ProbeStats.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:1fdfeb227c4f576a58517b675608187207f10190c5a73558116632696c5598cb"
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 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import json\n",
      "from collections import OrderedDict\n",
      "from copy import deepcopy\n",
      "import urllib2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "probes = []\n",
      "probes_by_id = {}\n",
      "for l in open('meta-20150223.txt').readlines():\n",
      "    probes.append(json.loads(l))\n",
      "    probes_by_id[probes[-1]['id']] = probes[-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stats_country = OrderedDict()\n",
      "for p in probes:\n",
      "    cc = p['country_code']\n",
      "    stats_country[cc] = stats_country.get(cc, 0)+1\n",
      "stats_country = OrderedDict(sorted(stats_country.items(), key=lambda t: t[1], reverse=True))\n",
      "stats_country_reduced = deepcopy(stats_country)\n",
      "while len(stats_country_reduced) > 100:\n",
      "     stats_country_reduced.popitem()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure(figsize=(25,4))\n",
      "bar(np.arange(len(stats_country_reduced)), stats_country_reduced.values())\n",
      "devnull = xticks(np.arange(0.5,len(stats_country_reduced)+0.5), stats_country_reduced.keys(), horizontalalignment='center', rotation=90)\n",
      "#yscale('log')\n",
      "title('Probe numbers by country')\n",
      "ylabel('#probes')\n",
      "xlabel('country code')\n",
      "grid(axis='y')\n",
      "savefig('probes-per-country.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZYMJakiRJkiRJkjQTTFhLkiRJkiRJkmbCiiSsk/xjkm8kOT3J+5JcKcmOSY5P8u0kxyXZ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\n3gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10dedde10>"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "probes = []\n",
      "data = json.loads(urllib2.urlopen('https://atlas.ripe.net/api/v1/measurement/1911027/result/').read())\n",
      "probes = [probes_by_id[p['prb_id']] for p in data if p['prb_id'] in probes_by_id]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stats_country = OrderedDict()\n",
      "for p in probes:\n",
      "    cc = p['country_code']\n",
      "    stats_country[cc] = stats_country.get(cc, 0)+1\n",
      "stats_country = OrderedDict(sorted(stats_country.items(), key=lambda t: t[1], reverse=True))\n",
      "stats_country_reduced = deepcopy(stats_country)\n",
      "while len(stats_country_reduced) > 120:\n",
      "     stats_country_reduced.popitem()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure(figsize=(25,4))\n",
      "bar(np.arange(len(stats_country)), stats_country.values())\n",
      "devnull = xticks(np.arange(0.5,len(stats_country)+0.5), stats_country.keys(), horizontalalignment='center', rotation=90)\n",
      "#yscale('log')\n",
      "title('Probe numbers by country (WW500)')\n",
      "ylabel('#probes')\n",
      "xlabel('country code')\n",
      "grid(axis='y')\n",
      "savefig('WW500-probes-per-country.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SpNaZfJYkSZIkSZIktc7ksyRJkiRJkiSpdSafJUmSJEmSJEmtM/ksSZIkSZIk\nSWqdyWdJkiRJkiRJUutMPkuSJEmSJEmSWrd4rjsgNRURrcbLzM7ijzK2JEmSJEmSNJdMPmuBaCvx\nOiwZ3Eb8uYgtSZIkSZIkzQ3LbkiSJEmSJEmSWufIZ2mB6rociSRJkiRJkjQdk8/SgtZ1ORJJkiRJ\nkiRpapbdkCRJkiRJkiS1zuSzJEmSJEmSJKl1Jp8lSZIkSZIkSa0z+SxJkiRJkiRJap3JZ0mSJEmS\nJElS60w+S5IkSZIkSZJaZ/JZkiRJkiRJktQ6k8+SJEmSJEmSpNYtnusOSBo/EdFqvMxsNZ4kSZIk\nSZLmnslnSbPUVsK43US2JEmSJEmS5odOy25ExMcjYmVE/Khv2WYRcVJEXBIRJ0bEki77IEmSJEmS\nJEkava5rPn8CeNLAskOAkzLzfsDJtS1JkiRJkiRJWkA6TT5n5mnA9QOL9wKOrLePBJ7WZR8kSZIk\nSZIkSaPX9cjnqSzNzJX19kpg6Rz0QZIkSZIkSZLUoTmdcDAzMyKmmbXsQGCi3l4C7AYsq+0V9ffa\n2rW1orSXLVvX9dctft+SeR6/xOztj7bjT97fvcfMtr9ri98s3vg/X7r9fw7GX/2Y2cZbsz24v23b\ntm3btm3btm3btm3btm3btm3P3/aKFStYvnw5ABMTEwwTmdPkflsQERPAlzNz59q+GFiWmVdFxJbA\nKZm5wxTrJbTRt2Dwb4wI2ok9OX6XsduNP66xJ8d3n3cde3L8rve5JEmSJEmSxkdEkJkxuHy9OejL\nCcAB9fYBwPFz0AdJkiRJkiRJUoc6HfkcEUcBjwHuTqnv/BbgS8CxwNbAZcC+mXnDFOs68nlsR8q6\nzxdW7MnxR7PP2zHK2JIkSZIkSXdEw0Y+d152Y7ZMPk+O3W78cY09Ob77vOvYk+O7z6eOLUmSJEmS\ndEc0n8puSJIkSZIkSZIWOJPPkiRJkiRJkqTWmXyWJEmSJEmSJLXO5LMkSZIkSZIkqXUmnyVJkiRJ\nkiRJrVs81x2QpIUiYtKkro1k5khiS5IkSZIkdcHksyS1qq2k7lTJ5i5jS5IkSZIktcuyG5IkSZIk\nSZKk1jnyWZLUalmPwZIe4xJ7ML6lTiRJkiRJasbksySpaiM5OixhO99jD4tvqRNJkiRJkmbLshuS\nJEmSJEmSpNY58lmSpDnQZTkSSZIkSZLmA5PPkiTNmS7LkUiSJEmSNLcsuyFJkiRJkiRJap0jnyVJ\nWmC6LOl16yoZAAAgAElEQVTRZuyp4kuSJEmSFg6Tz5IkLUhdlvRoK2FsyRBJkiRJWsgsuyFJkiRJ\nkiRJap3JZ0mSJEmSJElS6yy7IUmS5oWu60mPSy1s62xLkiRJWihMPkuSpHmk63rS870WtnW2JUmS\nJC0clt2QJEmSJEmSJLXOkc+SJEl3UJY66T72YHz3efexB+OP8z6XJEkad3OWfI6IJwGHA4uAIzLz\nnXPVF0mSpDsuS510F3tYfPd5d7GHxR/nfS5JkjS+5qTsRkQsAj4APAl4ALB/ROw4+4grWunX3MQ3\n9ujjG3v08Y09+vjGHn18Y48+vrFHH9/Yo49v7NHHH8/YK1Z0F7vr+MYefXxjjz6+sUcf39ijj29s\nTWWuRj7vAfw0My8DiIijgb2Bi2YXbgWwrJWOjT6+sUcf39ijj2/s0cc39ujjG3v08Y09+vjGHn18\nY48+/vyJbamTybHbjj+usQfju8+7jz0Y333efezB+O7z7mMPxnefTx1b62auJhy8N/DLvvav6jJJ\nkiRJ0io5w5+3TnNf09izid9l7PmyX9znCyu2+9x9Pl/2i/t8PGJrpuYq+ex/T5IkSZIkSZIWsJiL\noeMR8VDg0Mx8Um2/Hri9f9LBiDBBLUmSJEmSJEljIDMn1TuZq+TzYuAnwOOBK4GzgP0zc5Y1nyVJ\nkiRJkiRJ88mcTDiYmbdFxCuAbwKLgI+ZeJYkSZIkSZKkhWNORj5LkiRJkiRJkha2ORn5LEmSJEmS\nJEn9ImKzgUUJ3JCOnh1bC2bkc0RsnZmXdxB3U8b4SR4Rl2fm1g3Wv39m/mTIfY/IzO/OvncQESdm\n5l83iTFN7H/qaybQX/Q8M/M9DWI/YyBmAtcA52bmTbONW2M/NzM/XW+vsY8j4hWZ+YGG8V8KrMjM\nSyIigI8DzwAuAw7MzB82iT/Ndu+UmX9qsH6n+6VLA89FWP18OT0z/69h7E7e+2rsx2Xmt+vtbfv7\nGhFPz8wvdrHdrkXEKzPz8Abr/yWwfWZ+qbYPBzah/F8/0OQ1FBG/q3F6EvgN8G3gdZl57Wxj1/hL\ngZcDO9VFPwY+lJkrm8SdwXYbPU8j4kzgjZl58hT3nZyZj28Q+8vT3J2ZuddsY4+7iFgEbJqZv6nt\nuwAHAK/OzB062uammXl9g/XvnJm3Drlv26bvuTXOgcBBQG8fXAi8PzOPbBg3gKcB9wXOz8xvNok3\nEHtsPysi4kGs+b4IfcdfHR63ND7OHYh3H2C92vx1k2OiabaxCNgvMz/TYsy7AWTmjS3FO2DIXVm3\n88mWtrNRjfe7NuJ1relx8h1NRCzOzNvmuh/TiYgHAtsDF7RZYjQiJjLzsrbircN22/juv0NmXlxv\n3zUz/9B330Mz88ym/exCRLwK+C7ww/n+vBsUEe/va06VEzloxF2akYi4jMmf/RsD5wIvbvIaiIhj\nM3Pfevudmfm6vvs6y0/d0a239ofMLxHx8Ih4ZkTco7Z3jYjPAme0EPutEbFjvX2XiDgF+BmwMiKe\n2DD2FhFxaEQcFBEbR8SHI+KCiPhSRNy3ad+n23TD9S+KiE/2DuAGtJHs26KFGMNsDGxUf/9zvb1R\n37Im9qw/T60/ewKvAX4UEbNOglT9icrBffyihrEBDgZ6Xwz3B3YFtgVeDby3hfirRPGEiPgY8KuG\n4TrbLxFxbN/tdw7cd2KT2FXvudj//HsI8I2I2L9h7C81XH867+67PZg8eHMXG4yIrSPin7uI3Wfw\nZMC6+ndKQrjnr4GvACuAtzQJnJkbZebGfT93Ax5MSW59pEnsiHgEZYJfgCOBT1I+I86KiEc2id23\nja4+o7cGPhAR/xkRdxq4b3BkxLp691p+Zi0i3h8R76u/B3/e17DfTHdsMvheNovY+wHXUT7XTo2I\nv6EcEz0ZeHbD2B8bsnwr4PQmsYEv1ST5YOxdKa/RRmri7GDK+8i9gHsDrwUOiojnNwz/IeCVlOf0\n2yKi0fvJgHH+rJjqdfmf9afpa3RxRDw7Il5TTywSEXtGxBnABxvGfkNEvLVv0RnAV4GTKMeMTWJv\nUuN/MCL+OiLWi4h/pLxGn9Ukdo0fEfHKiLiCMjjhsoi4tHfMUl+rs/UQyuda/89DgLcBn2jU8dK3\nl0XE5cDlwOURcXlEvLyFuM/tu/2Igfte0TQ+cEVEHBERj68noloVEQdGxA8j4ub6c/Y0JwLWJe5E\nRCzpaz+ufu69OiLu3DD2VyJiYorlTwDOaxK7xrn/NPc9Yth9M4z9FuAYygCfr0UZ/NOWb0XE6yOi\n9avYu3xPrI7quz14bPjhJoEj4hkR8fT6u3f7URHR9Hs/wH2Aw4FrIuI7EfH2iHhqTB6d26qmz8Pq\nB8DZ9ffefbd7P7MWEQcM+Xl+02OizJzIzG0Hfu5OOVZq9J0I+Iu+24OJ5i7zU3dsmTk2P8B/ABdR\n3rTOBv4NuIpyoH7XFuJfyOrR4C+lfElZBOwIfL9h7JOAt1OSZhdSvqjsCLyEMgq1q332y4br/6j2\n+1LgYQP3ndNC/34OPJ3ywTz48/QW90Pjvs5wO9sAZ7XV18F+t7TPz+27/VnglW3vJ+BhwPsoB/6/\nAw4ENpuv+6XrfT7Ndjdrs+8d9G8k+wW4B2U07un1PeHdXf1NdXtN3xd/MND+Xt/t747i/zHL9b8H\nPHCK5bv1/w0N4nf2GQ2cA2wAHAH8ENihxf2yTYf/sz/Vvr+eMmL4gPp+eCBwQAvxLwGeOrBsEbAc\n+GbD2BcA9623dwduBfZsab8cCXwaWK9v2QPq6//AhrH/FTgZ2KBv2TLKCdAnttD37wHbTrF8ounr\nqO7zRfX2BpQRVm09F8f2swLYA9iyr30AcALwfmDzhrGPrM+Xd1CuMPkMcDHwtDb2C7DR4L6or9FG\nnxX1718O/D1wLHAq8B1gt5b+p4cCXwO261u2HfBl4BDgZy1tZz3guZTvG8cAuzSM96Yh/f4K8Oam\n/8+pbk/VnmX8uwP/QPn+eQVlQMhDW9rPB9Tn42OBJcCmwOMoiafnN4x9FnCvens34FrKyblPAkc0\njP2c+rnwRuBOlJN9x9ZtPqiF/XJ77edGU9zX9NjiQurnELA5cHYb/8sa726UROj5wKPbiltjd/ae\nOLhf234d1ffETwz8fIlyAu3xLfX/LsAjKCcQvwj8GrioYczFlJP6rwH+si7bk5KcP7dpn9vcx1PE\n+wDls7j/5wPAL4A/t7mtlp8rc/Ld/47+M241n59K+eL8hyjlMH4F7JTtXXbyx6zPOOBJwNGZ+WfK\n6N+m++oemfmGeib7F5n5rrr8oqZn42PyJf39phqxvC5uq/3+BvDpiPgk8LbMvL1h3J5NKG+uw4zV\nZf2Z+YspRuXNN7dHxL0oI9oeTzm50LN+k8AR8Q5gH0rS+SjgMMrB1vImcReqzLyuhcEt964jKKcK\nlDl/L6W6G+XE0/7A/Smv9W0z895z2rGZWWMERWb+VV/zHl1ssL6vLGoY5m6Zec7gwsw8t/4/mur0\nMzozbwZeHBFPB06MiHdkZqNRMtXxwAMBIuILmfmMFmL2bEl5T9wX+DMlufK5zLyhpfh/A3w9SqmJ\nL0bE+sDngBsp/48mbs3MnwJk5g8j4pLMnK5Eybp4AWXUyjF1hPVfUfbN/8vMrzQJnJlviog3Ad+M\niL+ljGg5nPLF+eyG/QbYOKco3ZGZl7UwuurWetxJZt7c8ujHsfysqD5KOV4hIh5NufrkFZTX7UeB\nZzaI/WBg58y8PSLuSjlhtn02LHHUk2uWfHhvXfbn+lptYtusJYEi4ghKAmSbzLylYdye51L2y6p4\nmfnziNiXUjbsOU2C18+0AyjJlu8Bz8whZf7W0fOBXafo9z6URN3bWthGJ7KUN/oI8JF6nL4v8F9R\nriQ6JjPf0CD8yyiDevrfu74dpYzgMZQE7GzdNTOvrLefC3wsM98dEevRcHRyZn4mIr4KvJOSAF1M\n+d7y333f2Zu4gHKsck5EPD8z/7eFmD1/rMctZOa1dX+0IksJnFdGxIMpo6CvoCTS6925S4Pwnb4n\ndikzD5xqeURsQzk22qOFzaxPSf5vUn+upLy3NPExysjqs4D3RcSvgQcBh2Tm8Q1jdyozV131UZ/j\nzwZeB5xJGYTSuihX5Dc9Plo/InavcXq36bUbxtYQ45Z8/kPWukCZeX1EXNpi4hng1ojYmfImu4x6\nSVw9+N+gYezboXwaRMTgm3fTD8+Nh8QIyheuxjLzO1Fq7n0EOK3/0rOGLs/MF7QUa85FxA7AH9b6\nwOntEBE/qre377sNpW5YU28Bvk95/Z+QmT8GiIhllMs1m3gx8BPKZVMnZOYfW/zu3OV+mZMPoIh4\nLDDr2qbVLZSRK8HkOuRNbRcRJ9SY28aatXG3bRh7JeUg683AafW98e8axlwlJtdO7tf0/fzKmKIu\nXUQ8jDJaadZicj15KCOUngV8vknsGn+zzLxucBnND+Kg+89oauwvRsT3gCMj4sk0P8nab7sWY/WS\nCR8GPhyl3ut+wIUR8brM/FQL8f+vXn78zZqgeB7lSq1XNo0NbBERr2b1c2NJXzuzwbwJ9QT2S6PU\nITyVUlZl37a++Gfmv0bELZRR8lBGPF3aRmym/4xv8/Mf4L597aYJhcHPip7B9mxs1/f50PZnBZQR\n8r33rWcBH83MLwBfiIiml97f2htQUU+c/V+LSZYNo68Gee9EfJSyME1PVKyqOVqT2Ve0mHiGMvhk\nUrzMvKVua9ZlXKKUqDiIMrryb6c6mdPA7dP0+88tbqdTmXlllPJE11PK4r0YaJJ87vKkWf/xw+Mp\nV/pQk5cNQwPlqpg9KMeMD6ac5L8T5WqcprocZLXdwHthfzuz4XwSUco8Hk65IuyDtHPcD92+JwLc\np+9E6OBJ0U4GoLQxOCwi/ofyXLyJ8lw8A3hPNpijos/YJvyhu5OJQwZYbgrsRfPyr1exumxX/20o\nJ3PVgXFLPg++iU+0+SZOqeH3OcqH2n9l5s/r8iez+gvMbHV2YJ6ZhzZZfx22cwOwX5T6YKcxz88K\nDSYnB9qNvsTF1BNUbUqp/9g0Mb9jw/WnlZm9+mkbDyShvk/zOoFbAk+kjGY9PErd9PWjnUlUutwv\nnX4ADTz3ejatsZvWCL0up5jkKiIeRfk/NJkAa+++24N1Nf+zQVwoX072p9TtOioijmkYbw2Z2WZC\nctBrKSM2l1M+G4JSluBAmr+G9mTyhIPXAodn5lcbxv4vyojh17C6xtuDKSOK2jhR2eVn9NX9jcy8\nIkq949dQ3nPmtXrydj9KX79Owxp7A3GTchn8cuBbwKd6J9Cy2URsR7BmgqzXbpysrEnnXowdKa+j\nZ0fEs2k4CnfgObgFpWzYe2oSpI1jxR2HvKdD8xOhnUziWE35WQGrJlJqYm9WnzR7N2s+P5p+VgAs\n6juOeAKlNF5P0+8ygwn//uPFpgn/z1NGsf5jZv4eVo3Y+gDNTybuGhH9E1yv39fOLPMFNHFlRDwh\nM7/Vv7AmvBqdZKWUZbsaeCTwyIEEZdN9Pl2/mx7PTTcIopUTl1FGxO9J+bx4BPANyijCkxqG7vKk\n2SkR8TnK/l1CKdVAlNHbf2wSuCbgdwdelpn/W18/hwHnRZk8upVJWTsaZNX/vgjtvBcCEBFHA1sB\nz87MYZ9Hs9XleyKUeZh6+2XwWKiNq5MmaWlw2NaUkhuXUt4DrwDauoqt04R/RPye1SPj1x/47Gj0\nedHxycTBAZZJeZ95TtPnfWYua7K+ZqdX33gs1JGZw2Rmntow/uDZlaRcWnZ60xdS7fvQUYlN+h5r\nTmayRtga+18axD6IUnPw9IHlj6HUTnvCbGPXOH/ZG3nbtoj4C2Apkye624oyy/hPG8ReNrColyC6\nNDMbHWgN2d7dgWuzhRdsRLw2a9mXiNgnMz/Xd9/bs9llff3buSvlsu/9KV8wTs7MRpNUdSUi9qDU\nAf51bR9AqTv+C+DQpgcAMXmylKT8P39XD55nnfiLiDMz86H19u6U/b0Ppb7ZFzLz/dOsvi7b2QIg\nM69pI15f3O0pX7L2o0z+8FbguMy8pGHc9Sm1E7en1JL8WLY4O3VELKVc9v2AuugC4IOZubKtbXQh\nIp5K+SLb6/eFwLuyhXIKXX7OdamOhru5NtenjBDtaXpQ/jbKCeyLgKMpdZibnojrj7+C1ft5UlI4\nMx/b1rbaFBEH1pu958saz5thSdIZxl42zd1tHCtOTHd/Npt9/aZp7v4j8FPgTYOJtRnGXvVZMcV9\nv8zMWU8gFxFPA+6TmR+o7bNYPWHPa/uPM2YZ/43AUygTvW5FqfV6ez3OW56Zs56QqeP/5yLKZccv\nppQjg9L/jwNvbPKZFBHnZOYDZ7v+DOLvRKmVejqrR8w/iHI8t1dmXtAgdu/4/JesOWq2jePz6fq9\nd5PvHEOO5YKSlDokM58829g1/mcpJyhPpZSu+1pvFHcLx4q3UN4/prJdZm7YIParKEm5PwOfzcwr\n6vIHUspOzjpBHOVqm/dmLUfUt3xn4EOZ+ajZxq5xJr2O6neAfwPWz8zNG8Tu7H0xIl6cmUcMLGvl\nu2KX74ldW9vgsMxsNPl1lLISO1HmNno4sDPl+/+ZmTnrCYKneH1uz+qrkRsn/Lv8vIiI2yknE6f6\nftjGyYpOjCofojWNW/J5m8z8RYfxD2XyaJ7NKTUVD83MoyatNPPYXX4AvYbJ/d4QeBFw94YHFF8F\nXp+Z5w8s3wV4e2Y2qikZ018a3/RL/1cpB4M/Gli+C/BvmTldrenZbnMRsF9mfqZBjIdRJnm4jjJx\n0icpk5AsokwK8vWGfVz1ATT4YdT0w6nvBE7///Q3wLmUSXBmXVOu4+fKOZTLsq+LUk/yGFbXk9wh\nM5vUk1zbtpt+6b8/JeG8H+UA6BjgNZm5dQt9C0oy+BWsrjf8Z+D9mXlYw9h/ASztP7FVv0y8jzJ5\nSqP6xhFxLOWSzNOBvwUuy8yDm8Rcy/a2AH7TwkF//8mCwfIbmfO4LmvHn3OdnWTtUj0o/z9WJ7f7\ntfGForMTZ+P8XOzp6qTZFNtp7QTxNNtYTPnS+9nM3Knl2E0/h86gHPtcXtvnUi6/35CSHH5cC318\nGHBP4MRcPYr4fpSJwppenTi4rbaSOHtQEqy/Be4LPIZyufBFlNfnddOsvrbYnSaf6zbWp9Tv3Iny\nHnAh5fnXqLzHWr5bND4+7+t3/0nWxv0e2EbrJ/vr+/cXM3PSSagWXqMTA4taS5xHxLspybgdKSf7\nT6eUJDijyXN8FCLi5Zn5wSmWP5oy+e0LG8Tu7H2x6++KXaoJ4sFjip7MBlcnTXHy+XbKPtqM8r94\n2WxjD2xnK0ry+RGUgVabZ+YmDeJNDCxq+8RWl8nnr1PqsP+SKf6vDU/gdvlc6SwfouHGrexGl5MC\nDS1fEaUe5smUs9Cz9VpKcqjnzpTLnTekXCo76y/lmbnqMp4ok0YdRJnM52gmXyq/rpYOHhzWbZ6/\ntjOjM5HdXhq/dDDxXLd5fkQ0KnUSEZsAL6fUpvoS5VLnl1Nmdz6fMjPwbH2AUpJgE8qla0/KzDOj\nXDJ0NOWS7flqqvrj21BmqT60SeCOnytd1pPs2kWUMjh7Zq1pGs0voe55FeXA6iFZr/6IiO0olxG/\nOhvUfKWUeXh9/4LM/FFEHMyak2DO1o6ZuTNAlMmYvt9CTGq8/oP+twGfoh70R5mwpslr9AesPtA6\njFKjvZU63gMJ3EkjlFtI4Hb2OQf8nmlOsgLzMvnM5Eux1/hC0UL8Lidi6/K52OUXikknzaKMbm98\n0qzGGvqlv4XX/1BZRsmeN3BSYL64cy/BUp1eT3xcGxGzHgDRL6eoB54Nr5CBzv+fH6Wc2L45IpZQ\navb2Xp//TbPX52BN9n7Z8PO5F+QWykRYbZvuu0XjGuFT9TsiFkXEcxoODpnqZP962dLl29ngio8Z\nxL6sd3uqxHnD2P9U496F8pn/MOCFwP9ExA2ZOeuyeV1+VtQAqxLPfftlX8pJ40b7hW7fFzv7rtjl\nYJ/qoZSrko+i1AeGlo4tMnNF73bb/8+I+GdWP79vo5xg+S7lvabRVdxdvj6rLj8vvgm8izK6/Bjg\nqJxicvNZ6uy5orkxbsnnfq1OCjSdOhqyaZhOD8wjYnNKoug5lAPn3bOdAvhLprnvri3E71KXff8U\n5YvK/1Iup3wj5c3waZl5bsPYizLzRICI+Jesk5pl5sURMa/faDs+gdOlLutJdu3plIOUU+rZ52OY\n+uBiNp4PPLF/1GCWWeOfQ6k/2ORgpdMTW6w5GdNtLbyH9+s/6D+FFg/6s05IBRARB7f8ZbTrBG5n\nn3Mdn2TtzAi+UHR24qzj52KXXyi6PGkGc3yCODM/Mpv11pJQaDoJ66b9jcx8RV9zC+a3Lv+fXZ7Y\nXkTzSQuH6jgB1dnxeceDQ7o82d+prhPn1frA3SivpU2AKyn7vIlOk0/16olns+Z+iZb2S5fvi519\nV+x4sA+sOT/Q/sBXKQnLWZfy6RnyPG/r//n/KPWqX5WZV7YQb5URvD47+7zIUg7o8PrdbT/g4xGx\nAfBZyv+1yUnizp4rmhvzPakyL0TEYymzDTfR2QdQRPwn8HeUURS7THW5VgNnR8RLM/O/B7b5Elqa\nMKlDXfZ9297Z9jqq8tfANtnOJX39Bw1NJ0eYyi6xuq7k4KQDnUwi2dIJnC4dBZwaEb+hXB5/Gqwq\nDdF4Mokuv/Rn5vHA8VEmYdkbeCXlDPeHKbWTT2wQfnFOcbl6Zl4T5RLwJro+sbXL4HM72puMaSxP\nEI0ggdtpAqrDk6ydGcUXijE9cdblF4ouT5rB+L7+u0wofG/I8dY/sDphNF91+f/s8vV5VRsj+Yfp\n+PnS5fF5b3DImbQ/OKTLk/1dnyDqLHEeEf9DKXFyE3AWZUToe1r6fO46+XQx3Z1Q6PJ9sevvip2p\nV/F8Hfh6HS2/P+U72KFZy7Y10OUJot/Wk4dd6PrEVqefF7BqsMW/A/8epd77JyhXzM26jGLHz5WR\n50M0v7+YTGW6J0njy0Bi6tnLN6UkFp/fJDbdfgC9mlLb9E3Am2LyrNFN9ssrgePql7beAeGDKBNL\n/F2DuKPQZd/7R1X+OSKuaCnxDB2/GWbDWrqz0dIJnM5k5r9FxLdZXU+yNyNwAP/YQvyuRxGQmb+j\njOj5TB1p/kzKZf1Nks/TTYzWdNK0Tk9sdfw8H9uD/o4TuJ19znV8krVLXX+h6PTEWVc6/kLR5Ukz\nGOPXf4deRTkJ+mygV395d8qJxKfNWa9mpsv/51i+Pkegy+Pz/sEh/0OLg0M6Ptnf9bFil4nzrSn/\nu0uBK+pPK8/vjj8roNv90uX74lgnzqJMSv8Uyon5CeC9wHEthO7y/9ll6YpOT2yNQj2+ejLlf/p4\nypWhw+ZrWZe4nTxX5iIfIsZrwsGuxdTF3q+tSZ2msZdSalb/kSk+gDLzqqbb6EqUbPZjgb+k7JML\nMvPbc9urmemq71HqR/ZPILU+0DuwbXwiZFyt7QROZl404i6pgSme5/3Wz8xZJ3Ii4p6Ug4dbmeLL\nZ9YJ1Oajgf3S/9qH5vulf+TTYOxG7y0DCdwPtZ3A7fJzLsrEfbcy9UmPefueG2USxv0pZSB6XyiO\nyMyJFrfRyURsXT4Xa/zBLxQnAB/PzCsaxh06Ucx0961D/M5e/+OsHm89jtWT043FsWLX/88OX5+b\nZ4MJRedah8fnI504qu9k/37ZwsSaXetLnO9P2f+fpIXEecT/b+/eQuWq7jiOf3/RWmO8VOOLgkEo\nKsQLggpJUzFYkhdLoYgXFKN57EONiuCDIoj4VhGMtFCRiBETIyq00Iv3W46x9MQkLVHf1IfkRUUw\nUls9/n2Ydeh4eqYczZ45mXO+HzjMnrX3rPXfM3vOnv3fa6+dJfS++6vp3YjtfHpX++yqqrsPs+6h\n7CtmtDGs92Us/y8OU5Kt9N6PPwFP1iz3Z+qgjc4/zyQHgYHDXnXRs3iI2+HQ9hdJ1tP7bl5B78qH\nbcAfOsqfDX1b0WiZfB4hd0Ba6IZ5AkcLzzif2BpHo0jgup+b3bAOKMbVMA8ohnnSTNKRzc4hczeM\nxHmSM+gln9cAPweWV9VJh1HfyJNP43ZCYdy036KfD5jd+Xe0q89z2CeyZmlvLLbDduXwNuDp+u89\nDrqqe6TbiobP5LMkSdKIjMsBxTB5QCFJC0OSTfQSzqvpDUs4Aexsj/+sqqnDqNt9hY4Io04+SwuR\nyWdJkiRJkvSdJHkAeAN4s6oOzHc80jCM+1BH0pHA5LMkSZIkSZIkqXNL5jsASZIkSZIkSdLCY/JZ\nkiRJkiRJktQ5k8+SJEmSJEmSpM6ZfJYkSZI6lOSWJEvnsf1Xklw0X+1LkiRJ00w+S5IkSd3aBBw3\n24wko/j9Xe1PkiRJmlcmnyVJkrSoJNmQZG+SPUkea2VnJnmplb+Q5IxW/miSK/tee6g9rm09jJ9K\n8k6Sx1v5zcDpwMtJXpx+TZLfJNkD3Jnk2b761iV5ZpYYL0mys8X4VpJlSY5NsiXJviS7k6xtyy5N\nsj3J/lbX0r561ieZSDKZZEeSZZ2/oZIkSdIAR893AJIkSdKoJDkXuBNYXVWfJPlRm7UZ2FJVW5Ns\nBB4Efsn/9iDuf34hsBI4COxM8pOqejDJrcDaqvqkLXccsKuqbm8xvJNkeVV9DGwEHpkR4zHAduDq\nqppMcjzwBXALMFVVFyQ5B3guydnAr4BDVbUyyfnA7lbPqW1df1ZV/0pyB3AbcO/3fwclSZKkubPn\nsyRJkhaTy4Ed04nhqvq0la8CnmjTjwM/nUNdf6uqA1VVwB7gzAHLTQFP9z3fCtzQEt+rgD/PWP4c\n4GBVTbYYD1XVFLCmxUZVvQd8AJwNXNpX/g9gX986rQQmkrwNbABWzGG9JEmSpE7Y81mSJEmLSQEZ\nMG+28q9oHTbaeM3H9M37d9/0FIN/W3/REtTTtgB/pNebeUdVfT2HuP9fjLOVTz9/vqqu+w71S5Ik\nSSw6Jb8AAAFiSURBVJ2x57MkSZIWk5eAq5KcApDk5FY+AVzbpq8HXmvT7wMXtelfAD+YQxufAScO\nmllVB4EDwF30EtEzvQecluTiFuMJSY4CXm+x0YbbWAG822K9rpWfB1xAL8m+C1iT5Mdt3rIkZ80h\nfkmSJKkTJp8lSZK0aFTVfuA+4NV2A8D726xfAxuT7KWX4N3Uyh8GLmvLrgIO9Vc3oJnfA3+ZvuHg\ngOWeAD5sw2fMjPE/wDXA5tbuX4EfAr8FliTZR29M6Bur6kvgd8DxSfYD9wB/b/V8BNwEbGvrNUFv\nSA9JkiRpJPLtKwAlSZIkDVuSh4DJqpqt57MkSZK0IJh8liRJkkYoySS9oTnWtZ7LkiRJ0oJk8lmS\nJEmSJEmS1DnHfJYkSZIkSZIkdc7ksyRJkiRJkiSpcyafJUmSJEmSJEmdM/ksSZIkSZIkSeqcyWdJ\nkiRJkiRJUudMPkuSJEmSJEmSOvcNnyAfXgwZnbEAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d43bb50>"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "probes_in_result = []\n",
      "data = json.loads(urllib2.urlopen('https://atlas.ripe.net/api/v1/measurement/1911024/result/').read())\n",
      "probes_in_result = [probes_by_id[p['prb_id']] for p in data]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stats_country_result = OrderedDict()\n",
      "for p in probes_in_result:\n",
      "    cc = p['country_code']\n",
      "    stats_country_result[cc] = stats_country_result.get(cc, 0)+1\n",
      "stats_country_result = OrderedDict(sorted(stats_country_result.items(), key=lambda t: t[1], reverse=True))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure(figsize=(25,4))\n",
      "bar(np.arange(len(stats_country_result)), stats_country_result.values())\n",
      "devnull = xticks(np.arange(0.5,len(stats_country_result)+0.5), stats_country_result.keys(), horizontalalignment='center', rotation=90)\n",
      "#yscale('log')\n",
      "title('Probe numbers by country in result (SB500)')\n",
      "ylabel('#probes')\n",
      "xlabel('country code')\n",
      "grid(axis='y')\n",
      "savefig('SB500-probes-per-country.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SJEnSyBl8liRJkiRJkiSNnMFnSZIkSZIkSdLIGXyWJEmSJEmSJI2cwWdJkiRJ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\nxOD8P4LLOi8CL+QtAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10cea6350>"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "probes_in_result = []\n",
      "data = json.loads(urllib2.urlopen('https://atlas.ripe.net/api/v1/measurement/1911261/result/').read())\n",
      "probes_in_result = [probes_by_id[p['prb_id']] for p in data if p['prb_id'] in probes_by_id]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stats_country_result = OrderedDict()\n",
      "for p in probes_in_result:\n",
      "    cc = p['country_code']\n",
      "    stats_country_result[cc] = stats_country_result.get(cc, 0)+1\n",
      "stats_country_result = OrderedDict(sorted(stats_country_result.items(), key=lambda t: t[1], reverse=True))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure(figsize=(25,4))\n",
      "bar(np.arange(len(stats_country_result)), stats_country_result.values())\n",
      "devnull = xticks(np.arange(0.5,len(stats_country_result)+0.5), stats_country_result.keys(), horizontalalignment='center', rotation=90)\n",
      "#yscale('log')\n",
      "title('Probe numbers by country in result (SB480 in NL,DE)')\n",
      "ylabel('#probes')\n",
      "xlabel('country code')\n",
      "grid(axis='y')\n",
      "savefig('SB500-probes-per-country.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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JCaYBAAAAAJiUYBoAAAAAgEkJpgEAAAAAmJRgGgAAAACASQmmAQAAAACYlGAa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\nNAAAAAAAk/r/b6qBxTN+jqwAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109c7d810>"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}