{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3bc57bc3-aa12-446d-9a7f-941b38d9fad7",
   "metadata": {},
   "source": [
    "# Calculating Viscosity from liquid compositions\n",
    "- This notebook shows how to calculate Viscosity using Giordano et al. (2008)\n",
    "- You can download the Excel spreadsheet from: https://github.com/PennyWieser/Thermobar/blob/main/docs/Examples/Other_features/Viscoity_Giordano.xlsx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "57c0802b-aeb7-4e18-bc23-86556992829b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# If you haven't done so, pip install Thermobar by removing the # symbol\n",
    "#!pip install Thermobar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0e98ab7b-15e4-415a-835a-e1d449238f6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import Thermobar as pt\n",
    "import matplotlib.pyplot as plt\n",
    "pd.options.display.max_columns = None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c08fcf6-9512-4937-87c4-519f5dda0abe",
   "metadata": {},
   "source": [
    "## Lets load in some melt compositions from a MELTS model published in Wieser et al. (2022)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "510b12cc-296c-4f8b-bb13-fcd059d37af2",
   "metadata": {},
   "outputs": [],
   "source": [
    "Liqs_import2=pt.import_excel('Viscoity_Giordano.xlsx', sheet_name='MELTSTest', suffix=\"_Liq\")\n",
    "Liqs2=Liqs_import2['Liqs']\n",
    "Liqs_input2=Liqs_import2['my_input']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f986accf-bfc9-401d-ac8b-a5f8c2e6b643",
   "metadata": {},
   "source": [
    "## Inspect the liquid data you have loaded in to make sure it makes sense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "46ca2c45-38a0-46b7-884b-ccfcd6fc41f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SiO2_Liq</th>\n",
       "      <th>TiO2_Liq</th>\n",
       "      <th>Al2O3_Liq</th>\n",
       "      <th>FeOt_Liq</th>\n",
       "      <th>MnO_Liq</th>\n",
       "      <th>MgO_Liq</th>\n",
       "      <th>CaO_Liq</th>\n",
       "      <th>Na2O_Liq</th>\n",
       "      <th>K2O_Liq</th>\n",
       "      <th>Cr2O3_Liq</th>\n",
       "      <th>P2O5_Liq</th>\n",
       "      <th>H2O_Liq</th>\n",
       "      <th>Fe3Fet_Liq</th>\n",
       "      <th>NiO_Liq</th>\n",
       "      <th>CoO_Liq</th>\n",
       "      <th>CO2_Liq</th>\n",
       "      <th>Sample_ID_Liq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51.456179</td>\n",
       "      <td>2.601690</td>\n",
       "      <td>13.529073</td>\n",
       "      <td>11.114610</td>\n",
       "      <td>0.185873</td>\n",
       "      <td>6.698477</td>\n",
       "      <td>10.974609</td>\n",
       "      <td>2.406926</td>\n",
       "      <td>0.483801</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.248535</td>\n",
       "      <td>0.508277</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.462403</td>\n",
       "      <td>2.641448</td>\n",
       "      <td>13.717522</td>\n",
       "      <td>11.175642</td>\n",
       "      <td>0.189570</td>\n",
       "      <td>6.497934</td>\n",
       "      <td>10.814571</td>\n",
       "      <td>2.451116</td>\n",
       "      <td>0.493423</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.253478</td>\n",
       "      <td>0.518386</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51.491657</td>\n",
       "      <td>2.771451</td>\n",
       "      <td>13.666925</td>\n",
       "      <td>11.521905</td>\n",
       "      <td>0.200504</td>\n",
       "      <td>6.275374</td>\n",
       "      <td>10.459901</td>\n",
       "      <td>2.509225</td>\n",
       "      <td>0.520502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.268098</td>\n",
       "      <td>0.548285</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51.508276</td>\n",
       "      <td>2.872896</td>\n",
       "      <td>13.569897</td>\n",
       "      <td>11.795709</td>\n",
       "      <td>0.208908</td>\n",
       "      <td>6.138356</td>\n",
       "      <td>10.217900</td>\n",
       "      <td>2.543802</td>\n",
       "      <td>0.541088</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279336</td>\n",
       "      <td>0.571268</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.506960</td>\n",
       "      <td>3.058952</td>\n",
       "      <td>13.350351</td>\n",
       "      <td>12.214913</td>\n",
       "      <td>0.223782</td>\n",
       "      <td>5.856971</td>\n",
       "      <td>9.977521</td>\n",
       "      <td>2.592645</td>\n",
       "      <td>0.577148</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.299224</td>\n",
       "      <td>0.611940</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SiO2_Liq  TiO2_Liq  Al2O3_Liq   FeOt_Liq   MnO_Liq   MgO_Liq    CaO_Liq  \\\n",
       "0  51.456179  2.601690  13.529073  11.114610  0.185873  6.698477  10.974609   \n",
       "1  51.462403  2.641448  13.717522  11.175642  0.189570  6.497934  10.814571   \n",
       "2  51.491657  2.771451  13.666925  11.521905  0.200504  6.275374  10.459901   \n",
       "3  51.508276  2.872896  13.569897  11.795709  0.208908  6.138356  10.217900   \n",
       "4  51.506960  3.058952  13.350351  12.214913  0.223782  5.856971   9.977521   \n",
       "\n",
       "   Na2O_Liq   K2O_Liq  Cr2O3_Liq  P2O5_Liq   H2O_Liq  Fe3Fet_Liq  NiO_Liq  \\\n",
       "0  2.406926  0.483801        0.0  0.248535  0.508277         0.0      0.0   \n",
       "1  2.451116  0.493423        0.0  0.253478  0.518386         0.0      0.0   \n",
       "2  2.509225  0.520502        0.0  0.268098  0.548285         0.0      0.0   \n",
       "3  2.543802  0.541088        0.0  0.279336  0.571268         0.0      0.0   \n",
       "4  2.592645  0.577148        0.0  0.299224  0.611940         0.0      0.0   \n",
       "\n",
       "   CoO_Liq  CO2_Liq  Sample_ID_Liq  \n",
       "0      0.0      0.0              0  \n",
       "1      0.0      0.0              1  \n",
       "2      0.0      0.0              2  \n",
       "3      0.0      0.0              3  \n",
       "4      0.0      0.0              4  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Liqs2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da9c8258-9f78-4b4b-b473-f40048cf48d5",
   "metadata": {},
   "source": [
    "## Lets calculate viscosity at the temperature stored in the column \"Temp HT1987_K\"\n",
    "- Here, we had already calculated temperature using Helz and Thornber, which was stored in the input spreadsheet in a column named 'Temp HT1987_K'\n",
    "- The dataframe Liqs_input2 contains all input columns, so we can access the values stored in this column using Liqs_input2['Temp HT1987_K']\n",
    "- This temperature needs to be in Kelvin!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "99c8b1c8-a81b-4041-9a36-528180cd3583",
   "metadata": {},
   "outputs": [],
   "source": [
    "Calc_ExcelT=pt.calculate_viscosity_giordano_2008(liq_comps=Liqs2, \n",
    "                                                  T=Liqs_input2['Temp HT1987_K'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3483d7c2-9667-4df7-8021-a73ce260c738",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'SiO2 content Liq (Wt%)')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,5))\n",
    "ax1.plot(Calc_ExcelT['MgO_Liq'], Calc_ExcelT['n_melt'], '-k')\n",
    "ax2.plot(Calc_ExcelT['SiO2_Liq'], Calc_ExcelT['n_melt'], '-k')\n",
    "\n",
    "ax1.set_ylabel('Viscosity (PaS)')\n",
    "ax1.set_xlabel('MgO content Liq (Wt%)')\n",
    "ax2.set_xlabel('SiO2 content Liq (Wt%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5366396a-299a-405d-8584-3c83b98e0f4d",
   "metadata": {},
   "source": [
    "## Using a different thermometer for temperature\n",
    "- You can get a list of all thermometers in Thermobar using the help function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b8e7e79c-cc52-46ca-aea9-2df3dacefe98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function calculate_liq_only_temp in module Thermobar.liquid_thermometers:\n",
      "\n",
      "calculate_liq_only_temp(*, liq_comps, equationT, P=None, H2O_Liq=None, print=False)\n",
      "     Liquid-only thermometery. Returns a temperature in Kelvin.\n",
      "    \n",
      "    Parameters\n",
      "     -------\n",
      "    \n",
      "     liq_comps: pandas.DataFrame\n",
      "         liquid compositions with column headings SiO2_Liq, MgO_Liq etc.\n",
      "    \n",
      "     equationT: str\n",
      "         If has _sat at the end, represents the saturation surface of that mineral.\n",
      "    \n",
      "         Equations from Putirka et al. (2016).\n",
      "             | T_Put2016_eq3_amp_sat (saturation surface of amphibole)\n",
      "    \n",
      "         Equations from Putirka (2008) and older studies:\n",
      "    \n",
      "             | T_Put2008_eq13\n",
      "             | T_Put2008_eq14\n",
      "             | T_Put2008_eq15\n",
      "             | T_Put2008_eq16\n",
      "             | T_Put2008_eq34_cpx_sat\n",
      "             | T_Put2008_eq28b_opx_sat\n",
      "             | T_Put1999_cpx_sat\n",
      "             * Following 3 thermometers are adaptations of olivine-liquid thermometers with  DMg calculated using Beattie 1993,\n",
      "             This means you can use them without knowing an olivine composition. ocan be applied when you haven't measured an olivine composiiton.\n",
      "             | T_Put2008_eq19_BeattDMg\n",
      "             | T_Put2008_eq21_BeattDMg\n",
      "             | T_Put2008_eq22_BeattDMg\n",
      "    \n",
      "         Equations from Sugawara (2000):\n",
      "    \n",
      "             | T_Sug2000_eq1\n",
      "             | T_Sug2000_eq3_ol\n",
      "             | T_Sug2000_eq3_opx\n",
      "             | T_Sug2000_eq3_cpx\n",
      "             | T_Sug2000_eq3_pig\n",
      "             | T_Sug2000_eq6a\n",
      "             | T_Sug2000_eq6b\n",
      "    \n",
      "         Equations from Helz and Thornber (1987):\n",
      "    \n",
      "             | T_Helz1987_MgO\n",
      "             | T_Helz1987_CaO\n",
      "    \n",
      "         Equation from Molina et al. (2015)\n",
      "    \n",
      "             | T_Molina2015_amp_sat\n",
      "    \n",
      "         Equation from Montrieth 1995\n",
      "            | T_Montierth1995_MgO\n",
      "    \n",
      "         Equation from Beattie (1993)\n",
      "            | T_Beatt1993_opx\n",
      "    \n",
      "     P: float, int, pandas.Series, str  (\"Solve\")\n",
      "         Pressure in kbar\n",
      "         Only needed for P-sensitive thermometers.\n",
      "         If enter P=\"Solve\", returns a partial function\n",
      "         Else, enter an integer, float, or panda series\n",
      "    \n",
      "     H2O_Liq: optional.\n",
      "         If None, uses H2O_Liq column from input.\n",
      "         If int, float, pandas.Series, uses this instead of H2O_Liq Column\n",
      "    \n",
      "    \n",
      "     Returns\n",
      "     -------\n",
      "     pandas series\n",
      "        Temperature in K\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(pt.calculate_liq_only_temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4119cf31-ca43-4fdb-a50e-259a2f812d4b",
   "metadata": {},
   "source": [
    "### Lets use \"T_Put2008_eq13\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fa8b7a3a-ea75-4bd8-997b-dd7f894c889f",
   "metadata": {},
   "outputs": [],
   "source": [
    "CalcT_eq13=pt.calculate_liq_only_temp(liq_comps=Liqs2, equationT=\"T_Put2008_eq13\")\n",
    "Calc_Puteq13=pt.calculate_viscosity_giordano_2008(liq_comps=Liqs2, \n",
    "                                                  T=CalcT_eq13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6f998329-f081-45f9-96c0-73d700a3e30d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'SiO2 content Liq (Wt%)')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,5))\n",
    "ax1.plot(Calc_Puteq13['MgO_Liq'], Calc_Puteq13['n_melt'], '-r', label='TPut13')\n",
    "ax2.plot(Calc_Puteq13['SiO2_Liq'], Calc_Puteq13['n_melt'], '-r')\n",
    "\n",
    "\n",
    "ax1.plot(Calc_ExcelT['MgO_Liq'], Calc_ExcelT['n_melt'], '-k', label='HT87')\n",
    "ax2.plot(Calc_ExcelT['SiO2_Liq'], Calc_ExcelT['n_melt'], '-k')\n",
    "ax1.legend()\n",
    "ax1.set_ylabel('Viscosity (PaS)')\n",
    "ax1.set_xlabel('MgO content Liq (Wt%)')\n",
    "ax2.set_xlabel('SiO2 content Liq (Wt%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59d44ab6-4d4d-41d6-94e8-feda75df356a",
   "metadata": {},
   "source": [
    "## With different F2O contents\n",
    "- By default, we perform calculations with no F, to use the same input structure as the rest of the liquids\n",
    "- However, Giordano parameterize in terms of F2O, so you can enter this straight in the function\n",
    "- We have 2 functions, allowing you to convert from F2O to F and back"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b6bc488f-9b15-434c-8386-cf076452fbd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14210723396066502"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F2O_calc=pt.convert_F_to_F2O(F_ppm=1000)\n",
    "F2O_calc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "78ca6c38-d232-449d-85e4-9f0ccdb1e9aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F_calc=pt.convert_F2O_to_F_ppm(F2O_wt=F2O_calc)\n",
    "F_calc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f07540b4-f39c-4f4d-8029-a748f7b800fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "WithF=pt.calculate_viscosity_giordano_2008(liq_comps=Liqs2, \n",
    "                                    T=Liqs_input2['Temp HT1987_K'],\n",
    "                                     F2O_content=F2O_calc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fa075518-a820-4d9f-9637-4fbb2488596b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'MgO content Liq')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot( Calc_ExcelT['MgO_Liq'], Calc_ExcelT['n_melt'], '-k', label='No F')\n",
    "plt.plot( WithF['MgO_Liq'], WithF['n_melt'], '-g', label='With 1000 ppm F')\n",
    "plt.legend()\n",
    "plt.ylabel('Viscosity')\n",
    "plt.xlabel('MgO content Liq')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}