Harmonic_Oscillator_with_Noise.ipynb

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    "colab": {
      "name": "Harmonic Oscillator with Noise.ipynb",
      "provenance": [],
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      "name": "python3",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Sistem Osilator Harmonik dengan Gangguan"
      ],
      "metadata": {
        "id": "9xSNFJ4M7Grh"
      }
    },
    {
      "cell_type": "code",
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        "id": "Sc8Ebz7J5jww"
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      "source": [
        "#Import necessary library case uts\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython.display import display, Math"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def potential(xmin, xmax, noise, xpow):\n",
        "  '''\n",
        "  Set harmonic oscillator potential within the system\n",
        "  Input   : x interval, noise, x power\n",
        "  Return  : potential\n",
        "  '''\n",
        "\n",
        "  #Harmonic Oscillator Potential V = 1/2*k*x**2 + lambda*x**xpow for k = 1 and xpow = [1, 2, 3, 4]\n",
        "  potential_v = (1/2 * xmin * xmin) + (noise*xmin**xpow)\n",
        "\n",
        "  return potential_v"
      ],
      "metadata": {
        "id": "3LPAljGs5zcJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def normalize(n, h, psi):\n",
        "  '''\n",
        "  Normalize wave function\n",
        "  Input   : initial wave function, number of points, step\n",
        "  Return  : normalized wave function\n",
        "  '''\n",
        "  norm = psi[0]**2+psi[n]**2\n",
        "  for i in range(1,n-3,2):\n",
        "    norm = norm+4*psi[i]**2+2*psi[i+1]**2\n",
        "\n",
        "  norm = norm + 4*psi[n-1]**2\n",
        "  norm = 1/np.sqrt((norm*h/3))\n",
        "\n",
        "  for i in range(0, len(psi)):\n",
        "   psi[i] = psi[i]*norm\n",
        "\n",
        "  return psi"
      ],
      "metadata": {
        "id": "ck3lRMKk6PoJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def numerov(nx, x, e_init, psi, noise, xpow):\n",
        "  '''\n",
        "  Integrates the wave function using Numerov's method\n",
        "  Input   : number of points, x inteval, initial energy, wave function\n",
        "  Return  : integrated wave function\n",
        "  '''\n",
        "\n",
        "  #Calculates the steps\n",
        "  h = xmax/nx\n",
        "  h2 = h**2\n",
        "  h12 = h2/12\n",
        "\n",
        "  #Initialize wave function\n",
        "  psi[0] = 0\n",
        "  psi[1] = 0.0001\n",
        "\n",
        "  #Calculates using Numerov's nethod\n",
        "  fn = 2*(potential(-x, x, noise, xpow)-e_init)\n",
        "  q0 = psi[0]*(1-h12*fn)\n",
        "  fn = 2*(potential(-x+h, x, noise, xpow)-e_init)\n",
        "  q1 = psi[1]*(1-h12*fn)\n",
        "  \n",
        "  for n in range(2, 2*nx+1):\n",
        "    q2 = h2*fn*psi[n-1]+2*q1-q0\n",
        "    fn = 2*(potential(n*h-x, x, noise, xpow)-e_init)\n",
        "    psi[n] = q2/(1-h12*fn)\n",
        "    q0 = q1\n",
        "    q1 = q2\n",
        "\n",
        "  psi = normalize(2*nx, h, psi)\n",
        "\n",
        "  return psi"
      ],
      "metadata": {
        "id": "7AT8_n4h7Jx4"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "def calculates(nx, x, e_init, de, eps, psi, noise, xpow):\n",
        "  '''\n",
        "  Starting course search and approximate energy level using bisection method\n",
        "  Input   : number of points, x inteval, initial energy,\n",
        "            eps, lists to store energy and deviation\n",
        "  Return  : Energy approximation, wave function\n",
        "  '''\n",
        "  #Starting course search\n",
        "  psi = numerov(nx, x, e_init, psi, noise, xpow)\n",
        "  dev1 = psi[2*nx]\n",
        "\n",
        "  while True:\n",
        "    #Update energy with de step\n",
        "    e2 = e_init + de\n",
        " \n",
        "    psi = numerov(nx, x, e2, psi, noise, xpow)\n",
        "    dev2 = psi[2*nx]\n",
        "\n",
        "    #Check if deviation is zero\n",
        "    if dev1*dev2 < 0:\n",
        "      break\n",
        "\n",
        "    #Update energy and deviation value for next iteration  \n",
        "    e_init = e2\n",
        "    dev1 = dev2\n",
        "\n",
        "  #Starting bisection\n",
        "  while abs(e_init-e2) > eps:\n",
        "    e3 = (e_init+e2)/2\n",
        "    psi = numerov(nx,x,e3,psi, noise, xpow)\n",
        "    dev3 = psi[2*nx]\n",
        "\n",
        "    if dev3*dev1 <= 0.000000 :\n",
        "      e2 = e3\n",
        "      dev2 = dev3\n",
        "    else:\n",
        "      e_init = e3\n",
        "      dev2 = dev3\n",
        "\n",
        "  return e3, psi"
      ],
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      "cell_type": "code",
      "source": [
        "def schrod(nx, xmax, e0, de, filename, n, noise, xpow):\n",
        "  '''\n",
        "  Function to calculates energy and wave function\n",
        "  Input   : number of points, x interval, initial energy, delta e, filename, energy level\n",
        "  Return  : approximated energy, psi .date file\n",
        "  '''\n",
        "  #Initialize Parameters\n",
        "  eps = 10e-6\n",
        "  dx = xmax/nx\n",
        "\n",
        "  #Initialize wave function\n",
        "  psi = np.zeros(2*nx+1)\n",
        "\n",
        "  #Calculates energy and deviation\n",
        "  energy, psi = calculates(nx, xmax, e0, de, eps, psi, noise, xpow)\n",
        "\n",
        "  #Save output as dat file\n",
        "  filename = filename + str(n) + '.dat'\n",
        "  x = np.zeros(2*nx+1)\n",
        "  for i in range(1, 2*nx+2):\n",
        "    x[i-1] = (i-1)*dx-xmax\n",
        "    \n",
        "  with open(filename, 'w') as out_file:\n",
        "    for item in range(len(psi)):\n",
        "      # out_file.write(str(x[item]) + '      ' + (psi[item]) + '\\n')\n",
        "      out_file.write(\"{}      {}      \\n\".format(x[item], psi[item]))\n",
        "\n",
        "  return energy"
      ],
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      "execution_count": null,
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    {
      "cell_type": "code",
      "source": [
        "def analytics(e_init, noise, n, xpow):\n",
        "  '''\n",
        "  Calculates analytics value\n",
        "  Input   : energy, noise, energy level, x power\n",
        "  Return  : final energy\n",
        "  '''\n",
        "\n",
        "  energy_add = 0\n",
        "  m = 1\n",
        "  omega = 1\n",
        "  hbar = 1\n",
        "  #Check x power\n",
        "  if xpow == 0:\n",
        "    energy_add = noise\n",
        "  elif xpow == 1:\n",
        "    energy_add = -(noise**2)/(2*m*omega**2)\n",
        "  elif xpow == 2:\n",
        "    energy_add = e_init*(noise/4 - (noise**2)/32)\n",
        "  elif xpow == 3:\n",
        "    energy_add = (-(15*noise**2 * hbar**2)/(4*m**3 * omega**4))*(n**2 - n + (11/30))\n",
        "  elif xpow == 4:\n",
        "    energy_add = ((3*noise*hbar**2)/(4*m**2 * omega**2))*(2*n**2 + 2*n + 1)\n",
        "  else:\n",
        "    energy_add = 0\n",
        "\n",
        "  final_energy = e_init + energy_add\n",
        "\n",
        "  return final_energy\n"
      ],
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        "id": "3FQGp7Q5-7D-"
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      "execution_count": null,
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    {
      "cell_type": "code",
      "source": [
        "#Initialize Parameters\n",
        "nx = 100\n",
        "xmax = 5 #L = 10\n",
        "de = 0.001\n",
        "energy_level = [i for i in range(0, 11)]\n",
        "xpower = [i for i in range(0, 5)]\n",
        "noise = 0.01\n",
        "columns = ['Energy Level', 'Energy (a.u.)', 'Analytics', 'Error']\n",
        "\n",
        "#Create dataframe to store output\n",
        "output0 = pd.DataFrame(columns=columns)\n",
        "output1 = pd.DataFrame(columns=columns)\n",
        "output2 = pd.DataFrame(columns=columns)\n",
        "output3 = pd.DataFrame(columns=columns)\n",
        "output4 = pd.DataFrame(columns=columns)\n",
        "\n",
        "for n in xpower:\n",
        "  e0 = 0\n",
        "  energy_value = []\n",
        "  analytic_value = []\n",
        "  filename = 'psi-out'+str(n)\n",
        "\n",
        "  for i in energy_level:\n",
        "    energy = schrod(nx, xmax, e0, de, filename, i, noise, n)\n",
        "    energy_value.append(energy)\n",
        "\n",
        "    #Update for the next energy level\n",
        "    e0 = energy + 0.005\n",
        "\n",
        "  for j in energy_level:\n",
        "    e_init = j + 0.5\n",
        "    energy_ana = analytics(e_init, noise, j, n)\n",
        "    analytic_value.append(energy_ana)\n",
        "\n",
        "  if n == 0:\n",
        "    for i in range(len(energy_level)):\n",
        "      output0.loc[i] = [energy_level[i], energy_value[i], analytic_value[i], abs(analytic_value[i]-energy_value[i])]\n",
        "  \n",
        "  if n == 1:\n",
        "    for i in range(len(energy_level)):\n",
        "      output1.loc[i] = [energy_level[i], energy_value[i], analytic_value[i], abs(analytic_value[i]-energy_value[i])]\n",
        "\n",
        "  if n == 2:\n",
        "    for i in range(len(energy_level)):\n",
        "      output2.loc[i] = [energy_level[i], energy_value[i], analytic_value[i], abs(analytic_value[i]-energy_value[i])]\n",
        "\n",
        "  if n == 3:\n",
        "    for i in range(len(energy_level)):\n",
        "      output3.loc[i] = [energy_level[i], energy_value[i], analytic_value[i], abs(analytic_value[i]-energy_value[i])]\n",
        "\n",
        "  if n == 4:\n",
        "    for i in range(len(energy_level)):\n",
        "      output4.loc[i] = [energy_level[i], energy_value[i], analytic_value[i], abs(analytic_value[i]-energy_value[i])]"
      ],
      "metadata": {
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      "outputs": []
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    {
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      "source": [
        "display(Math('V_g = \\lambda'))\n",
        "output0"
      ],
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Math object>"
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            "text/latex": "$$V_g = \\lambda$$"
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          "data": {
            "text/plain": [
              "    Energy Level  Energy (a.u.)  Analytics         Error\n",
              "0            0.0       0.509992       0.51  7.812500e-06\n",
              "1            1.0       1.510000       1.51  5.528911e-14\n",
              "2            2.0       2.509992       2.51  7.812500e-06\n",
              "3            3.0       3.510000       3.51  2.744471e-13\n",
              "4            4.0       4.510008       4.51  7.812500e-06\n",
              "5            5.0       5.510094       5.51  9.375000e-05\n",
              "6            6.0       6.510586       6.51  5.859375e-04\n",
              "7            7.0       7.512906       7.51  2.906250e-03\n",
              "8            8.0       8.521445       8.51  1.144531e-02\n",
              "9            9.0       9.546547       9.51  3.654688e-02\n",
              "10          10.0      10.606148      10.51  9.614844e-02"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Energy Level</th>\n",
              "      <th>Energy (a.u.)</th>\n",
              "      <th>Analytics</th>\n",
              "      <th>Error</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.509992</td>\n",
              "      <td>0.51</td>\n",
              "      <td>7.812500e-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.510000</td>\n",
              "      <td>1.51</td>\n",
              "      <td>5.528911e-14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.0</td>\n",
              "      <td>2.509992</td>\n",
              "      <td>2.51</td>\n",
              "      <td>7.812500e-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.0</td>\n",
              "      <td>3.510000</td>\n",
              "      <td>3.51</td>\n",
              "      <td>2.744471e-13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.0</td>\n",
              "      <td>4.510008</td>\n",
              "      <td>4.51</td>\n",
              "      <td>7.812500e-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5.0</td>\n",
              "      <td>5.510094</td>\n",
              "      <td>5.51</td>\n",
              "      <td>9.375000e-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6.0</td>\n",
              "      <td>6.510586</td>\n",
              "      <td>6.51</td>\n",
              "      <td>5.859375e-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7.0</td>\n",
              "      <td>7.512906</td>\n",
              "      <td>7.51</td>\n",
              "      <td>2.906250e-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8.0</td>\n",
              "      <td>8.521445</td>\n",
              "      <td>8.51</td>\n",
              "      <td>1.144531e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9.0</td>\n",
              "      <td>9.546547</td>\n",
              "      <td>9.51</td>\n",
              "      <td>3.654688e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>10.0</td>\n",
              "      <td>10.606148</td>\n",
              "      <td>10.51</td>\n",
              "      <td>9.614844e-02</td>\n",
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      "source": [
        "display(Math('V_g = \\lambda x'))\n",
        "output1"
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        "outputId": "c61319ff-870f-4193-976b-cbdcda4fa357"
      },
      "execution_count": null,
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        {
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            "text/plain": [
              "<IPython.core.display.Math object>"
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            "text/latex": "$$V_g = \\lambda x$$"
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              "    Energy Level  Energy (a.u.)  Analytics     Error\n",
              "0            0.0       0.499945    0.49995  0.000005\n",
              "1            1.0       1.499953    1.49995  0.000003\n",
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              "5            5.0       5.500047    5.49995  0.000097\n",
              "6            6.0       6.500539    6.49995  0.000589\n",
              "7            7.0       7.502875    7.49995  0.002925\n",
              "8            8.0       8.511414    8.49995  0.011464\n",
              "9            9.0       9.536531    9.49995  0.036581\n",
              "10          10.0      10.596148   10.49995  0.096198"
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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    {
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        "display(Math('V_g = \\lambda x^2'))\n",
        "output2"
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          "height": 416
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        "outputId": "0b234a43-74fd-48d3-f9fe-6db48ee7ad5d"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Math object>"
            ],
            "text/latex": "$$V_g = \\lambda x^2$$"
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              "    Energy Level  Energy (a.u.)  Analytics     Error\n",
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              "          const element = document.querySelector('#df-1fe87370-182e-499e-8057-4859d10e5609');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
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        "output3"
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        "id": "jcP_BkwvFl2g",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Math object>"
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            "text/latex": "$$V_g = \\lambda x^3$$"
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Energy Level  Energy (a.u.)  Analytics     Error\n",
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              "1            1.0       1.499109   1.499863  0.000753\n",
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              "9            9.0       9.519297   9.472862  0.046434\n",
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              "    .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>Energy Level</th>\n",
              "      <th>Energy (a.u.)</th>\n",
              "      <th>Analytics</th>\n",
              "      <th>Error</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.499867</td>\n",
              "      <td>0.499862</td>\n",
              "      <td>0.000005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.499109</td>\n",
              "      <td>1.499863</td>\n",
              "      <td>0.000753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.0</td>\n",
              "      <td>2.497602</td>\n",
              "      <td>2.499112</td>\n",
              "      <td>0.001511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.0</td>\n",
              "      <td>3.495344</td>\n",
              "      <td>3.497612</td>\n",
              "      <td>0.002269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.0</td>\n",
              "      <td>4.492336</td>\n",
              "      <td>4.495362</td>\n",
              "      <td>0.003027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5.0</td>\n",
              "      <td>5.488703</td>\n",
              "      <td>5.492362</td>\n",
              "      <td>0.003659</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6.0</td>\n",
              "      <td>6.485039</td>\n",
              "      <td>6.488613</td>\n",
              "      <td>0.003573</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7.0</td>\n",
              "      <td>7.483641</td>\n",
              "      <td>7.484113</td>\n",
              "      <td>0.000472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8.0</td>\n",
              "      <td>8.490898</td>\n",
              "      <td>8.478862</td>\n",
              "      <td>0.012036</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9.0</td>\n",
              "      <td>9.519297</td>\n",
              "      <td>9.472862</td>\n",
              "      <td>0.046434</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>10.0</td>\n",
              "      <td>10.586523</td>\n",
              "      <td>10.466112</td>\n",
              "      <td>0.120411</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d5db0caf-a1f9-48ba-bfbe-4692a2f36a51')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d5db0caf-a1f9-48ba-bfbe-4692a2f36a51 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d5db0caf-a1f9-48ba-bfbe-4692a2f36a51');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "display(Math('V_g = \\lambda x^4'))\n",
        "output4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "id": "MkNezmQTFltX",
        "outputId": "f4c0edd3-ed1d-47d3-fd40-a8c64d163b91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Math object>"
            ],
            "text/latex": "$$V_g = \\lambda x^4$$"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Energy Level  Energy (a.u.)  Analytics     Error\n",
              "0            0.0       0.507258     0.5075  0.000242\n",
              "1            1.0       1.535641     1.5375  0.001859\n",
              "2            2.0       2.590852     2.5975  0.006648\n",
              "3            3.0       3.671094     3.6875  0.016406\n",
              "4            4.0       4.774914     4.8075  0.032586\n",
              "5            5.0       5.901016     5.9575  0.056484\n",
              "6            6.0       7.048336     7.1375  0.089164\n",
              "7            7.0       8.215938     8.3475  0.131562\n",
              "8            8.0       9.403227     9.5875  0.184273\n",
              "9            9.0      10.610266    10.8575  0.247234\n",
              "10          10.0      11.838727    12.1575  0.318773"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b871b786-6605-4056-a573-aa53a4b5c23c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Energy Level</th>\n",
              "      <th>Energy (a.u.)</th>\n",
              "      <th>Analytics</th>\n",
              "      <th>Error</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.507258</td>\n",
              "      <td>0.5075</td>\n",
              "      <td>0.000242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.535641</td>\n",
              "      <td>1.5375</td>\n",
              "      <td>0.001859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.0</td>\n",
              "      <td>2.590852</td>\n",
              "      <td>2.5975</td>\n",
              "      <td>0.006648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.0</td>\n",
              "      <td>3.671094</td>\n",
              "      <td>3.6875</td>\n",
              "      <td>0.016406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.0</td>\n",
              "      <td>4.774914</td>\n",
              "      <td>4.8075</td>\n",
              "      <td>0.032586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5.0</td>\n",
              "      <td>5.901016</td>\n",
              "      <td>5.9575</td>\n",
              "      <td>0.056484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6.0</td>\n",
              "      <td>7.048336</td>\n",
              "      <td>7.1375</td>\n",
              "      <td>0.089164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7.0</td>\n",
              "      <td>8.215938</td>\n",
              "      <td>8.3475</td>\n",
              "      <td>0.131562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8.0</td>\n",
              "      <td>9.403227</td>\n",
              "      <td>9.5875</td>\n",
              "      <td>0.184273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9.0</td>\n",
              "      <td>10.610266</td>\n",
              "      <td>10.8575</td>\n",
              "      <td>0.247234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>10.0</td>\n",
              "      <td>11.838727</td>\n",
              "      <td>12.1575</td>\n",
              "      <td>0.318773</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
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              "\n",
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              "\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b871b786-6605-4056-a573-aa53a4b5c23c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b871b786-6605-4056-a573-aa53a4b5c23c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plot wave function\n",
        "\n",
        "filename = 'psi-out0'\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(8, 10))\n",
        "\n",
        "for n in range(0, 11):\n",
        "  data = np.loadtxt(filename+str(n)+'.dat', unpack=True)\n",
        "  x_pos = data[0]\n",
        "  psii = data[1]\n",
        "\n",
        "  plt.plot(x_pos, psii + (n+0.5), label='n='+str(n), linewidth=2)\n",
        "  plt.plot(x_pos, 1/2*x_pos*x_pos)\n",
        "\n",
        "plt.xlabel('Jarak (a.u)', fontsize=18)\n",
        "plt.ylabel('$\\Psi$', fontsize=18)\n",
        "plt.xticks(fontsize=18)\n",
        "plt.yticks(fontsize=18)\n",
        "plt.legend(bbox_to_anchor=(1.04,0), loc=\"lower left\", borderaxespad=0, fontsize=14)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Z_wLwucKcMSz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        },
        "outputId": "cc909601-e622-4f2a-8758-17da6864bad7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}