example_read_raman.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### An example notebook for importing Raman spectra in .spc file format, performing principle component analysis (PCA) and plotting the spectra"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Import Python package that are required for analysis. The 'functions' prefix is pointing to the folder containing the custom functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd # python's no.1 pacakge for manipulating data\n",
    "import glob\n",
    "import os\n",
    "import matplotlib.pyplot as plt # python's plotting package\n",
    "import functions.spc as spc # custom function for reading .spc files and converting to a dataframe\n",
    "import functions.base_corr as base_corr # custom function for performing basline correction of spectra\n",
    "from sklearn.decomposition import PCA # sklearn is python's no.1 package for machine learning\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Define the path (directory) to the folder containing the files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = '/Users/leebarrett/Documents/data_projects/read_raman/data/20131003 Samples C1-12/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(path):\n",
    "    raise OSError(f\"Directory: '{path}' not found. Please check directory and try again.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Import all the .spc files from the path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "files = glob.glob(f\"{path}*.spc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Convert the .spc files to a Pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gx-y(1)\n",
      "gx-y(1)\n",
      "gx-y(1)\n",
      "gx-y(1)\n",
      "gx-y(1)\n",
      "gx-y(1)\n",
      "gx-y(1)\n",
      "gx-y(1)\n",
      "gx-y(1)\n",
      "gx-y(1)\n",
      "gx-y(1)\n",
      "gx-y(1)\n"
     ]
    }
   ],
   "source": [
    "spc = [spc.read_spc(file, \"wide\") for file in files]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Join all the dataframes together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat(spc).reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. Get the baseline for each spetrum uing ALS algorithm defined in the base_corr.py module. Parameters passed to .baseline_als_optimized() could be played around with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "bline = []\n",
    "\n",
    "for index, row in df.iloc[:, 1:].iterrows():\n",
    "\n",
    "    bline.append(\n",
    "        base_corr.baseline_als_optimized(row.values, 10000, 0.0025)\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7. Perform baseline correction on original data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "bcorr = []\n",
    "\n",
    "for index, row in df.iloc[:, 1:].iterrows():\n",
    "    bcorr.append(\n",
    "        row.values-bline[index]\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ 8.9477978 , 10.14140153, 11.33500302, ..., 49.32648965,\n",
       "        53.57489046, 57.82327682]),\n",
       " array([ 6.09753093,  7.37267345,  8.64781444, ..., 53.43006352,\n",
       "        56.67785989, 58.92564154]),\n",
       " array([10.37430364, 11.53262697, 11.6909477 , ..., 28.25163136,\n",
       "        30.55663029, 31.86162125]),\n",
       " array([11.31283611, 12.56618215, 13.81952537, ..., 43.15208925,\n",
       "        46.27168698, 48.39127262]),\n",
       " array([ 4.37159091,  5.56361707,  6.75564214, ..., 42.67685838,\n",
       "        45.97909681, 47.28132343]),\n",
       " array([ 3.10125826,  3.29080975,  4.48036047, ..., 51.31217759,\n",
       "        55.41628284, 58.52037345]),\n",
       " array([ 6.85065827,  7.10241368,  8.35416737, ..., 48.25294949,\n",
       "        51.54889865, 52.84483461]),\n",
       " array([ 9.03562274, 10.25478203, 12.47393907, ..., 37.40998747,\n",
       "        40.58303792, 42.75607768]),\n",
       " array([11.35875518, 11.54384006, 11.7289221 , ..., 46.31305712,\n",
       "        50.50593911, 53.69880767]),\n",
       " array([ 6.442428  ,  7.78088537, 10.11934113, ..., 67.52195682,\n",
       "        72.35150443, 76.18103301]),\n",
       " array([ 6.4505682 ,  7.70540071, 10.9602316 , ..., 47.7008679 ,\n",
       "        50.80251973, 52.90415832]),\n",
       " array([10.05078657, 10.27219856, 11.49360804, ..., 53.48329004,\n",
       "        55.67678982, 56.87027538])]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bcorr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8. Create new dataframe with baseline corrected spectra."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>1599.5</th>\n",
       "      <th>1600.0</th>\n",
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       "      <td>10.141402</td>\n",
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       "      <td>13.528598</td>\n",
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       "      <td>17.109246</td>\n",
       "      <td>18.302700</td>\n",
       "      <td>18.496073</td>\n",
       "      <td>19.689335</td>\n",
       "      <td>...</td>\n",
       "      <td>25.583549</td>\n",
       "      <td>26.833432</td>\n",
       "      <td>28.082890</td>\n",
       "      <td>29.332008</td>\n",
       "      <td>33.580862</td>\n",
       "      <td>38.829521</td>\n",
       "      <td>44.078047</td>\n",
       "      <td>49.326490</td>\n",
       "      <td>53.574890</td>\n",
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       "      <th>1</th>\n",
       "      <td>6.097531</td>\n",
       "      <td>7.372673</td>\n",
       "      <td>8.647814</td>\n",
       "      <td>10.922951</td>\n",
       "      <td>12.198076</td>\n",
       "      <td>13.473183</td>\n",
       "      <td>13.748260</td>\n",
       "      <td>13.023293</td>\n",
       "      <td>13.298262</td>\n",
       "      <td>13.573148</td>\n",
       "      <td>...</td>\n",
       "      <td>29.691072</td>\n",
       "      <td>32.940469</td>\n",
       "      <td>36.189399</td>\n",
       "      <td>39.437957</td>\n",
       "      <td>42.686231</td>\n",
       "      <td>46.934298</td>\n",
       "      <td>50.182224</td>\n",
       "      <td>53.430064</td>\n",
       "      <td>56.677860</td>\n",
       "      <td>58.925642</td>\n",
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       "      <th>2</th>\n",
       "      <td>10.374304</td>\n",
       "      <td>11.532627</td>\n",
       "      <td>11.690948</td>\n",
       "      <td>11.849260</td>\n",
       "      <td>13.007557</td>\n",
       "      <td>13.165825</td>\n",
       "      <td>13.324051</td>\n",
       "      <td>13.482216</td>\n",
       "      <td>12.640300</td>\n",
       "      <td>12.798278</td>\n",
       "      <td>...</td>\n",
       "      <td>15.114269</td>\n",
       "      <td>17.420126</td>\n",
       "      <td>18.725732</td>\n",
       "      <td>21.031140</td>\n",
       "      <td>22.336396</td>\n",
       "      <td>24.641540</td>\n",
       "      <td>26.946609</td>\n",
       "      <td>28.251631</td>\n",
       "      <td>30.556630</td>\n",
       "      <td>31.861621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.312836</td>\n",
       "      <td>12.566182</td>\n",
       "      <td>13.819525</td>\n",
       "      <td>16.072860</td>\n",
       "      <td>17.326176</td>\n",
       "      <td>18.579461</td>\n",
       "      <td>18.832696</td>\n",
       "      <td>19.085859</td>\n",
       "      <td>19.338923</td>\n",
       "      <td>20.591856</td>\n",
       "      <td>...</td>\n",
       "      <td>22.311317</td>\n",
       "      <td>25.432212</td>\n",
       "      <td>28.552730</td>\n",
       "      <td>30.672947</td>\n",
       "      <td>33.792934</td>\n",
       "      <td>36.912752</td>\n",
       "      <td>40.032456</td>\n",
       "      <td>43.152089</td>\n",
       "      <td>46.271687</td>\n",
       "      <td>48.391273</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>6.755642</td>\n",
       "      <td>7.947664</td>\n",
       "      <td>9.139677</td>\n",
       "      <td>10.331677</td>\n",
       "      <td>11.523655</td>\n",
       "      <td>12.715599</td>\n",
       "      <td>12.907496</td>\n",
       "      <td>14.099328</td>\n",
       "      <td>...</td>\n",
       "      <td>22.557632</td>\n",
       "      <td>25.861161</td>\n",
       "      <td>29.164312</td>\n",
       "      <td>32.467164</td>\n",
       "      <td>34.769786</td>\n",
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       "      <td>40.374585</td>\n",
       "      <td>42.676858</td>\n",
       "      <td>45.979097</td>\n",
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       "<p>5 rows × 1201 columns</p>\n",
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      "text/plain": [
       "      1000.0     1000.5     1001.0     1001.5     1002.0     1002.5  \\\n",
       "0   8.947798  10.141402  11.335003  13.528598  14.722177  16.915732   \n",
       "1   6.097531   7.372673   8.647814  10.922951  12.198076  13.473183   \n",
       "2  10.374304  11.532627  11.690948  11.849260  13.007557  13.165825   \n",
       "3  11.312836  12.566182  13.819525  16.072860  17.326176  18.579461   \n",
       "4   4.371591   5.563617   6.755642   7.947664   9.139677  10.331677   \n",
       "\n",
       "      1003.0     1003.5     1004.0     1004.5  ...     1595.5     1596.0  \\\n",
       "0  17.109246  18.302700  18.496073  19.689335  ...  25.583549  26.833432   \n",
       "1  13.748260  13.023293  13.298262  13.573148  ...  29.691072  32.940469   \n",
       "2  13.324051  13.482216  12.640300  12.798278  ...  15.114269  17.420126   \n",
       "3  18.832696  19.085859  19.338923  20.591856  ...  22.311317  25.432212   \n",
       "4  11.523655  12.715599  12.907496  14.099328  ...  22.557632  25.861161   \n",
       "\n",
       "      1596.5     1597.0     1597.5     1598.0     1598.5     1599.0  \\\n",
       "0  28.082890  29.332008  33.580862  38.829521  44.078047  49.326490   \n",
       "1  36.189399  39.437957  42.686231  46.934298  50.182224  53.430064   \n",
       "2  18.725732  21.031140  22.336396  24.641540  26.946609  28.251631   \n",
       "3  28.552730  30.672947  33.792934  36.912752  40.032456  43.152089   \n",
       "4  29.164312  32.467164  34.769786  38.072242  40.374585  42.676858   \n",
       "\n",
       "      1599.5     1600.0  \n",
       "0  53.574890  57.823277  \n",
       "1  56.677860  58.925642  \n",
       "2  30.556630  31.861621  \n",
       "3  46.271687  48.391273  \n",
       "4  45.979097  47.281323  \n",
       "\n",
       "[5 rows x 1201 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2 = pd.DataFrame(bcorr, columns=df.columns[1:])\n",
    "\n",
    "display(df2.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9. Plot the baseline corrected spectra."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for index, row in df2.iterrows():\n",
    "    plt.plot(row.keys(), row.values, label=index+1)\n",
    "plt.title(\"Basline corrected spectra als algorithm\")\n",
    "plt.xlabel(\"Wavelength\")\n",
    "plt.ylabel(\"Raman Shift\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9. Scale the data for PCA preparation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.41686857  0.52432787  0.46661058 ...  0.21011071  0.28548079\n",
      "   0.45630822]\n",
      " [-0.68165674 -0.52701005 -0.59595159 ...  0.65827205  0.60805782\n",
      "   0.56444419]\n",
      " [ 0.96666017  1.05260223  0.60735744 ... -2.09152614 -2.10744063\n",
      "  -2.09038846]\n",
      " ...\n",
      " [-0.54872949 -0.37200433 -0.01408376 ...  2.1972822   2.23745115\n",
      "   2.25710389]\n",
      " [-0.54559216 -0.40066727  0.3184193  ...  0.03257258 -0.00272802\n",
      "  -0.02623045]\n",
      " [ 0.841973    0.57399396  0.52932583 ...  0.66408505  0.50398905\n",
      "   0.36282399]]\n"
     ]
    }
   ],
   "source": [
    "scaled = StandardScaler().fit_transform(df2.values)\n",
    "\n",
    "print(scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10. Create the pca object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit PCA model\n",
    "pca = PCA()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "11. Perform PCA on the baseline corrected spectra."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.41363290e+00 -1.05185695e+01 -5.59228948e+00  5.81748917e+00\n",
      "  -7.29553307e+00  1.14370359e+01  1.24795777e+01  8.90375693e-01\n",
      "   2.30965213e+00  3.75694571e-01  2.56894914e+00  4.84830653e-15]\n",
      " [-4.96658837e+00 -1.23779082e+01  3.16907191e+00 -8.56127582e+00\n",
      "  -7.27399848e+00 -1.26687720e+00 -6.34303193e+00 -6.73591220e+00\n",
      "  -6.77413757e+00 -7.41455227e+00  3.55417215e+00  4.84830653e-15]\n",
      " [-4.11401222e+01  6.69913305e+00  9.66826453e-01  3.31246241e+00\n",
      "   4.96125043e+00  3.00548385e+00 -3.59264839e+00  2.44370051e+00\n",
      "  -7.01981483e+00  6.67387490e+00  8.55924587e+00  4.84830653e-15]\n",
      " [-3.37685472e+00  1.25777221e+00  2.81452798e-01 -9.41947864e+00\n",
      "   9.14783698e+00 -4.86449890e-01 -4.16268585e-02 -4.12790368e+00\n",
      "   1.36106623e+01 -1.63901868e+00  5.43071624e+00  4.84830653e-15]\n",
      " [-1.26524409e+01  4.47075422e+00 -1.12423918e+01  1.29356391e+01\n",
      "  -1.62793823e+00 -5.21661631e+00 -4.97544497e+00  7.07562889e+00\n",
      "   2.92968586e+00 -8.72748316e+00  6.99969337e-02  4.84830653e-15]]\n"
     ]
    }
   ],
   "source": [
    "pca_dt = pca.fit_transform(scaled)\n",
    "print(pca_dt[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "12. Create new dataframe for PCA results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "      <th>PC5</th>\n",
       "      <th>PC6</th>\n",
       "      <th>PC7</th>\n",
       "      <th>PC8</th>\n",
       "      <th>PC9</th>\n",
       "      <th>PC10</th>\n",
       "      <th>PC11</th>\n",
       "      <th>PC12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.413633</td>\n",
       "      <td>-10.518569</td>\n",
       "      <td>-5.592289</td>\n",
       "      <td>5.817489</td>\n",
       "      <td>-7.295533</td>\n",
       "      <td>11.437036</td>\n",
       "      <td>12.479578</td>\n",
       "      <td>0.890376</td>\n",
       "      <td>2.309652</td>\n",
       "      <td>0.375695</td>\n",
       "      <td>2.568949</td>\n",
       "      <td>4.848307e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-4.966588</td>\n",
       "      <td>-12.377908</td>\n",
       "      <td>3.169072</td>\n",
       "      <td>-8.561276</td>\n",
       "      <td>-7.273998</td>\n",
       "      <td>-1.266877</td>\n",
       "      <td>-6.343032</td>\n",
       "      <td>-6.735912</td>\n",
       "      <td>-6.774138</td>\n",
       "      <td>-7.414552</td>\n",
       "      <td>3.554172</td>\n",
       "      <td>4.848307e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-41.140122</td>\n",
       "      <td>6.699133</td>\n",
       "      <td>0.966826</td>\n",
       "      <td>3.312462</td>\n",
       "      <td>4.961250</td>\n",
       "      <td>3.005484</td>\n",
       "      <td>-3.592648</td>\n",
       "      <td>2.443701</td>\n",
       "      <td>-7.019815</td>\n",
       "      <td>6.673875</td>\n",
       "      <td>8.559246</td>\n",
       "      <td>4.848307e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-3.376855</td>\n",
       "      <td>1.257772</td>\n",
       "      <td>0.281453</td>\n",
       "      <td>-9.419479</td>\n",
       "      <td>9.147837</td>\n",
       "      <td>-0.486450</td>\n",
       "      <td>-0.041627</td>\n",
       "      <td>-4.127904</td>\n",
       "      <td>13.610662</td>\n",
       "      <td>-1.639019</td>\n",
       "      <td>5.430716</td>\n",
       "      <td>4.848307e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-12.652441</td>\n",
       "      <td>4.470754</td>\n",
       "      <td>-11.242392</td>\n",
       "      <td>12.935639</td>\n",
       "      <td>-1.627938</td>\n",
       "      <td>-5.216616</td>\n",
       "      <td>-4.975445</td>\n",
       "      <td>7.075629</td>\n",
       "      <td>2.929686</td>\n",
       "      <td>-8.727483</td>\n",
       "      <td>0.069997</td>\n",
       "      <td>4.848307e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14.277830</td>\n",
       "      <td>22.746623</td>\n",
       "      <td>1.872603</td>\n",
       "      <td>0.125297</td>\n",
       "      <td>-6.253164</td>\n",
       "      <td>3.286355</td>\n",
       "      <td>3.411038</td>\n",
       "      <td>-8.826048</td>\n",
       "      <td>-1.851917</td>\n",
       "      <td>-2.140856</td>\n",
       "      <td>-1.907213</td>\n",
       "      <td>4.848307e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-11.284798</td>\n",
       "      <td>1.304846</td>\n",
       "      <td>6.321942</td>\n",
       "      <td>-8.490855</td>\n",
       "      <td>-5.011890</td>\n",
       "      <td>9.523280</td>\n",
       "      <td>-8.165903</td>\n",
       "      <td>8.883933</td>\n",
       "      <td>3.368178</td>\n",
       "      <td>1.752524</td>\n",
       "      <td>-6.729231</td>\n",
       "      <td>4.848307e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-17.404073</td>\n",
       "      <td>-0.185094</td>\n",
       "      <td>-4.988616</td>\n",
       "      <td>-9.843842</td>\n",
       "      <td>11.065550</td>\n",
       "      <td>-3.188409</td>\n",
       "      <td>9.671098</td>\n",
       "      <td>3.406802</td>\n",
       "      <td>-6.695559</td>\n",
       "      <td>-3.060768</td>\n",
       "      <td>-5.771544</td>\n",
       "      <td>4.848307e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-5.327133</td>\n",
       "      <td>-3.347730</td>\n",
       "      <td>-13.525542</td>\n",
       "      <td>-2.860324</td>\n",
       "      <td>-7.456799</td>\n",
       "      <td>-9.511717</td>\n",
       "      <td>-1.789940</td>\n",
       "      <td>-4.329010</td>\n",
       "      <td>1.936761</td>\n",
       "      <td>9.473859</td>\n",
       "      <td>-3.268145</td>\n",
       "      <td>4.848307e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>79.596925</td>\n",
       "      <td>-0.379055</td>\n",
       "      <td>-2.501263</td>\n",
       "      <td>-1.459826</td>\n",
       "      <td>3.610698</td>\n",
       "      <td>-0.255208</td>\n",
       "      <td>-2.133338</td>\n",
       "      <td>5.778388</td>\n",
       "      <td>-3.430775</td>\n",
       "      <td>2.067652</td>\n",
       "      <td>3.265165</td>\n",
       "      <td>4.848307e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.495621</td>\n",
       "      <td>-7.987715</td>\n",
       "      <td>5.025477</td>\n",
       "      <td>13.582411</td>\n",
       "      <td>10.576924</td>\n",
       "      <td>3.746613</td>\n",
       "      <td>-4.705079</td>\n",
       "      <td>-8.433394</td>\n",
       "      <td>-0.261724</td>\n",
       "      <td>1.374980</td>\n",
       "      <td>-6.060412</td>\n",
       "      <td>4.848307e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-1.804733</td>\n",
       "      <td>-1.683056</td>\n",
       "      <td>20.212730</td>\n",
       "      <td>4.862304</td>\n",
       "      <td>-4.442936</td>\n",
       "      <td>-11.073491</td>\n",
       "      <td>6.185299</td>\n",
       "      <td>3.973441</td>\n",
       "      <td>1.878989</td>\n",
       "      <td>1.264094</td>\n",
       "      <td>0.288300</td>\n",
       "      <td>4.848307e-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PC1        PC2        PC3        PC4        PC5        PC6  \\\n",
       "0   -1.413633 -10.518569  -5.592289   5.817489  -7.295533  11.437036   \n",
       "1   -4.966588 -12.377908   3.169072  -8.561276  -7.273998  -1.266877   \n",
       "2  -41.140122   6.699133   0.966826   3.312462   4.961250   3.005484   \n",
       "3   -3.376855   1.257772   0.281453  -9.419479   9.147837  -0.486450   \n",
       "4  -12.652441   4.470754 -11.242392  12.935639  -1.627938  -5.216616   \n",
       "5   14.277830  22.746623   1.872603   0.125297  -6.253164   3.286355   \n",
       "6  -11.284798   1.304846   6.321942  -8.490855  -5.011890   9.523280   \n",
       "7  -17.404073  -0.185094  -4.988616  -9.843842  11.065550  -3.188409   \n",
       "8   -5.327133  -3.347730 -13.525542  -2.860324  -7.456799  -9.511717   \n",
       "9   79.596925  -0.379055  -2.501263  -1.459826   3.610698  -0.255208   \n",
       "10   5.495621  -7.987715   5.025477  13.582411  10.576924   3.746613   \n",
       "11  -1.804733  -1.683056  20.212730   4.862304  -4.442936 -11.073491   \n",
       "\n",
       "          PC7       PC8        PC9      PC10      PC11          PC12  \n",
       "0   12.479578  0.890376   2.309652  0.375695  2.568949  4.848307e-15  \n",
       "1   -6.343032 -6.735912  -6.774138 -7.414552  3.554172  4.848307e-15  \n",
       "2   -3.592648  2.443701  -7.019815  6.673875  8.559246  4.848307e-15  \n",
       "3   -0.041627 -4.127904  13.610662 -1.639019  5.430716  4.848307e-15  \n",
       "4   -4.975445  7.075629   2.929686 -8.727483  0.069997  4.848307e-15  \n",
       "5    3.411038 -8.826048  -1.851917 -2.140856 -1.907213  4.848307e-15  \n",
       "6   -8.165903  8.883933   3.368178  1.752524 -6.729231  4.848307e-15  \n",
       "7    9.671098  3.406802  -6.695559 -3.060768 -5.771544  4.848307e-15  \n",
       "8   -1.789940 -4.329010   1.936761  9.473859 -3.268145  4.848307e-15  \n",
       "9   -2.133338  5.778388  -3.430775  2.067652  3.265165  4.848307e-15  \n",
       "10  -4.705079 -8.433394  -0.261724  1.374980 -6.060412  4.848307e-15  \n",
       "11   6.185299  3.973441   1.878989  1.264094  0.288300  4.848307e-15  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca_df = pd.DataFrame(pca_dt, columns=[f\"PC{x + 1}\" for x, y in enumerate(pca_dt)])\n",
    "\n",
    "display(pca_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "13. Create PCA plot of PC1 vs PC2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "labels = pca_df.index.values\n",
    "\n",
    "for x, y, z in zip(pca_df[\"PC1\"], pca_df[\"PC2\"], labels):\n",
    "\n",
    "    plt.plot(x, y, marker=\".\", linestyle='', label=z+1)\n",
    "plt.xlabel(\"PC1\")\n",
    "plt.ylabel(\"PC2\")\n",
    "plt.title(\"Principal Component Analysis of Raman Spectra\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "14. Get the varaince values (as a percentage) from the PCA data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_var = pca.explained_variance_ratio_*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "15. Plot the PCA variance as a bar chart. Most of the variance can be attributed to PC1. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(x=[f\"PC{x+1}\" for x, y in enumerate(pca_var)], height=pca_var)\n",
    "plt.ylabel(\"Explained Variance (%)\")\n",
    "plt.xlabel(\"Principal Component\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "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": "3.9.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "1ebd42a5a388c096f9f490269a3e975bfaa1494d3c823c8f6407b0580eafdd24"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}