Find_unscaled_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function, division\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def without_inf(x):\n",
    "    invalid = np.isinf(x['speed']) | np.isinf(x['course'])\n",
    "    return x[~invalid]\n",
    "\n",
    "def mask_for(x, mmsi):\n",
    "    mask = np.zeros(x.shape, dtype=bool)\n",
    "    for m in mmsi:\n",
    "        mask |= (m == x['mmsi'])\n",
    "    return mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def evaluate(x, name):\n",
    "    print(name)\n",
    "    # Use course to find out of range MMSI since course should \n",
    "    # never be above 360 (speed has a softer cutoff).\n",
    "    # There turn out to be a few due to noise or soemthing, so\n",
    "    # we use the fraction of course values above 360. There should\n",
    "    # only be a few or (none) if the data has been scaled, most of \n",
    "    # the values should be greater than 360 if it has not been since\n",
    "    # the range is 0-3600\n",
    "    mmsi = sorted(set(x['mmsi']))\n",
    "    out_of_range = set()\n",
    "    for m in mmsi:\n",
    "        x_w_m = x[x['mmsi'] == m]\n",
    "        metric = (x_w_m['course'] > 360).sum() / len(x_w_m)\n",
    "        if metric > 0.5:\n",
    "            out_of_range.add(m)\n",
    "    print(\"MMSI with course out of range\", sorted(out_of_range))\n",
    "    # Plot histograms for course\n",
    "    f, (ax1, ax2) = plt.subplots(1,2, figsize=(16,4))\n",
    "    mask = mask_for(x, out_of_range)\n",
    "    ax1.hist(x['course'][~mask])\n",
    "    ax1.set_xlabel(\"course\")\n",
    "    if out_of_range:\n",
    "        ax2.hist(x['course'][mask])\n",
    "        ax2.set_xlabel(\"course\")\n",
    "    # Plot histograms for speed\n",
    "    f, (ax1, ax2) = plt.subplots(1,2, figsize=(16,4))\n",
    "    ax1.hist(x['speed'][~mask])\n",
    "    ax1.set_xlabel(\"speed\")\n",
    "    if out_of_range:\n",
    "        ax2.hist(x['speed'][mask])\n",
    "        ax2.set_xlabel(\"speed\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "longliner\n",
      "MMSI with course out of range [224068000.0, 224098250.0, 224108130.0]\n",
      "trawl\n",
      "MMSI with course out of range []\n",
      "ps\n",
      "MMSI with course out of range []\n"
     ]
    },
    {
     "data": {
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JrgP+ZlVdl+Qa4FerasOcGCx0F9DeC7y9F1OLj1Fb8UBrx629YwatPUbQ4uPU3mM0l4Xu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\nSZKkXrHQlSRJkiT1ioWuJEmSJKlX/n92LrRPyfBq6AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108c90198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AI8CqtmjlsVW1pdW7vm8fSZKGjgmvJEnddyrwN0muTfLtJP8hyauAZVW1u9XZ\nDSxr5ZN44fGBtPLJU8THW1ySpKE0q+fwSpKkBWUJ8FbgX1TVvUk+TZu+PKGqKklNufcsjI6O8uCD\nD/Lss9uBMWBkvg4tSeq4sbExxsbG5uVYJrySJHXfTmBnVU086u/zwMXAriSvr6pdbbry4237OLC8\nb/9T2jHGW7k/Pj7VB46OjnLjjTeyefPt7Ns3Mn89kSR13sjICCMjI8+/37Bhw6yP5ZRmSZI6rqp2\nAY8mOa2F3gV8F/gSsLbF1gK3tvJtwJokRyU5FVgBbGnHebqt8Bzgwr59JEkaOl7hlSRpcfht4MYk\nRwH/D/Ah4OXAzUnWATuA8wGqaluSm4FtwH5gfVVNTHdeD1wHHENv1ec7jmQnJEk6FCa8kiQtAlX1\nHeBtU2x61wHqXwpcOkX8PuD0+W2dJEmHhwnvYfDkk4/xwuMKh8sLX9BLkiRJUreZ8B42w5hYDmcS\nLkmSJEmHgwmvJEmShnJ2mjPTJM2VCa8kSZKaYUowhy8Bl7TwmPBKkiRJBzGMV78lzYwJryRJkjQt\nr35LC9HLBt0ASZIkSZIOBxNeSZIkSVInmfBKkiRJkjrJhFeSJEmS1EnTJrxJrkmyO8nWvtjxSTYn\neTjJpiRL+7ZdnGR7koeSnN0XPzPJ1rbtir740Uk+1+J3J3lj37a17TMeTvLB+emyJEmSJGkxmMkV\n3muB1ZNiFwGbq+o04M72niQrgQuAlW2fK/PCOu5XAeuqagWwIsnEMdcBe1r8cuCydqzjgY8DZ7XX\nJf2JtSRJkiRJBzNtwltV3wD2TgqfA2xs5Y3Aea18LnBTVe2rqh3AI8CqJCcCx1bVllbv+r59+o91\nC/DOVn4PsKmqnqyqJ4HNvDTxliRJkiRpSrO9h3dZVe1u5d3AslY+CdjZV28ncPIU8fEWp/37KEBV\n7QeeSnLCQY4lSZIkSdK0lsz1AFVVSQb8JO7RvvJIe0mSNL2xsTHGxsYG3QxJknQYzDbh3Z3k9VW1\nq01XfrzFx4HlffVOoXdldryVJ8cn9nkD8FiSJcBxVbUnyTgvzlyXA3dN3ZzRWXZDkrTYjYyMMDIy\n8vz7DRs2DK4xkiRpXs12SvNtwNpWXgvc2hdfk+SoJKcCK4AtVbULeDrJqraI1YXAF6c41vvpLYIF\nsAk4O8nSJK8F3g18ZZbtlSRp0Uvy8iT3J/lSez9vT12QJGkYzeSxRDcB3wT+hySPJvkQ8Eng3Uke\nBn6xvad+TzoQAAAOa0lEQVSqtgE3A9uAPwfWV9XEdOf1wGeA7cAjVXVHi18NnJBkO/A7tBWfq+oJ\n4BPAvcAWYENbvEqSJM3OR+iN0RNj83w+dUGSpKEz7ZTmqvrAATa96wD1LwUunSJ+H3D6FPFngfMP\ncKxr6T0WSZIkzUGSU4D3Av8b8L+08DnA21t5IzBGL+l9/qkLwI4kE09d+CumfurCxJfYkiQNldlO\naZYkSQvL5cDvAs/1xebzqQuSJA2dOa/SLEmShluS9wGPV9X9SUamqjPfT10YHR3lwQcf5Nlnt9O7\ncDzlx0qS9BLz+QQFE15JkrrvZ4BzkrwX+CHgNUluYH6eujA+1QeOjo5y4403snnz7ezbNzK/vZEk\nddp8PkHBKc2SJHVcVX2sqpZX1anAGuCuqrqQ+Xnqwq1Ikg4oyVC9Fhuv8EqStPhMTF3+JHBzknXA\nDtoiklW1LcnEUxf289KnLlwHHAPc3vfUBUnSlObtbpF5YMIrSZI6rKq+Dny9lZ9gnp66IEnSMDLh\nlSRJ0lBajNMvJc0vE15JkqQBMJmbiWGZCup/K2mhMuGVJEkamGFJ6MCkTlIXuUqzJEmSJKmTTHgl\nSZIkSZ1kwitJkiRJ6iQTXkmSJElSJ5nwSpIkSZI6yYRXkiRJktRJJrySJEmSpE4y4ZUkSZIkdZIJ\nryRJkiSpk+aU8CbZkeTBJPcn2dJixyfZnOThJJuSLO2rf3GS7UkeSnJ2X/zMJFvbtiv64kcn+VyL\n353kjXNpryRJkiRp8ZjrFd4CRqrqjKo6q8UuAjZX1WnAne09SVYCFwArgdXAlUnS9rkKWFdVK4AV\nSVa3+DpgT4tfDlw2x/ZKkiRJkhaJ+ZjSnEnvzwE2tvJG4LxWPhe4qar2VdUO4BFgVZITgWOrakur\nd33fPv3HugV45zy0V5IkSZK0CMzHFd6vJvlWkl9vsWVVtbuVdwPLWvkkYGffvjuBk6eIj7c47d9H\nAapqP/BUkuPn2GZJkiRJ0iIw14T3Z6vqDOCXgN9K8vP9G6uq6CXFkiRpQJIsT/K1JN9N8pdJPtzi\n87buhiRJw2jJXHauqr9u//5Nkv8EnAXsTvL6qtrVpis/3qqPA8v7dj+F3pXd8VaeHJ/Y5w3AY0mW\nAMdV1RMvbcloX3mkvSRJmt7Y2BhjY2ODbsbhtg/4aFU9kOTVwH1JNgMforfuxh8m+Vf01t24aNK6\nGyfTm821on2RPbHuxpYktydZXVV3DKZbkiQd3KwT3iSvBF5eVX+b5FXA2cAG4DZgLb0FptYCt7Zd\nbgM+m+RT9AbPFcCWqqokTydZBWwBLgT+tG+ftcDdwPvpLYI1hdHZdkOStMiNjIwwMjLy/PsNGzYM\nrjGHSVXtAna18g+SfI/eWHwO8PZWbSMwRi/pfX7dDWBHkol1N/6KqdfdMOGVJA2luVzhXQb8p7bQ\n8hLgxqralORbwM1J1gE7gPMBqmpbkpuBbcB+YH37phhgPXAdcAxwe983xVcDNyTZDuwB1syhvZIk\nLXpJ3gScAdzDwdfduLtvt4l1N/Zx4HU3JEkaOrNOeKvq+8Bbpog/AbzrAPtcClw6Rfw+4PQp4s/S\nEmZJkjQ3bTrzLcBH2gyt57e1GVfztu7G6OgoDz74IM8+u53eheOR+Tq0JKnj5vN2ozndwytJkhaG\nJK+gl+zeUFUTtxvNx7ob41N93ujoKDfeeCObN9/Ovn0j89gTSVLXzeftRvPxHF5JkjTE0ruUezWw\nrao+3bdpYq0MeOm6G2uSHJXkVF5Yd2MX8HSSVe2YF/btI0nS0PEKryRJ3fezwK8ADya5v8UuBj7J\n/K27IUnS0DHhlSSp46rqLzjwrK55WXdDkqRh5JRmSZIkSVInmfBKkiRJkjrJhFeSJEmS1EkmvJIk\nSZKkTjLhlSRJkiR1kgmvJEmSJKmTfCyRJElaNH75l3950E2QJB1BJrySJGmRqUE3oMmgGyBJneeU\nZkmSJElSJ5nwSpIkSZI6yYRXkiRJktRJJrySJEmSpE4y4ZUkSZIkdZIJryRJkiSpk0x4JUmSJEmd\nNPQJb5LVSR5Ksj3Jvxp0eyRJWuwcmyVJC8VQJ7xJXg78W2A1sBL4QJIfG2yrumdsbGzQTegEz+Pc\neQ7nznOow82xud/YoBtwhIwNugFHyNigG3CEjA26AUfI2KAbcISMDboBQ2+oE17gLOCRqtpRVfuA\nPwPOHXCbOsc/kOeH53HuPIdz5znUEeDY/LyxQTfgCBkbdAOOkLFBN+AIGRt0A46QsUE34AgZG3QD\nht6wJ7wnA4/2vd/ZYpIkaTAcmyVJC8aSQTdgGjWTSq95zS8f7nbMWNV/42//dtCtkCTpsJnR2Dxh\n//6xoRmnn3lmy6CbIEk6wlJ1SOPWEZXkp4DRqlrd3l8MPFdVl/XVGd4OSJIWpKrKoNswrBybJUmD\nMNuxedgT3iXAfwbeCTwGbAE+UFXfG2jDJElapBybJUkLyVBPaa6q/Un+BfAV4OXA1Q6okiQNjmOz\nJGkhGeorvJIkSZIkzdawr9J8UD74/tAlWZ7ka0m+m+Qvk3y4xY9PsjnJw0k2JVk66LYOuyQvT3J/\nki+1957DQ5BkaZLPJ/lekm1JVnkOD02Si9vP8tYkn01ytOfw4JJck2R3kq19sQOes3aOt7ex5uzB\ntHph6erYvJjGz8Uyvi2Gcair48Ri+V1+gH7+Uft/9jtJvpDkuL5tneln37Z/meS5JMf3xQ6pnws2\n4Y0Pvp+tfcBHq+rHgZ8Cfqudt4uAzVV1GnBne6+D+wiwjRdWLPUcHporgNur6seANwMP4TmcsSRv\nAn4deGtVnU5vaukaPIfTuZbeuNFvynOWZCVwAb0xZjVwZZIFO24eCR0fmxfT+LlYxrdOj0MdHycW\ny+/yqfq5CfjxqvoJ4GHgYuhkP0myHHg38Fd9sUPu50I5CVPxwfezUFW7quqBVv4B8D16z088B9jY\nqm0EzhtMCxeGJKcA7wU+A0ysGOc5nKH2beTPV9U10LsnsKqewnN4KJ6m9wf4K9NbROiV9BYQ8hwe\nRFV9A9g7KXygc3YucFNV7auqHcAj9MYeHVhnx+bFMn4ulvFtkYxDnR0nFsvv8qn6WVWbq+q59vYe\n4JRW7lQ/m08Bvzcpdsj9XMgJrw++n6P2zd8Z9H5YllXV7rZpN7BsQM1aKC4Hfhd4ri/mOZy5U4G/\nSXJtkm8n+Q9JXoXncMaq6gngT4D/Su8PmCerajOew9k40Dk7id7YMsFxZnqLYmzu+Pi5WMa3zo9D\ni3CcWIy/y38NuL2VO9XPJOcCO6vqwUmbDrmfCznhdbWtOUjyauAW4CNV9bf926q3kpnn9wCSvA94\nvKru54Vvv1/EczitJcBbgSur6q3A/8ekKVWew4NL8iPA7wBvovfL/9VJfqW/jufw0M3gnHk+D67z\n56fL4+ciG986Pw4t5nFiMfwuT/L7wN9X1WcPUm1B9jPJK4GPAZf0hw+yy0H7uZAT3nFged/75bw4\n29cBJHkFvcH6hqq6tYV3J3l9234i8Pig2rcA/AxwTpLvAzcBv5jkBjyHh2InvW/t7m3vP0/vD49d\nnsMZ+0ngm1W1p6r2A18AfhrP4Wwc6Gd38jhzSovpwDo9Ni+C8XMxjW+LYRxabOPEovldnuRX6d16\n8D/1hbvUzx+h90XNd9rvo1OA+5IsYxb9XMgJ77eAFUnelOQoejcv3zbgNg29JAGuBrZV1af7Nt0G\nrG3ltcCtk/dVT1V9rKqWV9Wp9BZ/uKuqLsRzOGNVtQt4NMlpLfQu4LvAl/AcztRDwE8lOab9XL+L\n3iIznsNDd6Cf3duANUmOSnIqsALYMoD2LSSdHZsXw/i5mMa3RTIOLbZxYlH8Lk+ymt5tB+dW1TN9\nmzrTz6raWlXLqurU9vtoJ73F13Yzm35W1YJ9Ab8E/Gd6NytfPOj2LIQX8HP07st5ALi/vVYDxwNf\npbfa2yZg6aDbuhBewNuB21rZc3ho5+4ngHuB79D71vk4z+Ehn8Pfo/cH2lZ6C3S8wnM47Tm7id69\nbH9P717TDx3snNGbUvUIvT8c3zPo9i+EV1fH5sU2fi6G8W0xjENdHScWy+/yKfr5a8B2eqsWT/we\nurJD/Xx24r/npO3/BTh+tv1M20mSJEmSpE5ZyFOaJUmSJEk6IBNeSZIkSVInmfBKkiRJkjrJhFeS\nJEmS1EkmvJIkSZKkTjLhlSRJkiR1kgmvpANKMpbkzEG3Q5Ik9Tg2S4fGhFfSwVR7SZKk4eDYLB0C\nE15pgUnyqiRfTvJAkq1Jzk+yI8llSR5Mck+SH2l1fzjJ55Nsaa+f6TvGNa3ut5Oc0+LHJPmzJNuS\nfAE4BsjgeitJ0vBzbJaG15JBN0DSIVsNjFfVPwZI8hrgMuDJqnpzkguBTwO/DFwBXF5V/1eSNwB3\nACuB3wfurKpfS7IUuCfJV4F/DvygqlYmOR34Nn6LLEnSdBybpSGVKn9epIUkyQpgE/A54P+oqr9I\n8n3gHVW1I8krgL+uqtcleRwY79v9dcA/BL4OHA3sb/HX0hus/wC4oqrG2mfdB/x6VX37CHRNkqQF\nybFZGl5e4ZUWmKranuQM4B8D/ybJXVNVa/8GWFVVf9+/MQnA/1hV26eIO01KkqRD4NgsDS/v4ZUW\nmCQnAs9U1Y3AHwNntE0X9P37zVbeBHy4b9+faMWvTIpPHOP/BP5pi/0j4M2HoQuSJHWKY7M0vLzC\nKy08pwN/lOQ54O+B9cDngdcm+Q7wDPCBVvfDwL9r8SX0pkutBz4BfDrJg/S++PovwDnAVcC1SbYB\n3wO+dcR6JUnSwuXYLA0p7+GVOqDdJ3RmVT0x6LZIkiTHZmlYOKVZ6ga/uZIkabg4NktDwCu8kiRJ\nkqRO8gqvJEmSJKmTTHglSZIkSZ1kwitJkiRJ6iQTXkmSJElSJ5nwSpIkSZI6yYRXkiRJktRJ/z9/\nAtl/FZmU4wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10abeb710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x108c90dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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uJEmSJKlzTHYlSZIkSZ1jsitJkiRJ6hyTXUmSJElS55jsSpIkSZI6x2RXkiRJ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\nSVU/tXBsF/8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1075d9cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XPdBlYjAYTDuE3vMcHj/P4WR4HqejEwkt8GpgZ1XtqqoDwEeBy6Yc0wk0mHYA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ee70cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tUelL88o2ZfYN9KbJ/0r/yhHqyxzgWOATVXUsvTvZ/tT02BHqC0n2At4I/O+J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\nOYZdSZIkSVLn/H9MQrYTdyq+UgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109cb2470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name in [\"longliner\", \"trawl\", \"ps\"]: \n",
    "    x_x = without_inf(np.load('datasets/kristina_{0}.npz'.format(name))['x'])\n",
    "    evaluate(x_x, name)"
   ]
  }
 ],
 "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": "3.4.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}