{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2a. Calculate closed-system degassing path assuming melt composition is the bulk composition (with and without sulfur saturation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python set-up\n",
    "You need to install VolFe once on your machine, if you haven't yet. Then we need to import a few Python packages (including VolFe). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install VolFe on your machine. Don't remove the # from this line!\n",
    "# pip install VolFe # Remove the first # in this line if you have not installed VolFe on your machine before.\n",
    "\n",
    "# import python packages\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import VolFe as vf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the inputs\n",
    "\n",
    "At a minimum to run a degassing calculation, we need a dataframe of the melt composition, an estimate of oxygen fugacity and Fe in the melt [note 1], and temperature at some point along the degassing path:\n",
    "\n",
    "- Sample is just the name for this analysis.\n",
    "\n",
    "- Temperature is in °C.\n",
    "\n",
    "- Volatile-free melt composition is in wt% oxides [note 2]. All these oxides must be present in the dataframe, so set them to 0. if you have no data for them (although see [note 1] around FeOT).\n",
    "\n",
    "- H2O is all hydrogen in the melt reported as H<sub>2</sub>O in wt% [note 3]. \n",
    "\n",
    "- CO2ppm is all carbon in the melt reported as CO<sub>2</sub> in ppm [note 3].\n",
    "\n",
    "- STppm is all sulfur in the melt reported as S in ppm [note 3].\n",
    "\n",
    "- Xppm is all \"X\" in the melt reported as \"X\" in ppm [note 3]. \"X\" is an unreactive melt species whose identity can be changed - this is explored in Example 2e.\n",
    "\n",
    "- Fe3+FeT is the ratio of Fe<sup>3+</sup> to Fe<sub>T</sub> in the melt.\n",
    "\n",
    "[note 1] In this example we specify oxygen fugacity using Fe<sup>3+</sup>/Fe<sub>T</sub> and Fe in the melt as FeOT as is quite common for melt inclusion and matrix glass analyses - other options are possible, see Example 1a for more information.\n",
    "\n",
    "[note 2] It does not matter what the non-volatile oxides sum too - they are renormalised to 100 wt% minus the total of the volatiles (i.e., H<sub>2</sub>O + CO<sub>2</sub> + S<sub>T</sub> + X).\n",
    "\n",
    "[note 3] The volatile concentrations are absolute for the melt = i.e., the non-volatile melt composition is normalised to 100 wt% minus the volatiles. The volatiles are not added to the oxides and then renormalised.\n",
    "\n",
    "The following composition is analysis Sari15-04-33 from Brounce et al. (2014) with the updated Fe<sup>3+</sup>/Fe<sub>T</sub> from Cottrell et al. (2021), with a temperature chosen as 1200 °C."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the melt composition, fO2 estimate, and T as a dictionary.\n",
    "my_analysis = {'Sample':'Sari15-04-33',\n",
    "           'T_C': 1200., # Temperature in 'C\n",
    "           'SiO2': 47.89, # wt%\n",
    "           'TiO2': 0.75, # wt%\n",
    "           'Al2O3': 16.74, # wt%\n",
    "           'FeOT': 9.43, # wt%\n",
    "           'MnO': 0.18, # wt%\n",
    "           'MgO': 5.92, # wt%\n",
    "           'CaO': 11.58, # wt%\n",
    "           'Na2O': 2.14, # wt%\n",
    "           'K2O': 0.63, # wt%\n",
    "           'P2O5': 0.17, # wt%\n",
    "           'H2O': 4.17, # wt%\n",
    "           'CO2ppm': 1487., # ppm\n",
    "           'STppm': 1343.5, # ppm\n",
    "           'Xppm': 0., # ppm\n",
    "           'Fe3FeT': 0.177}\n",
    "\n",
    "# Turn the dictionary into a pandas dataframe, setting the index to 0.\n",
    "my_analysis = pd.DataFrame(my_analysis, index=[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this example we will mostly use the default options in VolFe, which can be found below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            option\n",
      "type                              \n",
      "COH_species     yes_H2_CO_CH4_melt\n",
      "H2S_m                         True\n",
      "species X                       Ar\n",
      "Hspeciation                   none\n",
      "fO2                       Kress91A\n",
      "...                            ...\n",
      "error                          0.1\n",
      "print status                 False\n",
      "output csv                    True\n",
      "setup                        False\n",
      "high precision               False\n",
      "\n",
      "[78 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "# print default options in VolFe\n",
    "print(vf.default_models)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But I'll just highlight the specific options that are important to the degassing calculation and what their default values are:\n",
    "\n",
    "**bulk_composition = melt-only** This means the composition (including volatiles and fO2 estimate) represent the bulk composition of the system - there is only melt present and all volatiles are dissolved in the melt.\n",
    "\n",
    "**starting_P = Pvsat** This means the calculation will start at Pvsat for the composition given.\n",
    "\n",
    "**gasssing_style = closed** This means the melt and vapor remain in chemical equilibrium throughout and the bulk composition is constant during the calculation\n",
    "\n",
    "**gassing_direction = degas** This means pressure will decrease during the calculations and therefore the melt will degas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the calculation\n",
    "\n",
    "### Composition from data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████▉| 3799.0/3800 [00:29<00:00, 128.39it/s]\n"
     ]
    }
   ],
   "source": [
    "degas1 = vf.calc_gassing(my_analysis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Composition from csv\n",
    "\n",
    "We could run the same calculation from a csv file if we preferred.\n",
    "\n",
    "We'll use the examples_marianas_wT csv in files and use all the default options again. \n",
    "The data in this file are from Brounce et al. (2014) and Kelley & Cottrell (2012) with updated Fe<sup>3+</sup>/Fe<sub>T</sub> from Cottrell et al. (2021) where available.\n",
    "Sari15-04-33 is row 48 in that file. \n",
    "\n",
    "Uncomment the lines to use them (i.e., remove the # at the start of each line)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the csv file <- do not remove # from this line\n",
    "# my_analyses = pd.read_csv(\"../files/example_marianas.csv\") # remove first # in this line to run it\n",
    "\n",
    "# run degassing calculation for row 48 - Sari15-04-33 <- do not remove # from this line\n",
    "# vf.calc_gassing(my_analyses,row=48) # remove first # in this line to run it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Include sulfide and anhydrite saturation\n",
    "\n",
    "Turns out the melt was sulfide-saturated during the degassing path. \n",
    "Under those conditions, the results for the melt and vapor are metastable with respect to saturation of sulfide.\n",
    "\n",
    "We can run the same calculation, but instead limit the sulfur content in the melt to sulfide or anhydrite saturation, if it reaches the limit. \n",
    "To do that we change the \"sulfur_saturation\" option from \"False\" (sulfur phases cannot saturate) to \"True\" (sulfur phases can saturate if the sulfur content is high enough)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choose the options I want to change for the calculation - everything else will use the default options\n",
    "my_models = [['sulfur_saturation','True']]\n",
    "\n",
    "# turn to dataframe with correct column headers and indexes    \n",
    "my_models = vf.make_df_and_add_model_defaults(my_models)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then we run the degassing calculation with those model options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████▉| 3799.0/3800 [00:49<00:00, 76.67it/s] \n"
     ]
    }
   ],
   "source": [
    "degas2 = vf.calc_gassing(my_analysis, models=my_models)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting\n",
    "\n",
    "And below we can plot the two results to see the difference.\n",
    "\n",
    "The black solid curves are the first calculation (not allowing sulfur to saturate), whilst the black dotted curves are the second calculation (sulfur can saturate as sulfide or anhydrite if the concentration in the melt is high enough).\n",
    "\n",
    "It has no effect on the H<sub>2</sub>O or CO<sub>2</sub> contents of the melt, but the sulfur content is much less at the start of degassing because it is in a sulfide phase instead!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4000.0, 0.0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(12,4))\n",
    "\n",
    "data1 = degas1 # sulfur cannot saturate\n",
    "data2 = degas2 # sulfur can saturate\n",
    "\n",
    "# Plotting results\n",
    "ax1.plot(data1['CO2T-eq_ppmw'], data1['P_bar'], '-k')\n",
    "ax1.plot(data2['CO2T-eq_ppmw'], data2['P_bar'], ':k')\n",
    "ax2.plot(data1['H2OT-eq_wtpc'], data1['P_bar'], '-k')\n",
    "ax2.plot(data2['H2OT-eq_wtpc'], data2['P_bar'], ':k')\n",
    "ax3.plot(data1['ST_ppmw'], data1['P_bar'], '-k')\n",
    "ax3.plot(data2['ST_ppmw'], data2['P_bar'], ':k')\n",
    "\n",
    "ax1.set_ylabel('P (bar)')\n",
    "ax1.set_xlabel('CO2,T-eq (ppmw)')\n",
    "ax2.set_xlabel('H2OT-eq (wt%)')\n",
    "ax3.set_xlabel('ST (ppmw)')\n",
    "ax1.set_ylim([4000,0])\n",
    "ax2.set_ylim([4000,0])\n",
    "ax3.set_ylim([4000,0])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "volfe-dev",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}