gistfile1.txt

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "max 7 items\n"
     ]
    }
   ],
   "source": [
    "\n",
    "COUNTS = [\n",
    "    4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, \n",
    "    24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, 60, 70, 75, 80, 90, 100, 125, \n",
    "    150, 200\n",
    "]\n",
    "  \n",
    "def nextval(rem):\n",
    "  for n in COUNTS[::-1]:\n",
    "    if (n <= rem and (rem-n) % 4 == 0):\n",
    "      return n\n",
    "  return 0\n",
    "  \n",
    "def solve(wings):\n",
    "  order = {}\n",
    "  wings_left = wings\n",
    "  while True:\n",
    "    nval = nextval(wings_left)\n",
    "    if nval > 0:\n",
    "      wings_left -= nval\n",
    "      if nval not in order:\n",
    "        order[nval] = 0\n",
    "      order[nval] += 1\n",
    "      if wings_left == 0:\n",
    "        return order\n",
    "    else:\n",
    "      return -1\n",
    "\n",
    "histo = {}\n",
    "max_ct = 0\n",
    "\n",
    "vals = []\n",
    "sizes = []\n",
    "for n in range(100, 1000):\n",
    "  ord = solve(n)\n",
    "  ct = 0\n",
    "  for key, value in ord.items():\n",
    "    sizes.append(key)\n",
    "    ct += value\n",
    "  #print (str(n) + \": \" + str(ct) + \" orders: \" + str(ord))\n",
    "  if (ct > max_ct):\n",
    "    max_ct = ct\n",
    "  vals.append(ct)\n",
    "\n",
    "print (\"\\nmax \" + str(max_ct) + \" items\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_xlabel('Orders')\n",
    "ax.set_ylabel('Frequency')\n",
    "ax.hist(vals, bins=max_ct)\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XPQg9otlzkUbHHoQkqZcBIUnqZUBIknoZEJKkXgaEJKmXASFJ6uVprmPgcBiSHgkMiElMdX79tvglL2lH4C4mSVIvA0KS1MuAkCT1MiAkSb0MCElSLwNCktTLgJAk9TIgJEm9RhYQST6Y5M4k1wy07ZfkoiQ3tMfHt/YkOTXJxiRXJVk6qrokScMZZQ/iw8CLt2pbBayrqiXAujYPcASwpP2sBE4bYV2SpCGMLCCq6qvAd7dqXg6sadNrgGMG2s+qziXAvkkOGFVtkqSpzfYxiPlVdVubvh2Y36YXALcMLLeptUmSxmRsB6mrqoCa7uuSrEyyPsn6LVu2jKAySRLMfkDcMbHrqD3e2do3AwcOLLewtf0bVbW6qpZV1bJ58+aNtFhJ2pnNdkCsBVa06RXA+QPtx7ezmQ4D7h7YFSVJGoOR3Q8iyceB3wT2T7IJeCtwMnBOkhOBm4Fj2+IXAkcCG4H7gBNGVZckaTgjC4iqeuUkTx3es2wBJ42qFknS9HkltSSplwEhSeplQEiSehkQkqReBoQkqZcBIUnqZUBIknoZEJKkXgaEJKmXASFJ6mVASJJ6GRCSpF4GhCSp18hGc92ZLV51wbhLkKSHzR6EJKmXASFJ6mVASJJ6GRCSpF4GhCSplwEhSeo1p05zTfJi4BRgF+CMqjp5zCVpB7etU5JvOvmoWaxEmnvmTA8iyS7A+4EjgEOBVyY5dLxVSdLOay71IJ4FbKyqGwGSfAJYDlw71qp2IFNdwOdfzJIGzZkeBLAAuGVgflNrkySNQapq3DUAkOTlwIur6g/a/O8B/7GqXrPVciuBlW32EOD6KVa9P/CdGS53pljb9pmrtc3VusDatteOWtuTqmreVAvNpV1Mm4EDB+YXtraHqKrVwOphV5pkfVUte/jlzTxr2z5ztba5WhdY2/ba2WubS7uYvgYsSXJQkt2B44C1Y65JknZac6YHUVUPJHkN8Hm601w/WFXfGHNZkrTTmjMBAVBVFwIXzvBqh94dNQbWtn3mam1ztS6wtu21U9c2Zw5SS5Lmlrl0DEKSNIfs0AGR5MVJrk+yMcmqMdZxYJIvJbk2yTeSvK61vy3J5iRXtp8jx1TfTUmubjWsb237JbkoyQ3t8fFjqOuQgW1zZZJ7krx+XNstyQeT3JnkmoG23u2Uzqnts3dVkqVjqO2dSb7Z3v+8JPu29sVJfjSw/U4fQ22T/g6TvLFtt+uT/JdZruvsgZpuSnJla5/tbTbZd8bsft6qaof8oTvQ/S3gYGB34OvAoWOq5QBgaZveG/gXuuFE3gb88RzYVjcB+2/V9n+BVW16FfCOOfD7vB140ri2G/A8YClwzVTbCTgS+AcgwGHApWOo7UXArm36HQO1LR5cbkzbrfd32P5ffB14NHBQ+z+8y2zVtdXz7wLeMqZtNtl3xqx+3nbkHsQvhu6oqvuBiaE7Zl1V3VZVl7fpHwDXMfevEl8OrGnTa4BjxlgLwOHAt6rq5nEVUFVfBb67VfNk22k5cFZ1LgH2TXLAbNZWVV+oqgfa7CV01xbNukm222SWA5+oqp9U1b8CG+n+L89qXUkCHAt8fBTvPZVtfGfM6udtRw6IOTl0R5LFwNOBS1vTa1qX8IPj2I3TFPCFJBvSXakOML+qbmvTtwPzx1PaLxzHQ/+zzoXtBpNvp7n2+ft9ur8wJxyU5IokX0ny3DHV1Pc7nCvb7bnAHVV1w0DbWLbZVt8Zs/p525EDYs5J8ljgU8Drq+oe4DTg3wNPA26j69KOw3OqaindSLonJXne4JPV9WHHdrpbugsnjwY+2ZrmynZ7iHFvp8kkeTPwAPDR1nQbsKiqng68AfhYksfNcllz8nc44JU89A+SsWyznu+MX5iNz9uOHBBDDd0xW5LsRveL/mhVfRqgqu6oqp9V1c+Bv2NEXempVNXm9ngncF6r446JLmp7vHMctTVHAJdX1R0wd7ZbM9l2mhOfvySvBn4LeFX7QqHtvrmrTW+g28//y7NZ1zZ+h2Pfbkl2BV4GnD3RNo5t1vedwSx/3nbkgJgzQ3e0/ZlnAtdV1bsH2gf3Eb4UuGbr185CbXsl2Xtimu7A5jV022pFW2wFcP5s1zbgIX/NzYXtNmCy7bQWOL6dXXIYcPfAroFZke4GXH8CHF1V9w20z0t3/xWSHAwsAW6c5dom+x2uBY5L8ugkB7XaLpvN2oD/DHyzqjZNNMz2NpvsO4PZ/rzN1lH5cfzQHdn/F7q0f/MY63gOXVfwKuDK9nMk8BHg6ta+FjhgDLUdTHfWyNeBb0xsJ+AJwDrgBuBiYL8xbbu9gLuAfQbaxrLd6ELqNuCndPt4T5xsO9GdTfL+9tm7Glg2hto20u2XnvjMnd6W/e32u74SuBx4yRhqm/R3CLy5bbfrgSNms67W/mHgv2617Gxvs8m+M2b18+aV1JKkXjvyLiZJ0sNgQEiSehkQkqReBoQkqZcBIUnqZUBop5FkYZLz20iY30pySrtGZpjXfjjJyx/Ge89P8tkkX28jdF7Y2n8pybnbu15plAwI7RTahUefBj5TVUvoroJ9LPD2nmUf9p0We9bxF8BFVfXUqjqUbiROqurWqtru4JFGyYDQzuIFwI+r6kMAVfUz4H8Cv59kzySvTrI2yReBde2K1L9Jd0+Ci4EnTqwoyTPagG0bknx+YOiDLyd5b7p7arxuq/c/gO5iLNr7X9VeszjtfgRJzsiD9xvYkuStrf1/J/laG9juz1vbXkkuaD2Sa5K8YjSbTTuzOXVPammEfhXYMNhQVfck+Tbw5Na0FPj1qvpukpcBh9CNwT8fuBb4YBsf533A8qra0r6Y3043WirA7lW1rOf93w+cneQ1dFfAfqiqbt2qnj8ASPIk4HPAh5O8iG5Yh2fRXS27tg2mOA+4taqOaq/ZZ3s3jDQZA0J60EVVNXF/gOcBH289jVtbzwK60Pg14KJurxW70A3XMOFselTV59sYPi+mG3zwiiS/tvVySfagG7X2tVV1c5LX0o2PdUVb5LF0gfGPwLuSvAP4bFX94/b+o6XJGBDaWVwLPGRffxuueRHdmEVLgR8OsZ4A36iqZ0/y/KTraOHzMbqhoj9LF0IbtlrsdODTVXXxwPv9VVV94N8U0t1W8kjgL5Osq6q/GKJ+aWgeg9DOYh2wZ5LjAdrInO8CPlwDI50O+CrwiiS7tGMMz2/t1wPzkjy7rWe3JL861ZsneUGSPdv03nT3Qvj2VsucBOxdVScPNH+e7jjJY9syC5I8MckvAfdV1d8D76QLOGlG2YPQTqGqKslLgb9N8n/o/ji6EHjTJC85j+7A9rV0X+T/3NZzfzvd9dS2339X4L10I31uyzOAv0nyQHvvM6rqa+nuFjbhj4GfJrmyzZ9eVacn+RXgn9surXuB36U7bvLOJD+nG430vw23JaThOZqrJKmXu5gkSb0MCElSLwNCktTLgJAk9TIgJEm9DAhJUi8DQpLUy4CQJPX6/57Uj4yy4B5bAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.set_xlabel('Order Sizes')\n",
    "ax.set_ylabel('Frequency')\n",
    "ax.hist(sizes, bins=len(COUNTS))\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}