{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2c. Calculate open-system degassing path\n",
    "\n",
    "During open-system degassing, vapor is lost from the system as soon as it is formed such that the bulk composition of the system changes continuously."
   ]
  },
  {
   "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": [
    "Check version"
   ]
  },
  {
   "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": [
    "## Quick test\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, and *lower* initial volatile content as a test case.\n",
    "\n",
    "Because the vapor is being removed at each pressure-step, the pressure step-size does matter. \n",
    "The smaller the step-size, the more true to pure open-system degassing the results will be but the longer it will take.\n",
    "The automatic step size is 1 bar.\n",
    "\n",
    "The open-system calculations can take a long time because the <i>P</i>-step size is 1 bar, so this example will run quicker than the next one if you just want to see how it looks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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': 1., # wt%\n",
    "           'CO2ppm': 50., # ppm\n",
    "           'STppm': 100., # 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": [
    "Running open-system calculations requires changing the \"gassing_style\" to \"open\".\n",
    "We'll use default options for everything else."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# choose the options I want - everything else will use the default options\n",
    "my_models = [['gassing_style','open']]\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": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 99%|█████████▉| 196.0/197 [00:58<00:00,  3.37it/s]\n"
     ]
    }
   ],
   "source": [
    "degas1 = vf.calc_gassing(my_analysis, models=my_models)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the calculation\n",
    "\n",
    "### Open-system - actual example\n",
    "\n",
    "This one will run slower because the volatile content is higher!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
    "           '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": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 62%|██████▏   | 2406.0/3862 [20:56<12:40,  1.91it/s]  \n"
     ]
    }
   ],
   "source": [
    "degas2 = vf.calc_gassing(my_analysis, models=my_models)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison to closed-system\n",
    "\n",
    "We can also run the closed-system calculations for comparison...  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████▉| 3799.0/3800 [00:25<00:00, 147.04it/s]\n"
     ]
    }
   ],
   "source": [
    "# closed-system degassing calculation\n",
    "degas3 = vf.calc_gassing(my_analysis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting\n",
    "\n",
    "... and plot them: open-system degassing is the solid black curve, whereas closed-system degassing is the dotted black curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4000.0, 0.0)"
      ]
     },
     "execution_count": 9,
     "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 = degas2 # open-system\n",
    "data2 = degas3 # closed-system\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
}