Hydrogen atom using Function and Class-Copy3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hydrogen Atom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"img/Hydrogen_GIF.gif\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from IPython.display import Image\n",
    "Image(url='img/Hydrogen_GIF.gif')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Radius of  nth orbit $$ r_n= \\frac{n^2h^2\\varepsilon_0}{\\pi m e^2}=(0.528)n^2Angstrom$$\n",
    "#### Velocity of electron in nth orbit $$ v_n=\\frac{e^2}{2nh\\varepsilon_0} = \\frac{2.2*10^6}{n}m/s$$\n",
    "#### Total energy of electron in nth orbit $$ E_n=\\frac{-me^4}{8n^2h^2\\varepsilon_0^2}=-\\frac{13.6}{n^2}eV$$\n",
    "#### Wave Number   $$ \\nu =\\frac{me^4}{8n^2h^3\\varepsilon_0^2}(\\frac{1}{n_1^2}-\\frac{1}{n_2^2})=R(\\frac{1}{n_1^2}-\\frac{1}{n_2^2})$$ \n",
    "where R is the Rydberg Constant and $ R = 1.0973*10^7 /m$\n",
    "\n",
    "#### Wavelength  $$ \\lambda=\\frac{1}{\\nu}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.528,\n",
       " 2.112,\n",
       " 4.752000000000001,\n",
       " 8.448,\n",
       " 13.200000000000001,\n",
       " 19.008000000000003,\n",
       " 25.872,\n",
       " 33.792,\n",
       " 42.768,\n",
       " 52.800000000000004,\n",
       " 63.888000000000005)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def Hydrogen_radius(n):\n",
    "    f = n**2*k\n",
    "    return f\n",
    "k=0.528\n",
    "Hydrogen_radius(1),Hydrogen_radius(2),Hydrogen_radius(3),Hydrogen_radius(4),Hydrogen_radius(5),Hydrogen_radius(6),Hydrogen_radius(7),Hydrogen_radius(8),Hydrogen_radius(9),Hydrogen_radius(10),Hydrogen_radius(11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2200000.0,\n",
       " 1100000.0,\n",
       " 733333.3333333334,\n",
       " 550000.0,\n",
       " 440000.0,\n",
       " 366666.6666666667,\n",
       " 314285.71428571426,\n",
       " 275000.0,\n",
       " 244444.44444444444,\n",
       " 220000.0,\n",
       " 200000.0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def velocity_of_electron(n):\n",
    "    v=k1/n\n",
    "    return v\n",
    "k1=2.2*10**6\n",
    "velocity_of_electron(1),velocity_of_electron(2),velocity_of_electron(3),velocity_of_electron(4),velocity_of_electron(5),velocity_of_electron(6),velocity_of_electron(7),velocity_of_electron(8),velocity_of_electron(9),velocity_of_electron(10),velocity_of_electron(11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-13.6,\n",
       " -3.4,\n",
       " -1.511111111111111,\n",
       " -0.85,\n",
       " -0.544,\n",
       " -0.37777777777777777,\n",
       " -0.27755102040816326,\n",
       " -0.2125,\n",
       " -0.16790123456790124,\n",
       " -0.136,\n",
       " -0.11239669421487603)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def Total_Energy(n):\n",
    "    E=-k2/n**2\n",
    "    return E\n",
    "k2=13.6\n",
    "Total_Energy(1), Total_Energy(2), Total_Energy(3), Total_Energy(4), Total_Energy(5), Total_Energy(6), Total_Energy(7), Total_Energy(8), Total_Energy(9),Total_Energy(10),Total_Energy(11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8227500.0,\n",
       " 9751111.11111111,\n",
       " 10284375.0,\n",
       " 10531200.0,\n",
       " 10665277.777777778,\n",
       " 10746122.448979592,\n",
       " 10798593.75,\n",
       " 10834567.901234567,\n",
       " 10860300.0,\n",
       " 10879338.842975207,\n",
       " 10893819.444444444)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def Wave_Number(n1,n2):\n",
    "    g=R*(1/(n1)**2-1/(n2)**2)\n",
    "    return g\n",
    "R=1.097*10**7\n",
    "Wave_Number(1,2),Wave_Number(1,3),Wave_Number(1,4),Wave_Number(1,5),Wave_Number(1,6),Wave_Number(1,7),Wave_Number(1,8),Wave_Number(1,9),Wave_Number(1,10),Wave_Number(1,11), Wave_Number(1,12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.215436037678517e-07,\n",
       " 1.0255241567912489e-07,\n",
       " 9.723488301428137e-08,\n",
       " 9.495594044363416e-08,\n",
       " 9.376220862091418e-08,\n",
       " 9.305682163476147e-08,\n",
       " 9.260465048979179e-08,\n",
       " 9.22971741112124e-08,\n",
       " 9.207848770291797e-08,\n",
       " 9.191735034943786e-08,\n",
       " 9.179516927921668e-08)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def Wave_Length(n1,n2):\n",
    "    h=(R*(1/(n1)**2-1/(n2)**2))**-1\n",
    "    return h\n",
    "R=1.097*10**7\n",
    "Wave_Length(1,2),Wave_Length(1,3),Wave_Length(1,4),Wave_Length(1,5),Wave_Length(1,6),Wave_Length(1,7),Wave_Length(1,8),Wave_Length(1,9),Wave_Length(1,10),Wave_Length(1,11),Wave_Length(1,12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.528, 2200000.0, -13.6, 8227500.0, 1.215436037678517e-07)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class Hydrogen_atom():\n",
    "    def __init__(self,value_k,value_k1,value_k2,value_R, info,info1):\n",
    "        self.k=value_k\n",
    "        self.k1=value_k1\n",
    "        self.k2=value_k2\n",
    "        self.R=value_R\n",
    "        self.info=info\n",
    "        self.info1=info1\n",
    "    def Hydrogen_radius(self,n):\n",
    "        rn=self.k*n**2\n",
    "        #print(self.info)\n",
    "        return rn\n",
    "    def velocity_of_electron(self,n):\n",
    "        vn=self.k1/n\n",
    "        #print(self.info1)\n",
    "        return vn\n",
    "    def Total_Energy(self, n):\n",
    "        En=-self.k2/n**2\n",
    "        return En\n",
    "    def Wave_Number(self,n1,n2):\n",
    "        f=self.R*(1/(n1)**2-1/(n2)**2)\n",
    "        return f\n",
    "    def Wave_Length(self, n1,n2):\n",
    "        L=(self.R*(1/(n1)**2-1/(n2)**2))**-1\n",
    "        return L\n",
    "H=Hydrogen_atom(value_k=0.528,value_k1=2.2*10**6,value_k2=13.6,value_R=1.097*10**7,info='Wow', info1='Jaw Dropping!!')\n",
    "H.Hydrogen_radius(1),H.velocity_of_electron(1),H.Total_Energy(1),H.Wave_Number(1,2), H.Wave_Length(1,2)    \n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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1776.192, 1837.968, 1900.8000000000002, 1964.688, 2029.632, 2095.632, 2162.688, 2230.8, 2299.9680000000003, 2370.192, 2441.472, 2513.808] [2200000.0, 1100000.0, 733333.3333333334, 550000.0, 440000.0, 366666.6666666667, 314285.71428571426, 275000.0, 244444.44444444444, 220000.0, 200000.0, 183333.33333333334, 169230.76923076922, 157142.85714285713, 146666.66666666666, 137500.0, 129411.76470588235, 122222.22222222222, 115789.47368421052, 110000.0, 104761.90476190476, 100000.0, 95652.17391304347, 91666.66666666667, 88000.0, 84615.38461538461, 81481.48148148147, 78571.42857142857, 75862.06896551725, 73333.33333333333, 70967.74193548386, 68750.0, 66666.66666666667, 64705.882352941175, 62857.142857142855, 61111.11111111111, 59459.45945945946, 57894.73684210526, 56410.256410256414, 55000.0, 53658.53658536585, 52380.95238095238, 51162.79069767442, 50000.0, 48888.88888888889, 47826.086956521736, 46808.51063829787, 45833.333333333336, 44897.95918367347, 44000.0, 43137.25490196078, 42307.692307692305, 41509.43396226415, 40740.74074074074, 40000.0, 39285.71428571428, 38596.491228070176, 37931.03448275862, 37288.13559322034, 36666.666666666664, 36065.57377049181, 35483.87096774193, 34920.63492063492, 34375.0, 33846.153846153844, 33333.333333333336, 32835.82089552239, 32352.941176470587, 31884.057971014492] [-13.6, -3.4, -1.511111111111111, -0.85, -0.544, -0.37777777777777777, -0.27755102040816326, -0.2125, -0.16790123456790124, -0.136, -0.11239669421487603, -0.09444444444444444, -0.08047337278106509, -0.06938775510204082, -0.06044444444444444, -0.053125, -0.047058823529411764, -0.04197530864197531, -0.037673130193905814, -0.034, -0.030839002267573697, -0.028099173553719006, -0.025708884688090735, -0.02361111111111111, -0.021759999999999998, -0.020118343195266272, -0.018655692729766804, -0.017346938775510204, -0.016171224732461353, -0.01511111111111111, -0.014151925078043704, -0.01328125, -0.01248852157943067, -0.011764705882352941, -0.01110204081632653, 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68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356] [0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 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     ]
    }
   ],
   "source": [
    "N=[]\n",
    "R=[]\n",
    "V=[]\n",
    "E=[]\n",
    "F=[]\n",
    "G=[]\n",
    "for n in range(1,70):\n",
    "    rr=H.Hydrogen_radius(n)\n",
    "    vv=H.velocity_of_electron(n)\n",
    "    ee=H.Total_Energy(n)\n",
    "    for n1 in range(1,70):\n",
    "        for n2 in range(1,70):\n",
    "            if n1<n2:\n",
    "                ff=H.Wave_Number(n1,n2)\n",
    "                gg=H.Wave_Length(n1,n2)\n",
    "    N.append(n)\n",
    "    R.append(rr)\n",
    "    V.append(vv)\n",
    "    E.append(ee)\n",
    "    F.append(ff)\n",
    "    G.append(gg)\n",
    "print(N,R,V,E, F,G)      \n",
    "           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'PrincipalQuantumNumber': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69], 'radius': [0.528, 2.112, 4.752000000000001, 8.448, 13.200000000000001, 19.008000000000003, 25.872, 33.792, 42.768, 52.800000000000004, 63.888000000000005, 76.03200000000001, 89.232, 103.488, 118.80000000000001, 135.168, 152.592, 171.072, 190.608, 211.20000000000002, 232.848, 255.55200000000002, 279.312, 304.12800000000004, 330.0, 356.928, 384.91200000000003, 413.952, 444.048, 475.20000000000005, 507.408, 540.672, 574.9920000000001, 610.368, 646.8000000000001, 684.288, 722.832, 762.432, 803.0880000000001, 844.8000000000001, 887.5680000000001, 931.392, 976.272, 1022.2080000000001, 1069.2, 1117.248, 1166.352, 1216.5120000000002, 1267.728, 1320.0, 1373.328, 1427.712, 1483.152, 1539.6480000000001, 1597.2, 1655.808, 1715.472, 1776.192, 1837.968, 1900.8000000000002, 1964.688, 2029.632, 2095.632, 2162.688, 2230.8, 2299.9680000000003, 2370.192, 2441.472, 2513.808], 'Velocity': [2200000.0, 1100000.0, 733333.3333333334, 550000.0, 440000.0, 366666.6666666667, 314285.71428571426, 275000.0, 244444.44444444444, 220000.0, 200000.0, 183333.33333333334, 169230.76923076922, 157142.85714285713, 146666.66666666666, 137500.0, 129411.76470588235, 122222.22222222222, 115789.47368421052, 110000.0, 104761.90476190476, 100000.0, 95652.17391304347, 91666.66666666667, 88000.0, 84615.38461538461, 81481.48148148147, 78571.42857142857, 75862.06896551725, 73333.33333333333, 70967.74193548386, 68750.0, 66666.66666666667, 64705.882352941175, 62857.142857142855, 61111.11111111111, 59459.45945945946, 57894.73684210526, 56410.256410256414, 55000.0, 53658.53658536585, 52380.95238095238, 51162.79069767442, 50000.0, 48888.88888888889, 47826.086956521736, 46808.51063829787, 45833.333333333336, 44897.95918367347, 44000.0, 43137.25490196078, 42307.692307692305, 41509.43396226415, 40740.74074074074, 40000.0, 39285.71428571428, 38596.491228070176, 37931.03448275862, 37288.13559322034, 36666.666666666664, 36065.57377049181, 35483.87096774193, 34920.63492063492, 34375.0, 33846.153846153844, 33333.333333333336, 32835.82089552239, 32352.941176470587, 31884.057971014492], 'Energy': [-13.6, -3.4, -1.511111111111111, -0.85, -0.544, -0.37777777777777777, -0.27755102040816326, -0.2125, -0.16790123456790124, -0.136, -0.11239669421487603, -0.09444444444444444, -0.08047337278106509, -0.06938775510204082, -0.06044444444444444, -0.053125, -0.047058823529411764, -0.04197530864197531, -0.037673130193905814, -0.034, -0.030839002267573697, -0.028099173553719006, -0.025708884688090735, -0.02361111111111111, -0.021759999999999998, -0.020118343195266272, -0.018655692729766804, -0.017346938775510204, -0.016171224732461353, -0.01511111111111111, -0.014151925078043704, -0.01328125, -0.01248852157943067, -0.011764705882352941, -0.01110204081632653, -0.010493827160493827, -0.00993425858290723, -0.009418282548476454, -0.008941485864562787, -0.0085, -0.00809042236763831, -0.007709750566893424, -0.007355327203893996, -0.007024793388429752, -0.006716049382716049, -0.006427221172022684, -0.006156631960162969, -0.005902777777777778, -0.005664306538942107, -0.0054399999999999995, -0.00522875816993464, -0.005029585798816568, -0.004841580633677465, -0.004663923182441701, -0.0044958677685950415, -0.004336734693877551, -0.004185903354878424, -0.004042806183115338, -0.003906923297902902, -0.0037777777777777775, -0.003654931470034937, -0.003537981269510926, -0.0034265558075081883, -0.0033203125, -0.0032189349112426036, -0.0031221303948576677, -0.0030296279795054577, -0.0029411764705882353, -0.002856542743121193], 'WaveNumber': [68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356, 68.26705811128356], 'wavelength': [0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208, 0.014648353505579208]}\n"
     ]
    }
   ],
   "source": [
    "# construction of a dictionary\n",
    "data={}\n",
    "data.update({\"PrincipalQuantumNumber\":N, \"radius\":R, \"Velocity\":V, \"Energy\":E, \"WaveNumber\":F, \"wavelength\":G})\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "DF=pd.DataFrame(data)\n",
    "#print(DF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>PrincipalQuantumNumber</th>\n",
       "      <th>radius</th>\n",
       "      <th>Velocity</th>\n",
       "      <th>Energy</th>\n",
       "      <th>WaveNumber</th>\n",
       "      <th>wavelength</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.528</td>\n",
       "      <td>2.200000e+06</td>\n",
       "      <td>-13.600000</td>\n",
       "      <td>68.267058</td>\n",
       "      <td>0.014648</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2.112</td>\n",
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       "      <td>3</td>\n",
       "      <td>4.752</td>\n",
       "      <td>7.333333e+05</td>\n",
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       "      <td>68.267058</td>\n",
       "      <td>0.014648</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>8.448</td>\n",
       "      <td>5.500000e+05</td>\n",
       "      <td>-0.850000</td>\n",
       "      <td>68.267058</td>\n",
       "      <td>0.014648</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>13.200</td>\n",
       "      <td>4.400000e+05</td>\n",
       "      <td>-0.544000</td>\n",
       "      <td>68.267058</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PrincipalQuantumNumber  radius      Velocity     Energy  WaveNumber  \\\n",
       "0                       1   0.528  2.200000e+06 -13.600000   68.267058   \n",
       "1                       2   2.112  1.100000e+06  -3.400000   68.267058   \n",
       "2                       3   4.752  7.333333e+05  -1.511111   68.267058   \n",
       "3                       4   8.448  5.500000e+05  -0.850000   68.267058   \n",
       "4                       5  13.200  4.400000e+05  -0.544000   68.267058   \n",
       "\n",
       "   wavelength  \n",
       "0    0.014648  \n",
       "1    0.014648  \n",
       "2    0.014648  \n",
       "3    0.014648  \n",
       "4    0.014648  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF.to_csv(\"Hydrogen_atom.csv\")\n",
    "DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>PrincipalQuantumNumber</th>\n",
       "      <th>radius</th>\n",
       "      <th>Velocity</th>\n",
       "      <th>Energy</th>\n",
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       "      <td>2.200000e+06</td>\n",
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       "      <td>68.267058</td>\n",
       "      <td>0.014648</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.112</td>\n",
       "      <td>1.100000e+06</td>\n",
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       "      <td>68.267058</td>\n",
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       "      <th>2</th>\n",
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       "      <td>68.267058</td>\n",
       "      <td>0.014648</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>8.448</td>\n",
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       "      <td>68.267058</td>\n",
       "      <td>0.014648</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>13.200</td>\n",
       "      <td>4.400000e+05</td>\n",
       "      <td>-0.544000</td>\n",
       "      <td>68.267058</td>\n",
       "      <td>0.014648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>64</td>\n",
       "      <td>65</td>\n",
       "      <td>2230.800</td>\n",
       "      <td>3.384615e+04</td>\n",
       "      <td>-0.003219</td>\n",
       "      <td>68.267058</td>\n",
       "      <td>0.014648</td>\n",
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       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>65</td>\n",
       "      <td>66</td>\n",
       "      <td>2299.968</td>\n",
       "      <td>3.333333e+04</td>\n",
       "      <td>-0.003122</td>\n",
       "      <td>68.267058</td>\n",
       "      <td>0.014648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>66</td>\n",
       "      <td>67</td>\n",
       "      <td>2370.192</td>\n",
       "      <td>3.283582e+04</td>\n",
       "      <td>-0.003030</td>\n",
       "      <td>68.267058</td>\n",
       "      <td>0.014648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>67</td>\n",
       "      <td>68</td>\n",
       "      <td>2441.472</td>\n",
       "      <td>3.235294e+04</td>\n",
       "      <td>-0.002941</td>\n",
       "      <td>68.267058</td>\n",
       "      <td>0.014648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>68</td>\n",
       "      <td>69</td>\n",
       "      <td>2513.808</td>\n",
       "      <td>3.188406e+04</td>\n",
       "      <td>-0.002857</td>\n",
       "      <td>68.267058</td>\n",
       "      <td>0.014648</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  PrincipalQuantumNumber    radius      Velocity     Energy  \\\n",
       "0            0                       1     0.528  2.200000e+06 -13.600000   \n",
       "1            1                       2     2.112  1.100000e+06  -3.400000   \n",
       "2            2                       3     4.752  7.333333e+05  -1.511111   \n",
       "3            3                       4     8.448  5.500000e+05  -0.850000   \n",
       "4            4                       5    13.200  4.400000e+05  -0.544000   \n",
       "..         ...                     ...       ...           ...        ...   \n",
       "64          64                      65  2230.800  3.384615e+04  -0.003219   \n",
       "65          65                      66  2299.968  3.333333e+04  -0.003122   \n",
       "66          66                      67  2370.192  3.283582e+04  -0.003030   \n",
       "67          67                      68  2441.472  3.235294e+04  -0.002941   \n",
       "68          68                      69  2513.808  3.188406e+04  -0.002857   \n",
       "\n",
       "    WaveNumber  wavelength  \n",
       "0    68.267058    0.014648  \n",
       "1    68.267058    0.014648  \n",
       "2    68.267058    0.014648  \n",
       "3    68.267058    0.014648  \n",
       "4    68.267058    0.014648  \n",
       "..         ...         ...  \n",
       "64   68.267058    0.014648  \n",
       "65   68.267058    0.014648  \n",
       "66   68.267058    0.014648  \n",
       "67   68.267058    0.014648  \n",
       "68   68.267058    0.014648  \n",
       "\n",
       "[69 rows x 7 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"Hydrogen_atom.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "import random as random\n",
    "import json as json\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df.PrincipalQuantumNumber,df.radius,label='Radius of Orbit', color='r')\n",
    "plt.plot(df.PrincipalQuantumNumber,df.Velocity,label='Velocity of Electron', color='b')\n",
    "plt.plot(df.PrincipalQuantumNumber,-df.Energy,label='Total Energy', color='yellow')\n",
    "plt.plot(df.PrincipalQuantumNumber,df.WaveNumber,label='Wave Number', color='magenta')\n",
    "plt.plot(df.PrincipalQuantumNumber,df.wavelength,label='Wave Length', color='cyan')\n",
    "plt.title('Variation of H-functions with n')\n",
    "plt.xlabel('Principal Quantum Number')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.semilogy(df.PrincipalQuantumNumber,df.radius,label='Radius of Orbit', color='r')\n",
    "plt.semilogy(df.PrincipalQuantumNumber,df.Velocity,label='Velocity of Electron', color='violet')\n",
    "plt.semilogy(df.PrincipalQuantumNumber,-df.Energy,label='Total Energy', color='yellow')\n",
    "plt.semilogy(df.PrincipalQuantumNumber,df.WaveNumber,label='Wave Number', color='green')\n",
    "plt.semilogy(df.PrincipalQuantumNumber,df.wavelength,label='Wave Length', color='cyan')\n",
    "plt.xlabel('Principal Quantum Number')\n",
    "plt.title('Variation of H-functions with n')\n",
    "plt.grid(True)\n",
    "#plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1296x576 with 0 Axes>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1296x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.loglog(df.PrincipalQuantumNumber,df.radius,label='Radius of Orbit', color='r')\n",
    "plt.loglog(df.PrincipalQuantumNumber,df.Velocity,label='Velocity of Electron', color='violet')\n",
    "plt.loglog(df.PrincipalQuantumNumber,-df.Energy,label='Total Energy', color='g')\n",
    "plt.semilogy(df.PrincipalQuantumNumber,df.WaveNumber,label='Wave Number', color='magenta')\n",
    "plt.semilogy(df.PrincipalQuantumNumber,df.wavelength,label='Wave Length',color='cyan')\n",
    "plt.title('Variation of H-functions with n')\n",
    "plt.xlabel('Principal Quantum Number')\n",
    "plt.grid(True)\n",
    "plt.figure(figsize=[18,8])\n",
    "#plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hydrogen Wave functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Higher(quantum interpretation) level reference: https://docs.sympy.org/latest/modules/physics/index.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## what is JSON?\n",
    "\n",
    "#### JavaScript Object Notation (JSON) is a standard text-based format for representing structured data based on JavaScript object syntax. It is commonly used for transmitting data in web applications (e.g., sending some data from the server to the client, so it can be displayed on a web page, or vice versa).\n",
    "JSON is a text-based data format following JavaScript object syntax, which was popularized by Douglas Crockford. Even though it closely resembles JavaScript object literal syntax, it can be used independently from JavaScript, and many programming environments feature the ability to read (parse) and generate JSON.\n",
    "\n",
    "JSON exists as a string — useful when you want to transmit data across a network. It needs to be converted to a native JavaScript object when you want to access the data. This is not a big issue —  JavaScript provides a global JSON object that has methods available for converting between the two.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is seaborn?\n",
    "Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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