Untitled_15_recent.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from prime_sieve.array import PrimeArraySieve\n",
    "import matplotlib.pyplot as plt\n",
    "import cmath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.0008429095800031227"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = np.exp(-2 * np.pi * 1j * ((2 * dep) / 1600))\n",
    "cmath.phase(np.mean(p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-3.23321616e-06 -1.68581916e-03 -3.36577802e-03]\n",
      "[-3.233216155128718e-06, -0.0016858191599564791, -0.0033657780174889958]\n"
     ]
    }
   ],
   "source": [
    "d = np.array([np.min(dep), np.mean(dep), np.max(dep)])\n",
    "y = np.exp(-2 * np.pi * 1j * ((2 * d*1) / _lambda)) # calculate the expression for each d value\n",
    "print(np.angle(y))\n",
    "print([cmath.phase(m) for m in y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-3.233216155128718e-06 3.233216155128718e-06\n",
      "-0.0016858191599564791 0.0016858191599564793\n",
      "-0.0033657780174889958 0.0033657780174889958\n"
     ]
    }
   ],
   "source": [
    "import cmath\n",
    "for k in d:\n",
    "    _lambda = 800\n",
    "    m = np.exp(-2 * np.pi * 1j * ((2 * k) / _lambda))\n",
    "    print(cmath.phase(m), phase_change(k, _lambda))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "_lambda = 800\n",
    "d = np.array([np.min(dep), np.mean(dep), np.max(dep)])\n",
    "#d = #np.sort(dep)\n",
    "#d = np.arange(0, 10, 0.1) # create an array of d values from 0 to 10 with a step of 0.1\n",
    "y = np.exp(-2 * np.pi * 1j * ((2 * d) / _lambda)) # calculate the expression for each d value\n",
    "plt.plot(d, y) # plot d on the x-axis and y on the y-axis\n",
    "plt.xlabel('d') # label the x-axis\n",
    "plt.ylabel('exp[-2 * pi * i * ((2 * d) / _lambda)]') # label the y-axis\n",
    "plt.title('Plot of the expression') # give a title to the graph\n",
    "plt.grid(True) # show a grid on the graph\n",
    "plt.show() # display the graph\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.9996518205634257-0.02638631547276782j)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "d = 0.021\n",
    "_lambda = 10\n",
    "result = np.exp(-2 * np.pi * 1j * ((2 * d) / _lambda))\n",
    "print(result)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def return_matrix(arr, factor):\n",
    "    x, y = factor\n",
    "    mat = np.zeros((x,y), dtype=np.int64)\n",
    "    for i in range(x*y):\n",
    "        mat[i%x, i%y] = arr[i]\n",
    "    return mat\n",
    "\n",
    "def sequence(prime_num):\n",
    "    m = np.arange(1,prime_num)\n",
    "    n = np.zeros(prime_num-1, dtype=np.int64)\n",
    "    prime_root = np_roots(prime_num)\n",
    "    #print(prime_num, prime_root)\n",
    "    for i in range(1, prime_num):\n",
    "        n[i-1] = pow(prime_root, i, prime_num)\n",
    "        #n[i-1] = (prime_root ** i) % prime_num\n",
    "    return n\n",
    "\n",
    "def np_roots(number):\n",
    "    roots = []\n",
    "    for r in range(2, number):\n",
    "        k = r % number\n",
    "        for o in range(2, number):\n",
    "            k = (k*r) % number\n",
    "            if k <= 1:\n",
    "                break\n",
    "        if o == number-1:\n",
    "            return r\n",
    "            roots.append(r)\n",
    "    return roots\n",
    "\n",
    "def get_well_depth(matrix, _lambda, p):\n",
    "    return matrix * (_lambda / (2 * p)) # p = p-1???\n",
    "\n",
    "def phase_change(matrix, _lambda):\n",
    "    y = np.exp(-2 * np.pi * 1j * ((2 * matrix) / _lambda)) # calculate the expression for each d value\n",
    "    return np.angle(y)\n",
    "    #return (2 * matrix) * ((2 * np.pi) / _lambda)\n",
    "\n",
    "def get_lambda(fg):\n",
    "    return 343/fg\n",
    "\n",
    "def get_periods(fg, amount):\n",
    "    return (1/fg) * amount\n",
    "\n",
    "def pure_sine_wave(frequency, duration, sampling_rate, initial, periods=None):\n",
    "    if periods is not None:\n",
    "        duration = get_periods(frequency, periods)\n",
    "    phase_change = np.arange((1/frequency) * sampling_rate, dtype=int)\n",
    "    time = np.roll(np.linspace(0, duration, int(duration * sampling_rate)), -phase_change[int((phase_change.shape[0]/360)*initial)])\n",
    "    sinewave = np.sin(2 * np.pi * frequency * time)\n",
    "    return sinewave\n",
    "\n",
    "def _time_delay(depth): # in sec\n",
    "    return (2 * depth) / 343 # c (velocity sound) = 343\n",
    "\n",
    "def factor(n, min_x=1, max_x=1):\n",
    "    out = list()\n",
    "    lower = int(n / max_x) - 1\n",
    "    upper = int(n / min_x) + 1\n",
    "    if max_x == 1:\n",
    "        lower = 1\n",
    "    print(lower, upper)\n",
    "    for i in range(*sorted((lower, upper))):\n",
    "        if np.gcd(i, n//i) <= 1:\n",
    "            #if mi <= n//i <= ma:\n",
    "                #print(i, n//i)\n",
    "            #out.add(tuple(sorted(((n//i), i), reverse=True)))\n",
    "            out.append(((n//i), i))\n",
    "            #out.append((i, (n//i)))\n",
    "    return list(sorted(out))\n",
    "\n",
    "def get_panel_size(max_x, max_y, f_upper):\n",
    "    c = 343\n",
    "    column_width = (c / f_upper / 2) * 100\n",
    "    print((c/f_upper)/2, c/(f_upper/2), column_width)\n",
    "    print(max_x / column_width)\n",
    "    x_wells = max_x / column_width\n",
    "    y_wells = max_y / column_width\n",
    "    return {'x': x_wells, 'y': y_wells, 'area': x_wells * y_wells}\n",
    "    #return (max_x / column_width) * (max_y / column_width)\n",
    "    \n",
    "def reflection_factor(depth, _lambda):\n",
    "    return np.exp(-2 * np.pi * 1j * ((2 * depth) / _lambda))\n",
    "    \n",
    "def schroeder_grating(prime_num, width, alpha_incidence, alpha_diffraction, _lambda):\n",
    "    return (prime_num-1) * width * ((np.sin(alpha_incidence) + np.sin(alpha_diffraction)) / _lambda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "prime_num = 2083\n",
    "fg = 800\n",
    "_lambda = get_lambda(fg)\n",
    "factor = (71,29)\n",
    "seq = sequence(prime_num)\n",
    "mat = return_matrix(seq, factor)\n",
    "dep = get_well_depth(mat, _lambda, prime_num)\n",
    "pac = phase_change(dep, _lambda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1386c3b50>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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W+1Tbp59+Go888gjq16+PHj16IC0tDbNmzcI777wDV1dXxMbGIi4uDuHh4fD19cXMmTMBAAMGDKju/0pEREQuQ9VefDzwwAMwm81ISEjAhAkTEB4ejrfeegt33303gJMDpGVlZRg4cCDy8/PRoUMHrFixAn5+fs4+dhEREamFqr34AIARI0acc88Oi8WCxMREJCYmUgcmIiIilydd20VEREQMVe0dTi+2f3Y47e4/DK4u7lTLZOLXVrufbk43dt7HD0de++sguuHrUUw39m9uQDdCf+R3fP2rE78DZtOP+YFEADg4jXueAoDXV/ylByZN+JBuvHNdF7pxx/e/041Ph/eo/EaV2DOYH/TeOmgm3fimkB9a/+BoNN0AgNx/80PJx1vzg43fj0+gG18V8K9FCbPuoht3PPg93UjrxX9dij7kh7RdXgqi7l9eXoy130+9ODucioiIiDC0+BARERFDafEhIiIihtLiQ0RERAx1QafaGqHJkkK41+F2n5tej9utDQBGHSqnG82+vZ9uuLjxQ5pp7RfTjU8b8jtgTrAPoRv+2/k56Z1P8kOeAOC1iR849Rl8hG6szW9BN3I/4L++hRX84NuKzz+iG0MO3EQ3+t7zEN3IGM2/hnim1qEbALBwzit0Y+Sjj9ONu+5+hG5M/+gtuuHSm7uEPACcsHvQDTjhEiO5S+rTjetf3kTdv/REGdbeWLXb6p0PERERMZQWHyIiImIoLT5ERETEUFp8iIiIiKG0+BARERFD1djt1Tvf+gJc3bgp4qz7CunjaXT/Ibqx41V+i/aw5fw60WX033Tjuys/pxvtZo2mG4Xhdrrhns1v0Q4A9sZFdMPbm9/6vv7YE3QjO4mf3G8VwD/Pfj4cTjfGX7mabsSvv5VueBzityNv0X0v3QCA/Z81oRsPPbiUbiz/uy3dqBhiohtH3rTSDY9P/eiGLZz/u7hdnUs3WPbCEuy4+2Vtry4iIiI1jxYfIiIiYigtPkRERMRQWnyIiIiIoWrs9up1NhyAqwu3bXW5Bz9ctfPfLemG7zZ+sNHnN3777aA6uXSjdTI/LFpan98q3pzPr5sbv7aDbgCAyYMf0szu3ohu7HzRk254FJXSjR+28wPWzd7ltyT/7LdGdGPjrpl0Y2j3oXQjPpYf9AaAx9IephuvX9udbgQs9eIbjgN0Y0bbT+hG6w75dKPz6sfoRsTDx+lG7I/cJUkK8+0YXsXb6p0PERERMZQWHyIiImIoLT5ERETEUFp8iIiIiKFq7MDpA6t+gpcPN6iZ2DKXPo7jrTrSjb7D19KNT6+LpBu71obSjcbP/Uw3lh3eSDeePHot3diw+Wq6AQAvTnuHboz8NpY/kHL+3xIN79tHN27blEE3Ur7idxZ1DWhDN2559iq6Ufft/XRjVUErugEArrsO0403o36gG980b0c3Nt/Pv54l/dmNbuTH8LsT1+3L74Jb8CE/+L4+vxl1/5ITZQA2V+m2eudDREREDKXFh4iIiBhKiw8RERExlBYfIiIiYiiTw+FwXOqD+L9sNhusViuu6fsCXN24AZqls2bQx9P91xF0IzerDt34o9ccuvHQ4RvpRmkFP6O85W9+UOzVtovoxvQDMXQDAPbu4P8+4DfBxc1Xb6UbW7L5v0vBqhC6MfGhBXTjxeTBdKOsfQHd8PcppBudQg7SDQDY9hR/KXu33CK64WLjG3tf8qEbQQv5nVaff+k9ujGj+y10o8LqTTdKE05Q9y8vKEFq3zeQl5cHX1/f895W73yIiIiIobT4EBEREUNp8SEiIiKG0uJDREREDFVjdzitP2Yf3Lzdqca91/SnjyP3WSvdCPuGTuD21+7hI06YLTYdt9GNkI/5S1DPuLkP3TAfOEQ3AKC56S+6cWQRfxn6VX+0pBvu+/hdEsM28sOEXcbxX5uCpmV0o/VT2XTj8KzzD95VxVV1nPNcXd8kim6YS7nXZQAI/DqTbiS0X0E3Xn+Tf119bsoDdGPAslV0Y01ffkffY8sbUPe3l1R9t1e98yEiIiKG0uJDREREDKXFh4iIiBhKiw8RERExlBYfIiIiYqiae7aLZy4sXm5U49MJ19DH8UrPFLqx6/p6dMPu4NeJOeX8VsI7BzakG50D99CN76/qQjdazecn7gHg2+2t6EbL4bvpxrxt/PR/j/VP0A2ffx+hGw+26Ek37lq/kW4snMafHVLPI4duvPYhf+YeAMyOe4tufJHTgW78fqgd3fi7/He+0dFCN+p/z5+9l1fuSTf2DXPCZR6u5P4u9sJi4I2q3VbvfIiIiIihtPgQERERQ1Vr8fH999/DZDKd8REZGQkAKC0txbhx4xASEgJPT09ER0cjPT39Yhy3iIiI1FIXNPOxefPm0y6X6+5+cse7uLg4LFy4EMnJyahXrx5mzZqFmJgY7N27t9LL64qIiMj/BpPDUfU9t7///nt07doVOTk58PPzO+3PysvLERwcjKSkJAwePBgAYLfbERwcjISEBIwYMaJK/w+bzQar1YqGL78IF09uq+fGn5ZQ9wcAc3E53Tg2uZRulNv535DdFs4PaC3PuJJu2L8LoBv1V/HbXu8c7Uc3AKDe9ya60XAsP3C65esWdMPe7gTduKkRP1Cc9hc/2Jx3hL80QquX/qQbe0eG0Y0Ga/jXMgBwO8Zvff93tB/dCP6tkG4c7M0P0NfJoBMITucvOZHyxTt04+qVY+iGb2ABdX97YQm2D56OvLy8St9wuKCfaA0aNEBAQACioqKQnJwMANi3bx9yc3MRFfWf6XCz2YzOnTuf91cvJSUlsNlsp32IiIjI5atav3Zp3rw5li1bhvr166OwsBDLly/HyJEjYTab0bRpUwA4Y7UTGBiIzMxzn9IYHx+PqVOnXsChi4iISG1UrcVHaGgoQkP/cy5xly5dcOTIESQlJSEhIeFk0PXMpMl07relJ06ciPHjx5/6b5vNhrAw/m1KERERqZnoQYJmzZohJycHwcHBAICcnNM31MnOzkZQUNA572+xWODr63vah4iIiFy+qvXOh91uh9lsPu1zGzduRJs2bdC4cWNYrVakpqaiSZMmp26flpaG/v2rvzufdbcLzO7c2qhP0vfU/QFg5qZudAPH3OlEi0R+4GzeWH7H129umkU3Fjfid0j88Q1+mDCx51a6AQBPBg+gG583XEo3Rsy5nW446p77HwpVtfIJfsdX5HO7GwNAyAZ+EPiTdYvpxsIT4XQj6w4fugEAverwQ+frC5vSjRlf9qEbbk4YD7znMX5X4I25EXTj6qXj6EarFw/RjZ3PcM/ViqLiKt+2WouP4cOHIywsDP369YOHhwdSUlKwbNkybNy4Ea6uroiNjUVcXBzCw8Ph6+uLmTNnAgAGDOBfnEVEROTyUK3FR/fu3fHGG28gKSkJRUVFaN++PX788Ue0b98ewMnh0bKyMgwcOBD5+fno0KEDVqxYccZpuSIiIvK/q9rvfAwfPvycf26xWJCYmIjExET2uEREROQypWu7iIiIiKGqtcOpEf7Z4bTD4vEwe3OXO87M8OcPyAmPTkD6Be1if5rjV/M7rV7V+iDdKHoyhG40nL2Pbux/mt/N0327E7Y3BFDYkR84K/Hl/x1w+6TVdGNOale6YSrh/y5eR/hGwE7+e8bjGL87ce4kbtdIAPCeyQ9YA0Dx47l0Y027BXQjp4IfoL99y3C6EfgkP5RcsXs/3ci7K6ryG1UiqyP/w2rxHTOp+5/Ir8CNbY5cvB1ORURERC6UFh8iIiJiKC0+RERExFBafIiIiIihauzAaTf3gXA1cbsc/pHA76SZ0ucNunHPstF0o9XsY3Tj7xv5YdHimDy64f4tPzx34/0b6MaaD/kdXwGggt+ME5au/Nc3e18A3TBV0Ams7vcq3bjjtafpxqhRX9KN5Te3oxuDV/PP1QU9O9ENADjRvj7dcCnnf2QcuosfBh4R+RPd2F8USDc2LOafI0X1+W+8VpH8CQXbM+pR968oLMahUdM0cCoiIiI1jxYfIiIiYigtPkRERMRQWnyIiIiIobT4EBEREUPV2LNdupr702e7lHVtTx+P25pf6UaXX4voRm+fLXRjSudb6Mb+UU3phtvVuXQjpf17dMPDGad2ABjbZRDd2PlUGN1o+tjPdKPOWv6MqO2r+edIw2/4Lcnbv7mZbvwyJpJu5DfypBuFQfw24AAw8RF+a/QPO7alGxXNGtKNw3H89++yqDl0o8dnT9KNFrMz6YYjK5tuNFjFnYVUeqIUH3Wdr7NdREREpObR4kNEREQMpcWHiIiIGEqLDxERETGU66U+gHPJfCAKZncPqlHnTzt9HO6RrejGD09wfw8AyHuZH1rb8xg/CNg0cR/dKNrAD1dOfvZ2utGsDj/kBQDlR47SjRs65dONzBb813drKj9w6ss/HMicWEI3Glr4AbyeH3xMN+q72ujG2Hv5SzQAQKqtGd3IHHQl3TjepZRutA8+RDcebHgd3Qgcxg8D517Nf99Zt7vTDbvjMHn/qj8WeudDREREDKXFh4iIiBhKiw8RERExlBYfIiIiYqgau8PpR7+2hZePmWq9OH0ofTx2C52Ao2cO3SjY5Uc3gvnNWvHw84vpxoK+N9KNCm/+C1MQXoduAECd3bl0IzsqgG4c5+cAkTIgkW7c9cODdKPuFXl0w2u6lW54TzlCNw4vaEw3rljHv4YAwKE+/nSjMIwf5G81gx8GDvmAHxgfFpJKN8a+8RDduHv4arrx8a6OdMOx+fy7klbGXlKMPS/FaYdTERERqXm0+BARERFDafEhIiIihtLiQ0RERAxVY3c4fWHe3TBbuJ1B5096jT6O27/ndxZ02eNHNzwz+V30zMP+ohuz4wfQjYrr+b9LqQ+dgCXPObPWx/sE0o3iYP5YwleU0Y3np3WlG638/6Ybd67cRDdefqgX3UgJ/5JuPFT6GN3YN4gfFAWAiGn843rtphN04+fZbejGn4XccCQAhLnyOwtbuh2jG6k9wunGmk1z6MZg33uo+5cXlGBPFW+rdz5ERETEUFp8iIiIiKG0+BARERFDafEhIiIihqqxA6fBv5bC1ZVbGz0zqwd9HC7T+MsUuxbwA5YNF2bQjQkPL6MbD4c+TDecsWusuZhv2AL4rwsA7Lw/iW68b+Mvqf3uz/3oRscf+QG8X0c3oRufRvPbtTb9NItuDJvDD4uWtOCHieumOWc42tGuBd1wM22gG7uf9aIbbVz5XXBv/n4M3bijzWa6kd7xarox4sYhdCPlh3nU/fPzK9CyirfVOx8iIiJiKC0+RERExFBafIiIiIihtPgQERERQ2nxISIiIoaqsWe7lPq6osKNO7xDL7Slj8OllE6gf991dGPDD1F0o7EbfybD2kcS6Eahw043fEz8U7fPo/yZDABw/TX8WSYTmnxNN3z28l/fVXO60A2PhhV049hjDeiGKdWbbmx9NJFuRCXwZ1T0m/YN3QCAlcOi6cbaG0PpRvNgfov2zeP5LclbvMUfR8dP9tMNrxf5HzSpk66lG6sKG1H3LyoqB1C1yyvonQ8RERExlBYfIiIiYihq8XH48GE0aNAAd9xxx6nPlZaWYty4cQgJCYGnpyeio6ORnp7OHqeIiIhcJi548WGz2XDLLbegpKTktM/HxcVh4cKFSE5ORmpqKpo2bYqYmBjYbDb6YEVERKT2Mzkcjmrv21tWVoZbbrkFrVu3Rl5eHnJzc7FkyRKUl5cjODgYSUlJGDx4MADAbrcjODgYCQkJGDFiRKVtm80Gq9WKnF2N4etjrv7f6P9olvIQdX8AqNM8h26YvgmgG9YD5XTD4YRfsgU/ww9XZb0cQTfGvLaAbiT3uJFuAEDxe/w27b3rbqcbXevsoBsjZvNDuGFz/6Abn2z+im7cOnos3cjswL0GAYBvFL/Nu/2LYLoBANGjNtGNQHd+SPP9H6/nj+M3/mvjOego3fCeyA82H73Ol26EpBfSjb86cdve20uKsTMxDnl5efD1Pf/f6YJ+HI0cORJ16tTB66+/ftrn9+3bh9zcXERF/efMDLPZjM6dO5/zVy8lJSWw2WynfYiIiMjlq9qLj8mTJ2Pnzp1ISUmBi8vpd8/KOrnC/+8VT2BgIDIzM8/ai4+Ph9VqPfURFhZW3UMSERGRWqRai49ly5YhJSUFX375JTw9Pc95O1fXM/dgMJnO/tb0xIkTkZeXd+ojI4O/equIiIjUXNXaqWnPnj04ePDgae9OlJWVAQA8PDywYcPJSy3n5OQgIOA/cw7Z2dmIiDj77/ktFgssFidcY11ERERqhWotPoYNG4aYmJjTPjdx4kTk5+dj9uzZiIiIgNVqRWpqKpo0aQLg5MBpWloa+vfvX60Di/z0frh4eFTrPv+txWsHqPsDwEvrP6cb21ryOwKawe8a+dzmvnQjd3EzuhGcV0Q30gv4odVd8UF0AwCaTOJ3bF3zcnO68d6nN9ONgMP888zvS74R/erjdKOI3/AR9tBiunFiPT8s2jAtl24AwLq+/PdN0O176IbXM/ywaMi3R+jG36b6dCPpM37X56HxT9CNjJ7csCgABP/KvZaVl1X9/tVafPj7+8Pf3/+0z1mtVjgcDrRs2RIAEBsbi7i4OISHh8PX1xczZ84EAAwYMKA6/ysRERG5TDn92i7x8fEoKyvDwIEDkZ+fjw4dOmDFihXw8/Nz9v9KREREaiF68fH++++f9t8WiwWJiYlITOQvyCQiIiKXH13bRURERAzl9F+7OEvQr4DZnWu4pPDHMXrX3XTD4wU/urFnOP+l+qrHTLox5Lsn6YbrCf7y0fM3XkM3xnX5lm4AwBuP38BHfm5EJzzy+cOwbj5ON35Z2Ypu/PbULLrRatkjdGN9V/4d3A86RNKNOc2d8BwD0DWY33122oEf6MYb2fyOzUsRTTea9NpHN4od/Gvzt8/yQ6s3vcoPrf55I/d+REWxC7CsarfVOx8iIiJiKC0+RERExFBafIiIiIihtPgQERERQ5kcDofjUh/E/2Wz2WC1WtGjyTi4mslt18v4oabbvj771XirY/qaW+lGoy/4XSP/usaNbsTetZJuvPlDd7rR7CN+58ldD5ATzf+fay4/cLZi0Kt0Y35uVOU3qsTogF/oxtiM3nTj2E3817fOah+6cSDPv/IbVSJ4MH/ZdoTzuyQDgPdb/EDxnuOBdGNZ+3fpRrf1o+lG0yuy6EZBQgO68c7s1yu/USWaudWhG21ff5i6v72kGH/MjENeXt4ZF5j9b3rnQ0RERAylxYeIiIgYSosPERERMZQWHyIiImIoLT5ERETEUDX2bJcOg/8Fs7sH1XK5h59k9r3tIN0Ysu0A3ejlxTdu2zKcbhzby0+6j+vxNd346mp++n/53vV0AwC6x46iG5Zj/NkdxXU96YbX/ly60WDuEbrx48EmdKOsiD8Lqd6X/BlRpT4mumHrXUA3AKC0kD/j7eomh+hG4e382Xvwt9KJ4V99RzeSxgykG2FTdtGNtFVX0g3rXm45YC8txq8pk3S2i4iIiNQ8WnyIiIiIobT4EBEREUNp8SEiIiKG4ieyLpJj3Urg4sUNaoW+ww9HVnTmt6xdlsUPRi0YfA3d8Duyl2908qIb2zrXpxsFvdrSjfn5/JAXANyWsJpufPh2DN0YcP8aurFgTwe6sXNra7qBcn5IM7H7R3TjmR0j6Eb4u/zz7Ng1jekGALSakUM3GszjG77f8QPWP2Ty2+f/e/Y9dKPe4WN0IzHsG7oRXcAPnBZewX3f2Uuqfn+98yEiIiKG0uJDREREDKXFh4iIiBhKiw8RERExVI0dOPXe7AGzhdvhtDCEP46su/md+Dp48wNJfy8+/25xVToO32y6MWdnOd34KyWSblgfOUo3Evd2oxsA8P6VH9CNleui6cZPC8LoRlj+froBO/89U34tP7T6RBY/LNp7wAa6sTOB/zde8M9mugEAFRZ+h9Mf/mxKN+qNKaIbb/wwn26MyB9PN6xv86+ruRX86+ry0dPpxo0ruMejoqjqfw+98yEiIiKG0uJDREREDKXFh4iIiBhKiw8RERExVI0dOC33BhwWrpHfpow+jhbxdrqxpbQF3Tg+nR/i+2FHc7phOchfYry4Iz9s1iC2lG4UXRlENwDg3oZP0g335tylrAHApZE33TDxh4GhLy6lG3f7/EA3ojfxA6cFdvJFCEDG+Kvoxn33rqIbAPBjr2Z0w31hON0oas7vHJ1r505IAICgn/lh0b/+4nefHYbH6YbHd1vpxspdr1P3P5Ffgagq3lbvfIiIiIihtPgQERERQ2nxISIiIobS4kNEREQMVWMHTovr2uHiyQ17PtKJv8T4wnU96Ubwtxl0I2BQLt04PimYbhSH8jvx3dX6V7rxTb8udOPt8bPoBgDkVnjSjWIHP8j77JvD6EbdnwvpxsfP9KEb8f35AWsvX36w+dm6/KXOR33HDyQuyehBNwAgfu0cuvH46w/RDf9YfofiNSf4XXCP9nTO0Dmr472b6cZuR1u64eOymgtU4+0MvfMhIiIihtLiQ0RERAylxYeIiIgYSosPERERMZQWHyIiImKoGnu2S1DDHJi9uK2Nr/XaTR/Hp6X82S7hnx6jG6t+aE837B78VvF+dW10476An+jG1jtD6cbE2AfpBgA4JmXRjT/XNaAbYx74km4EP5RPN+Z2aEM30K8lnQh+x4tufN6CP4PgyI0+dOOKTSV0AwACXfizmUK/4s9U+bsXv716yvdd6caiJxLoxlN9+G38N4Lfgr/ps/zPu+jPn6DuX1FUDODZKt1W73yIiIiIobT4EBEREUNVa/GRmpqKm2++GaGhofDw8EDz5s2RkPCft61KS0sxbtw4hISEwNPTE9HR0UhPT3f6QYuIiEjtVa3Fx9atW9GxY0fMnz8fmzdvxtNPP42JEydi8eLFAIC4uDgsXLgQycnJSE1NRdOmTRETEwObjZ8TEBERkcuDyeFwOJhA+/bt0bdvXzz33HMIDg5GUlISBg8eDACw2+0IDg5GQkICRoyo2lCOzWaD1WrFdV2nwNXVgzk0ZHbgt6wOW5FDN5atSKEbt4RF0Q3zFSF0w3U+9XQBAJTfzw8CHu1Vl27kR/PbbwPAhhvepBuDB/JbVle48b9FbZawg24c6MrPsQ/9ZTvdmDv0NroxZ+EbdOO7wqZ04+Ox/Jb1AFBQz41uZHUrpRsbuyfSjdi9/enG0Q8i6MaJMBPdiPiEH1rf+XAg3YA/97WtKCrGoVHTkJeXB19f3/Pe9oJfrUpKSpCSkoKMjAzcfffd2LdvH3JzcxEV9Z8fkmazGZ07d9avXkREROSUC1p8vPTSS/D09MTYsWMxb948tGzZEllZJ1du/73aCQwMRGZm5jlbJSUlsNlsp32IiIjI5euCFh8jR47EL7/8gvj4eNx111347LPPTv2Zq+uZb7maTOd+Wyo+Ph5Wq/XUR1hY2IUckoiIiNQSF7T4CAwMRGRkJEaOHIl7770XM2bMQHDwycu15+ScPiORnZ2NoKBzX7Z44sSJyMvLO/WRkcFffl5ERERqLnoy7J8B0caNG8NqtSI1NRVNmjQBcHLgNC0tDf37n3swyGKxwGI5cyfT2W+8CR8fboBu4NSnqfsDQEHE+YdmquLGR/lhwvLB/DBhwIZz//qrqv5K5odW53w7g27EdYihG76HmtENAPg4kt+Ns1/yarqx9K7r6caW6ZF0w+Wzv+nGqwmD6YaPfxndGLJ9GN14KOJHujH5rWS6AQDTr4qmG41GcjtPA8DQrvfRjbhvFtONEc1G0w3/P5wwhL+T353U4eFPN6wbuJM87NWYV63W4mPQoEG49tpr0alTJ/j6+mLZsmVISUnB4sWL4erqitjYWMTFxSE8PBy+vr6YOXMmAGDAgAHV+guIiIjI5atai48uXbogJSUFL774IkpLS9G6dWssWrQId9xxB4CT8xtlZWUYOHAg8vPz0aFDB6xYsQJ+fn4X4dBFRESkNqrW4mPcuHEYN27cOf/cYrEgMTERiYn8OdwiIiJyedK1XURERMRQ/FaEF8mg98bBbOGGX6ZO+Jg+DjMq6Ia3C3857GenPUA3nvrmc7ox7aFYujF6wmN0o2Aov272uJnfVRAAlt1/E93o9s56ujH6syV047E0ftBzfrNFdGOUZSzdyBhSTjfqmfhhwo+PdKIbzXyd81w11ed3Bm7pw++CmxreiG50sPADxe/dlUQ3Jj0xim7sns0/R77oNZNujF0yhrp/eVnVvyZ650NEREQMpcWHiIiIGEqLDxERETGUFh8iIiJiKC0+RERExFA19myXuj8XwdWVmzR/az2/s6rD5dwXxauq59/jt0b2OVyNfWvP4fHX+W3e45Pe44/jo/vpRmmTYroxqtHPdAMA5j57Ld1Y+VcruvHDgPp0I7yjmW4M28WfqVLUhj9Thf+bAL6P8Ge7OLx96Ebdj/fSDQDY681vjR5h4c+8+d6L/3fvbUMfphuPvvUJ3ThyE/8zotFS/vm+pSf//f/x269T98/Pr8CVVXwp0zsfIiIiYigtPkRERMRQWnyIiIiIobT4EBEREUPV2IHT4609YXbntlcHPweEgJ381uiPJfCDUcXRdAL1bjhMN8Yu5IdFy+vxw1XN3rDTjYRHetINAGg55TjdiFj4N91Yd+/VdKOAn1lDkym/0I2die3oxt1tNtGNOl/wg82LDnSgG15m/nUIAOye/Eu+j7mIbgybvpRuzH/wFrqR1Lw53fB9lP83fPmT2XRjSRb/PCsM5AaSiwrKAWRW6bZ650NEREQMpcWHiIiIGEqLDxERETGUFh8iIiJiqBo7cFocAJjJzfieHPIZfRyvzutPN+yedAJN3/2Tbix+cDHd6H/rDXQjazg/GGXe8QfdCP62Nd0AgEMJ3nRj/1J+WLQsnN+Ns+7PfMMZWs4ppBsb342kG+Yj/CBgUAN+V1GPuWV0AwBKn8+lG28P418TsyK96MZVL/9ON8bXXU83JtwURjewyp1O2PxD6cbrPZpR97eXFAOo2mOqdz5ERETEUFp8iIiIiKG0+BARERFDafEhIiIihjI5HI6aMWH2/9lsNlitVkR9Ng6u5OWfj/1yBX08vw2bQTe6TnyMblhy+R09K9z4tebxlnyja790ujEgYCPdiP0xlm4AgNuf/LCY3cJ/G3of4bf07TR4M91o6X2UbpjBPx5HSv3pxndJnemGe7+q7fh4PpnHfegGAFjX8tPv703gLrsOAEPS+Z2SGwfyw8Bt/fhBfg8Xfhh46Rs30Y38cDpBqyguxr6pccjLy4Ovr+95b6t3PkRERMRQWnyIiIiIobT4EBEREUNp8SEiIiKGqrE7nB7bHgwXDw+qcc1NO+jjuGbWOLphDqATmDR5Ht3oaOEH33puGkU3dj7Thm7EDoikG+YTzll72z344civ73yVbvRL5782h8Y0oRtbnud3WvSf5EY3pn32Id349PpIutHi7mN0I+/RELoBANZ9pXRjUqfb6Eb5LDPfGM+/sP7iEkQ3MmL4YeBNk2fQDWcY0OVO6v7lFSXYV8Xb6p0PERERMZQWHyIiImIoLT5ERETEUFp8iIiIiKG0+BARERFD1dizXczhJ2D2Kqca6atb0cex5BH+LIRHdt1NN157bAjd8PrlEN1w62/lG2s30Q2XW66mG7uHvEU3AKBr7AN0o5c/vwX/Y9esphsrj15JNwY03Ek31rzenG7ctfIRunHb1b/RjYlb19CN3tNb0w0AKA7izyLyLOcv9eC7yptuZPEvAVg2+RW6cdfox+nGAwd70Y1wr+N0Y8n6JdT9bfkVCGlRtdvqnQ8RERExlBYfIiIiYigtPkRERMRQWnyIiIiIoWrswOl3HT+Arw+3NrrjnTH0cdzWZDTd8E7zpBvdX0ijG/cFrKcbY57gByO/PrCRbtzwSHu6Ma+PE/a9B3Dwdn4NH3IFPyy2ID6GbljrFdCNNXe0oxs7xgXTjZZv2+jGlrBIupH1xo90Y/q4d+gGAEyZdD/deG7TSiccCd9IOMI/39cW1acb3rtz6cb2Y1fQDZdg/jIPd7TnHtPyilIA71XptnrnQ0RERAylxYeIiIgYqlqLj2XLlqF79+6oV68e6tSpg6ioKCxduvTUn5eWlmLcuHEICQmBp6cnoqOjkZ6e7vSDFhERkdqrWouPjRs34oYbbsCiRYuQlpaGm2++GXfeeSd+++03AEBcXBwWLlyI5ORkpKamomnTpoiJiYHNxv/uVURERC4PJofDQU2pREREYMyYMRg7diyCg4ORlJSEwYMHAwDsdjuCg4ORkJCAESNGVKlns9lgtVpx5YP/htniwRwactuVUfcHAM/D/I6A4Utz6UZJsBfdePzN+XRjcVYU3egXxL8b9tr+nnTj8B/8kBcAtHrlMN0Yv2YF3Xjm5VF049bR/HDkx99fRzdGded3aw13z6Ybbz/an26Yn/mbbjzRyBlDnsDMQz3oRum/6tGNIw+W0o2wRDPduHH2z3Qj+ZcudKPlK/l0I7cdP0DvfqKCun95WTF++noy8vLy4Ovre97bUjMfZWVlyM3Nhb+/P/bt24fc3FxERf3nh5PZbEbnzp31qxcRERE5hTrV9rXXXoOLiwv69euHbdu2AcAZq53AwEBkZmaes1FSUoKSkpJT/61f0YiIiFzeLvidjwULFmDq1KlISUmBn5/fqc+7up65njGZTOfsxMfHw2q1nvoICwu70EMSERGRWuCCFh/JyckYNWoUFi1ahF69Tl6NLzj45KZAOTk5p902OzsbQUFB52xNnDgReXl5pz4yMjIu5JBERESklqjWr10cDgcmT56MpKQkrFixAl26/GfQpnHjxrBarUhNTUWTJk0AnBw4TUtLQ//+5x7aslgssFgsZ3z+ig83w9XkXp3DO8O3u/mhtZgpT9KNexasohtzD/FDTc/8xg/P1bXyvxZ75+7udOPI4/yw6KAb+GEzAEhvzb9b5+tSTDeC0/mvjTOGRZvN43dJXd6qDd3I/5IfjAzd8yfdKH2B36018fCtdAMA7lq2jm4s2tyWbnh/15xu5DzNDxSvjz73P4yrKmgA93MKAA735o/juVEpdOP5j++h7m8vcQW+rtptq7X4GDp0KFauXIkFCxYgNDQUBw4cOPVnjRo1QmxsLOLi4hAeHg5fX1/MnDkTADBgwIDq/G9ERETkMlatxcfatWuRmZmJnj3PPM3R4XAgPj4eZWVlGDhwIPLz89GhQwesWLHitJkQERER+d9WrcXH/32n42wsFgsSExORmJjIHJOIiIhcxnRtFxERETEUtc/HxVR045VwdeN2ON1dlkofh8fAv+jGgt7RdOPAs/xAktnDTjcKPwylG24HfqIbzT+oQzc+DYmkGwDQMJn/Nlo7gx/Aq0j/nW74X80PNu95jH883FP5YVHfPP4S43d+xQ8lHyzhv3e/+4t/fgDA21P70Y28B/l/szaae4BuhMcepxtDNvOvRfVdqzhheR7D/7iXbnTz4oejZ25ndzit+v31zoeIiIgYSosPERERMZQWHyIiImIoLT5ERETEUFp8iIiIiKFMDoeDHwl3IpvNBqvViuu6ToGrK3e2i8fuv+njyb6xAd3wX7yZbpjc+S18n0jnz/75OIs/G+L3d/mts491KqMbz1+/lG4AQNLeG+iG5d0AupHw2ht0Y9CqR+jGpltm0I2tpfzZTPH3DqUbWRP5be9teZ50Y/a1/NbZAPDYgvvpRsB2/keG/9ZculEa7EU37G78v78z7iunG1eH89c02/kZf0aUiTwh0l5SjO1JccjLyzvjCvf/Te98iIiIiKG0+BARERFDafEhIiIihtLiQ0RERAxVY7dXL7zCDWZ3N6qReXUYfRzXD/iFbmwuiaQbXqOO0I2E2++kG5O/5Affps7jh0X9d7agG4umRtENACgeGEw3gh47QDcmt7yOboSfecHqarun6SC6cWxhQ7qR82AJ3XAcPf/QXFU0TeEHEsccjaUbAOCRyzfKPUx0Y9DC7+jGzDf517PcNvzXxuLOX7Ziy2onDIv60AlYssljqMbu7HrnQ0RERAylxYeIiIgYSosPERERMZQWHyIiImKoGjtw+v7kmfDx4dZGD936AH0cqfYOdKPojkK6ERuyi258W/d6uvHkU6Ppxj3py+nGR/+OpBsfzltINwDg1p/4XUFtifyAZf5DjehGQWf+uer+Hf93CZ+3lW7ktGlNN/y3m+nGV/OS6Ebb98bQDQB462F+F9wJu/lBz46eB+hGXnN+0NNk5ndrrdjL78b70/2v0o0/y/n3Eu55czx1f3s1vl30zoeIiIgYSosPERERMZQWHyIiImIoLT5ERETEUCaHw8FP3DiRzWaD1WrF5LQe8KjD7XD6/qf8do0RM3fQjayP+B0wRzf5gW4Eup6gG7PvG0A3/rzem2407bOXbvy2ix+MBIBZN/G7vjZzy6IbvVc+RjfcM7nvOQDofjO/K/A36yPphuUY/2+r4C38UOPRLvxxhK3idwUGgIPD+b9Pw4/48xQO3soP8jafsIVu7Jl2Fd3o0Hk33Sgs57/vdv8YQTeu7LqHun9ZQSmW93oXeXl58PU9/+7AeudDREREDKXFh4iIiBhKiw8RERExlBYfIiIiYqgau8PpRyu6wcXDg2oE76jG9X3P4Y8Z/BBPk3j+OD58vjPd6Ff/N7pxsDc/LIqW/ODr5q2N6EbThc4Z4nu3Gb9z7Nbfw/nG7bPoxrUb+Uu3772mlG78dCiBbqwtrkc3+ngdpxud48fSDcsmbhDwH8+/sY1uTMnmdzjt2ZkfFn1zzzq60XpdU7rx25H6dGNj9By6cU3mKLpR8MQV1P3Ly4urfFu98yEiIiKG0uJDREREDKXFh4iIiBhKiw8RERExlBYfIiIiYqgau736os0t4eXDbcE7K/Ia+nj+HtKWbtRd+SfdKA+x0o1Fi/mJ6shvxtANUwG/tfKEnkvpxpK+19INANg5ht8+v8G3/BlRGf35RoMv+K9NvSf4re//LqpDN8Lq5NKN9K9b0w3/P/ivi/eRqp9FcD77BnBnEAJA8/dy6Ubw20fpxrG+/Jbk2+P5sxk7t9pHN9J2NqYbAWn847Huee6MOVt+Beq2OKTt1UVERKTm0eJDREREDKXFh4iIiBhKiw8RERExVI3dXv3laffC1Y0bjpq1NZE+jiW5uXQjZKyNbnz6TC+6Ebn0MbrRbPRGuoFr29GJd9NvpxvHHnXOrLXPPn4Nn9GbP5bXohfQjXde6k030g+G0Y3m/y6gGxuG8MOirw2dSzcyygLpxqvf9KEbABCwxcRHKvgB2t2zWtEN64nNdMPDjx/kze1ZRDc8H+eHRVuN2E43+gzhtmg/ub36tCrdVu98iIiIiKG0+BARERFDXdDio6KiAps2bUJQUBCWLFly2p+VlpZi3LhxCAkJgaenJ6Kjo5Genu6MYxUREZHLQLUXHwcPHoSrqys6duyI7OzsM/48Li4OCxcuRHJyMlJTU9G0aVPExMTAZuPnHkRERKT2q/bAaWhoKLZvPznY0qrV6UND5eXleO+995CUlIQ+fU4OSCUnJyM4OBiffvopRowYUeX/j0dOOVxdy6t7eKcZufU+6v4AEPwvC934u7M33aj7NT/o2cC1A9049mBnulHKb9aK0JfX040PpvENALhl6Xi60ajpX3RjS1FDurF9vD/dCP6W3yX105X8brx9dgygG7PuG0Q38BM/GPnBviT+OACs6cYP4S78sCvd8D3AD626+PC74N7YkN+Nd82kSLphbpFPN7Z9xH9ty6/m7m8vAbC2aret9jsfbm5uaNmyJVq2bHnGn+3btw+5ubmIioo69Tmz2YzOnTvrVy8iIiICwMmn2mZlZQHAGXu6BwYGIjMz86z3KSkpQUlJyan/1q9nRERELm8X5WwXV9cz1zQm09nPL4+Pj4fVaj31ERbG7xEgIiIiNZdTFx/BwSev7pmTk3Pa57OzsxEUFHTW+0ycOBF5eXmnPjIyMpx5SCIiIlLDOPXXLo0bN4bVakVqaiqaNGkCALDb7UhLS0P//v3Peh+LxQKL5cyhzoN3OeDiye36GLAsgLo/AByKoRPo0nsL3dhk6kg3kh+bSTeGfMTvknptz9/pxvFb6tKNcY2vpxsAYP6opPIbVcJ94vkvP10VP23lG96P8y8JntncoDgAtPvhQbph/cGTbnjVtdMN7zX16cYLg9rSDQA4eJsP3Zj+0Ad0I/45/mSAg2+F0I1PQpPpxl0z+RMK/rVhGd24e9vjdOOZuz6l7l90ohyPVnFj8Wq/0lRUVJw2l1FQUIDc3Fx4eXnB3d0dsbGxiIuLQ3h4OHx9fTFz5skfeAMG8JPnIiIiUvtVe/Fx6NAhREREnPrve++9FwAwd+5cDB8+HPHx8SgrK8PAgQORn5+PDh06YMWKFfDz83PaQYuIiEjtVe3FR6NGjeBwnPvXIRaLBYmJiUhM5C/qJiIiIpcfXdtFREREDOXUgVNner/LXHj7cGujxt1L6eO4L6bqu7Key4QR39CNh34/c1O36rr7k7F0w601vw9L5n3BdMPW9uxnT1XH4becc1r3mKtW041db/IDtM/V+5Zu3JrA74Lrnbqbbri35S+5XuZFJ+CzNYtu3BzCX+p8xJIddAMA7hzyCN1wu5cfKPb/bh9/HAWN6MZz4dF0w2Uh/2N00AJ+WLTRqkK6sfDV5tT9yx2lANKqdFu98yEiIiKG0uJDREREDKXFh4iIiBhKiw8RERExlBYfIiIiYiiT43ybdlwCNpsNVqsV18ZMg6ubB9UqDDHTx+P9Jz/ZndvUjW5smMjvm9JiJb9l9fCr19ONT+feRDfym/LbXjf/sIhuAIBLIX9WVVEDftvrvzrxz7Ob+2yiGyu+iaIbLvy3HQbd8SPdSBvFn/3j/9oRuvHHvBZ0AwAC7+SvnVXwXgO68fiU+XTjxe230I0GT/GXRtj1IL/Nu++es194tTo8syvohnX0Ier+5QUl+PaWOcjLyzvj6vb/Te98iIiIiKG0+BARERFDafEhIiIihtLiQ0RERAxVYwdOv94SQW+vfqCM34K7nYUfFpt+NIZuHHPCVvFZC/ntxAvSA+lG+LSqbb97PrduPUY3Tti5geZ/fPJud7qx+qlX6ca9tz5AN/YMsdINUwU/PNdkEb+Nf1bU+QfeqqKgZz7dMG3mh4nLWvNbZwOAxy/8nvOBN/9JNw4d4V9H7mvPv47MW3093bAc55/vZifMvhd3LKAb/+rwBXX/wnw7Yjv8qoFTERERqXm0+BARERFDafEhIiIihtLiQ0RERAzleqkP4FxeeuheuLpyA4GWF/6ij+Nfx/ih1YBPvOmG1Z/beQ4AbL/zQ17Xx2yhG4e/bkM3lg/nd/NLWPQ23QCA3wbyg7wxzz9JN9Z/xe+CWwF+/jx6yli6cSiGHxYtasIPaf/c6S26MXxUH7qxb1wrugEADRfxA/Q7mlxBN1q8ww/QTv1yG91YUHQD3Vg66hW6MbZ9X7pREMMPNicfiabuX15QAuDXKt1W73yIiIiIobT4EBEREUNp8SEiIiKG0uJDREREDFVjdzjt0WQcXM0WqrVjoj99PHW2udONmHt/phufr+5EN5q9f5xuZEY7YWfCsV/TjS/G96Abngdz6QYAHOwXTDfa991ONw69xF92PftKM9349mF+AG9Ei550o6gbP9hcVof/99nXr86gGy8d47//AeDXa7jXVADIGhZFN66M5YdF169tTTcafFdON9q9uJluLP/jSrrhYuaH8MdftZq6f9GJcjwZlaodTkVERKTm0eJDREREDKXFh4iIiBhKiw8RERExVI3d4bTi6F+oMHHDnpYMfic+lxty6MZna/hhsft7fUc3Ps7hL/3eYDV/2eb33+pNN9zr8XPSH707l24AQM+0h+nGHUG/0I1JHfkBvLY37aYbAx5/gm48+ttCuvHvtyPpRsAf/EDiXX3vpxthbx2gGwDw/B8/0o1pfRrRjXUt+efq1Nv558jH83rRjd09vOiG/VX+R3FIff5n1dFSP+r+JaVlVb6t3vkQERERQ2nxISIiIobS4kNEREQMpcWHiIiIGEqLDxERETFUjT3bZf8z7eDi4UE1yuuW0MdRts+Pbvi3yKYb7y/lz1R5dMhyupFkvpVuzBmeRDcmjxlJN0Zd3Y9uAED9lvy3Ua+ULLqR/DHf2HW8Gd244u8iutHRI4NuPDcqhW5klAXQjW8HX8MfR4Ef3QCANZ78WSbLVn1CN2LuiqUbM7bfRTfcW/Jbkh8d5UM3XCxVP0vkXIrL+deh1S9cR92/vKwYQNV+zuidDxERETGUFh8iIiJiKC0+RERExFBafIiIiIihTA6Hg9+n2olsNhusVivCZkyDiyc3cOpSZKaPp0XCIbrxyc+f0Y0eW+6lGzm/BdONRp35x+PvLxrSDa9MflBs6ORldAMA1ubwQ5ot6vxNN4b4baQbqwpa0I1vsq6kGyeeDaUb5hI73Th+pTfdyL25kG5YffghXgDwfMefbhTU419XZz31Bt3Id3A/HwBgRot2dMPFm99efeespnQjMZofsO7hmU/d35ZfgbotDiEvLw++vr7nva3e+RARERFDafEhIiIihrpoi48XXngBYWFhsFgsiIyMxKpVqy7W/0pERERqkYuy+Jg9ezamT5+Ol19+GWlpabj55pvRt29f7N+//2L870RERKQWuSgDp23btsWQIUMwYcKEU59r164d+vXrh6lTp573vv8MnIa/9C96h9PgdOruAIDGj+ykGz/vjaAbLn9Z6Ebzt/6iG/vvrUc3Zg59h248v+t2upFT4Ek3AMDbo5Ru+Mw+/3BWVbjn8cdx8HH+5cB7TR26YeJnRVEcyDfC3/6Dj3jw37uHBzbijwNA8riZdOPpBx6mGy7l/MB4w5d30Q1PM7+z6FebrqIbTeaX041SPze64fX1r9T9yx1l+K500aUZOC0uLsa2bdsQFRV12uevu+46pKc7YTUgIiIitZrTr+2SnZ0Nh8NxxqonMDAQmzZtOuP2JSUlKCn5zzVYbDabsw9JREREapCLNnDq6nrmusZkMp3xufj4eFit1lMfYWFhF+uQREREpAZw+jsfgYGBMJlMyMnJOe3z2dnZCAoKOuP2EydOxPjx40/9d15eHho2bIiK4mL6WOz8r8BRVsBHKgr5vwuK+d/Fl1fwV/m1O+HrUpDP/0K/vMAJf5fCMxfDF9Sp4H9vfPJqkByXcv65ai/kn2f2Uv5lxRkzH3b+KYLyCie8iPDjDbCXOOE1BMCJfP5gysud8Vzlj6P0BP+1MZv5WYuKIv7xKC/nj6O8zAmvqw7uteyf+1dllPSiDJy2adMGAwcOxPPPP3/qc5GRkejTpw9efPHF89738OHDevdDRESklsrIyECDBg3Oexunv/MBAKNGjcKkSZPQvn17RERE4KOPPsKOHTuwePHiSu8bGhqKjIwM+Pj4nPXXNMDJuZCwsDBkZGRUOlErVafH1fn0mF4celydT4/pxfG/9Lg6HA7k5+cjNLTyyyNclMXHmDFjcPz4cTz88MPIyspCq1atsHTpUjRtWvn+9S4uLpWumP7h6+t72X8xLwU9rs6nx/Ti0OPqfHpML47/lcfVarVW6XYXZfFhMpkwZcoUTJky5WLkRUREpBbTtV1ERETEULVy8WGxWPD888/DYuF3DpT/0OPqfHpMLw49rs6nx/Ti0ON6dhflbBcRERGRc6mV73yIiIhI7aXFh4iIiBhKiw8RERExlBYfIiIiYqhat/h44YUXEBYWBovFgsjISKxatepSH1Kt9v3338NkMp3xERkZeakPrdapqKjApk2bEBQUhCVLlpz2Z6WlpRg3bhxCQkLg6emJ6OhopKenX5oDrWXO97i+//77Z33+3nHHHZfkWGu6ZcuWoXv37qhXrx7q1KmDqKgoLF269NSf63l6YSp7XPU8PdNF2WTsYpk9ezamT5+OOXPmoHXr1khJSUHfvn2xfft2REREXOrDq9U2b9582u577u7ul/Boap+DBw8iIiLinBdUiouLw8KFC5GcnIx69eph1qxZiImJwd69e/8ndj28UJU9rgDg4+ODLVu2nPY5Ly+vi31otdLGjRtxww03YOrUqfD398e8efNw5513YuPGjYiMjNTz9AJV9rgCep6ewVGLtGnTxhEfH3/a59q2beuYPHnyJTqi2m/NmjUOAI6cnJxLfSi1WmlpqWPHjh2OHTt2OAA4Pv/881N/VlZW5vDz83PMnz//1OfKy8sd/v7+juTk5EtwtLXH+R5Xh8PhmDt3rsNqtV6SY7tcNGrUyJGQkKDnqZP987g6HHqenk2t+bVLcXExtm3bhqioqNM+f9111+ltQSdo0KABAgICEBUVheTk5Et9OLWOm5sbWrZsiZYtW57xZ/v27UNubu5pz12z2YzOnTvruVuJ8z2u/7DZbPD29kZwcDCuv/56fPHFFwYeYe1WVlaG3Nxc+Pv763nqRP/3cf2HnqenqzW/dsnOzobD4Tjjrb/AwEBs2rTpEh1V7de8eXMsW7YM9evXR2FhIZYvX46RI0fCbDZj2LBhl/rwLgtZWVkAcNbnbmZm5qU4pMtGly5dsGLFClxxxRXIy8tDSkoK7rzzTqxcuRLdunW71IdX47322mtwcXFBv379sG3bNgB6njrD/31cAT1Pz6bWLD7+4ep65iGbTKZLcCSXh9DQ0NMuf9ylSxccOXIESUlJWnw4mZ67zte8eXM0b9781H/fcMMN2LlzJ95+++3/2Rf1qlqwYAGmTp2Kzz//HH5+fqc+r+cp52yPq56nZ6o1v3YJDAyEyWRCTk7OaZ/Pzs5GUFDQJTqqy1OzZs3OeJzlwgUHBwOAnrsG0fO3csnJyRg1ahQWLVqEXr16AdDz1BnO9riey//687TWLD48PDzQunVrpKamnvb59evXo3379pfoqGo/u91+xuc2btyINm3aXIKjuTw1btwYVqv1tOeu3W5HWlqanruk/37+OhwObNq0Sc/fc3A4HHjuuefw9NNPY8WKFbj11ltP/ZmepxfufI8roOfp2dSqX7uMGjUKkyZNQvv27REREYGPPvoIO3bswOLFiy/1odVaw4cPR1hYGPr16wcPDw+kpKRg2bJl2Lhx46U+tFqloqICNpvt1H8XFBQgNzcXXl5ecHd3R2xsLOLi4hAeHg5fX1/MnDkTADBgwIBLdci1QmWPa+/evdGtWzfExMSgoqICs2fPxp49e7B8+fJLeNQ119ChQ7Fy5UosWLAAoaGhOHDgwKk/a9SokZ6nF6iyx1XP07O4pOfaVFNFRYXj+eefd4SGhjrc3Nwc7dq1c6xYseJSH1atNnfuXEdUVJTDz8/PYbFYHJ07d3asW7fuUh9WrbN//34HgDM+5s6d63A4HI7i4mLHo48+6ggKCnJYLBbHtdde69iwYcOlPehaoLLHdfr06Y62bds6fHx8HJ6eno6ePXs6tm3bdmkPugYLDw8/6+P5z48CPU8vTGWPq56nZzI5HOfZvUdERETEyWrNzIeIiIhcHrT4EBEREUNp8SEiIiKG0uJDREREDKXFh4iIiBhKiw8RERExlBYfIiIiYigtPkRERMRQWnyIiIiIobT4EBEREUNp8SEiIiKG0uJDREREDPX/ADgN+Ixv3NluAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(71)  # len = 11\n",
    "y = np.arange(29)  # len = 7\n",
    "#norm = np.interp(dep, [0, np.max(dep)], [0,2])\n",
    "fig, ax = plt.subplots()\n",
    "ax.pcolormesh(y,x, pac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19 42\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[(50, 41),\n",
       " (53, 39),\n",
       " (56, 37),\n",
       " (59, 35),\n",
       " (61, 34),\n",
       " (65, 32),\n",
       " (67, 31),\n",
       " (71, 29),\n",
       " (77, 27),\n",
       " (83, 25),\n",
       " (90, 23),\n",
       " (109, 19)]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factor(2083-1,50,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ld = {}\n",
    "sieve = PrimeArraySieve()\n",
    "for k in sieve.primes_in_range(3,2500):\n",
    "    #print(k)\n",
    "    k = int(k)\n",
    "    #print(np_roots(k))\n",
    "    sn = sequence(k)\n",
    "    u, c = np.unique(sn, return_counts=True)\n",
    "    if c.shape[0]-1 == sn.shape[0] or c.shape == sn.shape:\n",
    "        ld[k] = {'unique': u, 'count': c, 'sequence': sn}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.573234847485091"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_lambda(100) / (np.pi/4.18870)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "sine = pure_sine_wave(500, 1, 2000, 0)\n",
    "squ = np.zeros(1000)\n",
    "for i in range(0,1000,100):\n",
    "    squ[i:i+50] = 1\n",
    "    squ[i+50:i+100] = -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(squ[400:600])\n",
    "plt.show()\n",
    "conv = np.convolve(squ, pac.flatten())\n",
    "conv = conv / np.max(conv)\n",
    "plt.plot(conv[400:600])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv = np.convolve(sine, pa.flatten())\n",
    "conv = conv / np.max(conv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(102,)\n",
      "[[  2  18  59  16  41  60  25  19  68  97  49  29  55  83  26  28  46]\n",
      " [ 92   4  36  15  32  82  17  50  38  33  91  98  58   7  63  52  56]\n",
      " [  9  81   8  72  30  64  61  34 100  76  66  79  93  13  14  23   1]\n",
      " [  2  18  59  16  41  60  25  19  68  97  49  29  55  83  26  28  46]\n",
      " [ 92   4  36  15  32  82  17  50  38  33  91  98  58   7  63  52  56]\n",
      " [  9  81   8  72  30  64  61  34 100  76  66  79  93  13  14  23   1]]\n",
      "[[ 1.91116887 17.20051984 56.37948169 15.28935097 39.17896185 57.33506613\n",
      "  23.88961089 18.15610427 64.97974161 92.69169024 46.82363734 27.71194863\n",
      "  52.55714395 79.31350814 24.84519532 26.75636419 43.95688403]\n",
      " [87.91376806  3.82233774 34.40103968 14.33376653 30.57870193 78.35792371\n",
      "  16.2449354  47.77922177 36.31220855 31.53428637 86.95818363 93.64727467\n",
      "  55.42389726  6.68909105 60.20181943 49.69039064 53.51272838]\n",
      " [ 8.60025992 77.40233927  7.64467548 68.80207935 28.66753306 61.15740387\n",
      "  58.29065056 32.4898708  95.55844354 72.62441709 63.06857274 75.4911704\n",
      "  88.8693525  12.42259766 13.3781821  21.97844202  0.95558444]\n",
      " [ 1.91116887 17.20051984 56.37948169 15.28935097 39.17896185 57.33506613\n",
      "  23.88961089 18.15610427 64.97974161 92.69169024 46.82363734 27.71194863\n",
      "  52.55714395 79.31350814 24.84519532 26.75636419 43.95688403]\n",
      " [87.91376806  3.82233774 34.40103968 14.33376653 30.57870193 78.35792371\n",
      "  16.2449354  47.77922177 36.31220855 31.53428637 86.95818363 93.64727467\n",
      "  55.42389726  6.68909105 60.20181943 49.69039064 53.51272838]\n",
      " [ 8.60025992 77.40233927  7.64467548 68.80207935 28.66753306 61.15740387\n",
      "  58.29065056 32.4898708  95.55844354 72.62441709 63.06857274 75.4911704\n",
      "  88.8693525  12.42259766 13.3781821  21.97844202  0.95558444]]\n",
      "[[0.04803291 0.43229621 1.41697092 0.3842633  0.98467471 1.44098738\n",
      "  0.60041141 0.45631267 1.63311903 2.32959626 1.17680636 0.69647723\n",
      "  1.3209051  1.99336588 0.62442786 0.67246078 1.10475699]\n",
      " [2.20951398 0.09606583 0.86459243 0.36024685 0.7685266  1.96934942\n",
      "  0.40827976 1.20082282 0.91262534 0.79254306 2.18549753 2.35361272\n",
      "  1.39295447 0.16811519 1.51303675 1.24885573 1.34492155]\n",
      " [0.21614811 1.94533296 0.19213165 1.72918486 0.72049369 1.53705321\n",
      "  1.46500384 0.81655952 2.40164563 1.82525068 1.58508612 1.89730005\n",
      "  2.23353044 0.31221393 0.33623039 0.5523785  0.02401646]\n",
      " [0.04803291 0.43229621 1.41697092 0.3842633  0.98467471 1.44098738\n",
      "  0.60041141 0.45631267 1.63311903 2.32959626 1.17680636 0.69647723\n",
      "  1.3209051  1.99336588 0.62442786 0.67246078 1.10475699]\n",
      " [2.20951398 0.09606583 0.86459243 0.36024685 0.7685266  1.96934942\n",
      "  0.40827976 1.20082282 0.91262534 0.79254306 2.18549753 2.35361272\n",
      "  1.39295447 0.16811519 1.51303675 1.24885573 1.34492155]\n",
      " [0.21614811 1.94533296 0.19213165 1.72918486 0.72049369 1.53705321\n",
      "  1.46500384 0.81655952 2.40164563 1.82525068 1.58508612 1.89730005\n",
      "  2.23353044 0.31221393 0.33623039 0.5523785  0.02401646]]\n"
     ]
    }
   ],
   "source": [
    "seq = sequence(103)\n",
    "#seq = np.arange(1,103, dtype=int)\n",
    "print(seq.shape)\n",
    "mat = return_matrix(seq, (6,17))\n",
    "de = get_well_depth(mat, 500, 103) / 2.54\n",
    "pa = phase_change(de, 500)\n",
    "print(mat)\n",
    "print(de)\n",
    "print(pa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.]\n",
      "[0.]\n",
      "[33.33333333 16.66666667]\n",
      "[4.1887902 2.0943951]\n",
      "[20. 40. 30. 10.]\n",
      "[2.51327412 5.02654825 3.76991118 1.25663706]\n",
      "[14.28571429 28.57142857  7.14285714 14.28571429 28.57142857  7.14285714]\n",
      "[1.7951958 3.5903916 0.8975979 1.7951958 3.5903916 0.8975979]\n",
      "[11.11111111 22.22222222 44.44444444 38.88888889 27.77777778  5.55555556\n",
      " 11.11111111 22.22222222]\n",
      "[1.3962634  2.7925268  5.58505361 4.88692191 3.4906585  0.6981317\n",
      " 1.3962634  2.7925268 ]\n",
      "[ 9.09090909 18.18181818 36.36363636 22.72727273 45.45454545 40.90909091\n",
      " 31.81818182 13.63636364 27.27272727  4.54545455]\n",
      "[1.14239733 2.28479466 4.56958931 2.85599332 5.71198664 5.14078798\n",
      " 3.99839065 1.71359599 3.42719199 0.57119866]\n",
      "[ 7.69230769 15.38461538 30.76923077 11.53846154 23.07692308 46.15384615\n",
      " 42.30769231 34.61538462 19.23076923 38.46153846 26.92307692  3.84615385]\n",
      "[0.96664389 1.93328779 3.86657557 1.44996584 2.89993168 5.79986336\n",
      " 5.31654141 4.34989752 2.41660973 4.83321947 3.38325363 0.48332195]\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(1,11)\n",
    "p = [2,3,5,7,9,11,13]\n",
    "for i in range(len(p)):\n",
    "    seq = sequence(p[i])\n",
    "    de = get_well_depth(seq, 100, p[i])\n",
    "    pa = phase_change(de, 100)\n",
    "    print(de)\n",
    "    print(pa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import modules\n",
    "import numpy as np\n",
    "import scipy.signal as signal\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# define the system\n",
    "#system = ([1.0], [1.0, 2.0, 1.0]) # transfer function (numerator, denominator)\n",
    "\n",
    "# compute the impulse response\n",
    "t, y = signal.impulse(system) # time points and output values\n",
    "\n",
    "# plot the impulse response\n",
    "plt.plot(t, y) # line plot\n",
    "plt.xlabel('Time [s]') # x-axis label\n",
    "plt.ylabel('Amplitude') # y-axis label\n",
    "plt.title('Impulse Response') # plot title\n",
    "plt.grid(True) # show grid\n",
    "plt.show() # show plot\n"
   ]
  }
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