{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3e3260d9",
   "metadata": {},
   "source": [
    "## Using MELTS to find the liquidus temperature"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce0ce115",
   "metadata": {},
   "source": [
    "- Finding the liquidus temperature of a magma is one of the first steps of most crystallisation and/or decompression calculations.\n",
    "- Usually this command is used as part of the isobaric_crystallisation and isothermal_decompression functions (amongst others).\n",
    "- However, it's still useful to consider how the liquidus function works independently, and examine how well MELTS performs as a Thermometer in igneous systems.\n",
    "\n",
    "**Before any calculations can be run** users need to download and install the alphaMELTS for Python files. This can be easily achieved with one simple function, please see the installation guide available on ReadTheDocs.\n",
    "\n",
    "Data used in the calculations below can be downloaded from here: https://github.com/gleesonm1/PetThermoTools/blob/master/docs/Examples/LiquidusTests/MELTS_MAGEMin_Liquidus.xlsx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3de6fb57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alphaMELTS for Python files successfully located.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import petthermotools as ptt\n",
    "from tqdm.notebook import tqdm, trange\n",
    "# import pickle\n",
    "\n",
    "# If the alphaMELTS for Python files have not been added to your Python path (see installation guide) then use the two lines below to add\n",
    "# the location of the alphaMELTS files here.\n",
    "# import sys\n",
    "# sys.path.append(r'path_to_alphaMELTS_files')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3e58c4d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import platform\n",
    "if platform.system() == \"Darwin\":\n",
    "    # used to suppress MELTS outputs in MacOS systems (run twice)\n",
    "    import sys\n",
    "    import os\n",
    "    sys.stdout = open(os.devnull, 'w')\n",
    "    sys.stderr = open(os.devnull, 'w')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04a64d78",
   "metadata": {},
   "source": [
    "To test the ability of MELTS to act as a liquid-only thermometer we need some experiments to test them against. Here, we use a database of experimental data compiled by P. Wieser (Wieser et al. 2025). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "936c6ed6",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data = pd.read_excel('MELTS_MAGEMIN_Liquidus.xlsx')\n",
    "Data = Data[~np.isinf(Data[\"H2O_Liq\"])]\n",
    "Data = Data.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e67b0bb",
   "metadata": {},
   "source": [
    "The entire experimental dataset contains over 2000 samples. Therefore, to reduce the computational time in this example I isolate 200 experiments (randomly selected) to be used in the following calculations. A new DataFrame is constructed using only these experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "16a8f222",
   "metadata": {},
   "outputs": [],
   "source": [
    "# randomly select 200 rows from the original DataFrame\n",
    "Ch = np.random.choice(range(len(Data['SiO2_Liq'])), 200, replace=False)\n",
    "Test = Data.copy()\n",
    "Test = Test.loc[Ch]\n",
    "\n",
    "# reset the index in the new DataFrame\n",
    "Test = Test.reset_index(drop = True)\n",
    "Test = Test.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8ec06670",
   "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>CO2_Liq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>64.066798</td>\n",
       "      <td>0.874263</td>\n",
       "      <td>13.831041</td>\n",
       "      <td>6.218075</td>\n",
       "      <td>0.147348</td>\n",
       "      <td>1.640472</td>\n",
       "      <td>4.518664</td>\n",
       "      <td>5.157171</td>\n",
       "      <td>1.227898</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.255403</td>\n",
       "      <td>2.062868</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>16.400000</td>\n",
       "      <td>3.240000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.460000</td>\n",
       "      <td>4.180000</td>\n",
       "      <td>4.890000</td>\n",
       "      <td>1.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.220000</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49.871506</td>\n",
       "      <td>1.746048</td>\n",
       "      <td>14.087436</td>\n",
       "      <td>12.420753</td>\n",
       "      <td>0.218256</td>\n",
       "      <td>6.726254</td>\n",
       "      <td>11.646936</td>\n",
       "      <td>2.271847</td>\n",
       "      <td>0.198415</td>\n",
       "      <td>0.019841</td>\n",
       "      <td>0.158732</td>\n",
       "      <td>0.633976</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51.976977</td>\n",
       "      <td>1.561151</td>\n",
       "      <td>17.509859</td>\n",
       "      <td>8.666432</td>\n",
       "      <td>0.146426</td>\n",
       "      <td>4.394229</td>\n",
       "      <td>6.773000</td>\n",
       "      <td>3.322450</td>\n",
       "      <td>0.992945</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.253837</td>\n",
       "      <td>4.402694</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46.958829</td>\n",
       "      <td>0.583149</td>\n",
       "      <td>16.113323</td>\n",
       "      <td>7.560473</td>\n",
       "      <td>0.112537</td>\n",
       "      <td>10.486448</td>\n",
       "      <td>9.688456</td>\n",
       "      <td>2.772515</td>\n",
       "      <td>0.358074</td>\n",
       "      <td>0.122768</td>\n",
       "      <td>0.214844</td>\n",
       "      <td>5.028583</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>65.164083</td>\n",
       "      <td>0.242702</td>\n",
       "      <td>14.659290</td>\n",
       "      <td>2.293986</td>\n",
       "      <td>0.147794</td>\n",
       "      <td>0.526606</td>\n",
       "      <td>2.088591</td>\n",
       "      <td>3.429156</td>\n",
       "      <td>3.027627</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.220769</td>\n",
       "      <td>8.199396</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>66.200000</td>\n",
       "      <td>0.580000</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>3.190000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.890000</td>\n",
       "      <td>3.110000</td>\n",
       "      <td>5.050000</td>\n",
       "      <td>1.380000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.640000</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>69.566753</td>\n",
       "      <td>0.186007</td>\n",
       "      <td>12.648501</td>\n",
       "      <td>0.985839</td>\n",
       "      <td>0.102304</td>\n",
       "      <td>0.437117</td>\n",
       "      <td>1.320652</td>\n",
       "      <td>3.124924</td>\n",
       "      <td>4.352572</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.167407</td>\n",
       "      <td>7.107924</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>60.503949</td>\n",
       "      <td>0.149985</td>\n",
       "      <td>22.157784</td>\n",
       "      <td>1.359864</td>\n",
       "      <td>0.319968</td>\n",
       "      <td>0.099990</td>\n",
       "      <td>0.829917</td>\n",
       "      <td>8.629137</td>\n",
       "      <td>5.949405</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>67.227273</td>\n",
       "      <td>0.345455</td>\n",
       "      <td>12.945455</td>\n",
       "      <td>2.554545</td>\n",
       "      <td>0.063636</td>\n",
       "      <td>0.390909</td>\n",
       "      <td>2.872727</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.109091</td>\n",
       "      <td>9.090909</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      SiO2_Liq  TiO2_Liq  Al2O3_Liq   FeOt_Liq   MnO_Liq    MgO_Liq  \\\n",
       "0    64.066798  0.874263  13.831041   6.218075  0.147348   1.640472   \n",
       "1    63.000000  0.500000  16.400000   3.240000  0.100000   1.460000   \n",
       "2    49.871506  1.746048  14.087436  12.420753  0.218256   6.726254   \n",
       "3    51.976977  1.561151  17.509859   8.666432  0.146426   4.394229   \n",
       "4    46.958829  0.583149  16.113323   7.560473  0.112537  10.486448   \n",
       "..         ...       ...        ...        ...       ...        ...   \n",
       "195  65.164083  0.242702  14.659290   2.293986  0.147794   0.526606   \n",
       "196  66.200000  0.580000  14.900000   3.190000  0.060000   0.890000   \n",
       "197  69.566753  0.186007  12.648501   0.985839  0.102304   0.437117   \n",
       "198  60.503949  0.149985  22.157784   1.359864  0.319968   0.099990   \n",
       "199  67.227273  0.345455  12.945455   2.554545  0.063636   0.390909   \n",
       "\n",
       "       CaO_Liq  Na2O_Liq   K2O_Liq  Cr2O3_Liq  P2O5_Liq   H2O_Liq  Fe3Fet_Liq  \\\n",
       "0     4.518664  5.157171  1.227898   0.000000  0.255403  2.062868        0.15   \n",
       "1     4.180000  4.890000  1.010000   0.000000  0.000000  5.220000        0.15   \n",
       "2    11.646936  2.271847  0.198415   0.019841  0.158732  0.633976        0.15   \n",
       "3     6.773000  3.322450  0.992945   0.000000  0.253837  4.402694        0.15   \n",
       "4     9.688456  2.772515  0.358074   0.122768  0.214844  5.028583        0.15   \n",
       "..         ...       ...       ...        ...       ...       ...         ...   \n",
       "195   2.088591  3.429156  3.027627   0.000000  0.220769  8.199396        0.15   \n",
       "196   3.110000  5.050000  1.380000   0.000000  0.000000  4.640000        0.15   \n",
       "197   1.320652  3.124924  4.352572   0.000000  0.167407  7.107924        0.15   \n",
       "198   0.829917  8.629137  5.949405   0.000000  0.000000  0.000000        0.15   \n",
       "199   2.872727  2.100000  2.300000   0.000000  0.109091  9.090909        0.15   \n",
       "\n",
       "     CO2_Liq  \n",
       "0        0.0  \n",
       "1        0.0  \n",
       "2        0.0  \n",
       "3        0.0  \n",
       "4        0.0  \n",
       "..       ...  \n",
       "195      0.0  \n",
       "196      0.0  \n",
       "197      0.0  \n",
       "198      0.0  \n",
       "199      0.0  \n",
       "\n",
       "[200 rows x 14 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_comp = ptt.comp_fix(comp=Test)\n",
    "new_comp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e1194469",
   "metadata": {},
   "outputs": [],
   "source": [
    "T = ptt.estimate_t(comp = new_comp, P_bar = Test['P_kbar']*1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d6416f3",
   "metadata": {},
   "source": [
    "Now that we have this 'Test' DataFrame we can use the findLiq_multi function to identify the liquidus temperature of each sample. Ideally, we'd have information about either the Fe redox state or an estimate of the oxygen fugacity of each melt. Unfortunately, we don't have this information for every sample so instead we will define a constant Fe$^{3+}$/Fe$_{tot}$ ratio for all samples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "15d74041",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "898b5cce80f84886a63a6e38c41160d6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/25 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Results_pMELTS = ptt.findLiq_multi(Model = \"pMELTS\", # which MELTS model to use - \"pMELTS\", \"MELTSv1.0.2\", \"MELTSv1.1.0\", or \"MELTSv1.2.0\"\n",
    "                        bulk = Test, # initial composition(s) wither a dictionary (if one composition) or a DataFrame (multiple compositions)\n",
    "                        P_bar = Test['P_kbar'].values*1000, # Pressure (in bars) of the calculation\n",
    "                        Fe3Fet_Liq = 0.15) # initial Fe3Fet_Liq ratio of the sample(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f421b5d3",
   "metadata": {},
   "source": [
    "Examining the results reveals the information that is recorded by this function. Notably, the findLiq_multi code will return the composition of the melt at the liquidus (in wt% hydrous normalized), whether or not the melt is fluid saturated at the liquidus, the liquidus phase, and (critically) the liquidus temperature (in degrees Celsius)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a1989420",
   "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>T_Liq_C</th>\n",
       "      <th>liquidus_phase</th>\n",
       "      <th>fluid_saturated</th>\n",
       "      <th>SiO2_Liq</th>\n",
       "      <th>TiO2_Liq</th>\n",
       "      <th>Al2O3_Liq</th>\n",
       "      <th>Cr2O3_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>P2O5_Liq</th>\n",
       "      <th>H2O_Liq</th>\n",
       "      <th>CO2_Liq</th>\n",
       "      <th>Fe3Fet_Liq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1046.589636</td>\n",
       "      <td>plagioclase1</td>\n",
       "      <td>No</td>\n",
       "      <td>64.067405</td>\n",
       "      <td>0.874305</td>\n",
       "      <td>13.830595</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.217798</td>\n",
       "      <td>0.147355</td>\n",
       "      <td>1.640551</td>\n",
       "      <td>4.518543</td>\n",
       "      <td>5.157135</td>\n",
       "      <td>1.227931</td>\n",
       "      <td>0.255415</td>\n",
       "      <td>2.062968</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>963.322929</td>\n",
       "      <td>plagioclase1</td>\n",
       "      <td>No</td>\n",
       "      <td>63.002055</td>\n",
       "      <td>0.500139</td>\n",
       "      <td>16.396922</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.240599</td>\n",
       "      <td>0.100028</td>\n",
       "      <td>1.460405</td>\n",
       "      <td>4.178467</td>\n",
       "      <td>4.889776</td>\n",
       "      <td>1.010159</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.221450</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1196.603712</td>\n",
       "      <td>olivine1</td>\n",
       "      <td>No</td>\n",
       "      <td>49.872499</td>\n",
       "      <td>1.746130</td>\n",
       "      <td>14.088097</td>\n",
       "      <td>0.019842</td>\n",
       "      <td>12.419525</td>\n",
       "      <td>0.218056</td>\n",
       "      <td>6.725265</td>\n",
       "      <td>11.647463</td>\n",
       "      <td>2.271954</td>\n",
       "      <td>0.198424</td>\n",
       "      <td>0.158739</td>\n",
       "      <td>0.634005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1059.622056</td>\n",
       "      <td>olivine1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>53.089794</td>\n",
       "      <td>0.327077</td>\n",
       "      <td>17.999068</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.737000</td>\n",
       "      <td>0.130302</td>\n",
       "      <td>4.170554</td>\n",
       "      <td>6.959927</td>\n",
       "      <td>3.415276</td>\n",
       "      <td>1.020686</td>\n",
       "      <td>0.260929</td>\n",
       "      <td>3.889386</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.152851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1229.343347</td>\n",
       "      <td>olivine1</td>\n",
       "      <td>No</td>\n",
       "      <td>46.959433</td>\n",
       "      <td>0.583179</td>\n",
       "      <td>16.114143</td>\n",
       "      <td>0.122774</td>\n",
       "      <td>7.559720</td>\n",
       "      <td>0.112519</td>\n",
       "      <td>10.484852</td>\n",
       "      <td>9.688938</td>\n",
       "      <td>2.772656</td>\n",
       "      <td>0.358092</td>\n",
       "      <td>0.214855</td>\n",
       "      <td>5.028839</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149930</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       T_Liq_C liquidus_phase fluid_saturated   SiO2_Liq  TiO2_Liq  Al2O3_Liq  \\\n",
       "0  1046.589636   plagioclase1              No  64.067405  0.874305  13.830595   \n",
       "1   963.322929   plagioclase1              No  63.002055  0.500139  16.396922   \n",
       "2  1196.603712       olivine1              No  49.872499  1.746130  14.088097   \n",
       "3  1059.622056       olivine1             Yes  53.089794  0.327077  17.999068   \n",
       "4  1229.343347       olivine1              No  46.959433  0.583179  16.114143   \n",
       "\n",
       "   Cr2O3_Liq   FeOt_Liq   MnO_Liq    MgO_Liq    CaO_Liq  Na2O_Liq   K2O_Liq  \\\n",
       "0   0.000000   6.217798  0.147355   1.640551   4.518543  5.157135  1.227931   \n",
       "1   0.000000   3.240599  0.100028   1.460405   4.178467  4.889776  1.010159   \n",
       "2   0.019842  12.419525  0.218056   6.725265  11.647463  2.271954  0.198424   \n",
       "3   0.000000   8.737000  0.130302   4.170554   6.959927  3.415276  1.020686   \n",
       "4   0.122774   7.559720  0.112519  10.484852   9.688938  2.772656  0.358092   \n",
       "\n",
       "   P2O5_Liq   H2O_Liq  CO2_Liq  Fe3Fet_Liq  \n",
       "0  0.255415  2.062968      0.0    0.149921  \n",
       "1  0.000000  5.221450      0.0    0.149921  \n",
       "2  0.158739  0.634005      0.0    0.149929  \n",
       "3  0.260929  3.889386      0.0    0.152851  \n",
       "4  0.214855  5.028839      0.0    0.149930  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Results_pMELTS.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e8437f0",
   "metadata": {},
   "source": [
    "In this initial calculation, the liquidus temperature is calculated using the pMELTS model, which was designed for use on mantle-like bulk compositions at 1 - 3 GPa (Ghiorso et al. 2002). For the majorty of samples in this database it may be more appropriate to use the rhyoliteMELTSv1.0.2 (Gualda et al. 2012) or rhyoliteMELTSv1.2.0 (Ghiorso and Gualda, 2015) models. We can rerun the same calculations but change the Model selected:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "875c28c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3e5c958808bd4d5abbf9ed3dccbe4aff",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/25 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Results_MELTSv102 = ptt.findLiq_multi(Model = \"MELTSv1.0.2\", # which MELTS model to use - \"pMELTS\", \"MELTSv1.0.2\", \"MELTSv1.1.0\", or \"MELTSv1.2.0\"\n",
    "                        bulk = Test, # initial composition(s) wither a dictionary (if one composition) or a DataFrame (multiple compositions)\n",
    "                        P_bar = Test['P_kbar'].values*1000, # Pressure (in bars) of the calculation\n",
    "                        Fe3Fet_Liq = 0.15) # initial Fe3Fet_Liq ratio of the sample(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0c151d75",
   "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>T_Liq_C</th>\n",
       "      <th>liquidus_phase</th>\n",
       "      <th>fluid_saturated</th>\n",
       "      <th>SiO2_Liq</th>\n",
       "      <th>TiO2_Liq</th>\n",
       "      <th>Al2O3_Liq</th>\n",
       "      <th>Cr2O3_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>P2O5_Liq</th>\n",
       "      <th>H2O_Liq</th>\n",
       "      <th>CO2_Liq</th>\n",
       "      <th>Fe3Fet_Liq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1040.789636</td>\n",
       "      <td>orthopyroxene1</td>\n",
       "      <td>No</td>\n",
       "      <td>64.067685</td>\n",
       "      <td>0.874305</td>\n",
       "      <td>13.831670</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.216893</td>\n",
       "      <td>0.147355</td>\n",
       "      <td>1.639473</td>\n",
       "      <td>4.518827</td>\n",
       "      <td>5.157438</td>\n",
       "      <td>1.227962</td>\n",
       "      <td>0.255416</td>\n",
       "      <td>2.062976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>958.422929</td>\n",
       "      <td>orthopyroxene1</td>\n",
       "      <td>No</td>\n",
       "      <td>63.000489</td>\n",
       "      <td>0.500015</td>\n",
       "      <td>16.400524</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.239280</td>\n",
       "      <td>0.100004</td>\n",
       "      <td>1.459187</td>\n",
       "      <td>4.180103</td>\n",
       "      <td>4.890175</td>\n",
       "      <td>1.010036</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.220188</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1202.103712</td>\n",
       "      <td>clinopyroxene1</td>\n",
       "      <td>No</td>\n",
       "      <td>49.871918</td>\n",
       "      <td>1.746284</td>\n",
       "      <td>14.089198</td>\n",
       "      <td>0.019845</td>\n",
       "      <td>12.420523</td>\n",
       "      <td>0.218296</td>\n",
       "      <td>6.724750</td>\n",
       "      <td>11.645662</td>\n",
       "      <td>2.272224</td>\n",
       "      <td>0.198450</td>\n",
       "      <td>0.158760</td>\n",
       "      <td>0.634090</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1095.522056</td>\n",
       "      <td>olivine1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>52.516888</td>\n",
       "      <td>1.577381</td>\n",
       "      <td>17.691899</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.755330</td>\n",
       "      <td>0.147945</td>\n",
       "      <td>4.439146</td>\n",
       "      <td>6.843411</td>\n",
       "      <td>3.356992</td>\n",
       "      <td>1.003268</td>\n",
       "      <td>0.256476</td>\n",
       "      <td>3.411264</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1306.743347</td>\n",
       "      <td>olivine1</td>\n",
       "      <td>No</td>\n",
       "      <td>46.959380</td>\n",
       "      <td>0.583174</td>\n",
       "      <td>16.114021</td>\n",
       "      <td>0.122773</td>\n",
       "      <td>7.559750</td>\n",
       "      <td>0.112540</td>\n",
       "      <td>10.485116</td>\n",
       "      <td>9.688868</td>\n",
       "      <td>2.772635</td>\n",
       "      <td>0.358089</td>\n",
       "      <td>0.214854</td>\n",
       "      <td>5.028801</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       T_Liq_C  liquidus_phase fluid_saturated   SiO2_Liq  TiO2_Liq  \\\n",
       "0  1040.789636  orthopyroxene1              No  64.067685  0.874305   \n",
       "1   958.422929  orthopyroxene1              No  63.000489  0.500015   \n",
       "2  1202.103712  clinopyroxene1              No  49.871918  1.746284   \n",
       "3  1095.522056        olivine1             Yes  52.516888  1.577381   \n",
       "4  1306.743347        olivine1              No  46.959380  0.583174   \n",
       "\n",
       "   Al2O3_Liq  Cr2O3_Liq   FeOt_Liq   MnO_Liq    MgO_Liq    CaO_Liq  Na2O_Liq  \\\n",
       "0  13.831670   0.000000   6.216893  0.147355   1.639473   4.518827  5.157438   \n",
       "1  16.400524   0.000000   3.239280  0.100004   1.459187   4.180103  4.890175   \n",
       "2  14.089198   0.019845  12.420523  0.218296   6.724750  11.645662  2.272224   \n",
       "3  17.691899   0.000000   8.755330  0.147945   4.439146   6.843411  3.356992   \n",
       "4  16.114021   0.122773   7.559750  0.112540  10.485116   9.688868  2.772635   \n",
       "\n",
       "    K2O_Liq  P2O5_Liq   H2O_Liq  CO2_Liq  Fe3Fet_Liq  \n",
       "0  1.227962  0.255416  2.062976      0.0    0.149938  \n",
       "1  1.010036  0.000000  5.220188      0.0    0.149941  \n",
       "2  0.198450  0.158760  0.634090      0.0    0.149913  \n",
       "3  1.003268  0.256476  3.411264      0.0    0.149928  \n",
       "4  0.358089  0.214854  5.028801      0.0    0.149928  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Results_MELTSv102.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c3d0de3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ad39d79324f94486a2095f3dadd5b13f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/25 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Results_MELTSv120 = ptt.findLiq_multi(Model = \"MELTSv1.2.0\", # which MELTS model to use - \"pMELTS\", \"MELTSv1.0.2\", \"MELTSv1.1.0\", or \"MELTSv1.2.0\"\n",
    "                        bulk = Test, # initial composition(s) wither a dictionary (if one composition) or a DataFrame (multiple compositions)\n",
    "                        P_bar = Test['P_kbar'].values*1000, # Pressure (in bars) of the calculation\n",
    "                        Fe3Fet_Liq = 0.15) # initial Fe3Fet_Liq ratio of the sample(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c68fa761",
   "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>T_Liq_C</th>\n",
       "      <th>liquidus_phase</th>\n",
       "      <th>fluid_saturated</th>\n",
       "      <th>SiO2_Liq</th>\n",
       "      <th>TiO2_Liq</th>\n",
       "      <th>Al2O3_Liq</th>\n",
       "      <th>Cr2O3_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>P2O5_Liq</th>\n",
       "      <th>H2O_Liq</th>\n",
       "      <th>CO2_Liq</th>\n",
       "      <th>Fe3Fet_Liq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1041.189636</td>\n",
       "      <td>plagioclase1</td>\n",
       "      <td>No</td>\n",
       "      <td>64.067515</td>\n",
       "      <td>0.874309</td>\n",
       "      <td>13.830485</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.217820</td>\n",
       "      <td>0.147355</td>\n",
       "      <td>1.640557</td>\n",
       "      <td>4.518456</td>\n",
       "      <td>5.157161</td>\n",
       "      <td>1.227950</td>\n",
       "      <td>0.255416</td>\n",
       "      <td>2.062976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>971.222929</td>\n",
       "      <td>plagioclase1</td>\n",
       "      <td>No</td>\n",
       "      <td>63.002159</td>\n",
       "      <td>0.500098</td>\n",
       "      <td>16.397399</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.240332</td>\n",
       "      <td>0.100020</td>\n",
       "      <td>1.460285</td>\n",
       "      <td>4.178394</td>\n",
       "      <td>4.890115</td>\n",
       "      <td>1.010180</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.221019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1195.303712</td>\n",
       "      <td>clinopyroxene1</td>\n",
       "      <td>No</td>\n",
       "      <td>49.871992</td>\n",
       "      <td>1.746273</td>\n",
       "      <td>14.089123</td>\n",
       "      <td>0.019845</td>\n",
       "      <td>12.420486</td>\n",
       "      <td>0.218296</td>\n",
       "      <td>6.724819</td>\n",
       "      <td>11.645645</td>\n",
       "      <td>2.272221</td>\n",
       "      <td>0.198451</td>\n",
       "      <td>0.158760</td>\n",
       "      <td>0.634091</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1094.722056</td>\n",
       "      <td>spinel1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>52.568804</td>\n",
       "      <td>1.578587</td>\n",
       "      <td>17.709042</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.763007</td>\n",
       "      <td>0.148094</td>\n",
       "      <td>4.444079</td>\n",
       "      <td>6.850119</td>\n",
       "      <td>3.360281</td>\n",
       "      <td>1.004251</td>\n",
       "      <td>0.256727</td>\n",
       "      <td>3.317010</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1389.243347</td>\n",
       "      <td>spinel1</td>\n",
       "      <td>No</td>\n",
       "      <td>46.959313</td>\n",
       "      <td>0.583153</td>\n",
       "      <td>16.113383</td>\n",
       "      <td>0.122683</td>\n",
       "      <td>7.559774</td>\n",
       "      <td>0.112539</td>\n",
       "      <td>10.486498</td>\n",
       "      <td>9.688555</td>\n",
       "      <td>2.772543</td>\n",
       "      <td>0.358078</td>\n",
       "      <td>0.214847</td>\n",
       "      <td>5.028635</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149917</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       T_Liq_C  liquidus_phase fluid_saturated   SiO2_Liq  TiO2_Liq  \\\n",
       "0  1041.189636    plagioclase1              No  64.067515  0.874309   \n",
       "1   971.222929    plagioclase1              No  63.002159  0.500098   \n",
       "2  1195.303712  clinopyroxene1              No  49.871992  1.746273   \n",
       "3  1094.722056         spinel1             Yes  52.568804  1.578587   \n",
       "4  1389.243347         spinel1              No  46.959313  0.583153   \n",
       "\n",
       "   Al2O3_Liq  Cr2O3_Liq   FeOt_Liq   MnO_Liq    MgO_Liq    CaO_Liq  Na2O_Liq  \\\n",
       "0  13.830485   0.000000   6.217820  0.147355   1.640557   4.518456  5.157161   \n",
       "1  16.397399   0.000000   3.240332  0.100020   1.460285   4.178394  4.890115   \n",
       "2  14.089123   0.019845  12.420486  0.218296   6.724819  11.645645  2.272221   \n",
       "3  17.709042   0.000000   8.763007  0.148094   4.444079   6.850119  3.360281   \n",
       "4  16.113383   0.122683   7.559774  0.112539  10.486498   9.688555  2.772543   \n",
       "\n",
       "    K2O_Liq  P2O5_Liq   H2O_Liq  CO2_Liq  Fe3Fet_Liq  \n",
       "0  1.227950  0.255416  2.062976      0.0    0.149921  \n",
       "1  1.010180  0.000000  5.221019      0.0    0.149921  \n",
       "2  0.198451  0.158760  0.634091      0.0    0.149911  \n",
       "3  1.004251  0.256727  3.317010      0.0    0.149877  \n",
       "4  0.358078  0.214847  5.028635      0.0    0.149917  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Results_MELTSv120.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c5a9736",
   "metadata": {},
   "source": [
    "Now that we have the results of this analysis, we can compare the results of these calculations to the true experimental temperature. Experiments that have no reported water typically show a very poor match to the experimental temperature, and are thus excluded from the following comparison. Additionally, we exlude any calculations that did not return a result (Results['T_C_liq'] = 0).\n",
    "\n",
    "Overall, MELTS does an acceptable job in reproducing the experimental temperatures, although it is notable that the liquidus temperature is typically overpredicted by around 50 $^{o}$C."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eea88d76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(700.0, 1500.0)"
      ]
     },
     "execution_count": 14,
     "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": [
    "f, a = plt.subplots(1,3, figsize = (12,4), sharex = True, sharey = True)\n",
    "\n",
    "a[0].plot(Test['T_K'][(Test['H2O_Liq'] > 0) & (Results_pMELTS['T_Liq_C'] > 0)]-273.15,\n",
    "       Results_pMELTS['T_Liq_C'][(Test['H2O_Liq'] > 0) & (Results_pMELTS['T_Liq_C'] > 0)], \n",
    "        'ok')\n",
    "a[0].plot([800,1400],[800,1400], 'r-')\n",
    "a[0].set_ylabel('Estimated Temperature ($^{o}$C)')\n",
    "a[0].set_xlabel('Experimental Temperature ($^{o}$C)')\n",
    "a[0].text(800, 1460, 'pMELTS')\n",
    "\n",
    "\n",
    "a[1].plot(Test['T_K'][(Test['H2O_Liq'] > 0) & (Results_MELTSv102['T_Liq_C'] > 0)]-273.15,\n",
    "       Results_MELTSv102['T_Liq_C'][(Test['H2O_Liq'] > 0) & (Results_MELTSv102['T_Liq_C'] > 0)], \n",
    "        'ok')\n",
    "a[1].plot([800,1400],[800,1400], 'r-')\n",
    "a[1].set_xlabel('Experimental Temperature ($^{o}$C)')\n",
    "a[1].text(800, 1460, 'rhyoliteMELTSv1.0.2')\n",
    "\n",
    "\n",
    "a[2].plot(Test['T_K'][(Test['H2O_Liq'] > 0) & (Results_MELTSv120['T_Liq_C'] > 0)]-273.15,\n",
    "       Results_MELTSv120['T_Liq_C'][(Test['H2O_Liq'] > 0) & (Results_MELTSv120['T_Liq_C'] > 0)], \n",
    "        'ok')\n",
    "a[2].plot([800,1400],[800,1400], 'r-')\n",
    "a[2].set_xlabel('Experimental Temperature ($^{o}$C)')\n",
    "a[2].text(800, 1460, 'rhyoliteMELTSv1.2.0')\n",
    "\n",
    "a[2].set_ylim([700,1500])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5aa1ce3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}