{
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  {
   "cell_type": "markdown",
   "id": "2a210c53-41d0-4905-976a-ba95ee36bd7e",
   "metadata": {},
   "source": [
    "# Plag-Liq and Kspar-Liq Matching\n",
    "- This notebook shows how to assess all possible matches for Plag-Liq and Kspar-Liq pairs where there aren't obvious pairwise analyses\n",
    "- You can download the excel spreadsheet here: https://github.com/PennyWieser/Thermobar/blob/main/docs/Examples/Feldspar_Thermobarometry/Feldspar_Liquid.xlsx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2188e8d4-78c2-4925-a960-743f8991362a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import Thermobar as pt\n",
    "pd.options.display.max_columns = None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aaf3adc-fd47-4caf-a8e0-d9dfb1c9d614",
   "metadata": {},
   "source": [
    "## Example 1 - Plag and Liquid. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1fa8a354-f387-40cc-9875-c44420e658bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Load in the liquids\n",
    "out_Liq=pt.import_excel('Feldspar_Liquid.xlsx', sheet_name=\"Liq_only\")\n",
    "Liqs=out_Liq['Liqs']\n",
    "# Load in the Plags\n",
    "out_Plag=pt.import_excel('Feldspar_Liquid.xlsx', sheet_name=\"Plag_only\")\n",
    "Plags=out_Plag['Plags']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "102bc549-6521-47e5-8a9d-255dd767f39b",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SiO2_Plag</th>\n",
       "      <th>TiO2_Plag</th>\n",
       "      <th>Al2O3_Plag</th>\n",
       "      <th>FeOt_Plag</th>\n",
       "      <th>MnO_Plag</th>\n",
       "      <th>MgO_Plag</th>\n",
       "      <th>CaO_Plag</th>\n",
       "      <th>Na2O_Plag</th>\n",
       "      <th>K2O_Plag</th>\n",
       "      <th>Cr2O3_Plag</th>\n",
       "      <th>Sample_ID_Plag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>57.3</td>\n",
       "      <td>0.09</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>8.33</td>\n",
       "      <td>6.11</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Plag1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0.12</td>\n",
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       "      <td>0.47</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>8.95</td>\n",
       "      <td>5.66</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Plag2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57.6</td>\n",
       "      <td>0.11</td>\n",
       "      <td>26.3</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>8.50</td>\n",
       "      <td>6.27</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Plag3_core</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>57.2</td>\n",
       "      <td>0.16</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.06</td>\n",
       "      <td>9.03</td>\n",
       "      <td>5.58</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Plag4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56.7</td>\n",
       "      <td>0.14</td>\n",
       "      <td>27.6</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>9.46</td>\n",
       "      <td>5.58</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Plag5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SiO2_Plag  TiO2_Plag  Al2O3_Plag  FeOt_Plag  MnO_Plag  MgO_Plag  CaO_Plag  \\\n",
       "0       57.3       0.09        26.6       0.43       0.0      0.03      8.33   \n",
       "1       56.5       0.12        26.9       0.47       0.0      0.05      8.95   \n",
       "2       57.6       0.11        26.3       0.50       0.0      0.07      8.50   \n",
       "3       57.2       0.16        27.0       0.62       0.0      0.06      9.03   \n",
       "4       56.7       0.14        27.6       0.69       0.0      0.11      9.46   \n",
       "\n",
       "   Na2O_Plag  K2O_Plag  Cr2O3_Plag Sample_ID_Plag  \n",
       "0       6.11      0.49         0.0          Plag1  \n",
       "1       5.66      0.47         0.0          Plag2  \n",
       "2       6.27      0.40         0.0     Plag3_core  \n",
       "3       5.58      0.84         0.0          Plag4  \n",
       "4       5.58      0.48         0.0          Plag5  "
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>SiO2_Liq</th>\n",
       "      <th>TiO2_Liq</th>\n",
       "      <th>Al2O3_Liq</th>\n",
       "      <th>FeOt_Liq</th>\n",
       "      <th>MnO_Liq</th>\n",
       "      <th>MgO_Liq</th>\n",
       "      <th>CaO_Liq</th>\n",
       "      <th>Na2O_Liq</th>\n",
       "      <th>K2O_Liq</th>\n",
       "      <th>Cr2O3_Liq</th>\n",
       "      <th>P2O5_Liq</th>\n",
       "      <th>H2O_Liq</th>\n",
       "      <th>Fe3Fet_Liq</th>\n",
       "      <th>NiO_Liq</th>\n",
       "      <th>CoO_Liq</th>\n",
       "      <th>CO2_Liq</th>\n",
       "      <th>Sample_ID_Liq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49.1</td>\n",
       "      <td>3.22</td>\n",
       "      <td>14.4</td>\n",
       "      <td>14.8</td>\n",
       "      <td>0.14</td>\n",
       "      <td>3.20</td>\n",
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       "      <td>0.18</td>\n",
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       "      <td>16.4</td>\n",
       "      <td>0.16</td>\n",
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      "text/plain": [
       "   SiO2_Liq  TiO2_Liq  Al2O3_Liq  FeOt_Liq  MnO_Liq  MgO_Liq  CaO_Liq  \\\n",
       "0      49.1      3.22       14.4      14.8     0.14     3.20     6.72   \n",
       "1      49.2      3.89       15.3      13.7     0.12     3.88     6.76   \n",
       "2      49.6      3.79       15.8      13.0     0.14     4.26     6.59   \n",
       "3      47.1      4.21       12.0      17.8     0.18     3.40     7.28   \n",
       "4      48.1      3.88       13.2      16.4     0.16     4.02     6.51   \n",
       "\n",
       "   Na2O_Liq  K2O_Liq  Cr2O3_Liq  P2O5_Liq  H2O_Liq  Fe3Fet_Liq  NiO_Liq  \\\n",
       "0      3.34     1.70        0.0      1.13      0.0         0.0      0.0   \n",
       "1      3.44     1.22        0.0      0.83      0.0         0.0      0.0   \n",
       "2      3.65     1.04        0.0      0.63      0.0         0.0      0.0   \n",
       "3      2.93     2.02        0.0      2.32      0.0         0.0      0.0   \n",
       "4      3.36     1.36        0.0      1.59      0.0         0.0      0.0   \n",
       "\n",
       "   CoO_Liq  CO2_Liq Sample_ID_Liq  \n",
       "0      0.0      0.0        Glass1  \n",
       "1      0.0      0.0        Glass2  \n",
       "2      0.0      0.0        Glass3  \n",
       "3      0.0      0.0        Glass4  \n",
       "4      0.0      0.0        Glass5  "
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "## Inspect them to check they read in right\n",
    "display(Plags.head())\n",
    "display(Liqs.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5cf7408-64a7-4bac-9117-0ef52dc8f19f",
   "metadata": {},
   "source": [
    "## Please check your feldspars are actually plagioclases! If you apply plag-liq thermometers to kspars, you get stupid results, that mess up the iterative calculations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fc10d10-9af6-4ca4-afbc-b88b05691c57",
   "metadata": {},
   "source": [
    "## Lets plot to inspect and check they are actually plag, else you will have convergence issues\n",
    "-This function relies heavily on the ternary plot package from Marc Harper et al. 2015 - https://github.com/marcharper/python-ternary, if you use these figures, you must cite that (Marc Harper et al. (2015). python-ternary: Ternary Plots in Python. Zenodo. 10.5281/zenodo.594435) as well as Thermobar.\n",
    "\n",
    "You may have problems with this package if you have the separate “ternary” package installed (yes, there are python packages called ternary and python-ternary- Yay!). I (penny) got the error “module ternary has no attribute figure, so had to uninstall the ternary I had through pip (pip uninstall ternary), and re-install python-ternary through conda in the command line “conda install python-ternary”. If you have everything in pip, or conda, keep in 1 environment, don’t follow my bad example here!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "859ffb88-f57c-4b71-8036-47bb3f26738e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Plag_tern_points = pt.tern_points_fspar(fspar_comps=Plags)\n",
    "\n",
    "fig, tax = pt.plot_fspar_classification(major_grid=True, ticks=False)\n",
    "\n",
    "# Here we plot the data ontop, with plag as red triangles\n",
    "tax.scatter(\n",
    "    Plag_tern_points,\n",
    "    edgecolor=\"k\",\n",
    "    marker=\"^\",\n",
    "    facecolor=\"red\",\n",
    "    label='Plag',\n",
    "   s=90\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b719fdfc-fa9d-4210-9b42-d696fd17da70",
   "metadata": {},
   "outputs": [],
   "source": [
    "Plag_components=pt.calculate_cat_fractions_plagioclase(plag_comps=Plags)\n",
    "RealPlags=Plags.loc[Plag_components['An_Plag']>0.05].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55b30af5-91f7-478b-9385-0814b4737a9b",
   "metadata": {},
   "source": [
    "## Example 1a - All possible Liq-Plag matches, An-Ab equilibrium\n",
    "- You can specify to use the An-Ab equilibrium test of Putirka (2008) as in this example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "890ee427-77d6-4c51-97a7-e552676ca94f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Considering N=28 Fspar & N=24 Liqs, which is a total of N=672 Liq-Fspar pairs, be patient if this is >>1 million!\n",
      "Applying filter to only average those that pass the An-Ab eq test of Putirka, 2008\n",
      "Done!!! I found a total of N=327 Fspar-Liq matches using the specified filter. N=28 Fspar out of the N=28 Fspar that you input matched to 1 or more liquids\n"
     ]
    }
   ],
   "source": [
    "MM_dict=pt.calculate_fspar_liq_temp_matching(liq_comps=Liqs, plag_comps=RealPlags, \n",
    "                                             equationT=\"T_Put2008_eq24a\", P=5, \n",
    "                                                Ab_An_P2008=True, H2O_Liq=2)\n",
    "Av_Matches=MM_dict['Av_PTs']\n",
    "All_Matches=MM_dict['All_PTs']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa21261f-0f16-42cd-a4f8-96f33ffd8007",
   "metadata": {},
   "source": [
    "## Example 1b - All possible Liq-Plag matches, no filter\n",
    "- This could be used if you want to develop your own equilibrium filters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e6f3f0db-56b2-4d3f-a04a-d6d5ea9f0a36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Considering N=28 Fspar & N=24 Liqs, which is a total of N=672 Liq-Fspar pairs, be patient if this is >>1 million!\n",
      "We are returning all pairs, if you want to use the Ab-An equilibrium test of Putirka (2008), enter Ab_An_P2008=True\n",
      "Done!!! I found a total of N=672 Fspar-Liq matches using the specified filter. N=28 Fspar out of the N=28 Fspar that you input matched to 1 or more liquids\n"
     ]
    }
   ],
   "source": [
    "MM_dict=pt.calculate_fspar_liq_temp_matching(liq_comps=Liqs, plag_comps=RealPlags, \n",
    "                                             equationT=\"T_Put2008_eq24a\", P=5, \n",
    "                                                Ab_An_P2008=False, H2O_Liq=2)\n",
    "Av_Matches=MM_dict['Av_PTs']\n",
    "All_Matches=MM_dict['All_PTs']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a04260c-c993-4b1e-b770-82abb15ab18c",
   "metadata": {},
   "source": [
    "## Example 1c - Iterating T and H2O for all possible Plag-Liq matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "43ef6cf7-b62f-4faf-b925-e5577c78d66f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Considering N=28 Fspar & N=24 Liqs, which is a total of N=672 Liq- Fspar pairs, be patient if this is >>1 million!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Applying filter to only average those that pass the An-Ab eq test of Putirka, 2008\n",
      "Done!!! I found a total of N=327 Fspar-Liq matches using the specified filter. N=28 Fspar out of the N=28 Fspar that you input matched to 1 or more liquids\n"
     ]
    }
   ],
   "source": [
    "T_H_Iter_dict=pt.calculate_fspar_liq_temp_hygr_matching(liq_comps=Liqs, plag_comps=RealPlags,\n",
    "        equationT=\"T_Put2008_eq24a\", equationH=\"H_Waters2015\", P=5)\n",
    "T_H_Calc_Av=T_H_Iter_dict.get('Av_HTs')\n",
    "T_H_Calc_All=T_H_Iter_dict.get('All_HTs')\n",
    "T_H_Evol=T_H_Iter_dict.get('T_H_Evolution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a322a364-017a-4a2d-8536-f5623f4a7278",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'H$_2$O Calc (wt%)')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Maybe you want to plot a histogram of H2O contents\n",
    "plt.hist(T_H_Calc_Av['Mean_H2O_calc'])\n",
    "plt.xlabel('H$_2$O Calc (wt%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c7e9123-0c71-4e99-a1ac-f1ba85b3f7db",
   "metadata": {},
   "source": [
    "## Example 2 - Kspar-Liquid calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1b4314c4-f7b6-4843-b5de-091075d7b616",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in the Kspars\n",
    "out_Kspar=pt.import_excel('Feldspar_Liquid.xlsx', sheet_name=\"Kspar_only\")\n",
    "Kspars=out_Kspar['Kspars']\n",
    "\n",
    "# Load in the liquids you want to match with\n",
    "out_Liq2=pt.import_excel('Feldspar_Liquid.xlsx', sheet_name=\"Evolved_Liq_only\")\n",
    "Liqs=out_Liq2['Liqs']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20039fce-f055-4c5b-a0d1-583c3fbc1037",
   "metadata": {},
   "source": [
    "### Check actually kspar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "31713395-4450-44fc-a1f7-3694c55adad9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Kspar_tern_points = pt.tern_points_fspar(fspar_comps=Kspars)\n",
    "fig, tax = pt.plot_fspar_classification(major_grid=True, ticks=False)\n",
    "\n",
    "tax.scatter(\n",
    "    Kspar_tern_points,\n",
    "    edgecolor=\"k\",\n",
    "    marker=\"o\",\n",
    "    facecolor=\"cyan\",\n",
    "   s=60, label='Kspar'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "53b2cdb3-a9c7-47bf-b3c4-4c0252426cfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "Kspar_components=pt.calculate_cat_fractions_kspar(kspar_comps=Kspars)\n",
    "RealKspars=Kspars.loc[Kspar_components['An_Kspar']<0.05].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "840f2ea2-3ff7-4cf6-93e1-17030c1979ca",
   "metadata": {},
   "source": [
    "### 2a - Lets calculate temperature for all liq-kspar matches\n",
    "- Currently no equilibrium tests exist for Kspar-Liquid that we are aware off. If you know of ones I can implement, please reach out!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "16d46a40-34a4-48f6-b45b-c57694258b71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Considering N=15 Fspar & N=15 Liqs, which is a total of N=225 Liq-Fspar pairs, be patient if this is >>1 million!\n",
      "Sorry, no equilibrium tests implemented for Kspar-Liquid. Weve returned all possible pairs, you will have to filter them yourselves\n",
      "Done!!! I found a total of N=225 Fspar-Liq matches using the specified filter. N=15 Fspar out of the N=15 Fspar that you input matched to 1 or more liquids\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_Fspar</th>\n",
       "      <th>Mean_Sample_ID_Kspar</th>\n",
       "      <th>Mean_T_K_calc</th>\n",
       "      <th>Mean_SiO2_Liq</th>\n",
       "      <th>Mean_TiO2_Liq</th>\n",
       "      <th>Mean_Al2O3_Liq</th>\n",
       "      <th>Mean_FeOt_Liq</th>\n",
       "      <th>Mean_MnO_Liq</th>\n",
       "      <th>Mean_MgO_Liq</th>\n",
       "      <th>Mean_CaO_Liq</th>\n",
       "      <th>Mean_Na2O_Liq</th>\n",
       "      <th>Mean_K2O_Liq</th>\n",
       "      <th>Mean_Cr2O3_Liq</th>\n",
       "      <th>Mean_P2O5_Liq</th>\n",
       "      <th>Mean_H2O_Liq</th>\n",
       "      <th>Mean_Fe3Fet_Liq</th>\n",
       "      <th>Mean_NiO_Liq</th>\n",
       "      <th>Mean_CoO_Liq</th>\n",
       "      <th>Mean_CO2_Liq</th>\n",
       "      <th>Mean_Sample_ID_Liq</th>\n",
       "      <th>Mean_SiO2_Liq_mol_frac</th>\n",
       "      <th>Mean_MgO_Liq_mol_frac</th>\n",
       "      <th>Mean_MnO_Liq_mol_frac</th>\n",
       "      <th>Mean_FeOt_Liq_mol_frac</th>\n",
       "      <th>Mean_CaO_Liq_mol_frac</th>\n",
       "      <th>Mean_Al2O3_Liq_mol_frac</th>\n",
       "      <th>Mean_Na2O_Liq_mol_frac</th>\n",
       "      <th>Mean_K2O_Liq_mol_frac</th>\n",
       "      <th>Mean_TiO2_Liq_mol_frac</th>\n",
       "      <th>Mean_P2O5_Liq_mol_frac</th>\n",
       "      <th>Mean_Cr2O3_Liq_mol_frac</th>\n",
       "      <th>Mean_Si_Liq_cat_frac</th>\n",
       "      <th>Mean_Mg_Liq_cat_frac</th>\n",
       "      <th>Mean_Mn_Liq_cat_frac</th>\n",
       "      <th>Mean_Fet_Liq_cat_frac</th>\n",
       "      <th>Mean_Ca_Liq_cat_frac</th>\n",
       "      <th>Mean_Al_Liq_cat_frac</th>\n",
       "      <th>Mean_Na_Liq_cat_frac</th>\n",
       "      <th>Mean_K_Liq_cat_frac</th>\n",
       "      <th>Mean_Ti_Liq_cat_frac</th>\n",
       "      <th>Mean_P_Liq_cat_frac</th>\n",
       "      <th>Mean_Cr_Liq_cat_frac</th>\n",
       "      <th>Mean_Mg_Number_Liq_NoFe3</th>\n",
       "      <th>Mean_Mg_Number_Liq_Fe3</th>\n",
       "      <th>Mean_ID_liq</th>\n",
       "      <th>Mean_Sample_ID_liq</th>\n",
       "      <th># of Liqs Averaged</th>\n",
       "      <th>Std_Sample_ID_Kspar</th>\n",
       "      <th>Std_T_K_calc</th>\n",
       "      <th>Std_SiO2_Liq</th>\n",
       "      <th>Std_TiO2_Liq</th>\n",
       "      <th>Std_Al2O3_Liq</th>\n",
       "      <th>Std_FeOt_Liq</th>\n",
       "      <th>Std_MnO_Liq</th>\n",
       "      <th>Std_MgO_Liq</th>\n",
       "      <th>Std_CaO_Liq</th>\n",
       "      <th>Std_Na2O_Liq</th>\n",
       "      <th>Std_K2O_Liq</th>\n",
       "      <th>Std_Cr2O3_Liq</th>\n",
       "      <th>Std_P2O5_Liq</th>\n",
       "      <th>Std_H2O_Liq</th>\n",
       "      <th>Std_Fe3Fet_Liq</th>\n",
       "      <th>Std_NiO_Liq</th>\n",
       "      <th>Std_CoO_Liq</th>\n",
       "      <th>Std_CO2_Liq</th>\n",
       "      <th>Std_Sample_ID_Liq</th>\n",
       "      <th>Std_SiO2_Liq_mol_frac</th>\n",
       "      <th>Std_MgO_Liq_mol_frac</th>\n",
       "      <th>Std_MnO_Liq_mol_frac</th>\n",
       "      <th>Std_FeOt_Liq_mol_frac</th>\n",
       "      <th>Std_CaO_Liq_mol_frac</th>\n",
       "      <th>Std_Al2O3_Liq_mol_frac</th>\n",
       "      <th>Std_Na2O_Liq_mol_frac</th>\n",
       "      <th>Std_K2O_Liq_mol_frac</th>\n",
       "      <th>Std_TiO2_Liq_mol_frac</th>\n",
       "      <th>Std_P2O5_Liq_mol_frac</th>\n",
       "      <th>Std_Cr2O3_Liq_mol_frac</th>\n",
       "      <th>Std_Si_Liq_cat_frac</th>\n",
       "      <th>Std_Mg_Liq_cat_frac</th>\n",
       "      <th>Std_Mn_Liq_cat_frac</th>\n",
       "      <th>Std_Fet_Liq_cat_frac</th>\n",
       "      <th>Std_Ca_Liq_cat_frac</th>\n",
       "      <th>Std_Al_Liq_cat_frac</th>\n",
       "      <th>Std_Na_Liq_cat_frac</th>\n",
       "      <th>Std_K_Liq_cat_frac</th>\n",
       "      <th>Std_Ti_Liq_cat_frac</th>\n",
       "      <th>Std_P_Liq_cat_frac</th>\n",
       "      <th>Std_Cr_Liq_cat_frac</th>\n",
       "      <th>Std_Mg_Number_Liq_NoFe3</th>\n",
       "      <th>Std_Mg_Number_Liq_Fe3</th>\n",
       "      <th>Std_ID_liq</th>\n",
       "      <th>Std_Sample_ID_liq</th>\n",
       "      <th>Sample_ID_Kspar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Kspar1</td>\n",
       "      <td>1138.165453</td>\n",
       "      <td>62.976667</td>\n",
       "      <td>0.45</td>\n",
       "      <td>18.56</td>\n",
       "      <td>3.17</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.64</td>\n",
       "      <td>5.71</td>\n",
       "      <td>6.223333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.709623</td>\n",
       "      <td>0.003864</td>\n",
       "      <td>0.002577</td>\n",
       "      <td>0.029872</td>\n",
       "      <td>0.0198</td>\n",
       "      <td>0.123242</td>\n",
       "      <td>0.062378</td>\n",
       "      <td>0.044734</td>\n",
       "      <td>0.003814</td>\n",
       "      <td>0.000095</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.576767</td>\n",
       "      <td>0.00314</td>\n",
       "      <td>0.002094</td>\n",
       "      <td>0.024278</td>\n",
       "      <td>0.016092</td>\n",
       "      <td>0.200325</td>\n",
       "      <td>0.101348</td>\n",
       "      <td>0.072699</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.000155</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.114521</td>\n",
       "      <td>0.114521</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15</td>\n",
       "      <td>Kspar1</td>\n",
       "      <td>27.575211</td>\n",
       "      <td>0.703732</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.507093</td>\n",
       "      <td>0.351866</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.472136</td>\n",
       "      <td>0.007112</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.00003</td>\n",
       "      <td>0.000186</td>\n",
       "      <td>0.005578</td>\n",
       "      <td>0.002604</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>1.436252e-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009076</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>0.000947</td>\n",
       "      <td>0.008549</td>\n",
       "      <td>0.003937</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>7.329643e-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.472136</td>\n",
       "      <td>4.472136</td>\n",
       "      <td>Kspar1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Kspar2</td>\n",
       "      <td>1087.464362</td>\n",
       "      <td>62.976667</td>\n",
       "      <td>0.45</td>\n",
       "      <td>18.56</td>\n",
       "      <td>3.17</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.64</td>\n",
       "      <td>5.71</td>\n",
       "      <td>6.223333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.709623</td>\n",
       "      <td>0.003864</td>\n",
       "      <td>0.002577</td>\n",
       "      <td>0.029872</td>\n",
       "      <td>0.0198</td>\n",
       "      <td>0.123242</td>\n",
       "      <td>0.062378</td>\n",
       "      <td>0.044734</td>\n",
       "      <td>0.003814</td>\n",
       "      <td>0.000095</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.576767</td>\n",
       "      <td>0.00314</td>\n",
       "      <td>0.002094</td>\n",
       "      <td>0.024278</td>\n",
       "      <td>0.016092</td>\n",
       "      <td>0.200325</td>\n",
       "      <td>0.101348</td>\n",
       "      <td>0.072699</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.000155</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.114521</td>\n",
       "      <td>0.114521</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15</td>\n",
       "      <td>Kspar2</td>\n",
       "      <td>25.122349</td>\n",
       "      <td>0.703732</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.507093</td>\n",
       "      <td>0.351866</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.472136</td>\n",
       "      <td>0.007112</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.00003</td>\n",
       "      <td>0.000186</td>\n",
       "      <td>0.005578</td>\n",
       "      <td>0.002604</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>1.436252e-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009076</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>0.000947</td>\n",
       "      <td>0.008549</td>\n",
       "      <td>0.003937</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>7.329643e-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.472136</td>\n",
       "      <td>4.472136</td>\n",
       "      <td>Kspar2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Kspar3</td>\n",
       "      <td>977.039695</td>\n",
       "      <td>62.976667</td>\n",
       "      <td>0.45</td>\n",
       "      <td>18.56</td>\n",
       "      <td>3.17</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.64</td>\n",
       "      <td>5.71</td>\n",
       "      <td>6.223333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.709623</td>\n",
       "      <td>0.003864</td>\n",
       "      <td>0.002577</td>\n",
       "      <td>0.029872</td>\n",
       "      <td>0.0198</td>\n",
       "      <td>0.123242</td>\n",
       "      <td>0.062378</td>\n",
       "      <td>0.044734</td>\n",
       "      <td>0.003814</td>\n",
       "      <td>0.000095</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.576767</td>\n",
       "      <td>0.00314</td>\n",
       "      <td>0.002094</td>\n",
       "      <td>0.024278</td>\n",
       "      <td>0.016092</td>\n",
       "      <td>0.200325</td>\n",
       "      <td>0.101348</td>\n",
       "      <td>0.072699</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.000155</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.114521</td>\n",
       "      <td>0.114521</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15</td>\n",
       "      <td>Kspar3</td>\n",
       "      <td>20.190486</td>\n",
       "      <td>0.703732</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.507093</td>\n",
       "      <td>0.351866</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.472136</td>\n",
       "      <td>0.007112</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.00003</td>\n",
       "      <td>0.000186</td>\n",
       "      <td>0.005578</td>\n",
       "      <td>0.002604</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>1.436252e-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009076</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>0.000947</td>\n",
       "      <td>0.008549</td>\n",
       "      <td>0.003937</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>7.329643e-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.472136</td>\n",
       "      <td>4.472136</td>\n",
       "      <td>Kspar3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Kspar4</td>\n",
       "      <td>1023.046234</td>\n",
       "      <td>62.976667</td>\n",
       "      <td>0.45</td>\n",
       "      <td>18.56</td>\n",
       "      <td>3.17</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.64</td>\n",
       "      <td>5.71</td>\n",
       "      <td>6.223333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.709623</td>\n",
       "      <td>0.003864</td>\n",
       "      <td>0.002577</td>\n",
       "      <td>0.029872</td>\n",
       "      <td>0.0198</td>\n",
       "      <td>0.123242</td>\n",
       "      <td>0.062378</td>\n",
       "      <td>0.044734</td>\n",
       "      <td>0.003814</td>\n",
       "      <td>0.000095</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.576767</td>\n",
       "      <td>0.00314</td>\n",
       "      <td>0.002094</td>\n",
       "      <td>0.024278</td>\n",
       "      <td>0.016092</td>\n",
       "      <td>0.200325</td>\n",
       "      <td>0.101348</td>\n",
       "      <td>0.072699</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.000155</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.114521</td>\n",
       "      <td>0.114521</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15</td>\n",
       "      <td>Kspar4</td>\n",
       "      <td>22.177217</td>\n",
       "      <td>0.703732</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.507093</td>\n",
       "      <td>0.351866</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.472136</td>\n",
       "      <td>0.007112</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.00003</td>\n",
       "      <td>0.000186</td>\n",
       "      <td>0.005578</td>\n",
       "      <td>0.002604</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>1.436252e-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009076</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>0.000947</td>\n",
       "      <td>0.008549</td>\n",
       "      <td>0.003937</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>7.329643e-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.472136</td>\n",
       "      <td>4.472136</td>\n",
       "      <td>Kspar4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Kspar5</td>\n",
       "      <td>1073.413522</td>\n",
       "      <td>62.976667</td>\n",
       "      <td>0.45</td>\n",
       "      <td>18.56</td>\n",
       "      <td>3.17</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.64</td>\n",
       "      <td>5.71</td>\n",
       "      <td>6.223333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.709623</td>\n",
       "      <td>0.003864</td>\n",
       "      <td>0.002577</td>\n",
       "      <td>0.029872</td>\n",
       "      <td>0.0198</td>\n",
       "      <td>0.123242</td>\n",
       "      <td>0.062378</td>\n",
       "      <td>0.044734</td>\n",
       "      <td>0.003814</td>\n",
       "      <td>0.000095</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.576767</td>\n",
       "      <td>0.00314</td>\n",
       "      <td>0.002094</td>\n",
       "      <td>0.024278</td>\n",
       "      <td>0.016092</td>\n",
       "      <td>0.200325</td>\n",
       "      <td>0.101348</td>\n",
       "      <td>0.072699</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.000155</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.114521</td>\n",
       "      <td>0.114521</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15</td>\n",
       "      <td>Kspar5</td>\n",
       "      <td>24.463649</td>\n",
       "      <td>0.703732</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.507093</td>\n",
       "      <td>0.351866</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.472136</td>\n",
       "      <td>0.007112</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.00003</td>\n",
       "      <td>0.000186</td>\n",
       "      <td>0.005578</td>\n",
       "      <td>0.002604</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>1.436252e-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009076</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>0.000947</td>\n",
       "      <td>0.008549</td>\n",
       "      <td>0.003937</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>7.329643e-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.472136</td>\n",
       "      <td>4.472136</td>\n",
       "      <td>Kspar5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ID_Fspar Mean_Sample_ID_Kspar  Mean_T_K_calc  Mean_SiO2_Liq  Mean_TiO2_Liq  \\\n",
       "0        0               Kspar1    1138.165453      62.976667           0.45   \n",
       "1        1               Kspar2    1087.464362      62.976667           0.45   \n",
       "2        2               Kspar3     977.039695      62.976667           0.45   \n",
       "3        3               Kspar4    1023.046234      62.976667           0.45   \n",
       "4        4               Kspar5    1073.413522      62.976667           0.45   \n",
       "\n",
       "   Mean_Al2O3_Liq  Mean_FeOt_Liq  Mean_MnO_Liq  Mean_MgO_Liq  Mean_CaO_Liq  \\\n",
       "0           18.56           3.17          0.27          0.23          1.64   \n",
       "1           18.56           3.17          0.27          0.23          1.64   \n",
       "2           18.56           3.17          0.27          0.23          1.64   \n",
       "3           18.56           3.17          0.27          0.23          1.64   \n",
       "4           18.56           3.17          0.27          0.23          1.64   \n",
       "\n",
       "   Mean_Na2O_Liq  Mean_K2O_Liq  Mean_Cr2O3_Liq  Mean_P2O5_Liq  Mean_H2O_Liq  \\\n",
       "0           5.71      6.223333             0.0           0.02           2.0   \n",
       "1           5.71      6.223333             0.0           0.02           2.0   \n",
       "2           5.71      6.223333             0.0           0.02           2.0   \n",
       "3           5.71      6.223333             0.0           0.02           2.0   \n",
       "4           5.71      6.223333             0.0           0.02           2.0   \n",
       "\n",
       "   Mean_Fe3Fet_Liq  Mean_NiO_Liq  Mean_CoO_Liq  Mean_CO2_Liq  \\\n",
       "0              0.0           0.0           0.0           0.0   \n",
       "1              0.0           0.0           0.0           0.0   \n",
       "2              0.0           0.0           0.0           0.0   \n",
       "3              0.0           0.0           0.0           0.0   \n",
       "4              0.0           0.0           0.0           0.0   \n",
       "\n",
       "   Mean_Sample_ID_Liq  Mean_SiO2_Liq_mol_frac  Mean_MgO_Liq_mol_frac  \\\n",
       "0                 7.0                0.709623               0.003864   \n",
       "1                 7.0                0.709623               0.003864   \n",
       "2                 7.0                0.709623               0.003864   \n",
       "3                 7.0                0.709623               0.003864   \n",
       "4                 7.0                0.709623               0.003864   \n",
       "\n",
       "   Mean_MnO_Liq_mol_frac  Mean_FeOt_Liq_mol_frac  Mean_CaO_Liq_mol_frac  \\\n",
       "0               0.002577                0.029872                 0.0198   \n",
       "1               0.002577                0.029872                 0.0198   \n",
       "2               0.002577                0.029872                 0.0198   \n",
       "3               0.002577                0.029872                 0.0198   \n",
       "4               0.002577                0.029872                 0.0198   \n",
       "\n",
       "   Mean_Al2O3_Liq_mol_frac  Mean_Na2O_Liq_mol_frac  Mean_K2O_Liq_mol_frac  \\\n",
       "0                 0.123242                0.062378               0.044734   \n",
       "1                 0.123242                0.062378               0.044734   \n",
       "2                 0.123242                0.062378               0.044734   \n",
       "3                 0.123242                0.062378               0.044734   \n",
       "4                 0.123242                0.062378               0.044734   \n",
       "\n",
       "   Mean_TiO2_Liq_mol_frac  Mean_P2O5_Liq_mol_frac  Mean_Cr2O3_Liq_mol_frac  \\\n",
       "0                0.003814                0.000095                      0.0   \n",
       "1                0.003814                0.000095                      0.0   \n",
       "2                0.003814                0.000095                      0.0   \n",
       "3                0.003814                0.000095                      0.0   \n",
       "4                0.003814                0.000095                      0.0   \n",
       "\n",
       "   Mean_Si_Liq_cat_frac  Mean_Mg_Liq_cat_frac  Mean_Mn_Liq_cat_frac  \\\n",
       "0              0.576767               0.00314              0.002094   \n",
       "1              0.576767               0.00314              0.002094   \n",
       "2              0.576767               0.00314              0.002094   \n",
       "3              0.576767               0.00314              0.002094   \n",
       "4              0.576767               0.00314              0.002094   \n",
       "\n",
       "   Mean_Fet_Liq_cat_frac  Mean_Ca_Liq_cat_frac  Mean_Al_Liq_cat_frac  \\\n",
       "0               0.024278              0.016092              0.200325   \n",
       "1               0.024278              0.016092              0.200325   \n",
       "2               0.024278              0.016092              0.200325   \n",
       "3               0.024278              0.016092              0.200325   \n",
       "4               0.024278              0.016092              0.200325   \n",
       "\n",
       "   Mean_Na_Liq_cat_frac  Mean_K_Liq_cat_frac  Mean_Ti_Liq_cat_frac  \\\n",
       "0              0.101348             0.072699                0.0031   \n",
       "1              0.101348             0.072699                0.0031   \n",
       "2              0.101348             0.072699                0.0031   \n",
       "3              0.101348             0.072699                0.0031   \n",
       "4              0.101348             0.072699                0.0031   \n",
       "\n",
       "   Mean_P_Liq_cat_frac  Mean_Cr_Liq_cat_frac  Mean_Mg_Number_Liq_NoFe3  \\\n",
       "0             0.000155                   0.0                  0.114521   \n",
       "1             0.000155                   0.0                  0.114521   \n",
       "2             0.000155                   0.0                  0.114521   \n",
       "3             0.000155                   0.0                  0.114521   \n",
       "4             0.000155                   0.0                  0.114521   \n",
       "\n",
       "   Mean_Mg_Number_Liq_Fe3  Mean_ID_liq  Mean_Sample_ID_liq  \\\n",
       "0                0.114521          7.0                 7.0   \n",
       "1                0.114521          7.0                 7.0   \n",
       "2                0.114521          7.0                 7.0   \n",
       "3                0.114521          7.0                 7.0   \n",
       "4                0.114521          7.0                 7.0   \n",
       "\n",
       "   # of Liqs Averaged Std_Sample_ID_Kspar  Std_T_K_calc  Std_SiO2_Liq  \\\n",
       "0                  15              Kspar1     27.575211      0.703732   \n",
       "1                  15              Kspar2     25.122349      0.703732   \n",
       "2                  15              Kspar3     20.190486      0.703732   \n",
       "3                  15              Kspar4     22.177217      0.703732   \n",
       "4                  15              Kspar5     24.463649      0.703732   \n",
       "\n",
       "   Std_TiO2_Liq  Std_Al2O3_Liq  Std_FeOt_Liq  Std_MnO_Liq  Std_MgO_Liq  \\\n",
       "0           0.0            0.0           0.0          0.0          0.0   \n",
       "1           0.0            0.0           0.0          0.0          0.0   \n",
       "2           0.0            0.0           0.0          0.0          0.0   \n",
       "3           0.0            0.0           0.0          0.0          0.0   \n",
       "4           0.0            0.0           0.0          0.0          0.0   \n",
       "\n",
       "   Std_CaO_Liq  Std_Na2O_Liq  Std_K2O_Liq  Std_Cr2O3_Liq  Std_P2O5_Liq  \\\n",
       "0          0.0      0.507093     0.351866            0.0           0.0   \n",
       "1          0.0      0.507093     0.351866            0.0           0.0   \n",
       "2          0.0      0.507093     0.351866            0.0           0.0   \n",
       "3          0.0      0.507093     0.351866            0.0           0.0   \n",
       "4          0.0      0.507093     0.351866            0.0           0.0   \n",
       "\n",
       "   Std_H2O_Liq  Std_Fe3Fet_Liq  Std_NiO_Liq  Std_CoO_Liq  Std_CO2_Liq  \\\n",
       "0          0.0             0.0          0.0          0.0          0.0   \n",
       "1          0.0             0.0          0.0          0.0          0.0   \n",
       "2          0.0             0.0          0.0          0.0          0.0   \n",
       "3          0.0             0.0          0.0          0.0          0.0   \n",
       "4          0.0             0.0          0.0          0.0          0.0   \n",
       "\n",
       "   Std_Sample_ID_Liq  Std_SiO2_Liq_mol_frac  Std_MgO_Liq_mol_frac  \\\n",
       "0           4.472136               0.007112              0.000006   \n",
       "1           4.472136               0.007112              0.000006   \n",
       "2           4.472136               0.007112              0.000006   \n",
       "3           4.472136               0.007112              0.000006   \n",
       "4           4.472136               0.007112              0.000006   \n",
       "\n",
       "   Std_MnO_Liq_mol_frac  Std_FeOt_Liq_mol_frac  Std_CaO_Liq_mol_frac  \\\n",
       "0              0.000004               0.000045               0.00003   \n",
       "1              0.000004               0.000045               0.00003   \n",
       "2              0.000004               0.000045               0.00003   \n",
       "3              0.000004               0.000045               0.00003   \n",
       "4              0.000004               0.000045               0.00003   \n",
       "\n",
       "   Std_Al2O3_Liq_mol_frac  Std_Na2O_Liq_mol_frac  Std_K2O_Liq_mol_frac  \\\n",
       "0                0.000186               0.005578              0.002604   \n",
       "1                0.000186               0.005578              0.002604   \n",
       "2                0.000186               0.005578              0.002604   \n",
       "3                0.000186               0.005578              0.002604   \n",
       "4                0.000186               0.005578              0.002604   \n",
       "\n",
       "   Std_TiO2_Liq_mol_frac  Std_P2O5_Liq_mol_frac  Std_Cr2O3_Liq_mol_frac  \\\n",
       "0               0.000006           1.436252e-07                     0.0   \n",
       "1               0.000006           1.436252e-07                     0.0   \n",
       "2               0.000006           1.436252e-07                     0.0   \n",
       "3               0.000006           1.436252e-07                     0.0   \n",
       "4               0.000006           1.436252e-07                     0.0   \n",
       "\n",
       "   Std_Si_Liq_cat_frac  Std_Mg_Liq_cat_frac  Std_Mn_Liq_cat_frac  \\\n",
       "0             0.009076             0.000015              0.00001   \n",
       "1             0.009076             0.000015              0.00001   \n",
       "2             0.009076             0.000015              0.00001   \n",
       "3             0.009076             0.000015              0.00001   \n",
       "4             0.009076             0.000015              0.00001   \n",
       "\n",
       "   Std_Fet_Liq_cat_frac  Std_Ca_Liq_cat_frac  Std_Al_Liq_cat_frac  \\\n",
       "0              0.000115             0.000076             0.000947   \n",
       "1              0.000115             0.000076             0.000947   \n",
       "2              0.000115             0.000076             0.000947   \n",
       "3              0.000115             0.000076             0.000947   \n",
       "4              0.000115             0.000076             0.000947   \n",
       "\n",
       "   Std_Na_Liq_cat_frac  Std_K_Liq_cat_frac  Std_Ti_Liq_cat_frac  \\\n",
       "0             0.008549            0.003937             0.000015   \n",
       "1             0.008549            0.003937             0.000015   \n",
       "2             0.008549            0.003937             0.000015   \n",
       "3             0.008549            0.003937             0.000015   \n",
       "4             0.008549            0.003937             0.000015   \n",
       "\n",
       "   Std_P_Liq_cat_frac  Std_Cr_Liq_cat_frac  Std_Mg_Number_Liq_NoFe3  \\\n",
       "0        7.329643e-07                  0.0                      0.0   \n",
       "1        7.329643e-07                  0.0                      0.0   \n",
       "2        7.329643e-07                  0.0                      0.0   \n",
       "3        7.329643e-07                  0.0                      0.0   \n",
       "4        7.329643e-07                  0.0                      0.0   \n",
       "\n",
       "   Std_Mg_Number_Liq_Fe3  Std_ID_liq  Std_Sample_ID_liq Sample_ID_Kspar  \n",
       "0                    0.0    4.472136           4.472136          Kspar1  \n",
       "1                    0.0    4.472136           4.472136          Kspar2  \n",
       "2                    0.0    4.472136           4.472136          Kspar3  \n",
       "3                    0.0    4.472136           4.472136          Kspar4  \n",
       "4                    0.0    4.472136           4.472136          Kspar5  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "melt_match_2=pt.calculate_fspar_liq_temp_matching(liq_comps=Liqs, kspar_comps=RealKspars, \n",
    "                                             equationT=\"T_Put2008_eq24b\", P=5, \n",
    "                                                Ab_An_P2008=True, H2O_Liq=2)\n",
    "melt_match_2['Av_PTs'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99d06aa2-eb61-470c-bea7-db316a8ac619",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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