{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4d. Calculate isobars\n",
    "\n",
    "This allows you to calculate H<sub>2</sub>O-CO<sub>2</sub> isobars for a given melt composition and temperature."
   ]
  },
  {
   "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": 9,
   "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": [
    "Check version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.4.1'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vf.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define inputs\n",
    "\n",
    "The melt composition and temperature can be given in a dataframe, or read from a csv file.\n",
    "\n",
    "In this example it is read from a dataframe,which is from Brounce et al. (2014) with the updated Fe<sup>3+</sup>/Fe<sub>T</sub> from Cottrell et al. (2021).\n",
    "\n",
    "Note the volatile content of the melt is not used in this calculation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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",
    "           '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": [
    "We'll mostly use the default options..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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 the 'COH_species' must be set to 'H2O-CO2 only' because the isobars are calculated assuming the only melt and vapor species are H<sub>2</sub>O and CO<sub>2</sub>O."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change just the \"COH_species\" option to \"H2O-CO2 only\"\n",
    "my_models = [['COH_species','H2O-CO2 only']]\n",
    "\n",
    "# turn \"my_models\" 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": [
    "## Run calculation\n",
    "\n",
    "The calculation is run as below. The initial and final pressure, as well as the pressure step, must be specified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate isobars starting at 1000 bars, ending at 4000 bars at 1000 bar intervals\n",
    "isobars = vf.calc_isobar(my_analysis,models=my_models,initial_P=1000.,final_P=4000.,step_P=1000.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting\n",
    "\n",
    "For plotting, we have to split out the different isobars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into each pressure for plotting\n",
    "isobar1000 = isobars[isobars[\"P_bar\"]==1000.]\n",
    "isobar2000 = isobars[isobars[\"P_bar\"]==2000.]\n",
    "isobar3000 = isobars[isobars[\"P_bar\"]==3000.]\n",
    "isobar4000 = isobars[isobars[\"P_bar\"]==4000.]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can plot them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'H2O (wt%)')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1) = plt.subplots(1, 1, figsize=(4,4))\n",
    "\n",
    "data1 = isobar1000\n",
    "data2 = isobar2000\n",
    "data3 = isobar3000\n",
    "data4 = isobar4000\n",
    "\n",
    "# Plotting results\n",
    "ax1.plot(data1['H2O_wtpc'], data1['CO2_ppm'], '-k')\n",
    "ax1.plot(data2['H2O_wtpc'], data2['CO2_ppm'], '-k')\n",
    "ax1.plot(data3['H2O_wtpc'], data3['CO2_ppm'], '-k')\n",
    "ax1.plot(data4['H2O_wtpc'], data4['CO2_ppm'], '-k')\n",
    "\n",
    "ax1.set_ylabel('CO2 (ppmw)')\n",
    "ax1.set_xlabel('H2O (wt%)')"
   ]
  }
 ],
 "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
}