tmp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import rcParams\n",
    "rcParams[\"savefig.dpi\"] = 100\n",
    "rcParams[\"figure.dpi\"] = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import requests\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tmp 139069390\n"
     ]
    }
   ],
   "source": [
    "token = \"INSERT TOKEN HERE\"\n",
    "url = \"https://api.github.com/search/code\"\n",
    "search = \"\\\"{0}\\\"\".format\n",
    "\n",
    "r = requests.get(url, auth=(\"megbedell\", token), params={\"q\": search(\"tmp\")})\n",
    "results = [r.json().get(\"total_count\", 0)]\n",
    "print(\"tmp\", results[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tmp 127709851.0\n",
      "tmp1 6604048.0\n",
      "tmp2 7051989.0\n",
      "tmp3 2278634.0\n",
      "tmp4 1413577.0\n",
      "tmp5 998331.0\n",
      "tmp6 897767.0\n",
      "tmp7 787554.0\n",
      "tmp8 926114.0\n",
      "tmp9 376948.0\n",
      "tmp10 577504.0\n",
      "tmp11 510651.0\n",
      "tmp12 507298.0\n",
      "tmp13 466591.0\n",
      "tmp14 254057.0\n",
      "tmp15 232334.0\n",
      "tmp16 780963.0\n",
      "tmp17 200581.0\n",
      "tmp18 163951.0\n",
      "tmp19 162249.0\n",
      "tmp20 152271.0\n",
      "tmp21 163481.0\n",
      "tmp22 152630.0\n",
      "tmp23 157663.0\n",
      "tmp24 130981.0\n",
      "tmp25 128986.0\n",
      "tmp26 122255.0\n",
      "tmp27 119894.0\n",
      "tmp28 115103.0\n",
      "tmp29 112432.0\n",
      "tmp30 106441.0\n",
      "tmp31 111913.0\n",
      "tmp32 609762.0\n",
      "tmp33 95135.0\n",
      "tmp34 106244.0\n",
      "tmp35 80344.0\n",
      "tmp36 82484.0\n",
      "tmp37 91504.0\n",
      "tmp38 79766.0\n",
      "tmp39 81543.0\n",
      "tmp40 74029.0\n",
      "tmp41 75219.0\n",
      "tmp42 79359.0\n",
      "tmp43 72344.0\n",
      "tmp44 62093.0\n",
      "tmp45 74513.0\n",
      "tmp46 76982.0\n",
      "tmp47 0.0\n",
      "tmp48 0.0\n",
      "tmp49 0.0\n",
      "tmp50 0.0\n",
      "tmp51 0.0\n",
      "tmp52 0.0\n",
      "tmp53 0.0\n",
      "tmp54 0.0\n",
      "tmp55 53187.0\n",
      "tmp56 63580.0\n",
      "tmp57 58773.0\n",
      "tmp58 55353.0\n",
      "tmp59 55405.0\n",
      "tmp60 50217.0\n",
      "tmp61 53383.0\n",
      "tmp62 52839.0\n",
      "tmp63 47576.0\n",
      "tmp64 470514.0\n",
      "tmp65 54236.0\n",
      "tmp66 42643.0\n",
      "tmp67 53753.0\n",
      "tmp68 46048.0\n",
      "tmp69 49768.0\n",
      "tmp70 44573.0\n",
      "tmp71 45565.0\n",
      "tmp72 40718.0\n",
      "tmp73 44140.0\n",
      "tmp74 0.0\n",
      "tmp75 0.0\n",
      "tmp76 0.0\n",
      "tmp77 0.0\n",
      "tmp78 44502.0\n",
      "tmp79 34118.0\n",
      "tmp80 42193.0\n",
      "tmp81 36023.0\n",
      "tmp82 64429.0\n",
      "tmp83 38255.0\n",
      "tmp84 37865.0\n",
      "tmp85 41713.0\n",
      "tmp86 35191.0\n",
      "tmp87 33972.0\n",
      "tmp88 37232.0\n",
      "tmp89 46470.0\n",
      "tmp90 36197.0\n",
      "tmp91 30607.0\n",
      "tmp92 36427.0\n",
      "tmp93 35302.0\n",
      "tmp94 26289.0\n",
      "tmp95 32082.0\n",
      "tmp96 27216.0\n",
      "tmp97 34824.0\n",
      "tmp98 37316.0\n",
      "tmp99 39593.0\n",
      "tmp100 121462.0\n",
      "tmp101 119759.0\n",
      "tmp102 239966.0\n",
      "tmp103 87734.0\n",
      "tmp104 28587.0\n",
      "tmp105 144522.0\n",
      "tmp106 30962.0\n",
      "tmp107 29026.0\n",
      "tmp108 41366.0\n",
      "tmp109 26276.0\n",
      "tmp110 31142.0\n",
      "tmp111 29024.0\n",
      "tmp112 44455.0\n",
      "tmp113 29307.0\n",
      "tmp114 25893.0\n",
      "tmp115 28508.0\n",
      "tmp116 30243.0\n",
      "tmp117 22944.0\n",
      "tmp118 25672.0\n",
      "tmp119 28860.0\n",
      "tmp120 25469.0\n",
      "tmp121 135093.0\n",
      "tmp122 30307.0\n",
      "tmp123 103619.0\n",
      "tmp124 25630.0\n",
      "tmp125 28748.0\n",
      "tmp126 24416.0\n",
      "tmp127 25757.0\n",
      "tmp128 27150.0\n",
      "tmp129 23864.0\n",
      "tmp130 22055.0\n",
      "tmp131 26175.0\n",
      "tmp132 23273.0\n",
      "tmp133 24175.0\n",
      "tmp134 27352.0\n",
      "tmp135 20034.0\n",
      "tmp136 23884.0\n",
      "tmp137 24619.0\n",
      "tmp138 26676.0\n",
      "tmp139 24616.0\n",
      "tmp140 20924.0\n",
      "tmp141 22167.0\n",
      "tmp142 22300.0\n",
      "tmp143 22148.0\n",
      "tmp144 21450.0\n",
      "tmp145 22716.0\n",
      "tmp146 22817.0\n",
      "tmp147 27407.0\n",
      "tmp148 22012.0\n",
      "tmp149 22178.0\n"
     ]
    }
   ],
   "source": [
    "names = [\"tmp{0}\".format(i) for i in range(1,150)]\n",
    "results = np.append(results, np.zeros(len(names)))\n",
    "names = np.append(\"tmp\", names)\n",
    "for i,n in enumerate(names):\n",
    "    r = requests.get(url, auth=(\"megbedell\", token), params={\"q\": search(n)})\n",
    "    results[i] = r.json().get(\"total_count\", 0)\n",
    "    print(n, results[i])\n",
    "    time.sleep(2.1) # rate cap at 30 requests per minute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tmp47 61482.0\n",
      "tmp48 64164.0\n",
      "tmp49 62281.0\n",
      "tmp50 53500.0\n",
      "tmp51 56349.0\n",
      "tmp52 64488.0\n",
      "tmp53 61243.0\n",
      "tmp54 60102.0\n",
      "tmp74 42964.0\n",
      "tmp75 111981.0\n",
      "tmp76 43856.0\n",
      "tmp77 36321.0\n"
     ]
    }
   ],
   "source": [
    "# Fill in timed-out gaps\n",
    "for i,n in enumerate(names):\n",
    "    if results[i] > 0.0:\n",
    "        continue\n",
    "    r = requests.get(url, auth=(\"megbedell\", token), params={\"q\": search(n)})\n",
    "    results[i] = r.json().get(\"total_count\", 0)\n",
    "    print(n, results[i])\n",
    "    time.sleep(2.1) # rate cap at 30 requests per minute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.loglog(results, \".\")\n",
    "plt.xlabel(\"tmp*\", fontsize=14)\n",
    "plt.ylabel(\"occurrences on GitHub\", fontsize=14)\n",
    "plt.savefig(\"tmp.png\")"
   ]
  },
  {
   "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}