{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2b. Calculate closed-system degassing path when there is melt and vapor at the start of calculation"
   ]
  },
  {
   "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": [
    "## Run the calculation\n",
    "\n",
    "### Bulk composition from intial CO<sub>2</sub>\n",
    "\n",
    "Sometimes we do not know the bulk composition of the system in terms of dissolved volatile content of the melt and an <i>f</i><sub>O<sub>2</sub></sub> estimate at <i>P<sup>v</sup></i><sub>sat</sub>.\n",
    "\n",
    "Instead, we might know the melt composition some way along the degassing path (e.g., from melt inclusion analyses) that was in equilibrium with a vapor and an idea of how much vapor was present.\n",
    "\n",
    "We can still calculate a closed-system degassing path if we add together the melt and vapor composition.\n",
    "\n",
    "So instead of Sari15-04-33 from Brounce et al. (2014) being representative of the bulk composition of the system, let's instead say its a melt composition some way along the degassing path (still assuming a temperature of 1200 °C). \n",
    "\n",
    "We estimate that the initial melt contained 4 wt% CO<sub>2</sub>-eq, which we add to the input dataframe under \"initial_CO2wtpc\"."
   ]
  },
  {
   "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",
    "           'initial_CO2wtpc': 4.} # initial CO2 content of the system in wt%\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": [
    "To use the initial CO<sub>2</sub> in the calculation, we have to change the \"bulk_composition\" option to \"melt+vapor_initialCO2\" so it knows how to calculate the bulk composition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# choose the options I want - everything else will use the default options\n",
    "my_models = [['bulk_composition','melt+vapor_initialCO2']]\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": [
    "VolFe will then calculate the bulk composition of the system by calculating the melt and vapor composition at Pvsat based on the input composition, and then add that vapor composition to the melt such that the bulk composition is 4 wt% CO<sub>2</sub>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0 : Switching solve species from OCS to OCH (first time)\n",
      "2.0 : Switching solve species from OCH to OHS (second time)\n",
      "1.0 : Switching solve species from OCS to OCH (first time)\n",
      "1.0 : Switching solve species from OCH to OHS (second time)\n",
      "solver failed, calculation aborted at P =  1.0 2025-03-06 08:13:50.253376\n"
     ]
    }
   ],
   "source": [
    "degas1 = vf.calc_gassing(my_analysis, models=my_models)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bulk composition from amount of co-existing vapor\n",
    "\n",
    "We might not have an estimate of the initial CO<sub>2</sub> content of the melt.\n",
    "\n",
    "Instead we might estimate that there is 3 wt% vapor present in equilibrium with the known melt composition.\n",
    "\n",
    "In that case, we specify the amount of vapor present as wt% of the system instead of the initial CO2 content in the input data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
    "           'wt_g': 3.} # wt% vapor in equilibrium with the melt\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": [
    "To use the amount of vapor present in the calculation, we have to change the \"bulk_composition\" option to \"melt+vapor_wtg\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# choose the options I want - everything else will use the default options\n",
    "my_models = [['bulk_composition','melt+vapor_wtg']]\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": [
    "VolFe will then calculate the bulk composition of the system by calculating the melt and vapor composition at Pvsat based on the input composition, and then add 3 wt% of that vapor composition to the melt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 : Switching solve species from OCS to OCH (first time)\n",
      "1.0 : Switching solve species from OCH to OHS (second time)\n",
      "1.0 : Switching solve species from OCS to OCH (first time)\n",
      "1.0 : Switching solve species from OCH to OHS (second time)\n",
      "solver failed, calculation aborted at P =  1.0 2025-03-06 08:24:12.920491\n"
     ]
    }
   ],
   "source": [
    "degas2 = vf.calc_gassing(my_analysis, models=my_models)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting\n",
    "\n",
    "And then we can plot them for comparison, where 4 wt% initial CO<sub>2</sub> is the solid black curve and 3 wt% additional vapor is the dotted black curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4000.0, 0.0)"
      ]
     },
     "execution_count": 8,
     "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
}