mtf_to_psf.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mtf_file = \"mtf/UltraScan1000FT.csv\"\n",
    "mtf_file = \"mtf/K3.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 1.        ],\n",
       "       [0.01587302, 0.99200644],\n",
       "       [0.03174603, 0.98448253],\n",
       "       [0.04761905, 0.97722011],\n",
       "       [0.06349206, 0.97003662],\n",
       "       [0.07936508, 0.96277324],\n",
       "       [0.09523809, 0.95529344],\n",
       "       [0.11111111, 0.94748151],\n",
       "       [0.12698413, 0.93924122],\n",
       "       [0.14285714, 0.93049444],\n",
       "       [0.15873016, 0.92117985],\n",
       "       [0.17460317, 0.91125164],\n",
       "       [0.19047619, 0.90067828],\n",
       "       [0.20634921, 0.88944133],\n",
       "       [0.22222222, 0.87753423],\n",
       "       [0.23809524, 0.86496122],\n",
       "       [0.25396825, 0.85173623],\n",
       "       [0.26984127, 0.83788179],\n",
       "       [0.28571429, 0.82342804],\n",
       "       [0.3015873 , 0.80841175],\n",
       "       [0.31746032, 0.79287534],\n",
       "       [0.33333333, 0.77686601],\n",
       "       [0.34920635, 0.76043481],\n",
       "       [0.36507936, 0.74363586],\n",
       "       [0.38095238, 0.72652551],\n",
       "       [0.3968254 , 0.70916157],\n",
       "       [0.41269841, 0.69160261],\n",
       "       [0.42857143, 0.67390724],\n",
       "       [0.44444445, 0.65613347],\n",
       "       [0.46031746, 0.63833806],\n",
       "       [0.47619048, 0.620576  ],\n",
       "       [0.49206349, 0.60289987],\n",
       "       [0.50793651, 0.58535943]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the mtf\n",
    "mtf = np.genfromtxt(mtf_file, delimiter=\",\")\n",
    "\n",
    "# Sort the MTF by spatial frequnecy\n",
    "mtf = mtf[mtf[:, 0].argsort(), :]\n",
    "\n",
    "plt.plot(mtf[:, 0], mtf[:, 1])\n",
    "plt.xlabel(\"Spatial Frequnecy (1/pixel)\")\n",
    "plt.ylabel(\"MTF\")\n",
    "\n",
    "mtf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'MTF')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ayfPdnRExvhcszWSIvZSH0G/5IFWiRyXa31q5ublQq9VwcHCost3BwQHZ2dk1vickJAQrV65EQkICBEFAfHw8IiIiUFFRgdxc7W2iHTt2RGRkJLZu3Yp169ZBoVAgMDAQFy5cuG+WuXPnQqVS6V4ZGRn1d6BETYRaI+D9Tafw7Z2JuPMGe2HuIC9IjfSBpmJ50rMF1k/pA3srMyRnFeDFb+KQcbNE7FhEBkf0f7bdu0aIIAj3XTdkwYIFGDRoEHr37g1TU1MMGzYM48ePBwDIZNpr4b1798bYsWPRtWtXBAUF4ZdffoGnpyeWLVt23wxyuRw2NjZVXkT0t7JKNd5adxxR8RmQSoAlI7rg1Sc9xI5ltDo7K7Hp9QC0sbVA+s0SjPwmDhdzisSORWRQRCuA7O3tIZPJqnV7cnJyqnWF7jI3N0dERARKSkpw+fJlpKenw83NDdbW1rC3t6/xPVKpFL169XpgB4iI7q+kvBKT18Rj2yntAofLX+qBUb14mVhsrnaW2DC1D9q3tEJ2QSlGfRuH05kqsWMRGQzRCiAzMzP4+voiOjq6yvbo6GgEBAQ88L2mpqZwdnaGTCbD+vXrMWTIEEilNR+KIAhISkqCk5NTvWUnMhaqkgqMXXkEMRdyYW4qw6rxPTGoM/9b0hcONgpEvdYHnVsrcbO4HGO+P4z4yzfFjkVkEES9BDZ79mysXLkSERERSElJwaxZs5Ceno6pU6cC0M7NGTdunG78+fPn8eOPP+LChQs4evQoRo8ejdOnT+O///2vbszChQuxc+dOpKamIikpCZMmTUJSUpJun0RUOzmFpQj9Lg7H0/NhozDBj5P9EdS+hdix6B62lmb46VV/9HLTrhodtuooYi7cEDsWkd4TdR2g0NBQ5OXl4eOPP0ZWVhZ8fHywbds2uLq6AgCysrKQnp6uG69Wq/HZZ5/h3LlzMDU1Rb9+/RAbGws3NzfdmPz8fEyZMgXZ2dlQKpXo3r07Dhw4AD8/v8Y+PCKDdb2gFGO+O4zU3GK0sJbjh0l+6OjIuXH6ykZhirUT/fHajwk4cP4GJkXG46sx3THQx1HsaER6S9R1gPRVXdYRIGpqslWlGPP9YaTlFqN1M3P8/Ko/XO0sxY5FtVBWqcbM9UnYfjobMqkE/zeyC57v7ix2LKJGYxDrABGR/rmWfxuh38Xpip/1U3qz+DEgchMZlo3pjhd9naHWCJj9ywn8evyq2LGI9BILICICAGTm38bo7w7jSl4JXGzNEfVab7jYWogdi+rIRCbFkhFd8JJ/GwgC8PaGE9iUwCKI6F6izgEiIv1w9VYJxnx/GBk3b6ONrQXWTemN1s3MxY5Fj0gqleDfw3wgAfDTkXS8s/EEAGCELy+HEd3FAojIyGXc1BY/V2/dhqudBda92hutWPwYPKlUgk+G+UAiAX48rC2CBAAvsggiAsACiMioZdwswejvDiMz/zbc7S2x7tXecFQqHv5GMghSqQQfP+cDQFsEvbvxBARBwMieXMiSiHOAiIzUlbxihH4bh8z82/Cwt8T6KSx+mqK7naCxvbVzgt7bdJITo4nAAojIKF3JK8bo7w7jmqoUbVtoix8HGxY/TZVEUrUIemfDCWw9cU3sWESiYgFEZGSyVLfx0vdHkKUqRbuWVlg3pTdasvhp8iQS7eWw0b1coBGAWVFJ2HYqS+xYRKJhAURkRPKKyjB25RHdnJ+fX/VHS2sWP8ZCKpXgv8931q0TNH1dIqKTr4sdi0gULICIjERBaQXGRRzFpRvFaKVU4MfJLH6MkVQqweIRXTC8WytUagRM+/k44i7liR2LqNGxACIyArfL1ZgUeQxnrhXAztIMP0z25zo/Rkz7mIyuGODtgPJKDV5dG49TV1VixyJqVCyAiJq4sko1XvsxAccu34K1wgRrJ/mhbQsrsWORyExkUiwb0x19POxQVFaJV1YfxcWcIrFjETUaFkBETVilWoOZ65Nw4PwNmJvKEDmhFzq1Uoodi/SEwlSG78b5onNrJW4Wl2PcKu38MCJjwAKIqInSaAS8/+spbD+dDTOZFN+N84Wvq63YsUjPWCtMETmhFzxaWOKaqhRhq44gr6hM7FhEDY4FEFETtWh7CjYmXIVUAnw1phuC2rcQOxLpKTsrOX6c5I9WSgVSbxRj/OpjKCytEDsWUYNiAUTUBEUcTMP3MWkAgCUvdsVAHyeRE5G+a9XMHD9M9oetpRlOZarw6tp4lFaoxY5F1GBYABE1MdtPZeGTP5MBAO8N7MCHX1KttW1hhTUT/GAlN8Hh1JuYuT4Jao0gdiyiBsECiKgJib98EzOikiAIwNjebfB637ZiRyID09lZie/H9YSZTIodZ7Lx320pYkciahAsgIiaiIs5RZi0Jh7llRo84+WAhc/5QCKRiB2LDFCftnb4dGQXAMCqg2mIPJQmciKi+scCiKgJyCksxSsRR6G6XYFuLs2wbEx3yKQsfujRDevWGu+GdAAALPwjGbvOZIuciKh+sQAiMnBFZZWYGHkMmfm34WZngVWv9IS5mUzsWNQEvPFUW4zxc4EgANPXJ+JERr7YkYjqDQsgIgNWodZg2k/HcTpT+4iLNRP9YGclFzsWNRESiQSfDPNBX88WKK3QYNKaY8i4WSJ2LKJ6wQKIyEAJgoAPfjuN/edvQGEqxarxveBqZyl2LGpiTGRSLH+5B7ydbJBbVI7xq49CVcI1gsjwsQAiMlCRsZex7mgGJBLg6zE90M2lmdiRqImykpsgYnwvOCkVuHSjGFN+iEdZJdcIIsPGAojIAO0/fwOf/KFd6+dfg7zwjLeDyImoqXNUKhAxvhes5CY4knYTczedgiBwjSAyXCyAiAzMxZwivPnzcWgEYKSvMyYHuYsdiYyEl5MNwsf2gEwqwa+JmVh1kLfHk+FiAURkQPJLyjF5zTEUllail1tz/Pt5rvVDjSuofQvMf9YLAPDfbSmIuXBD5EREj4YFEJGBqFBrMO3n47icV4LWzcwRPtYXchPe7k6Nb3yAG170dYZGAN78ORHpebwzjAwPCyAiA/HJH8k4dDEPFmYyrHylJ+x5uzuJRCKR4N/DfdDVpRlUtyvw6tp4FJdVih2LqE5YABEZgB8OX8HauCuQSIClod3g5WQjdiQycgpTGb4L80ULaznOXS/EOxtOcFI0GRTRC6AVK1bA3d0dCoUCvr6+iImJeeD45cuXw8vLC+bm5ujQoQPWrl1bbcymTZvg7e0NuVwOb29vbN68uaHiEzW42Iu5+GjrGQDAuyEdENzJUeRERFoONgp8M9YXZjIptp/Oxtd/XRQ7ElGtiVoARUVFYebMmZg3bx4SExMRFBSEQYMGIT09vcbx4eHhmDt3Lj766COcOXMGCxcuxLRp0/D777/rxsTFxSE0NBRhYWE4ceIEwsLCMGrUKBw5cqSxDouo3mTm38a0n49DrREwvFsrPt2d9I6va3N8MrwTAOCz6PPYnXxd5EREtSMRROxZ+vv7o0ePHggPD9dt8/LywvDhw7Fo0aJq4wMCAhAYGIhPP/1Ut23mzJmIj4/HwYMHAQChoaEoKCjA9u3bdWMGDhyI5s2bY926dbXKVVBQAKVSCZVKBRsbXmogcZRWqDHq2zicvKpC59ZKbJjaBwpTTnom/fTBb6exNu4KrOQm2DItAO1aWosdiYxQXb6/ResAlZeXIyEhAcHBwVW2BwcHIzY2tsb3lJWVQaFQVNlmbm6Oo0ePoqJCuzR7XFxctX2GhITcd59391tQUFDlRSS2hb8n4+RVFZpZmGLFyz1Y/JBeWzDEG37utigqq8SraxOgus3HZZB+E60Ays3NhVqthoND1RVsHRwckJ2dXeN7QkJCsHLlSiQkJEAQBMTHxyMiIgIVFRXIzc0FAGRnZ9dpnwCwaNEiKJVK3cvFxeUxj47o8fxyLAPrjqZDIgG+HN0dLrYWYkcieiBTmRQrXu6BVkoF0nKLMWN9ItQaToom/SX6JOh7F3ETBOG+C7stWLAAgwYNQu/evWFqaophw4Zh/PjxAACZ7O9/HddlnwAwd+5cqFQq3SsjI+MRj4bo8Z3OVGH+b6cBALOf8URfzxYiJyKqHXsrOb4b1xMKUyn2nbuBz6PPiR2J6L5EK4Ds7e0hk8mqdWZycnKqdXDuMjc3R0REBEpKSnD58mWkp6fDzc0N1tbWsLe3BwA4OjrWaZ8AIJfLYWNjU+VFJIZbxeWY+mMCyis16N+xJab1ayd2JKI68WmtxOIRXQAAy/dewt5zOSInIqqZaAWQmZkZfH19ER0dXWV7dHQ0AgICHvheU1NTODs7QyaTYf369RgyZAikUu2h9OnTp9o+d+3a9dB9EolNrREwIyoJV2/dhqudBT4P7QaplI+5IMMzrFtrhPV2BQDMikrCtfzbIiciqs5EzA+fPXs2wsLC0LNnT/Tp0wffffcd0tPTMXXqVADaS1OZmZm6tX7Onz+Po0ePwt/fH7du3cLnn3+O06dPY82aNbp9zpgxA08++SQWL16MYcOG4bfffsPu3bt1d4kR6asv91zAgfM3oDCVIvxlXyjNTcWORPTI5g/xQlJGPk5lqjDt5+OImtIHZiaiz7og0hH1T2NoaCiWLl2Kjz/+GN26dcOBAwewbds2uLpq/+WQlZVVZU0gtVqNzz77DF27dsWAAQNQWlqK2NhYuLm56cYEBARg/fr1WL16Nbp06YLIyEhERUXB39+/sQ+PqNb+OnsdX+25AABY9EJneLfiZVgybHITGVa83APWChMkpudj8Y6zYkciqkLUdYD0FdcBosaUmX8bg7+Mgep2Bcb1ccXHw3zEjkRUb3adycaUHxIAAN+M9cVAH65kTg3HINYBIiKgUq3BjHWJUN2uQFdnJeY/6y12JKJ6FdzJEa8GuQMA3t1wAlfyikVORKTFAohIREt3X0D8lVuwlptg2ZgenCNBTdJ7AzvC17U5Cssq8cZPx1FaoRY7EhELICKxHLyQi+X7tA+PXDSiM9rYcbFDappMZVJ8/VJ3NLcwxZlrBfjkj2SxIxGxACISw43CMsyMSoIgAGP82mBIl1ZiRyJqUE5Kc3wR2g0SCfDTkXT8lpQpdiQyciyAiBqZRiNg9i9JyC0qg6eDFT4Ywnk/ZBye6tASb95Z3HPe5tNIzysROREZMxZARI3s2wOpiLmQC4WpFMtf6gFzMz7klIzHjP7t0dO1OYrKKjF9fSIq1BqxI5GRYgFE1IgSrtzC/+3SPh9p4XOd0N7BWuRERI3LRCbF0tHdYK0wQVJGvm79K6LGxgKIqJGoSiowfZ32CdnPdW2FUT1dxI5EJArn5hb4z/OdAQDL917EkdQ8kRORMWIBRNQIBEHA+7+eRGa+9jlf/3neBxIJn/NFxuu5rq3woq8zNIL2eWGqkgqxI5GRYQFE1Ag2JFzF9tPZMJVJsGxMd1gr+Jwvoo+e6wQ3OwtcU5Vi7uaT4IMJqDGxACJqYOl5JVi49QwAYPaADuji3EzcQER6wkpugi9Hd4eJVIJtp7KxIf6q2JHIiLAAImpA6ju3vBeXq+HnZospT3qIHYlIr3R1aYbZwZ4AgI9+P4PUG0UiJyJjwQKIqAF9s/8S4q/cgpXcBJ+N6gqZlPN+iO712pNt0cfDDiXlasxYn4TySt4aTw2PBRBRAzl1VYUvos8D0M51cLHloy6IaiKTSvB5aFc0szDFqUwVPr/z3w1RQ2IBRNQAbperMTMqEZUaAYN8HDGiR2uxIxHpNSelOf73QhcAwLcHLiH+8k2RE1FTxwKIqAH8b3sKLt0oRktrOf77fGfe8k5UCwN9HDGihzMEAXh7wwmUlFeKHYmaMBZARPVs//kbWBN3BQDw6ciuaG5pJnIiIsPxwVBvOCkVuJJXgv9tPyt2HGrCWAAR1aNbxeV4d8MJAMArfVzR17OFyImIDIvS3BSfvtgVALA27goOXsgVORE1VSyAiOqJIAiYt+UUcgrL0LaFJd4f5CV2JCKD9ER7e4T1dgUAvLvxBApKuUo01T8WQET15I+TWdh2KhsmUgmWhnbnU96JHsPcwR3hameBLFUpFm5NFjsONUEsgIjqQW5RGT747TQAYFq/dujsrBQ5EZFhszAzwWcju0IiATYdv4pdZ7LFjkRNDAsgonrwwW+ncaukAh0drTGtXzux4xA1CT3dbDElSLt6+r82n8LN4nKRE1FTwgKI6DH9eefSl0wqwf+N7AozE/5nRVRfZg3whKeDFXKLyjF/yyk+MJXqDf+mJnoMeUVlWHD30tdTbeHTmpe+iOqTwlSGz0Z20z0wdeuJa2JHoiaCBRDRY/hg6xncLC5HR0drvPl0e7HjEDVJnZ2VePNp7aXlD7eewY3CMpETUVPAAojoEW0/lYU/T2ZBJpXg0xd56YuoIU3r1w7eTjbIL6nAR7+fETsONQH8G5voEdwsLtdd+nq9b1ve9UXUwExlUix5sQtkUgn+PJmFnbwrjB4TCyCiR/DR1jPILSqHp4MV3urPu76IGoNPayWmPKm9K2zBltNQ3eYCifToWAAR1dGO09qJmHfv+pKbcMFDosYyo397eNhbIqewDIu2pYgdhwwYCyCiOlCVVGD+Fu2lr9ee9EAX52biBiIyMgpTGf43ogsAYP2xDBy6yGeF0aMRvQBasWIF3N3doVAo4Ovri5iYmAeO/+mnn9C1a1dYWFjAyckJEyZMQF5enu73kZGRkEgk1V6lpaUNfShkBP67LQW5RdpnfU3vz7u+iMTg526re1bY3F9PoaS8UuREZIhELYCioqIwc+ZMzJs3D4mJiQgKCsKgQYOQnp5e4/iDBw9i3LhxmDRpEs6cOYMNGzbg2LFjmDx5cpVxNjY2yMrKqvJSKBSNcUjUhMVdykNUfAYA4H8jukBhyktfRGJ5b2AHtFIqkH6zBJ/vOi92HDJAohZAn3/+OSZNmoTJkyfDy8sLS5cuhYuLC8LDw2scf/jwYbi5uWH69Olwd3fHE088gddeew3x8fFVxkkkEjg6OlZ5ET2O0go15m0+BQB4yb8NernZipyIyLhZK0zxn+c7AwAiDqUhMf2WyInI0IhWAJWXlyMhIQHBwcFVtgcHByM2NrbG9wQEBODq1avYtm0bBEHA9evXsXHjRjz77LNVxhUVFcHV1RXOzs4YMmQIEhMTH5ilrKwMBQUFVV5E/7Ri70Wk5hajpbUccwZ2FDsOEQHo17Elnu/eGhoBmLPpJMorNWJHIgMiWgGUm5sLtVoNBweHKtsdHByQnV3z+g4BAQH46aefEBoaCjMzMzg6OqJZs2ZYtmyZbkzHjh0RGRmJrVu3Yt26dVAoFAgMDMSFCxfum2XRokVQKpW6l4uLS/0cJDUJ568XInz/JQDAwuc6QWluKnIiIrprwRBv2Fma4fz1Iizfe1HsOGRARJ8ELZFIqvwsCEK1bXclJydj+vTp+OCDD5CQkIAdO3YgLS0NU6dO1Y3p3bs3xo4di65duyIoKAi//PILPD09qxRJ95o7dy5UKpXulZGRUT8HRwZPoxHw/qaTqFALeMbLAQN9eDmVSJ/YWprho+c6AQBW7LuIizlFIiciQyFaAWRvbw+ZTFat25OTk1OtK3TXokWLEBgYiHfffRddunRBSEgIVqxYgYiICGRlZdX4HqlUil69ej2wAySXy2FjY1PlRQQAPx1Nx/H0fFiayfDxsE73Lc6JSDxDujjh6Y4tUaEWMG8znxhPtSNaAWRmZgZfX19ER0dX2R4dHY2AgIAa31NSUgKptGpkmUx7J879/sALgoCkpCQ4OTnVQ2oyJtmqUizZfhYA8N7AjmjVzFzkRERUE4lEgoXPdYLCVIojaTex6Xim2JHIAIh6CWz27NlYuXIlIiIikJKSglmzZiE9PV13SWvu3LkYN26cbvzQoUPx66+/Ijw8HKmpqTh06BCmT58OPz8/tGrVCgCwcOFC7Ny5E6mpqUhKSsKkSZOQlJRU5TIZUW18tPUMCssq0c2lGcbeWXOEiPSTi60FZvT3BKBdr+tWcbnIiUjfmYj54aGhocjLy8PHH3+MrKws+Pj4YNu2bXB11X7ZZGVlVVkTaPz48SgsLMTXX3+Nt99+G82aNcPTTz+NxYsX68bk5+djypQpyM7OhlKpRPfu3XHgwAH4+fk1+vGR4dp5Jhs7zmTDRCrB/0Z0hkzKS19E+m5ykDu2JGbi3PVC/G/7WSx+sYvYkUiPSQReLK2moKAASqUSKpWK84GMUGFpBQZ8fgDZBaWY1q8t3g3hbe9EhiLhyk2MCI8DAPzyWh/4uXPNLmNSl+9v0e8CI9I3S3dfQHZBKVztLPDW03zcBZEh8XW1xRg/7VIm8zaf4tpAdF8sgIj+ISWrAJGxlwEAHw/z4eMuiAzQnIEdYWdphgs5Rfg+JlXsOKSnWAAR3aHRCFiw5TTUGgGDfBzR17OF2JGI6BE0szDDvGe9AABf7bmA9LwSkRORPmIBRHTHpuNXEX/lFizMZFgwxFvsOET0GJ7v3hoBbe1QVqnBB1tPc20gqoYFEBGA/JJy/O/Omj8z+rfnmj9EBk4ikeCT4T4wk0mx79wNbDtV8yOWyHixACIC8OnOc8grLkf7llaY+IS72HGIqB60bWGF159qCwBY+PsZFJVVipyI9AkLIDJ6JzLy8fNR7XpTnwz3gamM/1kQNRWvP9UWbnYWyCksw7I9938kEhkf/k1PRk2tETB/y2kIAvBC99bo7WEndiQiqkcKUxk+HKp9WGrEoTQ+LJV0WACRUfv5aDpOZapgrTDB3MFeYschogbQr2NL9L/zsNSFv5/hhGgCwAKIjFhuURk+3aGd+PxOcAe0sJaLnIiIGsoHQ71hJpMi5kIudiVfFzsO6QEWQGS0Fm07i4LSSnRqZcOHnRI1ca52lpjypAcA4OPfk1FaoRY5EYmNBRAZpfjLN7Hp+FVIJMC/h/vwYadERuCNfm3RSqlAZv5thO+7JHYcEhkLIDI6ao2Aj34/AwAY5euC7m2ai5yIiBqDhZkJ5j2rXeT0m/2XkHGTK0QbMxZAZHR+ic/A6cwCWCtM8O7ADmLHIaJGNLizo26F6E/+SBY7DomIBRAZFdXtCny68xwAYNYznrC34sRnImMikUiw8LlOMJFKsCv5OvafvyF2JBIJCyAyKkt3n8fNOys+h/XhxGciY9TewRqvBLgBABZuPYPySo24gUgULIDIaJy/Xoi1cVcAaG+J5YrPRMZrxjPtYW8lR2puMVYfShM7DomA3wBkFARBuwCaWiMg2NsBQe1biB2JiERkozDF+4M6AgC+2nMB1wtKRU5EjY0FEBmFnWeu49DFPJiZSDH/zl0gRGTcXujeGj3aNENxuRqLtqWIHYcaGQsgavJKK9T495/auz2mBHmgjZ2FyImISB9IpRJ8PMwHEgmwJekajqbdFDsSNSIWQNTkfX8gFVdv3YajjQJv9Gsrdhwi0iM+rZUY49cGAPDhVu1lcjIOLICoSbuWfxsr7qz4+q9nvWBhZiJyIiLSN+8Ed4CNwgQpWQXYEJ8hdhxqJCyAqElbtP0sbleo4edmi6FdnMSOQ0R6yNbSDDOe8QQA/N+u8ygsrRA5ETUGFkDUZB27fBO/n7gGqQT48DlvSCR83hcR1Systys87C2RW1Sm6xpT08YCiJokjUbAx79rJz6H9mqDTq2UIiciIn1mZiLFvwZ7AQBWxaTxOWFGgAUQNUmbEzNxKlMFK7kJZg/wFDsOERmA/l4t8UQ7e5SrNVi0nbfFN3UsgKjJKSmvxJKdZwEA0/q1QwtrPu+LiB5OIpFg/hAvSCXAtlPZOJKaJ3YkakB1KoCefPJJ5Ofn637eunUrbt++Xd+ZiB7Lt/tTcb2gDM7NzTEh0E3sOERkQDo62mD0ndviP/kzGRreFt9k1akAOnjwIMrLy3U/jx07FllZWfUeiuhRZalu49sD2gmMcwd5QWEqEzkRERma2QM8YS03wenMAmw6flXsONRAHusSmCCwMib98umOcyit0KCXW3MM7uwodhwiMkD2VnK8+XQ7AMCSnedQXFYpciJqCJwDRE3GiYx8/JqYCQCY/yxveyeiRzc+0A1tbC1wo7AM4bwtvkmqcwG0c+dObN26FVu3boVGo8GePXt0P9991cWKFSvg7u4OhUIBX19fxMTEPHD8Tz/9hK5du8LCwgJOTk6YMGEC8vKqTlTbtGkTvL29IZfL4e3tjc2bN9f1MMnACIKAT/7Q3vb+QvfW6OrSTNxARGTQ5CYy3W3x38ek4uot3hbf1EiEOlzHkkofXi9JJBKo1epa7S8qKgphYWFYsWIFAgMD8e2332LlypVITk5GmzZtqo0/ePAg+vbtiy+++AJDhw5FZmYmpk6divbt2+uKnLi4OAQFBeGTTz7B888/j82bN+ODDz7AwYMH4e/vX6tcBQUFUCqVUKlUsLGxqdV7SFx/nszCtJ+PQ2Eqxd53noKT0lzsSERk4ARBwOjvDuNI2k0M7doKy8Z0FzsSPURdvr/rVADVN39/f/To0QPh4eG6bV5eXhg+fDgWLVpUbfz//d//ITw8HJcu/d2OXLZsGZYsWYKMDO3zW0JDQ1FQUIDt27frxgwcOBDNmzfHunXrasxRVlaGsrIy3c8FBQVwcXFhAWQgyirVGPD5AaTfLMGM/u0xi+v+EFE9OZ2pwtCvD0IQgM1vBKB7m+ZiR6IHqEsBVKdLYBMnTkRhYeFjhburvLwcCQkJCA4OrrI9ODgYsbGxNb4nICAAV69exbZt2yAIAq5fv46NGzfi2Wef1Y2Ji4urts+QkJD77hMAFi1aBKVSqXu5uLg8xpFRY/vxcDrSb5aghbUcU570EDsOETUhPq2VeKG7MwDgv9tSePNPE1KnAmjNmjX1tu5Pbm4u1Go1HBwcqmx3cHBAdnZ2je8JCAjATz/9hNDQUJiZmcHR0RHNmjXDsmXLdGOys7PrtE8AmDt3LlQqle51t5tE+k91uwLL/roAQHvrqqWcT3snovr1Togn5CZSHLt8C7uSr4sdh+pJnQqghqh8771TRxCE+969k5ycjOnTp+ODDz5AQkICduzYgbS0NEydOvWR9wkAcrkcNjY2VV5kGFbsvYj8kgq0b2mFkb7OYschoibISWmOSU+4AwAWbz+LCrVG5ERUH+p8F1h93Vpsb28PmUxWrTOTk5NTrYNz16JFixAYGIh3330XXbp0QUhICFasWIGIiAjdgoyOjo512icZroybJVh96DIAYO7gjjCRcVUHImoYU59qC1tLM6TmFmP90XSx41A9qPM3hqenJ2xtbR/4qg0zMzP4+voiOjq6yvbo6GgEBATU+J6SkpJqd6LJZNqVfu92p/r06VNtn7t27brvPslw/d+ucyhXa9DHww79OrQUOw4RNWE2ClPM6N8eALB09wUUllaInIgeV50nTCxcuBBKpbJePnz27NkICwtDz5490adPH3z33XdIT0/XXdKaO3cuMjMzsXbtWgDA0KFD8eqrryI8PBwhISHIysrCzJkz4efnh1atWgEAZsyYgSeffBKLFy/GsGHD8Ntvv2H37t04ePBgvWQm/XDyaj5+S7oGAJj3rBcXPSSiBveSfxtExl5GWm4xvt2findCOogdiR5DnQug0aNHo2XL+vnXdmhoKPLy8vDxxx8jKysLPj4+2LZtG1xdXQEAWVlZSE//u9U4fvx4FBYW4uuvv8bbb7+NZs2a4emnn8bixYt1YwICArB+/XrMnz8fCxYsQNu2bREVFVXrNYBI/wmCgP9uSwEAPN+9NXxa109BTkT0IKYyKeYM7IipPyZg5cFUjO3tCkelQuxY9IjqtA6QTCZDVlZWvRVA+ooLIeq3PSnXMWlNPMxMpPjr7b5wbm4hdiQiMhKCIGDkN3GIv3ILI32d8enIrmJHon9osHWAHnY3FVFDq1RrsGj7WQDAhEA3Fj9E1KgkEgn+9az2ERkbj19F8rUCkRPRo6rTJbBXXnkFc+bMeei4iIiIRw5E9CBR8Rm4mFOE5hameOOpdmLHISIj1KNNczzb2Ql/nsrCou0p+GESp1gYojoVQGvWrIGrqyu6d+/O1TCp0RWVVeKLaO2ih2893R5Kc1ORExGRsXpvYAfsSs5GzIVcHDh/A096thA7EtVRnQqgqVOnYv369UhNTcXEiRMxduzYWt/2TvS4vj+QityiMrjaWWBsb1ex4xCREXO1s0RYbzdEHErDf7el4Il29pBKOUXEkNRpDtCKFSuQlZWFOXPm4Pfff4eLiwtGjRqFnTt3siNEDepGYRm+j0kFALwX0hFmJlz0kIjE9dbT7WCtMMHZ7EL8diJT7DhUR3X+FpHL5RgzZgyio6ORnJyMTp064Y033oCrqyuKiooaIiMRlu+9iJJyNbo4KzG4s6PYcYiI0NzSDFP7tgUAfLbrPMoq1SInorp4rH9GSyQSSCQSCIIAjYbPRqGGkZ5Xgp+OXAEAzBnYkXciEpHemBjojpbWcly9dRs/H+EjMgxJnQugsrIyrFu3DgMGDECHDh1w6tQpfP3110hPT4eVlVVDZCQj98Xu86hQCwhqb4/AdvZixyEi0jE3k2H6nUdkfP3XRRSVVYqciGqrTgXQG2+8AScnJyxevBhDhgzB1atXsWHDBgwePLjaM7qI6kNKVgG2JGmvrb8X0lHkNERE1YX2coG7vSXyisux8s5cRdJ/dVoJWiqVok2bNujevfsDL0P8+uuv9RJOLFwJWn9MWH0Ue8/dwLNdnLD8pR5ixyEiqtEfJ6/hzZ8TYWkmw/73+sHeSi52JKNUl+/vOt0GP27cOM6/oEZzJDUPe8/dgEwqwTvBfOggEemvwT5O6Nw6FacyVfj6r4v46LlOYkeih6hTARQZGdlAMYiqEgQBi3doH3lxt71MRKSvpFIJ5gzsiLGrjuCnI1cw6Ql3uNjyUT36jBN3SC/tTsnB8fR8KEylmHFngiERkT57or09nmhnjwq1gM+jz4sdhx6CBRDpHbVGwKc7td2fiYHucLBRiJyIiKh25gzU3qyxJSkTKVl8UKo+YwFEeufX41dx/noRlOameO3OImNERIags7MSz3ZxgiAAn+48J3YcegAWQKRXSivUWLpb+8DTN55qyweeEpHBeSe4A0ykEvx1NgdH026KHYfugwUQ6ZUfD19BZv5tONoo8EqAm9hxiIjqzN3eEqG9XAAA/9uewmdl6ikWQKQ3CksrsHzvRQDAzGfaQ2EqEzkREdGjmd6/PRSmUhxPz0d08nWx41ANWACR3vj+QCpulVTAo4UlXvR1FjsOEdEjc7BRYGKgOwDtXCC1hl0gfcMCiPTCjcIyrDyYBgB4N7gDTGT8o0lEhu21vtp5jBdyirDp+FWx49A9+C1DeuHrvy6gpFyNri7NMNDHUew4RESPTWluimn9tHeyLo0+j7JKtciJ6J9YAJHo0vNK8PPRdADAnIEd+LgVImoyxvVxg6ONAtdUpVh3JF3sOPQPLIBIdJ9Hn0OFWkBQe3sEtLUXOw4RUb1RmMrwVv92AICv915ESXmlyInoLhZAJKqUrAL8duIagL9XUCUiakpG9XRBG1sL5BaVIzL2sthx6A4WQCSqz3adhyAAQ7o4wae1Uuw4RET1zlQmxcxntM80/HZ/KlS3K0RORAALIBJRUkY+dqdch1QCzBrgKXYcIqIGM6xba7RvaQXV7QqsjEkVOw6BBRCJ6LNd2ufkvNDDGW1bWImchoio4cikErwdrP2HXsTBNOQVlYmciFgAkSiOpOYh5kIuTKQSzOjfXuw4REQNLqSTIzq3VqK4XI3wfZfEjmP0WABRoxMEAZ/tOg8ACO3lAhdbC5ETERE1PIlEgndCOgAA1h6+gizVbZETGTcWQNToYi7k4ujlmzAzkeLNp9uJHYeIqNE82d4efm62KK/UYNlfF8WOY9REL4BWrFgBd3d3KBQK+Pr6IiYm5r5jx48fD4lEUu3VqVMn3ZjIyMgax5SWljbG4dBDaLs/2rk/Yb1d4aQ0FzkREVHj+WcX6JdjGUjPKxE5kfEStQCKiorCzJkzMW/ePCQmJiIoKAiDBg1CenrNq2V++eWXyMrK0r0yMjJga2uLkSNHVhlnY2NTZVxWVhYUCkVjHBI9xO6UHJy4qoK5qQyvP9VW7DhERI3Oz90WfT1boFIjYOnu82LHMVqiFkCff/45Jk2ahMmTJ8PLywtLly6Fi4sLwsPDaxyvVCrh6Oioe8XHx+PWrVuYMGFClXESiaTKOEdHPltKH2g0f3d/JgS6wd5KLnIiIiJxvBOs7QJtTsrE+euFIqcxTqIVQOXl5UhISEBwcHCV7cHBwYiNja3VPlatWoVnnnkGrq6uVbYXFRXB1dUVzs7OGDJkCBITEx+4n7KyMhQUFFR5Uf3781QWzmYXwlpugilPeogdh4hINJ2dlRjYyRGCAHy+i10gMYhWAOXm5kKtVsPBwaHKdgcHB2RnZz/0/VlZWdi+fTsmT55cZXvHjh0RGRmJrVu3Yt26dVAoFAgMDMSFCxfuu69FixZBqVTqXi4uLo92UHRflWoNvrjT6n31SQ80szATORERkbjeDvaERALsOJON05kqseMYHdEnQd/75G9BEGr1NPDIyEg0a9YMw4cPr7K9d+/eGDt2LLp27YqgoCD88ssv8PT0xLJly+67r7lz50KlUuleGRkZj3QsdH9bkq4h9UYxmluYYkKgm9hxiIhE197BGs91bQUAnAskAtEKIHt7e8hksmrdnpycnGpdoXsJgoCIiAiEhYXBzOzBnQSpVIpevXo9sAMkl8thY2NT5UX1p7xSgy/3aP/jntq3LawVpiInIiLSDzP6t4dUor1BJCkjX+w4RkW0AsjMzAy+vr6Ijo6usj06OhoBAQEPfO/+/ftx8eJFTJo06aGfIwgCkpKS4OTk9Fh56dH9Ep+BjJu30cJajnF93MSOQ0SkNzxaWOGFHs4AgC+i2QVqTKJeAps9ezZWrlyJiIgIpKSkYNasWUhPT8fUqVMBaC9NjRs3rtr7Vq1aBX9/f/j4+FT73cKFC7Fz506kpqYiKSkJkyZNQlJSkm6f1LhKK9RY9pe2+/Zmv3YwN5OJnIiISL9Mf7o9TKQS7D9/AwlXboodx2iYiPnhoaGhyMvLw8cff4ysrCz4+Phg27Zturu6srKyqq0JpFKpsGnTJnz55Zc17jM/Px9TpkxBdnY2lEolunfvjgMHDsDPz6/Bj4eq++lIOq4XlKGVUoHRfpxcTkR0rzZ2FhjZ0xnrjmbgs13n8fOrvcWOZBQkgiAIYofQNwUFBVAqlVCpVJwP9BhKyivx5JK9yC0qx/9e6IzRfm3EjkREpJeu3ipBv//bhwq1gHWv9kaftnZiRzJIdfn+Fv0uMGq61sZdQW5ROVztLDDC11nsOEREesu5uQVG99L+I/GL6PNgb6LhsQCiBlFcVonvDqQCAN56uj1MZfyjRkT0INP6tYOZiRRHL9/EoYt5Ysdp8vitRA1iTdxl3Cwuh7u9JYZ3ayV2HCIiveeoVOBlf20X6LPoc+wCNTAWQFTviqp0f9rBhN0fIqJaef2ptlCYSpGYno9952+IHadJ4zcT1bs1sZeRX1IBD3tL3SqnRET0cC2tFbr10jgXqGGxAKJ6VVhage9jtN2f6f3bs/tDRFRHrz3pAQszGU5eVWF3So7YcZosfjtRvdJ1f1pYYii7P0REdWZnJcf4ADcAwOfR56HRsAvUEFgAUb0pKK3A9zFpALTPt5FJH/5QWyIiqu7VIA9YyU2QklWAnWeyH/4GqjMWQFRvIg9dhup2Bdq1tMKQLuz+EBE9quaWZpj4hDsA4Ivd56FmF6jesQCieqG6XYGV/5j7w+4PEdHjmfSEO2wUJjh/vQh/nsoSO06TwwKI6sXqQ2koKK1E+5ZWeLazk9hxiIgMntLcFK8GeQAAlrILVO9YANFjU92uwKqDd+b+PMPuDxFRfRkf6AaluSlSbxTjj5PXxI7TpLAAose26mAaCksr0cHBGoN92P0hIqov1gpTvBqknQv01Z4L7ALVIxZA9FhUJRVY/Y/uj5TdHyKievVKgLYLdIldoHrFAogey8qDqSgsq0RHR2sM7OQodhwioiaHXaCGwQKIHll+STlWH7oMQLvuD7s/REQN459dIN4RVj9YANEjWxmThqI73Z8Qdn+IiBqMtcIUk59gF6g+sQCiR3KruByrD2nn/sx8xpPdHyKiBvbKnTvCLuZwXaD6wAKIHsn3MakoLlfD28kGIZ0cxI5DRNTk2bALVK9YAFGd3Swux5rYywCAmc+0h0TC7g8RUWNgF6j+sACiOvvugLb749PaBgO82f0hImosNgpTTLrTBVrGLtBjYQFEdZJXVIa1cZcBADP7e7L7Q0TUyMYHusFGYYILOUXYxi7QI2MBRHXy3YFUlJSr0cVZif5eLcWOQ0RkdGwUpph85xlhnAv06FgAUa3lFpVhbdwVAJz7Q0QkJnaBHh8LIKq17w+k4naFGl2dlejXgd0fIiKx3NsF0rALVGcsgKhWbhaX44fD2u7PDHZ/iIhEV6ULdJpdoLpiAUS1suqgdu6PT2sbdn+IiPSA9o4wbRfoy93sAtUVCyB6qPyScqyJ1XZ/3nqa3R8iIn3BLtCjYwFEDxVx6LLumV8DvLjuDxGRvlCa/90F4lygumEBRA9UUFqhe+bXW0/zie9ERPpmfKAbrBUmOH+9CNtPZ4sdx2CwAKIHWnPoMgpLK9G+pRUG+fCJ70RE+kbbBdKuDv3lnvPsAtWS6AXQihUr4O7uDoVCAV9fX8TExNx37Pjx4yGRSKq9OnXqVGXcpk2b4O3tDblcDm9vb2zevLmhD6NJKiqrxKo73Z83n27H7g8RkZ6aEOjOLlAdiVoARUVFYebMmZg3bx4SExMRFBSEQYMGIT09vcbxX375JbKysnSvjIwM2NraYuTIkboxcXFxCA0NRVhYGE6cOIGwsDCMGjUKR44caazDajJ+iLuC/JIKeNhbYkiXVmLHISKi+1Cam2Ji4J1nhP3FuUC1IREEQbSz5O/vjx49eiA8PFy3zcvLC8OHD8eiRYse+v4tW7bghRdeQFpaGlxdXQEAoaGhKCgowPbt23XjBg4ciObNm2PdunU17qesrAxlZWW6nwsKCuDi4gKVSgUbG5tHPTyDVlJeiaDFe5FXXI7PRnbFCF9nsSMREdEDqEoqELj4LxSVVeLbMF+EdDK+aQsFBQVQKpW1+v4WrQNUXl6OhIQEBAcHV9keHByM2NjYWu1j1apVeOaZZ3TFD6DtAN27z5CQkAfuc9GiRVAqlbqXi4tLHY6kafr5SDryisvRxtYCw7qx+0NEpO+UFqYYH+AGQHtHmIj9DYMgWgGUm5sLtVoNB4eqt1U7ODggO/vh1y+zsrKwfft2TJ48ucr27OzsOu9z7ty5UKlUuldGRkYdjqTpKa1Q49sDqQCAaf3awkQm+lQxIiKqhYlPuMPCTIYz1wrw19kcsePoNdG/2e5dVE8QhFottBcZGYlmzZph+PDhj71PuVwOGxubKi9jtv5oOm4UlqF1M3M8352XvoiIDIWtpRnC+mivinz110V2gR5AtALI3t4eMpmsWmcmJyenWgfnXoIgICIiAmFhYTAzM6vyO0dHx0faJ2mVVarxzX5t9+f1p9rCzET0GpmIiOrg1SAPKEylOJGRjwMXcsWOo7dE+3YzMzODr68voqOjq2yPjo5GQEDAA9+7f/9+XLx4EZMmTar2uz59+lTb565dux66T9LaEH8V2QWlcLRRYGRPdn+IiAyNvZUcL/tru0Bf7j7PLtB9iPrP+9mzZ2PlypWIiIhASkoKZs2ahfT0dEydOhWAdm7OuHHjqr1v1apV8Pf3h4+PT7XfzZgxA7t27cLixYtx9uxZLF68GLt378bMmTMb+nAMXnmlBuH7LgEApvb1gNxEJnIiIiJ6FK896QEzEymOp+cj9lKe2HH0kqgFUGhoKJYuXYqPP/4Y3bp1w4EDB7Bt2zbdXV1ZWVnV1gRSqVTYtGlTjd0fAAgICMD69euxevVqdOnSBZGRkYiKioK/v3+DH4+h25x4FZn5t2FvJcdovzZixyEiokfU0kaBl+78Pf7Vngsip9FPoq4DpK/qso5AU1Gp1uDpz/Yj/WYJ5j/rhclBHmJHIiKix5Cluo2+S/ahXK3B+im90dvDTuxIDc4g1gEi/fJb0jWk3yyBnaUZXvJn94eIyNA5Kc11czmX/cUu0L1YABHUGgHL914EAEwO8oCFmYnIiYiIqD68/lRbmEglOHQxDwlXboodR6+wACL8cfIaUnOL0czCVLd+BBERGT7n5hZ48c6jjL7ac1HkNPqFBZCR02gEfP2X9j+KSYHusJKz+0NE1JS88VQ7yKQS7D9/A0kZ+WLH0RssgIzcjjPZuJBTBGuFCV4JdBM7DhER1bM2dhYY3q01AGAZ7wjTYQFkxDQaQXd75IRAd9goTEVOREREDWFav7aQSoA9Z3NwOlMldhy9wALIiEWnXMfZ7EJYyU0wkd0fIqImy6OFFZ7r2goA7wi7iwWQkRIEQfcfwbg+rmhmYfaQdxARkSF78+l2kEiAnWeuIyWrQOw4omMBZKT2nsvB6cwCWJjJuOghEZERaNfSGoM7OwGA7uYXY8YCyAgJgqC7HTKstytsLdn9ISIyBm893Q4AsO10Fi5cLxQ5jbhYABmhmAu5SMrIh8JUyu4PEZER6ehog4GdHCEIwNd7jbsLxALIyGi7P9q5Py/5uaKFtVzkRERE1JjevNMF+v3ENaTeKBI5jXhYABmZuNQ8xF+5BTMTKV7ry+4PEZGx8WmtxDNeLaERgOV7L4kdRzQsgIzMsjtzf0b3coGDjULkNEREJIa3nm4PANiSlIkrecUipxEHCyAjcjTtJuJS82Aqk2Bq37ZixyEiIpF0dWmGvp4toNYIWGGkXSAWQEbk7ro/L/q6oFUzc5HTEBGRmKb313aBNh2/ioybJSKnaXwsgIzE8fRbiLmQC5lUgjeeYveHiMjY+bo2xxPt7FGpEfDNfuPrArEAMhJ3H4D3QvfWcLG1EDkNERHpg7tdoF/iM3At/7bIaRoXCyAjcOqqCnvP3YBUAkzr107sOEREpCf83G3h726LCrWAb42sC8QCyAh8dWfuz7BureFmbylyGiIi0icz7nSB1h3LQE5BqchpGg8LoCYu+VoBopOvQ8LuDxER1aBPWzv0dG2O8koNvj2QKnacRsMCqIn7eq+2+/NsZye0a2klchoiItI3EokEb93pAv105ApuFJaJnKhxsABqws5fL8T209kA/l70ioiI6F5PtrdHV5dmKK3QYGWMcXSBWAA1YV//dRGCAAzs5IgOjtZixyEiIj0lkUgw/c4zwn44fAU3i8tFTtTwWAA1UZduFOGPk9cAAG/159wfIiJ6sKc7tkSnVjYoKVcj4mCa2HEaHAugJmr53ovQCMAzXi3RqZVS7DhERKTnJBKJbrpEZOxlqEoqRE7UsFgANUFX8orxW9Kd7g/n/hARUS0Fezugg4M1isoqsTq2aXeBWAA1QSv2XoJaI6CvZwt0dWkmdhwiIjIQUqkEb96ZCxRxMA2FpU23C8QCqInJuFmCTcevAvh7iXMiIqLaGtzZCW1bWKKgtBJr466IHafBsABqYr7ZfwmVGgFPtLOHr2tzseMQEZGBkf2jC7QyJhXFZZUiJ2oYohdAK1asgLu7OxQKBXx9fRETE/PA8WVlZZg3bx5cXV0hl8vRtm1bRERE6H4fGRkJiURS7VVa2vSX985S3caGeG33562neecXERE9mqFdWsHVzgK3Sirw05Gm2QUStQCKiorCzJkzMW/ePCQmJiIoKAiDBg1Cenr6fd8zatQo7NmzB6tWrcK5c+ewbt06dOzYscoYGxsbZGVlVXkpFIqGPhzRfbs/FeVqDfzdbeHvYSd2HCIiMlAmMqnu8UnfHUhDaYVa5ET1z0TMD//8888xadIkTJ48GQCwdOlS7Ny5E+Hh4Vi0aFG18Tt27MD+/fuRmpoKW1tbAICbm1u1cRKJBI6Ojg2aXd/kFJTi56PawpFzf4iI6HE93701vtpzAVdv3ca6o+mYEOgudqR6JVoHqLy8HAkJCQgODq6yPTg4GLGxsTW+Z+vWrejZsyeWLFmC1q1bw9PTE++88w5u375dZVxRURFcXV3h7OyMIUOGIDEx8YFZysrKUFBQUOVlaL47kIrySg18XZsjoC27P0RE9HhMZVK8/lRbANr5pU2tCyRaAZSbmwu1Wg0HB4cq2x0cHJCdnV3je1JTU3Hw4EGcPn0amzdvxtKlS7Fx40ZMmzZNN6Zjx46IjIzE1q1bsW7dOigUCgQGBuLChQv3zbJo0SIolUrdy8XFpX4OspHkFpXhxzvXaN96uh0kEonIiYiIqCl40dcZTkoFrheUYUPCVbHj1CvRJ0Hf+2UtCMJ9v8A1Gg0kEgl++ukn+Pn5YfDgwfj8888RGRmp6wL17t0bY8eORdeuXREUFIRffvkFnp6eWLZs2X0zzJ07FyqVSvfKyMiovwNsBN/HpKK0QoOuzkr09WwhdhwiImoi5CYyTO17pwu07xLKKzUiJ6o/ohVA9vb2kMlk1bo9OTk51bpCdzk5OaF169ZQKv9+tIOXlxcEQcDVqzVXplKpFL169XpgB0gul8PGxqbKy1DcLC7HD3F3uz/t2f0hIqJ6FdrLBS2s5cjMv41fjzedLpBoBZCZmRl8fX0RHR1dZXt0dDQCAgJqfE9gYCCuXbuGoqIi3bbz589DKpXC2dm5xvcIgoCkpCQ4OTnVX3g9EnEwDSXlang72aC/V0ux4xARUROjMJXhtSc9AAAr9l1CpbppdIFEvQQ2e/ZsrFy5EhEREUhJScGsWbOQnp6OqVOnAtBemho3bpxu/EsvvQQ7OztMmDABycnJOHDgAN59911MnDgR5ubmAICFCxdi586dSE1NRVJSEiZNmoSkpCTdPpsSVUkF1sReBgBM78+5P0RE1DBe8m8DO0szpN8s0T1r0tCJeht8aGgo8vLy8PHHHyMrKws+Pj7Ytm0bXF1dAQBZWVlV1gSysrJCdHQ03nrrLfTs2RN2dnYYNWoU/v3vf+vG5OfnY8qUKcjOzoZSqUT37t1x4MAB+Pn5NfrxNbTVsWkoLKtEBwdrBHsb123/RETUeCzMTDA5yAOLd5zF8r0XMbx7a8ikhv2PbokgCILYIfRNQUEBlEolVCqV3s4HKiytQOD//kJBaSW+fqk7hnRpJXYkIiJqworKKvHE4r+QX1KBL0d3w7BurcWOVE1dvr9FvwuMHs3auCsoKK1E2xaWGOTTNOc3ERGR/rCSm2DincUQl++9CI3GsPsnLIAMUHFZJVbGpALQ3vll6G1IIiIyDK8EuMFaYYLz14uw80zNa/YZChZABujHw1dwq6QCbnYWGNKF3R8iImocSnNTTAhwAwB89ddFGPIsGhZABuZ2uRrf3+n+TOvXDiYy/l9IRESNZ+IT7rA0kyElqwC7U3LEjvPI+O1pYH4+mo7conI4NzfH8O76NwGNiIiatmYWZhh3pwu07K8LBtsFYgFkQEor1Ph2/yUA2u6PKbs/REQkgslPuMPcVIaTV1XYf/6G2HEeCb9BDcgv8RnIKSxDK6UCI3rUvPI1ERFRQ7OzkuNl/zYAgK/2GGYXiAWQgSirVCN8n7b78/pTbWFmwv/riIhIPFOe9ICZiRTH0/MReylP7Dh1xm9RA7Ex4SqyVKVwsJFjZE8XseMQEZGRa2mjwEt+f3eBDA0LIANQodZgxV5t92dq37ZQmMpETkRERAS81tcDZjIpjqTdxJFUw+oCsQAyAJuPZyIz/zbsreQYc6faJiIiEpuT0hwv9tTOSV3210WR09QNCyA9V6nW4Ou92j9Urz3pwe4PERHpldf7toWJVIKDF3NxPP2W2HFqjQWQntt64hrSb5bA1tIML/dm94eIiPSLi60FXuihXZdumQHNBWIBpMfUGgFf32kpTg5yh4WZiciJiIiIqnvjqXaQSoC9527g5NV8sePUCgsgPfbHyWtIzS1GMwtTjOvjJnYcIiKiGrnZW2JYtztdIAOZC8QCSE9p/tH9mRToDis5uz9ERKS/pvVrB4kEiE6+jpSsArHjPBQLID2140w2LuQUwVphglcC3cSOQ0RE9EDtWlrh2c5OAKD7B7w+YwGkhzQaQbeo1IRAd9goTEVORERE9HBvPt0OALDtdBYuXC8UOc2DsQDSQ9Ep13E2uxBWchNMZPeHiIgMREdHGwzs5AhBgG4JF33FAkjPCIKAZX9puz+vBLiimYWZyImIiIhq724X6PcT15B6o0jkNPfHAkjP7D2Xg9OZBbAwk2HSEx5ixyEiIqoTn9ZK9O/YEhoBWH7nMU76iAWQHhEEAV/u0bYMw3q7wtaS3R8iIjI8b/VvDwDYkpSJ9LwSkdPUjAWQHom5kIsTGflQmEoxOYjdHyIiMkzdXJrhSc8WUGsEhO/Xz7lALID0hLb7o53785KfK1pYy0VORERE9Oim35kLtDHhKjLzb4ucpjoWQHoi7lIeEq7cgpmJFK/1ZfeHiIgMW083W/TxsEOFWsA3+/RvLhALID1xt/szppcLHGwUIqchIiJ6fNPvzAWKis/A9YJSkdNUxQJIDxxJzcORtJswk0kx9am2YschIiKqF709bNHLrTnKKzX4dn+q2HGqYAGkB+4+OO7Fns5wUpqLnIaIiKh+SCQSvPW0tgv005EruFFYJnKiv7EAElnClVs4eDEXJlIJXu/L7g8RETUtQe3t0dWlGcoqNVgZoz9dIBZAIru76vOIHs5wsbUQOQ0REVH9kkgkmNFfe0fYD4ev4GZxuciJtEQvgFasWAF3d3coFAr4+voiJibmgePLysowb948uLq6Qi6Xo23btoiIiKgyZtOmTfD29oZcLoe3tzc2b97ckIfwyE5k5GPfuRuQSSV4ox+7P0RE1DT169ASPq1tUFKuRsTBNLHjABC5AIqKisLMmTMxb948JCYmIigoCIMGDUJ6evp93zNq1Cjs2bMHq1atwrlz57Bu3Tp07NhR9/u4uDiEhoYiLCwMJ06cQFhYGEaNGoUjR440xiHVyd3uz7BureBqZylyGiIiooYhkUjwZj/tXKDI2MtQlVSInAiQCIIgiPXh/v7+6NGjB8LDw3XbvLy8MHz4cCxatKja+B07dmD06NFITU2Fra1tjfsMDQ1FQUEBtm/frts2cOBANG/eHOvWratVroKCAiiVSqhUKtjY2NTxqGrndKYKQ5YdhFQCRM/ui7YtrBrkc4iIiPSBRiNg8FcxOJtdiJnPtMfMZzzr/TPq8v0tWgeovLwcCQkJCA4OrrI9ODgYsbGxNb5n69at6NmzJ5YsWYLWrVvD09MT77zzDm7f/nuFybi4uGr7DAkJue8+Ae1ltYKCgiqvhvb1nTu/hnRpxeKHiIiaPKlUontSfMTBNBSWitsFEq0Ays3NhVqthoODQ5XtDg4OyM7OrvE9qampOHjwIE6fPo3Nmzdj6dKl2LhxI6ZNm6Ybk52dXad9AsCiRYugVCp1LxcXl8c4soc7m12AHWeyIZFA94eBiIioqRvk44S2LSxRUFqJtXFXRM0i+iRoiURS5WdBEKptu0uj0UAikeCnn36Cn58fBg8ejM8//xyRkZFVukB12ScAzJ07FyqVSvfKyMh4jCN6uLvr/gz2cYKng3WDfhYREZG+kEn/Xhdo9aE0lFdqRMtiItYH29vbQyaTVevM5OTkVOvg3OXk5ITWrVtDqVTqtnl5eUEQBFy9ehXt27eHo6NjnfYJAHK5HHJ54zx89GJOIbadygLA7g8RERmfIV2ckJxVgDF+bWBmIl4fRrRPNjMzg6+vL6Kjo6tsj46ORkBAQI3vCQwMxLVr11BUVKTbdv78eUilUjg7OwMA+vTpU22fu3btuu8+G9vXf12EIADB3g7wcmqYCdZERET6ykQmxb8Ge8HdXty7n0W9BDZ79mysXLkSERERSElJwaxZs5Ceno6pU6cC0F6aGjdunG78Sy+9BDs7O0yYMAHJyck4cOAA3n33XUycOBHm5tpHSMyYMQO7du3C4sWLcfbsWSxevBi7d+/GzJkzxTjEKtJyi7H1xDUAfz8gjoiIiBqfaJfAAO0t63l5efj444+RlZUFHx8fbNu2Da6urgCArKysKmsCWVlZITo6Gm+99RZ69uwJOzs7jBo1Cv/+9791YwICArB+/XrMnz8fCxYsQNu2bREVFQV/f/9GP757Xb1VghbWcvi0UsKntfLhbyAiIqIGIeo6QPqqIdcBKq1Qo+B2BVraKOp1v0RERMauLt/fonaAjJHCVAaFqUzsGEREREZN9NvgiYiIiBobCyAiIiIyOiyAiIiIyOiwACIiIiKjwwKIiIiIjA4LICIiIjI6LICIiIjI6LAAIiIiIqPDAoiIiIiMDgsgIiIiMjosgIiIiMjosAAiIiIio8MCiIiIiIwOnwZfA0EQAAAFBQUiJyEiIqLauvu9ffd7/EFYANWgsLAQAODi4iJyEiIiIqqrwsJCKJXKB46RCLUpk4yMRqPBtWvXYG1tDYlEInYc0RUUFMDFxQUZGRmwsbERO06TxnPdOHieGw/PdePgedYSBAGFhYVo1aoVpNIHz/JhB6gGUqkUzs7OYsfQOzY2Nkb9H1Zj4rluHDzPjYfnunHwPOOhnZ+7OAmaiIiIjA4LICIiIjI6LIDooeRyOT788EPI5XKxozR5PNeNg+e58fBcNw6e57rjJGgiIiIyOuwAERERkdFhAURERERGhwUQERERGR0WQERERGR0WABRjW7duoWwsDAolUoolUqEhYUhPz+/1u9/7bXXIJFIsHTp0gbL2BTU9TxXVFRgzpw56Ny5MywtLdGqVSuMGzcO165da7zQBmLFihVwd3eHQqGAr68vYmJiHjh+//798PX1hUKhgIeHB7755ptGSmr46nKuf/31VwwYMAAtWrSAjY0N+vTpg507dzZiWsNV1z/Tdx06dAgmJibo1q1bwwY0MCyAqEYvvfQSkpKSsGPHDuzYsQNJSUkICwur1Xu3bNmCI0eOoFWrVg2c0vDV9TyXlJTg+PHjWLBgAY4fP45ff/0V58+fx3PPPdeIqfVfVFQUZs6ciXnz5iExMRFBQUEYNGgQ0tPTaxyflpaGwYMHIygoCImJifjXv/6F6dOnY9OmTY2c3PDU9VwfOHAAAwYMwLZt25CQkIB+/fph6NChSExMbOTkhqWu5/kulUqFcePGoX///o2U1IAIRPdITk4WAAiHDx/WbYuLixMACGfPnn3ge69evSq0bt1aOH36tODq6ip88cUXDZzWcD3Oef6no0ePCgCEK1euNERMg+Tn5ydMnTq1yraOHTsK77//fo3j33vvPaFjx45Vtr322mtC7969GyxjU1HXc10Tb29vYeHChfUdrUl51PMcGhoqzJ8/X/jwww+Frl27NmBCw8MOEFUTFxcHpVIJf39/3bbevXtDqVQiNjb2vu/TaDQICwvDu+++i06dOjVGVIP2qOf5XiqVChKJBM2aNWuAlIanvLwcCQkJCA4OrrI9ODj4vuc1Li6u2viQkBDEx8ejoqKiwbIaukc51/fSaDQoLCyEra1tQ0RsEh71PK9evRqXLl3Chx9+2NARDRIfhkrVZGdno2XLltW2t2zZEtnZ2fd93+LFi2FiYoLp06c3ZLwm41HP8z+Vlpbi/fffx0svvWT0D0C8Kzc3F2q1Gg4ODlW2Ozg43Pe8Zmdn1zi+srISubm5cHJyarC8huxRzvW9PvvsMxQXF2PUqFENEbFJeJTzfOHCBbz//vuIiYmBiQm/6mvCDpAR+eijjyCRSB74io+PBwBIJJJq7xcEocbtAJCQkIAvv/wSkZGR9x1jLBryPP9TRUUFRo8eDY1GgxUrVtT7cRi6e8/hw85rTeNr2k7V1fVc37Vu3Tp89NFHiIqKqvEfA1RVbc+zWq3GSy+9hIULF8LT07Ox4hkcloVG5M0338To0aMfOMbNzQ0nT57E9evXq/3uxo0b1f4FcldMTAxycnLQpk0b3Ta1Wo23334bS5cuxeXLlx8ruyFpyPN8V0VFBUaNGoW0tDT89ddf7P78g729PWQyWbV/Gefk5Nz3vDo6OtY43sTEBHZ2dg2W1dA9yrm+KyoqCpMmTcKGDRvwzDPPNGRMg1fX81xYWIj4+HgkJibizTffBKC91CgIAkxMTLBr1y48/fTTjZJdn7EAMiL29vawt7d/6Lg+ffpApVLh6NGj8PPzAwAcOXIEKpUKAQEBNb4nLCys2l9iISEhCAsLw4QJEx4/vAFpyPMM/F38XLhwAXv37uUX9D3MzMzg6+uL6OhoPP/887rt0dHRGDZsWI3v6dOnD37//fcq23bt2oWePXvC1NS0QfMaskc514C28zNx4kSsW7cOzz77bGNENWh1Pc82NjY4depUlW0rVqzAX3/9hY0bN8Ld3b3BMxsEESdgkx4bOHCg0KVLFyEuLk6Ii4sTOnfuLAwZMqTKmA4dOgi//vrrfffBu8Aerq7nuaKiQnjuuecEZ2dnISkpScjKytK9ysrKxDgEvbR+/XrB1NRUWLVqlZCcnCzMnDlTsLS0FC5fviwIgiC8//77QlhYmG58amqqYGFhIcyaNUtITk4WVq1aJZiamgobN24U6xAMRl3P9c8//yyYmJgIy5cvr/LnNz8/X6xDMAh1Pc/34l1g1bEAohrl5eUJL7/8smBtbS1YW1sLL7/8snDr1q0qYwAIq1evvu8+WAA9XF3Pc1pamgCgxtfevXsbPb8+W758ueDq6iqYmZkJPXr0EPbv36/73SuvvCL07du3yvh9+/YJ3bt3F8zMzAQ3NzchPDy8kRMbrrqc6759+9b45/eVV15p/OAGpq5/pv+JBVB1EkG4M9OPiIiIyEjwLjAiIiIyOiyAiIiIyOiwACIiIiKjwwKIiIiIjA4LICIiIjI6LICIiIjI6LAAIiIiIqPDAoiIiIiMDgsgIoJEIsGWLVtqPf6jjz5Ct27dGiyPsVq1ahWCg4Mb/HP27dsHiUSC/Pz8etvnP/8M5eTkoEWLFsjMzKy3/RPVNxZARHogJycHr732Gtq0aQO5XA5HR0eEhIQgLi6uXj/nfoVLVlYWBg0aVG+fc/nyZUgkkmqvsWPH1ttnNDVlZWX44IMPsGDBAt22M2fOYMSIEXBzc4NEIsHSpUvv+/7x48fj/fffr9VnBQQEICsrC0ql8nFj16hly5YICwvDhx9+2CD7J6oPfBo8kR4YMWIEKioqsGbNGnh4eOD69evYs2cPbt682Sif7+jo2CD73b17Nzp16qT72dzcvNoYQRCgVqthYmLcfx1t2rQJVlZWCAoK0m0rKSmBh4cHRo4ciVmzZt33vRqNBn/++Se2bt1aq88yMzNrsP/P75owYQL8/Pzw6aefonnz5g36WUSPgh0gIpHl5+fj4MGDWLx4Mfr16wdXV1f4+flh7ty5ePbZZ3XjJBIJwsPDMWjQIJibm8Pd3R0bNmyosq85c+bA09MTFhYW8PDwwIIFC1BRUQEAiIyMxMKFC3HixAldRyYyMlK3739eAnvQfurCzs4Ojo6OupdSqdRdftm5cyd69uwJuVyOmJgYCIKAJUuWwMPDA+bm5ujatSs2btxYZX/btm2Dp6cnzM3N0a9fP0RGRla5lFNTh2vp0qVwc3PT/Tx+/HgMHz4c//d//wcnJyfY2dlh2rRpVY6vvLwc7733Hlq3bg1LS0v4+/tj3759VfZ76NAh9O3bFxYWFmjevDlCQkJw69YtrF27FnZ2digrK6syfsSIERg3btx9z9X69evx3HPPVdnWq1cvfPrppxg9ejTkcvl933vo0CFIpVL4+/vrum/r169HQEAAFAoFOnXqVCX/vZfAJk6ciC5duugyV1RUwNfXFy+//LLuPb///jt8fX2hUCjg4eGBhQsXorKy8r6ZOnfuDEdHR2zevPm+Y4jExAKISGRWVlawsrLCli1bqn1p3mvBggUYMWIETpw4gbFjx2LMmDFISUnR/d7a2hqRkZFITk7Gl19+ie+//x5ffPEFACA0NBRvv/02OnXqhKysLGRlZSE0NLTGz3nQfurLe++9h0WLFiElJQVdunTB/PnzsXr1aoSHh+PMmTOYNWsWxo4di/379wMAMjIy8MILL2Dw4MFISkrC5MmTa33J51579+7FpUuXsHfvXqxZswaRkZG6YhDQdi8OHTqE9evX4+TJkxg5ciQGDhyICxcuAACSkpLQv39/dOrUCXFxcTh48CCGDh0KtVqNkSNHQq1WV+nG5Obm4o8//sCECRPumykmJgY9e/Z8pOPZunUrhg4dCqn077/S3333Xbz99ttITExEQEAAnnvuOeTl5dX4/q+++grFxcW687lgwQLk5uZixYoVAICdO3di7NixmD59OpKTk/Htt98iMjIS//nPfx6Yy8/PDzExMY90TEQNTtyH0RORIAjCxo0bhebNmwsKhUIICAgQ5s6dK5w4caLKGADC1KlTq2zz9/cXXn/99fvud8mSJYKvr6/u5w8//FDo2rVrtXEAhM2bNz/2fu5KS0sTAAjm5uaCpaWl7nX8+HFh7969AgBhy5YtuvFFRUWCQqEQYmNjq+xn0qRJwpgxYwRBEIS5c+cKXl5egkaj0f1+zpw5AgDh1q1b9831xRdfCK6urrqfX3nlFcHV1VWorKzUbRs5cqQQGhoqCIIgXLx4UZBIJEJmZmaV/fTv31+YO3euIAiCMGbMGCEwMPC+x//6668LgwYN0v28dOlSwcPDo0r2f7p165YAQDhw4MB99+nq6ip88cUXNf7O09NT2Lp1qyAIf5/7//3vf7rfV1RUCM7OzsLixYsFQRB0/x/cPW+CIAixsbGCqampsGDBAsHExETYv3+/7ndBQUHCf//73yqf+cMPPwhOTk66n2v6MzRr1izhqaeeuu8xEYnJuC+6E+mJESNG4Nlnn0VMTAzi4uKwY8cOLFmyBCtXrsT48eN14/r06VPlfX369EFSUpLu540bN2Lp0qW4ePEiioqKUFlZCRsbmzrnqa/9REVFwcvLS/ezi4uLbmL3P7sdycnJKC0txYABA6q8v7y8HN27dwcApKSkoHfv3pBIJLrf33s+aqtTp06QyWS6n52cnHDq1CkAwPHjxyEIAjw9Pau8p6ysDHZ2dgC0HaCRI0fed/+vvvoqevXqhczMTLRu3RqrV6/G+PHjq2T/p9u3bwMAFApFnY8lJSUFV69exTPPPFNl+z/PjYmJCXr27FmlW3ivPn364J133sEnn3yCOXPm4Mknn9T9LiEhAceOHavS8VGr1SgtLUVJSQksLCxq3Ke5uTlKSkrqfExEjYEFEJGeUCgUGDBgAAYMGIAPPvgAkydPxocfflilAKrJ3S/Vw4cPY/To0Vi4cCFCQkKgVCqxfv16fPbZZ3XKUV/7AbQFT7t27Wr8naWlpe5/azQaAMCff/6J1q1bVxl3d+6LIAgP/TypVFptXE1zl0xNTav8LJFIdBk0Gg1kMhkSEhKqFEmA9nIlUPNk7n/q3r07unbtirVr1yIkJASnTp3C77//ft/xdnZ2kEgkuHXr1gP3W5OtW7diwIABD80E4L4FGKA97kOHDkEmk+ku9f3zdwsXLsQLL7xQ7X0PKtpu3ryJFi1aPDQXkRg4B4hIT3l7e6O4uLjKtsOHD1f7uWPHjgC0E2FdXV0xb9489OzZE+3bt8eVK1eqjDczM4NarX7g59ZmP/XN29sbcrkc6enpaNeuXZWXi4uLbkxNx/9PLVq0QHZ2dpUi6J8dstro3r071Go1cnJyqmW5e+dUly5dsGfPngfuZ/LkyVi9ejUiIiLwzDPP6I6jJmZmZvD29kZycnKdsgLAb7/9Vm3yNFD13FRWViIhIUH3Z6Umn376KVJSUrB//37s3LkTq1ev1v2uR48eOHfuXLXz0a5duyrzju51+vRpXQePSN+wACISWV5eHp5++mn8+OOPOHnyJNLS0rBhwwYsWbIEw4YNqzJ2w4YNiIiIwPnz5/Hhhx/i6NGjePPNNwEA7dq1Q3p6OtavX49Lly7hq6++qnYHjpubG9LS0pCUlITc3NwaJ13XZj/1zdraGu+88w5mzZqFNWvW4NKlS0hMTMTy5cuxZs0aAMDUqVNx6dIlzJ49G+fOncPPP/9cZeIyADz11FO4ceMGlixZgkuXLmH58uXYvn17nbJ4enri5Zdfxrhx4/Drr78iLS0Nx44dw+LFi7Ft2zYAwNy5c3Hs2DG88cYbOHnyJM6ePYvw8HDk5ubq9vPyyy8jMzMT33//PSZOnPjQzw0JCcHBgwerbCsvL0dSUhKSkpJQXl6OzMxMJCUl4eLFiwC060cdO3YMQ4YMqba/5cuXY/PmzTh79iymTZuGW7du3TdHUlISPvjgA6xatQqBgYH48ssvMWPGDKSmpgIAPvjgA6xduxYfffQRzpw5g5SUFERFRWH+/Pn3PZ6SkhIkJCQ0ysKORI9E3ClIRFRaWiq8//77Qo8ePQSlUilYWFgIHTp0EObPny+UlJToxgEQli9fLgwYMECQy+WCq6ursG7duir7evfddwU7OzvByspKCA0NFb744gtBqVRW+awRI0YIzZo1EwAIq1ev1u37nxNYH7af2k6CTkxMrPa7mibgCoIgaDQa4csvvxQ6dOggmJqaCi1atBBCQkKqTMb9/fffhXbt2glyuVwICgoSIiIiqu0rPDxccHFxESwtLYVx48YJ//nPf6pNgh42bFiVz54xY4bQt29f3c/l5eXCBx98ILi5uQmmpqaCo6Oj8PzzzwsnT57Ujdm3b58QEBAgyOVyoVmzZkJISEi1YwoLCxNsbW2F0tLS+56ru1JSUgRzc3MhPz+/2nm893U368qVK6tNxr77np9//lnw9/cXzMzMBC8vL2HPnj26Mf/8/+D27duCt7e3MGXKlCr7ef7554WAgADdZPEdO3YIAQEBgrm5uWBjYyP4+fkJ3333nW78vX+Gfv75Z6FDhw4PPW4isUgEoRYX1olIdBKJBJs3b8bw4cPFjqI39u3bh379+uHWrVto1qyZ2HGqGTBgALy8vPDVV1/VavyoUaPQvXt3zJ07t1bjn3vuOTzxxBN47733dNsuX74Md3d3JCYmivq4Ej8/P8ycORMvvfSSaBmIHoSXwIiI6tnNmzexfv16/PXXX5g2bVqt3/fpp5/qJlrXxhNPPIExY8Y8SsQGlZOTgxdffFEvsxHdxbvAiIjqWY8ePXDr1i0sXrwYHTp0qPX7XF1d8dZbb9V6/D87P/qkZcuWepuN6C5eAiMiIiKjw0tgREREZHRYABEREZHRYQFERERERocFEBERERkdFkBERERkdFgAERERkdFhAURERERGhwUQERERGZ3/BzSjbPJx5JFYAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Image size\n",
    "N = 256\n",
    "\n",
    "# Compute the fft bins\n",
    "freqs = np.fft.fftshift(np.fft.fftfreq(N))\n",
    "\n",
    "# Interpolate the MTF to the frequnecy bins\n",
    "measured_mtfs = np.interp(abs(freqs), mtf[:, 0], mtf[:, 1])\n",
    "\n",
    "plt.plot(freqs, measured_mtfs)\n",
    "plt.xlabel(\"Spatial Frequnecy (1/pixel)\")\n",
    "plt.ylabel(\"MTF\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x125fe44a0>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Recover phase data with GS phase retrival\n",
    "initial_phase = np.random.uniform(0, 2 * np.pi, (N,))\n",
    "otf_i = measured_mtfs * np.exp(1j * initial_phase)\n",
    "\n",
    "# Limited support constaint mask\n",
    "counts = np.arange(0, N)\n",
    "mask = abs(counts - N // 2) < 20\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(mask)\n",
    "\n",
    "for i in range(10000):\n",
    "    # Compute the psf\n",
    "    psf_i = np.fft.ifft(np.fft.ifftshift(otf_i))\n",
    "    \n",
    "    # Apply a real valued constraint to the psf\n",
    "    psf_i = np.abs(psf_i)\n",
    "    \n",
    "    # Apply a limited support constraint to the psf\n",
    "    psf_i[~mask] = 0\n",
    "    \n",
    "    # Shift the PSF so the peak is at the center\n",
    "    peak_idx = np.argmax(psf_i)\n",
    "    shift = N // 2 - peak_idx\n",
    "    psf_i = np.roll(psf_i, shift)\n",
    "    \n",
    "    # Compute the otf\n",
    "    otf_i = np.fft.fftshift(np.fft.fft(psf_i))\n",
    "    \n",
    "    # Compute the mtf and adjust the amplitude to match the measured mtf\n",
    "    mtf_i = np.abs(otf_i)\n",
    "    \n",
    "    # Adjust the otf amplitude\n",
    "    otf_i = otf_i * (measured_mtfs / mtf_i)\n",
    "    \n",
    "    # Enforce positive phase\n",
    "    otf_i = np.real(otf_i) + abs(np.imag(otf_i)) * 1j\n",
    "\n",
    "plt.plot(np.abs(psf_i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12694fe00>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(np.angle(otf_i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1269e4b60>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.abs(otf_i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x126a769c0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaEAAAGeCAYAAAA9hL66AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAdIklEQVR4nO3df2xV9R3/8dfl1x3gvTer2N7bce0aLW6IkCgO2jEpbDT0+x0B64w/ElNiRvwBJE01ODDGbtkoY5FoUu3cXBhksvKHAiYi0gXbzrAuhUBs0BiMRbrY2shX7i0Vb/nx+f6xcMelpb233Pq+tzwfyUnWc8699+0nGU8O995Tj3POCQAAA+OsBwAAXL+IEADADBECAJghQgAAM0QIAGCGCAEAzBAhAIAZIgQAMEOEAABmJlgPcKWLFy/q888/l8/nk8fjsR4HAJAi55x6e3uVn5+vceOGudZxo+Tll1923//+953X63V33nmna2lpSepxnZ2dThIbGxsbW5ZvnZ2dw/6ZPypXQjt37lRVVZVeeeUV/fjHP9arr76q8vJyffjhh7r55puHfKzP55MkLdD/0QRNHI3xAACj6LzO6X3tjf95PhSPc+m/gem8efN05513qr6+Pr7vhz/8oVasWKHa2tohHxuNRhUIBFSq5ZrgIUIAkG3Ou3Nq0h5FIhH5/f4hz037BxP6+/t1+PBhlZWVJewvKyvTwYMHB5wfi8UUjUYTNgDA9SHtEfryyy914cIF5eXlJezPy8tTd3f3gPNra2sVCATiWzgcTvdIAIAMNWof0b7yk23OuUE/7bZ+/XpFIpH41tnZOVojAQAyTNo/mDBt2jSNHz9+wFVPT0/PgKsjSfJ6vfJ6vekeAwCQBdJ+JTRp0iTdddddamxsTNjf2NiokpKSdL8cACCLjcpHtKurq/XII49o7ty5Ki4u1p/+9CedPHlSjz/++Gi8HAAgS41KhB544AGdOnVKv/nNb9TV1aVZs2Zp7969KigoGI2XAwBkqVH5ntC14HtCAJDdTL8nBABAsogQAMAMEQIAmCFCAAAzRAgAYIYIAQDMECEAgBkiBAAwQ4QAAGaIEADADBECAJghQgAAM0QIAGCGCAEAzBAhAIAZIgQAMEOEAABmiBAAwAwRAgCYIUIAADNECABghggBAMwQIQCAGSIEADBDhAAAZogQAMAMEQIAmCFCAAAzRAgAYIYIAQDMECEAgBkiBAAwQ4QAAGaIEADADBECAJhJe4Rqamrk8XgStmAwmO6XAQCMARNG40lvv/12/eMf/4j/PH78+NF4GQBAlhuVCE2YMIGrHwDAsEblPaHjx48rPz9fhYWFevDBB/Xpp59e9dxYLKZoNJqwAQCuD2mP0Lx587R9+3a9++67+vOf/6zu7m6VlJTo1KlTg55fW1urQCAQ38LhcLpHAgBkKI9zzo3mC/T19emWW27RunXrVF1dPeB4LBZTLBaL/xyNRhUOh1Wq5ZrgmTiaowEARsF5d05N2qNIJCK/3z/kuaPyntDlpk6dqjvuuEPHjx8f9LjX65XX6x3tMQAAGWjUvycUi8X00UcfKRQKjfZLAQCyTNoj9PTTT6u5uVkdHR3697//rV/84heKRqOqrKxM90sBALJc2v857j//+Y8eeughffnll7rppps0f/58tba2qqCgIN0vBQDIcmmPUENDQ7qfEgAwRnHvOACAGSIEADBDhAAAZogQAMAMEQIAmCFCAAAzRAgAYIYIAQDMECEAgBkiBAAwQ4QAAGaIEADADBECAJghQgAAM0QIAGCGCAEAzBAhAIAZIgQAMEOEAABmiBAAwAwRAgCYIUIAADNECABghggBAMwQIQCAGSIEADBDhAAAZogQAMAMEQIAmCFCAAAzRAgAYIYIAQDMECEAgBkiBAAwQ4QAAGZSjlBLS4uWLVum/Px8eTwe7d69O+G4c041NTXKz8/X5MmTVVpaqmPHjqVrXgDAGJJyhPr6+jRnzhzV1dUNenzz5s3asmWL6urq1NbWpmAwqCVLlqi3t/eahwUAjC0TUn1AeXm5ysvLBz3mnNOLL76oZ599VhUVFZKkbdu2KS8vTzt27NBjjz12bdMCAMaUtL4n1NHRoe7ubpWVlcX3eb1eLVy4UAcPHkznSwEAxoCUr4SG0t3dLUnKy8tL2J+Xl6fPPvts0MfEYjHFYrH4z9FoNJ0jAQAy2Kh8Os7j8ST87JwbsO+S2tpaBQKB+BYOh0djJABABkprhILBoKT/XRFd0tPTM+Dq6JL169crEonEt87OznSOBADIYGmNUGFhoYLBoBobG+P7+vv71dzcrJKSkkEf4/V65ff7EzYAwPUh5feEzpw5o08++ST+c0dHh44ePaqcnBzdfPPNqqqq0saNG1VUVKSioiJt3LhRU6ZM0cMPP5zWwQEA2S/lCB06dEiLFi2K/1xdXS1Jqqys1F//+letW7dOZ8+e1ZNPPqmvvvpK8+bN0/79++Xz+dI3NQBgTPA455z1EJeLRqMKBAIq1XJN8Ey0HgcAkKLz7pyatEeRSGTYt1i4dxwAwAwRAgCYIUIAADNECABghggBAMwQIQCAGSIEADBDhAAAZogQAMAMEQIAmCFCAAAzRAgAYIYIAQDMECEAgBkiBAAwQ4QAAGaIEADADBECAJghQgAAM0QIAGCGCAEAzBAhAIAZIgQAMEOEAABmiBAAwAwRAgCYIUIAADNECABghggBAMwQIQCAGSIEADBDhAAAZogQAMAMEQIAmCFCAAAzRAgAYCblCLW0tGjZsmXKz8+Xx+PR7t27E46vXLlSHo8nYZs/f3665gUAjCEpR6ivr09z5sxRXV3dVc9ZunSpurq64tvevXuvaUgAwNg0IdUHlJeXq7y8fMhzvF6vgsHgiIcCAFwfRuU9oaamJuXm5mrGjBlatWqVenp6rnpuLBZTNBpN2AAA14e0R6i8vFyvv/66Dhw4oBdeeEFtbW1avHixYrHYoOfX1tYqEAjEt3A4nO6RAAAZyuOccyN+sMejXbt2acWKFVc9p6urSwUFBWpoaFBFRcWA47FYLCFQ0WhU4XBYpVquCZ6JIx0NAGDkvDunJu1RJBKR3+8f8tyU3xNKVSgUUkFBgY4fPz7oca/XK6/XO9pjAAAy0Kh/T+jUqVPq7OxUKBQa7ZcCAGSZlK+Ezpw5o08++ST+c0dHh44ePaqcnBzl5OSopqZG9913n0KhkE6cOKENGzZo2rRpuvfee9M6OAAg+6UcoUOHDmnRokXxn6urqyVJlZWVqq+vV3t7u7Zv367Tp08rFApp0aJF2rlzp3w+X/qmBgCMCSlHqLS0VEN9luHdd9+9poEAANcP7h0HADBDhAAAZogQAMAMEQIAmCFCAAAzRAgAYGbUb9sDjBkeT5LnfYt/t3MXkzxvxLeIBEYVV0IAADNECABghggBAMwQIQCAGSIEADBDhAAAZogQAMAMEQIAmOHLqoCU3BdRk/wSqmdckl9qTQN3Mdm/RybxpVa+0AoDXAkBAMwQIQCAGSIEADBDhAAAZogQAMAMEQIAmCFCAAAzRAgAYIYIAQDMcMcEQErqbgjJ3glh3I05wz/X1CnDnuP6vh72nIun/l9SMyV1ZwV3IannAtKJKyEAgBkiBAAwQ4QAAGaIEADADBECAJghQgAAM0QIAGCGCAEAzPBlVSDNkvki6tvv7x72nP+7YMXwL5bkl1WBTJXSlVBtba3uvvtu+Xw+5ebmasWKFfr4448TznHOqaamRvn5+Zo8ebJKS0t17NixtA4NABgbUopQc3OzVq9erdbWVjU2Nur8+fMqKytTX19f/JzNmzdry5YtqqurU1tbm4LBoJYsWaLe3t60Dw8AyG4p/XPcvn37En7eunWrcnNzdfjwYd1zzz1yzunFF1/Us88+q4qKCknStm3blJeXpx07duixxx5L3+QAgKx3TR9MiEQikqScnP/esLGjo0Pd3d0qKyuLn+P1erVw4UIdPHhw0OeIxWKKRqMJGwDg+jDiCDnnVF1drQULFmjWrFmSpO7ubklSXl5ewrl5eXnxY1eqra1VIBCIb+FweKQjAQCyzIgjtGbNGn3wwQf6+9//PuCYx5N4y3vn3IB9l6xfv16RSCS+dXZ2jnQkAECWGdFHtNeuXau33npLLS0tmj59enx/MBiU9N8rolAoFN/f09Mz4OroEq/XK6/XO5IxAABZLqUrIeec1qxZozfffFMHDhxQYWFhwvHCwkIFg0E1NjbG9/X396u5uVklJSXpmRgAMGakdCW0evVq7dixQ3v27JHP54u/zxMIBDR58mR5PB5VVVVp48aNKioqUlFRkTZu3KgpU6bo4YcfHpX/ACDTJPMbUZP5ImoyzwNku5QiVF9fL0kqLS1N2L9161atXLlSkrRu3TqdPXtWTz75pL766ivNmzdP+/fvl8/nS8vAAICxw+Occ9ZDXC4ajSoQCKhUyzXBM9F6HFwvxo0f9hTPuME/XDPgqW7MGf65kri1TzJXQheTvG2Pu5jE/80vXkjquYDhnHfn1KQ9ikQi8vv9Q57LDUwBAGaIEADADBECAJghQgAAM0QIAGCGCAEAzPCbVQFJcheTOCW5v7Ml9bHpNP1G1KQ+ev3fE9PyekC6cSUEADBDhAAAZogQAMAMEQIAmCFCAAAzRAgAYIYIAQDMECEAgBkiBAAwwx0TAElK6nc7JnfXgWTvrJAWyd4JIbN+dyUQx5UQAMAMEQIAmCFCAAAzRAgAYIYIAQDMECEAgBkiBAAwQ4QAAGb4siqQrGS/8OkujO4cwBjClRAAwAwRAgCYIUIAADNECABghggBAMwQIQCAGSIEADBDhAAAZogQAMBMShGqra3V3XffLZ/Pp9zcXK1YsUIff/xxwjkrV66Ux+NJ2ObPn5/WoQEAY0NKEWpubtbq1avV2tqqxsZGnT9/XmVlZerr60s4b+nSperq6opve/fuTevQAICxIaV7x+3bty/h561btyo3N1eHDx/WPffcE9/v9XoVDAbTMyEAYMy6pveEIpGIJCknJydhf1NTk3JzczVjxgytWrVKPT09V32OWCymaDSasAEArg8jjpBzTtXV1VqwYIFmzZoV319eXq7XX39dBw4c0AsvvKC2tjYtXrxYsVhs0Oepra1VIBCIb+FweKQjAQCyjMe5ZO9Pn2j16tV6++239f7772v69OlXPa+rq0sFBQVqaGhQRUXFgOOxWCwhUNFoVOFwWKVargmeiSMZDQBg6Lw7pybtUSQSkd/vH/LcEf0+obVr1+qtt95SS0vLkAGSpFAopIKCAh0/fnzQ416vV16vdyRjAACyXEoRcs5p7dq12rVrl5qamlRYWDjsY06dOqXOzk6FQqERDwkAGJtSek9o9erV+tvf/qYdO3bI5/Opu7tb3d3dOnv2rCTpzJkzevrpp/Wvf/1LJ06cUFNTk5YtW6Zp06bp3nvvHZX/AABA9krpSqi+vl6SVFpamrB/69atWrlypcaPH6/29nZt375dp0+fVigU0qJFi7Rz5075fL60DQ0AGBtS/ue4oUyePFnvvvvuNQ0EALh+cO84AIAZIgQAMEOEAABmiBAAwAwRAgCYIUIAADNECABghggBAMwQIQCAGSIEADBDhAAAZogQAMAMEQIAmCFCAAAzRAgAYIYIAQDMECEAgBkiBAAwQ4QAAGaIEADADBECAJghQgAAM0QIAGCGCAEAzBAhAIAZIgQAMEOEAABmiBAAwAwRAgCYIUIAADNECABghggBAMwQIQCAGSIEADBDhAAAZlKKUH19vWbPni2/3y+/36/i4mK988478ePOOdXU1Cg/P1+TJ09WaWmpjh07lvahAQBjQ0oRmj59ujZt2qRDhw7p0KFDWrx4sZYvXx4PzebNm7VlyxbV1dWpra1NwWBQS5YsUW9v76gMDwDIbh7nnLuWJ8jJydEf/vAHPfroo8rPz1dVVZWeeeYZSVIsFlNeXp5+//vf67HHHkvq+aLRqAKBgEq1XBM8E69lNACAgfPunJq0R5FIRH6/f8hzR/ye0IULF9TQ0KC+vj4VFxero6ND3d3dKisri5/j9Xq1cOFCHTx48KrPE4vFFI1GEzYAwPUh5Qi1t7frhhtukNfr1eOPP65du3Zp5syZ6u7uliTl5eUlnJ+Xlxc/Npja2loFAoH4Fg6HUx0JAJClUo7QbbfdpqNHj6q1tVVPPPGEKisr9eGHH8aPezyehPOdcwP2XW79+vWKRCLxrbOzM9WRAABZakKqD5g0aZJuvfVWSdLcuXPV1taml156Kf4+UHd3t0KhUPz8np6eAVdHl/N6vfJ6vamOAQAYA675e0LOOcViMRUWFioYDKqxsTF+rL+/X83NzSopKbnWlwEAjEEpXQlt2LBB5eXlCofD6u3tVUNDg5qamrRv3z55PB5VVVVp48aNKioqUlFRkTZu3KgpU6bo4YcfHq35AQBZLKUIffHFF3rkkUfU1dWlQCCg2bNna9++fVqyZIkkad26dTp79qyefPJJffXVV5o3b572798vn883KsMDALLbNX9PKN34nhAAZLdv5XtCAABcKyIEADBDhAAAZogQAMAMEQIAmCFCAAAzRAgAYIYIAQDMECEAgBkiBAAwQ4QAAGaIEADADBECAJghQgAAM0QIAGCGCAEAzBAhAIAZIgQAMEOEAABmiBAAwAwRAgCYIUIAADNECABghggBAMwQIQCAGSIEADBDhAAAZogQAMAMEQIAmCFCAAAzRAgAYIYIAQDMECEAgBkiBAAwQ4QAAGZSilB9fb1mz54tv98vv9+v4uJivfPOO/HjK1eulMfjSdjmz5+f9qEBAGPDhFROnj59ujZt2qRbb71VkrRt2zYtX75cR44c0e233y5JWrp0qbZu3Rp/zKRJk9I4LgBgLEkpQsuWLUv4+Xe/+53q6+vV2toaj5DX61UwGEzfhACAMWvE7wlduHBBDQ0N6uvrU3FxcXx/U1OTcnNzNWPGDK1atUo9PT1pGRQAMPakdCUkSe3t7SouLtY333yjG264Qbt27dLMmTMlSeXl5br//vtVUFCgjo4OPffcc1q8eLEOHz4sr9c76PPFYjHFYrH4z9FodIT/KQCAbONxzrlUHtDf36+TJ0/q9OnTeuONN/Taa6+pubk5HqLLdXV1qaCgQA0NDaqoqBj0+WpqavTrX/96wP5SLdcEz8RURgMAZIDz7pyatEeRSER+v3/Ic1OO0JV+9rOf6ZZbbtGrr7466PGioiL98pe/1DPPPDPo8cGuhMLhMBECgCyVSoRS/ue4KznnEiJyuVOnTqmzs1OhUOiqj/d6vVf9pzoAwNiWUoQ2bNig8vJyhcNh9fb2qqGhQU1NTdq3b5/OnDmjmpoa3XfffQqFQjpx4oQ2bNigadOm6d577x2t+QEAWSylCH3xxRd65JFH1NXVpUAgoNmzZ2vfvn1asmSJzp49q/b2dm3fvl2nT59WKBTSokWLtHPnTvl8vtGaHwCQxa75PaF0i0ajCgQCvCcEAFkqlfeEuHccAMAMEQIAmCFCAAAzRAgAYIYIAQDMECEAgBkiBAAwQ4QAAGaIEADADBECAJghQgAAM0QIAGCGCAEAzBAhAIAZIgQAMEOEAABmiBAAwAwRAgCYIUIAADNECABghggBAMwQIQCAGSIEADBDhAAAZogQAMAMEQIAmCFCAAAzRAgAYIYIAQDMECEAgBkiBAAwQ4QAAGaIEADADBECAJiZYD3AlZxzkqTzOic542EAACk7r3OS/vfn+VAyLkK9vb2SpPe113gSAMC16O3tVSAQGPIcj0smVd+iixcv6vPPP5fP55PH45EkRaNRhcNhdXZ2yu/3G0+YPOb+9mXr7Mz97WLu0eWcU29vr/Lz8zVu3NDv+mTcldC4ceM0ffr0QY/5/f6MXvirYe5vX7bOztzfLuYePcNdAV3CBxMAAGaIEADATFZEyOv16vnnn5fX67UeJSXM/e3L1tmZ+9vF3Jkj4z6YAAC4fmTFlRAAYGwiQgAAM0QIAGCGCAEAzGRFhF555RUVFhbqO9/5ju666y7985//tB5pSDU1NfJ4PAlbMBi0HmuAlpYWLVu2TPn5+fJ4PNq9e3fCceecampqlJ+fr8mTJ6u0tFTHjh2zGfYyw829cuXKAes/f/58m2EvU1tbq7vvvls+n0+5ublasWKFPv7444RzMnHNk5k7E9e8vr5es2fPjn+xs7i4WO+88078eCau9SXDzZ6J6z1SGR+hnTt3qqqqSs8++6yOHDmin/zkJyovL9fJkyetRxvS7bffrq6urvjW3t5uPdIAfX19mjNnjurq6gY9vnnzZm3ZskV1dXVqa2tTMBjUkiVL4vf3szLc3JK0dOnShPXfu9f+XoTNzc1avXq1Wltb1djYqPPnz6usrEx9fX3xczJxzZOZW8q8NZ8+fbo2bdqkQ4cO6dChQ1q8eLGWL18eD00mrvUlw80uZd56j5jLcD/60Y/c448/nrDvBz/4gfvVr35lNNHwnn/+eTdnzhzrMVIiye3atSv+88WLF10wGHSbNm2K7/vmm29cIBBwf/zjHw0mHNyVczvnXGVlpVu+fLnJPKno6elxklxzc7NzLnvW/Mq5ncueNf/ud7/rXnvttaxZ68tdmt257FnvZGT0lVB/f78OHz6ssrKyhP1lZWU6ePCg0VTJOX78uPLz81VYWKgHH3xQn376qfVIKeno6FB3d3fC2nu9Xi1cuDDj116SmpqalJubqxkzZmjVqlXq6emxHmmASCQiScrJyZGUPWt+5dyXZPKaX7hwQQ0NDerr61NxcXHWrLU0cPZLMnm9U5FxNzC93JdffqkLFy4oLy8vYX9eXp66u7uNphrevHnztH37ds2YMUNffPGFfvvb36qkpETHjh3TjTfeaD1eUi6t72Br/9lnn1mMlLTy8nLdf//9KigoUEdHh5577jktXrxYhw8fzphvmjvnVF1drQULFmjWrFmSsmPNB5tbytw1b29vV3Fxsb755hvdcMMN2rVrl2bOnBkPTSav9dVmlzJ3vUcioyN0yaVf6XCJc27AvkxSXl4e/9933HGHiouLdcstt2jbtm2qrq42nCx12bb2kvTAAw/E//esWbM0d+5cFRQU6O2331ZFRYXhZP+zZs0affDBB3r//fcHHMvkNb/a3Jm65rfddpuOHj2q06dP64033lBlZaWam5vjxzN5ra82+8yZMzN2vUcio/85btq0aRo/fvyAq56enp4Bf4PJZFOnTtUdd9yh48ePW4+StEuf5sv2tZekUCikgoKCjFn/tWvX6q233tJ7772X8GtLMn3Nrzb3YDJlzSdNmqRbb71Vc+fOVW1trebMmaOXXnop49dauvrsg8mU9R6JjI7QpEmTdNddd6mxsTFhf2Njo0pKSoymSl0sFtNHH32kUChkPUrSCgsLFQwGE9a+v79fzc3NWbX2knTq1Cl1dnaar79zTmvWrNGbb76pAwcOqLCwMOF4pq75cHMPJlPW/ErOOcVisYxd66Fcmn0wmbreSbH6RESyGhoa3MSJE91f/vIX9+GHH7qqqio3depUd+LECevRruqpp55yTU1N7tNPP3Wtra3u5z//ufP5fBk3c29vrzty5Ig7cuSIk+S2bNnijhw54j777DPnnHObNm1ygUDAvfnmm669vd099NBDLhQKuWg0mrFz9/b2uqeeesodPHjQdXR0uPfee88VFxe7733ve+ZzP/HEEy4QCLimpibX1dUV377++uv4OZm45sPNnalrvn79etfS0uI6OjrcBx984DZs2ODGjRvn9u/f75zLzLW+ZKjZM3W9RyrjI+Sccy+//LIrKChwkyZNcnfeeWfCR0Mz0QMPPOBCoZCbOHGiy8/PdxUVFe7YsWPWYw3w3nvvOUkDtsrKSufcfz8y/Pzzz7tgMOi8Xq+75557XHt7u+3Qbui5v/76a1dWVuZuuukmN3HiRHfzzTe7yspKd/LkSeuxB51Zktu6dWv8nExc8+HmztQ1f/TRR+N/btx0003upz/9aTxAzmXmWl8y1OyZut4jxa9yAACYyej3hAAAYxsRAgCYIUIAADNECABghggBAMwQIQCAGSIEADBDhAAAZogQAMAMEQIAmCFCAAAzRAgAYOb/A+/FhxSS5w3ZAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Revolve the PSF\n",
    "xs = np.arange(N)\n",
    "ys = np.arange(N)\n",
    "\n",
    "xs, ys = np.meshgrid(xs, ys)\n",
    "\n",
    "# Compute the radii\n",
    "rs = np.sqrt((xs - N // 2)**2 + (ys - N // 2)**2)\n",
    "\n",
    "# Interpolate the mtf\n",
    "full_psf = np.interp(rs, np.arange(N // 2), psf_i[N // 2:])\n",
    "\n",
    "# Plot the output\n",
    "# plt.imshow(full_psf)\n",
    "plt.imshow(full_psf[N // 2 - 20:N // 2 + 20, N // 2 - 20:N // 2 + 20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Generate the image vectors\n",
    "# max_freq = np.max(mtf[:, 0])\n",
    "# N = 2 * len(mtf[:, 0]) - 1\n",
    "# xs = np.linspace(-max_freq, max_freq, N)\n",
    "# ys = np.linspace(-max_freq, max_freq, N)\n",
    "\n",
    "# xs, ys = np.meshgrid(xs, ys)\n",
    "\n",
    "# # Compute the radii\n",
    "# rs = np.sqrt(xs**2 + ys**2)\n",
    "\n",
    "# # Interpolate the mtf\n",
    "# measured_mtfs = np.interp(rs, mtf[:, 0], mtf[:, 1])\n",
    "\n",
    "# # Plot the output\n",
    "# plt.imshow(measured_mtfs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Recover phase data with GS phase retrival\n",
    "# initial_phase = np.random.uniform(0, 2 * np.pi, (N, N))\n",
    "# otf_i = measured_mtfs * np.exp(1j * initial_phase)\n",
    "\n",
    "# # Limited support constaint mask\n",
    "# xs_i = np.arange(0, otf_i.shape[0])\n",
    "# ys_i = np.arange(0, otf_i.shape[1])\n",
    "\n",
    "# xs_i, ys_i = np.meshgrid(xs_i, ys_i)\n",
    "\n",
    "# rs_i = np.sqrt((xs_i - otf_i.shape[0] // 2)**2 + (ys_i - otf_i.shape[1] // 2)**2)\n",
    "# mask = rs_i < 15\n",
    "\n",
    "# for i in range(10000):\n",
    "#     # Compute the psf\n",
    "#     psf_i = np.fft.ifft2(np.fft.ifftshift(otf_i))\n",
    "    \n",
    "#     # Apply a real valued constraint to the psf\n",
    "#     psf_i = np.abs(psf_i)\n",
    "    \n",
    "#     # # Set the center value to 1\n",
    "#     # psf_i[psf_i.shape[0] // 2, psf_i.shape[0] // 2] = 1\n",
    "    \n",
    "#     # Apply a limited support constraint to the psf\n",
    "#     psf_i[~mask] = 0\n",
    "    \n",
    "#     # Compute the otf\n",
    "#     otf_i = np.fft.fftshift(np.fft.fft2(psf_i))\n",
    "    \n",
    "#     # Compute the mtf and adjust the amplitude to match the measured mtf\n",
    "#     mtf_i = np.abs(otf_i)\n",
    "    \n",
    "#     # Adjust the otf amplitude\n",
    "#     otf_i = otf_i * (measured_mtfs / mtf_i)\n",
    "\n",
    "# psf_i = np.fft.ifft2(np.fft.ifftshift(otf_i))\n",
    "# plt.imshow(np.abs(psf_i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Invalid shape (256,) for image data",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[67], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mangle\u001b[49m\u001b[43m(\u001b[49m\u001b[43motf_i\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\cttew\\Desktop\\code\\holo\\venv\\Lib\\site-packages\\matplotlib\\pyplot.py:3562\u001b[0m, in \u001b[0;36mimshow\u001b[1;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, interpolation_stage, filternorm, filterrad, resample, url, data, **kwargs)\u001b[0m\n\u001b[0;32m   3541\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mimshow)\n\u001b[0;32m   3542\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mimshow\u001b[39m(\n\u001b[0;32m   3543\u001b[0m     X: ArrayLike \u001b[38;5;241m|\u001b[39m PIL\u001b[38;5;241m.\u001b[39mImage\u001b[38;5;241m.\u001b[39mImage,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   3560\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m   3561\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m AxesImage:\n\u001b[1;32m-> 3562\u001b[0m     __ret \u001b[38;5;241m=\u001b[39m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimshow\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   3563\u001b[0m \u001b[43m        \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3564\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcmap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3565\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnorm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnorm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3566\u001b[0m \u001b[43m        \u001b[49m\u001b[43maspect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maspect\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3567\u001b[0m \u001b[43m        \u001b[49m\u001b[43minterpolation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minterpolation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3568\u001b[0m \u001b[43m        \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3569\u001b[0m \u001b[43m        \u001b[49m\u001b[43mvmin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvmin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3570\u001b[0m \u001b[43m        \u001b[49m\u001b[43mvmax\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvmax\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3571\u001b[0m \u001b[43m        \u001b[49m\u001b[43morigin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morigin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3572\u001b[0m \u001b[43m        \u001b[49m\u001b[43mextent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3573\u001b[0m \u001b[43m        \u001b[49m\u001b[43minterpolation_stage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minterpolation_stage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3574\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfilternorm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfilternorm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3575\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfilterrad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfilterrad\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3576\u001b[0m \u001b[43m        \u001b[49m\u001b[43mresample\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresample\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3577\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3578\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3579\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   3580\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3581\u001b[0m     sci(__ret)\n\u001b[0;32m   3582\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m __ret\n",
      "File \u001b[1;32mc:\\Users\\cttew\\Desktop\\code\\holo\\venv\\Lib\\site-packages\\matplotlib\\__init__.py:1473\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1470\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m   1471\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m   1472\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1473\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1474\u001b[0m \u001b[43m            \u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1475\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1476\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[43mk\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1478\u001b[0m     bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1479\u001b[0m     auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[0;32m   1480\u001b[0m                   \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n",
      "File \u001b[1;32mc:\\Users\\cttew\\Desktop\\code\\holo\\venv\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:5895\u001b[0m, in \u001b[0;36mAxes.imshow\u001b[1;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, interpolation_stage, filternorm, filterrad, resample, url, **kwargs)\u001b[0m\n\u001b[0;32m   5892\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m aspect \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   5893\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_aspect(aspect)\n\u001b[1;32m-> 5895\u001b[0m \u001b[43mim\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   5896\u001b[0m im\u001b[38;5;241m.\u001b[39mset_alpha(alpha)\n\u001b[0;32m   5897\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m im\u001b[38;5;241m.\u001b[39mget_clip_path() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   5898\u001b[0m     \u001b[38;5;66;03m# image does not already have clipping set, clip to Axes patch\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\cttew\\Desktop\\code\\holo\\venv\\Lib\\site-packages\\matplotlib\\image.py:729\u001b[0m, in \u001b[0;36m_ImageBase.set_data\u001b[1;34m(self, A)\u001b[0m\n\u001b[0;32m    727\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(A, PIL\u001b[38;5;241m.\u001b[39mImage\u001b[38;5;241m.\u001b[39mImage):\n\u001b[0;32m    728\u001b[0m     A \u001b[38;5;241m=\u001b[39m pil_to_array(A)  \u001b[38;5;66;03m# Needed e.g. to apply png palette.\u001b[39;00m\n\u001b[1;32m--> 729\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_A \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_normalize_image_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mA\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    730\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_imcache \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    731\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\cttew\\Desktop\\code\\holo\\venv\\Lib\\site-packages\\matplotlib\\image.py:697\u001b[0m, in \u001b[0;36m_ImageBase._normalize_image_array\u001b[1;34m(A)\u001b[0m\n\u001b[0;32m    695\u001b[0m     A \u001b[38;5;241m=\u001b[39m A\u001b[38;5;241m.\u001b[39msqueeze(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)  \u001b[38;5;66;03m# If just (M, N, 1), assume scalar and apply colormap.\u001b[39;00m\n\u001b[0;32m    696\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (A\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m A\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m3\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m A\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m4\u001b[39m]):\n\u001b[1;32m--> 697\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid shape \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mA\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m for image data\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    698\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m A\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m3\u001b[39m:\n\u001b[0;32m    699\u001b[0m     \u001b[38;5;66;03m# If the input data has values outside the valid range (after\u001b[39;00m\n\u001b[0;32m    700\u001b[0m     \u001b[38;5;66;03m# normalisation), we issue a warning and then clip X to the bounds\u001b[39;00m\n\u001b[0;32m    701\u001b[0m     \u001b[38;5;66;03m# - otherwise casting wraps extreme values, hiding outliers and\u001b[39;00m\n\u001b[0;32m    702\u001b[0m     \u001b[38;5;66;03m# making reliable interpretation impossible.\u001b[39;00m\n\u001b[0;32m    703\u001b[0m     high \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m255\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39missubdtype(A\u001b[38;5;241m.\u001b[39mdtype, np\u001b[38;5;241m.\u001b[39minteger) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;241m1\u001b[39m\n",
      "\u001b[1;31mTypeError\u001b[0m: Invalid shape (256,) for image data"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(np.angle(otf_i))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "hologram",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}