{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 2.4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fundamentals of Solar Cells and Photovoltaic Systems Engineering**\n",
    "\n",
    "**Solutions Manual - Chapter 2**\n",
    "\n",
    "**Problem 2.4**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Using the tabulated data provided in the online repository of this book, estimate the UV content (280–400 nm) and the total irradiance in the extraterrestrial reference spectrum AM0, the global reference spectrum AM1.5G, and the direct reference spectrum AM1.5D.**"
   ]
  },
  {
   "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": {
    "ExecuteTime": {
     "end_time": "2023-06-07T20:24:02.938773500Z",
     "start_time": "2023-06-07T20:24:01.854998200Z"
    }
   },
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-07T20:24:02.997407100Z",
     "start_time": "2023-06-07T20:24:02.950100800Z"
    }
   },
   "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>AM0</th>\n",
       "      <th>AM1.5G</th>\n",
       "      <th>AM1.5D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Wvlgth nm</td>\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",
       "      <td>280</td>\n",
       "      <td>8.20E-02</td>\n",
       "      <td>4.73E-23</td>\n",
       "      <td>2.54E-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>280.5</td>\n",
       "      <td>9.90E-02</td>\n",
       "      <td>1.23E-21</td>\n",
       "      <td>1.09E-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>281</td>\n",
       "      <td>1.50E-01</td>\n",
       "      <td>5.69E-21</td>\n",
       "      <td>6.13E-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>281.5</td>\n",
       "      <td>2.12E-01</td>\n",
       "      <td>1.57E-19</td>\n",
       "      <td>2.75E-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3980</td>\n",
       "      <td>8.84E-03</td>\n",
       "      <td>7.39E-03</td>\n",
       "      <td>7.40E-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3985</td>\n",
       "      <td>8.80E-03</td>\n",
       "      <td>7.43E-03</td>\n",
       "      <td>7.45E-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3990</td>\n",
       "      <td>8.78E-03</td>\n",
       "      <td>7.37E-03</td>\n",
       "      <td>7.39E-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3995</td>\n",
       "      <td>8.70E-03</td>\n",
       "      <td>7.21E-03</td>\n",
       "      <td>7.23E-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4000</td>\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": [
    "datafile = pd.read_csv('data/Reference_spectrum_ASTM-G173-03.csv', index_col=0, header=0) \n",
    "datafile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-07T20:24:03.007395500Z",
     "start_time": "2023-06-07T20:24:02.997407100Z"
    }
   },
   "outputs": [],
   "source": [
    "datafile.drop(datafile.index[0], inplace=True) #remove row including information on units\n",
    "datafile=datafile.astype(float) #convert values to float for easy operation\n",
    "datafile.index=datafile.index.astype(float) #convert indexes to float for easy operation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by calculating the total irradiance AM0, AM1.5G and AM1.5D."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-07T20:24:03.027396700Z",
     "start_time": "2023-06-07T20:24:03.007395500Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total irradiance in AM0 = 1348.0 W/m2\n",
      "Total irradiance in AM1.5G = 1000.5 W/m2\n",
      "Total irradiance in AM1.5D = 900.2 W/m2\n"
     ]
    }
   ],
   "source": [
    "for spectra in ['AM0', 'AM1.5G', 'AM1.5D']:\n",
    "    irradiance=np.trapz(datafile[spectra], x = datafile.index)\n",
    "    print('Total irradiance in ' + spectra + ' = ' + str(irradiance.round(1)) + ' W/m2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot the three spectra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-07T20:30:08.508978300Z",
     "start_time": "2023-06-07T20:30:08.016801800Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c185e12748>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(datafile, linewidth=2, label=datafile.columns)\n",
    "plt.ylabel('Spectral irradiance [$W·m^{-2}·nm^{-1}$]')\n",
    "plt.xlabel('Wavelength [nm]')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We calculate the UV content in AM0, AM1.5G, and AM1.5D."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-07T20:24:03.347397300Z",
     "start_time": "2023-06-07T20:24:03.337395200Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UV irradiance in AM0 = 102.8 W/m2\n",
      "UV irradiance in AM1.5G = 46.1 W/m2\n",
      "UV irradiance in AM1.5D = 30.5 W/m2\n"
     ]
    }
   ],
   "source": [
    "index=datafile.index<=400\n",
    "for spectra in ['AM0', 'AM1.5G', 'AM1.5D']:\n",
    "    irradiance=np.trapz(datafile[spectra][index], x = datafile.index[index])\n",
    "    print('UV irradiance in ' + spectra + ' = ' + str(irradiance.round(1)) + ' W/m2')"
   ]
  }
 ],
 "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": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}