{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6ec6587f-6e4f-42fd-bb7c-fec379bbce7f",
   "metadata": {},
   "source": [
    "## First lets load various python things"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "de8684e6-b0fb-417d-9a7f-1b0862caa915",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import Thermobar as pt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8bf6b87-b356-49a2-a625-b1df865af8e8",
   "metadata": {},
   "source": [
    "## This is the ideal datafram order for oxides, and their masses\n",
    "- To make this fancy matrix math work, its easiest to have a common column heading between them all. So, we load in our excel with Sample_ID_Liq. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aa02633a-3ade-41f6-a5db-610a62887e2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ideal_oxide = pd.DataFrame(columns=['Sample_ID_Liq', 'SiO2', 'TiO2', 'Al2O3',\n",
    "'FeOt', 'MnO', 'MgO', 'CaO', 'Na2O', 'K2O',\n",
    "'Cr2O3', 'P2O5'])\n",
    "\n",
    "oxide_mass = {'SiO2': 60.0843, 'MgO': 40.3044,\n",
    "'MnO': 70.9375, 'FeOt': 71.8464, 'CaO': 56.0774,\n",
    "'Al2O3': 101.961,'Na2O': 61.9789, 'K2O': 94.196,\n",
    "'TiO2': 79.8788, 'P2O5': 141.937, 'Cr2O3': 151.9982}\n",
    "\n",
    "# Now we turn this dictionary of oxide mass into a pandas dataframe so matrix functions can be used. \n",
    "oxide_mass_df = pd.DataFrame.from_dict(\n",
    "    oxide_mass, orient='index').T\n",
    "oxide_mass_df['Sample_ID_Liq'] = 'MolWt'\n",
    "oxide_mass_df.set_index('Sample_ID_Liq', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "741d049d-0f1f-4545-9d95-a9b9a41ddcd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "oxide_mass_df_altered=oxide_mass_df.set_index('Sample_ID_Liq')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bd1b9156-788c-4ebf-a7d4-8a11b9b0a57f",
   "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</th>\n",
       "      <th>MgO</th>\n",
       "      <th>MnO</th>\n",
       "      <th>FeOt</th>\n",
       "      <th>CaO</th>\n",
       "      <th>Al2O3</th>\n",
       "      <th>Na2O</th>\n",
       "      <th>K2O</th>\n",
       "      <th>TiO2</th>\n",
       "      <th>P2O5</th>\n",
       "      <th>Cr2O3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60.0843</td>\n",
       "      <td>40.3044</td>\n",
       "      <td>70.9375</td>\n",
       "      <td>71.8464</td>\n",
       "      <td>56.0774</td>\n",
       "      <td>101.961</td>\n",
       "      <td>61.9789</td>\n",
       "      <td>94.196</td>\n",
       "      <td>79.8788</td>\n",
       "      <td>141.937</td>\n",
       "      <td>151.9982</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      SiO2      MgO      MnO     FeOt      CaO    Al2O3     Na2O     K2O  \\\n",
       "0  60.0843  40.3044  70.9375  71.8464  56.0774  101.961  61.9789  94.196   \n",
       "\n",
       "      TiO2     P2O5     Cr2O3  \n",
       "0  79.8788  141.937  151.9982  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oxide_mass_df2 = pd.DataFrame.from_dict(\n",
    "    oxide_mass, orient='index').T\n",
    "oxide_mass_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "abb42bee-4c13-4421-9a4a-3cdc267dc771",
   "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</th>\n",
       "      <th>MgO</th>\n",
       "      <th>MnO</th>\n",
       "      <th>FeOt</th>\n",
       "      <th>CaO</th>\n",
       "      <th>Al2O3</th>\n",
       "      <th>Na2O</th>\n",
       "      <th>K2O</th>\n",
       "      <th>TiO2</th>\n",
       "      <th>P2O5</th>\n",
       "      <th>Cr2O3</th>\n",
       "      <th>Sample_ID_Liq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60.0843</td>\n",
       "      <td>40.3044</td>\n",
       "      <td>70.9375</td>\n",
       "      <td>71.8464</td>\n",
       "      <td>56.0774</td>\n",
       "      <td>101.961</td>\n",
       "      <td>61.9789</td>\n",
       "      <td>94.196</td>\n",
       "      <td>79.8788</td>\n",
       "      <td>141.937</td>\n",
       "      <td>151.9982</td>\n",
       "      <td>MolWt</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      SiO2      MgO      MnO     FeOt      CaO    Al2O3     Na2O     K2O  \\\n",
       "0  60.0843  40.3044  70.9375  71.8464  56.0774  101.961  61.9789  94.196   \n",
       "\n",
       "      TiO2     P2O5     Cr2O3 Sample_ID_Liq  \n",
       "0  79.8788  141.937  151.9982         MolWt  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oxide_mass_df2['Sample_ID_Liq'] = 'MolWt'\n",
    "oxide_mass_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8a8514a0-0e8e-4a30-9c40-64fc918fb786",
   "metadata": {},
   "outputs": [],
   "source": [
    "input1=pd.read_excel('Christy_input.xlsx', sheet_name=\"Sheet1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d747f344-bbb1-4a89-8a63-72a28d8205ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\penny\\onedrive - oregon state university\\postdoc\\pymme\\mybarometers\\thermobar_outer\\src\\Thermobar\\import_export.py:333: UserWarning: We notice you have specified a suffix, but some of your columns already have this suffix. e.g., If you already have _Liq in the file, you shouldnt specify suffix=\"_Liq\" during the import\n",
      "  w.warn('We notice you have specified a suffix, but some of your columns already have this suffix. '\n"
     ]
    }
   ],
   "source": [
    "import Thermobar as pt\n",
    "input2=pt.import_excel('Christy_input.xlsx', sheet_name=\"Sheet1\", suffix=\"_Liq\").get(\"Liqs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4d30d16-76d0-49bb-9754-da5bc525395b",
   "metadata": {},
   "source": [
    "## This cell defines the function, you must run it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6c16b987-22e5-4347-8133-ace2879bd4da",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_anhydrous_mol_proportions(df):\n",
    "    '''Import Liq compositions using liq_comps=My_Liquids, returns anhydrous mole proportions\n",
    "\n",
    "   Parameters\n",
    "    -------\n",
    "\n",
    "    inputs: df. pandas.DataFrame\n",
    "        Panda DataFrame of input compositions with column headings SiO2, TiO2\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    pandas DataFrame\n",
    "        anhydrous mole proportions with column headings of the form SiO2_Liq_mol_prop, stithced to original dataframe\n",
    "\n",
    "    '''\n",
    "    # This makes the input match the columns in the oxide mass dataframe\n",
    "    liq_wt = df.reindex(\n",
    "        df_ideal_oxide.columns, axis=1).fillna(0)\n",
    "    # Combine the molecular weight and weight percent dataframes\n",
    "    liq_wt_combo = pd.concat([oxide_mass_df, liq_wt],)\n",
    "    \n",
    "    # Divide throguh by the MolDrop the calculation column\n",
    "    mol_prop_anhyd = liq_wt_combo.div(\n",
    "        liq_wt_combo.loc['MolWt', :], axis='columns').drop(['MolWt'])\n",
    "    mol_prop_anhyd.columns = [\n",
    "        str(col) + '_mol_prop' for col in mol_prop_anhyd.columns]\n",
    "    return mol_prop_anhyd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9de6fc39-122e-466b-99f9-cc13e602dc7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=input1\n",
    "liq_wt = df.reindex(\n",
    "    df_ideal_oxide.columns, axis=1).fillna(0)\n",
    "# Combine the molecular weight and weight percent dataframes\n",
    "liq_wt_combo = pd.concat([oxide_mass_df, liq_wt],)\n",
    "\n",
    "# Divide throguh by the MolDrop the calculation column\n",
    "mol_prop_anhyd = liq_wt_combo.div(\n",
    "    liq_wt_combo.loc['MolWt', :], axis='columns').drop(['MolWt'])\n",
    "mol_prop_anhyd.columns = [\n",
    "    str(col) + '_mol_prop' for col in mol_prop_anhyd.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fead8dd5-247a-4322-aa0e-dae24d36451e",
   "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</th>\n",
       "      <th>MgO</th>\n",
       "      <th>MnO</th>\n",
       "      <th>FeOt</th>\n",
       "      <th>CaO</th>\n",
       "      <th>Al2O3</th>\n",
       "      <th>Na2O</th>\n",
       "      <th>K2O</th>\n",
       "      <th>TiO2</th>\n",
       "      <th>P2O5</th>\n",
       "      <th>Cr2O3</th>\n",
       "      <th>Sample_ID_Liq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MolWt</th>\n",
       "      <td>60.084300</td>\n",
       "      <td>40.30440</td>\n",
       "      <td>70.937500</td>\n",
       "      <td>71.84640</td>\n",
       "      <td>56.07740</td>\n",
       "      <td>101.961000</td>\n",
       "      <td>61.97890</td>\n",
       "      <td>94.196000</td>\n",
       "      <td>79.878800</td>\n",
       "      <td>141.937000</td>\n",
       "      <td>151.9982</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>57.023602</td>\n",
       "      <td>4.19180</td>\n",
       "      <td>0.103851</td>\n",
       "      <td>4.36174</td>\n",
       "      <td>6.94858</td>\n",
       "      <td>16.332899</td>\n",
       "      <td>3.59702</td>\n",
       "      <td>0.896895</td>\n",
       "      <td>0.623106</td>\n",
       "      <td>0.226584</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>liq1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57.658600</td>\n",
       "      <td>2.86892</td>\n",
       "      <td>0.084105</td>\n",
       "      <td>3.90621</td>\n",
       "      <td>5.91538</td>\n",
       "      <td>17.194799</td>\n",
       "      <td>3.85948</td>\n",
       "      <td>1.018600</td>\n",
       "      <td>0.654150</td>\n",
       "      <td>0.214935</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>liq2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            SiO2       MgO        MnO      FeOt       CaO       Al2O3  \\\n",
       "MolWt  60.084300  40.30440  70.937500  71.84640  56.07740  101.961000   \n",
       "0      57.023602   4.19180   0.103851   4.36174   6.94858   16.332899   \n",
       "1      57.658600   2.86892   0.084105   3.90621   5.91538   17.194799   \n",
       "\n",
       "           Na2O        K2O       TiO2        P2O5     Cr2O3 Sample_ID_Liq  \n",
       "MolWt  61.97890  94.196000  79.878800  141.937000  151.9982           NaN  \n",
       "0       3.59702   0.896895   0.623106    0.226584    0.0000          liq1  \n",
       "1       3.85948   1.018600   0.654150    0.214935    0.0000          liq2  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liq_wt_combo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "777c5b22-337a-4d99-a683-faefb3d8ac2d",
   "metadata": {},
   "source": [
    "## This cell uses the function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "91457e8a-26e1-495a-a84f-3687330ac9b0",
   "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_mol_prop</th>\n",
       "      <th>MgO_mol_prop</th>\n",
       "      <th>MnO_mol_prop</th>\n",
       "      <th>FeOt_mol_prop</th>\n",
       "      <th>CaO_mol_prop</th>\n",
       "      <th>Al2O3_mol_prop</th>\n",
       "      <th>Na2O_mol_prop</th>\n",
       "      <th>K2O_mol_prop</th>\n",
       "      <th>TiO2_mol_prop</th>\n",
       "      <th>P2O5_mol_prop</th>\n",
       "      <th>Cr2O3_mol_prop</th>\n",
       "      <th>Sample_ID_Liq_mol_prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.94906</td>\n",
       "      <td>0.104004</td>\n",
       "      <td>0.001464</td>\n",
       "      <td>0.060709</td>\n",
       "      <td>0.123911</td>\n",
       "      <td>0.160188</td>\n",
       "      <td>0.058036</td>\n",
       "      <td>0.009522</td>\n",
       "      <td>0.007801</td>\n",
       "      <td>0.001596</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.959628</td>\n",
       "      <td>0.071181</td>\n",
       "      <td>0.001186</td>\n",
       "      <td>0.054369</td>\n",
       "      <td>0.105486</td>\n",
       "      <td>0.168641</td>\n",
       "      <td>0.062271</td>\n",
       "      <td>0.010814</td>\n",
       "      <td>0.008189</td>\n",
       "      <td>0.001514</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  SiO2_mol_prop MgO_mol_prop MnO_mol_prop FeOt_mol_prop CaO_mol_prop  \\\n",
       "0       0.94906     0.104004     0.001464      0.060709     0.123911   \n",
       "1      0.959628     0.071181     0.001186      0.054369     0.105486   \n",
       "\n",
       "  Al2O3_mol_prop Na2O_mol_prop K2O_mol_prop TiO2_mol_prop P2O5_mol_prop  \\\n",
       "0       0.160188      0.058036     0.009522      0.007801      0.001596   \n",
       "1       0.168641      0.062271     0.010814      0.008189      0.001514   \n",
       "\n",
       "  Cr2O3_mol_prop Sample_ID_Liq_mol_prop  \n",
       "0            0.0                    NaN  \n",
       "1            0.0                    NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Calc_Prop=calculate_anhydrous_mol_proportions(df=input1)\n",
    "Calc_Prop"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "733b6e26-ecc4-4bcf-b994-a204ce03ccd3",
   "metadata": {},
   "source": [
    "## Here is an example of how you would write  afunction with differen normalizatoin schemes using if statements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "942334ec-8f23-47b4-b70c-a9c9f1962f16",
   "metadata": {},
   "outputs": [],
   "source": [
    "def demonstrate_classification_scheme(df, projection=None):\n",
    "    if projection == \"Cpx\":\n",
    "        return \"Im using the Cpx normalizatoin scheme\"\n",
    "    if projection == \"Plag-Amp\":\n",
    "        return \"Im using the Plag-Amp normalizatoin scheme\"\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "573dd659-abdb-427c-8318-e6dafb131917",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Im using the Cpx normalizatoin scheme'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demonstrate_classification_scheme(df=input1, projection=\"Cpx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e95681f5-fa61-4353-aaf1-e5bc56d8d38b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Im using the Plag-Amp normalizatoin scheme'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demonstrate_classification_scheme(df=input1, projection=\"Plag-Amp\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8eb20f0-efa6-45ac-a245-11a8936965d0",
   "metadata": {},
   "source": [
    "## Here is your function to get started"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d6d74e38-afab-4709-88eb-faea47974094",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_liq_components_as_minerals(df, projection=None):\n",
    "    # First, take the input dataframe of liquids, and calculate mole prop. \n",
    "    #Using the function you defined above\n",
    "    mol_prop_alone=calculate_anhydrous_mol_proportions(df=df)\n",
    "    \n",
    "    # Lets ocmbine mol_prop and the input to get 1 big dataframe\n",
    "    mol_prop=pd.concat([df, mol_prop_alone], axis=1)\n",
    "    \n",
    "    # Now add whatever calculation you want to it, e.g., say I want to sum MgO and SiO2 mol proportions\n",
    "    mol_prop['Sum_Mg_Si']=mol_prop['MgO_mol_prop'] + mol_prop['SiO2_mol_prop']\n",
    "    \n",
    "    # Now return this function with new columns added on. Initiall, the coluns go at the end, \n",
    "    #but I can send you some code later to shuffle column order once its all together\n",
    "    \n",
    "    # Add your iff statements, append the projection-specific things on. \n",
    "    # E.g., say projection = Cpx, I want to return a oclumn reading Cpx something \n",
    "    if projection == \"Cpx\":\n",
    "        mol_prop['Cpx_calc1']=5\n",
    "        \n",
    "    if projection == \"Plag\":\n",
    "        mol_prop['Plag_Calc']=10\n",
    "        \n",
    "    if projection == \"Amp\":\n",
    "        mol_prop['Amp_Calc']=20\n",
    "        \n",
    "    # You can either return a specific result beneath the if, or append to a column name and return the whole thing here\n",
    "    \n",
    "    return mol_prop"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30e855b9-76de-421f-85bb-9cb452e6ab89",
   "metadata": {},
   "source": [
    "## Now use the function!\n",
    "- Scroll to the end to see the new columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8158fea8-7fb5-4c22-b864-5236bd892011",
   "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>Sample_ID_Liq</th>\n",
       "      <th>SiO2</th>\n",
       "      <th>TiO2</th>\n",
       "      <th>Al2O3</th>\n",
       "      <th>FeOt</th>\n",
       "      <th>MnO</th>\n",
       "      <th>MgO</th>\n",
       "      <th>CaO</th>\n",
       "      <th>Na2O</th>\n",
       "      <th>K2O</th>\n",
       "      <th>...</th>\n",
       "      <th>CaO_mol_prop</th>\n",
       "      <th>Al2O3_mol_prop</th>\n",
       "      <th>Na2O_mol_prop</th>\n",
       "      <th>K2O_mol_prop</th>\n",
       "      <th>TiO2_mol_prop</th>\n",
       "      <th>P2O5_mol_prop</th>\n",
       "      <th>Cr2O3_mol_prop</th>\n",
       "      <th>Sample_ID_Liq_mol_prop</th>\n",
       "      <th>Sum_Mg_Si</th>\n",
       "      <th>Cpx_calc1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>liq1</td>\n",
       "      <td>57.023602</td>\n",
       "      <td>0.623106</td>\n",
       "      <td>16.332899</td>\n",
       "      <td>4.36174</td>\n",
       "      <td>0.103851</td>\n",
       "      <td>4.19180</td>\n",
       "      <td>6.94858</td>\n",
       "      <td>3.59702</td>\n",
       "      <td>0.896895</td>\n",
       "      <td>...</td>\n",
       "      <td>0.123911</td>\n",
       "      <td>0.160188</td>\n",
       "      <td>0.058036</td>\n",
       "      <td>0.009522</td>\n",
       "      <td>0.007801</td>\n",
       "      <td>0.001596</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.053063</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>liq2</td>\n",
       "      <td>57.658600</td>\n",
       "      <td>0.654150</td>\n",
       "      <td>17.194799</td>\n",
       "      <td>3.90621</td>\n",
       "      <td>0.084105</td>\n",
       "      <td>2.86892</td>\n",
       "      <td>5.91538</td>\n",
       "      <td>3.85948</td>\n",
       "      <td>1.018600</td>\n",
       "      <td>...</td>\n",
       "      <td>0.105486</td>\n",
       "      <td>0.168641</td>\n",
       "      <td>0.062271</td>\n",
       "      <td>0.010814</td>\n",
       "      <td>0.008189</td>\n",
       "      <td>0.001514</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.03081</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sample_ID_Liq       SiO2      TiO2      Al2O3     FeOt       MnO      MgO  \\\n",
       "0          liq1  57.023602  0.623106  16.332899  4.36174  0.103851  4.19180   \n",
       "1          liq2  57.658600  0.654150  17.194799  3.90621  0.084105  2.86892   \n",
       "\n",
       "       CaO     Na2O       K2O  ...  CaO_mol_prop  Al2O3_mol_prop  \\\n",
       "0  6.94858  3.59702  0.896895  ...      0.123911        0.160188   \n",
       "1  5.91538  3.85948  1.018600  ...      0.105486        0.168641   \n",
       "\n",
       "  Na2O_mol_prop K2O_mol_prop TiO2_mol_prop P2O5_mol_prop Cr2O3_mol_prop  \\\n",
       "0      0.058036     0.009522      0.007801      0.001596            0.0   \n",
       "1      0.062271     0.010814      0.008189      0.001514            0.0   \n",
       "\n",
       "  Sample_ID_Liq_mol_prop Sum_Mg_Si Cpx_calc1  \n",
       "0                    NaN  1.053063         5  \n",
       "1                    NaN   1.03081         5  \n",
       "\n",
       "[2 rows x 26 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_liq_components_as_minerals(df=input1, projection=\"Cpx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3aef38c-fc13-4f9e-b869-1b8a1384ec23",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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