benchmark_fftlog_pyfftlog.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Benchmark `FFTLog` vs `pyfftlog`\n",
    "\n",
    "Using \n",
    "- https://github.com/prisae/fftlog/blob/master/fftlogtest.ipynb ;\n",
    "- https://github.com/prisae/pyfftlog/blob/master/fftlogtest.ipynb .\n",
    "\n",
    "Consult these notebooks for any explanation, there are no comments here, just a benchmark.\n",
    "\n",
    "### Notes:\n",
    "\n",
    "To run, you need to\n",
    "- download [fftlog](https://github.com/prisae/fftlog) and run `python setup.py install`;\n",
    "- download [pyfftlog](https://github.com/prisae/pyfftlog) and place `pyfftlog.py` in this directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "nbpresent": {
     "id": "4cf9c8a2-cf5f-45cd-b5da-0b1c7fe23108"
    }
   },
   "outputs": [],
   "source": [
    "import fftlog\n",
    "import pyfftlog\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "nbpresent": {
     "id": "4cf9c8a2-cf5f-45cd-b5da-0b1c7fe23108"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ==> n ::    8 -- Result is the same: True (True)\n",
      "49.5 µs ± 3.6 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "1.58 µs ± 263 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "\n",
      " ==> n ::   16 -- Result is the same: True (True)\n",
      "69.7 µs ± 11.8 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "2.33 µs ± 586 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
      "\n",
      " ==> n ::   32 -- Result is the same: True (True)\n",
      "56.6 µs ± 3.21 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "2.22 µs ± 270 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "\n",
      " ==> n ::   64 -- Result is the same: True (True)\n",
      "52.4 µs ± 4.15 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "3.88 µs ± 195 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "\n",
      " ==> n ::  128 -- Result is the same: True (True)\n",
      "79 µs ± 24.7 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "7.67 µs ± 1.1 µs per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "\n",
      " ==> n ::  256 -- Result is the same: True (True)\n",
      "70.7 µs ± 4.84 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "13.2 µs ± 1.11 µs per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "\n",
      " ==> n ::  512 -- Result is the same: True (True)\n",
      "95 µs ± 5.21 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "25.5 µs ± 2.03 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "\n",
      " ==> n :: 1024 -- Result is the same: True (True)\n",
      "136 µs ± 10.7 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "48.7 µs ± 3.33 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "\n",
      " ==> n :: 2048 -- Result is the same: True (True)\n",
      "196 µs ± 23.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
      "98.7 µs ± 5.03 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "\n",
      " ==> n :: 4096 -- Result is the same: True (True)\n",
      "346 µs ± 20.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
      "216 µs ± 8.9 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "logrmin = -8\n",
    "logrmax = 8\n",
    "mu = 0\n",
    "q = 0\n",
    "kr = 1\n",
    "kropt = 1\n",
    "tdir = 1\n",
    "logrc = (logrmin + logrmax)/2\n",
    "\n",
    "nn = 2**np.arange(3, 13)\n",
    "result = np.zeros((2, nn.size))\n",
    "\n",
    "for i, n in enumerate(nn):\n",
    "    nc = (n + 1)/2.0\n",
    "    dlogr = (logrmax - logrmin)/n\n",
    "    dlnr = dlogr*np.log(10.0)\n",
    "\n",
    "    # Calculate input function\n",
    "    r = 10**(logrc + (np.arange(1, n+1) - nc)*dlogr)\n",
    "    ar = r**(mu + 1)*np.exp(-r**2/2.0)\n",
    "\n",
    "    # Initialize FFTLog\n",
    "    pykr, pywsave = pyfftlog.fhti(n, mu, dlnr, q, kr, kropt)\n",
    "    kr, wsave, _ = fftlog.fhti(n, mu, dlnr, q, kr, kropt)\n",
    "\n",
    "    logkc = np.log10(kr) - logrc\n",
    "    rk = 10**(logrc - logkc)\n",
    "\n",
    "    # Call FFTlog or pyfftlog\n",
    "    pyak = pyfftlog.fftl(ar.copy(), pywsave, rk, tdir)\n",
    "    ak = fftlog.fftl(ar.copy(), wsave, rk, tdir)\n",
    "    \n",
    "    # Ensure the result is the same\n",
    "    print(f\"\\n ==> n :: {n:4.0f} -- Result is the same: {np.allclose(pyak, ak)} ({np.allclose(pykr, kr)})\")\n",
    "\n",
    "    # Calculate runtime\n",
    "    out = %timeit -o pyfftlog.fftl(ar.copy(), pywsave, rk, tdir)\n",
    "    result[0, i] = out.best\n",
    "    out = %timeit -o fftlog.fftl(ar.copy(), wsave, rk, tdir)\n",
    "    result[1, i] = out.best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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TkxBCiDrJZa5CCCHqJAEhhBCiTk7fB5GQkMDu3bt56KGHSE9Pv6p9BAYGkpub28SVSR1SR8uqQepomXWEhYXVu87pAyI2NlbGVhFCCBuQJiYhhBB1koAQQghRJ6dvYrocpRQVFRWYzebLjr2SlZVFZWVlM1bmOHUopdDpdHh4eMj4NEKIWlp0QFRUVODq6nrFESX1er1D3NhjrzqMRiMVFRV4eno2+7GFEI6rRTcxmc3mqxpuuLXR6/WNnuNBCNHytehvT2kyaTh5r4RwTio5idLNKaiITmido5t03y06IIQQoiUzH9mLWvwiJUqBXo/uiT81aUi06CYmZ/TVV18RFxfH3XffDVjmDx47dizvvPMOq1atsk4EBHD33Xezb98+e5UqhLATZTZj3v4D6s2FYDaBMoPJiDp6oEmP4/RnEBfeSd0SfPrppyxcuJDhw4eTnZ1NQkICO3fuBCyBEB0dTWhoqJ2rFELYizp1AvMn70ByErSLhJxMS0i46NG6N+0otU4fEE19J7VKTkIdPYDWPaZJTtXS0tKYNm0a/fv359ChQ0RFRTFlyhQ++eQT3n//fcAyZv3y5cuJiYlh586dnD59mptuuonNmzeTl5fHjTfeyPjx49m3bx+///3v8fDw4Msvv6x1nDVr1vDGG2+glGLMmDHMnz8fgE8++YQ333yT0NBQoqKicHNz45VXXrnm1yWEaF6q6BxqzUrUtu+gjS/azEfRht0AJ4/hdSaFMumDuHrmT99FpZ2se52moZSC8jI4cxKUQmkaRESBp1e9+9Qio9Ddc+Wx+ZOTk4mPj2fQoEHMnTuX48ePc/z4cfLy8ggICGDVqlVMnjyZ8ePHs3XrVp577jn69u3LzJkzmTFjBt999x1gGXO/Zt2FMjMzeeWVV1i3bh2+vr5MnTqVdevW0a9fPxYvXsy6deswGAxMnjzZOpG7EMI5KKMRtWkt6stPoKoC7cbb0X41Bc3L27JB52i8h4yg3AZjQkkfxIXKS6FmegylLI+bQFhYGIMGDQLgzjvvZNeuXdx111189tlnFBYWsnv3bkaPHn3V+9+3bx/Dhg0jICAAvV7PnXfeyY4dO9i7dy9Dhw7Fz88PV1dXJkyY0CSvRwjRPNSRfZhfehS16j2I6obuhTfQTXrgl3CwsVZzBnG5v/T1ej1GoxGVnIQ5/lkwGcFFj+63TzTJKdvFl5BqmsaUKVOYOXMm7u7uTJgw4Zru16hvzieZC0oI56RyszCv/ickboegUHSPzIe+g5v9cnQ5g7iA1jnacpnY7dOa9HKxs2fPkpCQAMAXX3zBoEGDCA0NJSQkhNdff53Jkyc3aD/e3t6UlJRcsrx///7s2LGD/Px8TCYTa9asYdiwYfTr148dO3Zw7tw5jEYja9eubZLXI4SwDVVZifmLjzE//wgcTESbOB3di0vQ+g2xy71KreYMoqG0ztFN3tHTtWtXVq9ezTPPPENUVBQzZswALM1NeXl5dOvWrUH7mTx5Ms8888wlndQhISHMmzePSZMmoZRi9OjR3HzzzQDMmTOHCRMmEBoaSteuXWnTpk2TvjYhxLVTSsHuHy1nDfm5aINHot01E80/0K51tag5qS+eMKisrAwvr/o7mWvUNDHZQlpaGjNmzGDjxo2XrJs/fz69e/dm6tSpNqujtLQUb29vjEYjv/nNb7jnnnu45ZZbLtnuwveqJUyC0tLqcIQapA7b1KHOpGL+9F04egAiotBNfRCtW69mq6NFTxjkrMaNG4eXlxfPP/+8TY8THx/P1q1bqaysJC4ujnHjxtn0eEKIhlGlJagvP0ZtWgue3mjTHkYbeROazv4Dh9aQgLCxyMjIOs8e1q1b1yzHt3UACSEaR5lNqG3foT5fAaWlaKPGod0+Dc3b8Zp/JSCEEKKZqBOHLXdBn06Bbr3RTf0dWkSUvcuqlwSEEELYmCrIQ/3nA9TOzeAXiPbgU2ixwx1+FGUJCCGEsBFVXY36bg1q7WowmdAmTEEbdxeau0eTHeNgVhmnT6bRyQDRQU076ZcEhBBCNDGlFOzfhXnVe5bB9PoPtdwBHdS0A23+kHKOxdsz0QBXF42Xx7Rv0pBw+oBw9NFc33//fZYvX06vXr3Iy8sjPz+f3//+92RnZzN9+nTrNJ9Dhgzh22+/xdfX184VCyGuhco8YwmGg4nQLhLd4y+i9ezf5Mf56XQRS362DP+vAKNZcTCrTALiQk09mmtT+/DDD1m5ciU5OTksXLjQOvDekCFDuOuuu2QeaCFaCFVehvp6FWrDV+DmhjblN2ijfoXWxNMem5Xi4325rD6UR3tfNzJKqjGZFXqdRu+QK9/31RhOHxBNLSmnnINZZfQO8brmJH766ac5ffo09957LykpKXh7e3PjjTcyZcoUsrKymDRpEn5+fvznP/+p9bxly5axatUqAKZOncrvfmcZR2rRokV8/vnnhIWF4e/vT58+fZg1a9Y11SiEuDbKbEbt2IT674dQdA5t+Fi0O+5F82nb5McqrTLx9x/TSUgvZWxnX2YNCiE5v5KUEqQP4lq8l5DFyYKKOtdp54f7Lqs2cbKgCgVoQJSfG16u9d+0EuXnwW9jQ+pd/5e//IVNmzbx+eefk5SUxNtvv83y5csBePfdd1m9ejX+/v61nrN//37+/e9/87///Q+lFBMmTGDYsGGYTCbWrl3L+vXrMZlM3HzzzfTp06fR74MQommo5CSKPtuBeX8CpJ+GTt3RPfIsWlRXmxzvTGElC7ecJbO4iocGhXBL17ZomkZ0kCcjetjmzvJWExANUVplpmbcEXX+8eUCwhZ27txpvcsa4JZbbuHnn3/GbDZz8803W5ukbrzxxmatSwhhocwm1MavUav/SbnZbFn4q8nobvs1ms4245/uPFPM33/MwO18R3SvJm5Kqk+rCYjL/aVfMwZSUk45z31/GuP59ry5w8Ob/JTtSmTobiEcj1IKTh5D7dyCStgGhQW/rNTp0NzcbRIOZqVYfTCPj/fn0tnfg3kjwwnydm3y49RHhvu+QHSQJy+Pac+0PkFNfrnYxQwGQ51Ddw8dOpT169dTXl5OWVkZ69atY8iQIQwePJjvvvuOiooKSktL+f77721WmxDCQqWfxrxmJeb5D2H+85OozeugczTaxHvB1Q10OpvMBQ1QVm3ita1n+Xh/LqM6+vDnG9s3azhAKzqDaKjoIM9mOWuYNm0a06dPJzg4uFYndUxMDJMmTeJXv/oVYOmk7t27NwA33XQTN954IxEREfTt21eG7hbCBlReDmrXFtTPWyxTEGs66NHHMs1n/6HW2dxUdIzN5oLOKK5i4eYznCmq4oEBwdwW7WeXu65luG9sO9x3Y1ypjpqhu8vLy7nzzjt57bXXiIlpmr9cZLhvx67DEWpoyXWo4iLU7m2WUDhx2LKwU3e0wXFog4aj+fg1Sx0Aiekl/O3HdHTA/40Ip1+7K08vKsN9C5566imOHTtGZWUlkyZNarJwEKI1UhVlqL0/W0LhyF4wmaBdJNrE6ZYJe5r4rucr1qMUnx/JZ8XeHNr7uvPHuHBCDG7NWsPFJCCcyJtvvmnvEoRwaqq6Gg7tRv28BbV/J1RVgX8Q2o0T0YaMhPCOdmnKqTSaeWNHBltPFTO8fRv+MKwdHnr7dxG36IBoQa1nNifvlWiplNkERw9arkBK/AnKSsHgg3bdWEsodIq22eWpDZFVUsWft5wltaCSe/sFcVdPf4cZ5bVFB4ROp8NoNKJv4lvdWxqj0YjOjr8gQjQ1pRSknkDt3IzatQ0K88Hd09LJPGQkRPdt8iEwrsb+zFL+ui0dk1nx3KgIBoYb7F1SLfZ/h2zIw8ODiooKKisrL5vI7u7uVFZWNmNljlOHUgqdToeHR9MNPyyEvaiMM5YzhZ2bITsD9HroHYtuyEiIGYTm7m7vEgHL793/jhbwz8Rswtq4MT8ugjAf+/Y31KVFB4SmaQ0aDK+lXpkhRGug8nNQu7ZZQuF0CmgaRPexzLsw4Do0b8f6q7zKZGbpzkw2phQxJMLAY9e1a/YRGxqqRQeEEKLlUclJlKw/jKm4CE4ehWOHLCuiullGUI0dgdY2wL5F1iO3rJpXt5zleF4FU2MCmRwTgM5B+hvqIgEhhHAKqqoS87dr4MuPKa25qMI/CO32X1suSw2u/3p+R3Aku4xXt56lwqj448hwhkQ6/o2uEhBCCIelzGY4dhC14wfU7p+govyXlZoOLW4cuvGT7FdgA607XsC7CVkEebvy8tgI2vs6Rl/IlUhACCEcjjp7CrX9B9TOLVCQCx6eaAOug/adUJ99CCajzcZAakrVJsW7CVmsP3GOgWHezB0ehsHNMfsb6iIBIYRwCOpcnuUGth2bLGMg6XTQawDapPvR+gy2XoGkOna12RhITamg3MirW86SlFvO3b0C+HWfQFx0jtvfUBcJCCGE3aiKMlTiDtSOHyBpPyhl6Wy+50G0QSPqnJVN6xyN95ARlDvwFX/Hcst5dctZSqpMPDkijBEdfOxd0lVploB46623SExMxNfXl/j4eABKSkpYtGgROTk5BAUF8fjjj2MwGFBK8a9//Ys9e/bg7u7O7Nmz6dSpU3OUKYRoBspkgsN7Lf0Ke3dYhrsIDEH71WS0IaPQQsPtXeI1+T75HEt3ZuHnqecvN3cgys957zFqloAYNWoU48aNqzWW0Jo1a4iJiWHixImsWbOGNWvWMH36dPbs2UNmZiavv/46x48f57333mPhwoXNUaYQwkaUUnDqhGXu5p1boLgQvAxow0ajDb3BMseCA1/u2RBGs+Jfidn872gBfUK9eHJEOD7uztPfUJdmCYiePXuSnZ1da9muXbtYsGABAHFxcSxYsIDp06eTkJDAyJEj0TSNbt26UVpaSkFBAX5+dQ+3K4RwXCo3C/XzZku/QuYZy53NfQajGzoKYgai6Zt3AhxbKaww8tq2dA5mlXF7tB8z+gc7XX9DXezWB1FYWGj90vfz86OoqAiA/Px8AgMDrdsFBASQn59fZ0Bs2LCBDRs2APDqq6/Wel5j6PX6q35uU5I6pA5HrqGhdZhLiqj4aSMVm9ZTfWQfAK49++FxxzQ8rrsBneHa2+Md6f3IN3sw79sj5JdV8/zN3bg5Otguddji/XC4Tuq6RhWt79Rz7NixjB071vr4aoepcJQhLqQOqcORa7hcHaq6Gg4kYP55E+zfBUYjhEZY5lYYOgpzQDBlQFlFFVRc++twhPcjKaecb0+Wsjk5F18PPX++sT1dAnR2qavFTRjk6+trbToqKCjAx8fyV0VAQECtF5qXlyfNS0I4IKUUnDhi6VdI2AZlJeDTFm3UeEu/QvtOTt+vUJ9D2WU8u+E0ZgUaMHtQCF0CnLczuj52C4jY2Fg2b97MxIkT2bx5M4MGDbIuX7duHcOHD+f48eN4eXlJQAjhAFRyEqWbUzB7GiD9tKVfIS8b3Nwtw2gPHQU9+qG5OHfH7JVkFFcR/2M65vONHZoGqeeqiI2wb1220CwBsXjxYg4fPkxxcTGzZs1i8uTJTJw4kUWLFrFx40YCAwOZO3cuAP379ycxMZE//OEPuLm5MXv27MvuOyEhgd27d/PQQw81x0sRolUy79qGej+eEpPp/BINevZDu32aJRw8rjxqckuw6WQhb+/MQqHQ68CsQK/T6B3iZe/SbEJTLWgqsfT09Kt6niO0Z0odUocj1aDMZjh5zDJn896fLVcgWWlot9yF7s77mq2eizX3+1FWbWLZziw2pRbRM8iTucPDyCszklICnQwQHWTfgGxxfRBCCMeiqirhyD7Uvp2ofTuh6By4uEC33tB7IGxeCyaTZQykvoPtXW6zOZ5Xzt+2pZNdWs3UPoFM6hWAi04jyNuVET3s/8eDLUlACNGKqeIi1P5dlrOEw3ugqhI8vdB6D4S+g9FiBqJ5WSbcUbHDnWIMpKZiVorPD+fz0b4c/D31vDK2PT2DW2ZTUn2cPiCkD0KIxlFZ6Zamo30/w4kkUGbwC0S7bgxavyHQvXedN7A5wxhITSWvrJrF2zPYn1nG8PZtmD04FIOT3xV9NZw+IGJjY4mNjbV3GUI4LGt/wr6fUXt3QkaaZUVElGX8o35DWvQlqY2160wJr+/IoNJo5mxCpUgAACAASURBVJEhodzY2bfVvjdOHxBCiEtZ+hP2W0Jh/y4oLLAMn92tN1rcOLS+g9ECQ+xdpkOpMpn5YE8OXx8tIMrPnf8bHkaEk0zsYysSEEK0EKq4CHXgfH/CofP9CR6eF/QnxKJ5G+xdpkNKK6zkb9vSST1Xya3RfszoF4Sri87eZdmdBIQQTkxl1/Qn7ITjRyz9CW0D0K4bjdZ3CHSPQXNtGQPi2YJSim9PFPLe7iw89TqeGxVBbLiEaA2nDwjppBatiTKbIfX4L/cnWPsTOqKNv9vSn9ChS6ttM2+M4koTb/6cwfa0EvqFevHodWH4ezr9V2KTcvp3QzqpRUumKspRB3dTePQApnP5cPI4FOZb+hO69kIbebOlPyEo1N6lOpVDWWXE/5ROYYWRmf2DuL2HPzoJ1Us4fUAI4WxUdZXlJrSic1BYgCoqgMJzUFSAOr+sZh1VlQBU1Dy5ewza3TMt9yd4t7Hba3BWJrNi1cFcVh/MI8Tgyqs3daBrQOsYJuRqSEAI0QSUyWSZJe38l70quuBLvugcquZLv6gAykrr3ol3G/BpC75+aFHdwactKiPNcgObUqDTofXsZ5lsRzRadkk1f/8pnSM55Yzu5MPvYkPwcm199zY0hgSEEJdhPphIcfJhzAZfyxe49a/98yFQ88VfUmT5Er+Yhyf4+IFvWwhvj9ajL/j6WYbFrlnu4wc+vnXenKaSkzAfOwgmo2WIi+4xzfCqW54fTxXx5s+ZmBXMva4dcVG+9i7JKTh9QEgntWgqymiEMydRKUch+Sjq6H4oLKDs4g31rtYveYJC0Tr3OP9FX/Olf36djx+a+7VdR691jkb3xJ9a1RAXTanCaObdhCw2JBfSLcCDJ4aHEdrGzd5lOQ2nDwjppBZXSxUWQHISKiXJEgqnTkBVlWWlr7/ljKGwwPJY09DG3Ip261TLWEXN2KHZmoa4aEop+RX87cd00ouquLtXAFP7BKJvAfNENyenDwghGkIZqyHtJCo5CVKOWgIhL9uy0kVvGWpi5Djo1B2tUzT4B0LKUczxz/7SvBM7As3L274vRFyRUoqvjhbw4Z4cfNxdeGlMJH1C5XO7GhIQokVSBXnngyDJEgqnksFYbVnpFwiduqGNnmBpsmnfCc21jmYHad5xOucqjLy+PYPd6aUMjjAwZ0goPh7yNXe15J1zEDXTOSo7fxE5Sh2Noaqr4XSy5azgfCiQf745Rq+33Dh2w3i0Tt2hUzSaf2CD9y3NO85jT0Ypi39Kp7TKzIOxIYzv1lZuGLxGEhB2oCorIScdsjIsQyUcPwwHd1OiFKBZmjfczv9FW3NhjPUKGXXp1TI1jy9cfuH2l+znomU1/1NdDWUllIBlot32nSEw2HK9vZcBvA3gZTj/2NvSRu99frm7Z7P9Mqr8HEsQJJ8Pg9PJYDRaVvoHWTqNb+xmaSqK7CRDTbRw1SbFyn05rDmST3tfN14cHUlHPw97l9UiOH1AOOpVTKqqErIzINsSAmRnoLLSITsdzuXX3tjdo/YXuqcXWrtIy5f0hayPNdDqWGb9j1Z7nXU3FyyvY9/qdAqkHj9fhrJc119ZgSorgdISS1s8F+TKhVxcLCFSK0hqHrcBb2/wamNZduFyL0OdX+A1ZzLm0Eg0F/0vHcnJR+FcnmUjVzfL2cHoW9E6d7f0H7QNqKs60UKlF1Xxtx/TSc6v4Jaubbl/QDDuehlkr6k4fUDY8yomVV0F2ZmQnX5RCGRAwUVNEm18IbgdWo9+EBIGwWFowe0guB2kn67VGaq79xG7NO+o5KTadTz4pLUOpZTlrt7SYqgJjNISVGmx5cavC5arshIoLkRlnbVsV15qDcA6w8XN/Zdg8TZYtk1OosRsrv2cgGC0br0szUSdukNkxzrvHRAtn1KKjSmFLNuViV6nMW9kOEMj5c7ypub0AWFrqroacjMh63wIZGWgcjIgK90SAhc26xh8LCHQPQZC2llCICQMgtpd/uoXB+kMvdw195qmWc503D3AP+iX5Q3YrzKboLz80hApLbYESM2ymv/POgvnwwFAGzIKbdL9aL5+TflyhZPak1HCyvVpnMgtpXewJ48PDyPQS/5QsIVWHxAqOYmSjccxe/uieXiePxNI/+VMID+ndgh4t7GEQLdeENQOQsLQgsMsy65hrH1H6Qy1RR2azuWXs4OaZZfZ/uIzGe2G8RIOAoDvk8/x+o5MAFw0mNY3SMLBhlp1QKjkJMx/nUepyWR5XLPCy9vy13/nHnDd6F+ag0LCZIC0ZiB3D4uLKaVYd/wc7yRk/bIMOJxdTs9gL/sV1sK17oA4euCXpgxNswydPHE6eLeRy+PszFHOqIT9lVWbeOvnTLaeKqZbgAep5yoxmhV6nUbvEAkHW2rVAaF1j0HpXX9pyhg2Gs3gY++yhBDnpRZU8Jet6WSWVHFv3yDu7OXPsdwKUkqgkwGig2Sobltq3QEhTRlCOCSlFBuSC3knIQtvNxdeHtPeerYQHeTJiB6B5MrZpc05fUBc630Q0pQhhGOpMJp5e2cmP5wsok+oF09cF0ZbmQrULpz+XZfRXIVoOU4XVvLa1rOcKaxiakwgk3oH4CIjsNrNZQOiqKiILVu2kJiYyKlTpygrK8PLy4sOHTrQr18/Ro0ahY+PtNkLIa7dDymFLN2ZiYerjgWjI+nXTkZgtbd6A+Ljjz9m69at9O/fn9GjRxMeHo6npyfl5eWcPXuWw4cP8/TTTzNixAimTZvWnDULIVqQyvOT+nyXXEivYE+eGB5GgNzb4BDqDQg/Pz9ef/11XOsYJycqKooRI0ZQVVXFxo0bbVqgEKLlOltUxWtbz5J6rpK7ewXw6z6B0qTkQOoNiFtuueWKT3Zzc2PcuHFNWpAQonXYmlrEkp8zcXXReH5UBAPDr34kAmEbDeqkPnjwIMHBwQQHB1NQUMBHH32ETqfj17/+NW3btrV1jUKIFqTaZOb93dl8c/wc3QM9eXJEGEHe0qTkiBo0Lu7777+PTmfZdPny5ZhMJjRNY9myZTYtTgjRsmQWV/H0t6f45vg5JvbwZ+GN7SUcHFiDziDy8/MJDAzEZDKxb98+3nrrLfR6vcPNwSCEcFzb04p5Y3sGaPDHuHCGRMi4Zo6uQQHh6enJuXPnSEtLIyIiAg8PD4xGI8aaWbyEEKIe1SbFh3uz+SqpgK4BHjw5IowQQx1zgAuH06CAGDduHPPmzcNoNDJz5kwAkpKSCA8Pt2VtDeKoM8oJISC7pJq/bjvLsbwKftXdj/v7B+HqIjO+OYsGBcTEiRMZPHgwOp2O0NBQAPz9/Zk1a5ZNi2sIuZNaCMe060wJi7enY1bw1PVhDG8vN9U6mwYPtREWFnbZx0IIAWA0Kz7al8N/D+cT5efO09eH066NNCk5owYFxMMPP1zvuqVLlzZZMUII55ZXVs3ftqVzOKecm7u05bexwbhJk5LTalBAzJkzp9bjgoIC1q5dy/Dhw21SlBDC+SSml7DopwyqTGbmXteOuChfe5ckrlGDAqJnz56XLOvVqxevvPIK48ePb/KihBDOw2RWfHogl9UH82jv685T14cR4etu77JEE7jq4b71ej3Z2dlNWYsQwskUlBuJ/zGdA1lljOnky0ODQnDXS5NSS9GggFi1alWtx5WVlezZs4f+/fvbpCghhOPbn1lK/I/plFWb+cPQUMZ0lmF3WpoGBUReXl6tx+7u7kyYMIGRI0fapCghhOMyK8Xqg3l8eiCXsDZuvDSmPR3aSpNSS9SggJg9e7at6xBCOLiknHIOHUtle0oux/MqiOvow8ODQ/F0lSallqregEhNTaVjx45X3EFDtxNCOK+knHLmbziN0awAuKunP/f2C0LTZO6GlqzegHj//ffx8vLi+uuvp2fPnvj7+1vXFRQUcPjwYbZs2UJFRQUvvvhisxQrhGh+RRVGlu3KtIaDDvBydZFwaAXqDYiXX36Z3bt389133/H222+j0+msU44qpYiJieHmm29mwIABzVmvEKKZKKXYmFLIv/bkUFppwkUDBeh1Gr1DvOxdnmgGl+2DGDhwIAMHDsRoNJKZmUlpaSne3t60a9cOFxeX5qpRCNHMzhZVsXRnJgeyyogO9OSRsaGUVZtJKYFOBogO8rR3iaIZNKiTWq/XExERYetaroqM5ipE06k2mfnscD6rD+bh7qLx8OAQburSFt355qQRPQLJzc21c5WiuVz1jXKOQkZzFaJpHMou462fMzlTVMWIDm347cAQ/Dyd/itCXAP59IVo5YorTXy4J5vvkgsJ9tbz/KgIBoYb7F2WcAASEEK0UkoptqQW8X5iNsWVJu7o4c89fQLxkKEyxHmNCojc3Fzy8/Pp1q2breoRQjSDjOIq3t6Vxd6MUroGeLDghkg6+XvYuyzhYBoUELm5ufzjH/8gNTUVgBUrVrBjxw727t3rELPKCSEaxmhWrDmSz6oDubhoGg/GhjCua1tcdHJPg7hUg84l33nnHfr378+HH36IXm/JlD59+rB//36bFieEaDpHc8uZ+00qK/bmMCDMmyW3RvGr7n4SDqJeDTqDOHHiBM888ww63S954uXlRVlZmc0KE0I0jdIqEyv25rDu+Dn8vfT8cWQ4QyLb2Lss4QQaFBC+vr5kZmbWmof6zJkzBAYG2qwwIcS1UUrxU1ox7yZkU1hhZEJ3P37dNxAvV7nJVTRMgwLi1ltv5S9/+QsTJ07EbDazbds2Pv/8cyZOnGjr+oQQVyG7pJpluzJJSC+lk5878+PC6Rogdz+LxmlQQIwePRqDwcD3339PQEAAmzdvZsqUKQwePNjW9QkhGsFkVvzvaAEf7ctB0+CBAcFMkH4GcZUafJnr4MGDJRCEcGDH88p56+dMUgoqGRTuzYOxoQQbXO1dlnBiDQ6II0eOcPLkSSoqKmotv/POO5u8KCFEw5VVm/h4Xy5fHyvA10PPU9eHcV1kGxmOW1yzBgXEP//5T7Zv3050dDRubm7W5fIDKIR9/ZxWzLKELPLLjIzr2pZ7+wXh7Sad0KJpNCggtm7dSnx8fK1Jg4QQ9pNXVs07CVnsSCuhQ1t3nhoRLkNwiybXoIAIDAzE1VXaMoWwN5NZ8c3xAlbuzcWkFPf1C+L2Hv7opRNa2ECDAmLWrFksW7aM4cOH4+vrW2tdz549bVKYEMIiKaeclJNpeJgrWXv8HMfzKujXzpuHB4UQ2sbtyjsQ4io1KCBSUlLYs2cPR44cqdUHAbB06VKbFCaEsITDc9+fpspkmQ/a203HE8PDuL6DdEIL22tQQHzyySc8/fTT9OnTx9b1CCEu8F3yOWs4APyqmx8jO/rYsSLRmjQoINzd3aUpSYhmVFZt4oPEHDYkFwKWUTX1LhoDw2QiH9F8GhQQU6ZM4YMPPuDuu+/Gx6f2Xy8XDuAnhLh2CWdLeGtnJgXlRib28Cc2zJu0ch2dDMiVSqJZNSggavoZvvvuu0vWrVq16poKeOutt0hMTMTX15f4+HgASkpKWLRoETk5OQQFBfH4449jMMhfTqJlK6408d7uLDadLKK9rxvPXN+BboGWQLghMJDc3Fw7VyhamwYFxJIlS2xWwKhRoxg3bhxvvvmmddmaNWuIiYlh4sSJrFmzhjVr1jB9+nSb1SCEvf10uohlu7IorjQxuXcAk3sH4OoiZ+fCvhr0ExgUFFTvv2vVs2fPS84Odu3aRVxcHABxcXHs2rXrmo8jhCMqKDfy6paz/GVrOgFeeuJv6ci0vkESDsIh1HsGsWzZMh566CEA3njjjXovqfv973/f5EUVFhbi5+cHgJ+fH0VFRXVut2HDBjZs2ADAq6++etXzU+j1eoeY20LqaD11KKVYn5TD4s2pVBpNzLquA1MHRtR7w1tLfi+kDseto96ACA4Otv5/aGhokx+4KYwdO5axY8daH19tG22gg7TvSh2to46c0mqW7sxkd3op0YGezBkaSYSvO+fy85qthqsldbS8Oi6cCO5i9QbEHXfcwbZt2xgxYgSTJk26qgNfLV9fXwoKCvDz86OgoOCSK6eEcEZmpfj2xDk+SMzBrBS/HRjM+G4yV4NwXJdt6Hz33Xebq45aYmNj2bx5MwCbN29m0KBBdqlDiKaSUVzFc9+nsXRnFl0DPXj9V1HcGu0v4SAc2mWvYlJKXW51k1i8eDGHDx+muLiYWbNmMXnyZCZOnMiiRYvYuHEjgYGBzJ07t97nJyQksHv3bmt/iRCOpGaGt5X7ctDrNB4ZEsqNnX1lmAzhFC4bEGazmYMHD152B717976mAh577LE6lz///PMNen5sbCyxsbHXVIMQtpBWWMkbOzI4mlvBoHBvZg0OJdBLRkUWzuOyAVFdXc3bb79d75mEpmk2vUdCCGdkNCv+eziPVQfy8HTVMfe6dozs6CNnDcLpXDYgPDw8JACEaISU/Ape35HByYJKhrdvw4ODQmjr0eCZfYVwKE7/kyt9EMIRVJnMrDqQx38P5+Hr7sK8keEMjWxj77KEuCZ276S+VtIHIeztaG45r2/P4ExRFaM7+fKbAcEY3GVeaOH8LhsQy5cvb646hHA6FUYzK/fl8L+kAgK99LxwQwQDZDhu0YI4fROTEPawP7OUN3/OJLOkmlu6tuW+/kF4ucpZg2hZnD4gpA9CNKfSKhMf7slh/YlztGvjyitj29M7xMveZQlhE04fENIHIZrLxRP5/LpPIO56GXVVtFxOHxBC2FpheTWLfkqvcyIfIVoyCQgh6qGUYvWhPD47dIwqo5kpMQFM6iUT+YjWQwJCiDok51ewZEcGKQWVALjqNAa0M0g4iFZFAkKIC+SUVrNyXw6bThbh5qKhAQowKcXBrDKig6RpSbQeTv/nUEJCAsuWLbN3GcLJlVWbWLE3h9lfpfDjqWLu6unPH+PCcXXR0Gmg12lytZJodZz+DEKuYhLXwmi2TOLz6f5cCitNjOrow7S+QQQbLKOuvjymPSkl0MmAnD2IVsfpA0KIq6GUYueZEj7Yk0N6cRW9Q7x4vn8wXQI8am0XHeTJiB6OMa2kEM1NAkK0OsfzyvlXYjaHssuJ8HHj2bgIYsO9ZThuIS4iASFajaySKlbuy2VLahG+Hi7MGhTCTV3ayrSfQtRDAkK0eCVVJv5zMI//HS1A02By7wDu6OkvYycJcQVOHxAyFpOoT7VJse54AasO5FJSZeaGTr5M6xso034K0UBOHxByFZO4mFKK7WnFLN+bQ0ZxNX1Cvbi/fzCd/D2u/GQhhJXTB4QQFzqaW84/d2eTlFtOe183nh8VwYAw6YAW4mpIQIgWIbO4iuV7c/jxdDF+Hi48MiSUMZ18pQNaiGsgASGcWnGlidUHc/n6WAEumsY9MQFM7BGAp6vTDxIghN1JQAinVG0ys/bYOf59MJeyajNjOvkytU8gAdIBLUSTkYAQTkUpxY+nLR3QWSXVDGjnzYz+QXT0kw5oIZqaBIRwGkeyy/jXnmyO5lbQsa07C0ZH0r+dt73LEqLFcvqAkPsgWr6M4io+3JPD9rRi/D31zBkayg1R0gEthK05fUDIfRAtU1JOOYePpXIiq5AdacW4uuiY1ieQ23r44yHzQAvRLJw+IETLcyi7jOc2nMakLI+HRBiYPTiUtp7y4ypEc5I/xYRDOZFXwV+3nrWGgw7oFuAp4SCEHchvnXAIlUYzn+zP5YukfLxdXdDrNMxKyUxuQtiRBISwu/2Zpbz5cyaZJdXc1MWXGf2DOVNYJTO5CWFnEhDCbkqqTHyQmM13yYW0a+PKy2Mi6RNquWxVZnITwv4kIIRdbE8rZtmuLAorjNzZ0597YgJxl6uThHAoEhCiWRWUG1m2K4vtacVE+bnz3KgIOssw3EI4JAkI0SyUUnyfUsg/E7OpMiru7RfExB7+6OVmNyEcltMHhNxJ7fgyi6t4c2cm+zPL6BXsySND2hHu42bvsoQQV+D0ASF3Ujsuk1nx1dF8PtqXi16n8fDgEG7q0hadTN4jhFNw+oAQjim1oII3dmRyIr+CwREGZg0KkaG4hXAyEhCiSVWZzPz7QB7/PZyHwd2FJ0eEMbx9G5nyUwgnJAEhmszh7DKW/JzJ2aIqbojy4YGBIfi4u9i7LCHEVZKAENesrNrE8j05fHP8HMHerjJPgxAthASEuCYJZ0t4a2cm+WVGbo32Y1qfIJkPWogWQgJCXJXCCiPvJWSz5VQR7X3dePrmDnQPlDGThGhJJCBEoyil2HSyiPcTsymvNjG1TyB39QzA1UU6oYVoaSQgRINll1SzdGcmiRmldA/04PdD2tO+rbu9yxJC2IgEhLgik1nxzfECVuzNAeC3A4MZ381P5oQWooWTgBCXdbqwkiU7MjmaW07/dt7MHhxKsEFueBOiNZCAEHWqNik+O5TH6kO5eLq68Ph17Yjr6CM3vAnRikhAiFqScsrZsf8EP6XkklVqZGQHH34TG0xbD/lREaK1cfrfehnNtWmUV5tZeyyflftyMSvLspn9grijV4B9CxNC2I3TB4SM5nr1MoqrSDhbQkJ6KQezyjDWJAOg08CkLvNkIUSL5/QBIRqu2qQ4nFNmCYWzpaQXVwEQ7uPG+G5tCTa4snxPDkazQq/T6B3iZeeKhRD2JAHRwuWXG0lMLyHhbAl7M8ooN5qtX/7ju7UlNtxAuza/TN7T1d+TlBLoZIDoILkzWojWTAKihTErxfG8ChLOlrA7vYTk/EoAAjz1XN+xDbFhBvqEetc7XlJ0kCcjegSSm5vbnGULIRyQBEQLUFJlYm9GKQlnS0hML6Ww0oROg24BnkzvG0hsuIGObd3lElUhRKNIQDghpRRphTUdzCUcySnHrMDgpmNAOwMDw70ZEGaQuRiEENdEAsJJVBrNHMgqszYdZZcaAejY1p07ewYQG+ZNt0BPGf5CCNFkJCAcWHZJNQnnO5gPZJVRZVK4u2j0CfXmrl7eDAwzEOQtw14IIWxDAsJBJOWUcyIlDZfqCrLLqkk4W8LpQstlqKEGV27s0pbYMG96h3jh5iIT8gghbE8Cws5MZsX/jubzwZ4c6x3MOqBXiBf3d/YlNsxAuI+bdDALIZqdBIQdKKU4llfB5pOFbDtVTGGlybpOAybFBPDrPkH2K1AIIZCAaFZnCivZnFrEltQiMkuqcdVpDIow0NnfnVUH8qx3MA9oZ7B3qUIIIQFha3ll1Ww9VcTmk0WkFFSi0yAmxIvJvQMYGtkGbzfLpai9g73lDmYhhEORgLCBkioT208Xszm1iINZZSigi78HvxkYzIgOPvh7Xvq2yx3MQghHIwHRRKpMZhLOlrA5tYiEs6UYzYp2bVyZEhPAyI6+hPu4XXknQgjhQCQgroHJrDiQVcaW1CK2pxVTVm2mrYcLt3RtS1yUD138PeTqIyGE05KAaCSlFCfyK9iSWsTWU8UUlBvx1OsY1t5AXEdfYkK85G5mIUSLIAHRQBnFVWw+WcTm1CLSi6vQ6zQGhnkT19GH2HAD7nq5eU0I0bJIQFxGQbmRbacsoXA8rwINyw1sd/T057rINhhkMDwhRAsmAXGRsmoTO9JK2HyykP1ZZZgVRPm5M7N/ENd39CHQS8Y+EkK0Dg4bEG+99RaJiYn4+voSHx9vs+Mk5ZRzIvk0lRVlJOdXsutsCVUmRYjBlbt6BjAyyof2vu42O74QQjgqhw2IUaNGMW7cON58802bHSMpp5z5G05hNFsee7nqGNvZl7iOvnQPlCuQhBCtm8MGRM+ePcnOzrbpMQ5mlWE6Hw4acEcPfybHBNr0mEII4SwcNiAaYsOGDWzYsAGAV199lcDAxn25j+juxr8P5WE0mdG76BgZHUZgoI8tSm0QvV7f6NcgdbSOOhyhBqmj9dXh1AExduxYxo4da33c2GEqQl3hpdGR1jGQQl2r7DrURWCgYwy1IXU4Xh2OUIPU0TLrCAsLq3edUwdEU5AxkIQQom5yd5cQQog6OWxALF68mGeffZb09HRmzZrFxo0b69wuISGBZcuWNXN1QgjR8jlsE9Njjz3WoO1iY2OJjY21cTVCCNH6OOwZhBBCCPuSgBBCCFEnpw8I6YMQQgjb0JRSyt5FCCGEcDxOfwZxoSudSdS3/plnnrmq/TW1+uqoS1PXduH+bFVHQ7a9XB2Xe35d65rqPWrM+9FQja3tSjVc7Wtt7Od3LXVczbrG/s42p4a8H43Z17U8ryF1XM0xWlRADBw48JrWX+v2zampa7va/TXmeQ3Z9nLbNHadfH5N+7zm/vwaekx7acraHOXzu1iLCogrXe7a2MthHfny2aau7Wr315jnNWTby23T2HXy+TXt85r782voMe2lKWtzlM/vYi4LFixY0OhntUCdOnWydwmA1HExqcOxagCp42ItuQ7ppBZCCFGnFtXEJIQQoulIQAghhKiTw47F1Fz+97//sXHjRjRNIzIyktmzZ+Pm5mbz49Y35/Y333zDunXrcHFxYcCAAUyfPt1mNVRVVfHCCy9gNBoxmUwMHTqUyZMn8/rrr5OcnIxer6dz5848+OCD6PW2/VEpLS3l7bffJi0tDU3TePjhh+nWrRsAX375JStXruS9997Dx6dpJ3Sq63NYsWIFu3fvRq/XExISwuzZs/H29sZoNPL2229z8uRJzGYzI0eO5I477miSOnJzc3nzzTc5d+4cmqYxduxYxo8fz7///W++//576+ueOnUqAwYMAODUqVO88847lJeXo2kaf/7zn6/5Z/eRRx7Bw8MDnU6Hi4sLr776Ktu3b2f16tWcPXuWhQsX0rlzZwD279/PRx99hNFoRK/Xc++999K7d++rPnZdn0VJSQmLFi0iJyeHoKAgHn/8cQwGA1u3buWLL74AwMPDg9/+9rd07NjRui+z2cwzzzyDv79/oy5Fre9zqK+OGidOnGD+/Pk8/vjjDB06FICVK1eSmJiIUoqYmBjuv//+Rk1jfPFryM7OZvHixZSUlBAVFcWcs1Pa6wAAC6FJREFUOXPQ6/Xk5OSwdOlSioqKMBgMzJkzh4CAAOvrefvtt8nLywNg3rx5BAcHN7gGVCuWl5enZs+erSorK5VSSsXHx6sffvihWY596NAhlZycrObOnWtdduDAAfXSSy+pqqoqpZRS586ds2kNZrNZlZeXK6WUqq6uVvPmzVNHjx5Vu3fvVmazWZnNZrVo0SK1fv16m9ahlFJvvPGG2rBhg7WWkpISpZRSOTk56k9/+pN6+OGHVWFhYZMft67PYe/evcpoNCqllFqxYoVasWKFUkqprVu3qkWLFimllKqoqFCzZ89WWVlZTVJHfn6+Sk5OVkopVVZWpv7whz+otLQ0tWrVKvXFF19csr3RaFRPPPGEOnnypFJKqaKiImUyma65jtmzZ1/yPqelpamzZ8+qF154QZ04ccK6PCUlReXl5SmllDp16pR68MEHr+nYdX0WK1asUJ9//rlSSqnPP//c+lkkJSWp4uJipZRSiYmJat68ebX29dVXX6nFixerP//5z42qob7Pob46lFLKZDKpBQsWqIULF6rt27db63v22WeVyWRSJpNJ/fGPf1QHDx5sVC0Xv4b4+Hi1bds2pZRSy5Yts/5eXvi9deDAAfX6669b9/HCCy+offv2KaWUKi8vVxUVFY2qodU3MZnNZqqqqjCZTFRVVeHn59csx+3Zs2etv0AAvv32W26//XZcXV0B8PX1tWkNmqbh4eEBgMlkwmQyoWkaAwYMQNM0NE2jS5cu1r8+bKWsrIwjR44wevRowDJ9ore3NwAffvgh06ZNa9RfXo1R1+fQt29fXFxcAOjWrRv5+fnWdRUVFdafFb1ej5eXV5PU4efnZ70KxdPTk/Dw8FrHvdi+ffto37699a/mNm3aoNPZ5tc5IiKizlnHoqKi8Pf3ByAyMpLq6mqqq6uv+jh1fRa7du0iLi4OgLi4OHbt2gVA9+7drdt27dq11s9oXl4eiYmJjBkzptE11Pc51FcHWM76hwwZUuvsVtM0qqqqMBqNVFdXYzKZGvX7fPFrUEpx6NAh69nJqFGjrDWcOXOGmJgYAHr16kVCQoJ1uclk4v/bu9OQqLowgON/tQW0mhynBSpT0UptUco0SSGi5Y0WiGi3hGparIiopIWCQkxTWqdGkBChiKSNPvWhzVanpMVptExcKlNbpDFzinHm/SBd9O3aq+nkB5/fJ3VGz3Puuc5zznPP3Bk7dizQtNLq3bt3u45Hty4xabVa5syZw/r16+nVqxfjxo1j3LhxXRbP+/fvKSoq4ty5c/Ts2ZO4uDgCAwNd2qbD4SAxMZGqqipmzJhBUFCQ8pjdbufOnTvEx8e7NIaamhr69evHyZMnKS8vJyAggPj4eMxmM1qttkXp4G+7ceMG0dHRAERFRfH48WP0ej0/fvxg5cqVv7ygdYaamhpKS0sJDAykqKiIa9eukZubS0BAACtWrKBPnz68f/8eNzc3kpKSsFqtREdHM2/evE5pPykpCYBp06a1+Ejf38nLy8Pf31+Z3HSWL1++KJM2b29vrFbrL8+5ceMG4eHhyvdZWVksX76choaGDrXdfBxai+Pz58+YTCb27dvHqVOnlN8dMWIEoaGh6PV6nE4nM2fOZOjQoW1u+799qKurw9PTU5m4aLVaZQIxfPhw8vLymDVrFiaTiYaGBurq6qisrMTLy4u0tDRqamoYM2YMy5Yta9dEoluvIL5+/cqjR48wGAxkZGRgs9nIzc3tsngcDgdfv34lKSmJuLg4Dh8+jNPFu5Dd3d05dOgQRqORkpISKioqlMcyMzMJDg4mODjYpTE0NjZSWlrK9OnTSU1NpXfv3uTk5HDx4kUWLVrk0rZ/5+LFi3h4eBATEwM01Znd3d3JyMjgxIkTXL16lerq6k5t02azkZ6eTnx8PJ6enkyfPp3jx4+TmpqKt7c32dnZQNMxKyoqYtOmTezfvx+TyURBQUGH2z9w4AApKSns2rWLa9euYbFY/vd33rx5w5kzZ1izZk2H228vs9nMzZs3WbZsGQD5+floNJoOvyfgv+PQmqysLNUX3aqqKt69e4fRaCQjIwOz2dymY/knfYiLi8NisbBjxw4sFgtarRYPDw8cDgeFhYXExcWRnJxMdXU1t27datPf/KlbryAKCgoYOHCgsjSMjIzk1atXxMbGdkk8Wq2WyMhIpbTj7u5OXV1dp1+YVePl5UVISAhPnz7F19eXnJwcrFYrer3e5W37+Pjg4+OjrF6ioqLIycmhpqaG7du3A01L7sTERJKTk+nfv7/LY7p16xb5+fns3btXKW/dvXuXsLAwevTogUajYeTIkZSUlDBo0KBOadNut5Oenk5MTAyRkZEALfo6depUUlJSgKZjFhISopwb4eHhlJaWKqWGP/WzZKTRaIiIiOD169eEhIS0+vxPnz6RlpZGQkICgwcP7lDbajQaDbW1tXh7e1NbW9vif6G8vJyMjAx27txJ3759AXj58iWPHz/myZMn/Pjxg4aGBo4dO8bmzZvb3KbaOLQWR0lJCUePHgXAarXy5MkT3N3dqaqqIigoSCnhhoeHU1xc/Ntj+ZNaH7Kysvj27RuNjY14eHjw+fNnZay0Wi3btm0DmhJbXl4enp6eaLVa/P39lfNz4sSJvHr1SinltkW3XkHodDqKi4v5/v07TqeTgoIChgwZ0mXxREREYDabAaisrMRutysnvitYrVbq6+uBph1NP/t//fp1nj17xpYtW1xW126uf//++Pj4UFlZCTQlbn9/fzIzMzEYDBgMBnx8fEhJSfkryeHp06dcuXKFxMTEFjVbnU6H2WzG6XRis9koLi7utPPF6XRiNBoZMmQIs2fPVn5eW1urfG0ymRg2bBjQdJ2koqKC79+/09jYSGFhYbtKGGpsNptS0rDZbDx//hxfX99Wn19fX8/BgwdZsmQJo0aN6lDbrZkwYQK3b98G4Pbt20RERABNu3PS0tLYuHFji+sjS5cuxWg0YjAY2LJlC6NHj25XcmhtHFqL4+f5aTAYiIqKYvXq1UycOBGdTkdhYSGNjY3Y7XYsFkubz5XW+hAaGsrDhw+BpgnMz1tnWK1WHA4HAJcuXWLKlCkABAYGUl9fr5TDzGZzu8+Rbv9O6vPnz3P//n08PDzw8/Nj3bp1nV5HVXPkyBEsFgt1dXVoNBoWLlxIbGysUofvjG2D/6e8vByDwYDD4cDpdDJp0iQWLFjA4sWLGTBggDL7iYyMZMGCBS6LA6CsrAyj0YjdbmfgwIFs2LChRX0/ISGB5OTkTl9NqY3DpUuXsNvtLS6C6vV6bDYbJ0+e5O3btzidTqZMmcLcuXM7JY6ioiL27t2Lr6+vsmJZsmQJ9+7do6ysDDc3NwYMGIBer1dq4bm5uVy+fBk3NzfCw8M7vCW6urqatLQ0oKmENXnyZObPn4/JZOL06dNYrVa8vLzw8/Nj9+7dXLhwgcuXL7dYOezZs+ePN1eojUVERASHDx/m48eP6HQ6tm7dSp8+fTAajeTl5aHT6QCULbnNvXjxgqtXr7Zrm2tr4xAUFKQaR3MGg4Hx48cTFRWFw+EgMzOTwsJCAMLCwli5cmW7j0nzPlRXV/+yzbVnz548fPiQs2fP4ubmRnBwMKtWrVJew54/f052djZOp5OAgADWrl3bri3r3T5BCCGEUNetS0xCCCFaJwlCCCGEKkkQQgghVEmCEEIIoUoShBBCCFWSIIQQQqiSBCGEEEKVJAghhBCquvW9mIRwpYSEBGbMmEFubi4fPnwgLCyMhISEv/KBVEJ0BllBCOFCDx48YNeuXRgMBioqKtp9N00hupKsIIRwoX/++Ue56+b48eMpKyvr2oCEaAdZQQjhQs3vPturVy9sNlsXRiNE+0iCEEIIoUoShBBCCFWSIIQQQqiSz4MQQgihSlYQQgghVEmCEEIIoUoShBBCCFWSIIQQQqiSBCGEEEKVJAghhBCqJEEIIYRQJQlCCCGEqn8BX0FXqnA3unIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.title('Benchmark fftlog vs pyfftlog')\n",
    "\n",
    "plt.plot(nn, result[0, :]*1e6, '.-', label='pyfftlog')\n",
    "plt.plot(nn, result[1, :]*1e6, '.-', label='fftlog')\n",
    "plt.yscale('log')\n",
    "plt.xscale('log')\n",
    "plt.xticks(nn, nn)\n",
    "plt.yticks([1, 10, 100], [1, 10, 100])\n",
    "plt.xlabel('n')\n",
    "plt.ylabel('Time (us)')\n",
    "plt.legend()\n",
    "\n",
    "plt.savefig('./benchmark.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "     <td style='text-align: center; font-weight: bold; font-size: 1.2em; border: 2px solid #fff;' colspan='6'>Thu Aug 22 14:31:39 2019 CEST</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>Linux</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>OS</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>4</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>CPU(s)</td>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>x86_64</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>Machine</td>\n",
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       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>15.5 GB</td>\n",
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       "  <tr>\n",
       "     <td style='text-align: center; border: 2px solid #fff;' colspan='6'>Python 3.7.3 (default, Mar 27 2019, 22:11:17) \n",
       "[GCC 7.3.0]</td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>1.16.4</td>\n",
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       "  <tr>\n",
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       "    <td style='text-align: right; background-color: #ccc; border: 2px solid #fff;'>0.4.1</td>\n",
       "    <td style='text-align: left; border: 2px solid #fff;'>scooby</td>\n",
       "    <td style= border: 2px solid #fff;'></td>\n",
       "    <td style= border: 2px solid #fff;'></td>\n",
       "  </tr>\n",
       "  <tr>\n",
       "     <td style='text-align: center; background-color: #ddd;border: 2px solid #fff;' colspan='6'>Intel(R) Math Kernel Library Version 2019.0.4 Product Build 20190411 for Intel(R) 64 architecture applications</td>\n",
       "  </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "\n",
       "--------------------------------------------------------------------------------\n",
       "  Date: Thu Aug 22 14:31:39 2019 CEST\n",
       "\n",
       "             Linux : OS\n",
       "                 4 : CPU(s)\n",
       "            x86_64 : Machine\n",
       "             64bit : Architecture\n",
       "           15.5 GB : RAM\n",
       "           Jupyter : Environment\n",
       "\n",
       "  Python 3.7.3 (default, Mar 27 2019, 22:11:17)  [GCC 7.3.0]\n",
       "\n",
       "            1.16.4 : numpy\n",
       "             1.3.0 : scipy\n",
       "             7.6.0 : IPython\n",
       "             3.1.0 : matplotlib\n",
       "             0.4.1 : scooby\n",
       "\n",
       "  Intel(R) Math Kernel Library Version 2019.0.4 Product Build 20190411 for\n",
       "  Intel(R) 64 architecture applications\n",
       "--------------------------------------------------------------------------------"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scooby\n",
    "scooby.Report()"
   ]
  }
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