{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ccd708af-e72b-4a39-a2a2-cb1f5f460f14",
   "metadata": {},
   "source": [
    "# This file loads different calibration datasets, and saves them as pickles, which are then provided in Thermobar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "478013b5-0dfb-4a5c-85c7-811e56cb79f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import Thermobar as pt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pickle import dump\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10f4131a-70a5-47e2-af90-d63c9f2235fd",
   "metadata": {},
   "source": [
    "## Shea liquids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dc923f02-e0e6-4cea-87ee-edd82e426b2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "Shea2022_Cali_out=pt.import_excel('Calibration_Datasets.xlsx',\n",
    "                                  sheet_name='Shea_2022')\n",
    "Shea2022_Cali_Ol=Shea2022_Cali_out['Ols']\n",
    "Shea2022_Cali_Liqs=Shea2022_Cali_out['Liqs']\n",
    "Shea2022_Cali_input=Shea2022_Cali_out['my_input']\n",
    "\n",
    "from pickle import dump\n",
    "import pickle\n",
    "Shea2022_Cali_input.to_pickle(\"Shea2022_Cali_input.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f80e22c-f366-47e2-b635-e4a2a80a575f",
   "metadata": {},
   "source": [
    "## Amphiboles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5d4954c4-8e53-49e3-bb4e-f1cd06a0d252",
   "metadata": {},
   "outputs": [],
   "source": [
    "Ridolfi_Cali_out=pt.import_excel('Calibration_Datasets.xlsx', sheet_name='Ridolfi21_Cali')\n",
    "Ridolfi_Cali_Amp=Ridolfi_Cali_out['Amps']\n",
    "Ridolfi_Cali_input=Ridolfi_Cali_out['my_input']\n",
    "sites=pt.calculate_sites_ridolfi(amp_comps=Ridolfi_Cali_Amp)\n",
    "Combo=pd.concat([Ridolfi_Cali_input, sites], axis=1)\n",
    "\n",
    "\n",
    "Combo.to_pickle(\"Ridolfi_Cali_input.pkl\")\n",
    "Ridolfi_Cali_Amp.to_pickle(\"Ridolfi_Cali_Amp.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "56e29ccc-fa83-4bf0-ade0-2b60dc2ac12b",
   "metadata": {},
   "outputs": [],
   "source": [
    "Mutch_Cali_out=pt.import_excel('Calibration_Datasets.xlsx', sheet_name='Mutch2016_Amp')\n",
    "Mutch_Cali_Amp=Mutch_Cali_out['Amps']\n",
    "Mutch_Cali_input=Mutch_Cali_out['my_input']\n",
    "sites=pt.calculate_sites_ridolfi(amp_comps=Mutch_Cali_Amp)\n",
    "Combo=pd.concat([Mutch_Cali_input, sites], axis=1)\n",
    "\n",
    "\n",
    "\n",
    "Combo.to_pickle(\"Mutch_Cali_input.pkl\")\n",
    "Mutch_Cali_Amp.to_pickle(\"Mutch_Cali_Amp.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "595d74a0-8fe8-459a-b9b5-291d591c9253",
   "metadata": {},
   "outputs": [],
   "source": [
    "Putirka16_Cali_out=pt.import_excel('Calibration_Datasets.xlsx', sheet_name='Putirka16_Cali')\n",
    "Putirka16_Cali_Amp=Putirka16_Cali_out['Amps']\n",
    "Putirka16_Cali_input=Putirka16_Cali_out['my_input']\n",
    "sites=pt.calculate_sites_ridolfi(amp_comps=Putirka16_Cali_Amp)\n",
    "Combo=pd.concat([Putirka16_Cali_input, sites], axis=1)\n",
    "\n",
    "Combo.to_pickle(\"Putirka16_Cali_input.pkl\")\n",
    "Putirka16_Cali_Amp.to_pickle(\"Putirka16_Cali_Amp.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "82545f59-9de9-4612-99c1-ea8377c347be",
   "metadata": {},
   "outputs": [],
   "source": [
    "Zhang17_Cali_out=pt.import_excel('Calibration_Datasets.xlsx', sheet_name='Zhang2017_Cali')\n",
    "Zhang17_Cali_Amp=Zhang17_Cali_out['Amps']\n",
    "Zhang17_Cali_input=Zhang17_Cali_out['my_input']\n",
    "sites=pt.calculate_sites_ridolfi(amp_comps=Zhang17_Cali_Amp)\n",
    "Combo=pd.concat([Zhang17_Cali_input, sites], axis=1)\n",
    "\n",
    "Combo.to_pickle(\"Zhang17_Cali_input.pkl\")\n",
    "Zhang17_Cali_Amp.to_pickle(\"Zhang17_Cali_Amp.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d96375a2-3b14-4be9-9cfa-eb9a6f95461f",
   "metadata": {},
   "source": [
    "## Cpx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d3d228a3-b17c-4e07-9cc6-6e908f0406ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\penny\\anaconda3\\lib\\site-packages\\pandas\\core\\arraylike.py:364: RuntimeWarning: divide by zero encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "Jorgenson2022_Cali_out=pt.import_excel('Calibration_Datasets.xlsx', sheet_name='Jorgenson2022_Cali')\n",
    "Jorgenson2022_Cali_Cpx=Jorgenson2022_Cali_out['Cpxs']\n",
    "Jorgenson2022_Cali_Liqs=Jorgenson2022_Cali_out['Liqs']\n",
    "Jorgenson2022_Cali_input=Jorgenson2022_Cali_out['my_input']\n",
    "\n",
    "# Calculating Cpx components to also save. \n",
    "cpx_comps_Pet=pt.calculate_clinopyroxene_liquid_components(cpx_comps=Jorgenson2022_Cali_Cpx, liq_comps=Jorgenson2022_Cali_Liqs)\n",
    "cpx_comps_Pet['P_kbar']=Jorgenson2022_Cali_input['P_kbar']\n",
    "cpx_comps_Pet['T_K']=Jorgenson2022_Cali_input['T_K']\n",
    "\n",
    "from pickle import dump\n",
    "import pickle\n",
    "cpx_comps_Pet.to_pickle(\"Jorgenson2022_Cali_input.pkl\")\n",
    "Jorgenson2022_Cali_Cpx.to_pickle(\"Jorgenson2022_Cali_Cpx.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "666204b2-1b83-400c-909c-1a345c762cd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "NeavePutirka_2017_Cali_out=pt.import_excel('Calibration_Datasets.xlsx', sheet_name='NeavePutirka_2017_Cali')\n",
    "NeavePutirka_2017_Cali_Cpx=NeavePutirka_2017_Cali_out['Cpxs']\n",
    "NeavePutirka_2017_Cali_Liqs=NeavePutirka_2017_Cali_out['Liqs']\n",
    "NeavePutirka_2017_Cali_input=NeavePutirka_2017_Cali_out['my_input']\n",
    "\n",
    "# Calculating Cpx components to also save. \n",
    "cpx_comps_Pet=pt.calculate_clinopyroxene_liquid_components(cpx_comps=NeavePutirka_2017_Cali_Cpx, liq_comps=NeavePutirka_2017_Cali_Liqs)\n",
    "cpx_comps_Pet['P_kbar']=NeavePutirka_2017_Cali_input['P_kbar']\n",
    "cpx_comps_Pet['T_K']=NeavePutirka_2017_Cali_input['T_K']\n",
    "\n",
    "from pickle import dump\n",
    "import pickle\n",
    "cpx_comps_Pet.to_pickle(\"NeavePutirka_2017_Cali_input.pkl\")\n",
    "NeavePutirka_2017_Cali_Cpx.to_pickle(\"NeavePutirka_2017_Cali_Cpx.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fd1761a5-7c4e-4cef-88ce-f0d110dfaeec",
   "metadata": {},
   "outputs": [],
   "source": [
    "Masotta_2013_Cali_out=pt.import_excel('Calibration_Datasets.xlsx', sheet_name='Masotta_2013_Cali_Cpx')\n",
    "Masotta_2013_Cali_Cpx=Masotta_2013_Cali_out['Cpxs']\n",
    "Masotta_2013_Cali_Liqs=Masotta_2013_Cali_out['Liqs']\n",
    "Masotta_2013_Cali_input=Masotta_2013_Cali_out['my_input']\n",
    "\n",
    "# Calculating Cpx components to also save. \n",
    "cpx_comps_Pet=pt.calculate_clinopyroxene_liquid_components(cpx_comps=Masotta_2013_Cali_Cpx, liq_comps=Masotta_2013_Cali_Liqs)\n",
    "cpx_comps_Pet['P_kbar']=Masotta_2013_Cali_input['P_kbar']\n",
    "cpx_comps_Pet['T_K']=Masotta_2013_Cali_input['T_K']\n",
    "\n",
    "from pickle import dump\n",
    "import pickle\n",
    "cpx_comps_Pet.to_pickle(\"Masotta_2013_Cali_input.pkl\")\n",
    "Masotta_2013_Cali_Cpx.to_pickle(\"Masotta_2013_Cali_Cpx.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0c325828-9af0-4f50-9e11-01876ca97627",
   "metadata": {},
   "outputs": [],
   "source": [
    "Wang21_Cali_out=pt.import_excel('Calibration_Datasets.xlsx', sheet_name='Wang21_Cali_Cpx')\n",
    "Wang21_Cali_Cpx=Wang21_Cali_out['Cpxs']\n",
    "Wang21_Cali_Liqs=Wang21_Cali_out['Liqs']\n",
    "Wang21_Cali_input=Wang21_Cali_out['my_input']\n",
    "\n",
    "# Calculating Cpx components to also save. \n",
    "cpx_comps_Pet=pt.calculate_clinopyroxene_liquid_components(cpx_comps=Wang21_Cali_Cpx, liq_comps=Wang21_Cali_Liqs)\n",
    "cpx_comps_Pet['P_kbar']=Wang21_Cali_input['P_kbar']\n",
    "cpx_comps_Pet['T_K']=Wang21_Cali_input['T_K']\n",
    "\n",
    "from pickle import dump\n",
    "import pickle\n",
    "cpx_comps_Pet.to_pickle(\"Wang21_Cali_input.pkl\")\n",
    "Wang21_Cali_Cpx.to_pickle(\"Wang21_Cali_Cpx.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e741b419-1848-46bf-b47c-7385970dcc51",
   "metadata": {},
   "outputs": [],
   "source": [
    "Brugman_2019_Cali_out=pt.import_excel('Calibration_Datasets.xlsx', sheet_name='Brugman19_Cali')\n",
    "Brugman_2019_Cali_Cpx=Brugman_2019_Cali_out['Cpxs']\n",
    "Brugman_2019_Cali_Liqs=Brugman_2019_Cali_out['Liqs']\n",
    "Brugman_2019_Cali_input=Brugman_2019_Cali_out['my_input']\n",
    "\n",
    "# Calculating Cpx components to also save. \n",
    "cpx_comps_Pet=pt.calculate_clinopyroxene_liquid_components(cpx_comps=Brugman_2019_Cali_Cpx, liq_comps=Brugman_2019_Cali_Liqs)\n",
    "cpx_comps_Pet['P_kbar']=Brugman_2019_Cali_input['P_kbar']\n",
    "cpx_comps_Pet['T_K']=Brugman_2019_Cali_input['T_K']\n",
    "\n",
    "from pickle import dump\n",
    "import pickle\n",
    "cpx_comps_Pet.to_pickle(\"Brugman_2019_Cali_input.pkl\")\n",
    "Brugman_2019_Cali_Cpx.to_pickle(\"Brugman_2019_Cali_Cpx.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "089c3b0f-eee4-4cef-b84b-729856ded371",
   "metadata": {},
   "outputs": [],
   "source": [
    "Petrelli20_Cali_out=pt.import_excel('Calibration_Datasets.xlsx', sheet_name='Petrelli20_Cali')\n",
    "Petrelli20_Cali_Cpx=Petrelli20_Cali_out['Cpxs']\n",
    "Petrelli20_Cali_Liqs=Petrelli20_Cali_out['Liqs']\n",
    "Petrelli20_Cali_input=Petrelli20_Cali_out['my_input']\n",
    "\n",
    "# Calculating Cpx components to also save. \n",
    "cpx_comps_Pet=pt.calculate_clinopyroxene_liquid_components(cpx_comps=Petrelli20_Cali_Cpx, liq_comps=Petrelli20_Cali_Liqs)\n",
    "cpx_comps_Pet['P_kbar']=Petrelli20_Cali_input['P_kbar']\n",
    "cpx_comps_Pet['T_K']=Petrelli20_Cali_input['T_K']\n",
    "\n",
    "from pickle import dump\n",
    "import pickle\n",
    "cpx_comps_Pet.to_pickle(\"Petrelli20_Cali_input.pkl\")\n",
    "Petrelli20_Cali_Cpx.to_pickle(\"Petrelli20_Cali_Cpx.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f1c526c2-f885-4127-9932-39ab1bf1d891",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\penny\\anaconda3\\lib\\site-packages\\pandas\\core\\arraylike.py:364: RuntimeWarning: divide by zero encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "Putirka2008_Cali_out=pt.import_excel('Calibration_Datasets.xlsx', sheet_name='Putirka2008_CpxLiq')\n",
    "Putirka2008_Cali_Cpx=Putirka2008_Cali_out['Cpxs']\n",
    "Putirka2008_Cali_Liqs=Putirka2008_Cali_out['Liqs']\n",
    "Putirka2008_Cali_input=Putirka2008_Cali_out['my_input']\n",
    "\n",
    "# Calculating Cpx components to also save. \n",
    "cpx_comps_Pet=pt.calculate_clinopyroxene_liquid_components(cpx_comps=Putirka2008_Cali_Cpx, liq_comps=Putirka2008_Cali_Liqs)\n",
    "cpx_comps_Pet['P_kbar']=Putirka2008_Cali_input['P_kbar']\n",
    "cpx_comps_Pet['T_K']=Putirka2008_Cali_input['T_K']\n",
    "\n",
    "from pickle import dump\n",
    "import pickle\n",
    "cpx_comps_Pet.to_pickle(\"Putirka2008_Cali_input.pkl\")\n",
    "Putirka2008_Cali_Cpx.to_pickle(\"Putirka2008_Cali_Cpx.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aac2063c-5339-4088-93a7-4f8f3dc32dc6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e2e02c45-19c8-4b73-8310-3a288b33e5e4",
   "metadata": {},
   "source": [
    "## Plagioclase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "535116b8-d532-4560-8ff7-3ca7d17c532e",
   "metadata": {},
   "outputs": [],
   "source": [
    "Waters_Lange2015_Cali_out=pt.import_excel('Calibration_Datasets.xlsx', sheet_name='Waters_Lange2015_Cali')\n",
    "Waters_Lange2015_Cali_Amp=Waters_Lange2015_Cali_out['Plags']\n",
    "Waters_Lange2015_Cali_input=Waters_Lange2015_Cali_out['my_input']\n",
    "\n",
    "from pickle import dump\n",
    "import pickle\n",
    "Waters_Lange2015_Cali_input.to_pickle(\"Waters_Lange2015_Cali_input.pkl\")\n",
    "Waters_Lange2015_Cali_Amp.to_pickle(\"Waters_Lange2015_Cali_Amp.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "23fc9718-45c9-4b32-863d-cf0333de9c06",
   "metadata": {},
   "outputs": [],
   "source": [
    "Masotta2019_Cali_out=pt.import_excel('Calibration_Datasets.xlsx', sheet_name='Masotta2019_PlagLiq_Cali')\n",
    "Masotta2019_Cali_Amp=Masotta2019_Cali_out['Plags']\n",
    "Masotta2019_Cali_input=Masotta2019_Cali_out['my_input']\n",
    "\n",
    "from pickle import dump\n",
    "import pickle\n",
    "Masotta2019_Cali_input.to_pickle(\"Masotta2019_Cali_input.pkl\")\n",
    "Masotta2019_Cali_Amp.to_pickle(\"Masotta2019_Cali_Amp.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc9399d3-4728-497d-afae-843d558b2bec",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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