PCA_with_pandas_output.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA, by pandas, using MVDA_exploration_tool\n",
    "#### Plot the raw data, build a PCA model, and plot the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import MVDA_exploration_tools as mv\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "##%matplotlib widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python version 3.10.6 (main, May 29 2023, 11:10:38) [GCC 11.3.0]\n",
      "sklearn.__version__ 1.2.2\n",
      "pandas.__version__ 2.0.1\n",
      "MVDA_exploration_tools.__version__ 0.1.6\n"
     ]
    }
   ],
   "source": [
    "print('Python version', sys.version)\n",
    "print('sklearn.__version__', sklearn.__version__)\n",
    "print('pandas.__version__', pd.__version__)\n",
    "print('MVDA_exploration_tools.__version__', mv.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>sample</th>\n",
       "      <th>250</th>\n",
       "      <th>278</th>\n",
       "      <th>306</th>\n",
       "      <th>334</th>\n",
       "      <th>362</th>\n",
       "      <th>390</th>\n",
       "      <th>418</th>\n",
       "      <th>Fel</th>\n",
       "      <th>Met</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>fm01</td>\n",
       "      <td>0.455452</td>\n",
       "      <td>0.536366</td>\n",
       "      <td>0.013826</td>\n",
       "      <td>0.099335</td>\n",
       "      <td>0.211982</td>\n",
       "      <td>0.111913</td>\n",
       "      <td>0.008058</td>\n",
       "      <td>0.01972</td>\n",
       "      <td>0.19112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>fm02</td>\n",
       "      <td>0.564452</td>\n",
       "      <td>0.554564</td>\n",
       "      <td>0.020845</td>\n",
       "      <td>0.129672</td>\n",
       "      <td>0.271033</td>\n",
       "      <td>0.143549</td>\n",
       "      <td>0.009797</td>\n",
       "      <td>0.02465</td>\n",
       "      <td>0.19112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>fm03</td>\n",
       "      <td>0.692226</td>\n",
       "      <td>0.596313</td>\n",
       "      <td>0.051761</td>\n",
       "      <td>0.181787</td>\n",
       "      <td>0.351545</td>\n",
       "      <td>0.199277</td>\n",
       "      <td>0.038096</td>\n",
       "      <td>0.02958</td>\n",
       "      <td>0.19112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>fm04</td>\n",
       "      <td>0.483403</td>\n",
       "      <td>0.671500</td>\n",
       "      <td>0.014822</td>\n",
       "      <td>0.100429</td>\n",
       "      <td>0.213960</td>\n",
       "      <td>0.111585</td>\n",
       "      <td>0.005829</td>\n",
       "      <td>0.01972</td>\n",
       "      <td>0.23890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>fm05</td>\n",
       "      <td>0.599889</td>\n",
       "      <td>0.699944</td>\n",
       "      <td>0.037068</td>\n",
       "      <td>0.143525</td>\n",
       "      <td>0.284059</td>\n",
       "      <td>0.156597</td>\n",
       "      <td>0.023061</td>\n",
       "      <td>0.02465</td>\n",
       "      <td>0.23890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>fm06</td>\n",
       "      <td>0.703718</td>\n",
       "      <td>0.713386</td>\n",
       "      <td>0.044752</td>\n",
       "      <td>0.175482</td>\n",
       "      <td>0.346520</td>\n",
       "      <td>0.193078</td>\n",
       "      <td>0.029904</td>\n",
       "      <td>0.02958</td>\n",
       "      <td>0.23890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>fm07</td>\n",
       "      <td>0.696338</td>\n",
       "      <td>0.700592</td>\n",
       "      <td>0.029916</td>\n",
       "      <td>0.159379</td>\n",
       "      <td>0.328703</td>\n",
       "      <td>0.174273</td>\n",
       "      <td>0.010329</td>\n",
       "      <td>0.02958</td>\n",
       "      <td>0.23890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>fm08</td>\n",
       "      <td>0.514464</td>\n",
       "      <td>0.793404</td>\n",
       "      <td>0.020093</td>\n",
       "      <td>0.105247</td>\n",
       "      <td>0.219955</td>\n",
       "      <td>0.116894</td>\n",
       "      <td>0.009001</td>\n",
       "      <td>0.01972</td>\n",
       "      <td>0.28668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>fm09</td>\n",
       "      <td>0.611695</td>\n",
       "      <td>0.805055</td>\n",
       "      <td>0.020922</td>\n",
       "      <td>0.127295</td>\n",
       "      <td>0.269475</td>\n",
       "      <td>0.139565</td>\n",
       "      <td>0.004334</td>\n",
       "      <td>0.02465</td>\n",
       "      <td>0.28668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>fm10</td>\n",
       "      <td>0.720811</td>\n",
       "      <td>0.824102</td>\n",
       "      <td>0.035586</td>\n",
       "      <td>0.163817</td>\n",
       "      <td>0.333757</td>\n",
       "      <td>0.177520</td>\n",
       "      <td>0.014077</td>\n",
       "      <td>0.02958</td>\n",
       "      <td>0.28668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>fm11</td>\n",
       "      <td>0.529496</td>\n",
       "      <td>0.074750</td>\n",
       "      <td>0.028027</td>\n",
       "      <td>0.131487</td>\n",
       "      <td>0.270114</td>\n",
       "      <td>0.141439</td>\n",
       "      <td>0.009292</td>\n",
       "      <td>0.02465</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>fm12</td>\n",
       "      <td>0.133005</td>\n",
       "      <td>0.752452</td>\n",
       "      <td>0.008606</td>\n",
       "      <td>0.001916</td>\n",
       "      <td>0.002508</td>\n",
       "      <td>0.002149</td>\n",
       "      <td>0.012900</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.28668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0 sample       250       278       306       334       362   \n",
       "0            0   fm01  0.455452  0.536366  0.013826  0.099335  0.211982  \\\n",
       "1            1   fm02  0.564452  0.554564  0.020845  0.129672  0.271033   \n",
       "2            2   fm03  0.692226  0.596313  0.051761  0.181787  0.351545   \n",
       "3            3   fm04  0.483403  0.671500  0.014822  0.100429  0.213960   \n",
       "4            4   fm05  0.599889  0.699944  0.037068  0.143525  0.284059   \n",
       "5            5   fm06  0.703718  0.713386  0.044752  0.175482  0.346520   \n",
       "6            6   fm07  0.696338  0.700592  0.029916  0.159379  0.328703   \n",
       "7            7   fm08  0.514464  0.793404  0.020093  0.105247  0.219955   \n",
       "8            8   fm09  0.611695  0.805055  0.020922  0.127295  0.269475   \n",
       "9            9   fm10  0.720811  0.824102  0.035586  0.163817  0.333757   \n",
       "10          10   fm11  0.529496  0.074750  0.028027  0.131487  0.270114   \n",
       "11          11   fm12  0.133005  0.752452  0.008606  0.001916  0.002508   \n",
       "\n",
       "         390       418      Fel      Met  \n",
       "0   0.111913  0.008058  0.01972  0.19112  \n",
       "1   0.143549  0.009797  0.02465  0.19112  \n",
       "2   0.199277  0.038096  0.02958  0.19112  \n",
       "3   0.111585  0.005829  0.01972  0.23890  \n",
       "4   0.156597  0.023061  0.02465  0.23890  \n",
       "5   0.193078  0.029904  0.02958  0.23890  \n",
       "6   0.174273  0.010329  0.02958  0.23890  \n",
       "7   0.116894  0.009001  0.01972  0.28668  \n",
       "8   0.139565  0.004334  0.02465  0.28668  \n",
       "9   0.177520  0.014077  0.02958  0.28668  \n",
       "10  0.141439  0.009292  0.02465  0.00000  \n",
       "11  0.002149  0.012900  0.00000  0.28668  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataFrame = pd.read_excel('decimated_spectra.xlsx')\n",
    "display(dataFrame)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the primary ID (index) of the table as the column named sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>250</th>\n",
       "      <th>278</th>\n",
       "      <th>306</th>\n",
       "      <th>334</th>\n",
       "      <th>362</th>\n",
       "      <th>390</th>\n",
       "      <th>418</th>\n",
       "      <th>Fel</th>\n",
       "      <th>Met</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fm01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.455452</td>\n",
       "      <td>0.536366</td>\n",
       "      <td>0.013826</td>\n",
       "      <td>0.099335</td>\n",
       "      <td>0.211982</td>\n",
       "      <td>0.111913</td>\n",
       "      <td>0.008058</td>\n",
       "      <td>0.01972</td>\n",
       "      <td>0.19112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm02</th>\n",
       "      <td>1</td>\n",
       "      <td>0.564452</td>\n",
       "      <td>0.554564</td>\n",
       "      <td>0.020845</td>\n",
       "      <td>0.129672</td>\n",
       "      <td>0.271033</td>\n",
       "      <td>0.143549</td>\n",
       "      <td>0.009797</td>\n",
       "      <td>0.02465</td>\n",
       "      <td>0.19112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm03</th>\n",
       "      <td>2</td>\n",
       "      <td>0.692226</td>\n",
       "      <td>0.596313</td>\n",
       "      <td>0.051761</td>\n",
       "      <td>0.181787</td>\n",
       "      <td>0.351545</td>\n",
       "      <td>0.199277</td>\n",
       "      <td>0.038096</td>\n",
       "      <td>0.02958</td>\n",
       "      <td>0.19112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm04</th>\n",
       "      <td>3</td>\n",
       "      <td>0.483403</td>\n",
       "      <td>0.671500</td>\n",
       "      <td>0.014822</td>\n",
       "      <td>0.100429</td>\n",
       "      <td>0.213960</td>\n",
       "      <td>0.111585</td>\n",
       "      <td>0.005829</td>\n",
       "      <td>0.01972</td>\n",
       "      <td>0.23890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm05</th>\n",
       "      <td>4</td>\n",
       "      <td>0.599889</td>\n",
       "      <td>0.699944</td>\n",
       "      <td>0.037068</td>\n",
       "      <td>0.143525</td>\n",
       "      <td>0.284059</td>\n",
       "      <td>0.156597</td>\n",
       "      <td>0.023061</td>\n",
       "      <td>0.02465</td>\n",
       "      <td>0.23890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm06</th>\n",
       "      <td>5</td>\n",
       "      <td>0.703718</td>\n",
       "      <td>0.713386</td>\n",
       "      <td>0.044752</td>\n",
       "      <td>0.175482</td>\n",
       "      <td>0.346520</td>\n",
       "      <td>0.193078</td>\n",
       "      <td>0.029904</td>\n",
       "      <td>0.02958</td>\n",
       "      <td>0.23890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm07</th>\n",
       "      <td>6</td>\n",
       "      <td>0.696338</td>\n",
       "      <td>0.700592</td>\n",
       "      <td>0.029916</td>\n",
       "      <td>0.159379</td>\n",
       "      <td>0.328703</td>\n",
       "      <td>0.174273</td>\n",
       "      <td>0.010329</td>\n",
       "      <td>0.02958</td>\n",
       "      <td>0.23890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm08</th>\n",
       "      <td>7</td>\n",
       "      <td>0.514464</td>\n",
       "      <td>0.793404</td>\n",
       "      <td>0.020093</td>\n",
       "      <td>0.105247</td>\n",
       "      <td>0.219955</td>\n",
       "      <td>0.116894</td>\n",
       "      <td>0.009001</td>\n",
       "      <td>0.01972</td>\n",
       "      <td>0.28668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm09</th>\n",
       "      <td>8</td>\n",
       "      <td>0.611695</td>\n",
       "      <td>0.805055</td>\n",
       "      <td>0.020922</td>\n",
       "      <td>0.127295</td>\n",
       "      <td>0.269475</td>\n",
       "      <td>0.139565</td>\n",
       "      <td>0.004334</td>\n",
       "      <td>0.02465</td>\n",
       "      <td>0.28668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm10</th>\n",
       "      <td>9</td>\n",
       "      <td>0.720811</td>\n",
       "      <td>0.824102</td>\n",
       "      <td>0.035586</td>\n",
       "      <td>0.163817</td>\n",
       "      <td>0.333757</td>\n",
       "      <td>0.177520</td>\n",
       "      <td>0.014077</td>\n",
       "      <td>0.02958</td>\n",
       "      <td>0.28668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm11</th>\n",
       "      <td>10</td>\n",
       "      <td>0.529496</td>\n",
       "      <td>0.074750</td>\n",
       "      <td>0.028027</td>\n",
       "      <td>0.131487</td>\n",
       "      <td>0.270114</td>\n",
       "      <td>0.141439</td>\n",
       "      <td>0.009292</td>\n",
       "      <td>0.02465</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm12</th>\n",
       "      <td>11</td>\n",
       "      <td>0.133005</td>\n",
       "      <td>0.752452</td>\n",
       "      <td>0.008606</td>\n",
       "      <td>0.001916</td>\n",
       "      <td>0.002508</td>\n",
       "      <td>0.002149</td>\n",
       "      <td>0.012900</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.28668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0       250       278       306       334       362   \n",
       "sample                                                                 \n",
       "fm01             0  0.455452  0.536366  0.013826  0.099335  0.211982  \\\n",
       "fm02             1  0.564452  0.554564  0.020845  0.129672  0.271033   \n",
       "fm03             2  0.692226  0.596313  0.051761  0.181787  0.351545   \n",
       "fm04             3  0.483403  0.671500  0.014822  0.100429  0.213960   \n",
       "fm05             4  0.599889  0.699944  0.037068  0.143525  0.284059   \n",
       "fm06             5  0.703718  0.713386  0.044752  0.175482  0.346520   \n",
       "fm07             6  0.696338  0.700592  0.029916  0.159379  0.328703   \n",
       "fm08             7  0.514464  0.793404  0.020093  0.105247  0.219955   \n",
       "fm09             8  0.611695  0.805055  0.020922  0.127295  0.269475   \n",
       "fm10             9  0.720811  0.824102  0.035586  0.163817  0.333757   \n",
       "fm11            10  0.529496  0.074750  0.028027  0.131487  0.270114   \n",
       "fm12            11  0.133005  0.752452  0.008606  0.001916  0.002508   \n",
       "\n",
       "             390       418      Fel      Met  \n",
       "sample                                        \n",
       "fm01    0.111913  0.008058  0.01972  0.19112  \n",
       "fm02    0.143549  0.009797  0.02465  0.19112  \n",
       "fm03    0.199277  0.038096  0.02958  0.19112  \n",
       "fm04    0.111585  0.005829  0.01972  0.23890  \n",
       "fm05    0.156597  0.023061  0.02465  0.23890  \n",
       "fm06    0.193078  0.029904  0.02958  0.23890  \n",
       "fm07    0.174273  0.010329  0.02958  0.23890  \n",
       "fm08    0.116894  0.009001  0.01972  0.28668  \n",
       "fm09    0.139565  0.004334  0.02465  0.28668  \n",
       "fm10    0.177520  0.014077  0.02958  0.28668  \n",
       "fm11    0.141439  0.009292  0.02465  0.00000  \n",
       "fm12    0.002149  0.012900  0.00000  0.28668  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "named_Obs_dataFrame = dataFrame.set_index('sample')\n",
    "named_Obs_dataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the selection of the data table designated as X-data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>250</th>\n",
       "      <th>278</th>\n",
       "      <th>306</th>\n",
       "      <th>334</th>\n",
       "      <th>362</th>\n",
       "      <th>390</th>\n",
       "      <th>418</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fm01</th>\n",
       "      <td>0.455452</td>\n",
       "      <td>0.536366</td>\n",
       "      <td>0.013826</td>\n",
       "      <td>0.099335</td>\n",
       "      <td>0.211982</td>\n",
       "      <td>0.111913</td>\n",
       "      <td>0.008058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm02</th>\n",
       "      <td>0.564452</td>\n",
       "      <td>0.554564</td>\n",
       "      <td>0.020845</td>\n",
       "      <td>0.129672</td>\n",
       "      <td>0.271033</td>\n",
       "      <td>0.143549</td>\n",
       "      <td>0.009797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm03</th>\n",
       "      <td>0.692226</td>\n",
       "      <td>0.596313</td>\n",
       "      <td>0.051761</td>\n",
       "      <td>0.181787</td>\n",
       "      <td>0.351545</td>\n",
       "      <td>0.199277</td>\n",
       "      <td>0.038096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm04</th>\n",
       "      <td>0.483403</td>\n",
       "      <td>0.671500</td>\n",
       "      <td>0.014822</td>\n",
       "      <td>0.100429</td>\n",
       "      <td>0.213960</td>\n",
       "      <td>0.111585</td>\n",
       "      <td>0.005829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm05</th>\n",
       "      <td>0.599889</td>\n",
       "      <td>0.699944</td>\n",
       "      <td>0.037068</td>\n",
       "      <td>0.143525</td>\n",
       "      <td>0.284059</td>\n",
       "      <td>0.156597</td>\n",
       "      <td>0.023061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm06</th>\n",
       "      <td>0.703718</td>\n",
       "      <td>0.713386</td>\n",
       "      <td>0.044752</td>\n",
       "      <td>0.175482</td>\n",
       "      <td>0.346520</td>\n",
       "      <td>0.193078</td>\n",
       "      <td>0.029904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm07</th>\n",
       "      <td>0.696338</td>\n",
       "      <td>0.700592</td>\n",
       "      <td>0.029916</td>\n",
       "      <td>0.159379</td>\n",
       "      <td>0.328703</td>\n",
       "      <td>0.174273</td>\n",
       "      <td>0.010329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm08</th>\n",
       "      <td>0.514464</td>\n",
       "      <td>0.793404</td>\n",
       "      <td>0.020093</td>\n",
       "      <td>0.105247</td>\n",
       "      <td>0.219955</td>\n",
       "      <td>0.116894</td>\n",
       "      <td>0.009001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm09</th>\n",
       "      <td>0.611695</td>\n",
       "      <td>0.805055</td>\n",
       "      <td>0.020922</td>\n",
       "      <td>0.127295</td>\n",
       "      <td>0.269475</td>\n",
       "      <td>0.139565</td>\n",
       "      <td>0.004334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm10</th>\n",
       "      <td>0.720811</td>\n",
       "      <td>0.824102</td>\n",
       "      <td>0.035586</td>\n",
       "      <td>0.163817</td>\n",
       "      <td>0.333757</td>\n",
       "      <td>0.177520</td>\n",
       "      <td>0.014077</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             250       278       306       334       362       390       418\n",
       "sample                                                                      \n",
       "fm01    0.455452  0.536366  0.013826  0.099335  0.211982  0.111913  0.008058\n",
       "fm02    0.564452  0.554564  0.020845  0.129672  0.271033  0.143549  0.009797\n",
       "fm03    0.692226  0.596313  0.051761  0.181787  0.351545  0.199277  0.038096\n",
       "fm04    0.483403  0.671500  0.014822  0.100429  0.213960  0.111585  0.005829\n",
       "fm05    0.599889  0.699944  0.037068  0.143525  0.284059  0.156597  0.023061\n",
       "fm06    0.703718  0.713386  0.044752  0.175482  0.346520  0.193078  0.029904\n",
       "fm07    0.696338  0.700592  0.029916  0.159379  0.328703  0.174273  0.010329\n",
       "fm08    0.514464  0.793404  0.020093  0.105247  0.219955  0.116894  0.009001\n",
       "fm09    0.611695  0.805055  0.020922  0.127295  0.269475  0.139565  0.004334\n",
       "fm10    0.720811  0.824102  0.035586  0.163817  0.333757  0.177520  0.014077"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = named_Obs_dataFrame.loc['fm01':'fm10' , '250':'418']\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>250</th>\n",
       "      <th>278</th>\n",
       "      <th>306</th>\n",
       "      <th>334</th>\n",
       "      <th>362</th>\n",
       "      <th>390</th>\n",
       "      <th>418</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fm11</th>\n",
       "      <td>0.529496</td>\n",
       "      <td>0.074750</td>\n",
       "      <td>0.028027</td>\n",
       "      <td>0.131487</td>\n",
       "      <td>0.270114</td>\n",
       "      <td>0.141439</td>\n",
       "      <td>0.009292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm12</th>\n",
       "      <td>0.133005</td>\n",
       "      <td>0.752452</td>\n",
       "      <td>0.008606</td>\n",
       "      <td>0.001916</td>\n",
       "      <td>0.002508</td>\n",
       "      <td>0.002149</td>\n",
       "      <td>0.012900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             250       278       306       334       362       390       418\n",
       "sample                                                                      \n",
       "fm11    0.529496  0.074750  0.028027  0.131487  0.270114  0.141439  0.009292\n",
       "fm12    0.133005  0.752452  0.008606  0.001916  0.002508  0.002149  0.012900"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set = named_Obs_dataFrame.loc['fm11':'fm12' , '250':'418']\n",
    "test_set "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the X-data using the pandas plotting tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "raw_plt_axes = data.transpose().plot()\n",
    "raw_plt_axes.set_xlabel('nm')\n",
    "_ = raw_plt_axes.set_ylabel('Absorbance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define settings for the PCA model, and give it the name M1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "M1 = mv.PCA_model(n_components=3, is_center=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Enter the data table and run the calculations for the PCA model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "M1.fit(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the output of the PCA loadings (P)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>250</th>\n",
       "      <th>278</th>\n",
       "      <th>306</th>\n",
       "      <th>334</th>\n",
       "      <th>362</th>\n",
       "      <th>390</th>\n",
       "      <th>418</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p1</th>\n",
       "      <td>-0.721213</td>\n",
       "      <td>-0.499315</td>\n",
       "      <td>-0.073902</td>\n",
       "      <td>-0.202351</td>\n",
       "      <td>-0.370905</td>\n",
       "      <td>-0.212131</td>\n",
       "      <td>-0.039475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p2</th>\n",
       "      <td>-0.321281</td>\n",
       "      <td>0.854362</td>\n",
       "      <td>-0.069028</td>\n",
       "      <td>-0.174788</td>\n",
       "      <td>-0.298293</td>\n",
       "      <td>-0.192626</td>\n",
       "      <td>-0.073782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p3</th>\n",
       "      <td>0.414093</td>\n",
       "      <td>-0.117952</td>\n",
       "      <td>-0.478136</td>\n",
       "      <td>-0.314400</td>\n",
       "      <td>-0.114836</td>\n",
       "      <td>-0.352958</td>\n",
       "      <td>-0.591089</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         250       278       306       334       362       390       418\n",
       "p1 -0.721213 -0.499315 -0.073902 -0.202351 -0.370905 -0.212131 -0.039475\n",
       "p2 -0.321281  0.854362 -0.069028 -0.174788 -0.298293 -0.192626 -0.073782\n",
       "p3  0.414093 -0.117952 -0.478136 -0.314400 -0.114836 -0.352958 -0.591089"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M1.P"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 15,
     "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": [
    "M1.P.transpose().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the output of the PCA scores (T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>t1</th>\n",
       "      <th>t2</th>\n",
       "      <th>t3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fm01</th>\n",
       "      <td>0.228103</td>\n",
       "      <td>-0.045592</td>\n",
       "      <td>0.002747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm02</th>\n",
       "      <td>0.105065</td>\n",
       "      <td>-0.094688</td>\n",
       "      <td>0.013867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm03</th>\n",
       "      <td>-0.063565</td>\n",
       "      <td>-0.148152</td>\n",
       "      <td>-0.014956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm04</th>\n",
       "      <td>0.139599</td>\n",
       "      <td>0.060259</td>\n",
       "      <td>-0.001232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm05</th>\n",
       "      <td>-0.005208</td>\n",
       "      <td>0.007216</td>\n",
       "      <td>-0.014660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm06</th>\n",
       "      <td>-0.125013</td>\n",
       "      <td>-0.046938</td>\n",
       "      <td>-0.011066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm07</th>\n",
       "      <td>-0.097577</td>\n",
       "      <td>-0.041278</td>\n",
       "      <td>0.019798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm08</th>\n",
       "      <td>0.051490</td>\n",
       "      <td>0.150179</td>\n",
       "      <td>-0.011221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm09</th>\n",
       "      <td>-0.051967</td>\n",
       "      <td>0.106190</td>\n",
       "      <td>0.009409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm10</th>\n",
       "      <td>-0.180926</td>\n",
       "      <td>0.052805</td>\n",
       "      <td>0.007315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              t1        t2        t3\n",
       "sample                              \n",
       "fm01    0.228103 -0.045592  0.002747\n",
       "fm02    0.105065 -0.094688  0.013867\n",
       "fm03   -0.063565 -0.148152 -0.014956\n",
       "fm04    0.139599  0.060259 -0.001232\n",
       "fm05   -0.005208  0.007216 -0.014660\n",
       "fm06   -0.125013 -0.046938 -0.011066\n",
       "fm07   -0.097577 -0.041278  0.019798\n",
       "fm08    0.051490  0.150179 -0.011221\n",
       "fm09   -0.051967  0.106190  0.009409\n",
       "fm10   -0.180926  0.052805  0.007315"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M1.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='t1', ylabel='t2'>"
      ]
     },
     "execution_count": 17,
     "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": [
    "M1.T.plot.scatter(x='t1', y='t2', s=70)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the cumulative proportions of X-data variation modelled by the 3 components "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>R2X</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.655835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.993694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.999919</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        R2X\n",
       "1  0.655835\n",
       "2  0.993694\n",
       "3  0.999919"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M1.R2X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 19,
     "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": [
    "M1.R2X.plot.bar(rot=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a new PCA model with only 2 components and check the spectral residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "M2 = mv.PCA_model(n_components=2, is_center=True)\n",
    "M2.fit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Absorbance')"
      ]
     },
     "execution_count": 28,
     "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": [
    "resid_axes = M2.Epred(data).transpose().plot()\n",
    "resid_axes.set_xlabel('nm')\n",
    "resid_axes.set_ylabel('Absorbance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Also check the DModX residuals per spectrum in th training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='sample'>"
      ]
     },
     "execution_count": 30,
     "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": [
    "M2.DModXpred(data).plot.bar(rot=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Then make a PCA prediction for the selected test set data \n",
    "It is somewhat outside the desingn space of the training set and should thus have larger values than from the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>DModX</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fm11</th>\n",
       "      <td>3.651708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fm12</th>\n",
       "      <td>3.712513</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           DModX\n",
       "sample          \n",
       "fm11    3.651708\n",
       "fm12    3.712513"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M2.DModXpred(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'DModX')"
      ]
     },
     "execution_count": 39,
     "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": [
    "handle = M2.DModXpred(test_set).plot.bar(rot=0)\n",
    "handle.set_title('Test set residuals')\n",
    "handle.set_ylabel('DModX')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the average spectrum of the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 40,
     "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": [
    "M2.Xavg.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas plotting, further info\n",
    "https://pandas.pydata.org/docs/getting_started/intro_tutorials/04_plotting.html <br>\n",
    "https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}