Fig2_Laboratory_Diferences_Using_1133.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5b2a19ad-95f3-45c1-aa88-ecd4b785e285",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from matplotlib import cm\n",
    "import warnings as w\n",
    "pd.options.display.max_columns = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf0a55fc-bef4-4e94-9c11-8dbf30535ab8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment these pip installs once on each PC. \n",
    "#!pip install Thermobar\n",
    "#!pip install \"https://github.com/PennyWieser/Thermobar_onnx/archive/refs/tags/0.02.zip\"\n",
    "import Thermobar as pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5bbb9916-6b5d-467c-b583-e4062c1b7dd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "plt.style.use(\"seaborn-colorblind\")\n",
    "plt.rcParams[\"font.family\"] = 'arial'\n",
    "plt.rcParams[\"font.size\"] =12\n",
    "plt.rcParams[\"mathtext.default\"] = \"regular\"\n",
    "plt.rcParams[\"mathtext.fontset\"] = \"dejavusans\"\n",
    "plt.rcParams['patch.linewidth'] = 1\n",
    "plt.rcParams['axes.linewidth'] = 1\n",
    "plt.rcParams[\"xtick.direction\"] = \"in\"\n",
    "plt.rcParams[\"ytick.direction\"] = \"in\"\n",
    "plt.rcParams[\"ytick.direction\"] = \"in\"\n",
    "plt.rcParams[\"xtick.major.size\"] = 6 # Sets length of ticks\n",
    "plt.rcParams[\"ytick.major.size\"] = 4 # Sets length of ticks\n",
    "plt.rcParams[\"ytick.labelsize\"] = 12 # Sets size of numbers on tick marks\n",
    "plt.rcParams[\"xtick.labelsize\"] = 12 # Sets size of numbers on tick marks\n",
    "plt.rcParams[\"axes.titlesize\"] = 14 # Overall t_MITle\n",
    "plt.rcParams[\"axes.labelsize\"] = 14 # Axes labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af144583-ebc0-43df-8101-10055a7fe5f6",
   "metadata": {},
   "source": [
    "## Reading in Gales Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "736f0d2b-0f44-4892-af06-7423e57acd65",
   "metadata": {},
   "outputs": [],
   "source": [
    "gale=pd.read_excel('Supporting_Data_1.xlsx', sheet_name=\"Gale_Differences\")\n",
    "gale_df=pd.DataFrame(data={'Sample_ID': gale['short'],\n",
    "                     'SiO2': gale['SiO2'],\n",
    "                        'Al2O3': gale['Al2O3'],\n",
    "                      'MgO': gale['MgO'],\n",
    "                     'CaO': gale['CaO'] ,\n",
    "                     'Na2O': gale['Na2O'],\n",
    "                     'TiO2': gale['TiO2'],\n",
    "                     'FeOt': gale['FeOt']}\n",
    "                        \n",
    "                        )\n",
    "gale_df_cpx_head=gale_df.add_suffix('_Cpx')\n",
    "\n",
    "df_MIT=gale.loc[gale['loc']==\"EMP-MASSACHUSETTS INSTITUTE OF TECHNOLOGY\"]\n",
    "df_TAS=gale.loc[gale['loc']==\"EMP-UNIVERSITY OF TASMANIA\"]\n",
    "df_TUL=gale.loc[gale['loc']==\"EMP-UNIVERSITY OF TULSA\"]\n",
    "df_HI=gale.loc[gale['loc']==\"EMP-UNIVERSITY OF HAWAII\"]\n",
    "df_RI=gale.loc[gale['loc']==\"EMP-UNIVERSITY OF RHODE ISLAND\"]\n",
    "df_LaMere=gale.loc[gale['loc']==\"EMP-INSTITUT FRANCAIS POUR L'EXPLOITATION DE LA MER\"]\n",
    "df_Cape=gale.loc[gale['loc']==\"EMP-UNIVERSITY OF CAPE TOWN\"]\n",
    "df_SUNY=gale.loc[gale['loc']==\"EMP-SUNY AT STONY BROOK\"]\n",
    "df_RNAS=gale.loc[gale['loc']==\"EMP-RUSSIAN NATIONAL ACADEMY OF SCIENCES\"]\n",
    "df_UBC=gale.loc[gale['loc']==\"EMP-UNIVERSITY OF BRITISH COLUMBIA\"]\n",
    "df_ANU=gale.loc[gale['loc']==\"EMP-AUSTRALIAN NATIONAL UNIVERSITY\"]\n",
    "df_Houston=gale.loc[gale['loc']==\"EMP-UNIVERSITY OF HOUSTON\"]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12f84fbf-469c-450b-a728-57dcbd6e7d53",
   "metadata": {},
   "source": [
    "## Loading in Cpx B1133"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "35b41342-7db7-4541-8172-499804f018fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "out_Cpx_B1133=pt.import_excel('Supporting_Data_2.xlsx',\n",
    "                                 sheet_name='B1133', suffix=\"_Cpx\")\n",
    "Cpx_B1133_all=out_Cpx_B1133['Cpxs']\n",
    "Cpx_B1133_input_all=out_Cpx_B1133['my_input']\n",
    "\n",
    "# As has all minerals, need to filter out things that arent Cpx\n",
    "IsCpx_B1133=Cpx_B1133_input_all['phase']==\"cpx\"\n",
    "Cpx_B1133=Cpx_B1133_all.loc[IsCpx_B1133].reset_index(drop=True)\n",
    "\n",
    "Cpx_B1133_Mean=Cpx_B1133.mean(axis=0).to_frame().T\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "161f8d40-dd0c-47f8-a31e-cd67b65bfd93",
   "metadata": {},
   "source": [
    "## Correcting Cpx data for various labs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "793c66d3-7c8e-4b8b-bcad-b14b471fe7f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "Cpx_B1133_MIT=Cpx_B1133.copy()\n",
    "Cpx_B1133_MIT['SiO2_Cpx']=Cpx_B1133['SiO2_Cpx']*df_MIT['SiO2'].values\n",
    "Cpx_B1133_MIT['Al2O3_Cpx']=Cpx_B1133['Al2O3_Cpx']*df_MIT['Al2O3'].values\n",
    "Cpx_B1133_MIT['MgO_Cpx']=Cpx_B1133['MgO_Cpx']*df_MIT['MgO'].values\n",
    "Cpx_B1133_MIT['Na2O_Cpx']=Cpx_B1133['Na2O_Cpx']*df_MIT['Na2O'].values\n",
    "Cpx_B1133_MIT['TiO2_Cpx']=Cpx_B1133['TiO2_Cpx']*df_MIT['TiO2'].values\n",
    "Cpx_B1133_MIT['FeOt_Cpx']=Cpx_B1133['FeOt_Cpx']*df_MIT['FeOt'].values\n",
    "Cpx_B1133_MIT['CaO_Cpx']=Cpx_B1133['CaO_Cpx']*df_MIT['CaO'].values\n",
    "\n",
    "\n",
    "Cpx_B1133_MIT_TAS=Cpx_B1133_MIT.copy()\n",
    "Cpx_B1133_MIT_TAS['SiO2_Cpx']=Cpx_B1133_MIT['SiO2_Cpx']/df_TAS['SiO2'].values\n",
    "Cpx_B1133_MIT_TAS['Al2O3_Cpx']=Cpx_B1133_MIT['Al2O3_Cpx']/df_TAS['Al2O3'].values\n",
    "Cpx_B1133_MIT_TAS['MgO_Cpx']=Cpx_B1133_MIT['MgO_Cpx']/df_TAS['MgO'].values\n",
    "Cpx_B1133_MIT_TAS['Na2O_Cpx']=Cpx_B1133_MIT['Na2O_Cpx']/df_TAS['Na2O'].values\n",
    "Cpx_B1133_MIT_TAS['TiO2_Cpx']=Cpx_B1133_MIT['TiO2_Cpx']/df_TAS['TiO2'].values\n",
    "Cpx_B1133_MIT_TAS['FeOt_Cpx']=Cpx_B1133_MIT['FeOt_Cpx']/df_TAS['FeOt'].values\n",
    "Cpx_B1133_MIT_TAS['CaO_Cpx']=Cpx_B1133_MIT['CaO_Cpx']/df_TAS['CaO'].values\n",
    "\n",
    "Cpx_B1133_MIT_Houston=Cpx_B1133_MIT.copy()\n",
    "Cpx_B1133_MIT_Houston['SiO2_Cpx']=Cpx_B1133_MIT['SiO2_Cpx']/df_Houston['SiO2'].values\n",
    "Cpx_B1133_MIT_Houston['Al2O3_Cpx']=Cpx_B1133_MIT['Al2O3_Cpx']/df_Houston['Al2O3'].values\n",
    "Cpx_B1133_MIT_Houston['MgO_Cpx']=Cpx_B1133_MIT['MgO_Cpx']/df_Houston['MgO'].values\n",
    "Cpx_B1133_MIT_Houston['Na2O_Cpx']=Cpx_B1133_MIT['Na2O_Cpx']/df_Houston['Na2O'].values\n",
    "Cpx_B1133_MIT_Houston['TiO2_Cpx']=Cpx_B1133_MIT['TiO2_Cpx']/df_Houston['TiO2'].values\n",
    "Cpx_B1133_MIT_Houston['FeOt_Cpx']=Cpx_B1133_MIT['FeOt_Cpx']/df_Houston['FeOt'].values\n",
    "Cpx_B1133_MIT_Houston['CaO_Cpx']=Cpx_B1133_MIT['CaO_Cpx']/df_Houston['CaO'].values\n",
    "\n",
    "Cpx_B1133_MIT_Cape=Cpx_B1133_MIT.copy()\n",
    "Cpx_B1133_MIT_Cape['SiO2_Cpx']=Cpx_B1133_MIT['SiO2_Cpx']/df_Cape['SiO2'].values\n",
    "Cpx_B1133_MIT_Cape['Al2O3_Cpx']=Cpx_B1133_MIT['Al2O3_Cpx']/df_Cape['Al2O3'].values\n",
    "Cpx_B1133_MIT_Cape['MgO_Cpx']=Cpx_B1133_MIT['MgO_Cpx']/df_Cape['MgO'].values\n",
    "Cpx_B1133_MIT_Cape['Na2O_Cpx']=Cpx_B1133_MIT['Na2O_Cpx']/df_Cape['Na2O'].values\n",
    "Cpx_B1133_MIT_Cape['TiO2_Cpx']=Cpx_B1133_MIT['TiO2_Cpx']/df_Cape['TiO2'].values\n",
    "Cpx_B1133_MIT_Cape['FeOt_Cpx']=Cpx_B1133_MIT['FeOt_Cpx']/df_Cape['FeOt'].values\n",
    "Cpx_B1133_MIT_Cape['CaO_Cpx']=Cpx_B1133_MIT['CaO_Cpx']/df_Cape['CaO'].values\n",
    "\n",
    "Cpx_B1133_MIT_SUNY=Cpx_B1133_MIT.copy()\n",
    "Cpx_B1133_MIT_SUNY['SiO2_Cpx']=Cpx_B1133_MIT['SiO2_Cpx']/df_SUNY['SiO2'].values\n",
    "Cpx_B1133_MIT_SUNY['Al2O3_Cpx']=Cpx_B1133_MIT['Al2O3_Cpx']/df_SUNY['Al2O3'].values\n",
    "Cpx_B1133_MIT_SUNY['MgO_Cpx']=Cpx_B1133_MIT['MgO_Cpx']/df_SUNY['MgO'].values\n",
    "Cpx_B1133_MIT_SUNY['Na2O_Cpx']=Cpx_B1133_MIT['Na2O_Cpx']/df_SUNY['Na2O'].values\n",
    "Cpx_B1133_MIT_SUNY['TiO2_Cpx']=Cpx_B1133_MIT['TiO2_Cpx']/df_SUNY['TiO2'].values\n",
    "Cpx_B1133_MIT_SUNY['FeOt_Cpx']=Cpx_B1133_MIT['FeOt_Cpx']/df_SUNY['FeOt'].values\n",
    "Cpx_B1133_MIT_SUNY['CaO_Cpx']=Cpx_B1133_MIT['CaO_Cpx']/df_SUNY['CaO'].values\n",
    "\n",
    "Cpx_B1133_MIT_RNAS=Cpx_B1133_MIT.copy()\n",
    "Cpx_B1133_MIT_RNAS['SiO2_Cpx']=Cpx_B1133_MIT['SiO2_Cpx']/df_RNAS['SiO2'].values\n",
    "Cpx_B1133_MIT_RNAS['Al2O3_Cpx']=Cpx_B1133_MIT['Al2O3_Cpx']/df_RNAS['Al2O3'].values\n",
    "Cpx_B1133_MIT_RNAS['MgO_Cpx']=Cpx_B1133_MIT['MgO_Cpx']/df_RNAS['MgO'].values\n",
    "Cpx_B1133_MIT_RNAS['Na2O_Cpx']=Cpx_B1133_MIT['Na2O_Cpx']/df_RNAS['Na2O'].values\n",
    "Cpx_B1133_MIT_RNAS['TiO2_Cpx']=Cpx_B1133_MIT['TiO2_Cpx']/df_RNAS['TiO2'].values\n",
    "Cpx_B1133_MIT_RNAS['FeOt_Cpx']=Cpx_B1133_MIT['FeOt_Cpx']/df_RNAS['FeOt'].values\n",
    "Cpx_B1133_MIT_RNAS['CaO_Cpx']=Cpx_B1133_MIT['CaO_Cpx']/df_RNAS['CaO'].values\n",
    "\n",
    "Cpx_B1133_MIT_UBC=Cpx_B1133_MIT.copy()\n",
    "Cpx_B1133_MIT_UBC['SiO2_Cpx']=Cpx_B1133_MIT['SiO2_Cpx']/df_UBC['SiO2'].values\n",
    "Cpx_B1133_MIT_UBC['Al2O3_Cpx']=Cpx_B1133_MIT['Al2O3_Cpx']/df_UBC['Al2O3'].values\n",
    "Cpx_B1133_MIT_UBC['MgO_Cpx']=Cpx_B1133_MIT['MgO_Cpx']/df_UBC['MgO'].values\n",
    "Cpx_B1133_MIT_UBC['Na2O_Cpx']=Cpx_B1133_MIT['Na2O_Cpx']/df_UBC['Na2O'].values\n",
    "Cpx_B1133_MIT_UBC['TiO2_Cpx']=Cpx_B1133_MIT['TiO2_Cpx']/df_UBC['TiO2'].values\n",
    "Cpx_B1133_MIT_UBC['FeOt_Cpx']=Cpx_B1133_MIT['FeOt_Cpx']/df_UBC['FeOt'].values\n",
    "Cpx_B1133_MIT_UBC['CaO_Cpx']=Cpx_B1133_MIT['CaO_Cpx']/df_UBC['CaO'].values\n",
    "\n",
    "Cpx_B1133_MIT_ANU=Cpx_B1133_MIT.copy()\n",
    "Cpx_B1133_MIT_ANU['SiO2_Cpx']=Cpx_B1133_MIT['SiO2_Cpx']/df_ANU['SiO2'].values\n",
    "Cpx_B1133_MIT_ANU['Al2O3_Cpx']=Cpx_B1133_MIT['Al2O3_Cpx']/df_ANU['Al2O3'].values\n",
    "Cpx_B1133_MIT_ANU['MgO_Cpx']=Cpx_B1133_MIT['MgO_Cpx']/df_ANU['MgO'].values\n",
    "Cpx_B1133_MIT_ANU['Na2O_Cpx']=Cpx_B1133_MIT['Na2O_Cpx']/df_ANU['Na2O'].values\n",
    "Cpx_B1133_MIT_ANU['TiO2_Cpx']=Cpx_B1133_MIT['TiO2_Cpx']/df_ANU['TiO2'].values\n",
    "Cpx_B1133_MIT_ANU['FeOt_Cpx']=Cpx_B1133_MIT['FeOt_Cpx']/df_ANU['FeOt'].values\n",
    "Cpx_B1133_MIT_ANU['CaO_Cpx']=Cpx_B1133_MIT['CaO_Cpx']/df_ANU['CaO'].values\n",
    "\n",
    "\n",
    "\n",
    "Cpx_B1133_MIT_TUL=Cpx_B1133_MIT.copy()\n",
    "Cpx_B1133_MIT_TUL['SiO2_Cpx']=Cpx_B1133_MIT['SiO2_Cpx']/df_TUL['SiO2'].values\n",
    "Cpx_B1133_MIT_TUL['Al2O3_Cpx']=Cpx_B1133_MIT['Al2O3_Cpx']/df_TUL['Al2O3'].values\n",
    "Cpx_B1133_MIT_TUL['MgO_Cpx']=Cpx_B1133_MIT['MgO_Cpx']/df_TUL['MgO'].values\n",
    "Cpx_B1133_MIT_TUL['Na2O_Cpx']=Cpx_B1133_MIT['Na2O_Cpx']/df_TUL['Na2O'].values\n",
    "Cpx_B1133_MIT_TUL['TiO2_Cpx']=Cpx_B1133_MIT['TiO2_Cpx']/df_TUL['TiO2'].values\n",
    "Cpx_B1133_MIT_TUL['FeOt_Cpx']=Cpx_B1133_MIT['FeOt_Cpx']/df_TUL['FeOt'].values\n",
    "Cpx_B1133_MIT_TUL['CaO_Cpx']=Cpx_B1133_MIT['CaO_Cpx']/df_TUL['CaO'].values\n",
    "\n",
    "\n",
    "Cpx_B1133_MIT_HI=Cpx_B1133_MIT.copy()\n",
    "Cpx_B1133_MIT_HI['SiO2_Cpx']=Cpx_B1133_MIT['SiO2_Cpx']/df_HI['SiO2'].values\n",
    "Cpx_B1133_MIT_HI['Al2O3_Cpx']=Cpx_B1133_MIT['Al2O3_Cpx']/df_HI['Al2O3'].values\n",
    "Cpx_B1133_MIT_HI['MgO_Cpx']=Cpx_B1133_MIT['MgO_Cpx']/df_HI['MgO'].values\n",
    "Cpx_B1133_MIT_HI['Na2O_Cpx']=Cpx_B1133_MIT['Na2O_Cpx']/df_HI['Na2O'].values\n",
    "Cpx_B1133_MIT_HI['TiO2_Cpx']=Cpx_B1133_MIT['TiO2_Cpx']/df_HI['TiO2'].values\n",
    "Cpx_B1133_MIT_HI['FeOt_Cpx']=Cpx_B1133_MIT['FeOt_Cpx']/df_HI['FeOt'].values\n",
    "Cpx_B1133_MIT_HI['CaO_Cpx']=Cpx_B1133_MIT['CaO_Cpx']/df_HI['CaO'].values\n",
    "\n",
    "\n",
    "\n",
    "Cpx_B1133_MIT_RI=Cpx_B1133_MIT.copy()\n",
    "Cpx_B1133_MIT_RI['SiO2_Cpx']=Cpx_B1133_MIT['SiO2_Cpx']/df_RI['SiO2'].values\n",
    "Cpx_B1133_MIT_RI['Al2O3_Cpx']=Cpx_B1133_MIT['Al2O3_Cpx']/df_RI['Al2O3'].values\n",
    "Cpx_B1133_MIT_RI['MgO_Cpx']=Cpx_B1133_MIT['MgO_Cpx']/df_RI['MgO'].values\n",
    "Cpx_B1133_MIT_RI['Na2O_Cpx']=Cpx_B1133_MIT['Na2O_Cpx']/df_RI['Na2O'].values\n",
    "Cpx_B1133_MIT_RI['TiO2_Cpx']=Cpx_B1133_MIT['TiO2_Cpx']/df_RI['TiO2'].values\n",
    "Cpx_B1133_MIT_RI['FeOt_Cpx']=Cpx_B1133_MIT['FeOt_Cpx']/df_RI['FeOt'].values\n",
    "Cpx_B1133_MIT_RI['CaO_Cpx']=Cpx_B1133_MIT['CaO_Cpx']/df_RI['CaO'].values\n",
    "\n",
    "\n",
    "\n",
    "Cpx_B1133_MIT_LaMere=Cpx_B1133_MIT.copy()\n",
    "Cpx_B1133_MIT_LaMere['SiO2_Cpx']=Cpx_B1133_MIT['SiO2_Cpx']/df_LaMere['SiO2'].values\n",
    "Cpx_B1133_MIT_LaMere['Al2O3_Cpx']=Cpx_B1133_MIT['Al2O3_Cpx']/df_LaMere['Al2O3'].values\n",
    "Cpx_B1133_MIT_LaMere['MgO_Cpx']=Cpx_B1133_MIT['MgO_Cpx']/df_LaMere['MgO'].values\n",
    "Cpx_B1133_MIT_LaMere['Na2O_Cpx']=Cpx_B1133_MIT['Na2O_Cpx']/df_LaMere['Na2O'].values\n",
    "Cpx_B1133_MIT_LaMere['TiO2_Cpx']=Cpx_B1133_MIT['TiO2_Cpx']/df_LaMere['TiO2'].values\n",
    "Cpx_B1133_MIT_LaMere['FeOt_Cpx']=Cpx_B1133_MIT['FeOt_Cpx']/df_LaMere['FeOt'].values\n",
    "Cpx_B1133_MIT_LaMere['CaO_Cpx']=Cpx_B1133_MIT['CaO_Cpx']/df_LaMere['CaO'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72264c56-9337-4c2d-929d-7d446bf0aadc",
   "metadata": {},
   "source": [
    "## Loading in B1133 liquid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cb36eebe-01d7-4523-b218-d9f7837cd148",
   "metadata": {},
   "outputs": [],
   "source": [
    "out_Liq_B1133=pt.import_excel('Supporting_Data_2.xlsx', \n",
    "                sheet_name='B1133_Glass', suffix=\"_Liq\")\n",
    "Liq_B1133=out_Liq_B1133['Liqs']\n",
    "Liq_B1133_input_all=out_Liq_B1133['my_input']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2959f89d-3068-4ad1-909f-be419069659c",
   "metadata": {},
   "source": [
    "### Corecting liquid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7d0c34d5-c934-4410-a6c6-f70ad376f605",
   "metadata": {},
   "outputs": [],
   "source": [
    "Liq_B1133_MIT=Liq_B1133.copy()\n",
    "Liq_B1133_MIT['SiO2_Liq']=Liq_B1133['SiO2_Liq']*df_MIT['SiO2'].values\n",
    "Liq_B1133_MIT['Al2O3_Liq']=Liq_B1133['Al2O3_Liq']*df_MIT['Al2O3'].values\n",
    "Liq_B1133_MIT['MgO_Liq']=Liq_B1133['MgO_Liq']*df_MIT['MgO'].values\n",
    "Liq_B1133_MIT['Na2O_Liq']=Liq_B1133['Na2O_Liq']*df_MIT['Na2O'].values\n",
    "Liq_B1133_MIT['TiO2_Liq']=Liq_B1133['TiO2_Liq']*df_MIT['TiO2'].values\n",
    "Liq_B1133_MIT['FeOt_Liq']=Liq_B1133['FeOt_Liq']*df_MIT['FeOt'].values\n",
    "Liq_B1133_MIT['CaO_Liq']=Liq_B1133['CaO_Liq']*df_MIT['CaO'].values\n",
    "\n",
    "\n",
    "Liq_B1133_MIT_TAS=Liq_B1133_MIT.copy()\n",
    "Liq_B1133_MIT_TAS['SiO2_Liq']=Liq_B1133_MIT['SiO2_Liq']/df_TAS['SiO2'].values\n",
    "Liq_B1133_MIT_TAS['Al2O3_Liq']=Liq_B1133_MIT['Al2O3_Liq']/df_TAS['Al2O3'].values\n",
    "Liq_B1133_MIT_TAS['MgO_Liq']=Liq_B1133_MIT['MgO_Liq']/df_TAS['MgO'].values\n",
    "Liq_B1133_MIT_TAS['Na2O_Liq']=Liq_B1133_MIT['Na2O_Liq']/df_TAS['Na2O'].values\n",
    "Liq_B1133_MIT_TAS['TiO2_Liq']=Liq_B1133_MIT['TiO2_Liq']/df_TAS['TiO2'].values\n",
    "Liq_B1133_MIT_TAS['FeOt_Liq']=Liq_B1133_MIT['FeOt_Liq']/df_TAS['FeOt'].values\n",
    "Liq_B1133_MIT_TAS['CaO_Liq']=Liq_B1133_MIT['CaO_Liq']/df_TAS['CaO'].values\n",
    "\n",
    "Liq_B1133_MIT_Houston=Liq_B1133_MIT.copy()\n",
    "Liq_B1133_MIT_Houston['SiO2_Liq']=Liq_B1133_MIT['SiO2_Liq']/df_Houston['SiO2'].values\n",
    "Liq_B1133_MIT_Houston['Al2O3_Liq']=Liq_B1133_MIT['Al2O3_Liq']/df_Houston['Al2O3'].values\n",
    "Liq_B1133_MIT_Houston['MgO_Liq']=Liq_B1133_MIT['MgO_Liq']/df_Houston['MgO'].values\n",
    "Liq_B1133_MIT_Houston['Na2O_Liq']=Liq_B1133_MIT['Na2O_Liq']/df_Houston['Na2O'].values\n",
    "Liq_B1133_MIT_Houston['TiO2_Liq']=Liq_B1133_MIT['TiO2_Liq']/df_Houston['TiO2'].values\n",
    "Liq_B1133_MIT_Houston['FeOt_Liq']=Liq_B1133_MIT['FeOt_Liq']/df_Houston['FeOt'].values\n",
    "Liq_B1133_MIT_Houston['CaO_Liq']=Liq_B1133_MIT['CaO_Liq']/df_Houston['CaO'].values\n",
    "\n",
    "Liq_B1133_MIT_Cape=Liq_B1133_MIT.copy()\n",
    "Liq_B1133_MIT_Cape['SiO2_Liq']=Liq_B1133_MIT['SiO2_Liq']/df_Cape['SiO2'].values\n",
    "Liq_B1133_MIT_Cape['Al2O3_Liq']=Liq_B1133_MIT['Al2O3_Liq']/df_Cape['Al2O3'].values\n",
    "Liq_B1133_MIT_Cape['MgO_Liq']=Liq_B1133_MIT['MgO_Liq']/df_Cape['MgO'].values\n",
    "Liq_B1133_MIT_Cape['Na2O_Liq']=Liq_B1133_MIT['Na2O_Liq']/df_Cape['Na2O'].values\n",
    "Liq_B1133_MIT_Cape['TiO2_Liq']=Liq_B1133_MIT['TiO2_Liq']/df_Cape['TiO2'].values\n",
    "Liq_B1133_MIT_Cape['FeOt_Liq']=Liq_B1133_MIT['FeOt_Liq']/df_Cape['FeOt'].values\n",
    "Liq_B1133_MIT_Cape['CaO_Liq']=Liq_B1133_MIT['CaO_Liq']/df_Cape['CaO'].values\n",
    "\n",
    "Liq_B1133_MIT_SUNY=Liq_B1133_MIT.copy()\n",
    "Liq_B1133_MIT_SUNY['SiO2_Liq']=Liq_B1133_MIT['SiO2_Liq']/df_SUNY['SiO2'].values\n",
    "Liq_B1133_MIT_SUNY['Al2O3_Liq']=Liq_B1133_MIT['Al2O3_Liq']/df_SUNY['Al2O3'].values\n",
    "Liq_B1133_MIT_SUNY['MgO_Liq']=Liq_B1133_MIT['MgO_Liq']/df_SUNY['MgO'].values\n",
    "Liq_B1133_MIT_SUNY['Na2O_Liq']=Liq_B1133_MIT['Na2O_Liq']/df_SUNY['Na2O'].values\n",
    "Liq_B1133_MIT_SUNY['TiO2_Liq']=Liq_B1133_MIT['TiO2_Liq']/df_SUNY['TiO2'].values\n",
    "Liq_B1133_MIT_SUNY['FeOt_Liq']=Liq_B1133_MIT['FeOt_Liq']/df_SUNY['FeOt'].values\n",
    "Liq_B1133_MIT_SUNY['CaO_Liq']=Liq_B1133_MIT['CaO_Liq']/df_SUNY['CaO'].values\n",
    "\n",
    "Liq_B1133_MIT_RNAS=Liq_B1133_MIT.copy()\n",
    "Liq_B1133_MIT_RNAS['SiO2_Liq']=Liq_B1133_MIT['SiO2_Liq']/df_RNAS['SiO2'].values\n",
    "Liq_B1133_MIT_RNAS['Al2O3_Liq']=Liq_B1133_MIT['Al2O3_Liq']/df_RNAS['Al2O3'].values\n",
    "Liq_B1133_MIT_RNAS['MgO_Liq']=Liq_B1133_MIT['MgO_Liq']/df_RNAS['MgO'].values\n",
    "Liq_B1133_MIT_RNAS['Na2O_Liq']=Liq_B1133_MIT['Na2O_Liq']/df_RNAS['Na2O'].values\n",
    "Liq_B1133_MIT_RNAS['TiO2_Liq']=Liq_B1133_MIT['TiO2_Liq']/df_RNAS['TiO2'].values\n",
    "Liq_B1133_MIT_RNAS['FeOt_Liq']=Liq_B1133_MIT['FeOt_Liq']/df_RNAS['FeOt'].values\n",
    "Liq_B1133_MIT_RNAS['CaO_Liq']=Liq_B1133_MIT['CaO_Liq']/df_RNAS['CaO'].values\n",
    "\n",
    "Liq_B1133_MIT_UBC=Liq_B1133_MIT.copy()\n",
    "Liq_B1133_MIT_UBC['SiO2_Liq']=Liq_B1133_MIT['SiO2_Liq']/df_UBC['SiO2'].values\n",
    "Liq_B1133_MIT_UBC['Al2O3_Liq']=Liq_B1133_MIT['Al2O3_Liq']/df_UBC['Al2O3'].values\n",
    "Liq_B1133_MIT_UBC['MgO_Liq']=Liq_B1133_MIT['MgO_Liq']/df_UBC['MgO'].values\n",
    "Liq_B1133_MIT_UBC['Na2O_Liq']=Liq_B1133_MIT['Na2O_Liq']/df_UBC['Na2O'].values\n",
    "Liq_B1133_MIT_UBC['TiO2_Liq']=Liq_B1133_MIT['TiO2_Liq']/df_UBC['TiO2'].values\n",
    "Liq_B1133_MIT_UBC['FeOt_Liq']=Liq_B1133_MIT['FeOt_Liq']/df_UBC['FeOt'].values\n",
    "Liq_B1133_MIT_UBC['CaO_Liq']=Liq_B1133_MIT['CaO_Liq']/df_UBC['CaO'].values\n",
    "\n",
    "Liq_B1133_MIT_ANU=Liq_B1133_MIT.copy()\n",
    "Liq_B1133_MIT_ANU['SiO2_Liq']=Liq_B1133_MIT['SiO2_Liq']/df_ANU['SiO2'].values\n",
    "Liq_B1133_MIT_ANU['Al2O3_Liq']=Liq_B1133_MIT['Al2O3_Liq']/df_ANU['Al2O3'].values\n",
    "Liq_B1133_MIT_ANU['MgO_Liq']=Liq_B1133_MIT['MgO_Liq']/df_ANU['MgO'].values\n",
    "Liq_B1133_MIT_ANU['Na2O_Liq']=Liq_B1133_MIT['Na2O_Liq']/df_ANU['Na2O'].values\n",
    "Liq_B1133_MIT_ANU['TiO2_Liq']=Liq_B1133_MIT['TiO2_Liq']/df_ANU['TiO2'].values\n",
    "Liq_B1133_MIT_ANU['FeOt_Liq']=Liq_B1133_MIT['FeOt_Liq']/df_ANU['FeOt'].values\n",
    "Liq_B1133_MIT_ANU['CaO_Liq']=Liq_B1133_MIT['CaO_Liq']/df_ANU['CaO'].values\n",
    "\n",
    "\n",
    "\n",
    "Liq_B1133_MIT_TUL=Liq_B1133_MIT.copy()\n",
    "Liq_B1133_MIT_TUL['SiO2_Liq']=Liq_B1133_MIT['SiO2_Liq']/df_TUL['SiO2'].values\n",
    "Liq_B1133_MIT_TUL['Al2O3_Liq']=Liq_B1133_MIT['Al2O3_Liq']/df_TUL['Al2O3'].values\n",
    "Liq_B1133_MIT_TUL['MgO_Liq']=Liq_B1133_MIT['MgO_Liq']/df_TUL['MgO'].values\n",
    "Liq_B1133_MIT_TUL['Na2O_Liq']=Liq_B1133_MIT['Na2O_Liq']/df_TUL['Na2O'].values\n",
    "Liq_B1133_MIT_TUL['TiO2_Liq']=Liq_B1133_MIT['TiO2_Liq']/df_TUL['TiO2'].values\n",
    "Liq_B1133_MIT_TUL['FeOt_Liq']=Liq_B1133_MIT['FeOt_Liq']/df_TUL['FeOt'].values\n",
    "Liq_B1133_MIT_TUL['CaO_Liq']=Liq_B1133_MIT['CaO_Liq']/df_TUL['CaO'].values\n",
    "\n",
    "\n",
    "Liq_B1133_MIT_HI=Liq_B1133_MIT.copy()\n",
    "Liq_B1133_MIT_HI['SiO2_Liq']=Liq_B1133_MIT['SiO2_Liq']/df_HI['SiO2'].values\n",
    "Liq_B1133_MIT_HI['Al2O3_Liq']=Liq_B1133_MIT['Al2O3_Liq']/df_HI['Al2O3'].values\n",
    "Liq_B1133_MIT_HI['MgO_Liq']=Liq_B1133_MIT['MgO_Liq']/df_HI['MgO'].values\n",
    "Liq_B1133_MIT_HI['Na2O_Liq']=Liq_B1133_MIT['Na2O_Liq']/df_HI['Na2O'].values\n",
    "Liq_B1133_MIT_HI['TiO2_Liq']=Liq_B1133_MIT['TiO2_Liq']/df_HI['TiO2'].values\n",
    "Liq_B1133_MIT_HI['FeOt_Liq']=Liq_B1133_MIT['FeOt_Liq']/df_HI['FeOt'].values\n",
    "Liq_B1133_MIT_HI['CaO_Liq']=Liq_B1133_MIT['CaO_Liq']/df_HI['CaO'].values\n",
    "\n",
    "\n",
    "\n",
    "Liq_B1133_MIT_RI=Liq_B1133_MIT.copy()\n",
    "Liq_B1133_MIT_RI['SiO2_Liq']=Liq_B1133_MIT['SiO2_Liq']/df_RI['SiO2'].values\n",
    "Liq_B1133_MIT_RI['Al2O3_Liq']=Liq_B1133_MIT['Al2O3_Liq']/df_RI['Al2O3'].values\n",
    "Liq_B1133_MIT_RI['MgO_Liq']=Liq_B1133_MIT['MgO_Liq']/df_RI['MgO'].values\n",
    "Liq_B1133_MIT_RI['Na2O_Liq']=Liq_B1133_MIT['Na2O_Liq']/df_RI['Na2O'].values\n",
    "Liq_B1133_MIT_RI['TiO2_Liq']=Liq_B1133_MIT['TiO2_Liq']/df_RI['TiO2'].values\n",
    "Liq_B1133_MIT_RI['FeOt_Liq']=Liq_B1133_MIT['FeOt_Liq']/df_RI['FeOt'].values\n",
    "Liq_B1133_MIT_RI['CaO_Liq']=Liq_B1133_MIT['CaO_Liq']/df_RI['CaO'].values\n",
    "\n",
    "\n",
    "\n",
    "Liq_B1133_MIT_LaMere=Liq_B1133_MIT.copy()\n",
    "Liq_B1133_MIT_LaMere['SiO2_Liq']=Liq_B1133_MIT['SiO2_Liq']/df_LaMere['SiO2'].values\n",
    "Liq_B1133_MIT_LaMere['Al2O3_Liq']=Liq_B1133_MIT['Al2O3_Liq']/df_LaMere['Al2O3'].values\n",
    "Liq_B1133_MIT_LaMere['MgO_Liq']=Liq_B1133_MIT['MgO_Liq']/df_LaMere['MgO'].values\n",
    "Liq_B1133_MIT_LaMere['Na2O_Liq']=Liq_B1133_MIT['Na2O_Liq']/df_LaMere['Na2O'].values\n",
    "Liq_B1133_MIT_LaMere['TiO2_Liq']=Liq_B1133_MIT['TiO2_Liq']/df_LaMere['TiO2'].values\n",
    "Liq_B1133_MIT_LaMere['FeOt_Liq']=Liq_B1133_MIT['FeOt_Liq']/df_LaMere['FeOt'].values\n",
    "Liq_B1133_MIT_LaMere['CaO_Liq']=Liq_B1133_MIT['CaO_Liq']/df_LaMere['CaO'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b98bb704-155e-45b5-84a6-60307336ce72",
   "metadata": {},
   "source": [
    "## Loading B1133 liquid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c7828aa7-cdd5-40d1-9b1a-b04824b12ad2",
   "metadata": {},
   "outputs": [],
   "source": [
    "out_Liq_B1133=pt.import_excel('Supporting_Data_2.xlsx', sheet_name='B1133_Glass', suffix=\"_Liq\")\n",
    "Liq_B1133=out_Liq_B1133['Liqs']\n",
    "Liq_B1133_input_all=out_Liq_B1133['my_input']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da9d5217-6834-4cb5-9ced-5457265c064e",
   "metadata": {},
   "source": [
    "### Correcting B1133 liquid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "debe05e9-59b6-43c2-85b1-9e661a2d74ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "Liq_B1133_MIT=Liq_B1133.copy()\n",
    "Liq_B1133_MIT['SiO2_Liq']=Liq_B1133['SiO2_Liq']*df_MIT['SiO2'].values\n",
    "Liq_B1133_MIT['Al2O3_Liq']=Liq_B1133['Al2O3_Liq']*df_MIT['Al2O3'].values\n",
    "Liq_B1133_MIT['MgO_Liq']=Liq_B1133['MgO_Liq']*df_MIT['MgO'].values\n",
    "Liq_B1133_MIT['Na2O_Liq']=Liq_B1133['Na2O_Liq']*df_MIT['Na2O'].values\n",
    "Liq_B1133_MIT['TiO2_Liq']=Liq_B1133['TiO2_Liq']*df_MIT['TiO2'].values\n",
    "Liq_B1133_MIT['FeOt_Liq']=Liq_B1133['FeOt_Liq']*df_MIT['FeOt'].values\n",
    "Liq_B1133_MIT['CaO_Liq']=Liq_B1133['CaO_Liq']*df_MIT['CaO'].values\n",
    "\n",
    "Liq_B1133_MIT_Houston=Liq_B1133_MIT.copy()\n",
    "Liq_B1133_MIT_Houston['SiO2_Liq']=Liq_B1133_MIT['SiO2_Liq']/df_Houston['SiO2'].values\n",
    "Liq_B1133_MIT_Houston['Al2O3_Liq']=Liq_B1133_MIT['Al2O3_Liq']/df_Houston['Al2O3'].values\n",
    "Liq_B1133_MIT_Houston['MgO_Liq']=Liq_B1133_MIT['MgO_Liq']/df_Houston['MgO'].values\n",
    "Liq_B1133_MIT_Houston['Na2O_Liq']=Liq_B1133_MIT['Na2O_Liq']/df_Houston['Na2O'].values\n",
    "Liq_B1133_MIT_Houston['TiO2_Liq']=Liq_B1133_MIT['TiO2_Liq']/df_Houston['TiO2'].values\n",
    "Liq_B1133_MIT_Houston['FeOt_Liq']=Liq_B1133_MIT['FeOt_Liq']/df_Houston['FeOt'].values\n",
    "Liq_B1133_MIT_Houston['CaO_Liq']=Liq_B1133_MIT['CaO_Liq']/df_Houston['CaO'].values\n",
    "\n",
    "Liq_B1133_MIT_TAS=Liq_B1133_MIT.copy()\n",
    "Liq_B1133_MIT_TAS['SiO2_Liq']=Liq_B1133_MIT['SiO2_Liq']/df_TAS['SiO2'].values\n",
    "Liq_B1133_MIT_TAS['Al2O3_Liq']=Liq_B1133_MIT['Al2O3_Liq']/df_TAS['Al2O3'].values\n",
    "Liq_B1133_MIT_TAS['MgO_Liq']=Liq_B1133_MIT['MgO_Liq']/df_TAS['MgO'].values\n",
    "Liq_B1133_MIT_TAS['Na2O_Liq']=Liq_B1133_MIT['Na2O_Liq']/df_TAS['Na2O'].values\n",
    "Liq_B1133_MIT_TAS['TiO2_Liq']=Liq_B1133_MIT['TiO2_Liq']/df_TAS['TiO2'].values\n",
    "Liq_B1133_MIT_TAS['FeOt_Liq']=Liq_B1133_MIT['FeOt_Liq']/df_TAS['FeOt'].values\n",
    "Liq_B1133_MIT_TAS['CaO_Liq']=Liq_B1133_MIT['CaO_Liq']/df_TAS['CaO'].values\n",
    "\n",
    "Liq_B1133_MIT_Cape=Liq_B1133_MIT.copy()\n",
    "Liq_B1133_MIT_Cape['SiO2_Liq']=Liq_B1133_MIT['SiO2_Liq']/df_Cape['SiO2'].values\n",
    "Liq_B1133_MIT_Cape['Al2O3_Liq']=Liq_B1133_MIT['Al2O3_Liq']/df_Cape['Al2O3'].values\n",
    "Liq_B1133_MIT_Cape['MgO_Liq']=Liq_B1133_MIT['MgO_Liq']/df_Cape['MgO'].values\n",
    "Liq_B1133_MIT_Cape['Na2O_Liq']=Liq_B1133_MIT['Na2O_Liq']/df_Cape['Na2O'].values\n",
    "Liq_B1133_MIT_Cape['TiO2_Liq']=Liq_B1133_MIT['TiO2_Liq']/df_Cape['TiO2'].values\n",
    "Liq_B1133_MIT_Cape['FeOt_Liq']=Liq_B1133_MIT['FeOt_Liq']/df_Cape['FeOt'].values\n",
    "Liq_B1133_MIT_Cape['CaO_Liq']=Liq_B1133_MIT['CaO_Liq']/df_Cape['CaO'].values\n",
    "\n",
    "Liq_B1133_MIT_SUNY=Liq_B1133_MIT.copy()\n",
    "Liq_B1133_MIT_SUNY['SiO2_Liq']=Liq_B1133_MIT['SiO2_Liq']/df_SUNY['SiO2'].values\n",
    "Liq_B1133_MIT_SUNY['Al2O3_Liq']=Liq_B1133_MIT['Al2O3_Liq']/df_SUNY['Al2O3'].values\n",
    "Liq_B1133_MIT_SUNY['MgO_Liq']=Liq_B1133_MIT['MgO_Liq']/df_SUNY['MgO'].values\n",
    "Liq_B1133_MIT_SUNY['Na2O_Liq']=Liq_B1133_MIT['Na2O_Liq']/df_SUNY['Na2O'].values\n",
    "Liq_B1133_MIT_SUNY['TiO2_Liq']=Liq_B1133_MIT['TiO2_Liq']/df_SUNY['TiO2'].values\n",
    "Liq_B1133_MIT_SUNY['FeOt_Liq']=Liq_B1133_MIT['FeOt_Liq']/df_SUNY['FeOt'].values\n",
    "Liq_B1133_MIT_SUNY['CaO_Liq']=Liq_B1133_MIT['CaO_Liq']/df_SUNY['CaO'].values\n",
    "\n",
    "Liq_B1133_MIT_RNAS=Liq_B1133_MIT.copy()\n",
    "Liq_B1133_MIT_RNAS['SiO2_Liq']=Liq_B1133_MIT['SiO2_Liq']/df_RNAS['SiO2'].values\n",
    "Liq_B1133_MIT_RNAS['Al2O3_Liq']=Liq_B1133_MIT['Al2O3_Liq']/df_RNAS['Al2O3'].values\n",
    "Liq_B1133_MIT_RNAS['MgO_Liq']=Liq_B1133_MIT['MgO_Liq']/df_RNAS['MgO'].values\n",
    "Liq_B1133_MIT_RNAS['Na2O_Liq']=Liq_B1133_MIT['Na2O_Liq']/df_RNAS['Na2O'].values\n",
    "Liq_B1133_MIT_RNAS['TiO2_Liq']=Liq_B1133_MIT['TiO2_Liq']/df_RNAS['TiO2'].values\n",
    "Liq_B1133_MIT_RNAS['FeOt_Liq']=Liq_B1133_MIT['FeOt_Liq']/df_RNAS['FeOt'].values\n",
    "Liq_B1133_MIT_RNAS['CaO_Liq']=Liq_B1133_MIT['CaO_Liq']/df_RNAS['CaO'].values\n",
    "\n",
    "Liq_B1133_MIT_UBC=Liq_B1133_MIT.copy()\n",
    "Liq_B1133_MIT_UBC['SiO2_Liq']=Liq_B1133_MIT['SiO2_Liq']/df_UBC['SiO2'].values\n",
    "Liq_B1133_MIT_UBC['Al2O3_Liq']=Liq_B1133_MIT['Al2O3_Liq']/df_UBC['Al2O3'].values\n",
    "Liq_B1133_MIT_UBC['MgO_Liq']=Liq_B1133_MIT['MgO_Liq']/df_UBC['MgO'].values\n",
    "Liq_B1133_MIT_UBC['Na2O_Liq']=Liq_B1133_MIT['Na2O_Liq']/df_UBC['Na2O'].values\n",
    "Liq_B1133_MIT_UBC['TiO2_Liq']=Liq_B1133_MIT['TiO2_Liq']/df_UBC['TiO2'].values\n",
    "Liq_B1133_MIT_UBC['FeOt_Liq']=Liq_B1133_MIT['FeOt_Liq']/df_UBC['FeOt'].values\n",
    "Liq_B1133_MIT_UBC['CaO_Liq']=Liq_B1133_MIT['CaO_Liq']/df_UBC['CaO'].values\n",
    "\n",
    "Liq_B1133_MIT_ANU=Liq_B1133_MIT.copy()\n",
    "Liq_B1133_MIT_ANU['SiO2_Liq']=Liq_B1133_MIT['SiO2_Liq']/df_ANU['SiO2'].values\n",
    "Liq_B1133_MIT_ANU['Al2O3_Liq']=Liq_B1133_MIT['Al2O3_Liq']/df_ANU['Al2O3'].values\n",
    "Liq_B1133_MIT_ANU['MgO_Liq']=Liq_B1133_MIT['MgO_Liq']/df_ANU['MgO'].values\n",
    "Liq_B1133_MIT_ANU['Na2O_Liq']=Liq_B1133_MIT['Na2O_Liq']/df_ANU['Na2O'].values\n",
    "Liq_B1133_MIT_ANU['TiO2_Liq']=Liq_B1133_MIT['TiO2_Liq']/df_ANU['TiO2'].values\n",
    "Liq_B1133_MIT_ANU['FeOt_Liq']=Liq_B1133_MIT['FeOt_Liq']/df_ANU['FeOt'].values\n",
    "Liq_B1133_MIT_ANU['CaO_Liq']=Liq_B1133_MIT['CaO_Liq']/df_ANU['CaO'].values\n",
    "\n",
    "\n",
    "\n",
    "Liq_B1133_MIT_TUL=Liq_B1133_MIT.copy()\n",
    "Liq_B1133_MIT_TUL['SiO2_Liq']=Liq_B1133_MIT['SiO2_Liq']/df_TUL['SiO2'].values\n",
    "Liq_B1133_MIT_TUL['Al2O3_Liq']=Liq_B1133_MIT['Al2O3_Liq']/df_TUL['Al2O3'].values\n",
    "Liq_B1133_MIT_TUL['MgO_Liq']=Liq_B1133_MIT['MgO_Liq']/df_TUL['MgO'].values\n",
    "Liq_B1133_MIT_TUL['Na2O_Liq']=Liq_B1133_MIT['Na2O_Liq']/df_TUL['Na2O'].values\n",
    "Liq_B1133_MIT_TUL['TiO2_Liq']=Liq_B1133_MIT['TiO2_Liq']/df_TUL['TiO2'].values\n",
    "Liq_B1133_MIT_TUL['FeOt_Liq']=Liq_B1133_MIT['FeOt_Liq']/df_TUL['FeOt'].values\n",
    "Liq_B1133_MIT_TUL['CaO_Liq']=Liq_B1133_MIT['CaO_Liq']/df_TUL['CaO'].values\n",
    "\n",
    "\n",
    "Liq_B1133_MIT_HI=Liq_B1133_MIT.copy()\n",
    "Liq_B1133_MIT_HI['SiO2_Liq']=Liq_B1133_MIT['SiO2_Liq']/df_HI['SiO2'].values\n",
    "Liq_B1133_MIT_HI['Al2O3_Liq']=Liq_B1133_MIT['Al2O3_Liq']/df_HI['Al2O3'].values\n",
    "Liq_B1133_MIT_HI['MgO_Liq']=Liq_B1133_MIT['MgO_Liq']/df_HI['MgO'].values\n",
    "Liq_B1133_MIT_HI['Na2O_Liq']=Liq_B1133_MIT['Na2O_Liq']/df_HI['Na2O'].values\n",
    "Liq_B1133_MIT_HI['TiO2_Liq']=Liq_B1133_MIT['TiO2_Liq']/df_HI['TiO2'].values\n",
    "Liq_B1133_MIT_HI['FeOt_Liq']=Liq_B1133_MIT['FeOt_Liq']/df_HI['FeOt'].values\n",
    "Liq_B1133_MIT_HI['CaO_Liq']=Liq_B1133_MIT['CaO_Liq']/df_HI['CaO'].values\n",
    "\n",
    "\n",
    "\n",
    "Liq_B1133_MIT_RI=Liq_B1133_MIT.copy()\n",
    "Liq_B1133_MIT_RI['SiO2_Liq']=Liq_B1133_MIT['SiO2_Liq']/df_RI['SiO2'].values\n",
    "Liq_B1133_MIT_RI['Al2O3_Liq']=Liq_B1133_MIT['Al2O3_Liq']/df_RI['Al2O3'].values\n",
    "Liq_B1133_MIT_RI['MgO_Liq']=Liq_B1133_MIT['MgO_Liq']/df_RI['MgO'].values\n",
    "Liq_B1133_MIT_RI['Na2O_Liq']=Liq_B1133_MIT['Na2O_Liq']/df_RI['Na2O'].values\n",
    "Liq_B1133_MIT_RI['TiO2_Liq']=Liq_B1133_MIT['TiO2_Liq']/df_RI['TiO2'].values\n",
    "Liq_B1133_MIT_RI['FeOt_Liq']=Liq_B1133_MIT['FeOt_Liq']/df_RI['FeOt'].values\n",
    "Liq_B1133_MIT_RI['CaO_Liq']=Liq_B1133_MIT['CaO_Liq']/df_RI['CaO'].values\n",
    "\n",
    "\n",
    "\n",
    "Liq_B1133_MIT_LaMere=Liq_B1133_MIT.copy()\n",
    "Liq_B1133_MIT_LaMere['SiO2_Liq']=Liq_B1133_MIT['SiO2_Liq']/df_LaMere['SiO2'].values\n",
    "Liq_B1133_MIT_LaMere['Al2O3_Liq']=Liq_B1133_MIT['Al2O3_Liq']/df_LaMere['Al2O3'].values\n",
    "Liq_B1133_MIT_LaMere['MgO_Liq']=Liq_B1133_MIT['MgO_Liq']/df_LaMere['MgO'].values\n",
    "Liq_B1133_MIT_LaMere['Na2O_Liq']=Liq_B1133_MIT['Na2O_Liq']/df_LaMere['Na2O'].values\n",
    "Liq_B1133_MIT_LaMere['TiO2_Liq']=Liq_B1133_MIT['TiO2_Liq']/df_LaMere['TiO2'].values\n",
    "Liq_B1133_MIT_LaMere['FeOt_Liq']=Liq_B1133_MIT['FeOt_Liq']/df_LaMere['FeOt'].values\n",
    "Liq_B1133_MIT_LaMere['CaO_Liq']=Liq_B1133_MIT['CaO_Liq']/df_LaMere['CaO'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1253be0-62ad-4b38-bc44-545c0c45438d",
   "metadata": {},
   "source": [
    "## Calculating Cpx-Liq P and T For B1133\n",
    "- Here we calculate a mean composition of Cpx and Liq for different labs, where we have corrected both the liquid and the Cpx compositions for EPMA lab offsets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a532eef6-b34a-422f-bfbc-9f2bd8a6b614",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings as w\n",
    "with w.catch_warnings():\n",
    "    w.simplefilter('ignore')\n",
    "    ## Correcting liquid and cpx\n",
    "    Cpx_Liq_B1133_Calc_Meas=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1133.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1133.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Neave2017\")\n",
    "\n",
    "    Cpx_Liq_B1133_Calc_Corr_MIT=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1133_MIT.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1133_MIT.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Neave2017\")\n",
    "    Cpx_Liq_B1133_Calc_Corr_TAS=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1133_MIT_TAS.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1133_MIT_TAS.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Neave2017\")\n",
    "    Cpx_Liq_B1133_Calc_Corr_TUL=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1133_MIT_TUL.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1133_MIT_TUL.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Neave2017\")\n",
    "    Cpx_Liq_B1133_Calc_Corr_HI=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1133_MIT_HI.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1133_MIT_HI.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Neave2017\")\n",
    "    Cpx_Liq_B1133_Calc_Corr_RI=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1133_MIT_RI.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1133_MIT_RI.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Neave2017\")\n",
    "\n",
    "    Cpx_Liq_B1133_Calc_Corr_LaMere=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1133_MIT_LaMere.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1133_MIT_LaMere.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Neave2017\")\n",
    "\n",
    "    Cpx_Liq_B1133_Calc_Corr_Cape=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1133_MIT_Cape.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1133_MIT_Cape.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Neave2017\")\n",
    "\n",
    "    Cpx_Liq_B1133_Calc_Corr_SUNY=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1133_MIT_SUNY.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1133_MIT_SUNY.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Neave2017\")\n",
    "    Cpx_Liq_B1133_Calc_Corr_RNAS=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1133_MIT_RNAS.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1133_MIT_RNAS.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Neave2017\")\n",
    "    Cpx_Liq_B1133_Calc_Corr_UBC=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1133_MIT_UBC.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1133_MIT_UBC.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Neave2017\")\n",
    "    Cpx_Liq_B1133_Calc_Corr_ANU=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1133_MIT_ANU.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1133_MIT_ANU.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Neave2017\")\n",
    "    \n",
    "    Cpx_Liq_B1133_Calc_Corr_Houston=pt.calculate_cpx_liq_press_temp(cpx_comps=Cpx_B1133_MIT_Houston.mean(axis=0).to_frame().T,\n",
    "                                                                     liq_comps=Liq_B1133_MIT_Houston.mean(axis=0).to_frame().T,\n",
    "          equationT=\"T_Put2008_eq33\", equationP=\"P_Neave2017\")    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b31d8016-2e55-4c18-bcaa-9f4f7aaca82c",
   "metadata": {},
   "source": [
    "## Cpx only pressure and temperatures for B1133"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d2cd5d0d-a78d-42de-b3cb-055a1fdce8a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings as w\n",
    "with w.catch_warnings():\n",
    "    w.simplefilter('ignore')\n",
    "    Cpx_B1133_Calc_Meas=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1133, H2O_Liq=13)\n",
    "    Cpx_B1133_Calc_Corr_MIT=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1133_MIT, H2O_Liq=13)\n",
    "    Cpx_B1133_Calc_Corr_TAS=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1133_MIT_TAS, H2O_Liq=13)\n",
    "\n",
    "    Cpx_B1133_Calc_Meas=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1133.mean(axis=0).to_frame().T, H2O_Liq=13)\n",
    "    Cpx_B1133_Calc_Corr_MIT=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1133_MIT.mean(axis=0).to_frame().T, H2O_Liq=13)\n",
    "    Cpx_B1133_Calc_Corr_TAS=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1133_MIT_TAS.mean(axis=0).to_frame().T,\n",
    "                                                                     H2O_Liq=13)\n",
    "    Cpx_B1133_Calc_Corr_TUL=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1133_MIT_TUL.mean(axis=0).to_frame().T,\n",
    "                                                                     H2O_Liq=13)\n",
    "    Cpx_B1133_Calc_Corr_HI=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1133_MIT_HI.mean(axis=0).to_frame().T,\n",
    "                                                                     H2O_Liq=13)\n",
    "    Cpx_B1133_Calc_Corr_RI=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1133_MIT_RI.mean(axis=0).to_frame().T,\n",
    "                                                                     H2O_Liq=13)\n",
    "    Cpx_B1133_Calc_Corr_LaMere=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1133_MIT_LaMere.mean(axis=0).to_frame().T,\n",
    "                                                                     H2O_Liq=13)\n",
    "    Cpx_B1133_Calc_Corr_Cape=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1133_MIT_Cape.mean(axis=0).to_frame().T,\n",
    "                                                                     H2O_Liq=13)\n",
    "    Cpx_B1133_Calc_Corr_SUNY=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1133_MIT_SUNY.mean(axis=0).to_frame().T,\n",
    "                                                                     H2O_Liq=13)\n",
    "    Cpx_B1133_Calc_Corr_RNAS=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1133_MIT_RNAS.mean(axis=0).to_frame().T,\n",
    "                                                                     H2O_Liq=13)\n",
    "    Cpx_B1133_Calc_Corr_UBC=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1133_MIT_UBC.mean(axis=0).to_frame().T,\n",
    "                                                                     H2O_Liq=13)\n",
    "    Cpx_B1133_Calc_Corr_ANU=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1133_MIT_ANU.mean(axis=0).to_frame().T,\n",
    "                                                                     H2O_Liq=13)\n",
    "    Cpx_B1133_Calc_Corr_Houston=pt.calculate_cpx_only_press_all_eqs(cpx_comps=Cpx_B1133_MIT_Houston.mean(axis=0).to_frame().T,\n",
    "                                                                     H2O_Liq=13)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7214e99-f379-48b0-ac41-b8beee8242a2",
   "metadata": {},
   "source": [
    "## Figure showing correction factor and Cpx only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d9265137-1d27-4dcd-8ac0-d86c334495cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\penny\\AppData\\Local\\Temp\\ipykernel_18796\\503684177.py:69: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"-k\" (-> color='k'). The keyword argument will take precedence.\n",
      "  ax1.plot([1, 1.12], [1, 1], '-k', lw=0.3, color='grey')\n",
      "C:\\Users\\penny\\AppData\\Local\\Temp\\ipykernel_18796\\503684177.py:70: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"-k\" (-> color='k'). The keyword argument will take precedence.\n",
      "  ax1.plot([1, 1], [0.93, 1.09], '-k', lw=0.3, color='grey')\n",
      "C:\\Users\\penny\\AppData\\Local\\Temp\\ipykernel_18796\\503684177.py:71: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"-k\" (-> color='k'). The keyword argument will take precedence.\n",
      "  ax2.plot([0.95, 1.07], [1, 1], '-k', lw=0.3, color='grey')\n",
      "C:\\Users\\penny\\AppData\\Local\\Temp\\ipykernel_18796\\503684177.py:72: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"-k\" (-> color='k'). The keyword argument will take precedence.\n",
      "  ax2.plot([1, 1], [0.93, 1.01], '-k', lw=0.3, color='grey')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(12,10))\n",
    "\n",
    "ax2.plot(gale['Na2O'].loc[gale['loc']==\"EMP-UNIVERSITY OF HAWAII\"], \n",
    "         gale['Al2O3'].loc[gale['loc']==\"EMP-UNIVERSITY OF HAWAII\"], 'ok', ms=7, label='HI')\n",
    "ax2.plot(gale['Na2O'].loc[gale['loc']==\"EMP-UNIVERSITY OF RHODE ISLAND\"], \n",
    "         gale['Al2O3'].loc[gale['loc']==\"EMP-UNIVERSITY OF RHODE ISLAND\"], 'ok', mfc='cyan', ms=7, label='RI')\n",
    "ax2.plot(gale['Na2O'].loc[gale['loc']==\"EMP-MASSACHUSETTS INSTITUTE OF TECHNOLOGY\"], \n",
    "         gale['Al2O3'].loc[gale['loc']==\"EMP-MASSACHUSETTS INSTITUTE OF TECHNOLOGY\"], 'ok', mfc='yellow', ms=7, label='M_MIT')\n",
    "ax2.plot(gale['Na2O'].loc[gale['loc']==\"EMP-SUNY AT STONY BROOK\"], \n",
    "         gale['Al2O3'].loc[gale['loc']==\"EMP-SUNY AT STONY BROOK\"], 'pk', mfc='white', ms=10, label='SUNY')\n",
    "ax2.plot(gale['Na2O'].loc[gale['loc']==\"EMP-UNIVERSITY OF HOUSTON\"], \n",
    "         gale['Al2O3'].loc[gale['loc']==\"EMP-UNIVERSITY OF HOUSTON\"], 'ok', mfc='grey', ms=7, label='Houston'\n",
    "        )\n",
    "ax2.plot(gale['Na2O'].loc[gale['loc']==\"EMP-UNIVERSITY OF TULSA\"], \n",
    "         gale['Al2O3'].loc[gale['loc']==\"EMP-UNIVERSITY OF TULSA\"], 'ok', mfc='blue', ms=7, label='Tulsa')\n",
    "ax2.plot(gale['Na2O'].loc[gale['loc']==\"EMP-UNIVERSITY OF CAPE TOWN\"], \n",
    "         gale['Al2O3'].loc[gale['loc']==\"EMP-UNIVERSITY OF CAPE TOWN\"], 'ok', mfc='magenta', ms=7, label='Cape Town')\n",
    "\n",
    "ax2.plot(gale['Na2O'].loc[gale['loc']==\"EMP-INSTITUT FRANCAIS POUR L'EXPLOITATION DE LA MER\"], \n",
    "         gale['Al2O3'].loc[gale['loc']==\"EMP-INSTITUT FRANCAIS POUR L'EXPLOITATION DE LA MER\"], 'sk', mfc='red', ms=8, label='La Mere')\n",
    "\n",
    "ax2.plot(gale['Na2O'].loc[gale['loc']==\"EMP-UNIVERSITY OF TASMANIA\"], \n",
    "         gale['Al2O3'].loc[gale['loc']==\"EMP-UNIVERSITY OF TASMANIA\"], '*k', mfc='k', ms=10, label='Tasmania'\n",
    "        )\n",
    "ax2.plot(gale['Na2O'].loc[gale['loc']==\"EMP-RUSSIAN NATIONAL ACADEMY OF SCIENCES\"], \n",
    "         gale['Al2O3'].loc[gale['loc']==\"EMP-RUSSIAN NATIONAL ACADEMY OF SCIENCES\"], 'pk', mfc='orange', ms=10, label='Russian NAS')\n",
    "\n",
    "ax2.plot(gale['Na2O'].loc[gale['loc']==\"EMP-UNIVERSITY OF BRITISH COLUMBIA\"], \n",
    "         gale['Al2O3'].loc[gale['loc']==\"EMP-UNIVERSITY OF BRITISH COLUMBIA\"], 'dk', mfc='white', ms=7, label='UBC')\n",
    "\n",
    "ax2.plot(gale['Na2O'].loc[gale['loc']==\"EMP-AUSTRALIAN NATIONAL UNIVERSITY\"], \n",
    "         gale['Al2O3'].loc[gale['loc']==\"EMP-AUSTRALIAN NATIONAL UNIVERSITY\"], '+k', ms=15, lw=2, label='ANU')\n",
    "\n",
    "ax2.set_xlabel('Na$_2$O Correction Factor')\n",
    "ax2.set_ylabel('Al$_2$O$_3$ Correction Factor')\n",
    "\n",
    "ax1.plot(gale['MgO'].loc[gale['loc']==\"EMP-UNIVERSITY OF HAWAII\"], \n",
    "         gale['FeOt'].loc[gale['loc']==\"EMP-UNIVERSITY OF HAWAII\"], 'ok', ms=7)\n",
    "ax1.plot(gale['MgO'].loc[gale['loc']==\"EMP-UNIVERSITY OF RHODE ISLAND\"], \n",
    "         gale['FeOt'].loc[gale['loc']==\"EMP-UNIVERSITY OF RHODE ISLAND\"], 'ok', mfc='cyan', ms=7)\n",
    "ax1.plot(gale['MgO'].loc[gale['loc']==\"EMP-MASSACHUSETTS INSTITUTE OF TECHNOLOGY\"], \n",
    "         gale['FeOt'].loc[gale['loc']==\"EMP-MASSACHUSETTS INSTITUTE OF TECHNOLOGY\"], 'ok', mfc='yellow', ms=10)\n",
    "ax1.plot(gale['MgO'].loc[gale['loc']==\"EMP-SUNY AT STONY BROOK\"], \n",
    "         gale['FeOt'].loc[gale['loc']==\"EMP-SUNY AT STONY BROOK\"], 'pk', mfc='white', ms=10)\n",
    "ax1.plot(gale['MgO'].loc[gale['loc']==\"EMP-UNIVERSITY OF HOUSTON\"], \n",
    "         gale['FeOt'].loc[gale['loc']==\"EMP-UNIVERSITY OF HOUSTON\"], 'ok', mfc='grey', ms=7)\n",
    "ax1.plot(gale['MgO'].loc[gale['loc']==\"EMP-UNIVERSITY OF TULSA\"], \n",
    "         gale['FeOt'].loc[gale['loc']==\"EMP-UNIVERSITY OF TULSA\"], 'ok', mfc='blue', ms=7)\n",
    "ax1.plot(gale['MgO'].loc[gale['loc']==\"EMP-UNIVERSITY OF CAPE TOWN\"], \n",
    "         gale['FeOt'].loc[gale['loc']==\"EMP-UNIVERSITY OF CAPE TOWN\"], 'ok', mfc='magenta', ms=7)\n",
    "\n",
    "ax1.plot(gale['MgO'].loc[gale['loc']==\"EMP-INSTITUT FRANCAIS POUR L'EXPLOITATION DE LA MER\"], \n",
    "         gale['FeOt'].loc[gale['loc']==\"EMP-INSTITUT FRANCAIS POUR L'EXPLOITATION DE LA MER\"], 'sk', mfc='red', ms=8)\n",
    "\n",
    "ax1.plot(gale['MgO'].loc[gale['loc']==\"EMP-UNIVERSITY OF TASMANIA\"], \n",
    "         gale['FeOt'].loc[gale['loc']==\"EMP-UNIVERSITY OF TASMANIA\"], '*k', mfc='k', ms=10)\n",
    "\n",
    "ax1.plot(gale['MgO'].loc[gale['loc']==\"EMP-RUSSIAN NATIONAL ACADEMY OF SCIENCES\"], \n",
    "         gale['FeOt'].loc[gale['loc']==\"EMP-RUSSIAN NATIONAL ACADEMY OF SCIENCES\"], 'pk', mfc='orange', ms=10)\n",
    "\n",
    "ax1.plot(gale['MgO'].loc[gale['loc']==\"EMP-UNIVERSITY OF BRITISH COLUMBIA\"], \n",
    "         gale['FeOt'].loc[gale['loc']==\"EMP-UNIVERSITY OF BRITISH COLUMBIA\"], 'dk', mfc='white', ms=7)\n",
    "\n",
    "ax1.plot(gale['MgO'].loc[gale['loc']==\"EMP-AUSTRALIAN NATIONAL UNIVERSITY\"], \n",
    "         gale['FeOt'].loc[gale['loc']==\"EMP-AUSTRALIAN NATIONAL UNIVERSITY\"], '+k', ms=15, lw=2)\n",
    "\n",
    "ax1.set_xlabel('MgO Correction Factor')\n",
    "ax1.set_ylabel('FeO$_t$ Correction Factor')\n",
    "ax1.plot([1, 1.12], [1, 1], '-k', lw=0.3, color='grey')\n",
    "ax1.plot([1, 1], [0.93, 1.09], '-k', lw=0.3, color='grey')\n",
    "ax2.plot([0.95, 1.07], [1, 1], '-k', lw=0.3, color='grey')\n",
    "ax2.plot([1, 1], [0.93, 1.01], '-k', lw=0.3, color='grey')\n",
    "ax1.plot(1, 1, '*k', ms=20, mfc='grey')#, label='Lamont')\n",
    "ax2.plot(1, 1, '*k', ms=20, mfc='grey')#,  label='Lamont')\n",
    "\n",
    "# handles, labels = ax2.get_legend_handles_labels()\n",
    "# fig.legend(handles, labels, loc='upper center', ncol=7)\n",
    "\n",
    "#### Thermobarometry - Cpx only\n",
    "x='T_Put_Teq32d_Peq32b'\n",
    "y='P_Put_Teq32d_Peq32b'\n",
    "ax3.plot(Cpx_B1133_Calc_Meas[x], Cpx_B1133_Calc_Meas[y], 'ok', mfc='yellow', ms=10)\n",
    "# plt.plot(Cpx_B1133_Calc_Meas[x], Cpx_B1133_Calc_Meas[y], '.k', mfc='yellow')\n",
    "# plt.plot(Cpx_B1133_Calc_Corr_MIT[x], Cpx_B1133_Calc_Corr_MIT[y], '.r')\n",
    "ax3.plot(Cpx_B1133_Calc_Corr_MIT[x], Cpx_B1133_Calc_Corr_MIT[y], '*k', mfc='grey', ms=15)\n",
    "ax3.plot(Cpx_B1133_Calc_Corr_TAS[x], Cpx_B1133_Calc_Corr_TAS[y], '*k', mfc='k', ms=10)\n",
    "ax3.plot(Cpx_B1133_Calc_Corr_TUL[x], Cpx_B1133_Calc_Corr_TUL[y], 'ok', mfc='b')\n",
    "ax3.plot(Cpx_B1133_Calc_Corr_HI[x], Cpx_B1133_Calc_Corr_HI[y], 'ok', mfc='k')\n",
    "ax3.plot(Cpx_B1133_Calc_Corr_RI[x], Cpx_B1133_Calc_Corr_RI[y], 'ok', mfc='c')\n",
    "ax3.plot(Cpx_B1133_Calc_Corr_LaMere[x], Cpx_B1133_Calc_Corr_LaMere[y], 'sk', mfc='r', ms=7)\n",
    "ax3.plot(Cpx_B1133_Calc_Corr_LaMere[x], Cpx_B1133_Calc_Corr_LaMere[y], 'sk', mfc='r', ms=7)\n",
    "ax3.plot(Cpx_B1133_Calc_Corr_Cape[x], Cpx_B1133_Calc_Corr_Cape[y], 'ok', mfc='magenta', ms=7)\n",
    "ax3.plot(Cpx_B1133_Calc_Corr_SUNY[x], Cpx_B1133_Calc_Corr_SUNY[y], 'pk', mfc='white', ms=10)\n",
    "ax3.plot(Cpx_B1133_Calc_Corr_RNAS[x], Cpx_B1133_Calc_Corr_RNAS[y], 'pk', mfc='orange', ms=10)\n",
    "ax3.plot(Cpx_B1133_Calc_Corr_UBC[x], Cpx_B1133_Calc_Corr_UBC[y], 'dk', mfc='white', ms=7)\n",
    "ax3.plot(Cpx_B1133_Calc_Corr_ANU[x], Cpx_B1133_Calc_Corr_ANU[y], '+k',  ms=15)\n",
    "ax3.plot(Cpx_B1133_Calc_Corr_Houston[x], Cpx_B1133_Calc_Corr_Houston[y], 'ok', mfc='grey', ms=7)\n",
    "\n",
    "\n",
    "ax3.set_ylabel('Cpx-only Press (kbar, Putirka 2008 eq 32b)')\n",
    "ax3.set_xlabel('Cpx-only Temp (K, Putirka 2008 eq 32d)')\n",
    "ax3.plot()\n",
    "\n",
    "## Cpx-Liq, Liq and Cpx correctd\n",
    "x='T_K_calc'\n",
    "y='P_kbar_calc'\n",
    "ax4.plot(Cpx_Liq_B1133_Calc_Meas[x], Cpx_Liq_B1133_Calc_Meas[y], 'ok', mfc='yellow', ms=10)\n",
    "ax4.plot(Cpx_Liq_B1133_Calc_Corr_MIT[x], Cpx_Liq_B1133_Calc_Corr_MIT[y], '*k', mfc='grey', ms=15)\n",
    "ax4.plot(Cpx_Liq_B1133_Calc_Corr_TAS[x], Cpx_Liq_B1133_Calc_Corr_TAS[y], '*k', mfc='k', ms=10)\n",
    "ax4.plot(Cpx_Liq_B1133_Calc_Corr_TUL[x], Cpx_Liq_B1133_Calc_Corr_TUL[y], 'ok', mfc='b')\n",
    "ax4.plot(Cpx_Liq_B1133_Calc_Corr_HI[x], Cpx_Liq_B1133_Calc_Corr_HI[y], 'ok', mfc='k')\n",
    "ax4.plot(Cpx_Liq_B1133_Calc_Corr_RI[x], Cpx_Liq_B1133_Calc_Corr_RI[y], 'ok', mfc='c')\n",
    "ax4.plot(Cpx_Liq_B1133_Calc_Corr_LaMere[x], Cpx_Liq_B1133_Calc_Corr_LaMere[y], 'sk', mfc='r', ms=7)\n",
    "ax4.plot(Cpx_Liq_B1133_Calc_Corr_Cape[x], Cpx_Liq_B1133_Calc_Corr_Cape[y], 'ok', mfc='magenta', ms=7)\n",
    "ax4.plot(Cpx_Liq_B1133_Calc_Corr_SUNY[x], Cpx_Liq_B1133_Calc_Corr_SUNY[y], 'pk', mfc='white', ms=10)\n",
    "ax4.plot(Cpx_Liq_B1133_Calc_Corr_RNAS[x], Cpx_Liq_B1133_Calc_Corr_RNAS[y], 'pk', mfc='orange', ms=10)\n",
    "ax4.plot(Cpx_Liq_B1133_Calc_Corr_UBC[x], Cpx_Liq_B1133_Calc_Corr_UBC[y], 'dk', mfc='white', ms=7)\n",
    "ax4.plot(Cpx_Liq_B1133_Calc_Corr_ANU[x], Cpx_Liq_B1133_Calc_Corr_ANU[y], '+k',  ms=15)\n",
    "ax4.plot(Cpx_Liq_B1133_Calc_Corr_Houston[x], Cpx_Liq_B1133_Calc_Corr_Houston[y], 'ok', mfc='grey', ms=7)\n",
    "\n",
    "ax4.set_ylabel('Cpx-Liq Press (kbar, Neave & Putirka, 2017)')\n",
    "ax4.set_xlabel('Cpx-Liq Temp (K, Putirka 2008 eq 33)')\n",
    "ax4.plot()\n",
    "\n",
    "ax1.set_title('Glass Correction Factors (Gale et al. 2013)')\n",
    "ax2.set_title('Glass Correction Factors (Gale et al. 2013)')\n",
    "ax3.set_title('Average Cpx-Only PT \\n (Krawczynski et al., 2013, Exp B1133)')\n",
    "ax4.set_title('Average Cpx-Liq PT \\n (Krawczynski et al., 2013, Exp B1133)')\n",
    "\n",
    "ax1.annotate(\"a)\", \n",
    "             xy=(0.02, 0.95), xycoords=\"axes fraction\", fontsize=16)\n",
    "ax2.annotate(\"b) \", \n",
    "             xy=(0.02, 0.95), xycoords=\"axes fraction\", fontsize=16)\n",
    "ax3.annotate(\"c) \", \n",
    "             xy=(0.02, 0.95), xycoords=\"axes fraction\", fontsize=16)\n",
    "ax4.annotate(\"d) \", \n",
    "             xy=(0.02, 0.95), xycoords=\"axes fraction\", fontsize=16)\n",
    "plt.tight_layout()\n",
    "\n",
    "\n",
    "fig.savefig('Lab_Corrections_B1133.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f814915d-95b8-4603-8b67-660419d06318",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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