{
 "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",
      "If using the Green et al. (2025) or Weller et al. (2024) thermodynamic models please run `ptt.activate_petthermotools_env()` prior to any calculations.\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": "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": 6,
   "id": "15d74041",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0a4de6d975744f318489c4e1e6884355",
       "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": 7,
   "id": "a1989420",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>885.045528</td>\n",
       "      <td>alkali-feldspar1</td>\n",
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       "      <td>clinopyroxene1</td>\n",
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       "      <td>0.503895</td>\n",
       "      <td>15.601725</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.369638</td>\n",
       "      <td>0.087215</td>\n",
       "      <td>2.112275</td>\n",
       "      <td>4.864294</td>\n",
       "      <td>4.418883</td>\n",
       "      <td>1.376060</td>\n",
       "      <td>0.155049</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>clinopyroxene1</td>\n",
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       "      <td>0.141980</td>\n",
       "      <td>4.024817</td>\n",
       "      <td>8.881612</td>\n",
       "      <td>2.261485</td>\n",
       "      <td>0.557779</td>\n",
       "      <td>0.273819</td>\n",
       "      <td>10.117096</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>978.379922</td>\n",
       "      <td>plagioclase1</td>\n",
       "      <td>No</td>\n",
       "      <td>61.273117</td>\n",
       "      <td>0.165254</td>\n",
       "      <td>17.596388</td>\n",
       "      <td>0.092955</td>\n",
       "      <td>4.048346</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.344540</td>\n",
       "      <td>5.782476</td>\n",
       "      <td>3.191111</td>\n",
       "      <td>0.123935</td>\n",
       "      <td>0.206567</td>\n",
       "      <td>5.175310</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149921</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1067.517453</td>\n",
       "      <td>plagioclase1</td>\n",
       "      <td>No</td>\n",
       "      <td>60.388184</td>\n",
       "      <td>1.634061</td>\n",
       "      <td>15.426604</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.474746</td>\n",
       "      <td>0.243587</td>\n",
       "      <td>2.293775</td>\n",
       "      <td>6.038702</td>\n",
       "      <td>3.765357</td>\n",
       "      <td>1.603601</td>\n",
       "      <td>0.304483</td>\n",
       "      <td>1.826900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149921</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "       T_Liq_C    liquidus_phase fluid_saturated   SiO2_Liq  TiO2_Liq  \\\n",
       "0   885.045528  alkali-feldspar1              No  72.391194  0.210721   \n",
       "1  1072.731916    clinopyroxene1              No  64.247513  0.503895   \n",
       "2  1173.805892    clinopyroxene1              No  49.242231  1.054271   \n",
       "3   978.379922      plagioclase1              No  61.273117  0.165254   \n",
       "4  1067.517453      plagioclase1              No  60.388184  1.634061   \n",
       "\n",
       "   Al2O3_Liq  Cr2O3_Liq  FeOt_Liq   MnO_Liq   MgO_Liq   CaO_Liq  Na2O_Liq  \\\n",
       "0  12.905554   0.000000  1.388716  0.143674  0.229878  1.331236  2.920335   \n",
       "1  15.601725   0.000000  3.369638  0.087215  2.112275  4.864294  4.418883   \n",
       "2  17.036641   0.000000  6.408268  0.141980  4.024817  8.881612  2.261485   \n",
       "3  17.596388   0.092955  4.048346  0.000000  2.344540  5.782476  3.191111   \n",
       "4  15.426604   0.000000  6.474746  0.243587  2.293775  6.038702  3.765357   \n",
       "\n",
       "    K2O_Liq  P2O5_Liq    H2O_Liq  CO2_Liq  Fe3Fet_Liq  \n",
       "0  4.143710  0.000000   4.334981      0.0    0.149921  \n",
       "1  1.376060  0.155049   3.263454      0.0    0.149919  \n",
       "2  0.557779  0.273819  10.117096      0.0    0.149904  \n",
       "3  0.123935  0.206567   5.175310      0.0    0.149921  \n",
       "4  1.603601  0.304483   1.826900      0.0    0.149921  "
      ]
     },
     "execution_count": 7,
     "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": 8,
   "id": "875c28c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "15c9b50cd2a440e187fd2ed13a8333b8",
       "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": 9,
   "id": "0c151d75",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
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       "  <thead>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>812.145528</td>\n",
       "      <td>orthopyroxene1</td>\n",
       "      <td>No</td>\n",
       "      <td>72.389212</td>\n",
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       "      <td>0.229674</td>\n",
       "      <td>1.330969</td>\n",
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       "      <th>1</th>\n",
       "      <td>1112.431916</td>\n",
       "      <td>orthopyroxene1</td>\n",
       "      <td>No</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.087216</td>\n",
       "      <td>2.111717</td>\n",
       "      <td>4.864680</td>\n",
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       "      <td>1.376076</td>\n",
       "      <td>0.155051</td>\n",
       "      <td>3.263493</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1251.505892</td>\n",
       "      <td>garnet1</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>0.144999</td>\n",
       "      <td>4.108213</td>\n",
       "      <td>9.071375</td>\n",
       "      <td>2.309623</td>\n",
       "      <td>0.569638</td>\n",
       "      <td>0.279640</td>\n",
       "      <td>8.217166</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1031.379922</td>\n",
       "      <td>spinel1</td>\n",
       "      <td>No</td>\n",
       "      <td>61.272114</td>\n",
       "      <td>0.165237</td>\n",
       "      <td>17.597781</td>\n",
       "      <td>0.092926</td>\n",
       "      <td>4.047913</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.344301</td>\n",
       "      <td>5.783311</td>\n",
       "      <td>3.191148</td>\n",
       "      <td>0.123928</td>\n",
       "      <td>0.206547</td>\n",
       "      <td>5.174793</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1063.117453</td>\n",
       "      <td>orthopyroxene1</td>\n",
       "      <td>No</td>\n",
       "      <td>60.388161</td>\n",
       "      <td>1.634050</td>\n",
       "      <td>15.427047</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.474469</td>\n",
       "      <td>0.243585</td>\n",
       "      <td>2.293422</td>\n",
       "      <td>6.038864</td>\n",
       "      <td>3.765425</td>\n",
       "      <td>1.603604</td>\n",
       "      <td>0.304482</td>\n",
       "      <td>1.826891</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       T_Liq_C  liquidus_phase fluid_saturated   SiO2_Liq  TiO2_Liq  \\\n",
       "0   812.145528  orthopyroxene1              No  72.389212  0.210658   \n",
       "1  1112.431916  orthopyroxene1              No  64.247577  0.503913   \n",
       "2  1251.505892         garnet1             Yes  50.284931  1.077134   \n",
       "3  1031.379922         spinel1              No  61.272114  0.165237   \n",
       "4  1063.117453  orthopyroxene1              No  60.388161  1.634050   \n",
       "\n",
       "   Al2O3_Liq  Cr2O3_Liq  FeOt_Liq   MnO_Liq   MgO_Liq   CaO_Liq  Na2O_Liq  \\\n",
       "0  12.907554   0.000000  1.388024  0.143630  0.229674  1.330969  2.920481   \n",
       "1  15.601925   0.000000  3.369405  0.087216  2.111717  4.864680  4.418948   \n",
       "2  17.394800   0.000000  6.542481  0.144999  4.108213  9.071375  2.309623   \n",
       "3  17.597781   0.092926  4.047913  0.000000  2.344301  5.783311  3.191148   \n",
       "4  15.427047   0.000000  6.474469  0.243585  2.293422  6.038864  3.765425   \n",
       "\n",
       "    K2O_Liq  P2O5_Liq   H2O_Liq  CO2_Liq  Fe3Fet_Liq  \n",
       "0  4.146126  0.000000  4.333671      0.0    0.149947  \n",
       "1  1.376076  0.155051  3.263493      0.0    0.149932  \n",
       "2  0.569638  0.279640  8.217166      0.0    0.149980  \n",
       "3  0.123928  0.206547  5.174793      0.0    0.149919  \n",
       "4  1.603604  0.304482  1.826891      0.0    0.149925  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Results_MELTSv102.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c3d0de3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a94a048a9f9a4cfd928a624202a640d6",
       "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": 11,
   "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>849.145528</td>\n",
       "      <td>plagioclase1</td>\n",
       "      <td>No</td>\n",
       "      <td>72.390605</td>\n",
       "      <td>0.210686</td>\n",
       "      <td>12.905835</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.388483</td>\n",
       "      <td>0.143649</td>\n",
       "      <td>0.229839</td>\n",
       "      <td>1.330329</td>\n",
       "      <td>2.919742</td>\n",
       "      <td>4.146581</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.334251</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1082.531916</td>\n",
       "      <td>orthopyroxene1</td>\n",
       "      <td>No</td>\n",
       "      <td>64.247730</td>\n",
       "      <td>0.503916</td>\n",
       "      <td>15.602032</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.369230</td>\n",
       "      <td>0.087217</td>\n",
       "      <td>2.111482</td>\n",
       "      <td>4.864723</td>\n",
       "      <td>4.418996</td>\n",
       "      <td>1.376092</td>\n",
       "      <td>0.155053</td>\n",
       "      <td>3.263529</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1234.505892</td>\n",
       "      <td>garnet1</td>\n",
       "      <td>No</td>\n",
       "      <td>49.242979</td>\n",
       "      <td>1.054757</td>\n",
       "      <td>17.034953</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.407019</td>\n",
       "      <td>0.141987</td>\n",
       "      <td>4.024033</td>\n",
       "      <td>8.883451</td>\n",
       "      <td>2.261643</td>\n",
       "      <td>0.557804</td>\n",
       "      <td>0.273831</td>\n",
       "      <td>10.117545</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1100.279922</td>\n",
       "      <td>spinel1</td>\n",
       "      <td>No</td>\n",
       "      <td>61.272250</td>\n",
       "      <td>0.165237</td>\n",
       "      <td>17.597781</td>\n",
       "      <td>0.092859</td>\n",
       "      <td>4.047827</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.344288</td>\n",
       "      <td>5.783323</td>\n",
       "      <td>3.191155</td>\n",
       "      <td>0.123928</td>\n",
       "      <td>0.206547</td>\n",
       "      <td>5.174805</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1065.217453</td>\n",
       "      <td>plagioclase1</td>\n",
       "      <td>No</td>\n",
       "      <td>60.388328</td>\n",
       "      <td>1.634091</td>\n",
       "      <td>15.426328</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.474865</td>\n",
       "      <td>0.243591</td>\n",
       "      <td>2.293817</td>\n",
       "      <td>6.038577</td>\n",
       "      <td>3.765345</td>\n",
       "      <td>1.603633</td>\n",
       "      <td>0.304489</td>\n",
       "      <td>1.826934</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149921</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       T_Liq_C  liquidus_phase fluid_saturated   SiO2_Liq  TiO2_Liq  \\\n",
       "0   849.145528    plagioclase1              No  72.390605  0.210686   \n",
       "1  1082.531916  orthopyroxene1              No  64.247730  0.503916   \n",
       "2  1234.505892         garnet1              No  49.242979  1.054757   \n",
       "3  1100.279922         spinel1              No  61.272250  0.165237   \n",
       "4  1065.217453    plagioclase1              No  60.388328  1.634091   \n",
       "\n",
       "   Al2O3_Liq  Cr2O3_Liq  FeOt_Liq   MnO_Liq   MgO_Liq   CaO_Liq  Na2O_Liq  \\\n",
       "0  12.905835   0.000000  1.388483  0.143649  0.229839  1.330329  2.919742   \n",
       "1  15.602032   0.000000  3.369230  0.087217  2.111482  4.864723  4.418996   \n",
       "2  17.034953   0.000000  6.407019  0.141987  4.024033  8.883451  2.261643   \n",
       "3  17.597781   0.092859  4.047827  0.000000  2.344288  5.783323  3.191155   \n",
       "4  15.426328   0.000000  6.474865  0.243591  2.293817  6.038577  3.765345   \n",
       "\n",
       "    K2O_Liq  P2O5_Liq    H2O_Liq  CO2_Liq  Fe3Fet_Liq  \n",
       "0  4.146581  0.000000   4.334251      0.0    0.149921  \n",
       "1  1.376092  0.155053   3.263529      0.0    0.149937  \n",
       "2  0.557804  0.273831  10.117545      0.0    0.149969  \n",
       "3  0.123928  0.206547   5.174805      0.0    0.149911  \n",
       "4  1.603633  0.304489   1.826934      0.0    0.149921  "
      ]
     },
     "execution_count": 11,
     "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": 12,
   "id": "eea88d76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(700.0, 1500.0)"
      ]
     },
     "execution_count": 12,
     "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": []
  }
 ],
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