Q1 - one-dimensional infinite potential well.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1dc5a320",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Required Packages\n",
    "import numpy as np #Provides higher level mathematical functions\n",
    "import matplotlib.pyplot as plt #Provides plotting\n",
    "from scipy.constants import m_e, hbar, e, epsilon_0, h #Importing constants\n",
    "\n",
    "#Optional Figure Parameters\n",
    "fig_width_pt = 400  # Get this from LaTeX using \\showthe\\columnwidth\n",
    "inches_per_pt = 1.0/72.27               # Convert pt to inches\n",
    "golden_mean = (np.sqrt(5)-1.0)/2.0         # Aesthetic ratio\n",
    "fig_width = fig_width_pt*inches_per_pt  # width in inches\n",
    "fig_height =fig_width*golden_mean       # height in inches\n",
    "fig_size = [fig_width,fig_height]       \n",
    "SMALL_SIZE = 4\n",
    "MEDIUM_SIZE = 10\n",
    "BIGGER_SIZE = 12\n",
    "\n",
    "plt.rc('font', size=10)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=10)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=10)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=8)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=8)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=8)    # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title\n",
    "plt.rcParams['text.usetex'] = True\n",
    "plt.rcParams['figure.figsize'] = fig_size\n",
    "plt.rcParams['figure.dpi'] = 200 #Controls figure size in jupyter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f0f368dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def energy_level(n, W):\n",
    "    '''\n",
    "    Calculates the energy values for a given integer n and well width W (in m)\n",
    "    '''\n",
    "    if type(n) != int: #checks if n is an integer\n",
    "        print('n must be an integer!!!!')\n",
    "    else:\n",
    "    \n",
    "        return (h**2 * n**2)/(e*8*m_e*W**2)\n",
    "\n",
    "def well_width(n, eV, m_r): \n",
    "    '''Calculates the well width such that there are n states for a given energy level (eV). \n",
    "    m_r gives the relative electron mass\n",
    "    Used for Part D'''\n",
    "    return np.sqrt((h**2 * n**2)/(eV*e*8*m_r*m_e))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f99cb7ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3507744054260611"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy_level(3,12e-9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c4d60607",
   "metadata": {},
   "outputs": [],
   "source": [
    "n = np.arange(1,11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "36fcf3fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "En_3nm = energy_level(n,3e-9)\n",
    "En_6nm = energy_level(n,6e-9)\n",
    "En_9nm = energy_level(n,9e-9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e47d42bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x525 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context(['science', 'scatter']):\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.plot(n,En_3nm, label='3nm')\n",
    "    ax.plot(n,En_6nm, label='6nm')\n",
    "    ax.plot(n,En_9nm, label='9nm')\n",
    "    ax.legend(title='Well width', loc=2)\n",
    "    xlbl = r\"$n$\"\n",
    "    ylbl = r\"Energy eigenvalue $(eV)$\"\n",
    "    ax.set_xlabel(xlbl)\n",
    "    ax.set_ylabel(ylbl)\n",
    "\n",
    "    #fig.savefig('figures/q1_fig.png', dpi=1000)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1d4e4ec2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2371951951693332e-08"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "well_width(3, .33, 0.067)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9e47438",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}