{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f481cec8-9986-459d-84d9-13e8a2977a81",
   "metadata": {},
   "source": [
    "# This notebook shows how to convert from Oxide wt% to element wt% and back again\n",
    "- You can download the spreadsheet here: https://github.com/PennyWieser/Thermobar/blob/main/docs/Examples/Other_features/Ox_to_element.xlsx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "026609db-0bcf-4ab4-af9a-bedcab6b5fa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import Thermobar as pt#\n",
    "pd.options.display.max_columns = None\n",
    "pd.options.display.max_rows = None\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2dfa8de-429b-45d1-aa54-12cb3a4a48b0",
   "metadata": {},
   "source": [
    "## Load in oxide wt % values (in this case, from plag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a5340200-6999-48e5-b48e-9ba999e0ad78",
   "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_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>54.67</td>\n",
       "      <td>0.24</td>\n",
       "      <td>26.81</td>\n",
       "      <td>1.47</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.42</td>\n",
       "      <td>10.59</td>\n",
       "      <td>4.55</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52.05</td>\n",
       "      <td>0.07</td>\n",
       "      <td>29.26</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>12.26</td>\n",
       "      <td>4.07</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52.73</td>\n",
       "      <td>0.07</td>\n",
       "      <td>28.73</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.13</td>\n",
       "      <td>11.44</td>\n",
       "      <td>4.58</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56.89</td>\n",
       "      <td>0.01</td>\n",
       "      <td>26.88</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>8.55</td>\n",
       "      <td>6.06</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54.54</td>\n",
       "      <td>0.02</td>\n",
       "      <td>26.92</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>8.42</td>\n",
       "      <td>6.03</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</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      54.67       0.24       26.81       1.47       0.0      0.42     10.59   \n",
       "1      52.05       0.07       29.26       0.81       0.0      0.15     12.26   \n",
       "2      52.73       0.07       28.73       0.77       0.0      0.13     11.44   \n",
       "3      56.89       0.01       26.88       0.21       0.0      0.01      8.55   \n",
       "4      54.54       0.02       26.92       0.19       0.0      0.04      8.42   \n",
       "\n",
       "   Na2O_Plag  K2O_Plag  Cr2O3_Plag  Sample_ID_Plag  \n",
       "0       4.55      0.35         0.0               0  \n",
       "1       4.07      0.17         0.0               1  \n",
       "2       4.58      0.18         0.0               2  \n",
       "3       6.06      0.16         0.0               3  \n",
       "4       6.03      0.18         0.0               4  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ExcelIn_TB=pt.import_excel('Ox_to_element.xlsx', sheet_name=\"Plag\")\n",
    "ExcelIn_Plag=ExcelIn_TB['Plags']\n",
    "Excel_In=ExcelIn_TB['my_input']\n",
    "display(ExcelIn_Plag.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40321358-d1ad-492b-b443-ee6b804385a6",
   "metadata": {},
   "source": [
    "## Convert to element wt%\n",
    "- You have to specify the suffix on the data, so that Thermobar knows how to strip it off to do the processing, e.g. in this case _Plag\n",
    "- By default, it includes oxygen, this means that O is added to sum to 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d44f16db-735b-4eda-8f18-5a4351780602",
   "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>Si_wt</th>\n",
       "      <th>Mg_wt</th>\n",
       "      <th>Fe_wt</th>\n",
       "      <th>Ca_wt</th>\n",
       "      <th>Al_wt</th>\n",
       "      <th>Na_wt</th>\n",
       "      <th>K_wt</th>\n",
       "      <th>Mn_wt</th>\n",
       "      <th>Ti_wt</th>\n",
       "      <th>Cr_wt</th>\n",
       "      <th>P_wt</th>\n",
       "      <th>F_wt</th>\n",
       "      <th>H_wt</th>\n",
       "      <th>Cl_wt</th>\n",
       "      <th>O_wt_make_to_100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25.554795</td>\n",
       "      <td>0.253278</td>\n",
       "      <td>1.142613</td>\n",
       "      <td>7.568359</td>\n",
       "      <td>14.189250</td>\n",
       "      <td>3.375448</td>\n",
       "      <td>0.290555</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.143784</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>47.481919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24.330109</td>\n",
       "      <td>0.090456</td>\n",
       "      <td>0.629603</td>\n",
       "      <td>8.761859</td>\n",
       "      <td>15.485918</td>\n",
       "      <td>3.019357</td>\n",
       "      <td>0.141127</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.041937</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>47.499635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24.647966</td>\n",
       "      <td>0.078395</td>\n",
       "      <td>0.598511</td>\n",
       "      <td>8.175829</td>\n",
       "      <td>15.205414</td>\n",
       "      <td>3.397704</td>\n",
       "      <td>0.149428</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.041937</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>47.704814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26.592505</td>\n",
       "      <td>0.006030</td>\n",
       "      <td>0.163230</td>\n",
       "      <td>6.110432</td>\n",
       "      <td>14.226298</td>\n",
       "      <td>4.495652</td>\n",
       "      <td>0.132825</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005991</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>48.267037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25.494028</td>\n",
       "      <td>0.024122</td>\n",
       "      <td>0.147685</td>\n",
       "      <td>6.017525</td>\n",
       "      <td>14.247468</td>\n",
       "      <td>4.473396</td>\n",
       "      <td>0.149428</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.011982</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>49.434367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Si_wt     Mg_wt     Fe_wt     Ca_wt      Al_wt     Na_wt      K_wt  \\\n",
       "0  25.554795  0.253278  1.142613  7.568359  14.189250  3.375448  0.290555   \n",
       "1  24.330109  0.090456  0.629603  8.761859  15.485918  3.019357  0.141127   \n",
       "2  24.647966  0.078395  0.598511  8.175829  15.205414  3.397704  0.149428   \n",
       "3  26.592505  0.006030  0.163230  6.110432  14.226298  4.495652  0.132825   \n",
       "4  25.494028  0.024122  0.147685  6.017525  14.247468  4.473396  0.149428   \n",
       "\n",
       "   Mn_wt     Ti_wt  Cr_wt  P_wt  F_wt  H_wt  Cl_wt  O_wt_make_to_100  \n",
       "0    0.0  0.143784    0.0   0.0   0.0   0.0    0.0         47.481919  \n",
       "1    0.0  0.041937    0.0   0.0   0.0   0.0    0.0         47.499635  \n",
       "2    0.0  0.041937    0.0   0.0   0.0   0.0    0.0         47.704814  \n",
       "3    0.0  0.005991    0.0   0.0   0.0   0.0    0.0         48.267037  \n",
       "4    0.0  0.011982    0.0   0.0   0.0   0.0    0.0         49.434367  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wt_calc_withO=pt.convert_oxide_percent_to_element_weight_percent(df=ExcelIn_Plag, \n",
    "                                            suffix=\"_Plag\", without_oxygen=False)\n",
    "wt_calc_withO.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "527f711f-5562-4444-bf77-c92299bac253",
   "metadata": {},
   "source": [
    "## Lets compare this to that calculated by EPMA software"
   ]
  },
  {
   "attachments": {
    "b5818469-5c14-41b6-90fd-8b84db306c77.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "b922e009-1f9d-49ef-b735-3b171abf5920",
   "metadata": {},
   "source": [
    "![image.png](attachment:b5818469-5c14-41b6-90fd-8b84db306c77.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c95e0a71-c90a-4c80-845e-6d6cc338580a",
   "metadata": {},
   "source": [
    "### Without oxygen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6b713cbe-c4e6-45ee-91b9-17e32cea0ad5",
   "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>Si_wt_noO2</th>\n",
       "      <th>Mg_wt_noO2</th>\n",
       "      <th>Fe_wt_noO2</th>\n",
       "      <th>Ca_wt_noO2</th>\n",
       "      <th>Al_wt_noO2</th>\n",
       "      <th>Na_wt_noO2</th>\n",
       "      <th>K_wt_noO2</th>\n",
       "      <th>Mn_wt_noO2</th>\n",
       "      <th>Ti_wt_noO2</th>\n",
       "      <th>Cr_wt_noO2</th>\n",
       "      <th>P_wt_noO2</th>\n",
       "      <th>F_wt_noO2</th>\n",
       "      <th>H_wt_noO2</th>\n",
       "      <th>Cl_wt_noO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48.659041</td>\n",
       "      <td>0.482267</td>\n",
       "      <td>2.175656</td>\n",
       "      <td>14.410959</td>\n",
       "      <td>27.017838</td>\n",
       "      <td>6.427211</td>\n",
       "      <td>0.553247</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.273781</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46.342742</td>\n",
       "      <td>0.172296</td>\n",
       "      <td>1.199235</td>\n",
       "      <td>16.689139</td>\n",
       "      <td>29.496781</td>\n",
       "      <td>5.751116</td>\n",
       "      <td>0.268811</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.079880</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47.132382</td>\n",
       "      <td>0.149909</td>\n",
       "      <td>1.144487</td>\n",
       "      <td>15.634000</td>\n",
       "      <td>29.076126</td>\n",
       "      <td>6.497164</td>\n",
       "      <td>0.285740</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.080193</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51.403406</td>\n",
       "      <td>0.011657</td>\n",
       "      <td>0.315525</td>\n",
       "      <td>11.811486</td>\n",
       "      <td>27.499483</td>\n",
       "      <td>8.690111</td>\n",
       "      <td>0.256751</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.011581</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50.417698</td>\n",
       "      <td>0.047704</td>\n",
       "      <td>0.292065</td>\n",
       "      <td>11.900424</td>\n",
       "      <td>28.176188</td>\n",
       "      <td>8.846712</td>\n",
       "      <td>0.295513</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.023696</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Si_wt_noO2  Mg_wt_noO2  Fe_wt_noO2  Ca_wt_noO2  Al_wt_noO2  Na_wt_noO2  \\\n",
       "0   48.659041    0.482267    2.175656   14.410959   27.017838    6.427211   \n",
       "1   46.342742    0.172296    1.199235   16.689139   29.496781    5.751116   \n",
       "2   47.132382    0.149909    1.144487   15.634000   29.076126    6.497164   \n",
       "3   51.403406    0.011657    0.315525   11.811486   27.499483    8.690111   \n",
       "4   50.417698    0.047704    0.292065   11.900424   28.176188    8.846712   \n",
       "\n",
       "   K_wt_noO2  Mn_wt_noO2  Ti_wt_noO2  Cr_wt_noO2  P_wt_noO2  F_wt_noO2  \\\n",
       "0   0.553247         0.0    0.273781         0.0        0.0        0.0   \n",
       "1   0.268811         0.0    0.079880         0.0        0.0        0.0   \n",
       "2   0.285740         0.0    0.080193         0.0        0.0        0.0   \n",
       "3   0.256751         0.0    0.011581         0.0        0.0        0.0   \n",
       "4   0.295513         0.0    0.023696         0.0        0.0        0.0   \n",
       "\n",
       "   H_wt_noO2  Cl_wt_noO2  \n",
       "0        0.0         0.0  \n",
       "1        0.0         0.0  \n",
       "2        0.0         0.0  \n",
       "3        0.0         0.0  \n",
       "4        0.0         0.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wt_calc_withoutO=pt.convert_oxide_percent_to_element_weight_percent(df=ExcelIn_Plag, \n",
    "                                            suffix=\"_Plag\", without_oxygen=True)\n",
    "wt_calc_withoutO.head()"
   ]
  }
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