231 allinone.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from uncertainties import * \n",
    "from math import *\n",
    "from pylab import *\n",
    "from matplotlib import *\n",
    "import matplotlib.pyplot as pyplot\n",
    "import numpy as np\n",
    "from scipy.integrate import odeint\n",
    "from scipy.optimize import curve_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def diffract(theta:list,w,s,N,lmbda,scale):\n",
    "    k = 2*pi/lmbda;\n",
    "    v = 0.5*k*s*np.sin(theta);\n",
    "    u = 0.5*k*w*np.sin(theta);\n",
    "    y = w**2 * (((sin(u))/(u))*((sin(N*v))/(sin(v))))**2\n",
    "    y = y * scale\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  \"\"\"\n",
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  \"\"\"\n",
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  \"\"\"\n",
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'log(I)')"
      ]
     },
     "execution_count": 12,
     "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": [
    "fig, ax = pyplot.subplots()\n",
    "\n",
    "#All in code for other setup\n",
    "#Plot code ----------------------------------------------------------------\n",
    "PAI = pi;\n",
    "stheta = np.array([0.01467,0.01461]);\n",
    "lmbda = 650e-9;\n",
    "k = 2*pi/lmbda;\n",
    "w = 2*PAI/(k*stheta);\n",
    "meanw = mean(w)\n",
    "std = np.std(w)\n",
    "\n",
    "w = meanw;\n",
    "s = 999;\n",
    "N = 1;\n",
    "i = 0\n",
    "\n",
    "data = np.loadtxt('Red_1slit0.04.txt',delimiter=',', skiprows=10);\n",
    "#Plot code end ----------------------------------------------------------------\n",
    "resolution = 54.74 * 10**(-6)\n",
    "position = data[:,0]\n",
    "abspos = abs(position)\n",
    "\n",
    "intense = data[:,1]\n",
    "maxind = np.argmax(intense);\n",
    "pos = abspos - abspos[maxind]\n",
    "posInMeter = pos*resolution;\n",
    "theta = arctan(posInMeter)\n",
    "I = np.zeros(size(theta))\n",
    "scale = max(intense)/((N*w)**2)\n",
    "for angle in theta:\n",
    "    I[i] = diffract(angle,w,s,N,lmbda,scale);\n",
    "    i = i + 1;\n",
    "ax.plot(sin(theta),intense, label='s = 0.01467 exp')\n",
    "\n",
    "#Plot code ----------------------------------------------------------------\n",
    "#All in code for other setup for two or more slits\n",
    "PAI = pi;\n",
    "stheta = np.array([0.001916,0.002792]);\n",
    "sthetaw = np.array([0.0145]);\n",
    "#sthetaw  = np.array([0.06295,0.007663]);\n",
    "stheta = mean(stheta)\n",
    "sthetaw = mean(sthetaw)\n",
    "lmbda = 650e-9;\n",
    "N = 2;\n",
    "k = 2*pi/lmbda;\n",
    "s = 2*PAI/(N*k*stheta);\n",
    "w = 2*PAI/(k*sthetaw);\n",
    "meanw = mean(w)\n",
    "\n",
    "w = meanw;\n",
    "i = 0\n",
    "\n",
    "data = np.loadtxt('Red_2slits_d0.125_a=0.04.txt',delimiter=',', skiprows=10);\n",
    "#Plot code end----------------------------------------------------------------\n",
    "resolution = 54.74 * 10**(-6)\n",
    "position = data[:,0]\n",
    "abspos = abs(position)\n",
    "\n",
    "intense = data[:,1]\n",
    "maxind = np.argmax(intense);\n",
    "pos = abspos - abspos[maxind]\n",
    "posInMeter = pos*resolution;\n",
    "theta = arctan(posInMeter)\n",
    "I = np.zeros(size(theta))\n",
    "scale = max(intense)/((N*w)**2)\n",
    "for angle in theta:\n",
    "    I[i] = diffract(angle,w,s,N,lmbda,scale);\n",
    "    i = i + 1;\n",
    "ax.plot(sin(theta),I, label='s = 0.005857')\n",
    "ax.plot(sin(theta),intense, label='s = 0.005857 exp')\n",
    "\n",
    "#Plot code ----------------------------------------------------------------\n",
    "PAI = pi;\n",
    "#stheta = np.array([0.001259]);\n",
    "#sthetaw = np.array([0.016]);\n",
    "stheta = np.array([0.001149]);\n",
    "sthetaw = np.array([0.0145]);\n",
    "stheta = mean(stheta)\n",
    "sthetaw = mean(sthetaw)\n",
    "lmbda = 650e-9;\n",
    "N = 4;\n",
    "k = 2*pi/lmbda;\n",
    "s = 2*PAI/(N*k*stheta);\n",
    "w = 2*PAI/(k*sthetaw);\n",
    "\n",
    "i = 0\n",
    "\n",
    "data = np.loadtxt('Red_4slits_d0.125.txt',delimiter=',', skiprows=10);\n",
    "#Plot code end----------------------------------------------------------------\n",
    "resolution = 54.74 * 10**(-6)\n",
    "position = data[:,0]\n",
    "abspos = abs(position)\n",
    "\n",
    "intense = data[:,1]\n",
    "maxind = np.argmax(intense);\n",
    "pos = abspos - abspos[maxind]\n",
    "posInMeter = pos*resolution;\n",
    "theta = arctan(posInMeter)\n",
    "I = np.zeros(size(theta))\n",
    "scale = max(intense)/((N*w)**2)\n",
    "for angle in theta:\n",
    "    I[i] = diffract(angle,w,s,N,lmbda,scale);\n",
    "    i = i + 1;\n",
    "ax.plot(sin(theta),I, label='s = 0.003175')\n",
    "ax.plot(sin(theta),intense, label='s = 0.003175 exp')\n",
    "\n",
    "#Plot code ----------------------------------------------------------------\n",
    "PAI = pi;\n",
    "#stheta = np.array([0.00104,0.001095]);\n",
    "stheta = np.array(0.000927);\n",
    "sthetaw = np.array([0.018]);\n",
    "stheta = mean(stheta)\n",
    "sthetaw = mean(sthetaw)\n",
    "lmbda = 650e-9;\n",
    "N = 5;\n",
    "k = 2*pi/lmbda;\n",
    "s = 2*PAI/(N*k*stheta);\n",
    "w = 2*PAI/(k*sthetaw);\n",
    "\n",
    "i = 0\n",
    "\n",
    "data = np.loadtxt('Red_5slits_d0.125.txt',delimiter=',', skiprows=10);\n",
    "#Plot code end----------------------------------------------------------------\n",
    "resolution = 54.74 * 10**(-6)\n",
    "position = data[:,0]\n",
    "abspos = abs(position)\n",
    "\n",
    "intense = data[:,1]\n",
    "maxind = np.argmax(intense);\n",
    "pos = abspos - abspos[maxind]\n",
    "posInMeter = pos*resolution;\n",
    "theta = arctan(posInMeter)\n",
    "I = np.zeros(size(theta))\n",
    "scale = max(intense)/((N*w)**2)\n",
    "for angle in theta:\n",
    "    I[i] = diffract(angle,w,s,N,lmbda,scale);\n",
    "    i = i + 1;\n",
    "ax.plot(sin(theta),I, label='s = 0.003175')\n",
    "ax.plot(sin(theta),intense, label='s = 0.003175 exp')\n",
    "\n",
    "\n",
    "#END OF CODE----------------------------------------------------------------\n",
    "leg = ax.legend();\n",
    "pyplot.xlabel('sin(theta)')\n",
    "pyplot.ylabel('log(I)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.482758620689656e-05\n",
      "0.00011314186248912097\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  \"\"\"\n",
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  \"\"\"\n"
     ]
    },
    {
     "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": [
    "fig, ax = pyplot.subplots()\n",
    "\n",
    "#All in code for other setup\n",
    "#Plot code ----------------------------------------------------------------\n",
    "PAI = pi;\n",
    "pos = np.arange(-750,750,1)\n",
    "\n",
    "stheta = np.array([0.001149]);\n",
    "sthetaw = np.array([0.0145]);\n",
    "stheta = mean(stheta)\n",
    "sthetaw = mean(sthetaw)\n",
    "lmbda = 650e-9;\n",
    "N = 1;\n",
    "k = 2*pi/lmbda;\n",
    "s = 2*PAI/(N*k*stheta);\n",
    "w = 2*PAI/(k*sthetaw);\n",
    "\n",
    "i = 0\n",
    "\n",
    "#Plot code end ----------------------------------------------------------------\n",
    "resolution = 54.74 * 10**(-6)\n",
    "\n",
    "posInMeter = pos*resolution;\n",
    "theta = arctan(posInMeter)\n",
    "I = np.zeros(size(theta))\n",
    "scale = 1\n",
    "for angle in theta:\n",
    "    I[i] = diffract(angle,w,s,N,lmbda,scale);\n",
    "    i = i + 1;\n",
    "ax.plot(sin(theta),I, label='N = 1')\n",
    "maxI = max(I);\n",
    "#Plot code ----------------------------------------------------------------\n",
    "#All in code for other setup for two or more slits\n",
    "N = 2;\n",
    "i = 0\n",
    "s = 2*PAI/(N*k*stheta);\n",
    "w = 2*PAI/(k*sthetaw);\n",
    "#Plot code end----------------------------------------------------------------\n",
    "resolution = 54.74 * 10**(-6)\n",
    "\n",
    "I = np.zeros(size(theta))\n",
    "scale = maxI/(N*w)**2;\n",
    "for angle in theta:\n",
    "    I[i] = diffract(angle,w,s,N,lmbda,scale);\n",
    "    i = i + 1;\n",
    "#ax.plot(sin(theta),I, label='N = 2')\n",
    "\n",
    "#Plot code ----------------------------------------------------------------\n",
    "N = 4;\n",
    "i = 0\n",
    "s = 2*PAI/(N*k*stheta);\n",
    "w = 2*PAI/(k*sthetaw);\n",
    "#Plot code end----------------------------------------------------------------\n",
    "resolution = 54.74 * 10**(-6)\n",
    "\n",
    "I = np.zeros(size(theta))\n",
    "scale = maxI/(N*w)**2;\n",
    "for angle in theta:\n",
    "    I[i] = diffract(angle,w,s,N,lmbda,scale);\n",
    "    i = i + 1;\n",
    "#ax.plot(sin(theta),I, label='N = 4')\n",
    "\n",
    "#Plot code ----------------------------------------------------------------\n",
    "N = 5;\n",
    "i = 0\n",
    "s = 2*PAI/(N*k*stheta);\n",
    "w = 2*PAI/(k*sthetaw);\n",
    "#Plot code end----------------------------------------------------------------\n",
    "I = np.zeros(size(theta))\n",
    "scale = maxI/(N*w)**2;\n",
    "for angle in theta:\n",
    "    I[i] = diffract(angle,w,s,N,lmbda,scale);\n",
    "    i = i + 1;\n",
    "ax.plot(sin(theta),I, label='N = 5')\n",
    "\n",
    "#END OF CODE----------------------------------------------------------------\n",
    "leg = ax.legend();\n",
    "pyplot.xlabel('sin(theta)')\n",
    "pyplot.ylabel('I')\n",
    "\n",
    "print(w)\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  \"\"\"\n",
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'I')"
      ]
     },
     "execution_count": 98,
     "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": [
    "fig, ax = pyplot.subplots()\n",
    "\n",
    "#All in code for other setup\n",
    "#Plot code ----------------------------------------------------------------\n",
    "PAI = pi;\n",
    "stheta = np.array([0.001916,0.002792]);\n",
    "sthetaw = np.array([0.0145]);\n",
    "stheta = mean(stheta)\n",
    "sthetaw = mean(sthetaw)\n",
    "lmbda = 650e-9;\n",
    "N = 1;\n",
    "k = 2*pi/lmbda;\n",
    "s = 2*PAI/(N*k*stheta);\n",
    "w = 2*PAI/(k*sthetaw);\n",
    "pos = np.arange(-750,750,1)\n",
    "\n",
    "N = 1;\n",
    "i = 0\n",
    "\n",
    "#Plot code end ----------------------------------------------------------------\n",
    "resolution = 54.74 * 10**(-6)\n",
    "\n",
    "posInMeter = pos*resolution;\n",
    "theta = arctan(posInMeter)\n",
    "I = np.zeros(size(theta))\n",
    "scale = 1\n",
    "for angle in theta:\n",
    "    I[i] = diffract(angle,w,s,N,lmbda,scale);\n",
    "    i = i + 1;\n",
    "ax.plot(sin(theta),I, label='Red Laser')\n",
    "maxI = max(I);\n",
    "\n",
    "#Plot code ----------------------------------------------------------------\n",
    "PAI = pi;\n",
    "lmbda = 532e-9;\n",
    "N = 1;\n",
    "k = 2*pi/lmbda;\n",
    "i = 0\n",
    "\n",
    "#Plot code end ----------------------------------------------------------------\n",
    "resolution = 54.74 * 10**(-6)\n",
    "\n",
    "posInMeter = pos*resolution;\n",
    "theta = arctan(posInMeter)\n",
    "I = np.zeros(size(theta))\n",
    "scale = maxI/(N*w)**2;\n",
    "for angle in theta:\n",
    "    I[i] = diffract(angle,w,s,N,lmbda,scale);\n",
    "    i = i + 1;\n",
    "ax.plot(sin(theta),I,label='Green Laser')\n",
    "\n",
    "#END OF CODE----------------------------------------------------------------\n",
    "leg = ax.legend();\n",
    "pyplot.xlabel('sin(theta)')\n",
    "pyplot.ylabel('I')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  \"\"\"\n",
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'I')"
      ]
     },
     "execution_count": 110,
     "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": [
    "fig, ax = pyplot.subplots()\n",
    "\n",
    "#All in code for other setup\n",
    "#Plot code ----------------------------------------------------------------\n",
    "PAI = pi;\n",
    "stheta = np.array([0.001916,0.002792]);\n",
    "sthetaw = np.array([0.0145]);\n",
    "stheta = mean(stheta)\n",
    "sthetaw = mean(sthetaw)\n",
    "lmbda = 650e-9;\n",
    "N = 5;\n",
    "k = 2*pi/lmbda;\n",
    "s = 2*PAI/(N*k*stheta);\n",
    "w = 2*PAI/(k*sthetaw);\n",
    "pos = np.arange(-750,750,1)\n",
    "\n",
    "#Plot code end ----------------------------------------------------------------\n",
    "resolution = 54.74 * 10**(-6)\n",
    "\n",
    "posInMeter = pos*resolution;\n",
    "theta = arctan(posInMeter)\n",
    "I = np.zeros(size(theta))\n",
    "scale = 1\n",
    "\n",
    "i = 0\n",
    "for angle in theta:\n",
    "    I[i] = diffract(angle,w,s,N,lmbda,scale);\n",
    "    i = i + 1;\n",
    "ax.plot(sin(theta),I, label='Red Laser')\n",
    "maxI = max(I);\n",
    "\n",
    "#Plot code ----------------------------------------------------------------\n",
    "lmbda = 532e-9;\n",
    "N = 5;\n",
    "k = 2*pi/lmbda;\n",
    "i = 0\n",
    "\n",
    "#Plot code end ----------------------------------------------------------------\n",
    "resolution = 54.74 * 10**(-6)\n",
    "\n",
    "posInMeter = pos*resolution;\n",
    "theta = arctan(posInMeter)\n",
    "I = np.zeros(size(theta))\n",
    "scale = maxI/(N*w)**2;\n",
    "for angle in theta:\n",
    "    I[i] = diffract(angle,w,s,N,lmbda,scale);\n",
    "    i = i + 1;\n",
    "ax.plot(sin(theta),I,label='Green Laser')\n",
    "\n",
    "#END OF CODE----------------------------------------------------------------\n",
    "leg = ax.legend();\n",
    "pyplot.xlabel('sin(theta)')\n",
    "pyplot.ylabel('I')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  \"\"\"\n",
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  \"\"\"\n",
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'I')"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PPEJhYWHEfdK7XPCmTZsoLCzk3Xff5aKLLop0aMrOzub2229n1apVLF26lOXLl3PRRRcBcMcdd1BYWIjL5aKwsJDvfe97APzsZz9j27ZtLF68mAULFvDQQw8l5HUMB7HghQlJeVM5roBrAIGvh7RJdB0PkGLvcdGkdX/tTynAXPdhv2FhV8/2+u1cnCb7+uJh3bp1+P3+yPf79+9P2NyHDx+Oevzhhx+OfH355Zdz+eWXR73u+uuvj5rNc//990fNgMnLy+Opp56KaY2dnZ0xXT8QYsELE5LwpqSVk6Jb8DqtgC5foK8F3x1w9dpzoaux37DSrFLSreniphFGDSLwwoTkw7oPKcooIs+Z1/9kZwOhlAJC2sicCRMWe4+1ezer39NnmNlkZtmkZSLwwqhBBF6YcGit2dW4i8X5i6OdhM46fA5D+FN7u2i6LfhOS3eqpKu/Fb+8YDmH2g7R6kncbkRBGCki8MKEo6azhmZPM4vzogi8pw2CXjx2I7MmpU+QtVvgzd2bnaK4acIPjd2NCd4cJAgjQARemHCExXdR/qL+J7uMtpEumyHwqbbeQVZD4NtUt8BHseAX5i7EpEwi8MKoQARemHDsatiFw+ygNLu0/0lXEwBdFkPEU+z9LfgW1d1gu6up3/AUawolWSXsatyV4FULQuyIwAsTjt2Nu1mQuwCrydr/pMtovddpMgQ+rZcPPhxwbdLdAh/FggdYnLeYjxs/RmudwFULieTo0aOkpaUNWVXyxNK9Dz30EIsWLWLp0qWsXbs2kkPv8/n44he/yKJFi1iyZMmISg6YzWaWLl3K0qVLufTSS2MeHw0ReGFC4Q/6KW8qZ1FeFPcMRCz4Now6Ib198E6rIfatoRQwWaL64AFOyzuNNm8bRzuOJnDlQiK57bbbuPDCCwe9Jlrp3uuuu47du3ezY8cO7rjjDm6//XYA/u///g+A3bt3s3nzZr75zW8SCoViWpPT6WTHjh3s2LGD559/PqaxAyECL0wo9rXswxfyRfe/A7gNC75VGQLfOw/eZFI4rCbc/iCk5A5owYcfHrsaxE0zEpJZLhjg2Wefpbi4mIULFw54zUClezMyMiJfd3V1RUonlJWVRerRFBQUkJWVFdkV++qrr7J69WqWL1/O1VdfnbBNTMNBdrIKE4qw6C7JXxL9AlcTmO10BI0m2713soJh0RsCnxfVBw8wO2s2TouT3Y27uaTkksQt/hRw3wf3sbd5b0LnnJczjzs/ceeA55NZLrirq4v77ruPzZs3D+qeGax07y9+8QseeOABfD4fW7ZsAWDJkiU899xzXHPNNVRVVbF9+3aqqqooLi7m7rvv5rXXXiM1NZX77ruPBx54gO985zv95vV4PKxcuRKLxcJdd93FZZddNuD6hosIvDCh2N24m3xnPpNSJkW/wNUMKTl0+Yy312n2vv8iTqsZly8IqQNb8GaTmYW5C/m48eOErn2ikMxywd/97ne57bbbBm2JN1Tp3ptuuombbrqJP/zhD9x99908+uijfOlLX6K8vJyVK1cyc+ZM1qxZg8Vi4b333qOsrIwzzjDKSft8PlavXh113qNHjzJ16lQOHjzIueeey6JFiygpKRn2a4uGCLwwoShvKmdh7sKoVQmBboHPpcsbwKTAbunrxXTazHjCFnztzgHvszB3IU/ufRJ/yB89mDtGGMzSThbJLBf8/vvv88wzz3DHHXfQ2tqKyWTC4XD06c/60UcfDat07zXXXMPXv/51ACwWS58HzZo1aygtLaWyspL169fz5JNP9lvHV7/6VQC+//3vc+mllzJ16lQAiouL+dSnPsVHH30kAi8Iw8UdcHOo/RDri9YPclEzOLMjdWhOfBBELPjsvAEteIB5ufPwhXwcajvEnOw5iXoJE4ZklQt+8803I19/73vfi9p8+6KLLhqwdO+BAwcoLTXSa1988cXI1y6XC601qampbN68GYvFwoIFC8jPz+emm26KPDBcLhfV1dWcfvrp7NixI3KPlpYWUlJSsNvtNDY28vbbbw/ZKWo4iMALE4b9LfsJ6RDzcuYNfJGrCQoW4PIG+/nfwbDg3b5uC97TBkE/mPtb6AtyFgCwt3mvCPwIOPPMM/nhD3/I6tWrSU1NTWi54IH49Kc/zcMPPxyxpKPx4IMP8tprr2G1WsnOzubRRx8FoL6+ng0bNmAymZg2bRqPP/44APn5+TzyyCNce+21eL1eAO6++27mzOn7N1FeXs5Xv/pVTCYToVCIu+66iwULFsT9mkTghQnD3iYjWDg/p//b/AhhH3x7oE+hsTApNjPNXT7DBw/GAyF9cr/rZmbMxGlxUt5UzqUliclpnkgks1xwmHC99jAvvfRS1Ot6Z7389Kc/jXpNUVER+/bti3ru3HPPZevWrYOuZc2aNezenfjdz5ImKUwYypvLybBlMCV1gKbGoZDhoun2wafYoljw1m4L3pljHHC3RJ3KbDJTml1KeXP/5sqCcLIQgRcmDHub9zI/Z/7AAVZvG+gQOHNw+4OkWPtb8E5btw/e2V1RcgCBB+Odwr7mfYR0bBteBCFRiMALEwJ/yM+BlgPMzx3CPQOQkovbH8IxgAXv8QfBmW0ccA9cFnh+znw6/Z1Ud1THs/RTgpRZGH2M5HciAi9MCA61HcIX8g0dYAVIycHrD+Kw9P/3SIlY8GGBH9iCn5dr3GusuWkcDgdNTU0i8qMIrTVNTU04HI6YxkmQVZgQlDcZIjtkgBUgJQe3vx2HdQAfvD+IdmShYFCBL80qxaIslDeVs6FoQxyrP7kUFhZSXV1NQ0PDqV6K0AuHw0FhYWFMY0TghQnB3ua9OC1OZmbMHPiisAXvzMHjb4kUF+uNM9y2z5SGU5kHFXib2UZJVknCt/onG6vVyqxZs071MoQEIC4aYUJQ3lxOaXYpZlN/0Y7g7vHBe/whHNb+/x7O7mMuf3egdRCBB6PuSnlzubg7hFOCCLww7gnpEPua9w3ungHDgjdZwJ6O2x+MGmQNlw92hwOtQwj8/Nz5NHuaaXCLu0M4+YjAC+Oems4aOv2dzM2ZO/iF7hZwZhPS4AuEcFj6C3xY9N2+IDiyYIjm2uFdrAdaDoxs8YIQByLwwrinsrUSMIKeg+JpA0cWnkAQMHLeTySl2y8/XAs+fE8ReOFUIAIvjHsqWo1CUSVZQ1Tmc7eCIxOP39iYNFCaJNCTKjmEwGc5ssh35nOgVQReOPmIwAvjnoOtBylIKSDdlj74hZ42cGYZG5kgappkxEUzTAseoDS7VCx44ZQgAi+MeypaK5idNXvoCz1t4Mg0xJsBXDS9ffDObGNMKDjotKVZpVS2VhIIBWJfvCDEgQi8MK4J6RCH2g4N7Z4BI2Dq6LHg7VGCrOHceHfv3ayetkGnnZMzB1/Ix9F2acItnFySJvBKKYdS6gOl1E6l1B6l1H8l616CMBA1nTV4gh5KMocQeK0jFrxnEAs+fCySBw/DDrTub018yVtBGIxkWvBe4Fyt9RJgKXCBUuqTSbyfIPQjnEEzpAXv64JQYMgga9iC9/iGV3AMoDirGLMyix9eOOkkrVSBNrbuhSvlW7s/ZDufcFIZdgZN2M0yRJA1vNFpuAXHAOxmOzMyZojACyedpPrglVJmpdQOoB7YrLV+P8o1X1FKbVNKbZPiRkKiiSmDBoYMsppNCpvZ1JNFA8PLpMkqZX+LuGiEk0tSBV5rHdRaLwUKgU8opU6Lcs2vtdYrtdYr8/Pzk7kcYQIy/AyabjeLI6uXiyZ63RqH1XRCTfjhpUrWdNbQ5e8a1roFIRGclCwarXUr8AZwwcm4nyBArBk0/S14hy36v4cj3PTDMbwgK/SULAi7jAThZJDMLJp8pVRW99dO4DxgbNVNFcY0NR3DzKCBnkCpIxPvID54MFw3bn8QzBawpQ/bggcpWSCcXJJZD34K8KhSyozxIHlaa/2XJN5PEPpQ2TbMDBroFWTNxu0z6sIP5KKJNN4GcGSCt33I6aelTcNpcYrACyeVZGbR7AKWJWt+QRiKYWfQQI/A2zPwBOoxmxRWc/Tm3I7urk7GN5lDbnQCMCkTpVmlUpNGOKnITlZh3FLZWsmklElDZ9CAEWS1pYPZYjT7sJhQKrrAO61mvN2B2OEKPBgPmnBeviCcDETghXFLZWvl8Kx3iOxiBaOQWLQUyTAOq+kEC37wjU5hSrJKaPY00+IZ2mcvCIlABF4Yl8SUQQORUsEAHn8wah2aMJEgKxjlCoZpwRdnFgNwsO3g8NYkCHEiAi+MS8IZNMPKgYdIqWAwBD5aP9YwjhODrMMU+PBaxE0jnCxE4IVxSTiDJmw1D0kvF43HHxrUReO0mvEGegt8O4RCQ95icupkUiwpIvDCSUMEXhiXxJRBA5FSwdBtwQ/mojnRgkcPK1VSKUVxZnHk4SMIyUYEXhiXxJRBAzEGWQ0fvNY6MmbYfvisYg62ig9eODmIwAvjkpgyaEJBwwLv5aIZKsga0uALhmIW+NlZs2lwN9DmHd71ghAPIvDCuCMYCnKw7WBsKZIQU5AVwOOLXeDDa5JMGuFkIAIvjDuOdR7DG/TGlkEDfdIknQPUoYFeTT8CwdhdNN1BXwm0CicDEXhh3BEOsA4/g6anVDCELfjBXDTGv43b16ui5DAFfmraVJwWpwi8cFIQgRfGHTEVGYN+FvyQQdZu/7zbH7sFb1ImZmXOEoEXTgoi8MK4Y0QZNACOTLTWkVo0A+Gw9RJ4ewaghi3wACWZJZIqKZwUROCFcUdla+Xw/e/QUwvemYU3YGxYsg/HB+8LgslkiPww69GAkSpZ76qnw9cx/DUKwggQgRfGFeEMmuKsYfrfoY8FH264Pewga/e4WCz48MNHMmmEZCMCL4wrYs6gAcP6VmawpfX0Yx00yNrtovHFXjIYiHSYEj+8kGxE4IVxRcwlCqBnF6tSkSqRzgH6scIJQVaIWeCnpk3FbraLwAtJRwReGFfEXGQMTig01t2PdZCdrOFm3CMVeLPJLDVphJOCCLwwrog5gwaMIGv3Llb3EA234YQgK8Qs8GAEWsWCF5KNCLwwrog5gwaiW/CDCHykVMEImn6EKcks4XjXcTp9nbGtVRBiQAReGDeMKIMG+pQK9kaCrAP/a1jNJqxm1ddF4203ipYNk/AaD7Udim2tghADIvDCuGFEGTTQr1QwMOhOVugpGWx8E9tuVujV3Un88EISEYEXxg0jyqCBmIOsYAi8Jw6BL0wrxGayiR9eSCoi8MK4YUQZNH4PBDwxBVkhWlcnYs6kKcosEoEXkooIvDBuGFEGTb9SwYYPfrCdrOHz4WtHIvBgvNOQ3axCMhGBF8YNI8ug6V8qGMA+SJAVjIJj8fjgwcikqemsweV3xTROEIaLCLwwLhh5Bk3Ygu8ReKXAPkg1SQCn1RS/wHfHCiSTRkgWIvDCuCCuDBroE2R1WMwopQYd1jfIGlvTjzDhh5Fk0gjJQgReGBeMOIOmV6lgMIKsg+XAh+kTZLWlgTLFLPAz0mdgMVkk0CokDRF4YVwQ6eKUGWuKZNgH3xNkHSrACt1B1nC54EhN+NgE3mKyUJQhmTRC8hCBF8YFFa0VTEqZRJotLbaB/QR+8H6sYRw2c0+54PD4GJp+hCnJKhGBF5KGCLwwLjjYejB2/zsYVrfFCRa78e0wBd7Z2wcPIyo4Bj2ZNO6AO+axgjAUIvDCmCecQROz/x367GIFw0UzHB+8ozuLRmvdfWCEAp9VgkZzuO1wzGMFYShE4IUxT01nDd6gd2QC36tUMISDrMOz4IMhjT8Yv8BDT5BYEBKJCLww5gn7sBNjwQeHFWR19OvLGnvJYOjOpFEW2dEqJAUReGHMM+IMGuhTKhhi8MHb4m/6AWA1W5mRMUMCrUJSEIEXxjwjzqCBAXzww3PRQK+2fc4s8HVCMBDzEiSTRkgWIvDCmGfEGTQQ1UUzvCBrlMbbYDT+iJGSrBKqO6vxBDwxjxWEwUiawCulpiulXldKlSul9iil/jVZ9xImLnFl0GhtCPwIg6xA/5LB7paYl1GSWUJIhzjSfiTmsYIwGJbBTiqlOgAd7RSgtdYZgwwPAN/UWn+olEoHtiulNmuty0a+XEHoS1wZNN4O0KGIOGutYw+yxqrIKGAAACAASURBVFkyGHqCw5WtlczNmRvzeEEYiEEFXmsdQ2HtfmNrgdrurzuUUuXANEAEXkgYI65BA/1KBfuCIUJ66HZ90CvIGmdFSYCZGTMxK7OkSgoJ56T44JVSRcAy4P0o576ilNqmlNrW0NBwMpYjjCPC6YUjyqBx969DA0OXCoYoQdY4BN5mtjE9fbqkSgoJJ+kCr5RKA/4E3Kq17heB0lr/Wmu9Umu9Mj8/P9nLEcYZFa0VTE6dPPIMGoj44D3DbLgNRAKx/dv2xV6PBiSTRkgOSRV4pZQVQ9x/r7X+czLvJUxMKlsrR2a9w4DdnIZquA3RLPjuQK175AJf1VGFL+gb0XhBiEYys2gU8BugXGv9QLLuI0xcgqEgh9oOjcz/Dv1cNO5YLPgTffC2VDBZRm7BZ5YQ1EEOtx8e0XhBiEYyLfgzgBuAc5VSO7o/Pp3E+wkTjHAGTVw58NDLRWP44Ifb8MMY0y3wSo24XAH0BIkPtoofXkgcg2bRxIPW+i2MdEpBSArhrJOY+7CG8bQanZhsRrJY2J8+nDx4q9mExaR6XDRgPChG6KIpyizCpEzSvk9IKLKTVRizRIqMjdQH7241OjGZjH+DSJB1GAIfvq5v04+sEbto7GY709OnS6BVSCgi8MKY5UDrAaakThlZBg3028UaCbIOU+DtVnPCLHiA4sxiEXghoYjAC2OWytbKkfvfoV8lSXesFrzNhLdPV6eRW/Bg+OGPth/FH/SPeA5B6I0IvDAmCYQCHGo7FJ/Au1v7VZKE4VvwziRY8AEd4GjH0RHPIQi9EYEXxiRHO47iD/mZnR2PBd+/0BjE6IPvZ8G3GUXMRkD4YSUlC4REIQIvjEkqWuKoQRMmSrMPAPsw0iTBsPQjO1nBeFjooFHEbAQUZRahUJIqKSQMEXhhTFLRWoFCUZw5whRJiOKiCaLU8GrRgCHwnj4WfHzlCpwWJ9PSpkmqpJAwROCFMUlFawWF6YU4Lc6RTeD3QNDbL4vGYTFjbMIeGqfV3FMuGOIuVwBSk0ZILCLwwpikorUi/gwa6JdFM5wyBWGctihB1t5zj4CSrBIOtx/GH5JMGiF+ROCFMYcv6ONo+9H4M2igj4vG7QsNO8AK3T74E4OsveceASVZJQRCAao6qkY8hyCEEYEXxhyH2g4R1ME4Lfi+dWgAPIHgsAOs0O2iOTHICvFZ8JlSk0ZIHCLwwpgj7KOOL0UybMFn9xzyDa9dXxiH1ZRwC35W5ixAUiWFxCACL4w5KlorMCszRRlFI58kiovGExhew+0wTquZQEjjD3YHWu3poMxxWfAp1hSmpU0TC15ICCLwwpijorWCmRkzsZltI58kiovGHaMF368vq1LGAyMOCx6MHa0VbWLBC/EjAi+MOSpaK+Lb4AS9XDR9SxUMpxZ8GMeJXZ3AeGDEYcGD4Xo61HZIMmmEuBGBF8YU7oCb6o5qSrNK45yoFaypYLZGDnn8sbtoADwnlgyO04Kfkz2HQCjA4bbDcc0jCCLwwpjiYNtBNDoBFnzfOjQQu8Any4Kfkz0HgP0t++OaRxBE4IUxRUIyaKBfHRro3ugUkw++b6MQICEW/KyMWVhMFhF4IW5E4IUxxYGWA1hNVmakz4hvohPq0EDsO1mTZcFbzVaKM4tF4IW4EYEXxhT7W/ZTklWCxRRnO+ETXDRaayPIOsxCY9Djg++XC+9uHXHJ4DBzsueIwAtxIwIvjCn2Ne+L+Kjj4gQXjTfQ3ewjxlo0QP/drDoIvs64ljcnew71rnravG1xzSNMbETghTFDo7uRJk8Tc7Pnxj9ZlFLBAA5LDC4aywAWPPTk2Y8QCbQKiUAEXhgz7G82xG5uTpwCHwyAryN6N6eRWPC9SwY74y9XACLwQmIQgRfGDPta9gHEb8GHres+3ZzC/Vjj3OgUseDjE/g8Zx7Z9mwOtByIax5hYiMCL4wZ9rXsoyClgKwT0htjxt1sfE7J6Tnki60fa+9rPdFqwsdpwSulJNAqxI0IvDBm2Ne8LzH+d1e3wDt7CXykH+vwBd5qVphNqm9f1gRZ8ACl2aVUtFYQDAWHvlgQoiACL4wJfEEfh9sOx+9/h14WfE+pYK8/dgteKYXDYore1SlOCx4MP7w74Ka6szruuYSJiQi8MCaobK0koANJt+BjEXgwAq19XDS2dEAlxIKfkyOBViE+ROCFMUE4wBoWvbhwtxife/nge4KssQl8v7Z9JlNCSgaD0d3JpEwi8MKIEYEXxgT7mvfhMDuYmT4z/snczWCygD2j51A4Dz6GLBrobtvnP8FHnpLT8xCJA4fFwcyMmexr3hf3XMLERAReGBPsb9nP7KzZmE2xWdhRcTWDM9to0NGN2xcAYsuDD1/fJ8gKkJILrqa4lwkwL2cee5v3JmQuYeIhAi+MerTW7G/Zn5gAKxgWfC//O4CrW6RTbbHVuOnnooGECvyCnAXUdtXS4on/HYEw8RCBF0Y99a56Wr2tialBA4YFn9JX4LtGkAcPhsD32ckKxsMjHMiNk/m58wEobypPyHzCxEIEXhj1lDcb4hYWu7hxt/Sz4N2+AE6rGZNJDTAoOk6raQAffGIFvqy5LCHzCRMLEXhh1LOnaQ8mZUpMiiT0+OB70eULkmqP3b/vHMhF43eBzxXPKgHIsGVQmFZIWZMIvBA7IvDCqKesqYzizGJSrCmJmdDd3GeTExilCmINsMIgQdbwfRLAgtwF4qIRRoQIvDCq0Vqzp3EPC3IXJGZCnwsCnn4umi5vIOYAKwwUZO2eO0GB1vm586nurJba8ELMiMALo5o6Vx1NnqbECXyUQmMQe7u+MA6rGe+JQdawBZ+gQOuCHOO1S7qkECsi8MKoJux7Xpi7MDETRilTACO34J1WM75giECwl8hHBD5xFjwgfnghZpIm8Eqp3yql6pVSHyfrHsL4JxJgTVgOfP8yBWDkwY/IBx8uGRyIJvCJseCzHdlMSZ0ifnghZpJpwT8CXJDE+YUJQFlTGSVZJTgtzsRM6I5uwbt8QVJH4qLpHtO/ZLBKmAUP3YHWZhF4ITaSJvBa638AiTFhhAmJ1pqyprKIDzohuKL74A0LfmQuGjih6YfZ0l1wLHF//vNz5nO4/TCdcTbzFiYWp9wHr5T6ilJqm1JqW0NDw6lejjCKqHPV0expZmFegvzv0MuC75sm6fIFRmbBdxcn67/ZKXHlCoDIz0D88EIsnHKB11r/Wmu9Umu9Mj8//1QvRxhF7GncA5C4DBqArkawZ4LFHjkUCmnc/iAp9pFb8MmsRwOwKG8RALsadyVsTmH8c8oFXhAGYlfjLizKkrgdrABdDZDW15DwBIJoDSlxBFmTWVESINOeSVFGETvrdyZsTmH8IwIvjFp2NuxkXs48HBZH4ibtrIfUvgLfU0kyjiBrtM1OrsRWgFycv5hdjbvQWid0XmH8ksw0ySeBd4G5SqlqpdSXk3UvYfzhD/nZ07iHJQVLEjtxVyOk5vU55PJ2V5JMVJAVugU+cRY8wJL8JTR7mqVHqzBsYv+LHiZa62uTNbcw/tnfsh9P0MOS/EQLfAPMXN3nkMtvNPsYWZA1LPBRdrMG3ODrAlvqyNZ6AuGfxa6GXUxPn56QOYXxjbhohFFJ2NecUIEPBQ2r+gQXTVfEgo/DB3+iBZ9aYHzurI99nQMQ3g+wq0ECrcLwEIEXRiU7G3aS78xnSuqUxE3qagZ0P4EPB0hT48miOTHImjbJ+NyVuNRfi8nCaXmnicALw0YEXhiV7GzYyZL8JSgVWwOOQenqtqZP8MF3hfuxxtjNCcBhM/6F+lnw4UydzrqY5xyMxXmL2du8F0/Ak9B5hfGJCLww6mh0N1LTWcPSgqWJnThsTYfdJ93EY8HbzCZMKkqQNWzBJ1rg8xcT0AEpWyAMCxF4YdSxsyEJ/ncwMmigvw++24IfSR68Ugqn1RxJtYyQkgco6Ezs7uzwz+Sj+o8SOq8wPhGBF0YdH9V9hM1kS1wP1jARC76vi6bDYwh8umNkSWVpDgud3XNEMFuMTJoEW/C5zlxmZc5i2/FtCZ1XGJ+IwAujjq11W1mcvxi72T70xbHQ1QAmS3e1xx46PQHMJjUiHzxAmt1CpzcQ5cSkhAZZw6yatIoP6z8kEIpyT0HohQi8MKpo97Wzt3kvqyavSvzkXQ2G68TU98++w+MnzW4ZcUA33WGl3ePvfyItP+EWPMDKySvp8nexr3lfwucWxhci8MKo4qO6jwjpUHIEvrOhn/8doMMbGLF7BgzXzoAWfDIEftJKALYe35rwuYXxhQi8MKrYenwrNpONxfmLEz95xzFIn9z/sCdA2ggyaMKkOywRP34fUvONh0qCa8fkp+RTlFHEtjrxwwuDIwIvjCqS5n8H6DgOGf03TnV4/GQ4rCOeNt1upSOqi2aSUa7A2zHiuQdi5eSVbK/bTjAUHPpiYcIiAi+MGpLqfw/6jbIB6f0FvtMbIC0OF03ULBqAtO58+yQEWldOWkmnv5N9LeKHFwZGBF4YNWw/vj2J/vc6QEcV+A5P/D74Ll+QYOgEV0xY4DuOj3jugQj/jD6o/SDhcwvjBxF4YdTw9rG3cVqcid/gBD0iG82Cj1vgrZF5+p6Y2n3v2hHPPRAFKQXMzprNW8feSvjcwvhBBF4YFWiteavmLU6ffDo2sy3xN2g/ZnyO6oMPkGaPxwdvPBw6vCf44TOnGZ/bklO/fe20tXxY9yEuvysp8wtjHxF4YVRwtOMoNZ01nDHtjOTcYAAL3uMP4guG4nbRAP0zaezpRv/X9poRzz0Ya6etxR/y88FxcdMI0RGBF0YFb9UYrobkCfwxMFm7a8T0EM5fj0fgwwHaqLnwmdOgLTkCv6xgGU6LM/KzE4QTEYEXRgVv17zNjPQZyetU1F5r5MD328Uav8CHffBRUyUzpkJ7clw0NrON06eczls1b0mfViEqIvDCKcfld/HB8Q9YO21t8m7SXjNABo0hynH54Ady0QBkTOvx/yeBtVPXUtNZw+H2w0m7hzB2EYEXTjlvH3sbb9DLuhnrkneT1iOQNaP/YZch8FkpIxf4LKe1z1x9yCw08uAD3hHPPxhnFZ4FwJajW5IyvzC2EYEXTjmvHXmNLHsWyyctT84NggHDD549s9+pFpcPgOw4BD7TaUUpaO7y9T+Z0Z1Jk6RA65S0KZyWexqbj2xOyvzC2EYEXjil+II+/lH9D86Zfg4W08j94IPSXgM6CFn9Bb7Hgh95aqbFbCLDYY08LPqQ5FRJgPNmnseepj0c60yeK0gYm4jAC6eU92vfp9PfyXkzz0veTVqPGJ8HseDDbpaRkpNqi27BZxd13+hwXPMPxvqZ6wHEihf6IQIvnFJeOfwKadY0Tp9yevJu0tIt8ANY8OkOCxZzfP8K2SnW6D74jEIjPbP5YFzzD8aMjBnMzZ4rAi/0QwReOGW4/C42H9nM+UXnJ6d6ZJjWI6BMRsDzBFpcPrLjcM+EGdCCN1uMdw5JFHiAC2ZdwM6GnRxpP5LU+whjCxF44ZTxt6N/wxVwcUnxJcm9UcsRw5I293fDtLj8cWXQhMlKsUX3wQPklEBTcgX+kuJLMCkTz1U8l9T7CGMLEXjhlPFC5QtMS5uWvOyZMM0Ho/rfAVpdvrgCrGHCFnzUDUc5xcYakrgZaVLqJFZPXc3zlc9LjXghggi8cEqoaq/ivdr3uLTkUkwqiX+GWkPjfsifF/V0U6ePvNT4BT47xYY3EMLtjyKuOcXg7zLq0SeRy2ZfRp2rjvdr30/qfYSxgwi8cEp4ct+TmJWZq+ZcldwbddSCtx3y5/Y7pbWmvsNDQYYj7tuE8+hbogVac4uNz82Vcd9nMM6dfi7Z9mz+uO+PSb2PMHYQgRdOOi6/i2cPPMt5M8+jIKUguTdr6O54FMWCb3H58Qc1BenxB3hz04w5Gjqi7FjN63641JfFfZ/BsJltfHbuZ3mj6g0JtgqACPyEwR1wU9tZy5H2I9R11eEPRrE0TxKbKjbR4e/guvnXxTbQ2wGtRyEGH7PveLnxRRQLvr7DA0BBRvwCPyXTeBdwvM3T/2RmISF7Jr6aXcOfUGujho2rOaZ1XDPvGiwmC0+UPRHTuEQS0iEaXA1UtVdR1V5Fl7/rlK1lopOkrYPCqURrzd7mvbxR/QY76newt3kvzZ6+QmFWZqanT2dpwVLWTF3D2YVnk2JNSfraPAEPv9n9G1ZMWsHS/KXDG+Rqhr/+O3z8DIQCkDkDLrgH5l886LCyY+0ceG0za3U6j77dzO0b+r5bqG83rO1JCXDRTO4W+No2d79zz3xYQ6F7Ko6P3qF5Xh3nzps0+GRVW+Evt0Ldx8b3s9fDp38EObOGXEeeM4+Lii9iU8Um/nnxPyf/HRLgD/l5p+Yd3jn2DtvqtnG47TC+UN+MojRrGvNy5rE4fzFrp61lecFyzCZz0tc20RGBH0e0edvYuH8jT+97mtquWhSKOdlzOKvwLGZmzCTHkYPVZMUT9FDbWUtFawVbjm7h2YpnSbGkcMGsC7hh/g3Mzp6dtDU+s/8ZGtwN3HfWfSilhh7QehQev8LIZV/1z5BXCtt/B099Dj7zC1h2fdRhWmv+fdNufqwrOJYyn5+9XsnaOQV8YlZO5Jr6bndKQlw0qTZsZlM/C76m1c1/PLubB9LncI7rFU5/8kNe++Y5A/v9D/0DnrgS0ibDhnvA0wbv/S88vA4+txGmrRhyLV9Z/BX+UvkXfrXzV/zn6v+M+7UNRF1XHY+XPc4LB1+g2dOMw+xgWcEyzph6BlPTppJqTUWjaXI3UdNZQ1lTGY+VPcZvP/4t2fZsLph1AVfPuZrS7NKkrXGiIwI/DqjqqOKJsifYVLEJd8DNJ6d8km8s/QZnTjuTXGfuoGODoSA7GnbwbMWzvHzoZTYd2MT6meu5aelNFGcVJ3Sdbd42fr3r13xi8ieG11jb1QyPX25UY/z8czBzjXF82fXw5DXw/C1GjvnM1f2Gvr6vngNVxylxVBNccQ1579n43zcq+MSsT0SuqWkxrO1EWPBKKSZnOqg9QeCf+uAo3kCI0884F+fm55gWOMrPthzg7ssW9Z+ktQr++DnjNX3xJUjpfhgt/qzxc3jiKvjyZsgb/AE8PX06V825io37N3LDghsoyiyK+/X1pt5Vz692/opNFZsI6RDnzjiXz5R8hjVT12CNstegNy6/i7dq3uK1I6/xzP5neHLvkywrWMYNC25g3Yx1yc2omoCIwI9hdtTv4LGyx/jb0b9hUiY+PevTfH7B55mb09/fPBBmk5kVk1awYtIKbl9xO4+XPc4f9v6BLUe3cO38a/n6kq+TbktPyHp//tHPafe1c8eqO4a+OOCDpz9vWPCff76viFvs8NnH4KG1sOkr8LW3wZEROa215iebD7AuswaTN4RpxiquZyb/89oBDjZ0UpyfBsDhpi6mZjpwWBPjKpic6eBYa4+LJhjSbNxezVml+eTOXwCb4Ruz6rjtgyq+dnYJhdm9XGJaG26ZUBCu+2OPuAPklsDnn4WH18MTV8A/b4HUvp2pTuSrS77Kiwdf5PvvfZ/fnP+b4b1bGgJ/yM8fyv/AL3f8El/Ix+WzL+dLp32JwvT+O4QHIsWawvlF53N+0fm0eFp4vvJ5ntr3FLe/cTtFGUV86bQvcXHxxUM+KIThIY/LMUYwFGTzkc1c/9L13PDyDbxX+x5fXPhFXrnyFX649ocxifuJZDuyuWX5Lbx0xUt8ZvZneKLsCS7edDGbDhiWWjy8e+xdnt73NNfOu3boNWoNL/0bHH7TcMNEsdCxp8PlvzaqNG7u64Z4rbye3TVtfHVmLaCgcCXXfWIGZpNi4/aeqo6HGrsoykuN63X1pjgvlcqGzshmp7cqGqlt8/DZldONomPpUznPeQCl4KG/n5AyufNJqHgNzvteT4Gy3uQUw3VPQ2edYeUPUV8+z5nHN1d+k63Ht7Jx/8a4X9v7te9z1fNX8eNtP2bl5JU8/5nn+c7q78Qk7ieS7cjmCwu/wAuXvcCPzv4RDouD77zzHS748wU8tucxCc4mABF4jKi/N+ilw9dBs6eZdl87/tCpyzKJRpe/i9+X/56LN13M7W/cTqO7kbs+cRevXfUat664NaHBtBxHDt9b8z2evPhJZqTP4DvvfIcbXr6BPU17hjeB1uBuAZ8LgAZXA//vrf/HrMxZ3LL8lqHHf/B/8OGjcOY3DfdELzz+IF3h3qczTofVN8H2R+DgG9231vxk835m5qaw0LUVpi2HlBwKMhx8ak4+f/6wmkDQeFgdbkqswJdOSqfF5aepuybN09uqyE6xct6CAlAKitbirHmHq5dP5emt1T3++o46I4g8YzWsujHyOt2+E7KFClfAZf8LVe/B8zcPuTP2itIr+OSUT/KjrT+irKk7RdPvMX43w8xEOt51nG/9/Vvc+OqN+II+Hjz3QX6x7hdMz0hca0WzycwFRRfw9MVP89B5DzEjfQY/2vYj1m9cz0+2/4R6V3I3iMWC1hp/yI/L76LF00Kbtw1v0DtqWyZOCBeNP+SnuqOaw22HOdxufNR21tLkaaLR3UirtzWqhWo1WcmwZTA5dTKTUyczJXUKxVnFlGaVMjtrNmm2tKSv/Uj7Ef64949sqthEl7+LxfmLuW3FbaybsS7pWQgLcxfy2IWP8cLBF3hg2wNc+5druWrOVdyy7BayHFl9L/a54OM/wZ5NcPQ9Y+cm0J4xha/lZ9GlAjx03kM4Lc7Bb3rgNfjrXTD3IjjnPyKHGzq83P1iGS/trsUf1Jw9J5//unQhRed8G/a+ZAje19/lr/s7KKtt55eXTEZt3gafuisyx9UrC/nbE/W8eaCRhdMyaHX5KU6gwM+ZZPw97K/rwKwUm/fU8blPzsBu6f49zb8Ydj/NrSW1PLVd86t/VPLdixfAi7eD3w2X/pxWT4D/eqGMF3YeI6Q1a0vz+c+L5lM6qdtNdtoV0FQJr99tBJzP+taA61FKce+Z93LN81dx80tf4HdtfmY0HjJOmu3Gw2/+JbD4GkjtG6vxB/08Xv44D+18iJAO8Y0l3+BLi76U1KJwSinOmHYGZ0w7g10Nu3h0z6M8sucRHit7jItmXcS1865lQe6ChLibBsMdcHOw7SCVrZVUtFZQ1V5FvbueBlcDje7GqMafQpFmTSM/JZ+ClAIKUgooTCtkVuYsZmXOYkbGjKH/9pOASuaTRyl1AfBTwAw8rLW+d7DrV65cqbdt2zbi+/mDfg63H6aitYKK1goqWyupbK2kuqOagO7pl5nryGVq2lTynHnkOnPJceTgtDixmqxYTVaCOojL78IdcNPqbeV413Fqu2qp7arFHejxsU5JncKc7DnMyZ7D3Jy5zM2ey4yMGXEHiprcTWw+spmXDr3ER/UfYTFZ2FC0gevmXcfi/MVxzR2h4zjU7jSaYbhbweIAZzbkz4H8+WDrmzLZ4evglzt+yZN7nyTNlsY3lnyDK0qvwNFeCx/8Gj76PXjbjABhyTmQPYsqTwM3V7/EkZCbXxxvYE3+UsMqL12PBj6uaWdHVQveQIiS/DTOUDuxbbzeEK4vvmy4YYB/7G/g1qd20OkNcN0nZpDusPDoO4cB+N/rV3CG9QD87kKCK2/kvL0XYzEp/rr8fcxv/BBu+chwbwC+QIhP3vM3Plmcw2eWTuOrj2/nT19fzYqZOSSChg4vq374Gv9+4TzMJsXdL5bz11vPZN7k7viA3wM/ngNzNvBvoX/hhZ3H2HpxAxl/vRnW/4CK0i/ypUe2Udvm5nOnzyTFZubJD47i8Yf4/644jcuXdbtDtIZNX4VdT6EveoCdk6/kwyMtAMyfksGqomyj/HF9Ofzjx+zb/wI3Ts7HarbxwOTzWJoxy3BtHX4Tju8Csw0WfAZW3Uhw2kpePvJXHtr5EEfaj/Cp6Z/izlV39nfFaA2NB4zxXY3gdxm/r/TJULDAcDMlwACpaq/i8fLHebbiWdwBNyWZJVw6+1LWz1yfkAbtbd42ypvLKWsqo7ypnPLmco62H0Vj6KLFZGF6+nRDtJ0F5KXkkWpJxW62R+IE7oAbT8BDu6+dBlcD9e566l311HXVReYBmJo6lZKsEmZnz2Z2lvFRnFmMwxJfkF8ptV1rvTLquWQJvFLKDOwH1gPVwFbgWq31gNv5RiLwgVCAO7bcQmX7UY52VhPQxlvPcJ53SVZJ5ClalFFEUcZMMpQVfJ3Gh7f7s6/LeNtqdRhiZ3WCLQ1sqcZnawohBbVdtVS0VHCg9QD7W/ZzoGU/h9oOE+y+r9Nko9RZwFxbNnPNqcy1ZjM9ZRJZjmzMjixDRJ3dnx1ZeC12qruOsb9lP2VNZbx37D32tuwFYHbKFD6dOZfL7FPJd7cbotxxHHydaEcmAXsW3rTpdKXNxJ85C1v+LFJyp5PqdPRYOVqDqxlfzQ7chz6Amg+xN+zE4a4b8GeqlQl/zlxask6jyjmPSutcPDlzyc1MI2SpZWPlT9jRvJNszFzZ2sJ5bi9Z+efwTtZlbHGVcLC1hmDaezSaXsVutnPv6d/nnKYKePdBaKuiIW0uD/ov5fdtiwhgIYMubrS8yE3m56h3FlN/xUYWlxbj8gX55RsV/PKNSuYUpPPgdcsiluzRJhc3PraVgw1d/OCy07im8UHUB7/iJt8tXHf11ZzxysUw/XQjtbAX33+hjMffO8yn5hbwdkUjH/7n+oQFWQE2/OQfpDks1Hd4mJTu4Jmvr+l7wV//H7z/v9Rc9SK3PvkBj1vvwVa4jPfOepSv/2EnVrOJX39+BctnZANQ1+7h5j98xAeHm7li2TT+/dPzyU+309TajuuJa5ne+BZ/DHyKBwJXU082ihDnpB7mm+lbWNi6BW1LQ636Mh/PuYB/efe7NHvrKeBsTJ1rcTCV1al1XOh/hfTml3nDpvlzVi5HTSFKM4q4ds7N5FmWUtPipqu9hWnu/RR7yyjs3EV6/YcoV97lOQAADmdJREFUT8uAP4egxUlX9nwCk5bgmL4M58wVqPx5RvnkbkLBEF3tjbgajuBrqSLFXUeK5zj2rlpMHccMV5LVCfZ02tMm8Yo1yHPuana6jDjK9LTprJl2BqflLWRezjxmpM8w9nIE/eBuQXc1EupqJNjZQHtHLTWd1RwNtHMk2MXBQBtl3iZqfK2R9UxNncL83AXMzZlLSWYJJZklzMicgdXUHfDVGgIe492qv6vv54DX0Ap7uhHwt6fjMVk50mU0RD/UdijyruBQ26HIuwCFYnraNOZmFvPf6x4c0buTUyXwq4Hvaa03dH//7wBa63sGGjNSC/76X80lOxig1O+n2Bdgpk8z2W9GY8OrbJgI4dBenHiw48PMyAKGLhzdH3asOkAKHtJwETBpKqxW9tts7LPZ2Gezss9mo6NXEwmlNdmhEM5Qz8+7zWyi09RzjUVrlnh8rHG7OdvlZo7fj+p170aVTbPKoT1kxxnqIod2ClUDdtXz7iSkFQ1k4lN2zAqydBsp9KTuVYamsFOXsDs0i92hWVTpAtpIJdeumel0McV7mCJ/BYvVQRabDpKtOgHwaiu1OocU5SVftbLNYef/MnN4z2lFK9AhCzqQgdniR5s6AAh0nIbn+MXoQJax0zPo4wz363zd/DwlploCJjukTcLcUYPSQbZnrOcrLdfR5LdTkG6n3ePH4w9x5fJCfnDZQlJsfT2K7R4/N/3+Q9480MhkR5Cfhe7mE6Z9YHGCyQL//Ld+O1gr6ju54H/+QSCkuXpFIT+6esmI/hYG4qG/V3Lvy8YD+nf/tIpz5p0QG3E1w0NnQudxQqEQVaE8vmj6AQc96RTnpfLolz7B9Jy+754CwRA/31LBz7ccwKQUkzIcHG/3QCjAfdnPcYXnWZQOEsooJORuxervoFM7+V1wA3+yXkrAkU1duwe/duMoeBlr1jZQQcw6hVDQSUh1osxG0HaBB77c1sB5LjetOo02nUqacpNLByZl/O1WhqawLTSXPZZ5VDnncTyUTYvPTNDTwRQamWuqYoE6wkLTYRaqw6QqY24PNjpUOh5s2LWHTN3R528XIKBN1JFNPbl0mDJJMflICXVRoBvIow2AoxYLb6Y4eNvpZKvDgcfUI4rOUIisUAiTBhMatzLRajYR6CWcSmsKAwHme33M9/lZ4PUx3+cjOxSiSzvowIlL27EQxKKCWAnixIsTL2YVm156seLu1g0fVswEMRGgzhKi2hai2gYVNitdysavvl4e09yR13OKBP4q4AKt9Y3d398AnK61/pcTrvsK8BWAGTNmrDhyJPYaGk8/9ANMQTc27cOmvVi11/g6ZHwdUmb8yo7X5MCvHHhMTjymFPzmFAKWVHzmFIKWFJTJjE37MYW8WAJuzEE3lkAXlqALR8iNU7txdH9okxW/JRWfORWXKY1OUzodKp0OlUa7SsNryaTdGqCTGgK6noBuJqCb0SE3BH2Ygj5SQ5AZ0BQENbMDkE8GQWs2Hms2bns+bnseLns+LaYcWoJ2XL4goZAmw2kl02klw2Elw67ICzWS0nkYU1s15s5j2FzHCfm9+IMhXNZsvM7JuLPnEpqylIzsfHJSbfiDmja3n6ZOL42dXho7fbS4fKQ7LOSl2ZmW5aQwy8lMcwP57R8TqPoQX+sxOoMWjlsLKbPMY0doDqlpAfy2MgKWakKmDtJsTkoySzhnxjlk26bw0dEWdlW3UdnQiVkp5k5O57y5eRQ1vwmH3wJXk9GIY/6lMPX/b+/eY+QqyziOf387O7Mz29K9tJTebLnVSwtpC6VEEQKI3BIEAVMSE0DEf1QSNERraoxYlYAajDFKCCGQqIDcEpAqgrKiEm6lhZZL2WUBKV1oy5a2093uZebxj/NuOiyz2+nOzM7u6fNJmjlz3vc95z1PJ8+enMv7LmXPvgHWbuzimc5umhtTXLBkNsvC2Wwxg7k8dz33Duv/t5NTFjRycd/D6MO3YflVMKf4m7KPv/I+/+nYwbVnLazIUMGF+gZz3NLWyaymBlaeNL94pZ1vw9O/g2SGJ2es5KH2fo45fCpXfG7Bx/6IFercnuW+dVvY+mEv86dP4bzjZvGZ2dOia/KbHoAPOqKzyE+sYNf8s1nbnmXTu7vY2zfIrKYMyxe0sOLoVvbld9L2ThubuzeTHciSSRxGxuZR17eQrh2NzN3XzvG5VziGLTQn+shMbSbVOpf+mUvYOnUx7buTdO7Yy7bd+9jZM0B9QkxJ1dPSmKSpMUVzJknLlCSZZD0fZHvo7Xqdhu0v0bTrNVIDu0nTT74+TS7dSi5zOPlpc2DaXD6sn8k2a2JXX549+wbZ3TtA70COxlSCxlQ90xIDzLLtTM9to6n/fVI9XSR63qMrn+VtsrxXZ+yoE3sSIp9IkkukqE9MJV3fQio5g0xqPoclZjFNrSRzeVJ93aT7u0n3ddPY/wGN+SyN+Sxp6yGV34cpQY4Eg6qnvy7DQF0DfUrTV5emX2kG6jIMJjIM1mfIJxpI5npJDu4lOZglNZglmeuJ1uV6SeV7SDGAKUG+Lkle9fSpgV5lyNLIQEMzl3591Yj/96OpVYL/CnDOsAS/wsyuGalNudfgnXPuUDNagq/mY5JbgMK7IPMAn/bdOefGSTUT/HPAQklHSUoBlwEPVXF/zjnnClTtOXgzG5T0beBRosckbzezEt+Ucc45V66qvuhkZmuBtdXch3POueJ8qALnnIspT/DOORdTnuCdcy6mPME751xMVXWwsYMlaTtQqengZwA7KrStOPM4lcbjVBqPU+kqFasFZnZ4sYIJleArSdLzI73d5fbzOJXG41Qaj1PpxiNWfonGOediyhO8c87FVJwT/K217sAk4XEqjcepNB6n0lU9VrG9Bu+cc4e6OJ/BO+fcIc0TvHPOxdSkTvCSWiU9Jqk9fBad+kfSFaFOu6QripQ/JGlT9XtcG+XESVKjpEckvSbpZUmjTpw+GUk6V9JmSR2SPjatjqQGSfeE8mckHVlQ9oOwfrOkc8az3+NtrHGS9EVJ6yRtDJ9njnffx1M5v6dQPl9SVtJ1ZXfGzCbtP+AmYFVYXgXcWKROK9AZPlvCcktB+cXAn4BNtT6eiRgnoBE4I9RJAf8Gzqv1MVUwNgngDeDocHwvAouG1fkmcEtYvgy4JywvCvUbgKPCdhK1PqYJGKdlwJywfBzwbq2PZyLGqaD8fuBe4Lpy+zOpz+CBC4E7w/KdwEVF6pwDPGZm3Wa2E3gMOBdA0lTgu8BPx6GvtTTmOJlZj5k9AWBm/cALRLNzxcUKoMPMOsPx3U0Ur0KF8bsP+IIkhfV3m1mfmb0JdITtxdGY42Rm681saDa3l4G0pIZx6fX4K+f3hKSLiE6uKjJ3xmRP8EeYWRdA+JxZpM5c4J2C71vCOoA1wK+Anmp2cgIoN04ASGoGLgD+UaV+1sIBj7uwjpkNAruA6SW2jYty4lToEmC9mfVVqZ+1NuY4SZoCfB+4vlKdqeqEH5Ug6XFgVpGi1aVuosg6k7QUONbMvjP8GthkVK04FWy/HrgL+I2ZdR58DyesUY/7AHVKaRsX5cQpKpQWAzcCZ1ewXxNNOXG6HrjZzLLhhL5sEz7Bm9lZI5VJel/SbDPrkjQb2Fak2hbg9ILv84A24LPAiZLeIorDTEltZnY6k1AV4zTkVqDdzH5dge5OJKVMDj9UZ0v4Q9cEdJfYNi7KiROS5gEPApeb2RvV727NlBOnk4FLJd0ENAN5SfvM7Ldj7k2tb0qUeUPjF3z05uFNReq0Am8S3TBsCcutw+ocSbxvspYVJ6J7FPcDdbU+lirEpp7omudR7L8ptnhYnW/x0Ztifw7Li/noTdZO4nuTtZw4NYf6l9T6OCZynIbV+TEVuMla84CUGczpRNeD28PnUEJaDtxWUO8qohtgHcDXimwn7gl+zHEiOgMx4FVgQ/h3da2PqcLxOR94nejph9Vh3U+AL4XlNNFTDR3As8DRBW1Xh3abidHTRZWME/BDYG/B72cDMLPWxzPR4jRsGxVJ8D5UgXPOxdRkf4rGOefcCDzBO+dcTHmCd865mPIE75xzMeUJ3jnnYsoTvIstSbdJWlRCvWslXR6Wr5Q0p6DsLUkzDmKfSyWdX0K94yXdUep2nRsLT/AutszsajN7ZbQ64U3Cq4hGFAW4EpgzYoMDW0r0HPSB+rYRmCdpfhn7cm5UnuBdLEiaEsatf1HSJkkrJbVJWh7Ks5J+FsqflnREaHom8IKZDUq6lOjlrz9K2iApE+pcI+mFMJ75pwv2d7uk5yStl3ShpBTRCy0rQ/uVklZIeirUeUrSpwq6/TDRm4zOVYUneBcX5wJbzWyJmR0H/G1Y+RTgaTNbAjwJfCOsPwVYB2Bm9wHPA181s6Vm1hvq7DCzE4DfA0OTMKwG/mlmJwFnEA0HkQR+RDS+91Izuwd4DTjNzJaFsp8X9Ol54NTKHL5zH+cJ3sXFRuAsSTdKOtXMdg0r7wf+EpbXEQ1PATAb2H6AbT9QpN3ZwCpJG4gGZUsDxS63NAH3Kpox7Gai8WuGbKO8y0HOjWrCjybpXCnM7HVJJxJd/75B0t+HVRmw/eNy5Nj/2+8lSs6jGRq7vLCdiAbP2lxYUdLJw9quAZ4wsy+HYanbCsrSYf/OVYWfwbtYCE++9JjZH4BfAieU2PRV4NiC73uAw0po9yjRtfmhmXiWjdC+CXg3LF85bBufBGI7F7CrPU/wLi6OB54Nl0xWU/o0jH8FTiv4fgdwy7CbrMWsIbrm/lK4/LImrH8CWDR0k5VoPtwbJP2XaL7OQmcAj5TYT+cOmo8m6Q55kh4Evmdm7eO4zwbgX8DnLZq2zbmK8wTvDnnh0cUjzOzJcdznQmCumbWN1z7doccTvHPOxZRfg3fOuZjyBO+cczHlCd4552LKE7xzzsWUJ3jnnIup/wOf4s/iEjy7sAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = pyplot.subplots()\n",
    "\n",
    "#All in code for other setup\n",
    "#Plot code ----------------------------------------------------------------\n",
    "PAI = pi;\n",
    "pos = np.arange(-750,750,1)\n",
    "\n",
    "stheta = np.array([0.001149]);\n",
    "sthetaw = np.array([0.003175]);\n",
    "stheta = mean(stheta)\n",
    "sthetaw = mean(sthetaw)\n",
    "lmbda = 650e-9;\n",
    "N = 1;\n",
    "k = 2*pi/lmbda;\n",
    "s = 2*PAI/(N*k*stheta);\n",
    "w = 2*PAI/(k*sthetaw);\n",
    "\n",
    "i = 0\n",
    "\n",
    "#Plot code end ----------------------------------------------------------------\n",
    "resolution = 54.74 * 10**(-6)\n",
    "\n",
    "posInMeter = pos*resolution;\n",
    "theta = arctan(posInMeter)\n",
    "I = np.zeros(size(theta))\n",
    "scale = 1\n",
    "for angle in theta:\n",
    "    I[i] = diffract(angle,w,s,N,lmbda,scale);\n",
    "    i = i + 1;\n",
    "ax.plot(sin(theta),I, label='w = 2.047e-4')\n",
    "maxI = max(I);\n",
    "#Plot code ----------------------------------------------------------------\n",
    "#All in code for other setup for two or more slits\n",
    "N = 1;\n",
    "i = 0\n",
    "sthetaw = np.array([0.005857,0.005364]);\n",
    "stheta = mean(stheta)\n",
    "sthetaw = mean(sthetaw)\n",
    "s = 2*PAI/(N*k*stheta);\n",
    "w = 2*PAI/(k*sthetaw);\n",
    "#Plot code end----------------------------------------------------------------\n",
    "resolution = 54.74 * 10**(-6)\n",
    "\n",
    "I = np.zeros(size(theta))\n",
    "scale = maxI/(N*w)**2;\n",
    "for angle in theta:\n",
    "    I[i] = diffract(angle,w,s,N,lmbda,scale);\n",
    "    i = i + 1;\n",
    "ax.plot(sin(theta),I, label='w = 1.161e-4')\n",
    "\n",
    "#Plot code ----------------------------------------------------------------\n",
    "N = 1;\n",
    "i = 0\n",
    "s = 2*PAI/(N*k*stheta);\n",
    "sthetaw = np.array([0.01467,0.01461]);\n",
    "stheta = mean(stheta)\n",
    "sthetaw = mean(sthetaw)\n",
    "w = 2*PAI/(k*sthetaw);\n",
    "#Plot code end----------------------------------------------------------------\n",
    "resolution = 54.74 * 10**(-6)\n",
    "\n",
    "I = np.zeros(size(theta))\n",
    "scale = maxI/(N*w)**2;\n",
    "for angle in theta:\n",
    "    I[i] = diffract(angle,w,s,N,lmbda,scale);\n",
    "    i = i + 1;\n",
    "ax.plot(sin(theta),I, label='w = 4.439e-5')\n",
    "\n",
    "#END OF CODE----------------------------------------------------------------\n",
    "leg = ax.legend();\n",
    "pyplot.xlabel('sin(theta)')\n",
    "pyplot.ylabel('I')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00028285465622280247\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'I')"
      ]
     },
     "execution_count": 135,
     "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": [
    "fig, ax = pyplot.subplots()\n",
    "\n",
    "#All in code for other setup\n",
    "#Plot code ----------------------------------------------------------------\n",
    "PAI = pi;\n",
    "pos = np.arange(-750,750,1)\n",
    "\n",
    "stheta = np.array([0.001149]);\n",
    "sthetaw = np.array([0.01467,0.01461]);\n",
    "stheta = mean(stheta)\n",
    "sthetaw = mean(sthetaw)\n",
    "lmbda = 650e-9;\n",
    "N = 2;\n",
    "k = 2*pi/lmbda;\n",
    "#s = 2*PAI/(N*k*stheta);\n",
    "s = 0.00028285465622280247;\n",
    "w = 2*PAI/(k*sthetaw);\n",
    "\n",
    "i = 0\n",
    "print(s)\n",
    "#Plot code end ----------------------------------------------------------------\n",
    "resolution = 54.74 * 10**(-6)\n",
    "\n",
    "posInMeter = pos*resolution;\n",
    "theta = arctan(posInMeter)\n",
    "I = np.zeros(size(theta))\n",
    "scale = 1\n",
    "for angle in theta:\n",
    "    I[i] = diffract(angle,w,s,N,lmbda,scale);\n",
    "    i = i + 1;\n",
    "#ax.plot(sin(theta),I, label='s = 28.285e-5')\n",
    "maxI = max(I);\n",
    "#Plot code ----------------------------------------------------------------\n",
    "#All in code for other setup for two or more slits\n",
    "N = 2;\n",
    "i = 0\n",
    "sthetaw = np.array([0.01467,0.01461]);\n",
    "stheta = mean(stheta)\n",
    "sthetaw = mean(sthetaw)\n",
    "#s = 2*PAI/(N*k*stheta);\n",
    "s = 0.00018285465622280247;\n",
    "w = 2*PAI/(k*sthetaw);\n",
    "#Plot code end----------------------------------------------------------------\n",
    "resolution = 54.74 * 10**(-6)\n",
    "\n",
    "I = np.zeros(size(theta))\n",
    "scale = maxI/(N*w)**2;\n",
    "for angle in theta:\n",
    "    I[i] = diffract(angle,w,s,N,lmbda,scale);\n",
    "    i = i + 1;\n",
    "#ax.plot(sin(theta),I, label='s = 18.285e-5')\n",
    "\n",
    "#Plot code ----------------------------------------------------------------\n",
    "N = 2;\n",
    "i = 0\n",
    "sthetaw = np.array([0.01467,0.01461]);\n",
    "stheta = mean(stheta)\n",
    "sthetaw = mean(sthetaw)\n",
    "s = 0.00008285465622280247;\n",
    "w = 2*PAI/(k*sthetaw);\n",
    "#Plot code end----------------------------------------------------------------\n",
    "resolution = 54.74 * 10**(-6)\n",
    "\n",
    "I = np.zeros(size(theta))\n",
    "scale = maxI/(N*w)**2;\n",
    "for angle in theta:\n",
    "    I[i] = diffract(angle,w,s,N,lmbda,scale);\n",
    "    i = i + 1;\n",
    "ax.plot(sin(theta),I, label='s = 8.285e-5')\n",
    "\n",
    "#END OF CODE----------------------------------------------------------------\n",
    "leg = ax.legend();\n",
    "pyplot.xlabel('sin(theta)')\n",
    "pyplot.ylabel('I')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00028285465622280247\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  \"\"\"\n",
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'I')"
      ]
     },
     "execution_count": 133,
     "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": [
    "fig, ax = pyplot.subplots()\n",
    "\n",
    "#All in code for other setup\n",
    "#Plot code ----------------------------------------------------------------\n",
    "PAI = pi;\n",
    "pos = np.arange(-750,750,1)\n",
    "\n",
    "stheta = np.array([0.001149]);\n",
    "sthetaw = np.array([0.01467,0.01461]);\n",
    "stheta = mean(stheta)\n",
    "sthetaw = mean(sthetaw)\n",
    "lmbda = 650e-9;\n",
    "N = 2;\n",
    "k = 2*pi/lmbda;\n",
    "#s = 2*PAI/(N*k*stheta);\n",
    "s = 0.00028285465622280247;\n",
    "w = 2*PAI/(k*sthetaw);\n",
    "\n",
    "i = 0\n",
    "print(s)\n",
    "#Plot code end ----------------------------------------------------------------\n",
    "resolution = 54.74 * 10**(-6)\n",
    "\n",
    "posInMeter = pos*resolution;\n",
    "theta = arctan(posInMeter)\n",
    "I = np.zeros(size(theta))\n",
    "scale = 1\n",
    "for angle in theta:\n",
    "    I[i] = diffract(angle,w,s,N,lmbda,scale);\n",
    "    i = i + 1;\n",
    "#ax.plot(sin(theta),I, label='s = 28.285e-5')\n",
    "maxI = max(I);\n",
    "#Plot code ----------------------------------------------------------------\n",
    "#All in code for other setup for two or more slits\n",
    "N = 2;\n",
    "i = 0\n",
    "sthetaw = np.array([0.01467,0.01461]);\n",
    "stheta = mean(stheta)\n",
    "sthetaw = mean(sthetaw)\n",
    "#s = 2*PAI/(N*k*stheta);\n",
    "s = 0.00018285465622280247;\n",
    "w = 2*PAI/(k*sthetaw);\n",
    "#Plot code end----------------------------------------------------------------\n",
    "resolution = 54.74 * 10**(-6)\n",
    "\n",
    "I = np.zeros(size(theta))\n",
    "scale = maxI/(N*w)**2;\n",
    "for angle in theta:\n",
    "    I[i] = diffract(angle,w,s,N,lmbda,scale);\n",
    "    i = i + 1;\n",
    "ax.plot(sin(theta),I, label='s = 18.285e-5')\n",
    "\n",
    "#Plot code ----------------------------------------------------------------\n",
    "N = 2;\n",
    "i = 0\n",
    "sthetaw = np.array([0.01467,0.01461]);\n",
    "stheta = mean(stheta)\n",
    "sthetaw = mean(sthetaw)\n",
    "s = 0.00008285465622280247;\n",
    "w = 2*PAI/(k*sthetaw);\n",
    "#Plot code end----------------------------------------------------------------\n",
    "resolution = 54.74 * 10**(-6)\n",
    "\n",
    "I = np.zeros(size(theta))\n",
    "scale = maxI/(N*w)**2;\n",
    "for angle in theta:\n",
    "    I[i] = diffract(angle,w,s,N,lmbda,scale);\n",
    "    i = i + 1;\n",
    "#ax.plot(sin(theta),I, label='s = 8.285e-5')\n",
    "\n",
    "#END OF CODE----------------------------------------------------------------\n",
    "leg = ax.legend();\n",
    "pyplot.xlabel('sin(theta)')\n",
    "pyplot.ylabel('I')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00028285465622280247\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  \"\"\"\n",
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'I')"
      ]
     },
     "execution_count": 134,
     "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": [
    "fig, ax = pyplot.subplots()\n",
    "\n",
    "#All in code for other setup\n",
    "#Plot code ----------------------------------------------------------------\n",
    "PAI = pi;\n",
    "pos = np.arange(-750,750,1)\n",
    "\n",
    "stheta = np.array([0.001149]);\n",
    "sthetaw = np.array([0.01467,0.01461]);\n",
    "stheta = mean(stheta)\n",
    "sthetaw = mean(sthetaw)\n",
    "lmbda = 650e-9;\n",
    "N = 2;\n",
    "k = 2*pi/lmbda;\n",
    "#s = 2*PAI/(N*k*stheta);\n",
    "s = 0.00028285465622280247;\n",
    "w = 2*PAI/(k*sthetaw);\n",
    "\n",
    "i = 0\n",
    "print(s)\n",
    "#Plot code end ----------------------------------------------------------------\n",
    "resolution = 54.74 * 10**(-6)\n",
    "\n",
    "posInMeter = pos*resolution;\n",
    "theta = arctan(posInMeter)\n",
    "I = np.zeros(size(theta))\n",
    "scale = 1\n",
    "for angle in theta:\n",
    "    I[i] = diffract(angle,w,s,N,lmbda,scale);\n",
    "    i = i + 1;\n",
    "ax.plot(sin(theta),I, label='s = 28.285e-5')\n",
    "maxI = max(I);\n",
    "#Plot code ----------------------------------------------------------------\n",
    "#All in code for other setup for two or more slits\n",
    "N = 2;\n",
    "i = 0\n",
    "sthetaw = np.array([0.01467,0.01461]);\n",
    "stheta = mean(stheta)\n",
    "sthetaw = mean(sthetaw)\n",
    "#s = 2*PAI/(N*k*stheta);\n",
    "s = 0.00018285465622280247;\n",
    "w = 2*PAI/(k*sthetaw);\n",
    "#Plot code end----------------------------------------------------------------\n",
    "resolution = 54.74 * 10**(-6)\n",
    "\n",
    "I = np.zeros(size(theta))\n",
    "scale = maxI/(N*w)**2;\n",
    "for angle in theta:\n",
    "    I[i] = diffract(angle,w,s,N,lmbda,scale);\n",
    "    i = i + 1;\n",
    "#ax.plot(sin(theta),I, label='s = 18.285e-5')\n",
    "\n",
    "#Plot code ----------------------------------------------------------------\n",
    "N = 2;\n",
    "i = 0\n",
    "sthetaw = np.array([0.01467,0.01461]);\n",
    "stheta = mean(stheta)\n",
    "sthetaw = mean(sthetaw)\n",
    "s = 0.00008285465622280247;\n",
    "w = 2*PAI/(k*sthetaw);\n",
    "#Plot code end----------------------------------------------------------------\n",
    "resolution = 54.74 * 10**(-6)\n",
    "\n",
    "I = np.zeros(size(theta))\n",
    "scale = maxI/(N*w)**2;\n",
    "for angle in theta:\n",
    "    I[i] = diffract(angle,w,s,N,lmbda,scale);\n",
    "    i = i + 1;\n",
    "#ax.plot(sin(theta),I, label='s = 8.285e-5')\n",
    "\n",
    "#END OF CODE----------------------------------------------------------------\n",
    "leg = ax.legend();\n",
    "pyplot.xlabel('sin(theta)')\n",
    "pyplot.ylabel('I')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Intensity')"
      ]
     },
     "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": [
    "fig, ax = pyplot.subplots()\n",
    "\n",
    "#All in code for other setup\n",
    "#Plot code ----------------------------------------------------------------\n",
    "data = np.loadtxt('Red_1slit0.04.txt',delimiter=',', skiprows=10);\n",
    "#Plot code end ----------------------------------------------------------------\n",
    "resolution = 54.74 * 10**(-6)\n",
    "position = data[:,0]\n",
    "abspos = abs(position)\n",
    "\n",
    "intense = data[:,1]\n",
    "maxind = np.argmax(intense);\n",
    "pos = abspos - abspos[maxind]\n",
    "posInMeter = pos*resolution;\n",
    "theta = arctan(posInMeter)\n",
    "ax.plot(sin(theta),intense, label='Red Laser')\n",
    "#Plot code ----------------------------------------------------------------\n",
    "data = np.loadtxt('Green_1slit0.04.txt',delimiter=',', skiprows=10);\n",
    "#Plot code end ----------------------------------------------------------------\n",
    "resolution = 54.74 * 10**(-6)\n",
    "position = data[:,0]\n",
    "abspos = abs(position)\n",
    "\n",
    "intense = data[:,1]\n",
    "maxind = np.argmax(intense);\n",
    "pos = abspos - abspos[maxind]\n",
    "posInMeter = pos*resolution;\n",
    "theta = arctan(posInMeter)\n",
    "ax.plot(sin(theta),intense, label='Green Laser')\n",
    "#END OF CODE----------------------------------------------------------------\n",
    "leg = ax.legend();\n",
    "pyplot.xlabel('sin(theta)')\n",
    "pyplot.ylabel('Intensity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Intensity')"
      ]
     },
     "execution_count": 13,
     "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": [
    "fig, ax = pyplot.subplots()\n",
    "\n",
    "#All in code for other setup\n",
    "#Plot code ----------------------------------------------------------------\n",
    "data = np.loadtxt('Red_1slit0.04.txt',delimiter=',', skiprows=10);\n",
    "#Plot code end ----------------------------------------------------------------\n",
    "resolution = 54.74 * 10**(-6)\n",
    "position = data[:,0]\n",
    "abspos = abs(position)\n",
    "\n",
    "intense = data[:,1]\n",
    "maxind = np.argmax(intense);\n",
    "pos = abspos - abspos[maxind]\n",
    "posInMeter = pos*resolution;\n",
    "theta = arctan(posInMeter)\n",
    "ax.plot(sin(theta),intense, label='w = 4.439e-5')\n",
    "#Plot code ----------------------------------------------------------------\n",
    "data = np.loadtxt('Red_1slit0.08.txt',delimiter=',', skiprows=10);\n",
    "#Plot code end ----------------------------------------------------------------\n",
    "resolution = 54.74 * 10**(-6)\n",
    "position = data[:,0]\n",
    "abspos = abs(position)\n",
    "\n",
    "intense = data[:,1]\n",
    "maxind = np.argmax(intense);\n",
    "pos = abspos - abspos[maxind]\n",
    "posInMeter = pos*resolution;\n",
    "theta = arctan(posInMeter)\n",
    "ax.plot(sin(theta),intense, label='a = 0.08')\n",
    "#Plot code ----------------------------------------------------------------\n",
    "data = np.loadtxt('Red_1slit0.16.txt',delimiter=',', skiprows=10);\n",
    "#Plot code end ----------------------------------------------------------------\n",
    "resolution = 54.74 * 10**(-6)\n",
    "position = data[:,0]\n",
    "abspos = abs(position)\n",
    "\n",
    "intense = data[:,1]\n",
    "maxind = np.argmax(intense);\n",
    "pos = abspos - abspos[maxind]\n",
    "posInMeter = pos*resolution;\n",
    "theta = arctan(posInMeter)\n",
    "ax.plot(sin(theta),intense, label='a = 0.16')\n",
    "#END OF CODE----------------------------------------------------------------\n",
    "leg = ax.legend();\n",
    "pyplot.xlabel('sin(theta)')\n",
    "pyplot.ylabel('Intensity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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