{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numba\n",
    "import numpy as np\n",
    "\n",
    "def gather_from_detector():\n",
    "    return np.random.random((1000, 1000))\n",
    "\n",
    "@numba.jit\n",
    "def smooth(x):\n",
    "    out = np.empty_like(x)\n",
    "    for i in range(1, x.shape[0] - 1):\n",
    "        for j in range(1, x.shape[1] - 1):\n",
    "            out[i, j] = (x[i + -1, j + -1] + x[i + -1, j + 0] + x[i + -1, j + 1] +\n",
    "                         x[i +  0, j + -1] + x[i +  0, j + 0] + x[i +  0, j + 1] +\n",
    "                         x[i +  1, j + -1] + x[i +  1, j + 0] + x[i +  1, j + 1]) // 9\n",
    "\n",
    "    return out\n",
    "\n",
    "def save(x, filename):\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 3.17 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "for i in range(50):\n",
    "    img = gather_from_detector()\n",
    "    img = smooth(img)\n",
    "    img = np.fft.fft2(img)\n",
    "    save(img, \"file-\" + str(i) + \"-.dat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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