{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 12.8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fundamentals of Solar Cells and Photovoltaic Systems Engineering**\n",
    "\n",
    "**Solutions Manual - Chapter 12**\n",
    "\n",
    "**Problem 12.8**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**For the spectra in Problem S12.7 and assuming that the lamps are located at the correct position, what is the error that is made at each of the nine 100 nm broadbands from 300 to 1200 nm for each of the lamps with respect to the solar spectrum?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the package [pandas](https://pandas.pydata.org/) to handle the data and [matplotlib.pyplot](https://matplotlib.org/stable/index.html) to plot the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by importing the data for the solar spectra."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>AM0</th>\n",
       "      <th>AM1.5G</th>\n",
       "      <th>AM1.5D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Wvlgth nm</th>\n",
       "      <td>Etr W*m-2*nm-1</td>\n",
       "      <td>Global tilt  W*m-2*nm-1</td>\n",
       "      <td>Direct+circumsolar W*m-2*nm-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>8.20E-02</td>\n",
       "      <td>4.73E-23</td>\n",
       "      <td>2.54E-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280.5</th>\n",
       "      <td>9.90E-02</td>\n",
       "      <td>1.23E-21</td>\n",
       "      <td>1.09E-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>1.50E-01</td>\n",
       "      <td>5.69E-21</td>\n",
       "      <td>6.13E-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281.5</th>\n",
       "      <td>2.12E-01</td>\n",
       "      <td>1.57E-19</td>\n",
       "      <td>2.75E-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3980</th>\n",
       "      <td>8.84E-03</td>\n",
       "      <td>7.39E-03</td>\n",
       "      <td>7.40E-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3985</th>\n",
       "      <td>8.80E-03</td>\n",
       "      <td>7.43E-03</td>\n",
       "      <td>7.45E-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3990</th>\n",
       "      <td>8.78E-03</td>\n",
       "      <td>7.37E-03</td>\n",
       "      <td>7.39E-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3995</th>\n",
       "      <td>8.70E-03</td>\n",
       "      <td>7.21E-03</td>\n",
       "      <td>7.23E-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4000</th>\n",
       "      <td>8.68E-03</td>\n",
       "      <td>7.10E-03</td>\n",
       "      <td>7.12E-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2003 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      AM0                   AM1.5G  \\\n",
       "Wvlgth nm  Etr W*m-2*nm-1  Global tilt  W*m-2*nm-1   \n",
       "280              8.20E-02                 4.73E-23   \n",
       "280.5            9.90E-02                 1.23E-21   \n",
       "281              1.50E-01                 5.69E-21   \n",
       "281.5            2.12E-01                 1.57E-19   \n",
       "...                   ...                      ...   \n",
       "3980             8.84E-03                 7.39E-03   \n",
       "3985             8.80E-03                 7.43E-03   \n",
       "3990             8.78E-03                 7.37E-03   \n",
       "3995             8.70E-03                 7.21E-03   \n",
       "4000             8.68E-03                 7.10E-03   \n",
       "\n",
       "                                  AM1.5D  \n",
       "Wvlgth nm  Direct+circumsolar W*m-2*nm-1  \n",
       "280                             2.54E-26  \n",
       "280.5                           1.09E-24  \n",
       "281                             6.13E-24  \n",
       "281.5                           2.75E-22  \n",
       "...                                  ...  \n",
       "3980                            7.40E-03  \n",
       "3985                            7.45E-03  \n",
       "3990                            7.39E-03  \n",
       "3995                            7.23E-03  \n",
       "4000                            7.12E-03  \n",
       "\n",
       "[2003 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reference = pd.read_csv('data/Reference_spectrum_ASTM-G173-03.csv', index_col=0, header=0) \n",
    "reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "reference.drop(reference.index[0], inplace=True)   # remove row including information on units\n",
    "reference=reference.astype(float)                  # convert values to float for easy operation\n",
    "reference.index=reference.index.astype(float)      # convert indexes to float for easy operation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We import the data for the Xe-arc lamp."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Xe arc lamp (W*m-2*nm-1)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wavelength (nm)</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>300.36966</th>\n",
       "      <td>0.000793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300.58005</th>\n",
       "      <td>0.000816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300.79044</th>\n",
       "      <td>0.000837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301.00082</th>\n",
       "      <td>0.000858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301.21124</th>\n",
       "      <td>0.000876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199.77246</th>\n",
       "      <td>0.213920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199.84302</th>\n",
       "      <td>0.213600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199.91345</th>\n",
       "      <td>0.213280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199.98401</th>\n",
       "      <td>0.212960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1200.05444</th>\n",
       "      <td>0.212640</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6283 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Xe arc lamp (W*m-2*nm-1)\n",
       "wavelength (nm)                          \n",
       "300.36966                        0.000793\n",
       "300.58005                        0.000816\n",
       "300.79044                        0.000837\n",
       "301.00082                        0.000858\n",
       "301.21124                        0.000876\n",
       "...                                   ...\n",
       "1199.77246                       0.213920\n",
       "1199.84302                       0.213600\n",
       "1199.91345                       0.213280\n",
       "1199.98401                       0.212960\n",
       "1200.05444                       0.212640\n",
       "\n",
       "[6283 rows x 1 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xe_arc = pd.read_csv('data/Xe lamp spectral irradiance.csv', index_col=0, header=0) \n",
    "xe_arc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We import the data for the quartz tungsten lamp."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>W halogen lamp (W*m-2*nm-1)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wavelength (nm)</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>0.000010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>0.000100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>0.000200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>0.000300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>0.654304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>0.653941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>0.651544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199</th>\n",
       "      <td>0.647531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1200</th>\n",
       "      <td>0.644677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>901 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 W halogen lamp (W*m-2*nm-1)\n",
       "wavelength (nm)                             \n",
       "300                                 0.000001\n",
       "301                                 0.000010\n",
       "302                                 0.000100\n",
       "303                                 0.000200\n",
       "304                                 0.000300\n",
       "...                                      ...\n",
       "1196                                0.654304\n",
       "1197                                0.653941\n",
       "1198                                0.651544\n",
       "1199                                0.647531\n",
       "1200                                0.644677\n",
       "\n",
       "[901 rows x 1 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quartz_w = pd.read_csv('data/W lamp spectral irradiance.csv', index_col=0, header=0) \n",
    "quartz_w"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot the AM1.5G spectra, the Xe-arc lamp, and the quarz tungsten lamp spectra."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x238d2d4fd88>"
      ]
     },
     "execution_count": 6,
     "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": [
    "plt.plot(reference['AM1.5G'], \n",
    "         linewidth=2, label='AM1.5G')\n",
    "plt.plot(xe_arc, \n",
    "         linewidth=2, label='Xe-arc lamp')\n",
    "plt.plot(quartz_w, \n",
    "         linewidth=2, label='Quartz tungsten lamp')\n",
    "plt.ylabel('Spectral distribution (W*m-2*nm-1)')\n",
    "plt.xlabel('Wavelength (nm)')\n",
    "plt.xlim([250,1300])\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can calculate the total irradiance from every spectra in each 100 nm-bands from 300 to 1200 nm. "
   ]
  },
  {
   "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>AM1.5G</th>\n",
       "      <th>Xe arc lamp</th>\n",
       "      <th>Quartz W halogen lamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>46.3471</td>\n",
       "      <td>7.745697</td>\n",
       "      <td>4.3254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>137.925</td>\n",
       "      <td>59.513764</td>\n",
       "      <td>22.750901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>151.4</td>\n",
       "      <td>68.860502</td>\n",
       "      <td>51.50123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>140.4</td>\n",
       "      <td>71.229846</td>\n",
       "      <td>77.502158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>111.44</td>\n",
       "      <td>58.938656</td>\n",
       "      <td>93.554867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>95.685</td>\n",
       "      <td>118.622972</td>\n",
       "      <td>99.165299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>900</th>\n",
       "      <td>56.28</td>\n",
       "      <td>112.40382</td>\n",
       "      <td>97.922396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>64.92</td>\n",
       "      <td>38.525673</td>\n",
       "      <td>85.919091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1100</th>\n",
       "      <td>31.3655</td>\n",
       "      <td>30.108108</td>\n",
       "      <td>74.070708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1200</th>\n",
       "      <td>43.635</td>\n",
       "      <td>21.265237</td>\n",
       "      <td>64.467675</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       AM1.5G Xe arc lamp Quartz W halogen lamp\n",
       "300   46.3471    7.745697                4.3254\n",
       "400   137.925   59.513764             22.750901\n",
       "500     151.4   68.860502              51.50123\n",
       "600     140.4   71.229846             77.502158\n",
       "700    111.44   58.938656             93.554867\n",
       "800    95.685  118.622972             99.165299\n",
       "900     56.28   112.40382             97.922396\n",
       "1000    64.92   38.525673             85.919091\n",
       "1100  31.3655   30.108108             74.070708\n",
       "1200   43.635   21.265237             64.467675"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bands = np.array(np.arange(300,1300,100))\n",
    "w=5\n",
    "irradiance_bands = pd.DataFrame(columns=['AM1.5G', 'Xe arc lamp', 'Quartz W halogen lamp'])\n",
    "AM15G=reference['AM1.5G']\n",
    "Xe=xe_arc['Xe arc lamp (W*m-2*nm-1)']\n",
    "Quartz_W=quartz_w['W halogen lamp (W*m-2*nm-1)']\n",
    "for band in bands:    \n",
    "    AM15G_interpolated=np.interp(np.arange(band, band+100, w), AM15G.index, AM15G.values)   \n",
    "    irradiance_bands.loc[band,'AM1.5G'] = AM15G_interpolated.sum()*w\n",
    "    \n",
    "    Xe_interpolated=np.interp(np.arange(band, band+100, w), Xe.index, Xe.values)   \n",
    "    irradiance_bands.loc[band,'Xe arc lamp'] = Xe_interpolated.sum()*w\n",
    "    \n",
    "    Quartz_W_interpolated=np.interp(np.arange(band, band+100, w), Quartz_W.index, Quartz_W.values)   \n",
    "    irradiance_bands.loc[band,'Quartz W halogen lamp'] = Quartz_W_interpolated.sum()*w\n",
    "irradiance_bands"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the total irradiance per band from the different lamps. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Wavelength (nm)')"
      ]
     },
     "execution_count": 8,
     "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": [
    "ax = irradiance_bands.plot.bar(rot=0)\n",
    "plt.ylabel('Irradiance (W*m-2)')\n",
    "plt.xlabel('Wavelength (nm)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also calculate the errors in percentage. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>AM1.5G</th>\n",
       "      <th>Xe arc lamp</th>\n",
       "      <th>Quartz W halogen lamp</th>\n",
       "      <th>error Xe arc lamp</th>\n",
       "      <th>error Quartz W halogen lamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>46.3471</td>\n",
       "      <td>7.745697</td>\n",
       "      <td>4.3254</td>\n",
       "      <td>-83.287635</td>\n",
       "      <td>-90.667377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>137.925</td>\n",
       "      <td>59.513764</td>\n",
       "      <td>22.750901</td>\n",
       "      <td>-56.850633</td>\n",
       "      <td>-83.504875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>151.4</td>\n",
       "      <td>68.860502</td>\n",
       "      <td>51.50123</td>\n",
       "      <td>-54.517502</td>\n",
       "      <td>-65.983335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>140.4</td>\n",
       "      <td>71.229846</td>\n",
       "      <td>77.502158</td>\n",
       "      <td>-49.266492</td>\n",
       "      <td>-44.799033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>111.44</td>\n",
       "      <td>58.938656</td>\n",
       "      <td>93.554867</td>\n",
       "      <td>-47.111759</td>\n",
       "      <td>-16.049114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>95.685</td>\n",
       "      <td>118.622972</td>\n",
       "      <td>99.165299</td>\n",
       "      <td>23.97238</td>\n",
       "      <td>3.637246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>900</th>\n",
       "      <td>56.28</td>\n",
       "      <td>112.40382</td>\n",
       "      <td>97.922396</td>\n",
       "      <td>99.722494</td>\n",
       "      <td>73.991465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>64.92</td>\n",
       "      <td>38.525673</td>\n",
       "      <td>85.919091</td>\n",
       "      <td>-40.656696</td>\n",
       "      <td>32.346104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1100</th>\n",
       "      <td>31.3655</td>\n",
       "      <td>30.108108</td>\n",
       "      <td>74.070708</td>\n",
       "      <td>-4.008838</td>\n",
       "      <td>136.153444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1200</th>\n",
       "      <td>43.635</td>\n",
       "      <td>21.265237</td>\n",
       "      <td>64.467675</td>\n",
       "      <td>-51.265643</td>\n",
       "      <td>47.743038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       AM1.5G Xe arc lamp Quartz W halogen lamp error Xe arc lamp  \\\n",
       "300   46.3471    7.745697                4.3254        -83.287635   \n",
       "400   137.925   59.513764             22.750901        -56.850633   \n",
       "500     151.4   68.860502              51.50123        -54.517502   \n",
       "600     140.4   71.229846             77.502158        -49.266492   \n",
       "700    111.44   58.938656             93.554867        -47.111759   \n",
       "800    95.685  118.622972             99.165299          23.97238   \n",
       "900     56.28   112.40382             97.922396         99.722494   \n",
       "1000    64.92   38.525673             85.919091        -40.656696   \n",
       "1100  31.3655   30.108108             74.070708         -4.008838   \n",
       "1200   43.635   21.265237             64.467675        -51.265643   \n",
       "\n",
       "     error Quartz W halogen lamp  \n",
       "300                   -90.667377  \n",
       "400                   -83.504875  \n",
       "500                   -65.983335  \n",
       "600                   -44.799033  \n",
       "700                   -16.049114  \n",
       "800                     3.637246  \n",
       "900                    73.991465  \n",
       "1000                   32.346104  \n",
       "1100                  136.153444  \n",
       "1200                   47.743038  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "irradiance_bands['error Xe arc lamp'] = 100*(irradiance_bands['Xe arc lamp']/irradiance_bands['AM1.5G']-1)\n",
    "irradiance_bands['error Quartz W halogen lamp'] = 100*(irradiance_bands['Quartz W halogen lamp']/irradiance_bands['AM1.5G']-1)\n",
    "\n",
    "irradiance_bands"
   ]
  }
 ],
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