{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e1fb8284",
   "metadata": {},
   "source": [
    "# Cross Section Calculation with Cthulhu"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce605f56",
   "metadata": {},
   "source": [
    "This quick start guide demonstrates how Cthulhu offers you the power to download a line list, calculate a high-resolution molecular cross section for a grid of temperatures and pressures, and package the output into a HDF5 opacity database. All on your laptop!\n",
    "\n",
    "This quick start guide assumes at least a passing familiarity with cross sections (or that the world is ending and you need a cross section ASAP). For complete beginners, we recommend starting with the [Cthulhu tutorials](https://cthulhu.readthedocs.io/en/latest/content/notebooks/cross_section_basic.html).\n",
    "\n",
    "Here we'll use AlO as an example, since this is a small line list (~ 5 million transitions) that can easily run on a laptop. We recommend running Cthulhu on a laptop for line lists that are less than about ~100 million transitions. For larger line lists, or if you need a very large grid of pressure-temperature calculations, we recommend sacrificing your cluster to Cthulhu (as shown in [our cluster tutorial](https://cthulhu.readthedocs.io/en/latest/content/notebooks/cluster_tutorial.html))."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5e3f0f0",
   "metadata": {},
   "source": [
    "## Download ExoMol Line List for AlO\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5dc67760",
   "metadata": {},
   "outputs": [],
   "source": [
    "from Cthulhu.core import summon\n",
    "\n",
    "species = 'AlO'          # Chemical species name\n",
    "\n",
    "database = 'ExoMol'      # Line list Database  \n",
    "linelist = 'ATP'         # Specific name of the line list for AlO\n",
    "\n",
    "broadening = 'H2-He'     # Background gas broadening\n",
    "\n",
    "nu_min = 200             # Minimum wavenumber (1/wavelength) in units of cm^-1\n",
    "nu_max = 35000           # Maximum wavenumber (1/wavelength) in units of cm^-1\n",
    "\n",
    "input_directory = './input/'   # Where the line list will be downloaded"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa000751",
   "metadata": {},
   "source": [
    "Download the AlO line list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "95944f31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ***** Downloading requested data from ExoMol. You have chosen the following parameters: ***** \n",
      "\n",
      "Molecule: AlO \n",
      "Isotopologue: 27Al-16O \n",
      "Line List: ATP\n",
      "\n",
      "Starting by downloading the .broad, .pf, and .states files...\n",
      "Fetched the broadening coefficients, partition functions, and energy levels.\n",
      "Now downloading the ATP line list...\n",
      "\n",
      "Downloading .trans file 1 of 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 32.6M/32.6M [00:04<00:00, 7.52MiB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Converting this .trans file to HDF to save storage space...\n",
      "This file took 13.1 seconds to reformat to HDF.\n",
      "\n",
      "Line list ready.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "summon(database=database, species = species, linelist = linelist)     # Embrace the Old One"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00aa2aa6",
   "metadata": {},
   "source": [
    "## Calculate AlO Cross Section"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1da248c",
   "metadata": {},
   "source": [
    "First, let's calculate the cross section of AlO for a single pressure-temperature point to get a handle on the runtime. We'll choose 10 bar and 1000 K."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "be112142",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning cross-section computations...\n",
      "Loading ExoMol format\n",
      "Loading partition function\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.44821538099859026 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1.0 bar, T = 1000.0 K\n",
      "Computing transitions from ATP.h5 | 0.0% complete\n",
      "Completed 4943804 transitions in 18.843246871998417 s\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 18.844000367998888 s\n",
      "\n",
      "Total runtime: 23.395633052999983 s\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from Cthulhu.core import compute_cross_section\n",
    "\n",
    "P = [1.0]         # Pressure (bar)\n",
    "T = [1000.0]      # Temperature (K)\n",
    "\n",
    "# There are many more optional arguments to play with!\n",
    "compute_cross_section(database = database, species = species, linelist = linelist,\n",
    "                      pressure = P, temperature = T, S_cut = 0.0,                    # No intensity cut-off (S_cut)\n",
    "                      input_dir = input_directory,\n",
    "                      nu_out_min = nu_min, nu_out_max = nu_max, dnu_out = 0.01,      # Wavenumber spacing of 0.01 cm^-1  (R ~ 10^6)\n",
    "                      broad_type = broadening,\n",
    "                      verbose = True, N_cores = 4)                                   # Grant the mighty Cthulhu 4 cores  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dec84d8",
   "metadata": {},
   "source": [
    "We see that AlO only takes about 20 s for a single (P, T) point, so we can easily compute a cross section for a grid of pressures and temperatures on a laptop.\n",
    "\n",
    "Let's plot the cross section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ce3524e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v374YMmQI7r77bmRmZtp87gceeABKpfHbPnToEJRKJd555x0sWrQIWVlZDOZsUK3RYntqTqPtuu8dZ8ARERHJz+N65lJTU/HMM8+ge/fuJvdv2LABS5cuxcmTJ6FWq7FkyRJMnjwZR44caRSk2SorKwtHjhzBhg0b4O/vjwEDBqBz585ITEx04p1QQ1zfQEREJD2P65lTq9XYunUrBgwYYHL/ypUrMWvWLKjVagDA/Pnzcfz4cezYsUP/mqFDhzb6N336dLPXDA0NRZcuXRAcHAyVSoWePXvi8OHD0r6xJshc36VuexFrsBIREcnO43rmYmNjze6rra3F4cOHMW/ePP22iIgIJCQkICUlBWPHjgUA7N+/365rDhw4EHl5eRBCQKFQICsrCx07dnTsDZDe2Wsl6Nkmwt3NICIiatI8LpizJDc3FxqNBpGRkUbbo6KikJPTeO6WKd9++y327t0LHx8fdOvWDdOmTYNarcarr76K+fPnIyQkBHfeeSf69u0rx1toHm52zbEGKxERkfy8KpizxNaEs9OnTzc55Dpz5kzMnDlT6mY1adbWiJSwnBcREZHsvCqYU6vVUKlUKCwsNNpeUFCAuLg4l7Zl4cKF8PPzAwCMGzcO48aNc+n1vRH76YiIiIBt27Zh27ZtAICaGufnl3tVMOfn54cBAwbgxIkT+m1FRUVIT0/HkCFDXNqWZcuWISwszKXX9FQXr5eiY8tQ/dfOJHUprqhBeJCf840iIiLyUIadQCUlJXj//fedOp/HrWa15vnnn8e6deuQn58PAHjvvffQq1cvjBkzxs0ta76uF1dJdq5fL+dLdi4iIqLmwON65iorKzF+/Hj9gobk5GQkJSVh+fLlAICpU6ciJycHY8eORUBAACIiIrBp0yaHc8yR4xRm+uBsSbicXVCB1lFBUjeJiIio2fG4YC4wMBC7d++2+Jp58+YZpSch97J1WNVwkcrxjEKTwZy5AJGIiIhMY3cWSY7hGBERkeswmCPnORC9aepMr21lSVwiIiL7MJgjpzWMv2wJyK6XmF40cflGmfMNIiIiakYYzJHjbgZtZ7JL3NsOIiKiZozBHDmtzsbqG0RERCQ9BnMkg1vjrKzPSkREJC8Gc+Qww6lx1bV1Jl9zhEmAiYiIZMVgjiShNTPUmmNQHYKjsURERNJjMEeSMBeo3TCzapWIiIikwWCOJMdccURERK7DYI5sorWykCG3tNpFLSEiIiJDDObIJp/suWRxv6ZO66KWEBERkSEGc2QTa2sXFAZjq+ZGWbn+gYiISHoM5shmv14yn2Ykp6jShS0hIiIiHQZzZLPjGYVm953KLtb/f6WZnHNEREQkPQZzJLkgP5W7m0BERNRsMJgjySnNTJoTzBpMREQkOQZzJDmlk4nmuDKWiIjIdgzmSHLOJg3+fP8VaRpCTUJxRY27m0BE5NEYzJHknO2Zq7OSoJial38dysD+czfc3QwiIo/FYI4kZy6WY4hGjjpztcSh46xVLiEiagoYzJHkfMytgGigiilMSEZrdl3Ex1YqlxARNQUM5sguv1zMk+xc+aznSlZk5pc7fY4ajf0LajR1WpywkFeRiMiTMJgju5zMLLL7GHPVITgARtZsPXFN//+F5TUOLYbYc+a63cfsP5+LQ5fymU6HiLwCgzmSnUY3b6nB52KtHSlIfrmYh4pqjYStIndy5Ge5+Vg2UrOKrS6QSTmfa/R1ep5tvXsllbW4VlSJS9dLcSGnFEB9AElE5OkYzJFkzH1Am5uEbk+nx7mrJZxj10TUarT48kCa3cdV1NThdHYxfrtSYPY1xRU1OG1QWg6wvQc4q6ACJzIKUWbwe1xSWWt3O4mIXI3BHEnGcEjMUI2ZHjgOYTUvtRotCsqq8em+ywCs//zNPRxYOu5fhzIA1C9+MGRLIurKmjpk5Ffg10v5+m0/p+ZYPY6IyN0YzJFk6rSmPzAvXi81uZ1ZI5qXs9dK8N3hTP3X18zMpdRxpPfOnHUp1s91NM10j9+hS/Yv+rlWVIlvfklHWRV79ohIfgzmSDJFFfUfXA07TjLzK/T/n9wtxmCPfdHcCQcWX5DnaLgS+r/HrgIAthy/Kvu17Zmf2dCJjCKbX3vuWgnqtALFFTUoqazF+oPpAIAbxVXMeUdEsmEwRy7T8KPM3s823aR08jx7zzpeoSGroML6iww4Oo/N1oUQptg6JWDv2RvYfCwb+84ZL8LYeDTL7HQDIiJnMZgju2U48aFo6EyDieqWOFvvleRTVVOHc9dKcL24EgVl9uUOvFFSZff10hz8/fvppOk5nbb4aLftyYdzio3f01omLiYimTGYI7tJtcIv146kwVwr4bm+SLkCANh0NBu/p9uXaHfjb1kOXdNc7kJrqmqcWxFty0KKRsdweJWIZMZgjhzm2EcUu9iakoZpQi7fKHPJddMdrAyhCzwdUVxRg7V7L5vdX24ld549c++IiOzBYI4aKa6osVjKSOtgN5kzqUg4zOoex9ILcSHHfJF7cytA5XYio8hq8mBz8k0MBduSw1CX9sSc4grLPdbHnSwPJoTgIgoiMonBHDVSUFZjcbGBwobIakzPWCmbhGoH6muScyprNEjLLUNJpX3VGvJLq10SdNg7P0+n0sRQqxTtdaTUmK3qtAIf7b6ETzj/johMYDBHjWw/ZTlRqrUeEQHAR+lcVxp7INxvXUoackurcfaq7QtVAGDDkUys3XvJrjq+jtRP3XzMsZQmDVOkANLUCbZlDqijvdO64wSAgxdyG+3f5ODcQyJqGhjMkd2OXM63/iIzOFzqHfINApOKmjq7Vh4D9WlnTAVN5pzPKbV7cYGjqT6crbf6xX7T8+5sSX1y8XoZSp1cQJSa1fhncf3mqmBHFmgQkfdrdsFcamoqkpOT8dprrxltX7lyJRYsWID/+Z//wbJly9zUOs9haSjV0V4M9rV5jw1HMo2+3n++cW+Q1NbuvezwPDin2XFZc/PrbJl3t/vMdaRmFdl+sZsaDg2v2XURRQ2C0t/TCrB272VU1tg3LE5E3q9ZBnPDhw832lZSUoK33noLf//73/GPf/wD//jHP1BUVOSeBnoId9dNve5A/jGynRAC1TeDD1sDqIY9SrYMhV8trE8IbGtKkFM3Ax1bX687v70aBkKuZKpnzZqz1xovQjnXYGHKkZsri6X+0+WUByLP55HBXEFBAebMmWO2d2j16tXo27cvhgwZgrvvvhuZmZkmX2fKAw88AKXS+G37+fnB398fFRUVqKioQHx8PEJCQpx6D+Scsir2LsjpaFohPt9/BdeKKvHf37ON9pkbqvv6l3Sjrytt6In677Gr0NRpUWpjjdL0vHKczi62OYWIPfPyDG1PtTwvVG4VVtKY6By+nA9NnRbHTOTvO5FR5JJeuK9/SXd6aJiI5OVxwVxqaiqmTZuG0NBQk/s3bNiApUuX4ueff8aBAwcwePBgTJ48GVozRd5tERAQgMWLF2Pu3Ll46KGH8OCDD0KlUjl8vqagoLzGoez8OuYGaW2fMme6N+CKi/KYNXW6lCKaOi1qGqwULrbhg9ueuVknM4ts7i26UVJl1+9dRn4FDtkxN0+nsMHKU+HiSQC2PKxcyS3DsfRCfc1jU3KKGn+vLudK+zdSWaNxOB0REbmGxwVzarUaW7duxYABA0zuX7lyJWbNmgW1Wg0AmD9/Po4fP44dO3boXzN06NBG/6ZPn272mr///jvWrl2Lb775Bt9++y3+/e9/Y//+/dK+MS/kaOoHsyT4PCjnfCCnGQ6rmvqMzsy3PnS5du9lm+aIAfU/dluDJUdG9E442DvnTFk6Z6ugbDxqffWpLb2HWiEaVcM4eMH+4NbyNYDaOgZzRJ7M47qfYmPN5yerra3F4cOHMW/ePP22iIgIJCQkICUlBWPHjgUAuwOx7OxsREdH64d1W7Rogaoqztly9GFcivl2+8/lolNsmNPnocYMgzlTAZmllatHLuejb2IUACCrwLb5akLY97t0o9j+vz2tVkBpZzqc6yVVaKsOtvtaAHDoYh7G3hbn0LE6tRotfFXWn6e/P2J+GsmRywUosXEI2xn7zt7AlAFtZL8OETnG43rmLMnNzYVGo0FkZKTR9qioKOTk2DYH5ttvv8XevXuxZ88efPfddwCAu+66C2q1GgsXLsTChQuRmJiI0aNHS95+b+POZ3HWs5RHel45dhvkdLtRUqUf+zbMZWbO7+mF+sS1tgZoQgi7etxsGeZtaP3BNLuPMZyHZu/zR5oTvXo6WQ4u3jBkLpA74ILVx0TkOTyuZ85RtvYGTZ8+vdGQq0qlwpo1a+RolldzZJ6MqVJJhmypHkHOqa6tQ0F5DeIiAhvt23PmulE1jTNXSxAZ7IfsggpsPn4Vd/VyrrfJlN/TC5En9ZB9AxU2rn5tyNbeMVOKymsQEezn0LFA/TDq4yM7Ony8JaeyizGkcwvJzpdXVg1NnRYqH696/idqNrwqmFOr1VCpVCgsNF7ZVVBQgLg46T+ELFm4cCH8/Opv5OPGjcO4ceNcen2XcKBzzDAflp+P0uHEruY4O1epOcgvq8a+c7mYMTih0T5TZdHKqjT6YCunuEry1BaAbfPw3OFybhm6xIU51At9+HK+00Ot205cxbherRptlyL5b1lVLUICfB06duepHIzqYTzlZe3ey7IFn0TNzbZt27Bt2zYAQE2N86mSvCqY8/Pzw4ABA3DixAn9tqKiIqSnp2PIkCEubcuyZcsQFta053Rl5JejZ5sIk/vM9YTmlVYjOqQ+yPX3vRXMFVfU6CfBB/n5ONyTUm5jSofmrKC8xq5AvLZOi+M3hxxNpcAwp8wFc7XktvfsDXSJc+zvOC2v3OneqgwzQW7DFcaOWH8w3eHg69KNMozsLhr1pF8vrkTL8MY9vkRkH8NOoJKSErz//vtOnc/r+syff/55rFu3Dvn59SWl3nvvPfTq1Qtjxoxxc8uanuzCSusvauDazZV1DT8EDOfAmQsQbaGwI7lJc3XwQh5KqmqNAgJr0xBM9dhZc+Zq40S2zY1c+RClTi9iD11qmI92X8KaXReN9m06ms2SYUQeyON65iorKzF+/Hj9gobk5GQkJSVh+fLlAICpU6ciJycHY8eORUBAACIiIrBp06ZGiYBJXgXlNQj2d+zXh+GYa+w5cx0D2kejsKJG1vlZnsLRmqvFFY4PcZzILMLwrjEOH2+O1OlF7LHxN8tpU9buvYzHkjtw/iuRB/G4YC4wMBC7d++2+Jp58+YZpSch1zuVVYyBHaJN7hOoD9hiIwJRmlNqtM/Z2/8VN/ZYeIq80mqEBKgQ4OvTaJ9hmaq0vHKjVZdNvSzT3rM3HDpu56nrGNWjpUPHnrtW4nQwd+RyPm5vb/pvyVnl1RoE+PrAx860LdYcuVKAATK1mYjsx+4scki1xvqct0CDYKOgrEY/sT400PSkbF+ulLPJ/nM3cNXMEPi3v2aYPe7jmylFmipHK5Y4u9K21sn5bb/bMU/RXl8dSLMryLU1K8Cx9EJotcLmur5EJC9+epKkCspu9QwJo+23PjBNpcwAgK6tmvaCEqmYq7zw32PZJreTddW1jgdkutJonsrWSh1A/Tw5Wx28mOdQKTUikh6DOXJIWq7ppKkVZsptGeask2KlXnNWWqVBTvGtnjkhBK4VVZrtrSPrnFlw4Gg5MVeRq/fsdHYxk3sTeQgGc2RRalaRxf094sPN7jMcsrmmKwiuMP5wqbaj14BuOZVVrC+7tXbvZfznd/bKOeOkhwdkztCtMP/tivQ9iOeucUUzkSdgMEcWGQ6bNiKM58UZUgDwU93al19WrR92NZyL7Uj6k+Zo/7kbSM0qMkoVsf98LucteQhb69Sak5nvfHkwS85fK8HRtAK7hlyJyHt43GpW8izO5JRKVAebnE/kbxAAOppOorkxl9PNWhoJco0tx686dfzWE9cQHxWEji1D0Sk2VKJW3bLn5iKIGo3W5CpoZ2i1AkqJV8sSkX3YM0cWXbphfi6RQOMM8Td3AACiQ/1NHqeycOPnZ0K9k5lFOHVziLvcQmJauWuekutkFVRg95nr7m6G3dy9SloIYbTAiqg5YjDXzNVphUszujNWs80vF/Nw4Gbi2JQLuW5uDTUVcv39VdfW4USGfClWLKnRaPHd4Uy3XJvIUzCYa+ZOZhbpgwZ72VuQXfd6pYXM8SzXVT+/yVB6nrzzqcizeOOq5O2ncnDoUr7Lr6vVCv1cXK6Sp+aMwVwzJ4QwShviiKhgP7teb2l+Tb92UVaPbwoF3i3Z42AlA2oavDFfoNwB6PXiSn1SaCEEajRa/HAkEx/vuYTNx+rnK36x/7KsbSDyZAzmmjmFQmG1h81cVvgbJVVQKIAJfVo7fP3cUuOs/X4q67+SulQLTUVZVa3ZKgINC50TOcPZVbfu8nt6ITb+loXvfs3AuWsl2HwsG7ml9fPk8m/Ol+OibmrOGMw1YyWVtTh82fGhkZLK+h6yhnUfBQBba3Bn5tv/4aLPWddE7Dl7Q5+0lnn3SE4p5+WdfylHipU6rdDfJwrKa7DvXK4+kCOiegzmmjHdwoeL10sdGrrUivoJ1Sof2+e5mVz9asAwMDTXI2hroOjphBAor9bgamGlvsfE06sJkHcTqB+ylMu+c7mSJ7A+YscD5wGZg1UiT8VgjgAA5dXm019Y0zBAq5+IbCrisj4OYli3NbekaT99f3UgXd+TcflGGb5MSUNpE58PSO53PqdUtnOXV2sknwZhzwPOqexii6l8iJoqu5MG19XVYceOHdi9ezdycnJw48YNqFQqxMTEID4+HnfddRcGDhwoR1tJYoahVWmVBi3NVOaqqDE/9Geqp61GU4dgf8fyUYcF+jp0nDcpr9bAR6FARY0GJzKK9NsrajS4dN3xGqFEtjh7tQTDusTIeo0bxVWICQ+Q9RrmfHUwDfcPaovwIPsWZhF5M5t75jQaDRYtWgS1Wo0JEybg66+/RmpqKkpKSpCfn4/ffvsNa9asQVJSEhISErB27Vo5200S23XafLJSc/NszD2B2zNnzpJLN0z3IKTleneqjsOX8/FFyhUAQHEle+LI9bQyrxbYeNS9lUku8qGImhmbuk+qq6sxfvx4BAQE4F//+heGDx8Of3/T2f1LSkqwZcsWvPXWWzh69CjeffddSRtMrmcuz1mtRms6K5yFzwl7YjxzeaO8vb7kBRmHuYhscTq7GD3bRLi7GbI5mlaAOq3AwA7R7m4KkUvY1DO3bNky3HPPPdi8eTPGjh1rNpADgLCwMMyYMQO//vorqqqq8MMPP0jVVvIwJWZ6lbRC6IO2VpG35sDZnWTYwXYRkWVHrjSumSw1dye7Pu6mihRE7mBTz9zixYvtPrFSqcSaNWvsPo5cyI5oyV+lRHWDnrLK2jqTXW2GIzjqEH+HE4o6mcvYI5kLgIlcqbZOi9LKWoTKOEf1p5PXMKxLDLq2CpPtGtZU19bB39fHbdcnchWuZiWbRNg5mVg/Z86JuXOiCfbNXS30zqSt1PS4otJIep57567992Z1CKKmzqZgLj09HZMnT0bv3r3x5z//GaWl9XN+NmzYgLlz58raQPIM5hY0mNpcW3erBy8q2PyQvDXF5U2rFysttwz7zjEPFnmGa0WVqNFosUHGIvUZDiQFl1J+WbW+QgRRU2ZTMPfyyy/jkUcewfr169GzZ0/Mnj0bubm5mDp1Kv773//K3UaSiVz9XsfSC6G4GeZ1ig11+Dx5TeQmrNUKaOq0+Dk1x91NaVZinUyNMbpHrEQt8Vyf7bvc5IMdOYNVIk9hUzA3evRoTJ48Gd27d8cjjzyCdevW4b333sPVq+zCbi4CzMw7sVbRwVB5tcapYVdv9fGeS1i7l0XAXc2ROZd33hYnfUOauToPKJq687TnP0idzi52dxPIi9k8Z+6XX37Bo48+isLCQoSEhGDx4sXYvn07KiubVtHz5sSeXFPWJkpHhzQYTjW5MEK+m/r3Hvj0LYRAlYWEyyQvR37bEtTB+v+PDGbSWSnsOXNdlpqt9rh0vcxseUBPIXfdXGrabArmHn30UQghMGLECEREROi3z5kzh6lHmpCdp+x/etXFbLe3j5KsHZ0dGJr1lCHZWoMVvwcu5OmTA5PrBfn5YEB7x/OMRQb72T1U62PQU+1oFZSm5tKNMmw9cQ3HMwpRXFHjtnY4uqreFXS1sbVagbxSz7iXkXexuWcuKSkJc+bMaTSsNnr06EavPXr0qPMtI5e7dMPxlWdto4OtvsbWB+MgBz8EPeHJ+9N9t4ZTOWziHn0TIgEAwQEq9EmIxNDOLRw+18R+8Xa9/s5e3jlMe8WJv31b/Xop362peTYf99xpQesPpgMA0vPL8f0RzxtlIM8nS2qSxx57TI7TktTsmL9mLlCyZ5Wr3KWrjhvUOXW3c9dK3N0EjxMXEWj9RQ0M6aS2+5i+ifW9xP1u/rdznH15zoL9VWjXIsTu6wKAn+rWLVUBYJyXzMHbfirHJUl+t564Jvs1LDnjIQ9YGXnlOJZen9TY8N66/eYiKbnLrVHT41Awl52djVmzZiE+Ph6+vr7w8fEx+nf8+HGp20lNgNIg8guRYQjKMCWKK5kKdPe6IIeXN1Pa+CDhp6pfeDN3eHubz+2jVOD+QW31i3Z8bL3YTUkd1egS59gq7MAGC4XaqoMRESRfYl4p1Wia/vzO/edzoXHTfcJQcWUtckurAAAVJubVfrznkqubRF7OoU/UWbNmQaPR4Nlnn0V0dLTR0KsQAq+++qpkDSTPoFAoMLFva/z4e3bDPWZf39DxjELERdT3VPRsE4FfLubZfP3IYD8Ullueb+OuUdYv9l/BnGG3go3KGo17GuLhDH8lZt3RDp/vtz6fMDrEsUUI4XYmuTbULsaxXjnA+D3asdC7WckqqEB8VJDbrn/wYh6GdYlx2/Ub9rqZm6usqdNC5cO8/mQbh4K5zMxMnD9/Hkql6V80pixpmmIjAk2W9XKEpQ86U7uUHvzJ2PD7sS4lzT0NcaNgf1V96hkLCstuBeO2lliKCPLD6B6xdqXAcSeFwW+vwsvy8Ow+45re5C3Hr+LxkR1dci1Tzl4twYD20bhRXIW2autzfaVSVVuHAF8frN17CQM73Jo+kFNcZfL1N0qq0CrSfUEveReHwv7evXujrs58l/yAAQMcbhB5Jl1akSA/4/jfnjlztjI1LGY4F8kcd5b/yimqxGfNOJecLcPmlbX2D+MplQq0jwmxe6jUk3Ro6XjibJLHuWsl2HbSNfP3ajVafLL7Er7YfwVardDXrrYW7LNaDNnDoWDuvffew7Jly/Cf//wHZ8+eRUZGhtG/Z599Vup2kouYW4GZnls/OTok0PhD29GhTUu3sY4mPvyibBluc0Msd/nmKsAff89GjQfMxZGatSCtRWh9fsGGvxeWDOtSv7q0ZZh9aT/CA30dfkjwt+FhQHI3G9svMQo94sPtPtzHQm+kows0PEVZVS22nriKGgl6+R3x66V8l12rokaDups3St1cuF8u5uFKruUVxCWVtbh4vRT5ZdVIzSySu5nk5Ry6w+Xl5eG7777DpEmT0KNHD7Rr107/LzExEWfOnJG6nSQRa6ukCszkayu7OYTWcHVeaZWZFap2fOpW2dBjY8vpXB3L5ZVW6ycxN1UjulmeW+SrUtq1SrVXmwh0bVUf2LQIs69u7/2DEzB3RAeLQY45zgzT9moTYceFTP6vPl2KPZQWeiPVocbfu3ArSb09zfqD6cjMr0Cd1v0PQFqtwG9XCmQ5d0W1BgVl5uf6Wsspt+v0dWw4nOlU2ihqHhyaMzd37lzcdtttWLRoEaKiohotgHj88cclayBJ53pxJQ5fLsA9fVvXb3Ag+pFq7pLheQrKqu2eG+IJk4ObQz4oWxLfjukZizqtwKXr1j9wBnW0PdWIqd80H6UCd/aKwxY7c4Y5k4MwsUUwTtjYM6JAfW9lboMP6UA/+2+1DWO5qGA/FNxcBNTwz/DO2+Lw7a8Zdl+jubtRXIWyag2OphWgfzvpEp/rZOSXWxwubQ73EHINh4K50tJSfPnll2b3v/LKKw43iOSjqROosDJJ3V6GJbp6tY3AiZu53uwL+ay/umEQaWpysCuTBntCvUlXsGWhgrm6vaaCGkMKhQKJ6mCkmclvFm4mpYduaDc6xN8lReJVZhZ6maJQAC3DA5BbWt1o0Y4ti0QM9YiPwNG0Wz1GraOC9MFcozb6eO+cQnfaeDQLvrI+FErzcykoq3b7KmDybA79Fvfq1QulpaVm93vLyrPmRqB+HkZ5lfkPFHtjlBMGiXrtWb2nMuh2qGiQysPUYoeGZzYVt6VmuS4h6K+XbE+r4s3MBWo6hj+XyAbpQBJtmNcVHGD+efK+gW0tHuvva/vta2AHx8t6RYf649ERHWx6rQIK9GoTqfvCSJCfbSt4dXq3jTD6upXBcHbDv4eQAOmGWV2ZtsMDirbImp9Sqo9CjVYwETlZ5FAwd//992PKlCl4//33sXXrVuzdu9fo3+uvvy51O0kiAsC5HPM3BWduGMY5tizfxQyfMBtmnjfXG2RYZ9PdnwFlFgLipqBVZKDdPRbTBrax6/XWPufM/Q7ptg+2Y8hWN0/PUZbmrxlR3ApQh3QyLiPW8Gt7GfZU2hIoO6prK/sqZjjjLAMUm+WWVOFGcZXbFo2QZ3NomHXGjBkAgJ07d5rc76k9c6mpqVi+fDn69u2L06dPY/r06Rg/fjwA4Pvvv8fevXshhMCoUaMwadIkN7dWPr9dKUDP+AjJz2tP9gjD+quXb5RhVHfr4Vn31uE4fLl+FZrWDY/0QghcyS1HgoWhwabijk4t9D2mAb4+ZhepGPYI6RJLp5zPRVmVxmqvxG1tIiAAnLKzR9VPpcTYnrGIDrFvAQUA3D+oLa7klut/j6Sme8sBvj5oFWm8MMRSL6S9wly84KFVZKAshep/u1KAqpo6XC2sxD19WyPAzt5LTyflJ2FplQYbj2YhNjzA7prB1PQ5dHfp1q0bNm/ebHKfEAITJkxwqlFyyc3NxZNPPomhQ4ciNzcX/fr1Q2ZmJkpLS/Hqq6/i2LFjEEKgT58+GD16NIKDXZdQ0hUM55RphZA8L1u7FiE4mlZfb1COcN4wWKx2IGeZM4rKa/QTzFtH2l9j1NME+fkYlRF6fGRHrNl1Uf91RLAfIoLrh01nD21ntM9Qw+9FbEQgpg1si68PpllvQ4PFFZaCxoYc7ZkKD/JD77a+8gVzNyPYWXckNtpny2ISS6wFcAPaR8v2vtQh/rIEcwBw6mY6pMNX8t1amcFbaOoELl0vRfuYEI/tOCHXc2iY9S9/+QsSEhJM/ktMTMSKFSucalRBQQHmzJlj9hd19erV6Nu3L4YMGYK7774bmZm2rQgaOXIkhg4dCgDQarUICqof6vvll1/QqVMnKJVK+Pj4oH379jh8+LBT78ETGRW5lqFjy9nVpdZuS4YTyqtqXTvUYLhSMFumDzVPN6FP60bbzCXE7dAytFGCaUsig/xMBkBykPMDUHdmS9eIjwrCkE62DxHrz93gnFNuNx7WlrMGbMfYpp/4WI4HRDl+1fLKqrHz9HV8ts96OTxqPhz69J09e7bF/RMnTnSoMUD9UOi0adMQGmr65rFhwwYsXboUP//8Mw4cOIDBgwdj8uTJ0NqZr2jVqlV46623ANTnzTO8XmhoKHJzmX3bHHN5vgy3Gr4kKth6wl9ro6aiwTnPXnXdYoemqLdB3rPhXe3rDUm0oQTSgPbRCLMxkbCfjxLDuraQvZfB2V6fyTYMbdnyFlpFBqJFqG0Jkx+xsPBCHeqPBBeVo9INaQ9yYiGJNXIuRLDF5zcrNHgLd3+/yLM4FMylpKRg6tSpeOCBB4y2z549Gx999JFTDVKr1di6davZkmArV67ErFmzoFbXP9nOnz8fx48fx44dO/SvGTp0aKN/06dP1+//7LPPkJiYqJ8Xp1arjVbnlpaWokUL5yYrezoB4fBKsrG3xdr1+oY9CFIoqTSdrFjq1CtCCFwvblo9cS1C/dEzPgK338yrpau4YS3Y0VXhaJiw1vx1jAMWPzM9t9Gh/lZXzUrB2Yn9UkxLSFQHo0tsfTtUViaZ1mmFvoyZLohqOKw9tmcsOsS4phpEbHiAQ/nybHXpepm+ooq7SJ8EnMOg5BoOBXPvvvsuqqqq8NRTTxlt/+Mf/4gvv/wS77zzjsMNio2Nhb+/6Q+L2tpaHD58GL1799Zvi4iIQEJCAlJSUvTb9u/f3+jft99+CwD49NNPUV1djSeeeALbtm1DZWUlBg8ejAsXLkCr1aKurg6XLl1ifVkH+JopmWRzh4sd9z1zD9BSP63W1glsOpot6TldIdJCb+i9N4Pr225WNtAFDJaCAnVIfcD1+MiO+uMm9GllsQ1KpQJ9DHoAE1uY7kWa0KcVwoNsKNfmZras7rXWuzj2tjj9JH8/leUAVpei5/GRHdGrbf33MbpBIK1QKDCqR/3DVcM5iM4INAiuDatLyD1FK7uwQt4LWLHpaDb2n7sh2fnk/n5V1jTtVfVkO4f++s+cOYNDhw4hIMD4yXvo0KH48ccfMWzYMMyfP1+SBhrKzc2FRqNBZKRxaZyoqCjk5ORYPX7nzp1YsGAB+vTpg6+++goZGRk4evQoIiIisHjxYixYsABCCLz++utNbvGDlMz1TzjTu+I9gxveoW10MArNJJjVaTjH0dIHz5QBt3pXVT5KdG0VZlPVjr4JkTiWXmjxNd4yiTvKhtWzPvYs6bbC1PfltvgIo9yOhmLsrHVrycw7EvH9kSyjpMyto4IQJmE+O1PyrZS3coWMfPcGlPY4nlGEID8ffbBPzZdDwZxGo2kUyOmEhISgutr1f5C2ZP8fNWoUioqKTO6bMmUKpkyZInGrPFdBWY3DHzwtbBxm0/GWD+s6rUB2QQVaRQZC5aPEb1fy9atzvUWHliG4dL0MvdpE4HiGfW1vWLHAElvnnykUCvirlKiWKTfW0M4tsP+86+a33tO3Nf7zu/M9tepQf9zbPx5fHUxDHxsCXh0pe98sUSgUCPb3QX7ZrYe0fonSl7tqKLe0Gmm5ZUhsEYID53MxpLP56S79EiNl+fssr9Zge2oOxvS0bzqJO5y8WWbualEl7upluaecmjaHhllVKhX2799vct++ffvg6yvP05tarYZKpUJhofEfcEFBAeLi4swcJY+FCxfiz3/+M/785z9j27ZtLr22FDYfv+pwb5i14SHAsQDOngoScqiurcO2k9ewdu9llFdrvC6QA6Dv4nQkX5fNiXHt4KNUYPbQdgBs+514fGRHdImzfW5bt9aOJQMe08OxD+qWEvV+KZUKBAeoEBboK2mPmpRuv5mku1eDShQzhyTKet2iilpo6rT6lCXucCVXmrl7rrqjZd7sTdQ9FPyeVmB2XjF5hm3btuljiIULFzp9Poce8xYuXIixY8fi3nvvRf/+/REZGYnCwkIcOXIEmzZtwqeffup0w0zx8/PDgAEDcOLECf22oqIipKenY8iQIbJc05xly5YhLMx1mdLl4GgtU8MePd38KbnaUlpZi1A7EqRK1Qv4/WHvLIBt7ifaMz7cpnJns4e2w6Xr5kv1OUL3M+mbYNtQ0PCuMbKXLmoXEwLVGQU0bl69OGNwAjR1Wtw/qC3+dSjD+gEA7u1veVXtwA7R+PWS8/nmdOXHGvbgy907ePhyvkfkcqyqrXPJwhypnL1agsOX89EnIRLnc0rRMjwAYYG+qKqpa3LJmJuCcePGYdy4cQCAkpISvP/++06dz6GeuRkzZuDDDz/Evn378OKLL+Lxxx/Hiy++iJSUFHz44Ye4//77nWqUJc8//zzWrVuH/Pz6m9V7772HXr16YcyYMbJds6nS1Dn/QVZcYXlelrN0lR6EEFAoFIiLkOcmn11QYVQmp9LFSYml0qtNRKNVko+P7Gjz8QG+PughQ3UQwL75ZK5IHit1UGLv9AMdlY9SvwDE2gpXAGhhoSdvQPtoJERLM99Xt3I1VOZ5cqb88FuW1dc4m9fSmi/2X0GGF1V62Wdm4cYXKcxH1xw4fDebPXs2Zs2ahfPnzyMvLw9qtRqdO3d2umeksrIS48eP1y9oSE5ORlJSEpYvXw4AmDp1KnJycjB27FgEBAQgIiICmzZtglIp7x92kyRBJ5ZcWeF1c78adpw42ptozebjV2U5r1y6xIWZ7L1qERaAuQa5yawNC3Zo6Zq0FvayJ41IeKAvih0YUpo+sC0+3nNJ/7UtOdQUivqer+pabaM5iVIUu48JD3Dqb6qPjb2ftrLnQUAOlv7e4yODENxdhV2nr8t2/W0nrzn1PXDHfOETds6VpabBqUdThUKBLl26oEuXLlK1B4GBgdi9e7fF18ybNw/z5s2T7JrNVamMcyocCboM73u6+XMNz9OzTQRyis2vXL58o0zyDzRP1PD7ooDpIVZdPVBzvRijunv+JG9r7h+cYLbcmCUN5wjasiJQoVCgd9tIXC2saBTMje7R0u42mDKye0tZAxQp3NYmQj/53p1cESpZW4jhaQ5dymd2u2bIpu6sV199FatWrbL75H/84x/xww8/2H0cuYa75wtZ4utTfzsqqzLOo2RtDku5xEmDPVXDn5yp70tYgC/a3axhqlQo0MmNJZmGdYnR503zJFMHOJbQ2lRaFmd7YR4YnIA+bSNtGmp1N1ct2vjhiPXhVrmdyi52uLfUXT9JAeBGSbXR1BFq2my6uy5cuBA//PADJk6ciD179kCjMf+BWVFRge+//x6DBg2Cj48P7r33XqnaShKzdKOJjwqyqWyTXHRVCU40ePoPsjKRt6DM/Xmq3MFUHHFb24hGpdTMlWKTW9dWYbLPcXJEtA254ywJu7k4R4pAOTTQF62jrOfu8wTtXVR1Iq+s2iiBcUMtw10TVF7IkXdBjhwOX87HZ/suA5B/bjO5n03DrP7+/vjpp5+waNEiTJo0CVVVVWjTpg3UajUCAgIghEBlZSWuX7+O7OxsxMbG4n//93/xxBNPyN1+coKljrnxvVshv6waaVYmAJvrjTC3vaLG9MICIUzXdm2YRNRapYCcYvvL8VSZaZOnMpxoP7RzCxxLL9QvFDHU3SBtR/uYENRqtBjugoUF7jCgfTQOX3Z+Bac97uoVh+gQf3x5IA0RHlbBYs7Qdvh8/xX4KBWok6kHPkEdjHQXLBCwtBhJinmKtnDF+5RTZU0dwgKF1+T8JPvZPGfO19cXr7/+Ol599VX8/PPP2Lt3L3JycpCbmwuVSoVOnTohPj4ed955J5KSkvhL4wWsffjZUr7I3rlxlTYGTro5czUyF5Ourq1DoZc9tSoUCnSODcOFnFKEB/lBoQBC/X0t/rwslfdqCvokRCI1switHEhp4aNQoM6BOZ5tJFo1aorayR5Dfxek1OjUMtRlQU6Qn0+jB0FXThKRad2Vy/yWVoCE6GD0lCiVlLsUlFUjIsgPWiE8sqffnexeAOHn54cJEyZgwoQJcrSHPIilIc2pt7fBhiOZjYL2+KggZBVUOD1XRFfUXU4HL+TalHvNHYZ1aYF95+orG/Rqe6uEU8eWoUhsEYxWkYF4cEiiPjHoHZ1bwN+3ed/cgvxV6NDS/uHOoV1aYM9Z6epxOivYX4XYCGmGD319lKjTytPz7Mpkx11bheNoWoHLrteQ7qGyvEqDtLwy29P3eEifRmV1Hapq67D1xFWM7Rknadk5V/rucCZmDknElwfS3L7S2tM077s/OSwkwLGF0OZ68gSM53254maTluuZQye3tYlAm6hbvT5dDSoijOzeUr+oIdhfhRah/hjbMw7Rof4uG3LyVHf3aYU2Dsw562xHxQlTpE7v0iIsAEmdpFk9GRqgsjlZs91cGA9Yut+4av7eb1cK8NXBNKRmeuYDoCWFFTWoqdMiM79CtvROzvjhSH2SdiGE2fbpeoEbLoqjegzmyCxLAZW5Op66CeG2jLJb6vmT+35TXq1BmYeufB3cUY3gAJV+EYileYIqHyWiHUxW29QE+PrIUpLMmkEd1OhmR148V5k7vD38fX0cfvCy1WPJHdC/3a26rfEyLOLocDNgCzGR6HlUd2lSwlij6xmsEwJ5pd630OqUh41CrNl1EWk3y6bl3vx+HryYZ7b6y08nrwEAdpyqT011JbcM1bV10NRp8cvFPPz7UAYu5EhbvcabMJgjsyzNe9TtavgUZa2upuGrDc9f68Il9L9dyccWD00SbDjna3jXGDwwOMGNrSFbBPurXDJHzV4qHyX6t4uSdW4f4JrEuCofJXq1iTCZ3kahUOjnhA5obz3xs7PKqzX4/kgmqmvrcPaq5VWunjiY+cV+91aEEEKg4uaDtOE8yBqNFlW1daiq1VrsPdQ9hG9PzcHn+69g7d7LOJlZhMKKGuw+49n5GeXEYI4copt82vBPTm1HL5HhvDiNVgvDW59cnw8nM4twNK0QheWeueihc+ytYNhHqdDXpTXVI0FkTUxYAIJl+t1RKgB/E8FVoEx1QAd1VBsFzVUGq1yn3t4GDw9rr+/Bc4Wyag0OXcxrlDza02m0Aqeyitx2/aKKWn0dYsPb/Gf7LuPS9TIcvpyPj3Zf0ufI23kqp9nkD3UGgzmShcLMM6nhA5fhUG3DBzG5nvZ/uZgny3ntNfuOdia3m6vv6Yk9P9S8BfqpMNPE73H7FiFoEy1Pvrykjmr9/xsmxFUqFfBVKV06sf/nm8N+v15ybUocKRy4kIedp3NQ7YYa1AoAtTcXlOw/n2u2estn+y7jdHYxLt0oMzv0asqhS55xj3c1WYK5Tz75RI7TkgM22VCw2ikSzW0zlSdNanLl23JEQIPei8eS6+upmothJ/ePx/2D2srdrGbLMEhoiuypdWsPXfBkuEinrTpYtjmvQf63/m5MVT0xN5dXDqVVGqu3P09O0XXpehmOphVg87FsdzfFrJTz9Sv67ZkLdyKjCNeKKr0uf6izHO5/12q1uHTpEm7cuIG6OuNv2ooVK/DII4843Thy3vUS+5PoGooK9kOBI0OSZu9ht25/fRMi9SuUGq5mlYOctWgdERqgQunNlVnWbvo+SoXVhMnkOG/Pv+VuQf4q+KuUqL7ZWxYbHoCsggr9/rbRQcjIrzB3uM0C/Sx/ZAX4+WB0j1j9JHm51cqcB9MVsh0sVeZKJXbeu//zezb6JkTi9vbREKJ5JEt2KJjbvXs3Zs+ejatXr5qcqNgcvnHeQCtBT1R8VJDFYE440TXXwiBPlZwdc576xxwS4IvSKo1+1SqRXAZ1UCMrvwLtY0IalciTihC3FiD0TYzCkSu38sK1jgpC2+hg7L/Z0yInd5Qh/OZgOmYkNV6s5IG3HSPuyFLiqu9J5c2Vrl8fTMesoaantTQlDgVzTz31FObMmYOpU6ciKioKSuWt0VohBBMKe4hP9152+hzBVtIaqJS2jdQbPrWb4sgwa3m1xqbJ3Qcv5iEq2E+fhNfdZje4sYw0SK3giUEneT8/lRJ/GJKIs1eL0a5FCNJyyySvoNChZQg6W6hRK/2vtul3oFQqrN5vpFZSVQshBE5lF6Nbq3D98LMrh30dcSrb9elKXBVAnr1aol9trNUKt6QtciWH58y9/vrr6N+/P9q1a4eEhAT9v8TERLzzzjtStpEcpCtRVFbl+PBiTyuZzvsmmk5I2vDPRleI3Nwf8vXiKlmW8W9PzcGprGKPCeSAW3N9TK0EJJJTl7gwjO7RErfJMKw8tEsMgiw8XMVF2F9qzRRbkgS7IkVJQ5dulOHghTxoDIZebYkfElu4vifRndwR336855LF/Z6YSNleDn2a9OjRA0VFRWb3Z2XJPOme7LL7jHylimztSVKH1g+pmvuTkSNVSEZeOa7cTErpiYZ3NS56r4Bn5qWipkOhUEChUGCQGxZ8SDXnU1flw9IK726twyW5lj12na7Pcbb7zHW7HqDNrfx3pTW7Lrqszq67aLUCp7OLkVNUP0fQsNrElylpbmyZNBwaZp0+fTqmTJmCqVOnolOnTggKMl6G/vrrr+Ohhx6SpIHUNNxKMmx6v8qOLnBfHyVq67SoMDPMWlReg4hgv0aFuT2Npw/BEDmjc2wozutWIUrY8dGhZSj2nL2B6BDPrHySkV+B22u1CAmwbUjRU24Drsrl5q6pJB/vuYRgfxXKqzWYPbQdrtwoQ1FFDZI6tUClG1K0SM2hYG7GjBkAgD179pjcz3k/ZO+vwKnsYpuHRnRxX3FlrdEiCp3f0wsxsntL7Dvn3uLp/iolwoP8cOPmiuK5w9tb/NsI8PVhgEcu0z4mBJdvlGF41xjsPSv938rwrjGo0wpculFmsnKDo3yUCpuGWlVKBTRuSkd0rbjS5jJ7CgB39YrD1hPX5G2UFdeLq3D+Wgnuvb2NrNdx5x1OF7DWaQWqauuQmlWMqJsPBRevlyJRHQyFQoE6rZD0d9YVHGptt27dcOXKFZP/Ll++jK5du0rdTmoinFn9qtPByurP7ALnUyA4KzLYDyEBvuh4swj7jMEJUPkYJzVVKIDWBuW7/jAk0eqCEyKpjO4RC6C+p1sOCoUCSmX9IGInCwsjHKFruyUPD28v6TXtcfCCceLawRaGtRUKhV2Vc+Ry8Xopckur8ZGZJL5S8YTn1d/Tbq201j3I7Dp9HQcv5uFYeiE+2+f84kFXc+iT4y9/+QsSEszXjFyxYoXDDSLpyTW5M9hfhfAgX5P7Gs4DkfLvVze8Yu5taYXAeTsyhstBpVTAR6lAj/gI/HalwOREaJWPEnf3aa3/2pXZ64l0gmQqvwXUlxNLuNnbAQC3tYnASZlSozSku2agr49bhtE0dVr9o2uCOths9ZmoED+r+fNcSaC+VJqppMxS8IQ5gmfM1NQ9e7UE/RKjAADVtXUoq9YgwNdHtpJ4UnLokWz27NkAgAsXLuDDDz/E8uXL8eGHH+LChQsAgIkTJ0rXQnLIGYMl56VVGqMVVvYyt9oqyM8HPvY+ZkkQV1q7ZI1Giz0yDBvZo010EMb3bgUAeHBIIkICTAe9RO4WGxGIx0d2tP9v2QbdW4ejXYtbQ6KurJ2q466eoLV7L+uH9Sw9UAd5UCCn88X+KwCAgxdynfrsMMn9sZxFR2/22pVU1uLI5XyvWRjiUDBXV1eHJ554Al27dsWf/vQnvPLKK/jTn/6Erl274o9//CO0Wu/Piu3tDJNzlldrcK3I8Szf5p6k7unbGq0izaQbaHCIlPMorZ3JUxaZq24OX6lkGsYiclaCQYLdybfHuyRdjq+PEn8wkWBXDhP7tgagcFti7r1nb6B1ZCCC/VUYZZBP0lBUcONVvpby9blSalYxJJ926Ck3aCt+/D0bGfkVSM8rx4EL9Z+nnpzCxKG/3CVLlmDHjh1499138csvv+DcuXP45Zdf8M4772DHjh1YsmSJ1O0kJ1n6FbT2ZGhuGEblo7Q5SNONIErxp3Cr+99z/7CIvMGdt8UZfS334jWFon76QUiAr02LGJy/Xv2/kWYCKVe4rU0EVD5Ks3N9QwMb99qP6Oa+9urkONEB0BToanlnFVQgu6ACGXnl+O+xq8gvrXZzy0xzKJj78ssvkZKSgnnz5mHgwIHo1KkTBg4ciKeeegr79u3DunXrpG4nOWn3zRxIpvwhKcHiJOikTs7npJIyjYCup+toWqFk55RCD4PcVlEemjaByJyoYD/cP6itrNeICPLF3X3qpx/INSfLkAd3pDRye7sodzfByI+/ZwMAfk6VdpWtFIvgXK2oohbbTl7DtaJKlDbIIXghx73zs3UcCuZUKhViY02vJoqLi4OvL+cHeRpLpW2USgXG944zu9+Rp/WGR+iePi11U5u7TF2Dfn7d0K69xZfl0uLmSjRduRgfhcJonhCRN1AoFBYT8UpB5aPUP9gNkeAh0RqFwv0Li2wNXfomRiEmLAD9zFTVcRdP7Ylyl59Tc1BkkORezqT89nAomPP390dKSorJfQcOHGAw54X8VZZv4vbMpenaKszsDdSeZ7LAmx8stRYm4GYVVLh9HsNdvVsh0mDeiztTIhB5C1fkI40JC8AUmfOmWWN4e+oZ37gyheF3YXL/ePRvV59vc04TLQ7vTb2l5nz7awYqazRuz2VqyKFlNC+//DLGjBmDKVOmoF+/foiMjERhYSGOHj2KjRs34pNPPpG6nSQzSzUVgcbF4S0Z1iXG+ots0CcxslG+poa2HL+KuW4Mnib1a40AXx+0igzU35SbekFnIql0iQvDORnTCCkUCvip3Pv3qDUYWbDn3iB3L6mtauu0yCut9ohceJ5kXUqa/p7/3a8ZmDZQ3ikK1jgUzD3wwAOora3Fyy+/jK+//lq/vXXr1vjggw/0FSLIe1jLdi3VU7SmzsIwa4Ovq26W47pWaHkibmmVa8rQmNIyvH7I119Vvxhkupv/oIm8SXxUEIL8fPB7umvnv8odRBqqM+iK6hgTihMZRUb7Hb21hgf6otgFU020AtiRmgOFApg6oA1X5xvQ/WQLymtQVF6DIH+V2ypHOHzV2bNnIzMzE2fOnMG+fftw5swZZGRkYNasWVK2j5qYKjuSd+YU15fBspbw89+/ZjjVJin0S4xC34RIRJhIM0BEprWPCUF/g4n/fjIGCo+P7Kj/f1fmnqsyqBFta4kvQ5EecE8pqapFcWUt1u51vjJCExhlNWnX6evILmxcfajWwnz1qto65Ek0J9GpvxyFQoEuXbrgjjvuQJcuXfS9N2+//bYkjSPX8rTklZ48WtkyLMBoeFehULAmMZET4iICHQp2HCFHgmRz0qwknbV232htJpenQP3qYPIMeWXV2HNzMcS/D2WgRqNFWm4ZPt13GZU1jUePzl4twRf7r+D7I5mSXN/mT+/s7Gz4+/tDrVZj7969Fl/7wQcf4LnnnnO6ceRaCeogs2VO5GJY3qfhTU03v8TWBQ5zh7eX5MnRVhxuIJKWKxYzzR3eHmddeJ9T+Rjf14Z0UuOAwVxgS6HcmJ6xaBMVhNSs4sY7hZWDZXLwQi7ax4Top5jYy90L1uRUW6fFmpu1bQ3ru65LSTPqGQZgtHhCK0FmZpuDub59+6Jdu3Y4dOgQkpOToVAozP5Q2EPhnQZ1ULs8mFMa/K4cTy/EbW0i9F+3bxGCzPzG3dbmuCK48lEqUKcVHlEsmqgp6d46HOGBvtjQoKciMkjaYUaVj9KlQVBuifEwWqvIIKOvLd1L2rUIMVtOS0BA4dzgmkNSs4odDuSoXsOf6ef7ne+EsDmY++CDDxAWFgYA6NatGzZv3mzydUIITJgwwemGkev5umnipk7DuXG61VwC7nlAiAzyQ2FFDR4d0QEf77kEH6UCs+5oh9PZxcjM9456fUSeTtcnoKsIERMWgBslVfr9uhrHUnhoWP3UCFfeTRrmyWw4Qd7avc3c/gBfH8SEB6CwosbkfjntP3cDQggE+6sQG8HAzlYZ+eX49WK+LD8zm4O5KVOm6P//lVdeQUKC+dp6r7zyinOtoiarYW9uuIU5H6EB9b+etRqtW7rm1WH+KKyo0T85P5iUCD+VEm2igqDy5Al9RF6sYfnAADPlBB3RMJAa2CEav17Kl+z8pjS8dwX7q+yaEmLuTjOpXzyUSgVOmRqClVm1RosTGUUI8vdhMGeHrPwK2YJvh7piHnzwQaf2E+l0sFCfUZfIWDenztV0N9GGT8bRof7oaTAcTESOa/SY1iB6kaOCQ4eWoQgNUCFRHWy0ffYd0ifq7dW2cUUHlY8SbaODTLy6MXMdd+7OZ5lXVo2M/Aq7V2M681w+oH204wc3cQ4Fc4sWLTK5/ccff0T79u3xww8/ONEkak4szXPz8dEtgHBVa4wZPnG2igx0+82TqKkKN1FsXk4Bvj54ICkR4UF+uNtgGFfKXkCdhgsgdHyUtn38NnyYvM3DHiQb1iqVU6AMPx9X2XbiKk5ly9eL6lAwt2nTJpPbJ06ciG3btuHVV191qlHkXp6y3F1XiLumTuuWOXNd4sL0/z+hT2u3JYMkasp8lArcP9j8tB1XS+7W0kXXibGrp0lXk9rcnVCqyjv2apgE2Rpnns29+XE6w47FfI6Q/NMpKCgIFRXyNtoZqampmDlzJlauXIlHHnkEW7Zssbi9OWoRFiDbuR39Q84pslwFQiodWpof9iUi+bWJCjJ6kJJbo2kUIdKunjU3sqDyUaJPQuMhWFPG9IjFhD6t679QAD1a36rxqgsIu7Zy3ffM0I2SKtRotEjNKkJljQbVNiaGbzg30hbuXqTnyWxeALF48WIsWbJE/7WPj/kfxMyZM51rlYxyc3Px5JNPYujQocjNzUW/fv2QmZlpdjtJy9Eh01yJsmRb0z8xCn4+Sn2KlkAPqY9I1Fx0bRUOH2WJy8ptxUUYP7xGhfgjNEAlWZlAKQYV2hnMLVZAAWHwWNwpNhSHL9cv4nh4WHt8us91uTZ1jqYV4GRmEaKC/fDfY1cb5VQzdGtBiP3fmHYtQpDcLQa7z3hOgXtPYXMwd++99yIxMRFCCLz66qtGgZ2Or68vEhIScMcddzjdsIKCAixYsABffPGFyZWMq1evxpo1axAYGIiIiAh88MEHaNOmjdXzjhw5Uv//Wq0WQUFBFrc3R3KW1PF04UF+GNolRh/Mzbwj0b0NImqGEtUhAG5gbM9Y2a+l65kLMHhwU9wMNMICfFHiwjlhtqjPL2eau3qudIvUjtsx5GpvkDu8a/0wcqfYMOSVVhslUp7UrzU2Hc2274RNjM3BXO/evdG7d28AwNWrV/HQQw/J1qjU1FQ888wz6N69u8n9GzZswNKlS3Hy5Emo1WosWbIEkydPxpEjR6C0cVIpAKxatQpvvfWWzdubg8eSO8i24CDIz0efbsRZvdtG4niG/MW5mQCbyPV0QUliC9dMe3gsuYPR37qu50tYmRjSsWUoLl4vtfgapcT3kL4JUUYdHJ50h8oqsH2K1W1tIvDLxTzrL7zJ0qrm0ADPmOftTg6F8cOHD8fUqVPxwAMPGG2fPXs2PvroI6cbpVarsXXrVgwYMMDk/pUrV2LWrFlQq9UAgPnz5+P48ePYsWOH/jVDhw5t9G/69On6/Z999hkSExMxadIko3Ob295cKBQK2VZttmsRItmTo6X8dI6YMTgB/RKjrL+QiJqchg9tYTdX1/qpfBqlEDEMKmxZXdmuRbDV19gqPNAXfiqlPqE6UD/3rmGKFU8mAPj6KBETZl8dXlOfSnE3Mw5IHTB7I4e6Sd577z1UVVXh2WefNdr+xz/+EX/7299QUVGB+fPnO9yo2FjzXeu1tbU4fPgw5s2bp98WERGBhIQEpKSkYOzYsQCA/fv3mz3Hp59+ipqaGjzxxBPYtm0bhg8fjsDAQLPbqekLC/RF/3a3gjlPW/5PRK4zvncrpOWV1ycI91FCqxX4eM8lAMDgjmqknM8FAGhtGMYIkbDXyNSqXz+VEmNvi5PsGlLYfCwbraOC0LtBjj1NnRaZ+RVI6qR2qiRYn4RIpGYVIz4qCAnqYKP0LwG+PqiycREGUJ+1wFXzM+XkUDB35swZHDp0CAEBxhNHhw4dih9//BHDhg1zKpizJDc3FxqNBpGRxr8kUVFRyMnJsXr8zp07sWDBAvTp0wdfffUVMjIycPToURw8eNDkdgZzElLAuXXpNvDzUaLGTC1DQy1C/VGnFQgOUJms/zq4o1qO5hGRje6WsIyXvRQKBdoZDPEqlQoE+6tQXq3R9xB1jg3VJzb3RKN7xCKroMItgUp2YSWyCysbBXNlVRocvpyPETfnv9kzXSYi+NYq40A/FR4Z0aHR0Ks6xL9RWUhr4iICm28wp9FoGgVyOiEhIaiuds3Kw4ZsKfk0atQoFBUV2bydpNM/MUqyOWjmftTj+7TCxt+yrB7fu20kzuWUICE62GQwR0Tu1TrKsxah6e45ulvYoI5q+CgUOJpW4L5GWdA+JgQ1Gq3ZQGVA+2j9KlhXaXjbHtghGonqYGw8av2eHR1iPCzbMJCbMTgBgb4+yC6swM+p1jt2dFpHNo0OG4eCOZVKhf3792Po0KGN9u3btw++vvJNRlSr1VCpVCgsNI7mCwoKEBfnuq7mhQsXws+v/klh3LhxGDdunMuu7axBHaJxSOZ6hKb4uyDNh601U9vFhCC3tFpfJ5GIyJLb20dh79kb0M3eCvDQtEW2BmnB/sYf/5HBfigsr4GPUoE6rTRDKBevl6J9ixD9PGxdh4vhM33DdjhKN8/R3ioRPkoFHkxKREWNBj/Y0BEglVOH9+HU4X0AAE2t8/VaHfouLly4EGPHjsW9996L/v37IzIyEoWFhThy5Ag2bdqETz/91OmGmePn54cBAwbgxIkT+m1FRUVIT0/HkCFDZLtuQ8uWLUNYmHuSNFqiqdNaLJEF3Jo0CgC92kbI3CJ5mOvgs6Xnb+aQRAD1T4VERLboEheGvWdvNKoEk6AORnpeuZta1VifhEibgjndg6+vjxK1dVrEhAWgsLwGvdtGStbbuOv0dVR00EAdGoBWkYEmZ9kEB6gwvncrbDl+VZJrqkMDcP+gtvjXoQyrrx3WpQX8fX3g71vfDlfqMWAYegwYBgCoLC/F7o1fOnU+h1o/Y8YM1NTU4OWXX8Y333yj3966dWt8+OGHuP/++51qlDXPP/88nn76abz00kuIjo7Ge++9h169emHMmDGyXtfTHc8oxK+X8vFYcgeLrzOs8BAjY7UHOTmSPVx/rERPgkTUvLSNDkL7mBDsOHVrW+vIQI8K5myVeHOVbbfWYTiRUaQftgwLlPb+WFRRi0OX8hskEjZ+6PaXMD+ej1KB8CBpq3h4A4d/arNnz8asWbNw/vx55OXlQa1Wo3PnzpLMiaqsrMT48eP1CxqSk5ORlJSE5cuXAwCmTp2KnJwcjB07FgEBAYiIiMCmTZvsyjHXFB29Uv809ele12cAl0KYHau+Wkeank/DBepEJJdxvRovyvBRKvTz0+zJsyYnw6HL+Kggk+3SfVb73hzJ0eUADfb3lTSPp25O8n9+z0aSmYVlcpaQ9FR39YrD1hPXEOTng0oJngWcin4qKiqQk5ODvLw8dOnSBQUF0nTNBgYGYvfu3Th79iyEENi9e7c+kNOZN28ejh49igMHDmDz5s02VX9o6nR/nBob5jtYKrfiarqbiC3t1rEnF55uLgURkdS6xIVhRNcY/Ry6vjbWW5XTH5IapzAxp8/NFae9bv43LiIAUSF+iA2XJsCqqKkvi3atqFK/GMNUcNkwn5+z7upl/xz6zrGh+v+3lKRYCm2ig9GrbQTulCitjEPBnBACf/vb39CyZUuMHDlSn/PtmWeewcSJE1Fe7n1dzuQ+9w1sC+DWH70zTHUM33ri5PAqETnPMGBTKBRQ+SgR4KvEbW0iPGKIz54RMqVSoZ+aM3d4eygUCsSEBaCnDLk2T2XXl+DKK61qtK99TGijbc4I8mt8v5/cL77BFuPv04huLTH19jYm9shjUAfpUmA5FMy9/vrr+Oyzz/DKK6/g+++/1+d8++KLL9CnTx/85S9/kayBZJvDl/NRa0N+tYY8YVhSyuzdDQM2w/mDhk9dRESOur1948VTunGFji1dU4LMFgpF45RdhvnzHhnR4ebr6u/BusVzYYG+Rq+Tvl2N7/mdYkMxuV+8ySDMsYsYf6l7r4ZMpTOLCvHTB30P3lwsJ6eIID/c2dP53jmHgrkvvvgCKSkpePnllzF58mR9ig4fHx8sXrwYKSkpTjeM7FNSaVsx6DYNurJlzuFrEykrsTRcyatQKNAyPECymrBERKZEh/gjOsTPo+o5Rwb5mczXN7l/fbAi91CiOWVVpkdhYsIDMKij9FkGHh/Z0eR7bfh5CNR/ZkSF+GFMz1h954BuPvewLi0kb5uvSolWEuRUdCiYUyqVZueoKZVKVFZWOtUoks9dJibwupvUN7/urcMRaJADqn+7aDyQlCjpNYiIDHWJC0OnWON0VS1C7as/KrWY8IBGVRgA92cxsDSKJFWdWd2nSlKnW0OZAQZZEB4f2dFsqTWVjxJtom+1w9+3PlTy5IofDgVzKpUKu3fvNrkvJSXFbHUIko8tNQK9UUsHbjoqn/rVZfcPamu0Xc5hAyIinaibpafuvd0zFubNuiNR//9DOnl2qUJreVJtFRnsh9lD26FnfIR+m6OL4aJvBuXtYjz3M8ShsaeXXnoJY8aMwcSJE5GUlISCggK8//77OH78ONavXy9r0mAyLS3XsUUnUub3kcPADtH48fdsk/t0yS4bEQAUaDQROTrU32OzthNR0+Gu4UtzfA0CJHvybMZFBKKwvMauwvW28LEyGhPo62N3jdWGFAqFyft9fa5A++ZPD+6gxtmrnl2/1aFgbtasWdBoNPjrX/+KjRs3AqhfyRofH48PP/wQ06ZNk7SRJI8/JCUgUKrJpjKJjTBfN+/2dlE4eDGv0faOsaFmJwMaPqESEcnBUwdKBtlZ9eaevq0hhMBHuy/J1CLT7rwtzqZ6rY4wlSvQklaRgZLO6waAEBkyKzh8xocffhgPPfQQzp07h/z8fKjVanTp0kXKtpHMzM0X8BYRwaZTADQsyGzIkyYnE1HT1CM+HJdzy9zdjEZ6mZg/Z41CocCwLjHYd+6GDC0yLSY8AON6xWHbiWuIjwrC2J6xLrt2QxP6tJasVq1OiAwL8pw6o0KhQNeuXaVqC5FdwpkMmIg8UOe4MHTyoFRIPkoFxjgREHVtFSZpMFdnQ9dleKAvlApgfG/3L9ozNWr+WHIHnLtWgqparU21cA2ZWpTiLEnCw3PnzuHkyZOIjo7G8OHD4ePDeUkkP3ayEZGn8qRRAIVC4fQCMB+lQvIeKkuC/VUY2d19PXKGDH+WkcF+KCyvqe/MahUOAHYHc20lWrFryObZ719//TX69euHfv366WumAsDSpUvRo0cP3H///Rg9ejT69OmDGzdc1x1LzZfCICukp004JiJqSkb3iEWAr48kOTttKXmm8lGivQetHtXlmLtvYFtM7Nva4fNIVb6rIZuDuY0bN6JFixZYu3YtYmPro+VTp05h0aJF6NWrFw4cOIBffvkFrVu3xt/+9jdZGktkRGHyf4mISGKtIgIxsW9r3DewrdNTXFq4Oc+dI3S9cIDlhXmGEtTBiAz2MwrgfCVKvdKQzSH2qVOnsGfPHn3pLgD48MMPAQBr1qxB//79AQDr1q1DUlKSxM2k5sDPzl9yBnBERK7hq1IiQlW/6Cwi2A/FNlYdMkXt5mTKrpCgDsadt8Xhv8eyjQK4uAh5AlmbgzmVSmUUyAHAhg0b0L17d30gBwBqtRq+vpyY7kpXPHDVlL3u6NwCWQUV7m4GERHJrGEN7aZsfK9WUBpMA5JrLqXNXSENC9Lu378f2dnZuPfeexu91t+/6UfdnqS82nSdO29iquCxNQG+PpjQpxX62DD/goiIpGHqft29dbiJVzZfupBNF8g9PrIj+iVGyXY9m4O58PBwbNmyBQBQV1eHpUuXQqFQ4KGHHjJ63dmzZz1qFQ81XUqlAq0ig9CppeekACAiauqGdolBkJ9x1oqW4d43D05KSQ3KpJnqnujfzgOCuRdffBGTJ0/G7bffjsTERPz88894+OGH0bFjRwBAdnY21qxZg3vuuQfDhw+XrcFERETkPsH+Kn3+t443H6aFAGKbaUA3e2g7o9JhrSJtWyAhJZuDubvvvhv//ve/kZiYiP79++Ptt9/GP//5T/3+Cxcu4MCBAxg2bBjmzJkjS2PJtKbQD+rro4Sfh9eJJSKiekqFAj5KBe7oXJ+yo010EIZ2iXFzq9yjYcqWdi1CHJo65Ay7ZiFOmjQJkyZNMrkvOTkZycnJUrSJmqFOsaH6Jzx7hQX6YuqANhK3iIiIzPH3VaKzQZWLAF8fk4Xtm4uW4bd64xQwWx5cNs1nSQl5NIVC4XBFB6VSgfAg03VaiYhIeoF+KgztEoNajdZo+z19W+M/v2ebPe7+QW3lbprb6EqmhQX6wtfFI00M5oiIiMghDR/C46wk1G3KD96GJdOiQ1yb1YOTlJqA2jrbOnTbRAfJ3BIiImpOfJQKPJiUaLRtRNfmOXfOnRjMNQGBfrbNU3DxfEzJNFzyTUREnkGhUCC4Qb3WznFhSO7W0k0tap4YzDUByiae169nfIS7m0BERHbw92V44UqSfbeFECgsLJTqdEREROSlWkfWT+tp2l0NnsOhYO7777/HqFGjsHr1agDAiRMn0LZtW6jVavTu3RtpaWlStpGcpCsh4o5EhkRE1Pz4KBXo3jocj4zoAACY0KeVm1vUtDkUzP2///f/MHjwYEybNg0A8OSTT0KlUuG7777DnXfeieeff17SRpI15ifDBfur9CVEerf13hqmumzjRETkHe7o3EJfm9Rb52x7C4dSk9y4cQPLli0DAJw/fx4HDx7E+vXrce+992Ly5Mno27evpI0kx/0hKcHdTZBEfBRX4hIReSt1qD+igptuWhJ3c3rO3Lp16xAVFYWpU6cC0CV/5Si5p9D9LKzl/iEiIpKLv68Ppg1sugmD3c2hnrmYmBi88cYbSExMxLvvvou5c+fC19cXAHDkyBGX1yQj6+7p29rdTSAiIiIZONQz9/e//x1r1qzBH/7wB8THx+OVV14BADz77LMYOnSo2fqtRERERCQth3rmunfvjosXLyI/Px/R0dH67QsXLsSCBQvQsiWTBRIRERG5glO1WQ0DOSEEVCoVEhKaxoR7IiIiIm/APHNEREREXox55pqwLnFh7m4CERE1cwPaR1t/ETmFeeaasOFdY9zdBCIiaub6JHhvwnpvwTxzTQAzwRARETVfDgVzujxz33zzDd59913MmTPHK/LMpaamYubMmVi5ciUeeeQRbNmyxWj/pUuXEBYWhv3797uphURERET2cWiY9e9//zsmTZqEy5cvo0ePHkZ55v75z3/ixRdflLSRUsnNzcWTTz6JoUOHIjc3F/369UNmZiYAQKPR4M0330TPnj3d3ErTNh/LxrhereCjZK8nERER3eJQz5wuz1xubi5OnjypT1GycOFCnDt3DgsXLnSqUQUFBZgzZ47Z4drVq1ejb9++GDJkCO6++259QGbNyJEjMXToUACAVqtFUNCtep9vvvkmnnnmGfj5eWbtuOzCSnyy55K7m0FEREQexqk8cwUFBfjuu+/0yYNHjhyJTp06OdWg1NRUPPPMM+jevbvJ/Rs2bMDSpUtx8uRJqNVqLFmyBJMnT8aRI0egVNoem65atQpvvfUWACAlJQWhoaEe2ytHREREZI5DwVxdXR2efPJJfPzxx0bz4xQKBR577DH885//tCuwMqRWq7F161asX7/e5P6VK1di1qxZUKvVAID58+dj8eLF2LFjB8aOHQsA+t43Q3Fxcfj2228BAJ999hkSExP1Zcc2b96MkJAQrFixAhkZGfjyyy9RW1uLkSNHOvQe5JRTVInYiEB3N4OIiIg8hEPB3JIlS7Bjxw68++67uP322xEZGYnCwkIcPnwYf//737FkyRIsWrTIoQbFxsaa3VdbW4vDhw9j3rx5+m0RERFISEhASkqKPpiztIDh008/RU1NDZ544gls27YNw4cPx+uvv67fv3XrVsycOdNkQOgJ9p/LxX2D2rq7GUREROQhHArmvvzyS6SkpDQKvAYOHIipU6di2LBhDgdzluTm5kKj0SAy0jhnTVRUFHJycqwev3PnTixYsAB9+vTBV199hYyMDBw9ehSBgfU9XatXr0Z6ejo+//xzqNVqdO3aVfL3QERERCQlh4I5lUpltgctLi5On6bElWxJhzJq1CgUFRWZ3T9v3jyjXj8iIiIiT+dQMOfv74+UlBTccccdjfYdOHBAtmBOrVZDpVKhsLDQaHtBQQHi4uJkuaY5Cxcu1K98HTduHMaNGyfbtc5fK5Ht3ERERORa27Ztw7Zt2wAANTU1Tp/PoWDu5ZdfxpgxYzBlyhT069dPP2fu6NGj2LhxIz755BOnG2aKn58fBgwYgBMnTui3FRUVIT09HUOGDJHlmuYsW7YMYWHy1T4VQuC3KwW4vX009py9Yfm1srWCiIiIpGbYCVRSUoL333/fqfM5FMw98MADqK2txcsvv4yvv/5av71169b44IMPMGPGDKcaZcnzzz+Pp59+Gi+99BKio6Px3nvvoVevXhgzZoxs13SX39MLcXvDAsXMGUxEREQGHM4zN3v2bMyaNQvnz59HXl4e1Go1Onfu7HRd1srKSowfP16/oCE5ORlJSUlYvnw5AGDq1KnIycnB2LFjERAQgIiICGzatMnhVCieLqugwuprLl4vdUFLiIiIyBMphAOFVD///HMAwL333ivrUKMnKikpQXh4OIqLi2V97wcv5CI1qxhRwX4oKL81nu6jVGDWHe3gp7oVvK7ZddHkOR4f2VG29hEREZHzpIgrHOrOevjhh7F3717U1dU5dFGyLjWr2OT2Oq3AZ/su67+uqNa4qklERETkgRwaZu3Zsyc++ugjqdtCDvjyQJq7m0BERERu5FDPXPfu3S0m6b333nsdbQ8RERER2cGhnrlp06ZhypQpmD59Orp27YqQkBCj/adPn5akcQQUV9S6uwlERETkwRwK5nSpRw4dOmRyv7MrWumWOjPrU4QQ/D4TERGRY8Fct27dsHnzZpP7hBCYMGGCU40i67QC8GEsR0RE1Ow5FMz95S9/QUJCgtn9K1ascLhBZJsv9l/Bw8Pbu7sZRERE5GY2L4AoLCzEI488gkceeQQVFY0T2e7btw/PPvssCgoKMHHiREkbSY3V1mnN7usSF4aYsAAXtoaIiIjcxeZgbuPGjfj0009RVVWF3r17N9ofFxeH/fv3Izk5GUVFRVK2kewwrEsMereNwKAO0dZfTERERF7P5mDu+++/x5IlS/DVV19h8ODBjfZ37NgRhw8fxuDBg7Fy5UpJG9nc7DxlPu2LNR1bhiA8yA+xEYEStoiIiIg8lc3B3KlTp/Diiy9afd3KlSvx3//+16lGNVellbXYeToHl26U2fR6ByqxERERURNj8wKIwMBA+Pn5WX1dWFgYU2Y46Otf0u16/W9XCmRqCREREXkLm3vmlErbi0XU1jLRrStcyW3cg8dAmoiIqHmxOULr2LEjtm3bZvV1P/30E9q3Z8oMVygyUR3CR8lgjoiIqDmxOZh7/vnnMXPmTHzzzTcm52oJIfCvf/0Ls2bNwssvvyxpI8k2DOSIiIiaH5vnzCUlJWHJkiWYNWsWnn76afTr1w8xMTEAgBs3buDo0aMoKSnBP//5T5OrXckFuB6CiIio2bGrAsS8efPQr18/LF++HDt37kR5eTkAICgoCGPGjMHf/vY39O/fX5aGNnVarfOR2KgeLSVoCREREXkTu8t5DR48GBs3boRWq0VeXh4UCgWio6PtWiBBjX2855LT50hsESJBS4iIiMibOFSbFahf3aobZiUiIiIi92B3GhEREZEXYzDXRLSOZPkuIiKi5ojBXBNRWqlxdxOIiIjIDRjMNRGlVay6QURE1BwxmGsiQgIcXstCREREXozBnAeokyDHHKs/EBERNU8M5jzAJxLkmFMoGMwRERE1RwzmiIiIiLwYg7kmgv1yREREzRODuSaCw6xERETNE4M5N/rp5DVoJVj8ALBnjoiIqLliMOdG6Xnlkix+AAB2zBERETVPDObcTJp+OSIiImquGMw1ET3iI9zdBCIiInIDBnNNhK8Px1mJiIiaIwZzRERERF6MwZybCMHZckREROQ8BnNu8tFuaVaxEhERUfOmcncDXCk1NRXLly9H3759cfr0aUyfPh3jx48HABw7dgz//ve/4e/vj127dmH79u1QKhnrEhERkWdrVsFcbm4unnzySQwdOhS5ubno168fMjMzodFo8MILL+Cnn36CQqHAtGnTvK6igre1l4iIiKThkV1PBQUFmDNnjtkAZfXq1ejbty+GDBmCu+++G5mZmTadd+TIkRg6dCgAQKvVIigoCABw6NAhKJVKvPPOO1i0aBGysrJkDY6OXM6X7dxERETUvHhcz1xqaiqeeeYZdO/e3eT+DRs2YOnSpTh58iTUajWWLFmCyZMn48iRI3YNi65atQpvvfUWACArKwtHjhzBhg0b4O/vjwEDBqBz585ITEyU4i01ciKjSPJzckEFERFR8+RxwZxarcbWrVuxfv16k/tXrlyJWbNmQa1WAwDmz5+PxYsXY8eOHRg7diwA6HvfDMXFxeHbb78FAHz22WdITEzEpEmTAAChoaHo0qULgoODAQA9e/bE4cOHZQvmoABLPxAREZEkPC6Yi42NNbuvtrYWhw8fxrx58/TbIiIikJCQgJSUFH0wt3//frPn+PTTT1FTU4MnnngC27Ztw/DhwzFw4EDk5eVBCAGFQoGsrCx07NhRujdFREREJBOPC+Ysyc3NhUajQWRkpNH2qKgo5OTkWD1+586dWLBgAfr06YOvvvoKGRkZOHr0KNRqNV599VXMnz8fISEhuPPOO9G3b1+53gbqtNJ3y0UE+Ul+TiIiIvJ8XhXMWWLLnLFRo0ahqKjI5L6ZM2di5syZErfKdfxUHrmWhYiIiGTmVcGcWq2GSqVCYWGh0faCggLExcW5tC0LFy6En199b9i4ceMwbtw4l16fiIiIvNO2bduwbds2AEBNTY3T5/OqYM7Pzw8DBgzAiRMn9NuKioqQnp6OIUOGuLQty5YtQ1hYmEuvaQnTzBEREXkHw06gkpISvP/++06dz+vG5p5//nmsW7cO+fn1udree+899OrVC2PGjHFzy4iIiIhcz+N65iorKzF+/Hj9gobk5GQkJSVh+fLlAICpU6ciJycHY8eORUBAACIiIrBp0yaW3iIiIqJmSSGYbdYuJSUlCA8PR3FxscPDrGt2XZS4VcDc4e2h8mFAS0RE5E2kiCv46d8EtIkOYiBHRETUTDECaAJiwwPd3QQiIiJyEwZzLpJbUoVTWUWynLt1JIM5IiKi5srjFkA0RbUaLS7dKMOFnFLkllZLfv4WYQGSn5OIiIi8A3vmXOB6SRVOZhahqrYOF3JKHT5P34RI6y8iIiKiZoXBnAtIlc+3D4M5IiIiaoDBnBdR+Sjx8LD27m4GEREReRDOmXMBZ0ttzR7aDsqbJ/FVMf4mIiKiWxgZuIRz0ZxSoYAfgzgiIiIygRGCh5t1RyIDOSIiIjKLUYILODPMGujHkXAiIiIyj8GcC0i1mpWIiIioIXb7eKj4qCCEB/m6uxlERETk4RjMuYAjw6zje7eSviFERETU5HCY1cvNHtrO3U0gIiIiN2Iw5wIKGWfNBfj6yHZuIiIi8nwM5lxB4lhu1h2J0p6QiIiIvBaDOS/EdCVERESkw2BORht/y3J3E4iIiKiJYzAnoxslVQ4d1yEmxOprlExeR0RERGAwJ7tajdb+KXM2HDCiW0tHmkNERERNDIM5mX2677L9BwnrL4mLCETvtpH2n5uIiIiaFAZzHmZgh2ibXhfsr7L5tURERNR0MZhzgZLKWpteN3d4e5lbQkRERE0Nc1y4wM7T1216ncpHiRah/kwETERERDZjMCeTCzmldr0+LiIQANAqMkiO5hAREVETxWFWmew+Y1tvHBEREZEzGMx5iI4treeWIyIiImqIwZyH6Noq3N1NICIiIi/EYM4JqZlFKK6ocXcziIiIqBljMOeEc9dKUGxj2hEiIiIiOTCYc4INhRqIiIiIZMVgzgmF5TXIKapydzOIiIioGWMw56SL1+3LJ0dEREQkJQZzDtLUaQEA5dUaN7eEiIiImjMGcw7KLKhwdxOIiIiIGMw56tzVEnc3gYiIiKh51WZNTU3F8uXL0bdvX5w+fRrTp0/H+PHjAQArV65EVlYWhBBo2bIlFi5caPFcQnAtKxEREblfswrmcnNz8eSTT2Lo0KHIzc1Fv379kJmZiZKSErz11lvIyckBALRs2RLz5s1DRESE2XMVlNdAFeDropYTERERmeaRw6wFBQWYM2cOFAqFyf2rV69G3759MWTIENx9993IzMy06bwjR47E0KFDAQBarRZBQUEAAD8/P/j7+6OiogIVFRWIj49HSIjlWqm1NxdAAECtRmvhlURERETy8bhgLjU1FdOmTUNoaKjJ/Rs2bMDSpUvx888/48CBAxg8eDAmT54Mrda+gGrVqlV46623AAABAQFYvHgx5s6di4ceeggPPvggVCrbOy1PZRfbdW1DY3rEIrlbjMPHExERUfPmccOsarUaW7duxfr1603uX7lyJWbNmgW1Wg0AmD9/PhYvXowdO3Zg7NixAKDvfTMUFxeHb7/9FgDw2WefITExEZMmTQIA/P7771i7di327NkDAEhKSsLgwYNNnseUgrJq/f9X1mhwxo7FEe1iLPcAEhEREVniccFcbGys2X21tbU4fPgw5s2bp98WERGBhIQEpKSk6IO5/fv3mz3Hp59+ipqaGjzxxBPYtm0bhg8fjuzsbERHR+uHdVu0aIGqKtsrOxgOB1fU1OFERpHNxxIRERE5w+OCOUtyc3Oh0WgQGRlptD0qKkq/eMGSnTt3YsGCBejTpw+++uorZGRk4OjRo7jrrruwceNG/QrWxMREjB492uZ2GU7tu15cZTSfjoiIiEhOXhXMWWJLqpBRo0ahqKjI5L41a9ZI0o7f0wolOQ8RERGRLbwqmFOr1VCpVCgsNA6YCgoKEBcX59K2fP/R21D5+gEAxo0bh+RuMwAAFTW2l/eKCGJqEyIiouZm27Zt2LZtGwCgpqbG6fN5VTDn5+eHAQMG4MSJE/ptRUVFSE9Px5AhQ1zalimPPYfA4PoVt1HBfg6d476BbaVsEhEREXmBcePGYdy4cQCAkpISvP/++06dz+NSk1jz/PPPY926dcjPzwcAvPfee+jVqxfGjBnjtjYVlDsWVZvLo0dERERkK4/rmausrMT48eP1CxqSk5ORlJSE5cuXAwCmTp2KnJwcjB07FgEBAYiIiMCmTZugVLo3Lq2urUNWQYVb20BERETNj8cFc4GBgdi9e7fF18ybN88oPYknKKvSYM/ZG+5uBhERETUzXjfM6rEUQJ3W+opaIiIiIikxmHOTHq3D3d0EIiIiagIYzLnJkM4t3N0EIiIiagIYzEnE3nWptiQ5JiIiIrKGwZybMJYjIiIiKXjcatambnjXGHSODWWOOSIiIpIEe+YkYmtwplIqGMgRERGRZBjMEREREXkxBnMSsbWvjb1yREREJCUGc0RERERejMGci6mU7JkjIiIi6TCYczGOshIREZGUGMxJxNYgLcDXR96GEBERUbPCYM7FWoQFuLsJRERE1IQwmJNItUbr7iYQERFRM8RgTiKaOuvBXLA/C24QERGRtBjMScb6pLl2LYJd0A4iIiJqThjMudBtbSLc3QQiIiJqYhjMuVBIgK+7m0BERERNDIM5IiIiIi/GYE4iTAZMRERE7sBgjoiIiMiLMZiTSFVNncX9vj78VhMREZH0GGFIZNeZ6+5uAhERETVDDOYkUqcV7m4CERERNUMM5lxECAZ7REREJD0Gcy4S6Ofj7iYQERFRE8RgzkXYMUdERERyYDBHRERE5MVU7m5AU/f4yI7Ye/YG2kQFubspRERE1AQxmHOB4V1j3N0EIiIiaqI4zCqjR0d0cHcTiIiIqIljMCcjpZIFW4mIiEheDOaIiIiIvBiDOSIiIiIvxmCOiIiIyIsxmCMiIiLyYs0qNUllZSX+8Ic/ICkpCWfOnMGwYcPw6KOPAgC+//577N27F0IIjBo1CpMmTXJza4mIiIisa1bBnFarxeTJkzF37lyUlZUhPj4ejz76KEpLS/Hqq6/i2LFjEEKgT58+GD16NIKDg93dZCIiIiKLPHKYtaCgAHPmzIFCYTq1x+rVq9G3b18MGTIEd999NzIzM206b3BwMObOnQsASE9PR5cuXQAAv/zyCzp16gSlUgkfHx+0b98ehw8flubNEBEREcnI44K51NRUTJs2DaGhoSb3b9iwAUuXLsXPP/+MAwcOYPDgwZg8eTK0Wq3N1/joo48wb948rFixAgCQl5dndL3Q0FDk5uY690aIiIiIXMDjgjm1Wo2tW7diwIABJvevXLkSs2bNglqtBgDMnz8fx48fx44dO/SvGTp0aKN/06dP1+9/7LHHsH37drzwwgs4c+YM1Go1SktL9ftLS0vRokULh9p/6vA+h45rzrZt2+buJkjCU9+Hs+3y1PclJ296z57SVle2w1Pesy08pa1N5ecj9bmlOp+7f84eF8zFxsbC39/f5L7a2locPnwYvXv31m+LiIhAQkICUlJS9Nv279/f6N+3336L06dP49dffwUA+Pr6IjY2FmlpaRg8eDAuXLgArVaLuro6XLp0yWwwaQ2DOfu5+49AKp76PhjM2c+b3rOntLWpBAtS85S2NpWfD4M50zwumLMkNzcXGo0GkZGRRtujoqKQk5Nj9Xh/f3+88847WLFiBV544QUkJCTgzjvvRGhoKBYvXowFCxZgwYIFeP3115vF4gd3//K5g7e9Z29rryFH2m7vMXK/XorzWXuNs/sdPdaRfVJ9v+3Zbus1bTnWkdc4+7Wl61k61tZ9th5jaru957R0nLXvrS3H2rrP0n57/2vpfJaOtbXN1rZJdU9qMqtZhRBWX9OhQwesW7fO5L4pU6ZgypQpNl+nqqLM5H5NbQ0qy0sRFxGIkpISq+dzpx9//BFJSUnubgZqampc9r2S8z3L8T6kaK+z7XL0eEfabu8xcr3e1vdsy/msvcbZ/ZbaaulYR/ZZOsZUO+w9j6ntprbZeq2G2xx5jbNfN2yr4X5Lx9q6z9ZjDNuh227vOS0dZ+pa9h5ry76G53b2v5bOB8DssQ331dTU2HwOw20A0KNHDwC2xTFmCQ+1du1a0bB51dXVQqVSiS+++MJoe7t27cSrr77qknZlZmYKAPzHf/zHf/zHf/zHf5L9y8zMdDg28aqeOT8/PwwYMAAnTpzQbysqKkJ6ejqGDBnikja0atUKmZmZCA0NNZs6hYiIiMgWQgiUlpaiVatWDp/Dq4I5AHj++efx9NNP46WXXkJ0dDTee+899OrVC2PGjHHJ9ZVKJeLj411yLSIiImr6wsPDnTre44K5yspKjB8/Xr+gITk5GUlJSVi+fDkAYOrUqcjJycHYsWMREBCAiIgIbNq0CUqlV63lICIiIpKEQghnZtwRERERkTt5XM+cN1i9ejXWrFmDwMBARERE4IMPPkCbNm1MvvbNN9/Eli1b4OPjg9zcXHTp0gXvvPMOYmNjXdxq97Hn+5WcnNxo26OPPorZs2fL3Erb2PpedPkLu3btarT94MGD2Lp1K0aOHOnyNgHA1atX8ec//xnZ2dkoKirCgAEDsGrVKpMVVwoKCrBgwQJ88cUXzq2y8kD2vLeHH34YZ8+eRUBAgH7bhAkT8MILL8jdTLOEEPjss8/wxRdfAAAKCwsxcOBArFixAhEREW6/lhx/xxs3bsQHH3wAjUaDwsJCBAYG4s0338TgwYMdPqcrzJgxA4cOHUJaWppLr7to0SL88MMPRj+j/v374+2335b1ukeOHMFf//pXlJWVobS0FDExMVi/fr0+0b8junbt2ugz88yZM/jTn/6ExYsXO3TO/Px8PPvsszh16hSCg4MhhMCbb75p1+r46upqLFq0CLt374ZGo0F4eDjef/99falQUyzde4qKijBv3jxcvHgRtbW1mDRpEhYtWmTb/Hzn13c2L999952IjY0Vubm5QgghFi9eLPr27Svq6upMvl6tVou0tDQhhBA1NTVi6NCh4t5773VZe93N3u/XiBEjXNg6+9jzXtLT08WTTz5ptO38+fOibdu2QqPRuKVNGo1G9O3bVzz77LNCCCFqa2vF2LFjxX333dfotSdPnhTJycli3rx5oqndJux9bw899JC4cuWK/A2zQ2lpqQgODhbnzp0TQghRUVEhkpKSTP4s3XEtOf6Ox40bJ7777jv91y+99JKIjo42ey/xBNu3bxcREREiISHB5dd+9dVXxa5du1x6zdOnT4t27dqJM2fOCCGEqKurEzNmzBAZGRlOnXfChAlGX9fV1YmEhAT976QjZs6cKe644w5RU1MjhBDi3XffFZGRkaKkpMTmczzzzDMiKSlJVFVVCSGEWLRokUhISBDl5eUmX2/t3jNx4kTx2GOPCSGEKC8vFz169BD/93//Z1NbmtZd2gWSkpLE888/r/+6sLBQKJVK8dNPP5l8/aFDh4y+fu6550Tv3r3lbKJHsff75cnBnL3vpaEXX3xRLF682G1tOnDggAAgLl68qN/2/fffC4VC0ehme+3aNVFVVWUyRZC3s/e9eWIwV1VVJd566y2jbe+8844IDAwUWq3W7deS4+/4yJEjRoHbjz/+KACIwsJCya8lhZqaGjFgwADx+uuvN5tg7v777xcLFy6U/TqbN28WI0eOdOocPXr0EC+++KL+61OnTgkA4vDhwzYdX1dXJwIDA8VHH32k31ZYWCgAiHXr1pk8xtK959y5cwKASE1N1W9btWqVaN26tU1/01w1YAdby4kZGjhwoP7/L1++jB9++AFPP/207G31BI58vwDgmWeewfDhw5GcnIw33ngDtbW1rmiuRY6+F8Pj161bh0cffdRtbcrMzAQAo7rDcXFxEELgyJEjRq+1VFbP2zny3l5//XWMGDECw4YNw8KFC1FeXi5T62zj7++P559/3mhbRUUFYmJiJE+Z5Oi1pP477t+/v36hW0lJCdasWYPZs2dLPqwslb///e+YPn26U+kmnPXxxx8jOTkZd9xxB55++mnk5eXJdi0hBLZu3YqQkBDMmDEDd9xxByZPnozff/9d8mutWbMGf/zjH506x9SpU7F161YUFxcDAL7++mu0aNHC4hCpoby8PFRWVhrdTyMiIhAQEKAvG9qQpXtPSkoKfH190a1bN/22Pn36IDs7G+np6Vbbw2DODo6WE8vNzcXtt9+OXr16Ye7cuXjsscfkbqpHcOT71b9/f/zhD3/A3r178a9//QvffPMN/vSnP7miuRY5W0pu48aNuP3229G6dWu3tSkhIQEAkJGRod+WnZ0NoH4eB5nWrVs33HPPPdi9eze2bNmCo0ePYvLkye5ulhEhBP7973/jxRdf9Ihryfl3PGPGDLRu3RoBAQFYvXq1JOeUWnZ2Nv79739jwYIFbmtDYmIihg8fjh07dmD37t2oqKjA8OHDUVNTI8v18vLyUFJSgrfffhuLFi1CSkoKpkyZgqFDh+ofJKWQk5ODX375xaaKTZYsWbIEo0ePRkJCAjp16oSNGzdiz549JucPm9KiRQsEBgYa3U8LCgpQVVXl0P302rVrCA8PN8rMERUVBQA2fcYwmJOIsDCJukWLFjhy5AjS0tLw888/46GHHnJhyzyTue/X22+/rU8AHRMTg7/97W/45JNPZH2idJaln72OFE+S9jDVpoEDB6J///5YtmwZNBoNysrK8O677wJAk+2Fk8JLL72EyZMnQ6FQICQkBMuXL8eOHTvw22+/ubtpeqtWrULbtm3x5JNPesS15Pw7/uabb1BQUIBWrVph0KBBbu8lNeW5557D8uXL4evr67Y2PPzww3j88cfh4+MDX19frFy5EmfOnNGXkJJadXU1AGDixIn63qWHH34YUVFRWLt2rWTXWbt2LWbNmgU/Pz+nzrNgwQLs378fly9fxoULF/DCCy/gwQcfRGFhoU3HKxQK/OlPf8Lq1auRl5cHIQSWLFkCX19fye+ntnzGMJizg1qthkqlavTDLigoQFxcnE3H/+///i8+//xzXL58Wa5megxnv18A0K5dOwDAlStXJG+fPZx5L2lpaTh//jzuuusut7ZJoVDoh0GGDRuGqVOn6ntLOnToIGnbmjLd76Sn/A1/++23+Pnnn/Hll1/KXpXG0WtJ/Xfs6+uLN954A+fPn8c333wjyTmlsmPHDmi1WowePdrdTTESFRWF8PBw2X5vIyMjoVAoGt174uPjbRomtIUQAp988gmeeOIJp85TUVGBd999F/Pnz9f3fs2aNQslJSV47733bD7Pm2++iQcffBCTJ09GcnIy2rdvjy5dujh0P42Li0NxcTG0Wq1+m66Hz5bPSwZzdrC3nNj58+fx3XffGW0LDg4GAJSVlcnbWA9g7/frxo0beOONN4y2Xbt2DQDMptpwFWdKyX388cd49NFHJU9s7Uib1Go1PvzwQxw8eBA//fQTWrZsiRYtWmDAgAGStq0pee6554y+9pTfSQD417/+hU8//RTfffcdAgICcOHCBX0PibuuJdffsS5xvI6vry/8/Pw87l66detWpKWlITk5GcnJyVixYgVycnKQnJzc6HdJTg2vVV5ejpKSEtl+b4ODg9G9e/dGQ4K5ubmSXXPnzp1ISEhAx44dnTqPRqOBVqtt1HPq5+dn1xCpSqXCX//6V6SkpGDPnj145JFHcOXKFdx99912t+mOO+5AbW0tzp49q992/PhxtG7dWj9FxiKblm2Q3nfffSfi4uJEXl6eEEKIpUuXij59+oi6ujpx/fp1MWTIELF7924hhBC7du0SPXr0EMXFxUKI+lQQc+bMER07dtQvh27q7Pl+XblyRajVan2x4crKSjFmzBgxadIkt7XfkD3vRUej0YiEhARx9epVj2jT3XffLa5duyaEqE85MXz4cLFmzRqz52+Kq1l1TL23qqoqMWbMGLF+/Xr9Nn9/f3Hs2DEhRP0Ktjlz5oh+/fpJmmLGEV9//bUYPny4uHHjhigtLRWlpaVi5syZsqy8tXQtV/0dBwYGipMnT+q//vDDD4W/v78+DYanWrt2rVtWsyYmJootW7bov37llVdEfHy8Xak37PXRRx+JqKgokZWVJYQQYtu2bSIoKEiy38kZM2aIf/3rX5Kca/jw4WL8+PH6z+I9e/YIpVIptm7davM5/vrXv4rNmzfrv37uuefEzJkzrR5n7r46ceJE8cQTTwgh6lMA9ezZ0+bUJEwabCdL5cQqKipw9uxZ/byQXr16YcKECRgzZgyCgoJQWlqKDh06YMuWLW6dS+FK9ny/YmNj8T//8z+47777EBAQgLKyMgwaNAivv/66m99FPXvei87mzZvRt29fm4eV5W5Tu3btMHLkSKjVatTV1eHxxx/H3LlzG53XWlk9b2bpvWk0Gpw5c0bfkwQAb7zxBubNmwc/Pz+Ul5ejc+fO+PHHH+Hj4+Out4CcnBzMnDkTdXV1iImJMdr32muvufRarvo7Xr58OZ544gn4+/ujuroaKpUK//3vfxsl5vYk99xzDy5evKjvmXv66adx3333ueTar732GlasWIEVK1agqqoKMTEx2L59u80T/B3x6KOPorCwEHfeeSeioqKgUqmwfft2JCYmOn3uvLw8HDhwQJ+82lnr16/HSy+9hMGDByMoKAgVFRX45JNPMG7cOJvPkZiYiOeeew7Lli1DTU0NRo4ciY8//tjs663dVz///HM8+eSTGDRoEGprazF16lSbF9GwnBcRERGRF+OcOSIiIiIvxmCOiIiIyIsxmCMiIiLyYgzmiIiIiLwYgzkiIiIiL8ZgjoiIiMiLMZgjIiIi8mIM5oio2cnPz0efPn0QEhKChx9+2N3NaaSoqEiS9mm1Wrz++usuK3l18eJFfPTRRy65FhHdwmCOiCQ1cuRIBAcHIzAwEH369MHp06f1+1avXg2lUokbN27ot23atAk9e/ZESEiIXdnXnREdHY1jx47h9ttvd8n1LCkqKsKiRYuQlpam3xYREeF0+4QQmDVrFm7cuIGQkBAJWmpdYmIi/vvf/+KVV15xyfWIqB6DOSKS1K5duzBjxgz4+Pjg8OHD6N69u37f9u3bIYTAjh079NsmTZqE999/HzNnzsS2bdvc0WS3KioqwuLFi42COSl89NFHOHnyJP7xj39Iel5LVCoV1q1bhw8//BA7d+502XWJmjsGc0QkuTFjxqC8vBy//PKLfptWq8XZs2cRFRWF7du3G71+x44dGDNmjKub2aS99dZbePbZZ6FQKFx63eDgYDz++ONYuXKlS69L1JwxmCMiyY0ePRoAjHrgjhw5gv79+2PkyJFG24H63rxRo0ahqqoKL774Ivr06YN+/fqhb9++eO2116DRaAAA33zzDVq1agWFQoHevXvjwIEDAIBnn30WsbGxaNOmjb5378KFC5g4cSLatWuHdu3aYdq0acjIyLDadmvHDRkyBFFRUUhOTsbatWtxxx13ID4+Ho888giqq6uNznXgwAH0798f8fHxGDRoEL788kskJiYiNjYW9913H7Zt24a7774bAPDYY4+hT58++q8NvfHGGxg0aBDatGmDF198EdZKap86dQoXLlxAcnKyftumTZvQvXt3KBQK7N69G0B9T6mpbX369IFCocD777+PRx55BD169EDXrl2xc+dOlJaWYs6cOejatSv69++PkydPNrq+7mdcXFxs9ftNRBIQREQy6Nmzp7jjjjv0X7/++uti7dq1YvXq1QKAOH/+vBBCiJKSEjFo0CAhhBDXrl0TLVu2FFlZWfp9w4YNE2+88Yb+PDt37hQAxO7du42uN3r0aPH7778LIYTIyckRLVu2FA8//LDQarWirq5OPPTQQ6Jjx46ioqJCf8yIESPEQw89pP/a1uMeeugh0aJFC/H+++/r2x0VFSX++c9/6l9z7do1ERISIhYsWKA/19y5c4W/v7949dVX9a+7cuWKACB27drV6Hs4YsQIERsbK3788UchhBCnTp0SPj4+YsuWLRa/9++//75QqVSirq7OaLupa5m7PgDRq1cvkZ6eLoQQ4uWXXxbR0dFi0aJForCwUAghxIwZM0RSUlKj66elpQkAVttJRNJgzxwRyWL06NH49ddf9SspdUOpuuFU3VDrnj17MGLECACAWq1GSkoKWrduDQAIDQ3Fgw8+iE8++UR/3uTkZMTHx2PdunX6bVevXkVubi769OkDAPj73/+OvLw8vPHGG1AoFFAqlVi2bBkuXryIr776ymyb7TlOq9Xij3/8IwAgNjYWSUlJ2LNnj37/P/7xD9TW1mLRokX6c7322muNeu+sadmyJe655x4AQPfu3dG1a1ej65iSk5ODyMhIKJXO3eJHjx6Ntm3bAgCmTZuG/Px8REZGIiIiAgBw33334eDBg43ek1qt1reDiOTHYI6IZDFmzBjU1tZi7969qKysRH5+PuLj49GpUye0bdtWH8xt375dH+CpVCqkpqZizJgx6NGjB/r06YNly5bh8uXL+vMqFArMnDkT3377LaqqqgAAX331FR588EH9a3bt2oWOHTsiJiZGv61Vq1aIiIjA3r17zbbZnuPat28PHx8f/ddRUVFGq3QPHTqEDh06IDw8vNG57NG5c2ejrxtex5Tc3FwEBATYdR1TOnbsaHTdhtuio6MBoFF7dNe21k4ikgaDOSKSxYgRI6BSqbB9+3bs27cPQ4cO1e8bM2YMdu3aBa1Wi5SUFP2+H3/8Effeey+mTJmC1NRUHDt2DEuWLEFtba3RuWfNmoXi4mL8+OOPAID169cbBXP5+fnIyspCnz59jP4FBQVZ7Bmz57igoCCjr5VKJerq6vRfX7t2zWTgZhjc2cLadUxRqVRW59XZe23dQgpT2xq2R3dtlUrldBuIyDr+pRGRLEJDQzFw4EDs2LEDKpVKvygCqB++++STT7B582aEh4cjMDAQQH0PW1hYGJ566imL5+7Zsyd69+6NdevWoUePHggPD0ebNm30+9VqNcLCwnD06FG72uzocabExcXh+vXrjba7YlFATEwMKisrze43DPTKy8slv35FRYW+HUQkP/bMEZFsxowZg5MnT+L777/HyJEj9dtHjx4NhUKBV155xSjIq6mpaZRKw1RABACzZ8/Gli1b8I9//AOzZs0y2jd69GhcvHixUUCzevVqfPPNN2bb6+hxpgwaNAiXL182Ct6uXr2KoqIio9f5+voCuBVg7du3DwUFBXZdq6H4+HgUFhY26tHUKSws1P//uXPnnLqWKbrh1fj4eMnPTUSNMZgjItmMHj0aQghERUUZDTm2bNkSPXv2xIkTJ4zyy40fPx7FxcX49NNPAQB5eXlGix8MPfjgg6irq8O6detw3333Ge179tlnERYWhpdffhlarRYAcPjwYSxbtsxiVQVHjzNlwYIF8PX1xaJFiwDUL5hYsmRJo2oMMTExCAgIQFZWFurq6jB79myne+/Gjh0LIQTOnz9vcv/3338PAKisrMSXX37p1LVMOX36NEJDQ5GUlCT5uYnIBHcupSWipq2mpkYEBweLhQsXNtr37LPPisjISKP0GVqtVvzv//6viI+PF7179xYTJkwQ8+fPFwBE7969xeHDh43OMXbsWDF9+nST1758+bKYMmWKiI+PF/369RNjxowRhw4dEkIIkZeXJ3r37i2Cg4NFZGSkGDp0qE3HCSHE+PHjRWRkpAgODhYDBw4UQggxbdo0/bb+/fvrX7t//37Rt29f0bp1azFkyBCxadMmkZCQIBYtWmTU1lWrVomEhATRo0cP8Ze//EUIIYzad++99wohhEhKStJvmzhxosXv/cCBA8XKlSuNtunSkHTs2FG0b99ejBw5Umzbtk0AEB06dBBvvvmmOHz4sOjdu7cAINq0aSNee+01sXXrVtGtWzf96z755BPxySefiA4dOggAolu3bmLjxo366/zpT38SDz74oMX2EZF0FEJIMEuWiIhsEh4ejhUrVuDJJ5+U9Tpbt27F448/jvPnz+vnJKalpaFdu3bYtWuXUUJhKV27dg3dunXDgQMHjEq5EZF8OMxKRCSDgoIC/PnPfzbalpWVhZKSEnTr1k326991112YN28epk6danX1q1TKysowefJkvP/++wzkiFyIPXNERDLIyclBu3btsG/fPtx+++3QaDR4+OGHcfHiRaOatXLbsmULkpOTERgYKHvPXHZ2NrKysjBo0CDJz01E5jE1CRGRDMLDw/Hoo49i9uzZ8PX1RVFREYYMGaJffOAq48ePd9m1Wrdura/eQUSuw545IiIiIi/GOXNEREREXozBHBEREZEXYzBHRERE5MUYzBERERF5MQZzRERERF6MwRwRERGRF2MwR0REROTF/j913V4etUO+OwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from Cthulhu.misc import read_cross_section_file, cross_section_collection\n",
    "from Cthulhu.plot import plot_cross_section\n",
    "\n",
    "'''Read in the cross section we just computed'''\n",
    "\n",
    "# Read in cross section\n",
    "nu, sigma = read_cross_section_file(species = species, database = database, ionization_state = 1,\n",
    "                                    filename = species + '_T' + str(T[0]) + \n",
    "                                    'K_log_P' + str(round(np.log10(P[0]), 1)) + '_' + broadening + '_sigma.txt')\n",
    "\n",
    "cross_sections = []\n",
    "\n",
    "# Add first cross section to collection\n",
    "cross_sections = cross_section_collection(new_x = nu, new_y = sigma, collection = cross_sections)\n",
    "\n",
    "plot_cross_section(collection = cross_sections, labels = [species + ' (1000 K, 1 bar)'], \n",
    "                    filename = species, y_min = 1e-30, x_max = 10, save_fig = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e15a3da4",
   "metadata": {},
   "source": [
    "Looking good! Now let's iterate over pressures and temperatures to generate a full AlO cross section."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c7b9317",
   "metadata": {},
   "source": [
    "#### Full cross section calculation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7daa485e",
   "metadata": {},
   "source": [
    "We'll calculate the AlO cross section for the 162 pressure-temperature points used by the [POSEIDON](https://poseidon-retrievals.readthedocs.io/en/latest/) atmospheric retrieval code.\n",
    "\n",
    "The code below will take about an hour to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "31a5ffce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning cross-section computations...\n",
      "Loading ExoMol format\n",
      "Loading partition function\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.10546971799885796 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-06 bar, T = 100.0 K   [1 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 4.62069116100065 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.060283888000412844 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-06 bar, T = 200.0 K   [2 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 6.215454395000052 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05836472700138984 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-06 bar, T = 300.0 K   [3 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 7.4238048380011605 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.06429532099900825 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-06 bar, T = 400.0 K   [4 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 8.721320273001766 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.06346779900013644 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-06 bar, T = 500.0 K   [5 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 9.788798118999694 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.058995084998969105 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-06 bar, T = 600.0 K   [6 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 10.81321288899926 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.06205776299975696 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-06 bar, T = 700.0 K   [7 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 11.53036968500055 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.06566759800080035 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-06 bar, T = 800.0 K   [8 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 12.17591482699936 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.0640154810007516 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-06 bar, T = 900.0 K   [9 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 12.816160881000542 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.062270536000141874 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-06 bar, T = 1000.0 K   [10 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 13.268536744000812 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.062362770000618184 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-06 bar, T = 1200.0 K   [11 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 14.538872426001035 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.06938989899936132 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-06 bar, T = 1400.0 K   [12 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 15.655640807999589 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.062458004998916294 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-06 bar, T = 1600.0 K   [13 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 15.467001275999792 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.061746532999677584 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-06 bar, T = 1800.0 K   [14 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 15.800431188999937 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.06463787400025467 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-06 bar, T = 2000.0 K   [15 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 15.909217300000819 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.07239191699954972 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-06 bar, T = 2500.0 K   [16 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.497568828999647 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05805577799947059 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-06 bar, T = 3000.0 K   [17 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.084274851000373 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05812076300026092 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-06 bar, T = 3500.0 K   [18 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.694384349999382 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05757137699947634 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-05 bar, T = 100.0 K   [19 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 4.779953210998428 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.06403553299969644 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-05 bar, T = 200.0 K   [20 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 6.901809734999915 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.057692918000611826 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-05 bar, T = 300.0 K   [21 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 8.35688584899981 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05764136300058453 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-05 bar, T = 400.0 K   [22 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 9.464681937999558 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05763719499918807 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-05 bar, T = 500.0 K   [23 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 10.394521116999385 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.06389544200101227 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-05 bar, T = 600.0 K   [24 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 11.346709940999062 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05820776999826194 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-05 bar, T = 700.0 K   [25 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 11.82462625900007 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.06302041199887753 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-05 bar, T = 800.0 K   [26 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 11.92776576399956 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.057347294999999576 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-05 bar, T = 900.0 K   [27 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 12.562901769999371 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.07471023899961438 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-05 bar, T = 1000.0 K   [28 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 13.718352104000587 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.0629513420008152 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-05 bar, T = 1200.0 K   [29 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 14.471926796999469 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.06066127699887147 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-05 bar, T = 1400.0 K   [30 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 15.11918214400066 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.07139782500053116 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-05 bar, T = 1600.0 K   [31 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 17.41090236799937 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.06924844200148073 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-05 bar, T = 1800.0 K   [32 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.52717829699941 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.06235276599909412 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-05 bar, T = 2000.0 K   [33 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.246374966000076 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.06194135599980655 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-05 bar, T = 2500.0 K   [34 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.08461858299961 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.061349442999926396 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-05 bar, T = 3000.0 K   [35 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.200703463000536 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.06810821999897598 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1e-05 bar, T = 3500.0 K   [36 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.894236310999986 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05858913500014751 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.0001 bar, T = 100.0 K   [37 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 4.692225535000034 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.058775281000635005 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.0001 bar, T = 200.0 K   [38 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 6.40818345200023 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.058661353001298266 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.0001 bar, T = 300.0 K   [39 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 7.978126283000165 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.06563105100030953 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.0001 bar, T = 400.0 K   [40 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 8.658458298999903 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.059300385999449645 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.0001 bar, T = 500.0 K   [41 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 9.559867813999517 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.06171466500018141 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.0001 bar, T = 600.0 K   [42 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 10.558665742999437 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.07727862700085097 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.0001 bar, T = 700.0 K   [43 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 11.038289685000564 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.06907779599896458 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.0001 bar, T = 800.0 K   [44 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 11.853952135999862 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.06064921100005449 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.0001 bar, T = 900.0 K   [45 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 12.525188653999066 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05748046799999429 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.0001 bar, T = 1000.0 K   [46 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 12.944178352998279 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.058865061999313184 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.0001 bar, T = 1200.0 K   [47 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 14.1271740709999 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.06721223699969414 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.0001 bar, T = 1400.0 K   [48 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 14.975538669999878 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.0610431580007571 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.0001 bar, T = 1600.0 K   [49 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 15.557463359000394 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.058680804999312386 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.0001 bar, T = 1800.0 K   [50 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 15.707025387999238 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05950155100072152 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.0001 bar, T = 2000.0 K   [51 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 15.773835764999603 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.0662577209986921 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.0001 bar, T = 2500.0 K   [52 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 15.933989568999095 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05770615499932319 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.0001 bar, T = 3000.0 K   [53 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.350456430000122 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.057365562999621034 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.0001 bar, T = 3500.0 K   [54 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.504494570999668 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05178634600088117 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.001 bar, T = 100.0 K   [55 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 4.4603823390007165 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.06003466199945251 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.001 bar, T = 200.0 K   [56 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 6.116246013998534 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05498431900014111 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.001 bar, T = 300.0 K   [57 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 7.438589974999559 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.053402215000460274 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.001 bar, T = 400.0 K   [58 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 8.471458301999519 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05748031999974046 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.001 bar, T = 500.0 K   [59 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 9.32372347799901 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.060748210999008734 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.001 bar, T = 600.0 K   [60 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 10.193002051999429 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.055882820999613614 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.001 bar, T = 700.0 K   [61 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 11.684606929000438 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.06034544000067399 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.001 bar, T = 800.0 K   [62 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 12.08515334599906 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05447881300096924 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.001 bar, T = 900.0 K   [63 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 12.766471876999276 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.06337123199955386 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.001 bar, T = 1000.0 K   [64 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 13.43204164800045 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.056814998999470845 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.001 bar, T = 1200.0 K   [65 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 14.506861773999844 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05571075700026995 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.001 bar, T = 1400.0 K   [66 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 15.421213055000408 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05617146399890771 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.001 bar, T = 1600.0 K   [67 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 15.913143196999954 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.062863376999303 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.001 bar, T = 1800.0 K   [68 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.0580023170005 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.06123499999921478 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.001 bar, T = 2000.0 K   [69 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 15.78680959600024 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.06009700899994641 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.001 bar, T = 2500.0 K   [70 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.0885807930008 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.06077498699960415 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.001 bar, T = 3000.0 K   [71 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 15.975885116999052 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.06711022200033767 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.001 bar, T = 3500.0 K   [72 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 15.866799971001456 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.0489488330003951 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.01 bar, T = 100.0 K   [73 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 4.757207597000161 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.049794849001045804 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.01 bar, T = 200.0 K   [74 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 6.303391059000205 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05087563599954592 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.01 bar, T = 300.0 K   [75 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 7.536450887999308 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05787738299841294 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.01 bar, T = 400.0 K   [76 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 8.723042607000025 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05155102699973213 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.01 bar, T = 500.0 K   [77 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 9.451624115999948 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05263053199996648 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.01 bar, T = 600.0 K   [78 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 10.386030640998797 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05303990500033251 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.01 bar, T = 700.0 K   [79 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 10.96656744000029 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05997106100039673 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.01 bar, T = 800.0 K   [80 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 11.749899973001448 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05360926800130983 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.01 bar, T = 900.0 K   [81 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 12.437952697999208 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05333066699859046 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.01 bar, T = 1000.0 K   [82 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 13.06634925599974 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.053262565001205076 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.01 bar, T = 1200.0 K   [83 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 14.780734766000023 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.06133834800129989 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.01 bar, T = 1400.0 K   [84 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 15.521324280000044 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05338456000026781 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.01 bar, T = 1600.0 K   [85 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 15.919360277999658 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.059807670000736834 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.01 bar, T = 1800.0 K   [86 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.13489109299917 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.051426089001324726 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.01 bar, T = 2000.0 K   [87 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.208700795999903 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.06235055799879774 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.01 bar, T = 2500.0 K   [88 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 17.384556335000525 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05543997099994158 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.01 bar, T = 3000.0 K   [89 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.084435004000625 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05622042200047872 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.01 bar, T = 3500.0 K   [90 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.21406191599999 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04582002399911289 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.1 bar, T = 100.0 K   [91 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 9.819616508999388 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05275436800002353 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.1 bar, T = 200.0 K   [92 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 9.84709230199951 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04298924800059467 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.1 bar, T = 300.0 K   [93 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 10.222934935000012 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04455935300029523 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.1 bar, T = 400.0 K   [94 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 10.73287095699925 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.045357214999967255 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.1 bar, T = 500.0 K   [95 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 11.413441737999165 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05365506199996162 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.1 bar, T = 600.0 K   [96 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 12.265885136001089 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04756460600037826 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.1 bar, T = 700.0 K   [97 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 13.07073470999967 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04878524100058712 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.1 bar, T = 800.0 K   [98 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 14.000911336999707 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.045043350000923965 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.1 bar, T = 900.0 K   [99 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 14.021250534000501 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05278487400028098 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.1 bar, T = 1000.0 K   [100 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 15.047814039999139 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04552144900117128 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.1 bar, T = 1200.0 K   [101 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 15.749981185999786 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04588643200077058 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.1 bar, T = 1400.0 K   [102 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 15.581952427999568 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04605998199986061 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.1 bar, T = 1600.0 K   [103 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 15.776169975000812 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.053022477999547846 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.1 bar, T = 1800.0 K   [104 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.840266415998485 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04713797399926989 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.1 bar, T = 2000.0 K   [105 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.73868341999878 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04755043400109571 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.1 bar, T = 2500.0 K   [106 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.691129922000982 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04877234800005681 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.1 bar, T = 3000.0 K   [107 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.10913158400035 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05502600399995572 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 0.1 bar, T = 3500.0 K   [108 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 17.152950853998846 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04006979499899899 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1.0 bar, T = 100.0 K   [109 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.339697148001505 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.0409116399987397 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1.0 bar, T = 200.0 K   [110 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.417263453999112 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04089017300066189 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1.0 bar, T = 300.0 K   [111 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 17.40331688200058 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04825721000088379 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1.0 bar, T = 400.0 K   [112 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.865802681000787 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04130430599980173 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1.0 bar, T = 500.0 K   [113 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.90055703500002 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04256288999931712 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1.0 bar, T = 600.0 K   [114 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 17.42568979399948 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.042196774998956244 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1.0 bar, T = 700.0 K   [115 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 18.28409458999886 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05299391900007322 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1.0 bar, T = 800.0 K   [116 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 18.726948473999073 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04506582400063053 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1.0 bar, T = 900.0 K   [117 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 17.23070753600041 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04554977099905955 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1.0 bar, T = 1000.0 K   [118 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.629345795001427 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.046343342000909615 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1.0 bar, T = 1200.0 K   [119 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 18.083987022000656 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.06603379800071707 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1.0 bar, T = 1400.0 K   [120 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 17.080834921998758 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.046493599000314134 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1.0 bar, T = 1600.0 K   [121 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 17.41425520499979 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.047198557998854085 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1.0 bar, T = 1800.0 K   [122 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 17.08152515800066 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04296557199995732 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1.0 bar, T = 2000.0 K   [123 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 17.135871464000957 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05036492600083875 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1.0 bar, T = 2500.0 K   [124 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.65485159000127 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04365962500014575 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1.0 bar, T = 3000.0 K   [125 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 17.91298482899947 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04409900400059996 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 1.0 bar, T = 3500.0 K   [126 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 17.77858315100093 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04077887399944302 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 10.0 bar, T = 100.0 K   [127 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 17.37452030500026 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.048618291000821046 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 10.0 bar, T = 200.0 K   [128 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.849063138999554 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04033354499915731 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 10.0 bar, T = 300.0 K   [129 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.388469067000187 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04074289699929068 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 10.0 bar, T = 400.0 K   [130 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.411722419999933 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04157333299917809 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 10.0 bar, T = 500.0 K   [131 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.72449662500003 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04770589799954905 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 10.0 bar, T = 600.0 K   [132 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.63101890500002 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04125794899846369 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 10.0 bar, T = 700.0 K   [133 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.814465282999663 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.045934870999190025 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 10.0 bar, T = 800.0 K   [134 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 18.581262815001537 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04752360000020417 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 10.0 bar, T = 900.0 K   [135 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 18.382054599000185 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.048239425999781815 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 10.0 bar, T = 1000.0 K   [136 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.349018970000543 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04747526699975424 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 10.0 bar, T = 1200.0 K   [137 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.235597185999723 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04553105600098206 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 10.0 bar, T = 1400.0 K   [138 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.96454129599988 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.041908843000783236 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 10.0 bar, T = 1600.0 K   [139 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 19.683885205999104 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05313303100047051 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 10.0 bar, T = 1800.0 K   [140 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 18.15163825000127 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.0423780720011564 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 10.0 bar, T = 2000.0 K   [141 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 18.077768215000106 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.042453739000848145 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 10.0 bar, T = 2500.0 K   [142 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 17.904592849999972 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.042758290999699966 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 10.0 bar, T = 3000.0 K   [143 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 17.127518571998735 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04959292099920276 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 10.0 bar, T = 3500.0 K   [144 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 17.600959125000372 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04967358099929697 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 100.0 bar, T = 100.0 K   [145 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 16.918726161999075 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.041838122000626754 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 100.0 bar, T = 200.0 K   [146 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 17.460939992999556 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04261297499942884 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 100.0 bar, T = 300.0 K   [147 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 17.819036105000123 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.049077188999945065 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 100.0 bar, T = 400.0 K   [148 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 17.59993993900025 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.042013463000330376 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 100.0 bar, T = 500.0 K   [149 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 17.08782877700105 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.051814718999594334 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 100.0 bar, T = 600.0 K   [150 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 19.12059810500068 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04562003399951209 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 100.0 bar, T = 700.0 K   [151 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 18.806204050000815 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05406782299905899 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 100.0 bar, T = 800.0 K   [152 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 17.972807137999553 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04572573000041302 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 100.0 bar, T = 900.0 K   [153 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 17.50179054200089 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04764062100002775 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 100.0 bar, T = 1000.0 K   [154 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 17.669534290998854 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.046220585998526076 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 100.0 bar, T = 1200.0 K   [155 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 18.75191448499936 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05482008500075608 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 100.0 bar, T = 1400.0 K   [156 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 19.430974564998905 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04721740300010424 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 100.0 bar, T = 1600.0 K   [157 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 19.279682471000342 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04290186599973822 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 100.0 bar, T = 1800.0 K   [158 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 18.39183143899936 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.0443226040006266 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 100.0 bar, T = 2000.0 K   [159 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 19.6246190260008 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.05405659999996715 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 100.0 bar, T = 2500.0 K   [160 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 19.728187832999538 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04927537699950335 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 100.0 bar, T = 3000.0 K   [161 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 20.598830375000034 s\n",
      "\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.04957236599875614 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for AlO at P = 100.0 bar, T = 3500.0 K   [162 of 162]\n",
      "Calculation complete!\n",
      "Completed 4943804 transitions in 20.02548005599965 s\n",
      "\n",
      "Total runtime: 2735.3723237640006 s\n"
     ]
    }
   ],
   "source": [
    "from Cthulhu.core import compute_cross_section\n",
    "\n",
    "P = [1.0e-6, 1.0e-5, 1.0e-4, 1.0e-3, 1.0e-2, 1.0e-1, 1.0e0, 1.0e1, 1.0e2]    # Pressure (bar)\n",
    "log_P = [-6.0, -5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0]                  # log10 pressure\n",
    "T = [100.0, 200.0, 300.0, 400.0, 500.0, 600.0, 700.0, 800.0,                 # Temperature (K)\n",
    "     900.0, 1000.0, 1200.0, 1400.0, 1600.0, 1800.0, 2000.0,\n",
    "     2500.0, 3000.0, 3500.0]\n",
    "\n",
    "compute_cross_section(database = database, species = species, linelist = linelist,\n",
    "                      pressure = P, temperature = T, S_cut = 0.0,\n",
    "                      input_dir = input_directory,\n",
    "                      nu_out_min = nu_min, nu_out_max = nu_max, dnu_out = 0.01,\n",
    "                      broad_type = broadening,\n",
    "                      verbose = False, N_cores = 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1deaba19",
   "metadata": {},
   "source": [
    "## Plot Computed Cross Section"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25e5446a",
   "metadata": {},
   "source": [
    "We'll now plot two temperatures and pressures to show the T and P dependences of AlO's cross section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ad7ebea3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from Cthulhu.misc import read_cross_section_file, cross_section_collection\n",
    "from Cthulhu.plot import plot_cross_section\n",
    "\n",
    "# Read in cross section at 1000 K\n",
    "nu, sigma = read_cross_section_file(species = species, database = database, \n",
    "                                    filename = species + '_T1000.0K_log_P0.0_' + broadening + '_sigma.txt')\n",
    "\n",
    "# Read in cross section at 2000 K\n",
    "nu2, sigma2 = read_cross_section_file(species = species, database = database,\n",
    "                                        filename = species + '_T2000.0K_log_P0.0_' + broadening + '_sigma.txt')\n",
    "\n",
    "# Add both cross sections to collection in preparation for plotting\n",
    "\n",
    "cross_sections = []\n",
    "\n",
    "# Add first cross section to collection\n",
    "cross_sections = cross_section_collection(new_x = nu, new_y = sigma, collection = cross_sections)\n",
    "\n",
    "# Add second cross section to collection, making sure to specify the previous collection as a parameter\n",
    "cross_sections = cross_section_collection(new_x = nu2, new_y = sigma2, collection = cross_sections)\n",
    "\n",
    "plot_cross_section(collection = cross_sections, labels = [species + ' (1000 K, 1 bar)', species + ' (2000 K, 1 bar)'], \n",
    "                    filename = species + '_Temperature', y_min = 1.0e-30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1c771041",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read in cross section at 1 mbar\n",
    "nu, sigma = read_cross_section_file(species = species, database = database, \n",
    "                                    filename = species + '_T1000.0K_log_P-3.0_' + broadening + '_sigma.txt')\n",
    "\n",
    "# Read in cross section at 10 bar\n",
    "nu2, sigma2 = read_cross_section_file(species = species, database = database,\n",
    "                                        filename = species + '_T1000.0K_log_P1.0_' + broadening + '_sigma.txt')\n",
    "\n",
    "cross_sections = []\n",
    "\n",
    "# Add first cross section to collection\n",
    "cross_sections = cross_section_collection(new_x = nu, new_y = sigma, collection = cross_sections)\n",
    "\n",
    "# Add second cross section to collection, making sure to specify the previous collection as a parameter\n",
    "cross_sections = cross_section_collection(new_x = nu2, new_y = sigma2, collection = cross_sections)\n",
    "\n",
    "plot_cross_section(collection = cross_sections, labels = [species + ' (1000 K, 1 mbar)', species + ' (1000 K, 10 bar)'], \n",
    "                    filename = species + '_Pressure', y_min = 1.0e-30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7b167a1",
   "metadata": {},
   "source": [
    "## Create an Opacity Database"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5297afdd",
   "metadata": {},
   "source": [
    "### Package the cross section grid as a HDF5 file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e9b5ab6",
   "metadata": {},
   "source": [
    "By default, Cthulhu writes the cross section for each (P,T) pair as a .txt file in the ./output folder. \n",
    "\n",
    "We'll now combine all of the different (P,T) pairs into a single HDF5 file for storage efficiency and fast read time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0107d8b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now preparing HDF5 file for: AlO\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Progress: 100%|██████████| 162/162 [07:25<00:00,  2.75s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AlO done\n"
     ]
    }
   ],
   "source": [
    "from Cthulhu.hdf5_package import make_single_species_HDF5\n",
    "\n",
    "P = [1.0e-6, 1.0e-5, 1.0e-4, 1.0e-3, 1.0e-2, 1.0e-1, 1.0e0, 1.0e1, 1.0e2]    # Pressure (bar)\n",
    "log_P = [-6.0, -5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0]                  # log10 pressure\n",
    "T = [100.0, 200.0, 300.0, 400.0, 500.0, 600.0, 700.0, 800.0,                 # Temperature (K)\n",
    "     900.0, 1000.0, 1200.0, 1400.0, 1600.0, 1800.0, 2000.0,\n",
    "     2500.0, 3000.0, 3500.0]\n",
    "\n",
    "make_single_species_HDF5(species, database = database, linelist = linelist, \n",
    "                         log_P = log_P, T = T, isotope = 'default', \n",
    "                         broadening = broadening, output_dir = './output/HDF5/')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5051785",
   "metadata": {},
   "source": [
    "#### Combining different chemical species into an opacity database"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daef9a3a",
   "metadata": {},
   "source": [
    "Once you have made the HDF5 file for a single chemical species, you can add it into an opacity database HDF5 file which can contain the cross sections for multiple gases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b0ff2b98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AlO done\n"
     ]
    }
   ],
   "source": [
    "from Cthulhu.hdf5_package import extend_HDF5_database\n",
    "\n",
    "database_name = 'Combined_Opacity_database.hdf5'   # If this database file doesn't already exist, Cthulhu will create the file\n",
    "\n",
    "extend_HDF5_database(database_name, species)     # Calling this function for other gases will add them to the database"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e573b316",
   "metadata": {},
   "source": [
    "Let's print the attributes of our new opacity database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2b08ca78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AlO\n",
      "  Broadening: H2-He\n",
      "  Linelist: ATP\n",
      "  Modified: 2024-08-20\n",
      "  Source: ExoMol\n",
      "  Version: v1\n",
      "AlO/T\n",
      "  Units: K\n",
      "  Variable: Temperature\n",
      "AlO/log(P)\n",
      "  Units: log10(P/bar)\n",
      "  Variable: Pressure (log10 scale)\n",
      "AlO/log(sigma)\n",
      "  Units: log10(sigma/m^2)\n",
      "  Variable: Cross section (log10 scale)\n",
      "AlO/nu\n",
      "  Units: cm^-1\n",
      "  Variable: Wavenumber\n"
     ]
    }
   ],
   "source": [
    "import h5py\n",
    "\n",
    "def print_attrs(name, obj):\n",
    "    \"\"\"Print attributes of an HDF5 object.\"\"\"\n",
    "    print(name)\n",
    "    for key, val in obj.attrs.items():\n",
    "        print(f\"  {key}: {val}\")\n",
    "\n",
    "def list_all_attributes(filename):\n",
    "    \"\"\"List all attributes in an HDF5 file.\"\"\"\n",
    "    with h5py.File(filename, 'r') as f:\n",
    "        f.visititems(print_attrs)\n",
    "\n",
    "list_all_attributes('./Combined_Opacity_database.hdf5')"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Congratulations, you've made your first opacity database! 🎉\n",
    "\n",
    "You can now use this HDF5 file with your favorite exoplanet or brown dwarf modeling code."
   ]
  }
 ],
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