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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Benchmarking models for sulfur saturation\n",
    "\n",
    "This notebook benchmarks the models for sulfur saturation in VolFe where possible.\n",
    "\n",
    "## Python set-up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import VolFe as vf\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Version of VolFe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.4.1'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vf.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Composition and conditions used for benchmarking\n",
    "\n",
    "Average of high-SiO2 pillow-rim glasses in HSDP from Mauna Kea volcano from Brounce et al. (2017)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_analysis = {\n",
    "        \"Sample\": \"Hawaiian basalt\",\n",
    "        \"T_C\": 1200.0,  # Temperature in 'C\n",
    "        \"SiO2\": 51.29,  # wt%\n",
    "        \"TiO2\": 2.50,  # wt%\n",
    "        \"Al2O3\": 13.70,  # wt%\n",
    "        \"FeOT\": 11.04,  # wt%\n",
    "        \"MnO\": 0.02,  # wt%\n",
    "        \"MgO\": 6.70,  # wt%\n",
    "        \"CaO\": 11.03,  # wt%\n",
    "        \"Na2O\": 2.27,  # wt%\n",
    "        \"K2O\": 0.43,  # wt%\n",
    "        \"P2O5\": 0.21,  # wt%\n",
    "        \"H2O\": 3.,  # wt%\n",
    "        \"CO2ppm\": 1000.,  # ppm\n",
    "        \"STppm\": 0.,  # ppm\n",
    "        \"Xppm\": 0.0,  # ppm\n",
    "        \"Fe3FeT\": 0.1,\n",
    "    }\n",
    "\n",
    "my_analysis = pd.DataFrame(my_analysis, index=[0])\n",
    "\n",
    "PT = {\"P\":1000.}\n",
    "PT[\"T\"]=1200.\n",
    "\n",
    "melt_wf=vf.melt_comp(0.,my_analysis)\n",
    "melt_wf['CO2'] = my_analysis.loc[0.,\"CO2ppm\"]/1000000.\n",
    "melt_wf[\"H2OT\"] = my_analysis.loc[0,\"H2O\"]/100.\n",
    "melt_wf['ST'] = my_analysis.loc[0.,\"STppm\"]/1000000.\n",
    "melt_wf['CT'] = (melt_wf['CO2']/vf.species.loc['CO2','M'])*vf.species.loc['C','M']\n",
    "melt_wf['HT'] = (melt_wf['H2OT']/vf.species.loc['H2O','M'])*(2.*vf.species.loc['H','M'])\n",
    "melt_wf['XT'] = my_analysis.loc[0.,\"Xppm\"]/1000000.\n",
    "melt_wf[\"Fe3FeT\"] = my_analysis.loc[0.,\"Fe3FeT\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Models for Sulfide Content at Sulfide Saturation (S2-CSS)\n",
    "\n",
    "option = SCSS, function = SCSS\n",
    "\n",
    "Models calculated using PySulfSat have been benchmarked in PySulfSat:\n",
    "- 'Fortin15_pss' Fortin et al. (2015) using PySulfSat by Wieser & Gleeson (2023)\n",
    "- 'Liu21_pss' Liu et al. (2021) using PySulfSat by Wieser & Gleeson (2023)\n",
    "- 'ONeill22_pss' O'Neill & Mavrogenes (2022) using PySulfSat by Wieser & Gleeson (2023)\n",
    "- 'ONeill21_pss' O'Neill (2021) using PySulfSat by Wieser & Gleeson (2023)\n",
    "- 'Smythe17_pss' Smythe et al. (2017) using PySulfSat by Wieser & Gleeson (2023)\n",
    "- 'Li22_pss' Li and Zhang (2022) using PySulfSat by Wieser and Gleeson (2023)\n",
    "- 'Blanchard21eq11_pss' Eq. (11) from Blanchard et al. (2021) using PySulfSat by Wieser and Gleeson (2023)\n",
    "- 'Blanchard21eq12_pss' Eq. (12) from Blanchard et al. (2021) using PySulfSat by Wieser and Gleeson (2023)\n",
    "\n",
    "The following models do not have material available in the original papers for benchmarking:\n",
    "- 'ONeill21hyd' Eq. (10.34, 10.43, 10.45, 10.46, 10.49) from O'Neill (2021)\n",
    "- 'ONeill21dil' Eq. (10.34, 10.43, 10.45, 10.46) including water dilution from O'Neill (2021)\n",
    "- 'Liu07' Eq. (9) in Liu et al. (2007)\n",
    "\n",
    "### 'ONeill21' Eq. (10.34, 10.43, 10.45, 10.46) excluding water dilution from O'Neill (2021)\n",
    "\n",
    "Supplementary spreadsheet (sulfide,SCSS=ONeill21.xlsx)\n",
    "\n",
    "Cell AR14: 7.437\n",
    "\n",
    "Matches to 3 decimal places: Note spreadsheet uses +273 to convert to K, rather than 273.15 used in VolFe so T in spreadsheet = 1200.15 'C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.4369918428928115"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_models = [[\"SCSS\", \"ONeill21\"],[\"sulfide\", \"ONeill21\"]]\n",
    "my_models = vf.make_df_and_add_model_defaults(my_models)\n",
    "math.log(vf.SCSS(PT,melt_wf,models=my_models))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Models for Sulfate Content at Anhydrite Saturation (S6+CAS)\n",
    "\n",
    "option = SCAS, function = SCAS\n",
    "\n",
    "Models calculated using PySulfSat have been benchmarked in PySulfSat:\n",
    "- 'Chowdhury19_pss' Chowdhury & Dasgupta (2019) using PySulfSat by Wieser and Gleeson (2023)\n",
    "- 'Zajacz19_pss' Zajacz and Tsay (2019) using PySulfSat by Wieser and Gleeson (2023)\n",
    "\n",
    "The following models do not have material available in the original papers for benchmarking:\n",
    "- 'Liu23' Eq. (4) Liu et al. (2023)\n",
    "\n",
    "There are no other models to benchmark in VolFe."
   ]
  }
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