEzpWKiLFy9Kt79OX18fderUke5XRlRUFHJyctCtWzeFj3nz5/DgwQNcunQJHTp0gImJidxjWrZsiapVq8oEXVXeb2Xer9IqeD8LllMpYGdnB2tr60Lvd5MmTWBqaooDBw6o5folYfAhogpprJ+7yuFl0v/W2NOUgnEyjRs3Vun4IUOGYM+ePXjy5Any8vKwYcMGDBo0CBKJRG77I0eOIDMzU+6HdLVq1aCvr1/o69KlSwrXc/z4cbnnkPelrjlonj59CiEErKysCu0r2Pb48WOZf4tqW7BfGXfuvFp/sWAxWUW8+XNQ9Byurq7StgCwfv16hd/vAsq8X6X1+PFjGBoaolKlSnKv9eZ1dHV10ahRI/zxxx9quX5JOI8PEVVYBQsAvzmAuTiaDj3Aq4HNEokE1tbWKh3fq1cvjB07Fps2bYKLiwuSkpKKXQB0586d8Pb2ho2NTaF9R48ehYWFRaHtby6EWZwmTZogNjZWobb29vYKn1cRRYU9efuKalvcOdSpuJ9DcYQQMjV26dJF4ff7Tcq8X6Wh7HWqVq2q8mtSFoMPEVVoY/3ccSb+Mf64WfJ/zbapaa3x0AO8uu2gr68PXV1dlY6vVKkS+vTpg3Xr1sHZ2Rnt27eXGbfxupycHOzbtw/ffvut3P2NGjWSG8CUWRzW1NQUnp6eCrXV01PPx07lypUhkUjk9lI8efIEwP/3ZFSpUgXAq54IW1vbQm3l9YKUxMnJCQAQHx+vUHt5PwdFz3H79m04OjpKv7eyspIbVoujzPtVWlWqVEFWVhYyMjIK3cJ78uQJmjRpUugYIyMjZGZmquX6JeGtLiKq0H6IvKFQ6AGAUzdTCs3zownW1tZ4+fIl0tPTVT7HkCFDcO7cOezbt6/YQc1Hjx5FamoqunfvrvK1SlIWt7qMjY1Rs2ZNXLhwodC+CxcuwNjYGDVqvJqVu2CsyZttc3NzcfXqVXh4eCh9fV9fX+jr6xcaqFsUeT8HOzs71K9fH7///jsyMjLkHnf69Gk8fPgQ/v7+0m2q3OpS5v0qraLe76SkJKSkpMh9v588eaJyD6iy2ONDRBWWvHl8SrL4f+012fNTp04dAChysKkiWrZsiSFDhpQYanbu3IkWLVqgevXqKl1HEWV1q6t79+5YtmwZ7t69K+0Ref78OXbt2oWuXbtKe5eaN28OOzs7hIeHo0+fPtLjd+zYgRcvXqg0l0+1atUQHByMlStXYsOGDXKf7IqLi0N6ejoaNmxY5M/hyy+/RP/+/TF58mSsWLFCZl96ejrGjh0LExMTTJgwQbpd1Vtdir5fpdWpUycYGRkhPDwczZs3l24PDw+HRCKRO9bs1q1bKgVQVTD4vCXB3q7StYGISPNUCT0FNB1+CmZEjomJUTn4AEBoaGix+/Py8rBnzx5MmzZN5WsowszMDF5eXiodu2PHDgCQPlF09uxZmJqaAgB69uwpbefn54fjx4/LPKk0efJkbNy4EZ07d8Y333wDQ0NDzJs3D1lZWQgJCZG209XVxYIFC/DJJ59gxIgR6NevH27cuIGpU6fC398fnTp1kqlJIpGgbdu2iI6OLrb2JUuW4NatWxg0aBCOHDmC7t27w9bWFikpKYiIiEBYWBh+/fVX1K9fv8ifQ79+/fDPP/9g0aJFSEhIwJAhQ2Bra4tr165h6dKliIuLw+bNm2V6Y6pUqSK9facMRd8vAKhZsyaAV5NMFnj06BGOHz8O4P97cw4dOgQbGxvY2Nigbdu2AF7dMvvqq68wY8YMWFlZoUOHDoiNjUVISAiCg4NRr149mWs9fvwYN27cwGeffab0a1IFP4XfEi6ESPR2lXYeniUR1zUWfBwdHeHt7Y09e/Zg+PDhGrkG8GoJjJSUlFLNTqxpvXr1kvl++fLlWL58OYBXg3oL5OXlIS8vT6atjY0NTp48icmTJyMoKAi5ublo2bIloqOjpb1qBQYMGABdXV3MmzcP4eHhsLKywsCBAzF79myZdgVLWNjZ2ZVYu5GREQ4cOIBNmzZh/fr1GDFiBNLS0lC5cmV4eXlh3bp16NKlC6Kioor9OSxcuBDt2rXDTz/9hJEjRyItLQ1Vq1ZFu3btsH379kJBQVXKvF/yph64dOlSoZ/X6NGjAaBQUPzyyy9hZmaG5cuXY9GiRahWrRqmTZuGL7/8stB59+zZA319ffTu3VsNr7JkEvH6/7MIaWlpsLCwQGpqKszNzcu6HCKtlJWVhfj4eLi6uio1yPZ1penxAV7N4zNWg7e7du7ciT59+uD27dvS2x/R0dHw9fXF0aNH0bZt21Lfehg9ejTOnDmDv//+Wx0lIy8vD0II6OvrY8yYMfjpp5/Uct7y5ODBgwgMDMR///1XaB4aVan751DReHt7w8nJCZs2bSq2XUl/FxT9/ObgZiKqkIqbx8fUUE/m3zdpOvQArxaqbNq0KebOnVtoX/v27aGvry+zSKkqVqxYodYP2ypVqhS5FlhFERUVhb59+6ot9ADq/zlUJCdOnEBsbGyRTx1qAm91EVGFJW8en0n+tbDpzB28yM6FqaEehr9fQ2b/2wg9wKtxJGvWrMHevXuRn58PHR2dQoOE1XWLQ12io6Olt0CqVq1axtVoxsKFC8u6BK3y+PFjbNiwQW1PlCmCwYeIKrSCELM04jom/m9yQmMDXenDBgXj75ZGXMeEtxR6Cnh4eMg8yVKaQcJvg6Jz9RApSpPTLBSFY3zewDE+RGVPHWN8iKhi4RgfIiItNWfOHIUnzgOADRs2oG/fvqhduzZ0dHTg4uJSZNsXL15g/PjxsLe3h5GRETw9PfHrr7/KbfvPP/+gffv2MDU1haWlJXr06FHkQpc//vgj6tSpA0NDQ7i6umLWrFnIycmRaRMSEgKJRIKUlBSZ7XFxcahRowZsbW1x7tw5hV+3Jvn4+EinJSjO/v37MXDgQDRo0AD6+vpKLQuRkJAAiUQid+V2Uh2DDxHRO0bZ4LNx40ZcunQJzZo1g5ubW7Fte/TogfXr12PmzJk4dOgQmjZtin79+mHz5s0y7a5evQofHx+8fPkS27Ztw7p163D9+nV4e3vj0aNHMm1nz56NcePGoUePHjhy5AhGjx6NOXPmYMyYMSXWfuHCBXh7eyMvLw+nTp165263/fbbb4iJiUG9evXQqFGjsi6HwDE+REQV3pEjR6Cj8+q/cwMDA3Hx4kW57Q4ePIiIiAhs3rwZ/fr1A/BqaYbbt29jypQp6NOnj3R9sa+//hqGhobYv3+/9LZCkyZN4O7ujkWLFmH+/PkAXg1e/e677zBs2DDMmTMHwKvekpycHHz11VcYP358kYO4Y2JiEBAQAFtbW0RERMDBwUF9b8pbsmbNGul7/+mnn5bLp7tycnIgkUjUNnNzecceHyKiciArKwuTJk2Cp6cnLCwsYGVlhZYtW2LPnj0y7SQSCdLT07F+/XpIJBJIJJISb7kUfPCW5LfffoOpqWmhSeoGDx6M+/fv48yZMwBeTW63f/9+fPTRRzJjKZydneHr64vffvtNuu3w4cPIysrC4MGDC51TCFFkz1VERATat28PNzc3nDx5UqHQc/PmTQwePBju7u4wMTFB9erV0aVLl0JrRkVHR0MikWDLli348ssvYW9vD3Nzc7Rv3x7Xrl2TaSuEwIIFC+Ds7AwjIyM0btwYhw4dKrGWAoq+98XJz8/H7Nmz4eTkBCMjI3h5eSEyMlKmjbKvfePGjZg0aRKqV68OQ0ND3Lx5ExkZGZg8ebJ0DI2VlRW8vLywZcuWUr+G8kQ74h0RUTmXnZ2NJ0+eYPLkyahevTpevnyJo0ePokePHggLC5OuBXX69Gm0a9cOvr6+mDFjBgCo7UGMixcvom7duoX+y79gWY2LFy+iVatWiIuLQ2ZmptzlNho2bIiIiAhkZWXByMhI2rv05rw4dnZ2sLa2ltv7tHPnTowdOxatWrXC3r17YWZmplD99+/fR5UqVTBv3jzY2NjgyZMnWL9+PZo3b45///0XtWvXlmk/ffp0tG7dGmvXrkVaWho+//xzdOnSBVeuXJH2bM2aNQuzZs3C0KFD0bNnT9y9exfDhg1DXl5eofNpyk8//QRnZ2csW7YM+fn5WLBgAT744AMcP34cLVu2VOm1f/HFF2jZsiVWrVoFHR0dVK1aFRMnTsTGjRvx3Xff4b333kN6ejouXrwod0X3d5ogGampqQKASE1NLetSiLRWZmamuHz5ssjMzCzrUspMbm6uyMnJEUOHDhXvvfeezL5KlSqJoKAglc7buXNn4ezsLHefu7u76NixY6Ht9+/fFwDEnDlzhBBC/PHHHwKA2LJlS6G2c+bMEQDE/fv3hRBCDBs2TBgaGsq9Xq1atUSHDh2k38+cOVMAEABEjRo1Sv3zz83NFS9fvhTu7u5iwoQJ0u1RUVECgAgICJBpv23bNgFAnD59WgghxNOnT4WRkZHo3r27TLuC19+2bVul6hkzZoxQ5mM3Pj5eABD29vYy70VaWpqwsrIS7du3L/LYkl77+++/X+gYDw8P0a1bN4Xre9tK+rug6Oc3b3UREZUT27dvR+vWrWFqago9PT3o6+sjNDQUV65ceWs1FPfU0Zv7FG2rzDkBoGvXrrh161ahhTNLkpubizlz5qBevXowMDCAnp4eDAwMcOPGDbnvYdeuXWW+L+jBun37NoBXvWtZWVn4+OOPZdq1atUKzs7OStVWGj169JB5fNvMzAxdunTBiRMnpOuXKfvaP/roo0LbmjVrhkOHDmHatGmIjo5GZmam5l5UGWLwISIqB3bt2oXevXujevXq+OWXX3D69GnExsZiyJAhyMrKeis1VKlSRe5tjSdPngB4tep2QTsARbaVSCSwtLSUts3KykJGRobctgXnfN2aNWswaNAgzJ8/H1OnTlW4/okTJ2LGjBno1q0b9u3bhzNnziA2NhaNGjWS+yH+5grnhoaGACBtW/D6qlWrVuhYeds0pajrv3z5UrqoqrKvXd4irD/88AM+//xz7N69G76+vrCyskK3bt1w48YN9b+oMsQxPkRE5cAvv/wCV1dXbN26VaYXJDs7+63V0KBBA2zZsgW5ubky43wKBsgWzDLt5uYGY2PjQgNnC9rWrFlT2kNRMLbnwoULaN68ubRdUlISUlJSZGauLqCjo4PQ0FBIJBIsXLgQ+fn5Cs1l88svv2DgwIHSp8cKpKSkSIOYMgqCUVJSUqF9SUlJxc6HpE5FXd/AwACmpqYAlH/t8nraKlWqJB3T9PDhQ2nvT5cuXXD16lX1vJhygD0+RETlgEQigYGBgcwHUlJSUqGnuoBXPROauA3RvXt3vHjxAjt37pTZvn79etjb20uDi56eHrp06YJdu3bh+fPn0nZ37txBVFQUevToId3WqVMnGBkZITw8XOac4eHhkEgk6Natm9xaCsJPcHAwFi9ejIkTJ5ZYv0QikfbaFDhw4AASExNLPFaeFi1awMjIqNCq4X/++af0dtjbsGvXLplev+fPn2Pfvn3w9vaWDsJW92u3tbXFoEGD0K9fP1y7dk1uj927ij0+RETlQGBgIHbt2oXRo0dLnx769ttvYWdnV+hWQ4MGDRAdHY19+/bBzs4OZmZmxT5hdPnyZVy+fBnAqzCVkZGBHTt2AHi1EGrBPDoffPAB/P39MWrUKKSlpaFmzZrYsmULDh8+jF9++UX6IQu8etqpadOmCAwMxLRp05CVlYWvv/4a1tbWmDRpkrSdlZUVvvrqK8yYMQNWVlbo0KEDYmNjERISguDg4GIXYpVIJFi9ejUkEgmWLl0KIQSWLl1a7HsYHh6OOnXqoGHDhvj777+xcOFClef/qVy5MiZPnozvvvsOwcHB6NWrF+7evYuQkBCFb3Xdvn1buvBsXFwcAEjfexcXF4XWZtPV1YW/vz8mTpyI/Px8zJ8/H2lpaZg1a5a0jTpee/PmzREYGIiGDRuicuXKuHLlCjZu3IiWLVvCxMRE4fOUexoYeP1O41NdRGVPW5/qmjdvnnBxcRGGhoaibt26Ys2aNdInnV537tw50bp1a2FiYqLQ00WvPy315tfMmTNl2j5//lyMHTtWVKtWTRgYGIiGDRvKfXpLCCHOnj0r/Pz8hImJiTA3NxfdunUTN2/elNv2+++/F7Vq1RIGBgbCyclJzJw5U7x8+VJunY8ePZLZnp+fL0aOHCkAiLFjxxb5Op8+fSqGDh0qqlatKkxMTESbNm3EyZMnRdu2bWXeo4Inm7Zv3y5zfMFTVGFhYTLXnjt3rnB0dJS+H/v27St0zqKEhYUV+d6X9GReQT3z588Xs2bNEg4ODsLAwEC899574siRI2p97UIIMW3aNOHl5SUqV64sDA0NRY0aNcSECRNESkpKia/zbVDXU11cpPQNXKSUqOxxkVIiehMXKSUiIiJSEoMPERERaQ0GHyIiItIaDD5ERESkNSpE8Dl37hw6d+4MJycnGBsbS1c1/uWXX8q6NCIiIipHKsQ8Ps+ePYOjoyP69euH6tWrIz09HZs2bcInn3yChIQEfPXVV2VdIhGpgA+dElEBdf09qNCPs7do0QL379/HnTt3FD6Gj7MTlb28vDzcuHED1tbWsLa2LutyiKgcSElJQUpKCtzd3WUm0yyg6Od3hejxKYq1tTWSk5PLugwiUpKuri4sLCzw6NEjZGdnw9zcHHp6esWu8k1EFY8QArm5uUhLS0NaWhosLS3lhh5lVKjgk5+fj/z8fDx9+hTbt2/HkSNH8NNPP5V1WUSkgmrVqsHY2BjJyclIS0sr63KIqAzp6urCzs4OFhYWpT5XhQo+o0ePxs8//wwAMDAwwA8//IARI0YUe0x2drbM6sf8A0tUPkgkElhaWsLCwgJ5eXnIzc0t65KIqAzo6elBV1dXbT2+5S74REdHw9fXV6G2//77Lzw9PaXfT58+HcHBwUhOTsa+ffvw6aefIj09HZMnTy7yHHPnzpVZ6I2IyheJRAI9PT3o6ZW7P1dE9A4qd4ObHzx4gAMHDijUtkePHrCysipy/6hRo7B27Vrcv38fNjY2ctvI6/FxdHTk4GYiIqJ3yDs7uNnOzg7BwcFqOVezZs2watUq3Lp1q8jgY2hoCENDQ7Vcj4iIiMq3CjGBYVGioqKgo6ODGjVqlHUpREREVA6UqscnMzMTf/31F+7du4eUlBSYmJjAxsYGDRo0gJubm7pqLNHw4cNhbm6OZs2awdbWFikpKdi+fTu2bt2KKVOmFNnbQ0RERNpF6eCTmZmJLVu2ICwsDH/99Zf0SQshhMyIazs7O3Tv3h3Dhw9HgwYN1FexHC1btkRYWBjWr1+PZ8+ewdTUFI0aNcLGjRsxYMAAjV6biIiI3h0KD25++fIlli5divnz5+PZs2eoVKkSmjRpgiZNmsDW1hZWVlbIzMzEkydPcO3aNZw5cwbx8fGQSCRo164dFi5cKPMEVnnFmZuJiIjePWof3FyrVi0kJiaiW7duGDBgAAICAqCvr1/sMbdu3cLGjRuxYcMGeHl5Yc2aNRg8eLDir4KISI3WnryF51m5MDPSQ7A3x/4RaSOFe3wGDRqEGTNmqDR2Jy8vDxs2bICOjg6CgoKUPv5tYo8PUcXVYk4kktKyUM3cCDHT/cq6HCJSI7X3+ISHh6tcjK6uLnt6iIiIqMxV6MfZiYiIiF6n0eATHx+PQYMGafISRERERArTSPC5c+cOhg0bhjp16mDjxo2auAQRERGR0pQOPqdOnYKvry/Mzc1hZWWFDz/8ENeuXQMAZGRkYOLEiahVqxZCQ0NhY2ODH374Qe1FExEREalCqQkM//77b7Rv3x4vX76Ubtu3bx9iY2Nx4sQJdOvWDZcvX4a9vT0+//xzDB8+nOtgERERUbmhVI/PggUL8PLlS8ydOxfJyclITk7GN998g6SkJHh7e+Pq1av46quvcPPmTXz22WcMPURERFSuKNXj88cff6Bdu3b4/PPPpdu++uorREZG4sSJE1i4cCEmTpyo9iKJiIiI1EGpHp/k5GQ0adKk0PamTZsCQLmfnJCIiIi0m1LBJzc3F5UqVSq0vWBblSpV1FMVERERkQZwAkMiIiLSGkqN8QGAX375BTExMTLbbt68CQAICAgo1F4ikeDAgQMqlkdERESkPkoHn5s3b0qDzpsOHz5caJtEIlG+KiIiIiINUCr4xMfHa6oOIiIiIo1TKvg4Oztrqg4iIiIijePgZiIiItIaSvX4JCcnq3SRqlWrqnQcERERkTopFXyqVaum9GBliUSC3NxcpY4hIiIi0gSln+rS09ODl5cXDAwMNFEPERERkcYoFXyqVq2K5ORk3Lx5Ex9//DGGDBkCDw8PTdVGREREpFZKDW5OTEzErl270KJFC/z0009o1KgRmjdvjp9//hlpaWmaqpGIiIhILZQKPrq6uujWrRv27NmDu3fvYu7cuUhLS8OoUaNgZ2eHAQMGIDIyUlO1EhEREZWKyo+z29raYurUqbhy5QpOnjyJvn37Yu/evejQoQNq1KiBc+fOqbFMIiIiotJTyzw+rVu3RmhoKPbs2QN7e3vcvn0bd+7cUcepiYiIiNRG6ae63pSSkoKNGzciLCwMly5dgr6+Pnr06IFGjRqpoz4iIiIitVEp+OTn5+PgwYMICwvDgQMH8PLlSzRs2BBLlizBgAEDUKVKFXXXSURERFRqSgWf69evY926ddi4cSMePHiAypUrIzg4GEOGDEHjxo01VSMRERGRWigVfOrUqQNdXV34+flhyZIl6N69OycyJCIioneGSre6Tpw4gRMnTmDw4MEltpVIJEhPT1flMkRERERqpVTwcXJyUnqtLiIiIqLyQqngk5CQoKEyiIiIiDRPqXl8uMo6ERERvcuUCj729vaYPHkyLl++rKl6iIiIiDRGqeCTmpqKJUuWoEGDBmjVqhVCQ0Px4sULTdVGRFQqP0TegOu0A/gx8kax+38oYj8RVTxKBZ8HDx5g6dKlaNCgAWJiYjB8+HDY2dlh6NChOHXqlKZqJCJS2g+RN7Ak4joEgMUR1wuFn9f3L4m4zvBDpCUkQgihyoH//PMP1q1bhy1btuDp06eQSCSoVasWhgwZgoEDB8LW1lbdtb4VaWlpsLCwQGpqKszNzcu6HCJSQUGoeZOpoR5eZOdK/33TRP9aGOvn/jZKJCI1U/TzW+XgU+Dly5fYtWsX1q1bh2PHjkEIAV1dXXTu3BlDhw5FQEAAdHTUshbqW8HgQ/RuKyr0KIrhh+jd9NaCz+vu3buHsLAwhIeHIz4+HhKJBNWqVUNiYqK6LqFxDD5E7zbXaQdQmj9qEgDx8zqrqxwieksU/fxWa1eMg4MDZsyYgYMHD6J169YQQiApKUmdlyAiKtYE/1qlOn5iKY8novJNpSUr5ElPT8e2bduwbt06/PnnnxBCwMTEBD179lTXJYiISlRwm0qV212T/GvhM97mIqrQSh18Tp48iXXr1mHHjh3IyMiAEAJNmzbF0KFD0a9fP5iZmamjTiIihakSfhh6iLSDSsEnMTER69evR3h4OOLi4iCEQJUqVRAcHIyhQ4fCw8ND3XUSESllrJ879py+jJvPdYtdY1AIAXezPIYeIi2hVPDZtm0bwsLCcPToUeTl5UFHRwcdOnTAkCFD0K1bN+jr62uqTiIipfwQeQNxL/RQ0rrKEokEN1/o4cfIGww/RFpAqeDTt29fAICLiwsGDx6MwYMHw8HBQSOFERGpSpVH2hf/rz3DD1HFpnTwGTp0KPz8/DRVDxFRqZRmHh+GH6KKT63z+FQEnMeH6N3GeXyItJPa5/GJjY0tVUGZmZm4cuVKqc5BRFSS0s7jU9rjiah8Uzj4NG/eHF27dsXJkyeVukBKSgoWLlwIV1dXbN++XekCiYiUMdbPvchJCE0N9WT+fROXqyCq+BQOPtu2bcPVq1fh4+MDV1dXTJkyBTt27MCtW7eQnp4OAMjLy8OjR4/w559/YtmyZQgMDET16tUxffp0fPjhhxg5cqTGXggRUQF54WeSfy2Z4PPmfoYeIu2g8ODmnj17olu3bggLC8OqVauwePFimbkxdHV1kZeXJ/1eCAEzMzMMGTIE48aNQ506ddRbORFRMQpCzNKI65j4v8kJN525I3f/BIYeIq2h8uDmixcvIjIyEn/++Sfu3buHx48fw9jYGDY2NmjQoAHatm0LPz8/VKpUSd01axQHNxNVXC3mRCIpLQvVzI0QM51PpxJVJIp+fqu8ZIWHhwc8PDwwbtw4VU9BRERE9FapdXV2IiIiovKsQgaftWvXQiKRwNTUtKxLISIionKkwgWfxMRETJ48Gfb29mVdChEREZUzFS74jBw5Eu+//z78/f3LuhQiIiIqZypU8Pnll19w/PhxrFixoqxLISIionKowgSf5ORkjB8/HvPmzeOK8URERCSXyo+zlzejR49G7dq1MWrUKKWOy87ORnZ2tvT7tLQ0dZdGRERE5YRaenwuX76MXbt2YePGjaU+V3R0NCQSiUJf586dAwDs3LkT+/btw5o1a2Rmk1bE3LlzYWFhIf1ydHQs9WsgIiKi8qlUPT6xsbEYNmwYLly4IN32ySefAABOnDiBTp064ddff0XXrl0VPmft2rWxZs0ahdo6OTnhxYsXGDNmDD777DPY29vj2bNnAICXL18CAJ49ewZ9ff0iZ5D+4osvMHHiROn3aWlpDD9EREQVlMpLVly6dAktWrSAjo4Ohg0bhqtXr+LQoUPS9bqEEHB2dkbbtm3V0hNUlISEBLi6uhbb5sMPP8Tu3bsVOh+XrCCquLhkBVHFpfElK2bOnAkA+Pvvv1GzZk3MmjULhw4dku6XSCRo2bIlYmNjVb2EQqpVq4aoqKhC2+fNm4fjx4/j0KFDsLa21mgNRERE9G5QOfgcP34cH330EWrWrFlkGycnJxw+fFjVSyjEyMgIPj4+hbaHh4dDV1dX7j4iIiLSTioPbn7+/DmqVq1abJusrCzprS8iIiKisqZy8HF0dMTFixeLbfP333/Dzc1N1UuUSnh4OF68eFEm1yYiIqLySeXgExgYiN9//x3Hjh2Tu3/btm2IiYlBt27dVL0EERERkVqpPMZn+vTp2LFjBz744AMEBQXhwYMHAIAVK1bg9OnT2LJlC1xcXGQeFSciIiIqSyoHHxsbGxw/fhyffPIJ1q5dK93+6aefAgCaN2+OLVu2wMLCovRVEhEREalBqSYwrFGjBv744w+cO3cOMTExePLkCczNzdG8eXM0bdpUXTUSERERqYVa1ury9PSEp6enOk5FREREpDEqD25OTU3F+fPnkZGRIXd/eno6zp8/z0U/iYiIqNxQOfh88803aNWqVZHz9OTl5aF169aYPXu2ysURERERqZPKwefw4cPo0KEDzMzM5O43NzdHx44dcfDgQZWLIyIiIlInlYPPnTt34O7uXmwbNzc33LlzR9VLEBEREamVysFHIpEgOzu72DbZ2dlcsoKIiIjKDZWDT926dXH48GEIIeTuz8/Px6FDh1C7dm2ViyMiIiJSJ5WDT//+/XH9+nUMGTIEqampMvtSU1MxZMgQ3Lx5EwMGDCh1kURERETqoPI8PqNHj8auXbuwfv167NmzB02bNkX16tWRmJiI2NhYPHv2DO+//750JmciIiKisqZyj4++vj5+//13TJ48Gfn5+YiIiEB4eDgiIiKQn5+PKVOm4MiRI9DX11dnvUREREQqK9XMzYaGhliwYAHmzZuHq1ev4tmzZ7C0tETt2rWhq6urrhqJiIiI1EItS1bo6OigXr166jgVERERkcaofKuLiIiI6F1TquBz9OhRBAQEwMbGBvr6+tDV1S30paenlk4lIiIiolJTOZXs3LkTffr0QX5+PpydnVGnTh2GHCIiIirXVE4q33zzDYyNjbFnzx60a9dOnTURERERaYTKt7quXbuGvn37MvQQERHRO0Pl4GNtbQ0TExN11kJERESkUSrf6urduzeOHDmC3Nxcju0hondCsLcrnmflwsyIf7OItJVEFLXKaAkyMjLQoUMH2NraYunSpXByclJ3bWUiLS0NFhYWSE1Nhbm5eVmXQ0RERApQ9PNb5eBTo0YN5OTk4P79+wAAS0tLWFhYFL6ARIK4uDhVLlEmGHyIiIjePYp+fqvc35ufnw89PT2Znh55GUrFXEVERESkdioHn4SEBDWWQURERKR5XLKCiIiItAaDDxEREWmNUj/Tefr0aRw9ehT3799HdnZ2of0SiQShoaGlvQwRERFRqakcfHJzc9GvXz/s2rULQghIJBKZgcwF3zP4EBERUXmh8q2uxYsXY+fOnRg8eDDOnj0LIQTGjx+P06dPY/78+bC0tESvXr3eqUfZiYiIqGJTucdn06ZN8PDwwNq1a6XbLC0t0bx5czRv3hwBAQFo1qwZ2rVrhxEjRqilWCIiIqLSULnH5+bNm/Dx8ZF+L5FIkJOTI/2+fv366NKlC1auXFmqAomIiIjUReXgY2BgILNIqampKZKTk2XaODs748aNG6pXR0RERKRGKgcfR0dH3L17V/p9nTp1cOLECZkBzjExMbCysipdhURERERqonLwadu2rUzQ6dOnD65du4bAwEAsX74c/fr1w6lTp9CpUye1FUtERERUGioPbh4yZAjy8vJw7949ODo64rPPPkN0dDT279+PQ4cOAQCaNWuGefPmqa1YIiIiotJQeXX2opw9exZxcXFwdnZGs2bNoKPzbk0OzdXZiYiI3j0aX529KF5eXvDy8lL3aYmIiIhKTe3Bh4iIiEietSdv4XlWLsyM9BDsXaNMalA4+LRr106lC0gkEkRGRqp0LBEREVUca0/GIyktC9XMjcp/8ImOjpa7/c01ut7cLpFIVC6OiIiISJ0UHnmcn58v85WZmYnAwEDUqlULGzduREJCAjIzM5GQkIANGzagVq1aCAwMREZGhibrJyIiIlKYyo9czZw5ExcuXEBsbCw+/vhjODk5wdDQEE5OThgwYADOnDmD8+fPY+bMmeqsl4iIiEhlKgefzZs346OPPoKpqanc/ebm5vjoo4+wZcsWlYsjIiIiUieVg8+jR49kFiWVJzc3t9D6XURERERlReXg4+bmhu3bt+Px48dy9z969Ajbtm1DzZo1VS6OiIiISJ1UDj7jx49HUlISGjdujO+//x5///037t69i7///hvLli1DkyZNkJycjAkTJqizXiIiIiKVqTyBYXBwMB48eIBvv/0WEydOlNknhICuri5CQkIwZMiQUhdJREREpA6lmrl5xowZ6N+/PzZt2oTz588jNTUVFhYWaNSoEfr37w83Nzd11UlERERUaqVessLNzQ1ff/21OmohIiIi0qh3a+l0IiIiolIodY/PX3/9hdjYWDx79gx5eXmF9kskEsyYMaO0lyEiIiIqNZWDz5MnT9CtWzf88ccfctfqKsDgQ0REROWFysFn4sSJOHXqFHx8fBAUFAQHBwfo6ZW6A0kl0dHR8PX1lbvv9OnTaNGixVuuiIiIiMojlZPK/v370axZM0RGRpabFdjnzJlTKAB5eHiUUTVERERU3qgcfLKysvD++++Xm9ADAO7u7uzdISIioiKp/FTXe++9h4SEBDWWQkRERKRZKgefkJAQ7N27FzExMeqsp1TGjBkDPT09mJubo2PHjjh16lRZl0RERETliMq3uhITExEYGIi2bdvi448/xnvvvQcLCwu5bQcOHKhygYqwsLDAuHHj4OPjgypVquDmzZtYuHAhfHx8cODAAXTs2LHIY7Ozs5GdnS39Pi0tTaO1EhERUdmRiOKeRS+Gjo4OJBKJzKPsb473EUJAIpHInd+nKMU9ofWmf//9F56ennL3PXv2DA0aNICVlRX++++/Is8REhKCWbNmFdqempoKc3NzheogIiKikrWYE4mktCxUMzdCzHQ/tZ47LS0NFhYWJX5+q9zjExYWpuqhxapduzbWrFmjUFsnJ6ci91laWiIwMBCrVq1CZmYmjI2N5bb74osvZBZZTUtLg6Ojo3JFExER0TtB5eATFBSkzjqk7OzsEBwcrJZzFfRGFffkmaGhIQwNDdVyPSIiIirfKuxaXU+fPsX+/fvh6ekJIyOjsi6HiIiIygGVe3zu3LmjcNvibkmpQ//+/eHk5AQvLy9YW1vjxo0bWLx4MR4+fIjw8HCNXpuIiIjeHSoHHxcXF4UmL5RIJMjNzVX1Mgpp2LAhtm7dilWrVuHFixewsrJCmzZtsHHjRjRt2lSj1yYiIqJ3h8rBZ+DAgXKDT2pqKv777z/Ex8ejbdu2cHFxKU19Cpk2bRqmTZum8esQERHRu03l4FPcLSQhBBYvXowFCxYgNDRU1UsQERERqZVGBjdLJBJMnjwZ9evXx5QpUzRxCSIiIiKlafSpLi8vLxw7dkyTlyAiIiJSmEaDT1xcnMYHNhMREREpSuUxPkXJz89HYmIiwsPDsWfPHvj5qXdKaiIiIiJVqRx8CtbqKooQApaWlli4cKGqlyAiIiJSK5WDz/vvvy83+Ojo6KBy5crw8vLC4MGDYWtrW6oCiYiIiNRF5eATHR2txjKIiIiINK/CrtVFRERE9Ca1DG7+888/ce7cOaSmpsLCwgKenp5o1aqVOk5NREREpDalCj4nTpzAsGHDcPPmTQCvBjQXjPtxd3fHmjVr4O3tXfoqiYiIiNRA5eBz+vRpdOjQATk5OQgICIC3tzdsbW3x8OFDnDhxAocOHUKHDh0QFRWFFi1aqLNmIiIiIpWoHHymT58OiUSC6OjoQr06U6dOxfHjx9GxY0dMnz6dszcTERFRuaDy4ObY2Fj06dOnyFtZbdu2RZ8+ffDXX3+pXBwRERGROqkcfIyMjFC9evVi21SvXh1GRkaqXoKIiIhIrVQOPn5+fiXewjp27Bjat2+v6iWIiIiI1Erl4LN48WLcv38fgwcPRmJiosy+xMREDBo0CElJSVi0aFGpiyQiIiJSB4UHN7dr167QNisrK2zYsAGbNm2Cs7MzqlatiuTkZNy+fRt5eXlo2LAhgoKCEBkZqdaiiYiIiFShcPApbomK3NxcxMXFIS4uTmb7f//9V+xCpkRERERvk8LBJz8/X5N1EBEREWmcxtfqys3N1fQliIiIiBSiseBz+fJlTJo0CQ4ODpq6BBEREZFS1LJIaYEXL17g119/RWhoKP766y8IIWBgYKDOSxARERGpTC3B59SpU1i3bh22b9+OjIwMCCHw3nvvYfDgwejfv786LkFERERUaioHn4cPH2L9+vVYt24dbty4ASEEqlWrhvT0dAwcOBDh4eFqLJOIiIio9JQKPvn5+Thw4ABCQ0Nx8OBB5ObmwsjICL1798bAgQPRoUMH6Ovr8/YWERERlUtKBR8HBwc8fPgQANC6dWsMHDgQvXv3hrm5uUaKIyIiIlInpYJPUlISdHR0MGnSJHzxxRewtLTUUFlERERE6qfU4+wDBgyAkZERFi1aBDs7O/Tq1Qt79+7lXD1ERET0TlAq+GzYsAEPHjzAihUr0KBBA+zcuRPdu3dHtWrV8OmnnyImJkZTdRIRERGVmtITGJqZmWHEiBH466+/cP78eXz22WeQSCRYsWIFWrduDYlEgmvXruHOnTuaqJeIiIhIZaWaudnDwwPLli3D/fv38euvv8Lf3x8SiQQnT55EjRo14O/vjy1btqirViIiIqJSUcuSFfr6+ujduzcOHz6MhIQEhISEwMnJCZGRkRgwYIA6LkFERERUampfq8vBwQFff/01bt26hd9//x19+vRR9yWIiIjoHfBD5A24TjuAHyNvFLv/hyL2a4Ja1+p6U/v27dG+fXtNXoKIiIjKoR8ib2BJxHUAwOL//VvU/oJ/x/q5a7wuiRBCaPwq75C0tDRYWFggNTWVEzMSERGp4PVQ8zpTQz28yM6V/vumif61VA4/in5+q/1WFxEREWmvokIPAGnYkRd6gFc9P5q+7cXgQ0RERGqztIjQ87aOLwmDDxEREanNBP9apTp+YimPLwmDDxEREanNWD93lcPLJP9a+EzDA5wZfIiIiEitVAk/byP0AAw+REREpAFj/dzhZpqLkh4eF0KgpmnuWwk9AIMPERERacAPkTcQ90IPEomk2HYSiQQ3X+gVOcmhujH4EBERkVoV90h7URZHXH8r4YfBh4iIiNRGldBT4G2EHwYfIiIiUpvSzsOjamhSFIMPERERqU1p5/Ep7fElYfAhIiIitSnuUXZTQz2Zf99UmrW6FMXgQ0RERGolL/xM8q8lE3ze3P82Qg/A4ENEREQaUBB+JJA/OeHr+99W6AEA+X1NRERERKU01s+92EBT0n5NYI8PERERaQ0GHyIiItIaDD5ERESkNRh8iIiISGsw+BAREZHWqFDB59SpUwgICEDlypVhbGwMd3d3fPvtt2VdFhEREZUTFeZx9s2bN+OTTz5B7969sWHDBpiamiIuLg73798v69KIiIionKgQwScxMRHDhw/HiBEjsGLFCul2X1/fMqyKiIiIypsKcatr7dq1SE9Px+eff17WpRAREVE5ViGCz4kTJ2BlZYWrV6/C09MTenp6qFq1KkaOHIm0tLRij83OzkZaWprMFxEREVVMFSL4JCYmIiMjA7169UKfPn1w9OhRTJkyBRs2bEBAQACEEEUeO3fuXFhYWEi/HB0d32LlRERE9DaVu+ATHR0NiUSi0Ne5c+cAAPn5+cjKysL06dPxxRdfwMfHB1OmTMHcuXPxxx9/IDIyssjrffHFF0hNTZV+3b179y29UiIiInrbyt3g5tq1a2PNmjUKtXVycgIAVKlSBTdu3EDHjh1l9n/wwQcYP348/vnnH7Rv317uOQwNDWFoaFi6oomIiOidUO6Cj52dHYKDg5U6pmHDhoiJiSm0veAWl45OuevYIiIiojJQIRLBRx99BAA4dOiQzPaDBw8CAFq0aPHWayIiIqLyp9z1+KiiQ4cO6NKlC7755hvk5+ejRYsWOHv2LGbNmoXAwEC0adOmrEskIiKicqBC9PgAwNatWzF+/HisXr0aH3zwAVauXIkJEyZgx44dZV0aERERlRMVoscHAIyNjTFv3jzMmzevrEshIiKicqrC9PgQERERlYTBh4iIiLQGgw8RERFpDQYfIiIi0hoMPkRERKQ1GHyIiIhIazD4EBERkdZg8CEiIiKtweBDREREWoPBh4iIiLQGgw8RERFpDQYfIiIi0hoMPkRERKQ1GHyIiIhIazD4EBERkdZg8CEiIiKtweBDREREWoPBh4iIiLQGgw8RERFpDQYfIiIi0hoMPkRERKQ1GHyIiIhIazD4EBERkdZg8CEiIiKtweBDREREWoPBh4iIiLQGgw8RERFpDQYfIiIi0hoMPkRERKQ1GHyIiIhIazD4EBERkdZg8CEiIiKtweBDREREWoPBh4iIiLQGgw8RERFpDQYfIiIi0hoMPkRERKQ1GHyIiIhIazD4EBERkdZg8CEiIiKtweBDREREWoPBh4iIiLQGgw8RERFpDQYfIiIi0hp6ZV0AERERaYdgb1c8z8qFmVHZxQ8GHyIiInorgr1rlHUJvNVFRERE2oPBh4iIiLQGgw8RERFpDQYfIiIi0hoMPkRERKQ1GHyIiIhIazD4EBERkdZg8CEiIiKtweBDREREWqNCBJ9BgwZBIpEU+RUTE1PWJRIREVE5IBFCiLIuorTi4uLw6NGjQtu7dOkCQ0ND3L59G7q6ugqdKy0tDRYWFkhNTYW5ubm6SyUiIiINUPTzu0Ks1eXm5gY3NzeZbcePH0dKSgq++uorhUMPERERVWwV4laXPKGhoZBIJBgyZEhZl0JERETlRIXo8XlTamoqduzYAT8/P7i6uhbbNjs7G9nZ2dLv09LSNF0eERERlZEKGXy2bNmCzMxMDB06tMS2c+fOxaxZswptZwAiIiJ6dxR8bpc0dLncDW6Ojo6Gr6+vQm3//fdfeHp6FtretGlTxMfHIzExEYaGhsWe480en8TERNSrV0+pmomIiKh8uHv3LhwcHIrcX+56fGrXro01a9Yo1NbJyanQtvPnz+Ps2bMYN25ciaEHAAwNDWXamZqa4u7duzAzM4NEIlG88BKkpaXB0dERd+/e5dNiRGWEv4dEZUuTv4NCCDx//hz29vbFtit3wcfOzg7BwcEqHx8aGgoAKp9DR0en2KRYWubm5vyDS1TG+HtIVLY09TtoYWFRYpsK9VRXdnY2fvnlFzRr1gweHh5lXQ4RERGVMxUq+OzevRtPnjwpVY8RERERVVwVKviEhoaiUqVK6Nu3b1mXUoihoSFmzpyp0LgjItIM/h4Sla3y8DtY7p7qIiIiItKUCtXjQ0RERFQcBh8iIiLSGgw+REREpDUYfJQQHh4OiUSCs2fPyt0fGBgIFxcXAK8maZo9ezZ8fHxQrVo1mJqaokGDBpg/fz6ysrLkHn/lyhUMGjQITk5OMDAwgLW1NQICAnDo0CFNvSSiCuX8+fMYPHgwXF1dYWRkBFNTUzRu3BgLFizAkydPpO1ycnKwcuVKtGzZEhYWFjA2NkbdunUxbdo0PH78uAxfAdG7RRO/c3l5eViyZAk6deoEBwcHmJiYSNs+e/as9EULUlhYWJgAIGJjY+Xu79y5s3B2dhZCCHHhwgVhbW0tJkyYIPbs2SMiIyNFSEiIMDIyEn5+fiI/P1/m2J07dwpDQ0NRt25dsXr1anH8+HGxbds28cEHHwgAYsqUKZp+eUTvtNWrVws9PT1Rv359sXz5chEVFSV+//13MWfOHOHq6iq6desmhBAiPT1dtG3bVujq6opRo0aJAwcOiGPHjonZs2eLypUrC0dHR3H16tUyfjVE5Z+mfueeP38uzMzMxPDhw8X27dtFVFSUWLx4sahcubKoV6+eyMjIKFXdDD5KUCb4vHjxQrx48aJQm4ULFwoA4uTJk9JtN2/eFCYmJsLLy0vuMSNHjhQAxJYtW9TzQogqmD///FPo6uqKTp06iaysrEL7s7OzxZ49e4QQQgwfPlwAEL/++muhdteuXRMWFhaifv36Ijc3V+N1E72rNPk7l5ubK1JSUgq13b59uwAgNm7cWKraGXyUoEzwKcrx48cFALF582bptjFjxggA4vTp03KPSU9PF5aWlsLDw0Pl2okqssDAQKGnpyfu3LlTbLsHDx4IPT090bFjxyLbzJkzRwAQO3bsUHeZRBVGWfzO3b59WwAQc+bMUanmAhzjo4K8vDzk5uYW+hIKTIl07NgxAED9+vWl2yIiImBra4sWLVrIPcbExAQdOnTAxYsXkZSUpJ4XQVRB5OXl4dixY2jSpAkcHR2LbRsVFYXc3Fx069atyDYF+yIiItRYJVHFUVa/c/I+P1VR7hYpfRcUFVAAwNnZuch958+fx4IFC9C9e3c0bNhQuv3OnTvw9PQs9pqurq7SttWqVVOuYKIKLCUlBRkZGdLfkeLcuXMHAIpt+/rvGhEVVha/c4mJiZg2bRq8vLwQGBioZMWyGHxUsGHDBtStW7fQ9gkTJuDu3btyj0lISEBgYCAcHR2xdu1apa9Z0JskkUiUPpaIlMffNaK3q6jfuSdPniAgIABCCGzduhU6OqW7WcXgo4K6devCy8ur0HYLCwu5wef27dvw9fWFnp4eIiMjYWVlJbPfyckJ8fHxxV4zISEBAErsViTSNtbW1jAxMSnxdwh49bsGoNi2Bfv4u0Yk39v8nXv69Cn8/f2RmJiIY8eOoUaNGipW/f84xkfDbt++DR8fHwghEBUVBQcHh0Jt/P398fDhQ8TExMg9R0ZGBiIiIuDh4cHbXERv0NXVhZ+fH/7++2/cu3ev2LYF/wGye/fuItsU7PP391djlUQVx9v6nXv69Cnat2+P+Ph4REREyAwRKZVSDY3WMso+1XX79m3h4uIiHB0dRVxcXJHnvXnzpjA2Ni7ycfZRo0YV+SggEck+WpudnV1o/8uXL8XevXuFEHycnUgdNP079+TJE9G4cWNhaWlZ5GeuqnirS0OSk5Ph6+uLBw8eIDQ0FMnJyUhOTpbud3BwkPb+uLm5YePGjfj444/RtGlTTJw4EbVr18bDhw+xbt06HDp0CJMnT0afPn3K6uUQlWstW7bEypUrMXr0aDRp0gSjRo1C/fr1kZOTg3///RerV6+Gh4cHunTpgiVLluDatWsYMGAATpw4gS5dusDQ0BAxMTFYtGgRzMzMsHPnTujq6pb1yyIqtzT5O5eZmYmOHTvi33//xbJly5CbmytzR8TGxgZubm6qF6/WGFXBKdPjExUVJQAU+TVz5sxCx1+6dEkEBQUJBwcHoa+vL6ysrESnTp3EgQMHNPiqiCqOc+fOiaCgIOHk5CQMDAxEpUqVxHvvvSe+/vprkZycLG338uVLsXz5ctG8eXNhamoqDA0NRe3atcXUqVPlTpxGRPJp4ncuPj6+2M/PoKCgUtUsEUKByWeIiIiIKgAObiYiIiKtweBDREREWoPBh4iIiLQGgw8RERFpDQYfIiIi0hoMPkRERKQ1GHyIiIhIazD4EBERkdZg8CEiKsKgQYMgkUiQkJBQ1qUQkZow+BCRxmVkZGDOnDlo3LgxTE1NYWRkBAcHB3h7e+OLL75AXFxcWZdIRFqCS1YQkUY9f/4cbdq0wfnz51GzZk34+fnB0tISd+/exaVLl/Dff/9hzZo1CA4OLutSC3nw4AFSU1Ph5uYGfX39si6HiNSAq7MTkUYtW7YM58+fx9ChQ7FmzRpIJBKZ/fHx8cjOzi6j6opnZ2cHOzu7si6DiNSIt7qISKNOnz4NAPj0008LhR4AcHV1RZ06daTfu7i4wMXFBU+fPsWwYcNga2sLY2NjNGvWDHv37pV7DSEE1q1bh9atW8Pc3BwmJibw8vLCunXrimy/fv16vP/++7C0tISJiQnc3d0xcuRI3LlzR9quuDE+J06cQJcuXWBtbQ1DQ0O4u7vjq6++QkZGRqG2O3fuRNu2bVG1alUYGRnB0dERnTp1wu7du4t764hIA9jjQ0QaZWVlBQC4efMmPD09FTrm5cuXaN++PTIzMxEUFIRnz57h119/Rbdu3bBx40Z8/PHH0rZCCAwYMACbN29GrVq10L9/fxgYGCAiIgJDhw7F5cuXsWjRIpn2/fr1w9atW1G9enX069cP5ubmSEhIwNatW9GpUyc4OTkVW9+qVaswevRoVK5cGV26dIGNjQ1iY2Mxe/ZsREVFISoqCgYGBgCAlStXYvTo0bCzs0P37t1RpUoVPHjwAH/99Rd2796Nbt26KfeGElHpCCIiDdq9e7cAIMzNzcXnn38uIiMjxZMnT4ps7+zsLACIdu3aiZcvX0q3X7lyRRgbGwtLS0uRlpYm3b569WoBQAwdOlTk5ORIt2dnZ4suXboIAOLs2bPS7cuXLxcAhJ+fn8jIyJC5dkZGhnj8+LH0+6CgIAFAxMfHS7ddunRJ6Onpiffee0+mrRBCzJ07VwAQixYtkm5r3LixMDAwEMnJyYVea0pKSpHvAxFpBoMPEWncggULhKmpqQAg/XJzcxNjxowR169fl2lbEHz++OOPQucZM2aMACA2btwo3dawYUNRqVIlkZmZWaj9+fPnBQAxadIk6bZ69eoJXV3dQteVR17wGTt2rAAgTp48Wah9Xl6esLGxEU2aNJFua9y4sahUqZJ4+vRpidcjIs3jrS4i0rgpU6Zg5MiROHz4MP7880+cPXsWZ86cwfLlyxEaGoqtW7eia9eu0vb6+vpo0aJFofN4e3tj+fLlOHfuHAYMGICMjAxcuHAB9vb2mDdvXqH2OTk5AICrV68CANLT03H58mXUrFkT7u7uKr2WmJgYAMDhw4dx9OjRQvv19fWl1wOA3r17Y9q0afDw8EDfvn3h4+ODNm3awNLSUqXrE1HpMPgQ0VthZmaGXr16oVevXgCA1NRUTJ8+HStWrMDQoUORmJgoHRdTpUoV6OgUfvbC1tZWeiwAPH36FEIIJCYmYtasWUVeOz09HQDw7NkzAED16tVVfh1PnjwBAMyePVuh9lOnTkWVKlWwatUqLFmyBIsXL4aenh4CAgKwbNkyuLq6qlwLESmPT3URUZmwsLDATz/9BGdnZ6SkpODChQvSfY8fP0Z+fn6hYx4+fCg9FgDMzc0BAE2aNIF4dete7ldUVJTMcYmJiSrXXXDNtLS0Yq9ZQCKRIDg4GGfPnsWjR4/w22+/oUePHti7dy86d+6MvLw8lWshIuUx+BBRmZFIJDAxMSm0PScnR3pL6XUnT54EAOnTYWZmZqhbty6uXLki7c0pjqmpKerVq4f4+HjcuHFDpZqbN28OAHLrK0mVKlXQrVs3bN26Fe3atcOVK1dw8+ZNleogItUw+BCRRv3888+IjY2Vu2/Xrl24evUqLC0t4eHhIbNvxowZ0jE6wKtxOuvWrYOFhQU+/PBD6faxY8ciIyMDw4YNk97Sel18fLzMPDxjxoxBXl4eRo8ejczMTJm2WVlZ0ltZRRk9ejT09PTw2Wef4e7du4X2P3v2DP/++6/0+yNHjiA3N1emTU5OjvQ6xsbGxV6PiNSLY3yISKMOHTqEkSNHombNmmjdujXs7e3x4sULnDt3DidPnoSOjg5WrFgBQ0ND6TF2dnZ49uwZPD090blzZ6SmpmLLli3IysrCmjVrYGZmJm07YsQIxMTEYP369fjjjz/Qvn172Nvb4+HDh7h69SrOnDmDzZs3w8XFBQAwatQoHD9+HNu2bYO7uzu6du0Kc3Nz3LlzB0eOHEFoaGixc+t4eHhgxYoVGDVqFGrXro2AgAC4ubkhLS0Nt27dwvHjxzFo0CCsWrUKANCnTx+YmJigTZs2cHZ2Rk5ODiIiInD58mX06dOnxDmDiEjNyuBJMiLSIlevXhULFiwQ/v7+wtXVVRgZGQkjIyPh5uYmgoKCZObYEeLV4+zOzs7i8ePHIjg4WFStWlUYGhoKLy8vsWfPniKvs3XrVtG+fXtRuXJloa+vL6pXry58fHzE4sWLxaNHj2Ta5ufni7Vr14oWLVqISpUqCRMTE+Hu7i5Gjhwp7ty5I20n73H2An/99Zfo27evsLe3F/r6+sLa2lo0btxYTJs2TVy5ckXabsWKFaJr167C2dlZGBkZiSpVqojmzZuLn3/+WWbeISJ6O7hIKRGVKwU9M/KWiSAiKi2O8SEiIiKtweBDREREWoPBh4iIiLQGx/gQERGR1mCPDxEREWkNBh8iIiLSGgw+REREpDUYfIiIiEhrMPgQERGR1mDwISIiIq3B4ENERERag8GHiIiItMb/AULyV9G97SojAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = Abund()\n",
    "test_abunds = g.predict_abund(1.0,0.1,1000,1,adjust_C=True,verbose=True)\n",
    "\n",
    "#Setting abundances to fit to be those at 10x solar metallicity and C/O = 0.1\n",
    "g.species_abunds = test_abunds\n",
    "popt,pcov,best_fit_abunds = g.convert_species_abunds(1000, 1, plot_it=True) # Need to add errors or you get weird errorbars on M/H and C/O!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebc609f9-41c0-4a5a-9524-e4029108295a",
   "metadata": {},
   "source": [
    "### What do we mean by metallicity and C/O?\n",
    "Something that is sometimes overlooked is how exactly one defines metallicity and C/O. Metallicity is generally defined as a scaling of all elements heavier than He by some factor. But then, how does one change the C/O ratio? Some retrieval codes (PETRA, PLATON, POSEIDON) then change the C/H abundance to get the given C/O ratio, while other codes change O/H instead (pRT with pre-tabulated chemistry). The issue with this approach is that if C/O is changed after the metallicity has been set, then the new composition no longer reflects the correct metallicity! For example, in a low C/O scenario, if one sets C/H after the metallicity, then a whole bunch of carbon atoms have been removed, and the composition is now less metal enriched than it should be for the defined metallicity!\n",
    "\n",
    "Many other codes (CHIMERA, PICASO) change the C/O in such a way that the overal metallicity remains unchanged. In a low C/O scenario, an equal number of carbon atoms are removed as oxygen atoms are added, keeping the number of atoms heavier the He the same, but lowering the C/O accordingly. Functionally:\n",
    "```\n",
    "total = O/H + C/H\n",
    "C/H = C/O * total / (C/O + 1)\n",
    "O/H = total / (C/O +1)            \n",
    "```\n",
    "\n",
    "Some other codes just retrieve O/H and C/H separately and quote a metallicity based one of them or their sum (ATMO, PETRA, pRT with easyCHEM) with the latter being equivalent to the above treatment.\n",
    "\n",
    "In `ExoComp`, we can adjust how we define metallicity and C/O with the `adjust_C` parameter. By default, `ExoComp` changes the C/H ratio to adjust the C/O with `adjust_C = True`, meaning the metallicity is equivlanet to the O/H ratio. `adjust_C = False` will adjust the O/H ratio to match the C/O ratio instead and `adjust_C = 'Neither'` will adjust the C/O while conserving the total metallicity (so metallicity is basically [(O+C)/H].\n",
    "\n",
    "Below, the point is illustrated with the example abundances above. Our test abundances were from 10x solar metallicity and C/O = 0.1, but this was defined by adjusting C/H to obtain the given C/O (ExoComp's default). But what if we used the other methods?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0d284245-0c0e-48ca-b567-d6f02b47b7c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Adjust C/H to get C/O:\n",
      "H2O: -2.1935948210641625\n",
      "CO: -3.740758551917497\n",
      "CO2: -5.704870644717015\n",
      "\n",
      "Adjust O/H to get C/O\n",
      "H2O: -1.3902937541418852\n",
      "CO: -2.5332379909695253\n",
      "CO2: -3.674217006349617\n",
      "\n",
      "Adjust C/O, but preserve M/H\n",
      "H2O: -2.015773925761302\n",
      "CO: -3.4567423478404438\n",
      "CO2: -5.241189750109873\n"
     ]
    }
   ],
   "source": [
    "#Default: Adjust C/H to get C/O\n",
    "print('\\nAdjust C/H to get C/O:')\n",
    "test_abunds = g.predict_abund(1.0,0.1,1000,1,adjust_C=True,verbose=True)\n",
    "\n",
    "#Adjust O/H to get C/O\n",
    "print('\\nAdjust O/H to get C/O')\n",
    "test_abunds = g.predict_abund(1.0,0.1,1000,1,adjust_C=False,verbose=True)\n",
    "\n",
    "#Adjust C/O but preserve M/H constant\n",
    "print('\\nAdjust C/O, but preserve M/H')\n",
    "test_abunds = g.predict_abund(1.0,0.1,1000,1,adjust_C='Neither',verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "580834ed-40a9-4b1d-aa63-c26ef2bde669",
   "metadata": {},
   "source": [
    "They're so different! The different methods can result in almost 2 orders of magntiude difference in expected abundances for CO$_2$. What would the corresponding fits to metallicity and C/O be?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6d3bd4a9-4630-4d54-92e1-519fca988719",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Adjust O/H to get C/O\n",
      "Consider adding errors/uncertainty to the provided measurements!\n",
      "---\n",
      "Best fit [M/H]: 0.261  +/- 2.590\n",
      "Best fit C/O: 0.121  +/- 0.074\n",
      "---\n",
      "Best Fit Abundances\n",
      "---\n",
      "H2O: -2.1933361378098493\n",
      "CO: -3.7405011251604616\n",
      "CO2: -5.705127695480436\n",
      "Updating bulk abundances with mean/median fit\n",
      "\n",
      "Adjust C/O, but preserve M/H\n",
      "Consider adding errors/uncertainty to the provided measurements!\n",
      "---\n",
      "Best fit [M/H]: 0.822  +/- 0.116\n",
      "Best fit C/O: 0.108  +/- 0.052\n",
      "---\n",
      "Best Fit Abundances\n",
      "---\n",
      "H2O: -2.1934883965417504\n",
      "CO: -3.7406528166603428\n",
      "CO2: -5.704976246285373\n",
      "Updating bulk abundances with mean/median fit\n"
     ]
    }
   ],
   "source": [
    "print('\\nAdjust O/H to get C/O')\n",
    "popt,pcov,best_fit = g.convert_species_abunds(1000, 1, plot_it=False, adjust_C=False) \n",
    "\n",
    "print('\\nAdjust C/O, but preserve M/H')\n",
    "popt,pcov,best_fit = g.convert_species_abunds(1000, 1, plot_it=False, adjust_C='Neither') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c8089b1-a3f5-4851-8b74-b8edab6723b6",
   "metadata": {},
   "source": [
    "We can obtain the same species abundances, but the metallicity or C/O we infer is wildly different from the 10x solar and 0.1 C/O we input using the other C/O definition!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "322bcca6-9814-45ca-8336-d95139cf24cf",
   "metadata": {},
   "source": [
    "### Adding uncertainties into our measurements\n",
    "\n",
    "Any good measurement will have an associated uncertainty. We can incorporate these into our fits. ``exocomp`` handles symmetric errors, asymmetric errors, and the ability to input posterior samples. The latter can take a bit of computation if there are lots of samples because it is using curve_fit to fit each sample. You can reduce the number of fitted samples by defining ``posterior_samples``. Let's take a look:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "23085a29-854d-4da9-b2b9-42e419f510b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---\n",
      "Best fit [M/H]: 1.056  +/- 0.001\n",
      "Best fit C/O: 0.531  +/- 0.116\n",
      "---\n",
      "Best Fit Abundances\n",
      "---\n",
      "H2O: -2.19516931830578\n",
      "CO: -2.9537410033888496\n",
      "CO2: -4.916949667855418\n",
      "Chi^2 = 0.00016958376897597855, Reduced Chi^2 = 8.479188448798927e-05\n",
      "Updating bulk abundances with mean/median fit\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Symmetric errors\n",
    "g = Abund(species_abunds={'H2O': -2.196454926078874, 'CO': -2.95559683423187, 'CO2': -4.9118293333092335},\n",
    "          species_errs={'H2O': 0.25, 'CO': 0.3, 'CO2': 0.5})\n",
    "popt,pcov,best_fit_abunds = g.convert_species_abunds(1000, 1, plot_it=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "52149f6d-9312-4bdf-b6a2-c7d022c89701",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.000195\n",
      "         Iterations: 42\n",
      "         Function evaluations: 82\n",
      "---\n",
      "Best fit [M/H]: 1.055 \n",
      "Best fit C/O: 0.534\n",
      "Chi^2 = 39896627.976812825, Reduced Chi^2 = 19948313.988406412\n",
      "---\n",
      "Best Fit Abundances\n",
      "---\n",
      "H2O: -2.196227532130406\n",
      "CO: -2.953466315741787\n",
      "CO2: -4.917735870799223\n",
      "Updating bulk abundances with mean/median fit\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Asymmetric errors - will use scipy.optimize.fmin rather than curve_fit\n",
    "g = Abund(species_abunds={'H2O': -2.196454926078874, 'CO': -2.95559683423187, 'CO2': -4.9118293333092335},\n",
    "          species_errs={'H2O': [0.2,0.1], 'CO': [0.3,0.3], 'CO2': [0.5,0.4]})\n",
    "popt,pcov,best_fit_abunds = g.convert_species_abunds(1000, 1, plot_it=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "07634041-5909-44ae-8f35-1a5941c91849",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on 250 samples... May take a moment if >10\n",
      "---\n",
      "Metallicity - Median: 1.0539, Lower bound: 0.9747, Upper bound: 1.1328\n",
      "C/O - Median: 0.5501, Lower bound: 0.3275, Upper bound: 0.9885\n",
      "---\n",
      "Median Fit Abundances\n",
      "---\n",
      "H2O: -2.1996638510444004\n",
      "CO: -2.9443853957585833\n",
      "CO2: -4.912042947072754\n",
      "Updating bulk abundances with mean/median fit\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Posterior samples\n",
    "h2o_err = np.random.normal(-2.2,0.1,1000)\n",
    "co_err = np.random.normal(-2.96,0.2,1000)\n",
    "co2_err = np.random.normal(-4.91,0.3,1000)\n",
    "\n",
    "g = Abund(species_abunds={'H2O': -2.196454926078874, 'CO': -2.95559683423187, 'CO2': -4.9118293333092335},\n",
    "          species_errs={'H2O': h2o_err, 'CO': co_err, 'CO2': co2_err})\n",
    "popt,pcov,best_fit_abunds = g.convert_species_abunds(1000, 1, plot_it=True, posterior_samples=250) # Doesn't matter if C/O adjusts C or O here"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "508f4bf0-d8ac-46d2-a30f-8fd7614f3126",
   "metadata": {},
   "source": [
    "### Adding refractories\n",
    "\n",
    "Many folks [1,2,3,4] have pointed out the utility of measuring refractory elements to understand processes like 1) cloud formation, 2) rainout, and 3) planet formation. With ``exocomp``, we can add measurements of any refractory element (basically anything past oxygen on the periodic table) to constrain the refractory-to-volatile ratio by setting ``fit_refvol=True``. We can also just use the additional measurements to further constrain the metallicity. Both cases are shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "79bafe72-03ea-4c56-bb6c-2bcc435aeb3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---\n",
      "Best fit [M/H]: 0.921  +/- 0.000\n",
      "Best fit C/O: 0.192  +/- 0.000\n",
      "---\n",
      "Best Fit Abundances\n",
      "---\n",
      "H2O: -2.3801603061726535\n",
      "CO: -2.875074266378856\n",
      "CO2: -5.596428406271661\n",
      "S: -8.628516809069882\n",
      "Na: -4.680052969697716\n",
      "Chi^2 = 6.706780369973133, Reduced Chi^2 = 1.6766950924932833\n",
      "Updating bulk abundances with mean/median fit\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using Sulfur and Sodium to further constrain the metallicity\n",
    "# Symmetric errors\n",
    "g = Abund(species_abunds={'H2O': -2.20, 'CO': -2.96, 'CO2': -4.91,'S':-9.0,'Na':-5.5},\n",
    "          species_errs={'H2O': 0.25, 'CO': 0.3, 'CO2': 0.5, 'S':0.3, 'Na':0.5})\n",
    "popt,pcov,best_fit_abunds = g.convert_species_abunds(1500, 1, plot_it=True) # We need to increase the temperature so S is not condensed..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ac81d6b2-a7f2-479b-9f9a-f4f7a9b04e2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---\n",
      "Best fit [M/H]: 1.069  +/- 0.001\n",
      "Best fit C/O: 0.152  +/- 0.000\n",
      "Best fit R/V: -0.639  +/- 0.009\n",
      "---\n",
      "Best Fit Abundances\n",
      "---\n",
      "H2O: -2.107080366027173\n",
      "CO: -2.8282389963421037\n",
      "CO2: -5.274885927979525\n",
      "S: -9.118131964522485\n",
      "Na: -5.171683908984855\n",
      "Chi^2 = 1.449834641902057, Reduced Chi^2 = 0.3624586604755142\n",
      "Updating bulk abundances with mean/median fit\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using Sulfur to further co\n",
    "# Symmetric errors\n",
    "g = Abund(species_abunds={'H2O': -2.20, 'CO': -2.96, 'CO2': -4.91,'S':-9.0,'Na':-5.5},\n",
    "          species_errs={'H2O': 0.25, 'CO': 0.3, 'CO2': 0.5, 'S':0.3, 'Na':0.5})\n",
    "popt,pcov,best_fit_abunds = g.convert_species_abunds(1500, 1, plot_it=True, fit_refvol=True) # Doesn't matter if C/O adjusts C or O here"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31684040-6e2f-4db0-a283-23c8505aba9c",
   "metadata": {},
   "source": [
    "## Changing our solar reference abundances\n",
    "\n",
    "Metallicity, as defined here, is always relative to a reference abundance. Usually this is \"solar composition\" of some variety, but sometimes we have enough information about the host star to use the stellar abundances as our reference. Unfortunately, if we want to compare retrieval results, the reference abundances used in various studies or retrieval codes varies considerably!\n",
    "\n",
    "When it comes to solar abundances, many references exist. ``ExoComp`` has abundances from Lodders et al. 2010, 2021, and 2025, Asplund et al. 2005, 2009, 2021, and Caffau et al. 2011. These are listed in ``exocomp.Abund.possible_solars``. In ``exocomp.Abund.define_stellar``, one can input any known stellar elemental abundances to be used in the calculations (whatever is defined in ``exocomp.Abund.stellar`` will be used for anything not defined).\n",
    "\n",
    "Let's look at our example at 10x solar metallicity and $\\mathrm{C/O} = 0.1$, but with different solar abundances. The default we've been using so far is the most recent reference, Lodders et al. 2025. Again, the default treatment of C/O is to vary C/H."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "df76963e-0849-4fc2-832c-a2488267c354",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lodders+ 2025\n",
      "---\n",
      "Best fit [M/H]: 1.001  +/- 0.000\n",
      "Best fit C/O: 0.500  +/- 0.002\n",
      "---\n",
      "Best Fit Abundances\n",
      "---\n",
      "H2O: -2.196456010547247\n",
      "CO: -2.9555979209728966\n",
      "CO2: -4.911828253745222\n",
      "Chi^2 = 3.522536165426771e-10, Reduced Chi^2 = 1.7612680827133854e-10\n",
      "Updating bulk abundances with mean/median fit\n",
      "\n",
      "Asplund+ 2009\n",
      "---\n",
      "Best fit [M/H]: 1.058  +/- 0.000\n",
      "Best fit C/O: 0.529  +/- 0.002\n",
      "---\n",
      "Best Fit Abundances\n",
      "---\n",
      "H2O: -2.193703402171143\n",
      "CO: -2.9528386553972754\n",
      "CO2: -4.914568928043371\n",
      "Chi^2 = 0.0022683813605737756, Reduced Chi^2 = 0.0011341906802868878\n",
      "Updating bulk abundances with mean/median fit\n",
      "\n",
      "Lodders+ 2010\n",
      "---\n",
      "Best fit [M/H]: 1.095  +/- 0.000\n",
      "Best fit C/O: 0.521  +/- 0.002\n",
      "---\n",
      "Best Fit Abundances\n",
      "---\n",
      "H2O: -2.193696515820324\n",
      "CO: -2.9528316475014313\n",
      "CO2: -4.914575904597135\n",
      "Chi^2 = 0.0022798738648195493, Reduced Chi^2 = 0.0011399369324097746\n",
      "Updating bulk abundances with mean/median fit\n"
     ]
    }
   ],
   "source": [
    "# Our original example used Lodders+ 2025 to get abundances of H2O, CO, and CO2 at 10x solar metallicity and C/O=0.1\n",
    "# Again at 1000 K and 1 bar\n",
    "print('Lodders+ 2025')\n",
    "g = Abund(species_abunds={'H2O': -2.196454926078874, 'CO': -2.95559683423187, 'CO2': -4.9118293333092335},\n",
    "          species_errs={'H2O': 0.1, 'CO': 0.1, 'CO2': 0.1}, solar='Lodders25')\n",
    "popt,pcov,best_fit_abunds = g.convert_species_abunds(1000, 1, plot_it=False)\n",
    "\n",
    "print('\\nAsplund+ 2009')\n",
    "g = Abund(species_abunds={'H2O': -2.196454926078874, 'CO': -2.95559683423187, 'CO2': -4.9118293333092335},\n",
    "          species_errs={'H2O': 0.1, 'CO': 0.1, 'CO2': 0.1}, solar='Asplund09')\n",
    "popt,pcov,best_fit_abunds = g.convert_species_abunds(1000, 1, plot_it=False)\n",
    "\n",
    "print('\\nLodders+ 2010')\n",
    "g = Abund(species_abunds={'H2O': -2.196454926078874, 'CO': -2.95559683423187, 'CO2': -4.9118293333092335},\n",
    "          species_errs={'H2O': 0.1, 'CO': 0.1, 'CO2': 0.1}, solar='Lodders10')\n",
    "popt,pcov,best_fit_abunds = g.convert_species_abunds(1000, 1, plot_it=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60018a45-20a8-4395-a3c7-30ece9802f9b",
   "metadata": {},
   "source": [
    "### Case Study: WASP-77A b\n",
    "Let's look at a real-world example of this. Line et al. 2022 measured relatively high precision oxygen and carbon abundances in WASP-77Ab. Below, we use ``exocomp.Abund.define_solar()`` to re-define the reference oxygen and carbon abundances with values measured from Regginai et al. 2022."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8a7a07f3-5984-457f-8be3-72f0429dde88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Asplund09 solar abundances\n",
      "Asplund+ 2009 Solar Abundances\n",
      "[O/H] = -0.4911940877550265 +/- 0.14\n",
      "[C/H] = -0.460342076112882 +/- 0.14\n",
      "C/O_p = 0.5900000000000007 +/- 0.08\n",
      "C/O_* = 0.5495408738576247\n",
      "(C/O_p)/(C/O_*) = 1.0736235065798911\n",
      "\n",
      "Reggiani+ Stellar Abundances\n",
      "[O/H] = -0.7211940877550269 +/- 0.14\n",
      "[C/H] = -0.5903420761128828 +/- 0.14\n",
      "[(C+O)/H] = -0.6771073802690852 +/-     0.19798989873223333\n",
      "C/O_p = 0.5900000000000007 +/- 0.08\n",
      "C/O_* = 0.43651583224016655\n",
      "(C/O_p)/(C/O_*) = 1.351611915132986\n"
     ]
    }
   ],
   "source": [
    "from exocomp import Abund\n",
    "from IPython.display import display\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# initialize Abund instance with retrieval defined so we get correct mh_type and co_type\n",
    "g = Abund(retrieval='CHIMERA',solar = 'Asplund09')\n",
    "#display(g.solar_abundances_orig)\n",
    "\n",
    "# convert bulk abundances to elemental abundances\n",
    "g.convert_bulk_abundance(-0.48,0.59,mh_err=[0.12,0.14],co_err=[0.08,0.08],solar = 'Asplund09') \n",
    "#g.convert_bulk_abundance(-0.41,0.46,mh_err=[0.14,0.14],co_err=[0.08,0.08],solar = 'Asplund09') #Accounting for rainout\n",
    "\n",
    "print('Asplund+ 2009 Solar Abundances')\n",
    "print(f'[O/H] = {g.bulk_abunds['O']-g.solar_abundances['O'][0]} +/- {np.max(g.bulk_errs['O'])}') #errors here assume O/H and C/H are uncorrelated...\n",
    "print(f'[C/H] = {g.bulk_abunds['C']-g.solar_abundances['C'][0]} +/- {np.max(g.bulk_errs['C'])}')\n",
    "print(f'C/O_p = {10**g.bulk_abunds['C']/10**g.bulk_abunds['O']} +/- 0.08') #C/O errors not saved elsewhere\n",
    "print(f'C/O_* = {(10**g.solar_abundances['C'][0]/10**g.solar_abundances['O'][0])}')\n",
    "print(f'(C/O_p)/(C/O_*) = {(10**g.bulk_abunds['C']/10**g.bulk_abunds['O'])/(10**g.solar_abundances['C'][0]/10**g.solar_abundances['O'][0])}')\n",
    "\n",
    "# redefine solar based on Reggiani et al. 2022\n",
    "g.define_stellar({'O':(8.92,0.23),'C':(8.56,0.1)})\n",
    "\n",
    "print('\\nReggiani+ Stellar Abundances')\n",
    "print(f'[O/H] = {g.bulk_abunds['O']-g.solar_abundances['O'][0]} +/- {np.max(g.bulk_errs['O'])}')\n",
    "print(f'[C/H] = {g.bulk_abunds['C']-g.solar_abundances['C'][0]} +/- {np.max(g.bulk_errs['C'])}')\n",
    "print(f'[(C+O)/H] = {np.log10((10**g.bulk_abunds['C']+10**g.bulk_abunds['O'])/((10**g.solar_abundances['C'][0]+10**g.solar_abundances['O'][0])))} +/- \\\n",
    "    {np.sqrt(np.max(g.bulk_errs['C'])**2+np.max(g.bulk_errs['O'])**2)}')\n",
    "\n",
    "print(f'C/O_p = {10**g.bulk_abunds['C']/10**g.bulk_abunds['O']} +/- 0.08') #C/O errors not saved elsewhere\n",
    "print(f'C/O_* = {(10**g.solar_abundances['C'][0]/10**g.solar_abundances['O'][0])}')\n",
    "print(f'(C/O_p)/(C/O_*) = {(10**g.bulk_abunds['C']/10**g.bulk_abunds['O'])/(10**g.solar_abundances['C'][0]/10**g.solar_abundances['O'][0])}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee69db4e-58ad-44c9-972b-feed1969fe24",
   "metadata": {},
   "source": [
    "This is a super interesting case, where if we compared WASP-77Ab's C/O measurement to solar abundances, we would conclude that it has a C/O consistent with solar. But if we compare the C/O in the planet to new, state-of-the-art measurements of the C/O ratio of the star, then we see that the planet is actually more highly enriched in carbon than the star!\n",
    "\n",
    "We can calculate the elemental ratios relative to solar or stellar with the ``exocomp.Abund.solar_ratio_calc()`` routine, which outputs the elemental abundances relative to our reference abundances. However, we do not include the errors here because the uncertainty in the C/H and O/H abundances of WASP-77A are well-correlated such that their individual absolute abundances as input here are not measured as precise as their ratio.\n",
    "\n",
    "In any case:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3b1904dd-9833-43c1-99e3-57233a64c83a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Asplund09 solar abundances\n",
      "C/O relative to solar: 1.073623506579891\n",
      "C/O relative to stellar: 1.351611915132986\n"
     ]
    }
   ],
   "source": [
    "g = Abund(retrieval='CHIMERA',solar = 'Asplund09')\n",
    "#display(g.solar_abundances_orig)\n",
    "\n",
    "# convert bulk abundances to elemental abundances\n",
    "g.convert_bulk_abundance(-0.48,0.59,mh_err=[0.12,0.14],co_err=[0.08,0.08],solar = 'Asplund09') \n",
    "\n",
    "solar_ratios, solar_ratios_err = g.solar_ratio_calc()\n",
    "ratio = 10**solar_ratios['C']/10**solar_ratios['O']\n",
    "#exponent_error = np.sqrt(solar_ratios_err['C']**2 + solar_ratios_err['O']**2)\n",
    "#ratio_error = ratio * np.log(10) * exponent_error\n",
    "print(f'C/O relative to solar: {ratio}') #Note that \n",
    "\n",
    "#Redefine O and C using Reganni et al. stellar abundances\n",
    "g.define_stellar({'O':(8.92,0.23),'C':(8.56,0.1)})\n",
    "solar_ratios, solar_ratios_err = g.solar_ratio_calc()\n",
    "ratio = 10**solar_ratios['C']/10**solar_ratios['O']\n",
    "#exponent_error = np.sqrt(solar_ratios_err['C']**2 + solar_ratios_err['O']**2)\n",
    "#ratio_error = ratio * np.log(10) * exponent_error\n",
    "print(f'C/O relative to stellar: {ratio}') #Note that "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7ab3d9b-ca52-4416-82a7-51004e341be3",
   "metadata": {},
   "source": [
    "Let's do this again with the values from August et al. 2023 using JWST/NIRSpec data on the same system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "78146d8e-d31d-4442-a72e-bf0076231431",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Asplund09 solar abundances\n",
      "Asplund+ 2009 Solar Abundances\n",
      "[O/H] = -0.9100000000000001 +/- 0.24\n",
      "[C/H] = -1.0936974992327126 +/- 0.26\n",
      "C/O_p = 0.3600000000000003 +/- 0.08\n",
      "C/O_* = 0.5495408738576247\n",
      "(C/O_p)/(C/O_*) = 0.6550923090995944\n",
      "\n",
      "Reggiani+ Stellar Abundances\n",
      "[O/H] = -1.1400000000000006 +/- 0.24\n",
      "[C/H] = -1.2236974992327134 +/- 0.26\n",
      "[(C+O)/H] = -1.1637715084642928 +/-     0.3538361202590827\n",
      "C/O_p = 0.3600000000000003 +/- 0.08\n",
      "C/O_* = 0.43651583224016655\n",
      "(C/O_p)/(C/O_*) = 0.824712354996398\n"
     ]
    }
   ],
   "source": [
    "# initialize Abund instance with retrieval defined so we get correct mh_type and co_type\n",
    "g = Abund(retrieval='PLATON')\n",
    "#display(g.solar_abundances_orig)\n",
    "\n",
    "# convert bulk abundances to elemental abundances\n",
    "g.convert_bulk_abundance(-0.91,0.36,mh_err=[0.16,0.24],co_err=[0.09,0.10]) \n",
    "#g.convert_bulk_abundance(-0.41,0.46,mh_err=[0.14,0.14],co_err=[0.08,0.08],solar = 'Asplund09') #Accounting for rainout\n",
    "\n",
    "print('Asplund+ 2009 Solar Abundances')\n",
    "print(f'[O/H] = {g.bulk_abunds['O']-g.solar_abundances['O'][0]} +/- {np.max(g.bulk_errs['O'])}') #errors here assume O/H and C/H are uncorrelated...\n",
    "print(f'[C/H] = {g.bulk_abunds['C']-g.solar_abundances['C'][0]} +/- {np.max(g.bulk_errs['C'])}')\n",
    "print(f'C/O_p = {10**g.bulk_abunds['C']/10**g.bulk_abunds['O']} +/- 0.08') #C/O errors not saved elsewhere\n",
    "print(f'C/O_* = {(10**g.solar_abundances['C'][0]/10**g.solar_abundances['O'][0])}')\n",
    "print(f'(C/O_p)/(C/O_*) = {(10**g.bulk_abunds['C']/10**g.bulk_abunds['O'])/(10**g.solar_abundances['C'][0]/10**g.solar_abundances['O'][0])}')\n",
    "\n",
    "# redefine solar based on Reggiani et al. 2022\n",
    "g.define_stellar({'O':(8.92,0.23),'C':(8.56,0.1)})\n",
    "\n",
    "print('\\nReggiani+ Stellar Abundances')\n",
    "print(f'[O/H] = {g.bulk_abunds['O']-g.solar_abundances['O'][0]} +/- {np.max(g.bulk_errs['O'])}')\n",
    "print(f'[C/H] = {g.bulk_abunds['C']-g.solar_abundances['C'][0]} +/- {np.max(g.bulk_errs['C'])}')\n",
    "print(f'[(C+O)/H] = {np.log10((10**g.bulk_abunds['C']+10**g.bulk_abunds['O'])/((10**g.solar_abundances['C'][0]+10**g.solar_abundances['O'][0])))} +/- \\\n",
    "    {np.sqrt(np.max(g.bulk_errs['C'])**2+np.max(g.bulk_errs['O'])**2)}')\n",
    "print(f'C/O_p = {10**g.bulk_abunds['C']/10**g.bulk_abunds['O']} +/- 0.08') #C/O errors not saved elsewhere\n",
    "print(f'C/O_* = {(10**g.solar_abundances['C'][0]/10**g.solar_abundances['O'][0])}')\n",
    "print(f'(C/O_p)/(C/O_*) = {(10**g.bulk_abunds['C']/10**g.bulk_abunds['O'])/(10**g.solar_abundances['C'][0]/10**g.solar_abundances['O'][0])}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a46d128-4bef-4ae2-85b9-e5235bc9a4a3",
   "metadata": {},
   "source": [
    "## Sensitivity to Temperature and Pressure\n",
    "\n",
    "A given metallicity and C/O ratio will correspond to a different set of species abundances depending on the temperature and pressure of an atmosphere. The two biggest reasons are the CH$_4$-to-CO transition from low-to-high temperatures and thermal dissociation of molecules at high temperatures. You can see both these at work in the plots below, showing how abundances change at 10 times solar metallicity 1) at 1 mbar as a function of temperture and 2) at 1000 K as a function of pressure.\n",
    "\n",
    "There will be a sweet-spot where we can be a bit more confident in our inferences because the chemistry isn't quite so sensitive to the assumed pressure and temperature. For example, above 1000 K, we can be confident that we're safely in the CO-dominated regime and below 2500 K, we can be confident that we're not subject to much thermal dissociation. As with this whole exercise, disequilbrium chemistry processes like vertical mixing and photochemistry can also influence our inferences, so be aware of that too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "16f5e292-d414-4243-9c8f-e7659ca9ebd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Abundance')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temps = np.linspace(500,3500)\n",
    "pressures = np.linspace(-4,1)\n",
    "cs = ['#1f77b4', '#ff7f0e', '#d62728','#2ca02c', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf'] #colors\n",
    "\n",
    "fig,ax = plt.subplots(2,1,figsize=(10,6))\n",
    "for i,t in enumerate(temps):\n",
    "    test_abunds = g.predict_abund(1.0,0.1,t,1e-3,adjust_C=True,verbose=False,species=['H2O','CO','CO2','CH4'])\n",
    "    ax[0].plot(t,test_abunds['H2O'],'o',color=cs[0],label = 'H2O' if i == 0 else '')\n",
    "    ax[0].plot(t,test_abunds['CH4'],'s',color=cs[1],label = 'CH4' if t == np.min(temps) else '')\n",
    "    ax[0].plot(t,test_abunds['CO'],'d',color=cs[2],label = 'CO' if t == np.min(temps) else '')\n",
    "    ax[0].plot(t,test_abunds['CO2'],'^',color=cs[3],label = 'CO2' if t == np.min(temps) else '')\n",
    "\n",
    "ax[0].set_xlabel('Temperature')\n",
    "ax[0].set_ylabel('Abundance')\n",
    "ax[0].legend()\n",
    "\n",
    "for p in pressures:\n",
    "    test_abunds = g.predict_abund(1.0,0.1,1000,10**p,adjust_C=True,verbose=False,species=['H2O','CO','CO2','CH4'])\n",
    "    ax[1].plot(p,test_abunds['H2O'],'o',color=cs[0],label = 'H2O' if t == np.min(temps) else '')\n",
    "    ax[1].plot(p,test_abunds['CH4'],'s',color=cs[1],label = 'CH4' if t == np.min(temps) else '')\n",
    "    ax[1].plot(p,test_abunds['CO'],'d',color=cs[2],label = 'CO' if t == np.min(temps) else '')\n",
    "    ax[1].plot(p,test_abunds['CO2'],'^',color=cs[3],label = 'CO2' if t == np.min(temps) else '')\n",
    "\n",
    "ax[1].set_xlabel('Pressure')\n",
    "ax[1].set_ylabel('Abundance')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88339141-6c03-4c50-b494-4230ac397edd",
   "metadata": {},
   "source": [
    "Thus, on the flip-side, our inference of the metallicity and C/O will change for a given set of species abundances, depending on the pressure and temperature that we assume those abundances are representative of. Below, you can see that assuming a (very) wrong temperature will give us the wrong metallicity and C/O and abundances that don't fit very well! \n",
    "\n",
    "Assuming the wrong pressure is a little more forgiving, but our C/O estimate is off by a factor of four because there's actually more CH$_4$ and less CO and CO$_2$ at depth. Remember what we said above though, and if you chose to define C/O differently, you'll be biased in a different way! The only reason our metallicity didn't change when assuming a different pressure was because H$_2$O is relatively robust to pressure in this temperature regime and the metallicity was essentially defined as [O/H]. If we define things differently, our estimates are even more messed up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "32ca77c9-993e-4c26-b8e2-cadd5e9fc39b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting for bulk abundances at the correct T=1000K and P=1bar\n",
      "---\n",
      "Best fit [M/H]: 1.219  +/- 0.000\n",
      "Best fit C/O: 0.085  +/- 0.000\n",
      "---\n",
      "Best Fit Abundances\n",
      "---\n",
      "H2O: -1.9269685283939144\n",
      "CO: -3.3145903720687953\n",
      "CO2: -4.999774192669719\n",
      "Chi^2 = 0.002798471471121238, Reduced Chi^2 = 0.001399235735560619\n",
      "Updating bulk abundances with mean/median fit\n",
      "\n",
      "Fitting for bulk abundances at the wrong T=3000K\n",
      "---\n",
      "Best fit [M/H]: 1.531  +/- 0.000\n",
      "Best fit C/O: 0.033  +/- 0.000\n",
      "---\n",
      "Best Fit Abundances\n",
      "---\n",
      "H2O: -1.6024375664538895\n",
      "CO: -3.004024008397868\n",
      "CO2: -5.309866587763058\n",
      "Chi^2 = 29.21826991328298, Reduced Chi^2 = 14.60913495664149\n",
      "Updating bulk abundances with mean/median fit\n",
      "\n",
      "Fitting for bulk abundances at the wrong P=1e-4 bar\n",
      "---\n",
      "Best fit [M/H]: 1.219  +/- 0.000\n",
      "Best fit C/O: 0.031  +/- 0.000\n",
      "---\n",
      "Best Fit Abundances\n",
      "---\n",
      "H2O: -1.926799365189687\n",
      "CO: -3.314416514885071\n",
      "CO2: -4.999944236575657\n",
      "Chi^2 = 0.0024938703370249184, Reduced Chi^2 = 0.0012469351685124592\n",
      "Updating bulk abundances with mean/median fit\n",
      "\n",
      "Fitting for bulk abundances at the wrong P=1e-4 bar, defining C/O by O/H\n",
      "---\n",
      "Best fit [M/H]: 0.011  +/- 0.000\n",
      "Best fit C/O: 0.039  +/- 0.000\n",
      "---\n",
      "Best Fit Abundances\n",
      "---\n",
      "H2O: -1.9262285578622753\n",
      "CO: -3.3138649853768554\n",
      "CO2: -5.000495625767293\n",
      "Chi^2 = 0.0016220342566747273, Reduced Chi^2 = 0.0008110171283373637\n",
      "Updating bulk abundances with mean/median fit\n",
      "\n",
      "Fitting for bulk abundances at the wrong P=1e-4 bar, defining C/O consistently with M/H\n",
      "---\n",
      "Best fit [M/H]: 1.019  +/- 0.000\n",
      "Best fit C/O: 0.034  +/- 0.000\n",
      "---\n",
      "Best Fit Abundances\n",
      "---\n",
      "H2O: -1.9265749513052937\n",
      "CO: -3.3142002063068943\n",
      "CO2: -5.000160491748477\n",
      "Chi^2 = 0.0021293786280041182, Reduced Chi^2 = 0.0010646893140020591\n",
      "Updating bulk abundances with mean/median fit\n"
     ]
    }
   ],
   "source": [
    "# Assume our fiducial 10x solar metallicity, C/O = 0.1, T=1000K and pressure = 1bar\n",
    "test_abunds = g.predict_abund(1.0,0.1,1000,1,adjust_C=True,verbose=False)\n",
    "\n",
    "g = Abund(species_abunds=test_abunds,\n",
    "          species_errs={'H2O': 0.1, 'CO': 0.1, 'CO2': 0.1}, solar='Lodders25')\n",
    "\n",
    "print('Fitting for bulk abundances at the correct T=1000K and P=1bar')\n",
    "popt,pcov,best_fit_abunds = g.convert_species_abunds(1000, 1, plot_it=False)\n",
    "\n",
    "print('\\nFitting for bulk abundances at the wrong T=3000K')\n",
    "popt,pcov,best_fit_abunds = g.convert_species_abunds(3000, 1, plot_it=False)\n",
    "\n",
    "print('\\nFitting for bulk abundances at the wrong P=1e-4 bar')\n",
    "popt,pcov,best_fit_abunds = g.convert_species_abunds(1000, 1e-4, plot_it=False)\n",
    "\n",
    "print('\\nFitting for bulk abundances at the wrong P=1e-4 bar, defining C/O by O/H')\n",
    "popt,pcov,best_fit_abunds = g.convert_species_abunds(1000, 1e-4, plot_it=False, adjust_C=False)\n",
    "\n",
    "print('\\nFitting for bulk abundances at the wrong P=1e-4 bar, defining C/O consistently with M/H')\n",
    "popt,pcov,best_fit_abunds = g.convert_species_abunds(1000, 1e-4, plot_it=False, adjust_C='Neither')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea8b77fc-e7c2-486c-8475-7fe1cb26d5d9",
   "metadata": {},
   "source": [
    "In the near-future, we will implement the ability to incorporate uncertainties in the temperature and pressure into the errors of the inferred bulk abundances."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc62b5d7-4768-4315-8a69-7437ba3fda05",
   "metadata": {},
   "source": [
    "## Some Real-world Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5588ab2-4eea-460d-a8ca-b50d6d251cff",
   "metadata": {},
   "source": [
    "### WASP-17b\n",
    "\n",
    "Let's look at an actual example. Louie et al. 2025 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3f0c1bb5-bbe5-4518-8032-f72574b59b32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 2.778314\n",
      "         Iterations: 96\n",
      "         Function evaluations: 173\n",
      "---\n",
      "Best fit [M/H]: 0.260 \n",
      "Best fit C/O: 0.255\n",
      "Best fit R/V: -1.509\n",
      "Chi^2 = 22435859.901501104, Reduced Chi^2 = 4487171.980300221\n",
      "---\n",
      "Best Fit Abundances\n",
      "---\n",
      "H2O: -2.948740454529697\n",
      "CH4: -9.205096976006793\n",
      "CO2: -6.508029084442246\n",
      "K: -8.287357035986236\n",
      "Na: -6.846880531894575\n",
      "CO: -3.411282129239243\n",
      "Updating bulk abundances with mean/median fit\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#WASP-17b\n",
    "                      \n",
    "g = Abund(species_abunds={'H2O':-2.96,'CH4':-9.05,'CO2':-5.81,'K':-8.07,'Na':-9.31,'CO':-3.97},\n",
    "          species_errs={'H2O':[0.24,0.21],'CH4':[1.01,2.01],'CO2':[1.31,0.62],'K':[0.52,0.58],'Na':[0,1.75],'CO':[0,1.0]},\n",
    "          solar='Asplund09')\n",
    "g.convert_species_abunds(1271, 5e-3,plot_it=True,fit_refvol=True)\n",
    "#g.convert_species_abunds(1271, 5e-3,plot_it=True,fit_refvol=False,adjust_C=False)\n",
    "#g.convert_species_abunds(1271, 5e-3,plot_it=True,fit_refvol=False,adjust_C='Neither')\n",
    "\n",
    "plt.savefig('WASP17b_exocomp.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dd7b14a-4189-430b-b1e3-ab33523b062c",
   "metadata": {},
   "source": [
    "### WASP-178b\n",
    "\n",
    "WASP-178b is an ultra-hot Jupiter with an observed transit spectrum ranging from 0.2 to 5.1 $\\mu$m, with detections of refractories and volatiles. Here, we mostly ascribe the large NUV absorption to SiO, but not that it could also be from atomic species like Fe and Mg (and their ions), or some combination of each. In any case, we show here the constraints we would place on the bulk abundances from results from a free retrieval (with posteriors!) and compare them to an equivalent chemical equilibrium retrieval. Remember, as above, the challenge for ultra-hot Jupiters is dealing with thermal dissociation, where some (or lots) of the H$_2$O will have dissociated, making it challenging to infer a bulk O/H from measurements of H$_2$O.\n",
    "\n",
    "These retrievals correspond to the isothermal free retrieval case for the combined HST+JWST dataset from Lothringer et al. 2025. These retrievals were run with pRT, so let's take the posterior, for which the abundances will be in mass fractions, convert them to volume mixing ratios with ``ExoComp``, and fit bulk abundances to them. The standard deviation of the posterior is used as a weight for the fits to each sample- otherwise unconstrained species blow up the constraint on the bulk abundances (the same problem when trying to infer bulk abundances by adding up atoms per species!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2544be5d-aff7-411a-b219-4341f9ac4322",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median VMRs:\n",
      "{'H2O': -6.259975908812065, 'CO': -5.441280282739208, 'CO2': -9.6259416793431, 'FeH': -6.399704413781108, 'SiO': -5.084753936621808, 'TiO': -10.732461397889557, 'VO': -10.288929797996929, 'Fe': -6.447413285227768, 'Mg': -6.000271813664576}\n"
     ]
    }
   ],
   "source": [
    "samples_free = np.genfromtxt('W178_eqchem_JWST_retrieval_10bin_1000LP_iso_new_offs_hot_post_equal_weights.dat')\n",
    "\n",
    "#Columns are: UV offset, IR offset, radius, temperature, log10Pcloud, H2O, CO, CO2, FeH, SiO, TiO, VO, Fe, Fe+, Mg, Mg+\n",
    "species = ['H2O','CO','CO2','FeH', 'SiO', 'TiO', 'VO', 'Fe', 'Fe+', 'Mg', 'Mg+']\n",
    "# Unfortunately, these ion species are not included in EasyChem by default\n",
    "# They can be added manually though with the right thermochemical info\n",
    "not_included = ['Fe+','Mg+']\n",
    "offset = 5\n",
    "posterior_dict_mmw = {}\n",
    "for i,s in enumerate(species):\n",
    "    if s in not_included:\n",
    "        continue\n",
    "    posterior_dict_mmw[s] = samples_free[:,offset+i]\n",
    "\n",
    "g = Abund()\n",
    "posterior_dict_vmr = g.MMR_to_VMR(posterior_dict_mmw, mmw=2.3)\n",
    "\n",
    "# Make a dict of the median values to input into ExoComp- only used for reference,\n",
    "# it is the posteriors that matter!\n",
    "median_dict = {}\n",
    "for i,s in enumerate(species):\n",
    "    if s in not_included:\n",
    "        continue\n",
    "    median_dict[s] = np.median(posterior_dict_vmr[s])\n",
    "print('Median VMRs:')\n",
    "print(median_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b74e8836-1353-42bf-8a53-cd9b2381ebf1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on 250 samples... May take a moment if >10\n",
      "---\n",
      "Metallicity - Median: 0.7425, Lower bound: 0.6285, Upper bound: 0.8657\n",
      "C/O - Median: 0.0016, Lower bound: 0.0010, Upper bound: 0.0024\n",
      "Ref/Vol - Median: -1.2740, Lower bound: -1.4248, Upper bound: -1.1135\n",
      "---\n",
      "Median Fit Abundances\n",
      "---\n",
      "H2O: -6.342546942879467 + 0.6073334415111322 - 0.8608469086508288\n",
      "CO: -5.397260058990113 + 0.6274025678119353 - 0.8585983812006326\n",
      "CO2: -10.434123844104665 + 1.236983028705236 - 1.7336802832130527\n",
      "FeH: -8.719915084214614 + 2.7423852130688697 - 0.15434695463802584\n",
      "SiO: -3.9908787457581734 + 2.38025601660468 - 0.14779824058628188\n",
      "TiO: -7.622897024195767 + 2.4933889945239756 - 0.33218304283236755\n",
      "VO: -9.906240568173741 + 2.504401867489605 - 0.45163656039393274\n",
      "Fe: -3.808756048235038 + 2.742623297449149 - 0.15444587903315243\n",
      "Mg: -3.6916531144848 + 2.3871840172824914 - 0.1507595418080423\n",
      "Updating bulk abundances with mean/median fit\n"
     ]
    },
    {
     "data": {
      "image/png": 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Hjx5atGiRpk6d2qx9l5eXKzo6WmVlZYqKirKsDeg4Kisr1aVLF0k8KxJHdNQ80lHbBaD9dNQ80lHbFQj4HAd/0ZI8YvuZ8oaGBi1fvlyTJ0/2FuTSkcvWZ86cqYyMDCUmJioxMVEZGRmKiIjQhAkTbIwYAAAAAIC2YXtR/tZbb2n//v265ZZbGq2bM2eOqqurNW3aNJWWlmr48OFat26dIiMjbYgUVmpoaND+/fslSb169VJQkN/c7sBv0EeAvePACWPQCTECcCbyi3XoW/jF5etW4vIjZ/CnS438KZZj+WtcgaCj5hEntsvOceCEMeiEGNGxODGPNEdHbdfJcEp+cUqcx3JizPhxLckjfA0DAAAAAIBNKMoBAAAAALAJRTkAAAAAADahKAcAAAAAwCYU5QAAAAAA2ISiHAAAAAAAm9j+nHJAkkJCQjRt2jTvPBqjjwB7x4ETxqATYgTgTOQX69C34DnlwHF4ViSO11HzSEdtF4D201HzSEdtVyDgcxz8Bc8pBwAAAADAAbg+An7BNE19++23kqRTTz1VhmHYHJH/oY8Ae8eBE8agE2IE4EzkF+vQt6Aoh1+oqqpSjx49JHGp0YnQR4C948AJY9AJMQJwJvKLdehbcPk6AAAAAAA2oSgHAAAAAMAmFOUAAAAAANiEohwAAAAAAJtwozf4vT5zX7f8GHsXjrH8GAD8R2vySkPtIe98v9+uVVBYpx/cnrwCACdm1ee7Y3M14BScKQcAP9SnTx8ZhtFomj59uqQjj0/xeDyKi4tTeHi4UlJStH37dpujBgAAQEtxphx+ISQkRJMnT/bOozH6KLAUFBSovr7e+3rbtm1KTU3VddddJ0lavHixsrKytGLFCvXt21cPPfSQUlNTtWvXLkVGRtoVtuXsHAdGULA6J13qnfdH5AkAViG/WIe+Bf/q8Asul0srVqywOwy/Rh8Flu7du/u8XrhwoU4//XQlJyfLNE0tWbJE8+bN0/jx4yVJOTk5iomJUW5urqZOnWpHyO3CznFghITq1DGzbDl2c5EnAFiF/GId+hZcvg4Afq62tlbPP/+8brnlFhmGocLCQhUXFystLc27jcvlUnJysjZs2HDC/dTU1Ki8vNxnAgAAgL0oyuEXTNNUZWWlKisrZZqm3eH4JfoocL3yyiv6/vvvNWXKFElScXGxJCkmJsZnu5iYGO+6pmRmZio6Oto7xcfHWxazVewcB6ZpqqH2kBpqD/ntGCRPALAK+cU69C0oyuEXqqqq1KVLF3Xp0kVVVVV2h+OX6KPA9cwzz2j06NGKi4vzWW4Yhs9r0zQbLTtWenq6ysrKvFNRUZEl8VrJznFg1tWo6NFrVfTotTLratr12M1FngBgFfKLdehb8JtyAPBj+/bt01tvvaVVq1Z5l7ndbklHzpjHxsZ6l5eUlDQ6e34sl8sll8tlXbAAAABoMc6UA4AfW758uXr06KExY/77zOuEhAS53W7l5eV5l9XW1io/P18jR460I0wAAAC0EmfKAcBPNTQ0aPny5Zo8ebLPI1IMw9DMmTOVkZGhxMREJSYmKiMjQxEREZowYYKNEQMAAKClKMoBwE+99dZb2r9/v2655ZZG6+bMmaPq6mpNmzZNpaWlGj58uNatW9dhnlHeZ+7rTS5vqD3kne/327UKCuvUXiEBAABYgqIcAPxUWlraCe/CahiGPB6PPB5P+wYFAACANsVvygEAAAAAsAlnyuEXgoODde2113rn0Rh9BEhGUJAizrzAOx8ox24u8gQAq5BfrEPfgqIcfqFTp0568cUX7Q7Dr9FHgGSEhKn71ekBd+zmIk8AsIoT84vV9x7Zu3DMj2/UDE7sW7Qt//yqHwAAAACAAEBRDgAAAACATSjK4RcqKytlGIYMw1BlZaXd4fgl+gg48ki0fYvGat+isT6PR+vox24u8gQCgcfj8f6dH53cbrd3vWma8ng8iouLU3h4uFJSUrR9+3YbI+4YyC/WoW9BUQ4AAABHGTBggA4cOOCdtm7d6l23ePFiZWVlKTs7WwUFBXK73UpNTVVFRYWNEQPAiVGUAwAAwFFCQkLkdru9U/fu3SUdOUu+ZMkSzZs3T+PHj1dSUpJycnJUVVWl3Nxcm6MGgKZRlAMAAMBRdu/erbi4OCUkJOiGG27Qnj17JEmFhYUqLi5WWlqad1uXy6Xk5GRt2LDhhPurqalReXm5zwQA7YWiHAAAAI4xfPhwPffcc3rzzTf11FNPqbi4WCNHjtR3332n4uJiSVJMTIzPe2JiYrzrmpKZmano6GjvFB8fb2kbAOBYFOUAAABwjNGjR+uaa67RwIEDddlll+n111+XJOXk5Hi3MQzD5z2maTZadqz09HSVlZV5p6KiImuCB4AmUJQDAADAsTp37qyBAwdq9+7d3ruwH39WvKSkpNHZ82O5XC5FRUX5TADQXkLsDgCQpODgYF1xxRXeeTRGHwGSERSk8NOGeucD5djNRZ5AIKqpqdHOnTt10UUXKSEhQW63W3l5eRo8eLAkqba2Vvn5+Vq0aJHNkTob+cU69C0oyuEXOnXq5L38DE2jjwDJCAlTj+s8AXfs5iJPIBD8+te/1rhx49SrVy+VlJTooYceUnl5uSZPnizDMDRz5kxlZGQoMTFRiYmJysjIUEREhCZMmGB36I5GfrEOfQuKcgAAADjGF198oRtvvFHffvutunfvrvPPP18bN25U7969JUlz5sxRdXW1pk2bptLSUg0fPlzr1q1TZGSkzZEDQNMoygEAAOAYK1eu/MH1hmHI4/HI4/G0T0AAcJL880dxCDiVlZXq3LmzOnfurMrKSrvD8Uv0ESA11B7S/qxrtD/rGjXUHgqYYzcXeQKAVcgv1qFvYXtR/uWXX+qmm25St27dFBERoXPOOUebN2/2rjdNUx6PR3FxcQoPD1dKSoq2b99uY8SwSlVVlaqqquwOw6/RR4Bk1tXIrKsJuGM3F3kCgFXIL9ahbwObrUV5aWmpLrjgAoWGhuqNN97Qjh079Ic//EGnnHKKd5vFixcrKytL2dnZKigokNvtVmpqqioqKuwLHAAAAACANmDrb8oXLVqk+Ph4LV++3LusT58+3nnTNLVkyRLNmzdP48ePlyTl5OQoJiZGubm5mjp1anuHDAAAAABAm7H1TPmaNWs0dOhQXXfdderRo4cGDx6sp556yru+sLBQxcXFSktL8y5zuVxKTk7Whg0bmtxnTU2NysvLfSYAAAAAAPyRrUX5nj17tHTpUiUmJurNN9/UHXfcobvvvlvPPfecJKm4uFiSFBMT4/O+mJgY77rjZWZmKjo62jvFx8db2wgAAAAAAFrJ1qK8oaFB5557rjIyMjR48GBNnTpVt99+u5YuXeqznWEYPq9N02y07Kj09HSVlZV5p6KiIsviBwAAAADgZNj6m/LY2Fj179/fZ1m/fv308ssvS5LcbrekI2fMY2NjvduUlJQ0Ont+lMvlksvlsihiWCUoKEjJycneeTRGHwGSDEOu+CTvfMAcu5nIEwCsQn6xDn0LW4vyCy64QLt27fJZ9tlnn6l3796SpISEBLndbuXl5Wnw4MGSpNraWuXn52vRokXtHi+sEx4ervXr19sdhl+jjwApKNQl94SFAXfs5iJPALAK+cU69C1sLcpnzZqlkSNHKiMjQ7/4xS/00UcfadmyZVq2bJmkI5etz5w5UxkZGUpMTFRiYqIyMjIUERGhCRMm2Bk6AAAAAAAnzdaifNiwYVq9erXS09P14IMPKiEhQUuWLNHEiRO928yZM0fV1dWaNm2aSktLNXz4cK1bt06RkZE2Rg4AAAAAwMmztSiXpLFjx2rs2LEnXG8YhjwejzweT/sFhXZXWVnpfUb93r171blz53Y9fp+5r3vnG2oPeef7/XatgsI6tckx9i4cc1Lvt7uPAH/QUHtIX/7pFknST+94ts3Gp78fu7nIEwCsQn6xDn0L24ty4Khvv/3W7hD8Hn0ESA3V5QF57OYiTwCwCvnFOvRtYOP2fgAAAAAA2ISiHAAAAAAAm1CUAwAAAABgE4pyAAAAAABsQlEOAAAAAIBNuPs6/EJQUJCGDh3qnUdj9BEgyTAU5k70zgfMsZuJPAHAKuQX69C3oCiHXwgPD1dBQYHdYfg1+giQgkJdip38aMAdu7nIEwCsQn6xDn0LinIAAAAAXn3mvm53CEBA4foIAAAAAABsQlEOv1BVVaU+ffqoT58+qqqqsjscv0QfAVJD3SF9sfQWfbH0FjXUHQqYYzcXeQKAVZyQA52K3A0uX4dfME1T+/bt886jMfoIkGRK9eUl3vmAOXYzkScAWMYBOdCpyN3gTDkAAAAAADahKAcAP/Xll1/qpptuUrdu3RQREaFzzjlHmzdv9q43TVMej0dxcXEKDw9XSkqKtm/fbmPEAAAAaCmKcgDwQ6WlpbrgggsUGhqqN954Qzt27NAf/vAHnXLKKd5tFi9erKysLGVnZ6ugoEBut1upqamqqKiwL3AAAAC0CL8pBwA/tGjRIsXHx2v58uXeZX369PHOm6apJUuWaN68eRo/frwkKScnRzExMcrNzdXUqVPbO2QAAAC0AmfKAcAPrVmzRkOHDtV1112nHj16aPDgwXrqqae86wsLC1VcXKy0tDTvMpfLpeTkZG3YsKHJfdbU1Ki8vNxnAgAAgL04Uw6/YBiG+vfv751HY/RRYNmzZ4+WLl2q2bNn64EHHtBHH32ku+++Wy6XS7/85S9VXFwsSYqJifF5X0xMjPcOrsfLzMzUggULLI/dUoYU2q2Xdz5gjt1M5AkAlnFADnQqcjcoyuEXIiIiuEHVj6CPAktDQ4OGDh2qjIwMSdLgwYO1fft2LV26VL/85S+92x3/n7dpmif8Dz09PV2zZ8/2vi4vL1d8fLwF0VsnKLST4m57MuCO3VzkCQBWcUIOdCpyN7h8HQD8UGxsrPdb86P69eun/fv3S5Lcbrckec+YH1VSUtLo7PlRLpdLUVFRPhMAAADsRVEOAH7oggsu0K5du3yWffbZZ+rdu7ckKSEhQW63W3l5ed71tbW1ys/P18iRI9s1VgAAALQeRTn8QlVVlQYMGKABAwaoqqrK7nD8En0UWGbNmqWNGzcqIyNDn3/+uXJzc7Vs2TJNnz5d0pHL1mfOnKmMjAytXr1a27Zt05QpUxQREaEJEybYHL11GuoO6aunp+mrp6epoe5QwBy7ucgTAKzihBzoVORu8Jty+AXTNLVjxw7vPBqjjwLLsGHDtHr1aqWnp+vBBx9UQkKClixZookTJ3q3mTNnjqqrqzVt2jSVlpZq+PDhWrdunSIjI22M3GKmVPfdfu98wBy7mcgTACzjgBzoVORuUJQDgJ8aO3asxo4de8L1hmHI4/HI4/G0X1AAAABoU1y+DgAAAACATSjKAQAAAACwCUU5AAAAAAA2oSgHAAAAAMAm3OgNfsEwDO/zlw3DsDka/0QfAZIMKTiqh3c+YI7dTOQJAJZxQA50KnI3KMrhFyIiIrR37167w/Br9BEgBYV2Us87nw24YzcXeQKAVZyQA52K3A0uXwcAAAAAwCYU5QAAAAAA2ISiHH6hurpaw4YN07Bhw1RdXW13OH6JPgKkhroaHciZpQM5s9RQVxMwx24u8gQAqzghBzoVuRv8phx+oaGhQZs2bfLOozH6CJBkmqot3u2dD5hjNxN5AoBlHJADnYrcDc6UAwAAAABgE4pyAAAAAABsQlEOAAAAAIBNKMoBAAAAALAJRTkAAAAAADbh7uvwG6eeeqrdIfg9+giQgsKjAvLYzUWeAGAVJ+RApyJ3BzaKcviFzp0765tvvrE7DL9GHwFSUFgnxd+dG3DHbi7yBACrOCEHOhW5G1y+DgAAAACATSjKAQAAAACwCUU5/EJ1dbVSUlKUkpKi6upqu8PxS/QRIDXU1ag4d66Kc+eqoa4mYI7dXOQJAFZxQg50KnI3+E05/EJDQ4Py8/O982iMPgIkmaZqirZ55wPm2M1EngBgGQfkQKcid8PWM+Uej0eGYfhMbrfbu940TXk8HsXFxSk8PFwpKSnavn27jREDAAAAANB2bL98fcCAATpw4IB32rp1q3fd4sWLlZWVpezsbBUUFMjtdis1NVUVFRU2RgwAAAB/kZmZKcMwNHPmTO8yTuwAcBLbi/KQkBC53W7v1L17d0lHkumSJUs0b948jR8/XklJScrJyVFVVZVyc3kcAwAAQKArKCjQsmXLNGjQIJ/lnNgB4CS2F+W7d+9WXFycEhISdMMNN2jPnj2SpMLCQhUXFystLc27rcvlUnJysjZs2HDC/dXU1Ki8vNxnAgAAQMdy8OBBTZw4UU899ZR+8pOfeJdzYgeA09h6o7fhw4frueeeU9++ffX111/roYce0siRI7V9+3YVFxdLkmJiYnzeExMTo3379p1wn5mZmVqwYIGlcQOt0Wfu6yf1/obaQ975fr9dq6CwTo222btwzEkdAwAAp5g+fbrGjBmjyy67TA899JB3+Y+d2Jk6dWqjfdXU1Kim5r93FOekDoD2ZGtRPnr0aO/8wIEDNWLECJ1++unKycnR+eefL0kyDMPnPaZpNlp2rPT0dM2ePdv7ury8XPHx8W0cOawQERFhdwh+zwh12R0CYDs7x4ETxiC5FIFg5cqV+vjjj1VQUNBoXWtO7HBSp3mckAPb08mecDmqofaQt2+PP/HCCZfA4FePROvcubMGDhyo3bt36+qrr5Z0JLHGxsZ6tykpKWmUZI/lcrnkcpEwnKZz586qrKy0Owy/FhTWSb1mv2x3GICt7BwHThiD5FIEgqKiIt1zzz1at26dOnVqfNXYUS05scNJnR/nhBzoVPQtbP9N+bFqamq0c+dOxcbGKiEhQW63W3l5ed71tbW1ys/P18iRI22MEgAAAHbZvHmzSkpKNGTIEIWEhCgkJET5+fl6/PHHFRIS4j15c/SM+VE/dGLH5XIpKirKZwKA9mJrUf7rX/9a+fn5Kiws1D//+U9de+21Ki8v1+TJk72PtsjIyNDq1au1bds2TZkyRREREZowYYKdYQMAAMAml156qbZu3aotW7Z4p6FDh2rixInasmWLTjvtNE7sAHAUWy9f/+KLL3TjjTfq22+/Vffu3XX++edr48aN6t27tyRpzpw5qq6u1rRp01RaWqrhw4dr3bp1ioyMtDNsWODQoUO65pprJEkvv/zyD16OFqjMw7X6ZnWGJKn7zx+QERJmc0RA+7NzHDhhDJJLEQgiIyOVlJTks6xz587q1q2bd/nREzuJiYlKTExURkYGJ3ZOkhNyoFPRt7C1KF+5cuUPrjcMQx6PRx6Pp30Cgm3q6+v197//3TuPxsyGBlXv2eSdP/HtDoGOy85x4IQxSC4FjuDETttzQg50KvoWfnWjNwAAAKCl1q9f7/OaEzsAnMSvbvQGAAAAAEAgoSgHAAAAAMAmFOUAAAAAANiEohwAAAAAAJtQlAMAAAAAYJNW3X19xYoV+sUvfqGIiIi2jgcBqnPnzjJN0+4w/FpQWCf1vv81u8MAbGXnOHDCGCSXArCKE3KgU9G3aNWZ8vT0dLndbt16663asGFDW8cEAAAAAEBAaFVR/sUXX+j5559XaWmpLr74Yp111llatGiRiouL2zo+AAhIHo9HhmH4TG6327veNE15PB7FxcUpPDxcKSkp2r59u40RAwAAoDVaVZQHBwfryiuv1KpVq1RUVKRf/epXeuGFF9SrVy9deeWVevXVV9XQ0NDWsaIDO3TokK677jpdd911OnTokN3h+CXzcK2+eSVT37ySKfNwrd3hoB0MGDBABw4c8E5bt271rlu8eLGysrKUnZ2tgoICud1upaamqqKiwsaIrWfnOHDCGCSXArCKE3KgU9G3OOkbvfXo0UMXXHCBRowYoaCgIG3dulVTpkzR6aefrvXr17dBiAgE9fX1eumll/TSSy+pvr7e7nD8ktnQoKpdH6hq1wcy+dIrIISEhMjtdnun7t27SzpylnzJkiWaN2+exo8fr6SkJOXk5Kiqqkq5ubk2R20tO8eBE8YguRSAVZyQA52KvkWri/Kvv/5ajzzyiAYMGKCUlBSVl5frtddeU2Fhob766iuNHz9ekydPbstYASCg7N69W3FxcUpISNANN9ygPXv2SJIKCwtVXFystLQ077Yul0vJycnc5wMAAMBhWnX39XHjxunNN99U3759dfvtt+uXv/ylunbt6l0fHh6ue++9V48++mibBQoAgWT48OF67rnn1LdvX3399dd66KGHNHLkSG3fvt17/46YmBif98TExGjfvn0n3GdNTY1qamq8r8vLy60JHgAAAM3WqqK8R48eys/P14gRI064TWxsrAoLC1sdGAAEstGjR3vnBw4cqBEjRuj0009XTk6Ozj//fEmSYRg+7zFNs9GyY2VmZmrBggXWBAwAAIBWadXl68nJyTr33HMbLa+trdVzzz0n6ciHxd69e59cdAAASUeePz1w4EDt3r3bexf24594UVJS0ujs+bHS09NVVlbmnYqKiiyNGQAAAD+uVUX5zTffrLKyskbLKyoqdPPNN590UAAAXzU1Ndq5c6diY2OVkJAgt9utvLw87/ra2lrl5+dr5MiRJ9yHy+VSVFSUzwQAAAB7tery9RNdIvnFF18oOjr6pIMCgED361//WuPGjVOvXr1UUlKihx56SOXl5Zo8ebIMw9DMmTOVkZGhxMREJSYmKiMjQxEREZowYYLdoQMAAKAFWlSUDx48WIZhyDAMXXrppQoJ+e/b6+vrVVhYqMsvv7zNg0THFxERoYMHD3rn0ZgR6lL8rJe88+jYvvjiC91444369ttv1b17d51//vnauHGj92dBc+bMUXV1taZNm6bS0lINHz5c69atU2RkpM2RW8vOceCEMUguBWAVJ+RAp6Jv0aKi/Oqrr5YkbdmyRaNGjVKXLl2868LCwtSnTx9dc801bRogAoNhGOrcubPdYfg1wzBkhHWyOwy0k5UrV/7gesMw5PF45PF42icgP2HnOHDCGCSXArCKE3KgU9G3aFFRPn/+fElSnz59dP3116tTJ/54AAAAAABorVb9pnzy5MltHQcCXE1NjaZOnSpJ+vOf/yyXi0t3jmcertN3b2ZLkrqNmiEjJNTmiID2Z+c4cMIYJJcCsIoTcqBT0bdo9t3Xu3btqm+//VaS9JOf/ERdu3Y94QS01OHDh5WTk6OcnBwdPnzY7nD8ktlQr8pt/1Dltn/IbKi3OxzAFnaOAyeMQXIpAKs4IQc6FX2LZp8pf/TRR703EHr00UebvPs6AAAAAABovmYX5cdesj5lyhQrYgEAAAAAIKA0uygvLy9v9k6joqJaFQwAAAAAAIGk2UX5Kaec8qOXrJumKcMwVF/PbyEAAAAAAPgxzS7K33nnHSvjAAAAAAAg4DS7KE9OTrYyDgAAAAAAAk6zi/JPP/1USUlJCgoK0qeffvqD2w4aNOikA0NgiYiIUElJiXcejRmhLvW86wXvPBCI7BwHThiD5FIAVnFCDnQq+hbNLsrPOeccFRcXq0ePHjrnnHNkGIZM02y0Hb8pR2sYhqHu3bvbHYZfMwxDwRHRdocB2MrOceCEMUguBWAVJ+RAp6Jv0eyivLCw0PsffWFhoWUBAQAAAAAQKJpdlPfu3bvJeaAt1NTUaPbs2ZKkrKwsuVxcunM883Cd/vP205KkrpfcJiMk1OaIgPZn5zhwwhgklwKwihNyoFPRtwhq7Rt37dqlGTNm6NJLL9Vll12mGTNmaNeuXW0ZGwLI4cOH9eSTT+rJJ5/U4cOH7Q7HL5kN9Tr4yes6+MnrMhv4iQgCk53jwAljkFwKwCpOyIFORd+iVUX5Sy+9pKSkJG3evFlnn322Bg0apI8//lhJSUl68cUX2zpGAAAAAAA6pGZfvn6sOXPmKD09XQ8++KDP8vnz5+v+++/Xdddd1ybBAQAAAADQkbXqTHlxcbF++ctfNlp+0003qbi4+KSDAgAAAAAgELSqKE9JSdF7773XaPn777+viy666KSDAgAAAAAgEDT78vU1a9Z456+88krdf//92rx5s84//3xJ0saNG/Xiiy9qwYIFbR8lAAAAAAAdULOL8quvvrrRsqN3eD3W9OnTdccdd5x0YAAAAAAAdHTNLsobGhqsjAMBLjw8XIWFhd55NGaEhumndzzjnQcCkZ3jwAljkFwKwCpOyIFORd+iVXdfB9paUFCQ+vTpY3cYfs0wghQSHWN3GICt7BwHThiD5FIAVnFCDnQq+hatLsorKyuVn5+v/fv3q7a21mfd3XfffdKBAQAAAADQ0bWqKP/kk090xRVXqKqqSpWVleratau+/fZbRUREqEePHhTlaLHa2lrNmzdPkvTwww8rLIxLd45n1tfp+3f/P0nSKT+bJCM41OaIgPZn5zhwwhgklwKwihNyoFPRt2jVI9FmzZqlcePG6T//+Y/Cw8O1ceNG7du3T0OGDNEjjzzS1jEiANTV1emRRx7RI488orq6OrvD8Utmfb3KP1ql8o9WyayvtzscwBZ2jgMnjEFyKQCrOCEHOhV9i1YV5Vu2bNG9996r4OBgBQcHq6amRvHx8Vq8eLEeeOCBVgWSmZkpwzA0c+ZM7zLTNOXxeBQXF6fw8HClpKRo+/btrdo/AAAAAAD+plVFeWhoqAzDkCTFxMRo//79kqTo6GjvfEsUFBRo2bJlGjRokM/yxYsXKysrS9nZ2SooKJDb7VZqaqoqKipaEzYAAAAAAH6lVUX54MGDtWnTJknSxRdfrN/97nd64YUXNHPmTA0cOLBF+zp48KAmTpyop556Sj/5yU+8y03T1JIlSzRv3jyNHz9eSUlJysnJUVVVlXJzc1sTNgAAAAAAfqVVRXlGRoZiY2MlSf/zP/+jbt266c4771RJSYmWLVvWon1Nnz5dY8aM0WWXXeazvLCwUMXFxUpLS/Muc7lcSk5O1oYNG064v5qaGpWXl/tMAAAAAAD4o1bdfX3o0KHe+e7du+vvf/97qw6+cuVKffzxxyooKGi0rri4WNKRy+OPFRMTo3379p1wn5mZmVqwYEGr4gEAAAAAoD216kz5USUlJXrvvff0/vvv65tvvmnRe4uKinTPPffo+eefV6dOnU643dHfrh9lmmajZcdKT09XWVmZdyoqKmpRXAAAAAAAtJdWnSkvLy/X9OnTtXLlStX/v9v2BwcH6/rrr9cf//hHRUdH/+g+Nm/erJKSEg0ZMsS7rL6+Xu+++66ys7O1a9cuSUfOmB+9VF468kXA8WfPj+VyueRyuVrTLNgoPDxc27Zt886jMSM0TLG3/NE7DwQiO8eBE8YguRSAVZyQA52KvkWrivLbbrtNW7Zs0WuvvaYRI0bIMAxt2LBB99xzj26//Xb97W9/+9F9XHrppdq6davPsptvvllnnXWW7r//fp122mlyu93Ky8vT4MGDJUm1tbXKz8/XokWLWhM2/FhQUJAGDBhgdxh+zTCCFNa9t91hALaycxw4YQySSwFYxQk50KnoW7SqKH/99df15ptv6sILL/QuGzVqlJ566ildfvnlzdpHZGSkkpKSfJZ17txZ3bp18y6fOXOmMjIylJiYqMTERGVkZCgiIkITJkxoTdgAAAAAAPiVVhXl3bp1a/IS9ejoaJ/Hmp2sOXPmqLq6WtOmTVNpaamGDx+udevWKTIyss2OAf9QW1urjIwMSdIDDzygsDAu3TmeWV+nsg+PXIUSPeIXMoJDbY4IaH92jgMnjEFyKQCrOCEHOhV9i1YV5b/5zW80e/ZsPffcc97fexcXF+u+++7Tb3/721YHs379ep/XhmHI4/HI4/G0ep9whrq6Ou9d8++77z4+SDbBrK9X2Qd/kSRFnXcNCRsByc5x0NJj95n7uuUx7V04xuc1uRSAVfgcYh36Fs0uygcPHuxz1/Pdu3erd+/e6tWrlyRp//79crlc+uabbzR16tS2jxQAAAAAgA6m2UX51VdfbWEYAAAAAAAEnmYX5fPnz7cyDgAAAAAAAk6rflN+1ObNm7Vz504ZhqH+/ft7H10GAAAAAAB+XFBr3lRSUqJLLrlEw4YN0913360ZM2ZoyJAhuvTSS/XNN9+0dYwAAACAJGnp0qUaNGiQoqKiFBUVpREjRuiNN97wrjdNUx6PR3FxcQoPD1dKSoq2b99uY8QA8MNaVZTfddddKi8v1/bt2/Wf//xHpaWl2rZtm8rLy3X33Xe3dYwAENAyMzNlGIZmzpzpXcaHTgCBqmfPnlq4cKE2bdqkTZs26ZJLLtFVV13lzYGLFy9WVlaWsrOzVVBQILfbrdTUVFVUVNgcOQA0rVWXr69du1ZvvfWW+vXr513Wv39//fGPf1RaWlqbBYfA0alTJ3300UfeeTRmhITK/css7zwCQ0FBgZYtW6ZBgwb5LD/6oXPFihXq27evHnroIaWmpmrXrl2KjIy0KVrr2TkOnDAGyaUIBOPGjfN5/fDDD2vp0qXauHGj+vfvryVLlmjevHkaP368JCknJ0cxMTHKzc3lCUEnwQk50KnoW7TqTHlDQ4NCQxv/wYSGhqqhoeGkg0LgCQ4O1rBhwzRs2DAFBwfbHY5fMoKC5YrtK1dsXxlB9FEgOHjwoCZOnKinnnpKP/nJT7zLTdP0+dCZlJSknJwcVVVVKTc318aIrWfnOHDCGCSXItDU19dr5cqVqqys1IgRI1RYWKji4mKfk0Qul0vJycnasGHDCfdTU1Oj8vJynwm+nJADnYq+RauK8ksuuUT33HOPvvrqK++yL7/8UrNmzdKll17aZsEBQCCbPn26xowZo8suu8xnOR86AQS6rVu3qkuXLnK5XLrjjju0evVq9e/fX8XFxZKkmJgYn+1jYmK865qSmZmp6Oho7xQfH29p/ABwrFYV5dnZ2aqoqFCfPn10+umn64wzzlBCQoIqKir0xBNPtHWMCAC1tbX6/e9/r9///veqra21Oxy/ZNbXqeyfL6vsny/LrK+zOxxYbOXKlfr444+VmZnZaF0gf+i0cxw4YQySSxEozjzzTG3ZskUbN27UnXfeqcmTJ2vHjh3e9YZh+GxvmmajZcdKT09XWVmZdyoqKrIsdqdyQg50KvoWrfpNeXx8vD7++GPl5eXpX//6l0zTVP/+/RudzQGaq66uTnPmzJEkTZs2TWFhYTZH5H/M+np9v365JCly8BgZwfzmqKMqKirSPffco3Xr1v3g74Jb86Fz9uzZ3tfl5eWOK8ztHAdOGIPkUgSKsLAwnXHGGZKkoUOHqqCgQI899pjuv/9+SUe+vIyNjfVuX1JS0uiLzGO5XC65XC5rg3Y4J+RAp6Jv0eKi/PDhw+rUqZO2bNmi1NRUpaamWhEXAASszZs3q6SkREOGDPEuq6+v17vvvqvs7Gzt2rVLEh86AeAo0zRVU1OjhIQEud1u5eXlafDgwZKOXEGSn5+vRYsW2RwlADStxUV5SEiIevfurfr6eiviAYCAd+mll2rr1q0+y26++WadddZZuv/++3XaaafxoRNAwHrggQc0evRoxcfHq6KiQitXrtT69eu1du1a7+MjMzIylJiYqMTERGVkZCgiIkITJkywO3QAaFKrLl//zW9+o/T0dD3//PPq2rVrW8cEAAEtMjJSSUlJPss6d+6sbt26eZfzoRNAoPr66681adIkHThwQNHR0Ro0aJDWrl3rvXpzzpw5qq6u1rRp01RaWqrhw4dr3bp1HfpxkQCcrVVF+eOPP67PP/9ccXFx6t27tzp37uyz/uOPP26T4AAATeNDJ4BA9cwzz/zgesMw5PF45PF42icgADhJrSrKr776ahmGIdM02zoeAEAT1q9f7/OaD50AAAAdQ4uK8qqqKt1333165ZVXVFdXp0svvVRPPPGETj31VKviAwAAAACgw2pRUT5//nytWLFCEydOVHh4uHJzc3XnnXfqxRdftCo+BIhOnTrpnXfe8c6jMSMkVDE3ZnjngUBk5zhwwhgklwKwihNyoFPRt2hRUb5q1So988wzuuGGGyRJEydO1AUXXKD6+noFBwdbEiACQ3BwsFJSUuwOw68ZQcHq1GuQ3WEAtrJzHDhhDJJLAVjFCTnQqehbBLVk46KiIl100UXe1+edd55CQkL01VdftXlgAAAAAAB0dC06U15fX6+wsDDfHYSE6PDhw20aFAJPXV2dli1bJkn61a9+pdBQLt05nll/WAf/b60kqcvZl8sIbtV9GgFHs3McOGEMkksBWMUJOdCp6Fu06F/cNE1NmTJFLpfLu+zQoUO64447fB6LtmrVqraLEAGhtrZWM2bMkCRNmTKFD5JNMOsP6z95f5IkdU66jISNgGTnOHDCGCSXArCKE3KgU9G3aNG/+OTJkxstu+mmm9osGAAAAAAAAkmLivLly5dbFQcAAAAAAAGnRTd6AwAAAAAAbYeiHAAAAAAAm1CUAwAAAABgE4pyAAAAAABswv324RdcLpdee+017zwaM0JC1f3a+d55IBDZOQ6cMAbJpQCs4oQc6FT0LSjK4RdCQkI0ZswYu8Pwa0ZQsCJOH2Z3GICt7BwHThiD5FIAVnFCDnQq+hZcvg4AAAAAgE04Uw6/UFdXpxdeeEGSNHHiRIWGcunO8cz6w6rcsV6S1Ll/ioxghi8Cj53jwAljkFwKwCpOyIFORd+Cf3H4hdraWt18882SpOuuu44Pkk0w6w/ru78vkSRFnHkhCRsByc5x4IQxSC4FYBUn5ECnom/B5esAAAAAANiEohwAAAAAAJtQlAMAAAAAYBOKcgAAAAAAbEJRDgAAAACATSjKAQAAAACwCffbh19wuVz629/+5p1HY0ZIqE69aq53HghEdo4DJ4xBcikAqzghBzoVfQuKcviFkJAQXXfddXaH4deMoGB1PutCu8MAbGXnOHDCGCSXArCKE3KgU9G34PJ1AAAAAABswply+IXDhw9r9erVkqSf//znCgnhT/N4ZkO9qj77UJIU0XeEjKBgmyMC2p+d48AJY5BcCsAqTsiBTkXfgv+t4Rdqamr0i1/8QpJ08OBBPkg2wTxcp29fXShJip/1kowwEjYCj53jwAljkFwKwCpOyIFO9UN922fu65Yff+/CMZYfAz+My9cBAAAAALCJrUX50qVLNWjQIEVFRSkqKkojRozQG2+84V1vmqY8Ho/i4uIUHh6ulJQUbd++3caIAQAAAABoO7YW5T179tTChQu1adMmbdq0SZdccomuuuoqb+G9ePFiZWVlKTs7WwUFBXK73UpNTVVFRYWdYQMAAAAA0CZsLcrHjRunK664Qn379lXfvn318MMPq0uXLtq4caNM09SSJUs0b948jR8/XklJScrJyVFVVZVyc3PtDBsAAAAAgDbhN78pr6+v18qVK1VZWakRI0aosLBQxcXFSktL827jcrmUnJysDRs2nHA/NTU1Ki8v95kAAAAAAPBHthflW7duVZcuXeRyuXTHHXdo9erV6t+/v4qLiyVJMTExPtvHxMR41zUlMzNT0dHR3ik+Pt7S+AEAAAAAaC3bn5Vy5plnasuWLfr+++/18ssva/LkycrPz/euNwzDZ3vTNBstO1Z6erpmz57tfV1eXk5h7gBhYWFavny5dx6NGcEh6nbFTO88EIjsHAdOGIPkUgBWcUIOdCr6Frb/q4eFhemMM86QJA0dOlQFBQV67LHHdP/990uSiouLFRsb692+pKSk0dnzY7lcLrlcLmuDRpsLDQ3VlClT7A7DrxnBIeoy8DK7wwBsZec4cMIYJJcCsIoTcqBT0bew/fL145mmqZqaGiUkJMjtdisvL8+7rra2Vvn5+Ro5cqSNEQIAAAAA0DZsPVP+wAMPaPTo0YqPj1dFRYVWrlyp9evXa+3atTIMQzNnzlRGRoYSExOVmJiojIwMRUREaMKECXaGDQscPnxYb775piRp1KhRCgmx/SIOv2M21Ku68GNJUnjCuTKCgm2OCGh/do4DJ4xBcikAqzghBzoVfQtb/7f++uuvNWnSJB04cEDR0dEaNGiQ1q5dq9TUVEnSnDlzVF1drWnTpqm0tFTDhw/XunXrFBkZaWfYsEBNTY3Gjh0rSTp48CAfJJtgHq7TNy8tkCTFz3pJRhgJG4HHznHghDFILgVgFSfkQKeib2Hr/9bPPPPMD643DEMej0cej6d9AkKL9Zn7epvsp6H2kHe+32/XKiisU5vsFwAAAAD8md/9phwAAAAAgEBBUQ4Afmjp0qUaNGiQoqKiFBUVpREjRuiNN97wrjdNUx6PR3FxcQoPD1dKSoq2b99uY8QAAABoDYpyAPBDPXv21MKFC7Vp0yZt2rRJl1xyia666ipv4b148WJlZWUpOztbBQUFcrvdSk1NVUVFhc2RAwAAoCUoygHAD40bN05XXHGF+vbtq759++rhhx9Wly5dtHHjRpmmqSVLlmjevHkaP368kpKSlJOTo6qqKuXm5todOgAAAFqAohwA/Fx9fb1WrlypyspKjRgxQoWFhSouLlZaWpp3G5fLpeTkZG3YsOGE+6mpqVF5ebnPBAAAAHvxrBT4BSM4RF1T7/DOozH6KPBs3bpVI0aM0KFDh9SlSxetXr1a/fv39xbeMTExPtvHxMRo3759J9xfZmamFixYYGnMVrNzHDhhDIaFhSk7O9s7DwBtxQk50KnoW/CvDr9gBIco8tyxdofh1+ijwHPmmWdqy5Yt+v777/Xyyy9r8uTJys/P9643DMNne9M0Gy07Vnp6umbPnu19XV5ervj4+LYP3EJ2jgMnjMHQ0FBNnz7d7jAAdEBOyIFORd+CohwA/FRYWJjOOOMMSdLQoUNVUFCgxx57TPfff78kqbi4WLGxsd7tS0pKGp09P5bL5ZLL5bI2aAAAALQIvymHXzAb6nVo/6c6tP9TmQ31dofjl+gjmKapmpoaJSQkyO12Ky8vz7uutrZW+fn5GjlypI0RWs/OceCEMVhfX6/169dr/fr1qq/3zxgBOJMTcqBT0bfgTDn8gnm4Tl//5QFJUvysl2SEBdsckf+hjwLLAw88oNGjRys+Pl4VFRVauXKl1q9fr7Vr18owDM2cOVMZGRlKTExUYmKiMjIyFBERoQkTJtgduqXsHAdOGIOHDh3SxRdfLEk6ePCgOnfubHNEADoKJ+RAp6JvQVEOAH7o66+/1qRJk3TgwAFFR0dr0KBBWrt2rVJTUyVJc+bMUXV1taZNm6bS0lINHz5c69atU2RkpM2RAwAAoCUoygHADz3zzDM/uN4wDHk8Hnk8nvYJCAAAAJbgN+UAAAAAANiEohwAAAAAAJtQlAMAAAAAYBN+Uw4AgEP1mfu6z+uG2kPe+X6/XaugsE4nfYy9C8ec9D4AAMCJUZTDLxjBwTol5WbvPBqjjwB7x4ETxqATYgTgTOQX69C3oCiHXzCCQxU9/Bq7w/Br9BFg7zhwwhh0QozAycrMzNSqVav0r3/9S+Hh4Ro5cqQWLVqkM88807uNaZpasGCBli1b5n1s5B//+EcNGDDAxsidjfxiHfoW/KYcAAAAjpGfn6/p06dr48aNysvL0+HDh5WWlqbKykrvNosXL1ZWVpays7NVUFAgt9ut1NRUVVRU2Bg5ADSNM+XwC2ZDvWq//rckKSzmdBlBXLpzPPoIsHccOGEMOiFG4GStXbvW5/Xy5cvVo0cPbd68WT/72c9kmqaWLFmiefPmafz48ZKknJwcxcTEKDc3V1OnTrUjbMcjv1iHvgVnyuEXzMN1Kn5utoqfmy3zcJ3d4fgl+giwdxw4YQw6IUagrZWVlUmSunbtKkkqLCxUcXGx0tLSvNu4XC4lJydrw4YNTe6jpqZG5eXlPhN8kV+sQ9+CohwAAACOZJqmZs+erQsvvFBJSUmSpOLiYklSTEyMz7YxMTHedcfLzMxUdHS0d4qPj7c2cAA4BkU5AAAAHGnGjBn69NNP9Ze//KXROsMwfF6bptlo2VHp6ekqKyvzTkVFRZbECwBN4TflAAAAcJy77rpLa9as0bvvvquePXt6l7vdbklHzpjHxsZ6l5eUlDQ6e36Uy+WSy+WyNmAAOAHOlAMAAMAxTNPUjBkztGrVKr399ttKSEjwWZ+QkCC32628vDzvstraWuXn52vkyJHtHS4A/CjOlAMAAMAxpk+frtzcXL366quKjIz0/k48Ojpa4eHhMgxDM2fOVEZGhhITE5WYmKiMjAxFRERowoQJNkcPAI1RlAMAAMAxli5dKklKSUnxWb58+XJNmTJFkjRnzhxVV1dr2rRpKi0t1fDhw7Vu3TpFRka2c7QA8OMoyuEXjOBgRV9wo3cejdFHgL3jwAlj0AkxAifLNM0f3cYwDHk8Hnk8HusDChDkF+vQt6Aoh18wgkN1yoUT7Q7Dr9FHgL3jwAlj0AkxAjg5fea+bstxyS/WoW/Bjd4AAAAAALAJZ8rhF0yzQXXfHnkmaOip8TIMvi86Hn0E2DsOnDAGnRAjAGciv1iHvgVFOfyCWVerA89OlyTFz3pJRlgnmyPyP/QRYO84cMIYdEKMAJyJ/GId+hZ8DQMAAAAAgE0oygEAAAAAsAlFOQAAAAAANqEoBwAAAADAJhTlAAAAAADYhKIcAAAAAACb8Eg0+AUjOFhR5433zqMx+giwdxw4YQw6IUYAzkR+sQ59C4py+AUjOFQ/ufgWu8Pwa/QRYO84cMIYdEKMAJyJ/GId+hZcvg4AAAAAgE04Uw6/YJoNqi//RpIUHNVdhsH3RcejjwB7x4ETxqATYgTgTOQX69C3oCiHXzDravXln26VJMXPeklGWCebI/I/9BFg7zhwwhh0QowAnIn8Yh36FnwNAwAAAACATWwtyjMzMzVs2DBFRkaqR48euvrqq7Vr1y6fbUzTlMfjUVxcnMLDw5WSkqLt27fbFDEAAAAAAG3H1qI8Pz9f06dP18aNG5WXl6fDhw8rLS1NlZWV3m0WL16srKwsZWdnq6CgQG63W6mpqaqoqLAxcgAAAAAATp6tvylfu3atz+vly5erR48e2rx5s372s5/JNE0tWbJE8+bN0/jxR57dl5OTo5iYGOXm5mrq1Kl2hA0AAAAAQJvwq9+Ul5WVSZK6du0qSSosLFRxcbHS0tK827hcLiUnJ2vDhg22xAgAAAAAQFvxm7uvm6ap2bNn68ILL1RSUpIkqbi4WJIUExPjs21MTIz27dvX5H5qampUU1PjfV1eXm5RxAAAAAAAnBy/KcpnzJihTz/9VO+//36jdYZh+Lw2TbPRsqMyMzO1YMECS2KEdYygYHUZPMY7j8boI8DeceCEMeiEGAE4E/nFOvQt/KIov+uuu7RmzRq9++676tmzp3e52+2WdOSMeWxsrHd5SUlJo7PnR6Wnp2v27Nne1+Xl5YqPj7cocrQVIyRU3dLutDsMv0YfAfaOAyeMQSfECMCZyC/WoW9h62/KTdPUjBkztGrVKr399ttKSEjwWZ+QkCC32628vDzvstraWuXn52vkyJFN7tPlcikqKspnAgAAAADAH9lalE+fPl3PP/+8cnNzFRkZqeLiYhUXF6u6ulrSkcvWZ86cqYyMDK1evVrbtm3TlClTFBERoQkTJtgZOtqYaZqqrypTfVWZTNO0Oxy/RB8FlszMTA0bNkyRkZHq0aOHrr76au3atctnG9M05fF4FBcXp/DwcKWkpGj79u02Rdw+7BwHThiDTogRgDORX6xD38LWonzp0qUqKytTSkqKYmNjvdNf//pX7zZz5szRzJkzNW3aNA0dOlRffvml1q1bp8jISBsjR1sz62r0xRMT9cUTE2XW1fz4GwIQfRRY8vPzNX36dG3cuFF5eXk6fPiw0tLSVFlZ6d1m8eLFysrKUnZ2tgoKCuR2u5WamqqKigobI7eWnePACWPQCTECcCbyi3XoW9j6m/LmfBNkGIY8Ho88Ho/1AQGAn1i7dq3P6+XLl6tHjx7avHmzfvazn8k0TS1ZskTz5s3T+PHjJUk5OTmKiYlRbm6upk6dakfYAAAAaCG/ek45AKBpZWVlkqSuXbtKkgoLC1VcXKy0tDTvNi6XS8nJydqwYUOT+6ipqVF5ebnPBAAAAHtRlAOAnzNNU7Nnz9aFF16opKQkSUeeSiGp0ZMoYmJivOuOl5mZqejoaO/EkykAAADsR1EOAH5uxowZ+vTTT/WXv/yl0TrDMHxem6bZaNlR6enpKisr805FRUWWxAsAAIDm84vnlAMAmnbXXXdpzZo1evfdd9WzZ0/vcrfbLenIGfPY2Fjv8pKSkkZnz49yuVxyuVzWBgwAAIAW4Uw5APgh0zQ1Y8YMrVq1Sm+//bYSEhJ81ickJMjtdisvL8+7rLa2Vvn5+Ro5cmR7hwsAAIBW4kw5/IIRFKzOSZd659FYc/qoz9zXLY1h78Ixlu4f/zV9+nTl5ubq1VdfVWRkpPd34tHR0QoPD5dhGJo5c6YyMjKUmJioxMREZWRkKCIiQhMmTLA5euvYmSuckKecECMAZyK/WIe+BUU5/IIREqpTx8yyOwy/Rh8FlqVLl0qSUlJSfJYvX75cU6ZMkSTNmTNH1dXVmjZtmkpLSzV8+HCtW7dOkZGR7Rxt+7FzHDhhDDohRgDORH6xDn0LinIA8EOmaf7oNoZhyOPxyOPxWB8QAAAALEFRDr9gmqbMuhpJkhHqOuHdowMZfQTYOw6cMAadECMAZyK/WIe+BTd6g18w62pU9Oi1Knr0Wm9Sgi/6CLB3HDhhDDohRgDORH6xDn0LinIAAAAAAGxCUQ4AAAAAgE0oygEAAAAAsAlFOQAAAAAANqEoBwAAAADAJhTlAAAAAADYhOeUwy8YQUGKOPMC7zwao48Ae8eBE8agE2IE4EzkF+vQt6Aoh18wQsLU/ep0u8Pwa/QR/EWfua/bdmw7x4ETxqATYgTgTOQX69C34KsYAAAAAABsQlEOAAAAAIBNKMrhFxpqD2nforHat2isGmoP2R2OX6KPAHvHgRPGoBNiBOBM5Bfr0LegKAcAAAAAwCYU5QAAAAAA2ISiHAAAAAAAm1CUAwAAAABgE4pyAAAAAABsQlEOAAAAAIBNQuwOAJAkIyhI4acN9c6jMfoIsHccOGEMOiFGAM5EfrEOfQuKcvgFIyRMPa7z2B2GX6OPAHvHgRPGoBNiBOBM5Bfr0LfgqxgAAAAAAGxCUQ4AAAAAgE0oyuEXGmoPaX/WNdqfdY0aag/ZHY5foo8Ae8eBE8agE2IE2sK7776rcePGKS4uToZh6JVXXvFZb5qmPB6P4uLiFB4erpSUFG3fvt2eYDsI8ot16FtQlMNvmHU1Mutq7A7Dr9FHgL3jwAlj0AkxAiersrJSZ599trKzs5tcv3jxYmVlZSk7O1sFBQVyu91KTU1VRUVFO0fasZBfrEPfBjZu9NaB9Zn7ut0hAAAAtLnRo0dr9OjRTa4zTVNLlizRvHnzNH78eElSTk6OYmJilJubq6lTp7ZnqADwozhTDgAAgA6jsLBQxcXFSktL8y5zuVxKTk7Whg0bbIwMAJrGmXIAAAB0GMXFxZKkmJgYn+UxMTHat29fk++pqalRTc1/Lx0uLy+3LkAAOA5nygEAANDhGIbh89o0zUbLjsrMzFR0dLR3io+Pb48QAUASRTkAAAA6ELfbLem/Z8yPKikpaXT2/Kj09HSVlZV5p6KiIsvjBICjuHwd/sEw5IpP8s6jCfQRYO84cMIYdEKMgMUSEhLkdruVl5enwYMHS5Jqa2uVn5+vRYsWNfkel8sll8vVnmE6D/nFOvRtwKMoh18ICnXJPWGh3WH4NfoIsHccOGEMOiFGoC0cPHhQn3/+ufd1YWGhtmzZoq5du6pXr16aOXOmMjIylJiYqMTERGVkZCgiIkITJkywMWpnI79Yh74FRTkAAAAcZdOmTbr44ou9r2fPni1Jmjx5slasWKE5c+aourpa06ZNU2lpqYYPH65169YpMjLSrpAB4IQoygEAAOAoKSkpMk3zhOsNw5DH45HH42m/oACglSjK4Rcaag/pyz/dIkn66R3PKiisk80R+R/6CLB3HDhhDDohRqAj6zP3dbtDsAz5xTr0LSjK4Tcaqnkm6I+hjwB7x4ETxqATYgTgTOQX69C3gY1HogEAAAAAYBNbi/J3331X48aNU1xcnAzD0CuvvOKz3jRNeTwexcXFKTw8XCkpKdq+fbs9wQIAAAAA0MZsLcorKyt19tlnKzs7u8n1ixcvVlZWlrKzs1VQUCC3263U1FRVVFS0c6QAAAAAALQ9W39TPnr0aI0ePbrJdaZpasmSJZo3b57Gjx8vScrJyVFMTIxyc3M1derU9gwVAAAAAIA257e/KS8sLFRxcbHS0tK8y1wul5KTk7Vhw4YTvq+mpkbl5eU+EwAAAAAA/shvi/Li4mJJUkxMjM/ymJgY77qmZGZmKjo62jvFx8dbGifaiGEozJ2oMHeiZBh2R+Of6KOAwj03TsDOceCEMeiEGAE4E/nFOvRtwPP7R6IZx/1hmqbZaNmx0tPTNXv2bO/r8vJyCnMHCAp1KXbyo3aH4dfoo8By9J4bN998s6655ppG64/ec2PFihXq27evHnroIaWmpmrXrl2KjIy0IeL2Yec4cMIYdEKMAJyJ/GId+hZ+W5S73W5JR86Yx8bGepeXlJQ0Ont+LJfLJZfLZXl8AGAl7rkBAAAQGPz28vWEhAS53W7l5eV5l9XW1io/P18jR460MTIAsBf33AAAAOg4bD1TfvDgQX3++efe14WFhdqyZYu6du2qXr16aebMmcrIyFBiYqISExOVkZGhiIgITZgwwcaoYYWGukP66ulpkqS4255UUGgnmyPyP/QRjvqhe27s27fvhO/LzMzUggULLI3NanaOAyeMQSti7DP39ZPex4/Zu3CM5ccAcHKckAOdir6FrUX5pk2bdPHFF3tfH/0t+OTJk7VixQrNmTNH1dXVmjZtmkpLSzV8+HCtW7euQ/9eMmCZUn15iXceTaCPcJyAvOeGnePACWPQCTECcCbyi3Xo24Bna1GekpIi0zzxX55hGPJ4PPJ4PO0XFAD4Oe65AQAA0HH47W/KAQBN454bAAAAHYff3n0dAAIZ99wAAAAIDBTlAOCHuOcGAABAYKAoBwA/xD03AAAAAgNFOfyDIYV26+WdRxPoI8DeceCEMeiEGAE4E/nFOvRtwKMoh18ICu2kuNuetDsMv0YfAfaOAyeMQSfECMCZyC/WoW/B3dcBAAAAALAJRTkAAAAAADahKIdfaKg7pK+enqavnp6mhrpDdofjl+gjwN5x4IQx6IQYATgT+cU69C34TTn8gynVfbffO48m0EeAvePACWPQCTECcCbyi3Xo24BHUQ6g2frMfd3yY+xdOMbyYwAAAAD+gsvXAQAAAACwCUU5AAAAAAA2oSgHAAAAAMAmFOUAAAAAANiEG73BPxhScFQP7zyaQB8B9o4DJ4xBJ8QIwJnIL9ahbwMeRTn8QlBoJ/W881m7w/Br9BFg7zhwwhh0QowAnIn8Yh36Fly+DgAAAACATThTDgAAbNVn7uuWH2PvwjGWHwMAgNbgTDn8QkNdjQ7kzNKBnFlqqKuxOxy/RB8B9o4DJ4xBJ8QIwJnIL9ahb8GZcvgH01Rt8W7vPJpAHwH2jgMnjEEnxAjAmcgv1qFvAx5nygEAAAAAsAlFOQAAAAAANqEoBwAAAADAJhTlAAAAAADYhKIcAAAAAACbcPd1+I2g8Ci7Q/B79BFg7zhwwhh0QowAnIn8Yh36NrBRlNukz9zX7Q7BrwSFdVL83bl2h+HX6CPA3nHghDHohBgBOBP5xTr0LSjKAQAAACBAtcfJwr0Lx1h+DCfjN+UAAAAAANiEohx+oaGuRsW5c1WcO1cNdTV2h+OX6CPA3nHghDHohBgBOBP5xTr0Lbh8Hf7BNFVTtM07jybQR4C948AJY9AJMQJwJvKLdejbgEdRDsCv8LsmAAAABBKKcgAA0OHxhR8AwF/xm3IAAAAAAGzCmXIAAAAAgGXa42ql9mDVFVGcKQcAAAAAwCacKYffMEJddofg9+gjwN5x4IQx6IQYATgT+cU69G1goyiHXwgK66Res1+2Owy/Rh8B9o4DJ4xBJ8TYkXWEyzO5WR1OhPxiHfoWXL4OAAAAAIBNKMoBAAAAALAJl6/DL5iHa/XN6gxJUvefPyAjJMzmiPwPfQTYOw6cMAadECMAZyK/WIe+BUU5/ILZ0KDqPZu884bN8fgj+giwdxw4YQw6IUYAzkR+sQ59C4ryJnSEG7UAAAAAAPwfvykHAAAAAMAmjijKn3zySSUkJKhTp04aMmSI3nvvPbtDAgC/QY4EgKaRHwE4gd8X5X/96181c+ZMzZs3T5988okuuugijR49Wvv377c7NACwHTkSAJpGfgTgFH5flGdlZenWW2/Vbbfdpn79+mnJkiWKj4/X0qVL7Q4NAGxHjgSAppEfATiFX9/orba2Vps3b9bcuXN9lqelpWnDhg1NvqempkY1NTXe12VlZZKk8vLyZh+3oaaqFdHiZDTUHvrvfE2VZDYQy3H8NS4nakk+OHZ70zStCKfVWpoj2yI/SvbmSDvHgRPGoBNihH8jPx7hxPxoNafkF6fEeSwnxhyoWpITWpQfTT/25ZdfmpLMDz74wGf5ww8/bPbt27fJ98yfP9+UxMTExNTmU1FRUXukvmZraY4kPzIxMVk1kR+ZmJiYmp6akx/9+kz5UYbh+7Q+0zQbLTsqPT1ds2fP9r5uaGjQf/7zH3Xr1u2E72lv5eXlio+PV1FRkaKiouwOp13Q5sBos9Qx222apioqKhQXF2d3KE1qbo50Qn78IR3xb0vquO2SOm7baNd/kR+br6P+3RxF+5yrI7dNsq99LcmPfl2Un3rqqQoODlZxcbHP8pKSEsXExDT5HpfLJZfL5bPslFNOsSrEkxIVFdUh//B/CG0OHB2t3dHR0XaH0EhLc6ST8uMP6Wh/W0d11HZJHbdttOsI8mPLdNS/m6Non3N15LZJ9rSvufnRr2/0FhYWpiFDhigvL89neV5enkaOHGlTVADgH8iRANA08iMAJ/HrM+WSNHv2bE2aNElDhw7ViBEjtGzZMu3fv1933HGH3aEBgO3IkQDQNPIjAKfw+6L8+uuv13fffacHH3xQBw4cUFJSkv7+97+rd+/edofWai6XS/Pnz290mVRHRpsDR6C22y4dMUeeSEf92+qo7ZI6bttolzP4W37saP17PNrnXB25bZIz2meYpp89wwIAAAAAgADh178pBwAAAACgI6MoBwAAAADAJhTlAAAAAADYhKIcAAAAAACbUJRb5Mknn1RCQoI6deqkIUOG6L333mvW+z744AOFhITonHPOsTZAC7S0zTU1NZo3b5569+4tl8ul008/Xc8++2w7Rds2WtrmF154QWeffbYiIiIUGxurm2++Wd999107RXvy3n33XY0bN05xcXEyDEOvvPLKj74nPz9fQ4YMUadOnXTaaafpT3/6k/WBwi+1dLw052/n5ZdfVv/+/eVyudS/f3+tXr3aZ31r/mZbyo52ZWZmatiwYYqMjFSPHj109dVXa9euXW3aLsmeti1dulSDBg1SVFSUoqKiNGLECL3xxhuOb9exMjMzZRiGZs6cebJN8WFHuzwejwzD8JncbnebtsvJHn74YY0cOVIRERE65ZRTmv2+nTt36sorr1R0dLQiIyN1/vnna//+/dYF2kqtbd9RU6dOlWEYWrJkSZvHdrJa2ra6ujrdf//9GjhwoDp37qy4uDj98pe/1FdffWV9sK3Qmn870zTl8XgUFxen8PBwpaSkaPv27dYG2kqlpaWaNGmSoqOjFR0drUmTJun777//wfccPHhQM2bMUM+ePRUeHq5+/fpp6dKl7ROwiTa3cuVKMzQ01HzqqafMHTt2mPfcc4/ZuXNnc9++fT/4vu+//9487bTTzLS0NPPss89un2DbSGvafOWVV5rDhw838/LyzMLCQvOf//yn+cEHH7Rj1CenpW1+7733zKCgIPOxxx4z9+zZY7733nvmgAEDzKuvvrqdI2+9v//97+a8efPMl19+2ZRkrl69+ge337NnjxkREWHec8895o4dO8ynnnrKDA0NNV966aX2CRh+o6XjpTl/Oxs2bDCDg4PNjIwMc+fOnWZGRoYZEhJibty40btNS/9mndKuUaNGmcuXLze3bdtmbtmyxRwzZozZq1cv8+DBg45v25o1a8zXX3/d3LVrl7lr1y7zgQceMENDQ81t27Y5ul1HffTRR2afPn3MQYMGmffcc0+btMnOds2fP98cMGCAeeDAAe9UUlLSZu1yut/97ndmVlaWOXv2bDM6OrpZ7/n888/Nrl27mvfdd5/58ccfm//+97/N1157zfz666+tDbYVWtO+o1avXm2effbZZlxcnPnoo49aEt/JaGnbvv/+e/Oyyy4z//rXv5r/+te/zA8//NAcPny4OWTIEOuDbYXW/NstXLjQjIyMNF9++WVz69at5vXXX2/Gxsaa5eXl1gbbCpdffrmZlJRkbtiwwdywYYOZlJRkjh079gffc9ttt5mnn366+c4775iFhYXmn//8ZzM4ONh85ZVXLI+XotwC5513nnnHHXf4LDvrrLPMuXPn/uD7rr/+evM3v/mNOX/+fMcV5S1t8xtvvGFGR0eb3333XXuEZ4mWtvn3v/+9edppp/kse/zxx82ePXtaFqOVmlPgzJkzxzzrrLN8lk2dOtU8//zzLYwM/qil46U5fzu/+MUvzMsvv9xnm1GjRpk33HBDk/u0oij3h3aZpmmWlJSYksz8/PyWNuGE/KVtpmmaP/nJT8ynn366JeGfkJ3tqqioMBMTE828vDwzOTm5TYtyu9rlxM8sdli+fHmzC5/rr7/evOmmm6wNqI21pH2maZpffPGF+dOf/tTctm2b2bt3b78syo9qaduO9dFHH5mSfvTEnJ2a276GhgbT7XabCxcu9C47dOiQGR0dbf7pT3+yMMKW27FjhynJ5wvEDz/80JRk/utf/zrh+wYMGGA++OCDPsvOPfdc8ze/+Y1lsR7F5ettrLa2Vps3b1ZaWprP8rS0NG3YsOGE71u+fLn+/e9/a/78+VaH2OZa0+Y1a9Zo6NChWrx4sX7605+qb9+++vWvf63q6ur2CPmktabNI0eO1BdffKG///3vMk1TX3/9tV566SWNGTOmPUK2xYcfftioj0aNGqVNmzaprq7OpqjQ3lozXprzt3OibX4o17Ylf2pXWVmZJKlr164tbkdT/KVt9fX1WrlypSorKzVixIjWNsfL7nZNnz5dY8aM0WWXXXayTfFhd7t2796tuLg4JSQk6IYbbtCePXtOtkkBq6GhQa+//rr69u2rUaNGqUePHho+fLglP72xS0NDgyZNmqT77rtPAwYMsDscS5WVlckwjFZd2u9vCgsLVVxc7JMTXC6XkpOT2+3/3eb68MMPFR0dreHDh3uXnX/++YqOjv7BWC+88EKtWbNGX375pUzT1DvvvKPPPvtMo0aNsjxmivI29u2336q+vl4xMTE+y2NiYlRcXNzke3bv3q25c+fqhRdeUEhISHuE2aZa0+Y9e/bo/fff17Zt27R69WotWbJEL730kqZPn94eIZ+01rR55MiReuGFF3T99dcrLCxMbrdbp5xyip544on2CNkWxcXFTfbR4cOH9e2339oUFdpba8ZLc/52TrTNifbZ1vylXaZpavbs2brwwguVlJTU2ub4sLttW7duVZcuXeRyuXTHHXdo9erV6t+//8k2y9Z2rVy5Uh9//LEyMzNPuh3Hs7Ndw4cP13PPPac333xTTz31lIqLizVy5EhH3S/Fn5SUlOjgwYNauHChLr/8cq1bt04///nPNX78eOXn59sdXptYtGiRQkJCdPfdd9sdiqUOHTqkuXPnasKECYqKirI7nJN2dNzb+f9ucxUXF6tHjx6Nlvfo0eMHY3388cfVv39/9ezZU2FhYbr88sv15JNP6sILL7QyXEkU5ZYxDMPntWmajZZJR84CTJgwQQsWLFDfvn3bKzxLNLfN0pFvSQ3D0AsvvKDzzjtPV1xxhbKysrRixQrHnC2XWtbmHTt26O6779bvfvc7bd68WWvXrlVhYaHuuOOO9gjVNk31UVPL0fG1ZLycaPvjl7d0n1awu10zZszQp59+qr/85S8tirs57GrbmWeeqS1btmjjxo268847NXnyZO3YsaNVbWhunFa2q6ioSPfcc4+ef/55derU6aRi/yF2/HuNHj1a11xzjQYOHKjLLrtMr7/+uiQpJyendY1wgKZubnf8tGnTplbtu6GhQZJ01VVXadasWTrnnHM0d+5cjR07tt1ulGpl+zZv3qzHHntMK1assOVzgJVtO1ZdXZ1uuOEGNTQ06Mknn2yDyJunPdpn5/+7LWlfUzH9WKyPP/64Nm7cqDVr1mjz5s36wx/+oGnTpumtt96yrE1HOe+0rJ879dRTFRwc3OhbmJKSkkbfLElSRUWFNm3apE8++UQzZsyQdCQhm6apkJAQrVu3Tpdcckm7xN5aLW2zJMXGxuqnP/2poqOjvcv69esn0zT1xRdfKDEx0dKYT1Zr2pyZmakLLrhA9913nyRp0KBB6ty5sy666CI99NBDio2NtTzu9uZ2u5vso5CQEHXr1s2mqNDeWjNemvO3c6JtTrTPtuYP7brrrru0Zs0avfvuu+rZs+fJNMeH3W0LCwvTGWecIUkaOnSoCgoK9Nhjj+nPf/6zI9u1efNmlZSUaMiQId719fX1evfdd5Wdna2amhoFBwc7rl1N6dy5swYOHKjdu3e3pimOMGPGDN1www0/uE2fPn1ate9TTz1VISEhja4M6devn95///1W7bOlrGzfe++9p5KSEvXq1cu7rL6+Xvfee6+WLFmivXv3tmq/zWVl246qq6vTL37xCxUWFurtt99u17PkVrbv6FMViouLfT6ztuf/u81t36effqqvv/660bpvvvnmhLFWV1frgQce0OrVq70/LR00aJC2bNmiRx55pM1/dnQ8ivI2FhYWpiFDhigvL08///nPvcvz8vJ01VVXNdo+KipKW7du9Vn25JNP6u2339ZLL72khIQEy2M+WS1tsyRdcMEFevHFF3Xw4EF16dJFkvTZZ58pKCioTT9YWqU1ba6qqmr084SjH8KOnp3oaEaMGKH//d//9Vm2bt06DR06VKGhoTZFhfbWmvHSnL+dESNGKC8vT7NmzfLZZuTIkRa0ojE722Wapu666y6tXr1a69evb/P/K/zt38w0TdXU1LS2OV52tevSSy9t9H/9zTffrLPOOkv333//SRXkdrarKTU1Ndq5c6cuuuiik2mSXzv11FN16qmnWrLvsLAwDRs2rNEjDj/77DP17t3bkmMez8r2TZo0qVFxM2rUKE2aNEk333yzJcc8lpVtk/5bkO/evVvvvPNOu5+AsLJ9CQkJcrvdysvL0+DBgyUduZ9Ffn6+Fi1aZMkxj9fc9o0YMUJlZWX66KOPdN5550mS/vnPf6qsrOyE+auurk51dXUKCvK9kDw4ONh7BYulLL+VXAA6+liSZ555xtyxY4c5c+ZMs3PnzubevXtN0zTNuXPnmpMmTTrh+514J9OWtrmiosLs2bOnee2115rbt2838/PzzcTERPO2226zqwkt1tI2L1++3AwJCTGffPJJ89///rf5/vvvm0OHDjXPO+88u5rQYhUVFeYnn3xifvLJJ6YkMysry/zkk0+8dxU9vs1HH7kza9Ysc8eOHeYzzzzDI9ECVEvHS3P+dj744AMzODjYXLhwoblz505z4cKFjR7X9GN/s05t15133mlGR0eb69ev93kUVVVVVZu0y862paenm++++65ZWFhofvrpp+YDDzxgBgUFmevWrXN0u47X1ndft6td9957r7l+/Xpzz5495saNG82xY8eakZGR3uMGun379pmffPKJuWDBArNLly7efFRRUeHd5swzzzRXrVrlfb1q1SozNDTUXLZsmbl7927ziSeeMIODg8333nvPjib8oNa073j+evf1lratrq7OvPLKK82ePXuaW7Zs8cnNNTU1djXjhFrzb7dw4UIzOjraXLVqlbl161bzxhtv9OtHog0aNMj88MMPzQ8//NAcOHBgo0eiHd++5ORkc8CAAeY777xj7tmzx1y+fLnZqVMn88knn7Q8Xopyi/zxj380e/fubYaFhZnnnnuuz2NqJk+ebCYnJ5/wvU4syk2z5W3euXOnedlll5nh4eFmz549zdmzZ7fpB8r20NI2P/7442b//v3N8PBwMzY21pw4caL5xRdftHPUrffOO++YkhpNkydPNk2z6TavX7/eHDx4sBkWFmb26dPHXLp0afsHDr/Q0vHSnL+dF1980TzzzDPN0NBQ86yzzjJffvlln/U/9jfr1HY11SZJ5vLly9usXXa17ZZbbvEes3v37uall17aZgW5ne06XlsX5aZpT7uOPqc4NDTUjIuLM8ePH29u3769TdvlZJMnT25yrL7zzjvebZoau88884x5xhlnmJ06dTLPPvvsdnlOcmu0tn3H8teivKVtKywsPGFuPvY9/qI1/3YNDQ3m/PnzTbfbbbpcLvNnP/uZuXXr1vYPvhm+++47c+LEiWZkZKQZGRlpTpw40SwtLfXZ5vj2HThwwJwyZYoZFxdndurUyTzzzDPNP/zhD2ZDQ4Pl8Rr/LyAAAAAAANDOuPs6AAAAAAA2oSgHAAAAAMAmFOUAAAAAANiEohwAAAAAAJtQlAMAAAAAYBOKcgAAAAAAbEJRDgAAAACATSjK4WgpKSkyDEOGYWjLli3teuy9e/d6j33OOee067EBAAAAdAwU5XC822+/XQcOHFBSUpK3UA4JCdGXX37ps92BAwcUEhIiwzC0d+9en3X79u2Ty+VSeXm5PB5Pk0X20X0fLf7j4+N14MAB3XvvvRa1DAAAAP7gX//6l84//3x16tTJ0pMxU6ZM0dVXX23Z/uGfKMrheBEREXK73QoJCfEui4uL03PPPeezXU5Ojn760582uY9XX31VKSkpioqKavZxg4OD5Xa71aVLl9YFDgA2Ki4u1l133aXTTjtNLpdL8fHxGjdunP7xj3/4bJeQkKC1a9dKkurr6/Xoo49q0KBB6tSpk0455RSNHj1aH3zwgR1NAIBmmTJlivfqxpCQEPXq1Ut33nmnSktLm72P+fPnq3Pnztq1a5dPnqyurlZERIQefvhhRUdHq6qqqtF7Dx06pFNOOUVZWVlt0h50PBTl6JAmT56s5cuX+yxbsWKFJk+e3OT2r776qq688sr2CA0AbLd3714NGTJEb7/9thYvXqytW7dq7dq1uvjiizV9+nTvdp9++qm+++47XXzxxTJNUzfccIMefPBB3X333dq5c6fy8/MVHx+vlJQUvfLKK/Y1CAB+xOWXX64DBw5o7969evrpp/W///u/mjZtWrPf/+9//1sXXnihevfurW7dunmX5+XlKT4+Xr/61a9UXV2tl19+udF7X375ZVVVVWnSpElt0hZ0PBTl6JCuvPJKlZaW6v3335ckvf/++/rPf/6jcePGNdr2+++/13vvvUdRDiBgTJs2TYZh6KOPPtK1116rvn37asCAAZo9e7Y2btzo3e7VV1/VqFGj5HK59Le//U0vvfSSnnvuOd12221KSEjQ2WefrWXLlunKK6/UbbfdpsrKShtbBQAn5nK55Ha71bNnT6Wlpen666/XunXrvOuXL1+ufv36qVOnTjrrrLP05JNPetcZhqHNmzfrwQcflGEY8ng83nVHT+x0795d48aN07PPPtvo2M8++6x3m61bt+qSSy5ReHi4unXrpl/96lc6ePCgpW2H/6MoR4cUGhqqm266yZsYn332Wd10000KDQ1ttO3f//53DRw4UPHx8d5lW7duVZcuXXymAQMGtFv8AGCV//znP1q7dq2mT5+uzp07N1p/yimneOfXrFmjq666SpKUm5urvn37Nvnl5r333qvvvvtOeXl5lsUNAG1lz549Wrt2rfdz4VNPPaV58+bp4Ycf1s6dO5WRkaHf/va3ysnJkXTkvkQDBgzQvffeqwMHDujXv/61JKmhoUGvvfaaN0/eeuutys/PV2FhofdYe/fu1TvvvKNbb71VVVVVuvzyy/WTn/xEBQUFevHFF/XWW29pxowZ7dwD8DchP74J4Ey33nqrRowYoYyMDL344ov68MMPdfjw4UbbNXXp+plnnqk1a9b4LPvyyy+VkpJiZcgAYLnPP/9cpmnqrLPO+sHtvvzyS/3f//2frrjiCknSZ599pn79+jW57dHln332WdsGCwBt5LXXXlOXLl1UX1+vQ4cOSZL3N97/8z//oz/84Q8aP368pCP30tixY4f+/Oc/a/Lkyd57F3Xp0kVut9u7z40bN6qhoUEjR46UJI0aNUpxcXFasWKFFixYIOnIGfi4uDilpaXp2WefVXV1tZ577jnvl6LZ2dkaN26cFi1apJiYmHbrD/gXinJ0WElJSTrrrLN04403ql+/fkpKSmr02LS6ujqtXbtW6enpPsvDwsJ0xhln+Cw79kZyAOBUpmlKOnI55g9Zs2aNLrjgAnXt2rXZ+/6xfQKAXS6++GItXbpUVVVVevrpp/XZZ5/prrvu0jfffKOioiLdeuutuv32273bHz58WNHR0T+4z1dffVVjx45VUNCRi4+Dg4M1efJkrVixQvPnz5dhGMrJydGUKVMUHBysnTt36uyzz/a5SumCCy5QQ0ODdu3aRVEewLh8HR3aLbfcovXr1+uWW25pcv0777yjU045heeMAwgYiYmJMgxDO3fu/MHtjr10XZL69u2rHTt2NLnt0X0lJia2XaAA0IY6d+6sM844Q4MGDdLjjz+umpoaLViwQA0NDZKOXMK+ZcsW77Rt2zafe2w05fg8KR357FlUVKS3335b//jHP7R//37dfPPNko58KXqiLy/5UjOwUZSjQ7v99tv1zTff6Lbbbmty/Zo1a7jBG4CA0rVrV40aNUp//OMfm7wx2/fff6+DBw/qnXfe8cmPN9xwg3bv3q3//d//bfSeP/zhD+rWrZtSU1MtjR0A2sr8+fP1yCOPqL6+Xj/96U+1Z88enXHGGT5TQkLCCd+/e/du7d27V2lpaT7LTz/9dCUnJ2v58uV69tlnlZKSotNPP12S1L9/f23ZssUn937wwQcKCgpS3759rWkoHIGiHB1aSEiITj311BNeet7UN5wA0NE9+eSTqq+v13nnnaeXX35Zu3fv1s6dO/X4449rxIgRWrt2rRITE3Xaaad533PDDTfo5z//uSZPnqxnnnlGe/fu1aeffqqpU6dqzZo1evrpp5u8cRwA+KOUlBQNGDBAGRkZ8ng8yszM1GOPPabPPvtMW7du1fLly3/wueKvvvqqLrvsMkVERDRad+utt2rVqlVavXq1br31Vu/yiRMnqlOnTpo8ebK2bdumd955R3fddZcmTZrEpesBjqIcHUqfPn1kmuYJL0c/55xzZJqm+vTpo48//ljl5eVKTk722cbj8TT67Xlz9g0ATpGQkKCPP/5YF198se69914lJSUpNTVV//jHP7R06VK9+uqrjb6wNAxDf/vb3zRv3jw9+uijOuuss3TRRRdp3759euedd3T11Vfb0xgAaKXZs2frqaee0qhRo/T0009rxYoVGjhwoJKTk7VixYofPFPeVJ486pprrpHL5ZLL5fLePE6SIiIi9Oabb+o///mPhg0bpmuvvVaXXnqpsrOz27xtcBbDPHrHF8CBUlJStGHDBoWFhenDDz/UwIEDm/3ejz76SLt27dKkSZNadez9+/erf//+qq2t9V6OBABOV19frx49euiNN97QeeedZ3c4AOB3vv32W8XGxqqoqMjnbuxAa1GUw9G+/PJLVVdXS5J69eqlsLCwdjv24cOHtXfvXkmSy+Xyec45ADhVSUmJli1bpnnz5nHjIQBowmeffaY333xTd911l92hoIOgKAcAAAAAwCb8phwAAAAAAJtQlAMAAAAAYBOKcgAAAAAAbEJRDgAAAACATSjKAQAAAACwCUU5AAAAAAA2oSgHAAAAAMAmFOUAAAAAANiEohwAAAAAAJv8/5HP+eLGnQq4AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g.species_abunds = median_dict\n",
    "g.species_errs = posterior_dict_vmr\n",
    "popt,pcov,best_fit = g.convert_species_abunds(3300, 1e-3, plot_it=True,fit_refvol=True, posterior_samples=250)\n",
    "\n",
    "plt.savefig('WASP178b_exocomp.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5d8ad3a-e807-4b03-93ad-578032e2b3e5",
   "metadata": {},
   "source": [
    "TiO appears to be depleted relative to our chemical equilibrium expectations. This is seen in measurements of other planets too (CITE). It seems likely that some cold-trapping process is depleting Ti-species. Let's run again, without TiO since we have a prior expectation that our measurement of its abundance in WASP-178b may not be representative of the planet's bulk composition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3d1b3422-2271-421f-9666-264b9fdbf43a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on 250 samples... May take a moment if >10\n",
      "---\n",
      "Metallicity - Median: 0.7751, Lower bound: 0.6493, Upper bound: 0.9263\n",
      "C/O - Median: 0.0012, Lower bound: 0.0008, Upper bound: 0.0019\n",
      "Ref/Vol - Median: -1.2309, Lower bound: -1.4031, Upper bound: -1.0850\n",
      "---\n",
      "Median Fit Abundances\n",
      "---\n",
      "H2O: -6.257227796699308\n",
      "CO: -5.408058377209647\n",
      "SiO: -3.8292797052021577\n",
      "CO2: -10.349956032933735\n",
      "FeH: -8.65325791637293\n",
      "Fe: -3.7372753887939774\n",
      "Mg: -3.6439448402690777\n",
      "VO: -9.643689682077227\n",
      "Updating bulk abundances with mean/median fit\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Try without TiO\n",
    "ss = ['H2O','CO','SiO','CO2','FeH','Fe','Mg','VO']\n",
    "median_dict_tmp = {}\n",
    "posterior_dict_vmr_tmp = {}\n",
    "for s in ss:\n",
    "    median_dict_tmp[s] = median_dict[s]\n",
    "    posterior_dict_vmr_tmp[s] = posterior_dict_vmr[s]\n",
    "g.species_abunds = median_dict_tmp\n",
    "g.species_errs = posterior_dict_vmr_tmp\n",
    "popt,pcov,best_fit = g.convert_species_abunds(3300, 1e-3, plot_it=True,fit_refvol=True, posterior_samples=250)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee511bca-11db-4809-af19-a2f037004de2",
   "metadata": {},
   "source": [
    "Nice! The best-fit values are pretty consistent with the chemical equilibrium retrievals, which found [O/H] = $0.4^{+0.45}_{-0.43}$, C/O = $0.01^{+0.01}_{-0.00}$, and [Si/O] = $-0.18 \\pm 0.49$. The constraints from the chemical equilibrium retrieval are less constraining because they marginalize over the uncertainty in the temperature which is really important (see below)!\n",
    "\n",
    "A few caveats: 1) The R/V found here adds the contribution of the other refractories, including TiO, which is likely cold-trapped (CITE Pelletier, Gandhi) so it is no surprise that we find a somewhat lower R/V compared to the Si/O measured in the chemical equilibrium retrievals. 2) As described above, these results are sensitive to the temperature and pressure we assume, which is especially true here because of thermal dissociation. If the temperature was a bit higher, we'd infer a higher [O/H] because there'd be more dissociation, while for a lower temperature, we'd infer the opposite. E.g.:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "82d30d87-d3cb-4df7-bf5e-b79d439897b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 K Hotter\n",
      "Running on 250 samples... May take a moment if >10\n",
      "---\n",
      "Metallicity - Median: 1.2170, Lower bound: 1.0883, Upper bound: 1.3621\n",
      "C/O - Median: 0.0005, Lower bound: 0.0003, Upper bound: 0.0007\n",
      "Ref/Vol - Median: -1.6880, Lower bound: -1.8540, Upper bound: -1.5379\n",
      "---\n",
      "Median Fit Abundances\n",
      "---\n",
      "H2O: -6.273257233938325\n",
      "CO: -5.407219508416903\n",
      "SiO: -3.372907870307618\n",
      "CO2: -10.155719954411932\n",
      "FeH: -8.303757427011279\n",
      "Fe: -3.2984350075674014\n",
      "Mg: -3.206185961570291\n",
      "VO: -9.064218282698473\n",
      "Updating bulk abundances with mean/median fit\n",
      "\n",
      "100 K Cooler\n",
      "Running on 250 samples... May take a moment if >10\n",
      "---\n",
      "Metallicity - Median: 0.2877, Lower bound: 0.1574, Upper bound: 0.4495\n",
      "C/O - Median: 0.0040, Lower bound: 0.0027, Upper bound: 0.0059\n",
      "Ref/Vol - Median: -0.7210, Lower bound: -0.8772, Upper bound: -0.5546\n",
      "---\n",
      "Median Fit Abundances\n",
      "---\n",
      "H2O: -6.267570637248643\n",
      "CO: -5.391099784632492\n",
      "SiO: -4.343501020158031\n",
      "CO2: -10.559576363895035\n",
      "FeH: -9.047700767974849\n",
      "Fe: -4.223540242754351\n",
      "Mg: -4.128453451408211\n",
      "VO: -10.300507326164016\n",
      "Updating bulk abundances with mean/median fit\n"
     ]
    }
   ],
   "source": [
    "print('100 K Hotter')\n",
    "popt,pcov,best_fit = g.convert_species_abunds(3400, 1e-3, plot_it=False,fit_refvol=True, posterior_samples=250)\n",
    "\n",
    "print('\\n100 K Cooler')\n",
    "popt,pcov,best_fit = g.convert_species_abunds(3200, 1e-3, plot_it=False,fit_refvol=True, posterior_samples=250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53eccf17-aae5-46ec-a827-9a46bf0c7651",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}