{
 "metadata": {
  "name": "",
  "signature": "sha256:3e637d48c2e672049849b9af12b9d7ddcc284665998d698a9f83998de1867e5b"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Software In The Loop\n",
      "\n",
      "Run real FC code against our test data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from psas_packet import io\n",
      "import numpy\n",
      "\n",
      "with io.BinFile('./logfiles/logfile-000') as fclog:\n",
      "    adis_time = []\n",
      "    accel = []\n",
      "    roll_rate = []\n",
      "    fin_time = []\n",
      "    fin_angle = []\n",
      "    for fourcc, data in fclog.read():\n",
      "        if fourcc == 'ADIS':\n",
      "            adis_time.append(data['timestamp'])\n",
      "            roll_rate.append(data['Gyro_X']*360)\n",
      "            accel.append(data['Acc_X'])\n",
      "        elif fourcc == 'ROLL':\n",
      "            fin_time.append(data['timestamp'])\n",
      "            fin_angle.append(data['Angle'])\n",
      "        elif fourcc != 'SEQN':\n",
      "            print fourcc, data\n",
      "\n",
      "\n",
      "# Time in seconds\n",
      "fin_time = numpy.array(fin_time)\n",
      "adis_time = numpy.array(adis_time)\n",
      "adis_time -= adis_time[0]\n",
      "adis_time = adis_time / 1e9\n",
      "fin_time -= fin_time[0]\n",
      "fin_time = fin_time / 1e9"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Skipped unknown header: MESG\n",
        "\n",
        "MESG {'timestamp': 2765483312}\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Skipped unknown header: \u0000\u0000\u0001h\n",
        "\n",
        "\u0000\u0000\u0001h {'timestamp': 90500535943170}\n",
        "boundary?\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import utils\n",
      "from matplotlib import rcParams\n",
      "rcParams.update({'font.size': 14})\n",
      "\n",
      "fig = utils.Plot(r\"Fin Angle\", \"Time [$s$]\", r\"$\\alpha$ [${}^0$]\")\n",
      "fig.plot(fin_time, fin_angle)\n",
      "fig.ylim([-15, 15])\n",
      "fig.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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dsqwsM4jNZjP3CLA1OWYUsswI5JilW3xZBgAAgCllGQAAACaUZQAAAJhQlgEAAGBi8WXZ\nLn+MYr1ezz0CbE2OGYUsMwI5Zumqu+eeYVZV1Ut/DQAAAEZVVenuOu5xiz+zDAAAAFPKMgAAAEwo\nywAAADChLAMAAMDE4svyarWaewQ4EXZ2ZwRyzChkmRHIMUunLCvLDGKz2cw9AmxNjhmFLDMCOWbp\nFl+WAQAAYEpZBgAAgAllGQAAACaUZQAAAJhYfFm2yx+jWK/Xc48AW5NjRiHLjECOWbrq7rlnmFVV\n9dJfAwAAgFFVVbq7jnvc4s8sAwAAwJSyDAAAABPKMgAAAEwoywAAADCx+LK8Wq3mHgFOhJ3dGYEc\nMwpZZgRyzNIpy8oyg9hsNnOPAFuTY0Yhy4xAjlm6xZdlAAAAmFKWAQAAYEJZBgAAgAllGQAAACYW\nX5bt8sco1uv13CPA1uSYUcgyI5Bjlq66e+4ZZlVVvfTXAAAAYFRVle6u4x63+DPLAAAAMKUsAwAA\nwISyDAAAABPKMgAAAEwsviyvVqu5R4ATYWd3RiDHjEKWGYEcs3TKsrLMIDabzdwjwNbkmFHIMiOQ\nY5Zu2LJcVddV1ZnJ11vmngsAAIBL3xVzD3CBvSbJNYdu3zHTHAAAAOyQ0cvyHd1909xDAAAAsFuG\nfRv2gQ+vqj+pqj+uqh+rqofPPRAAAACXvpHL8m8leUqSz0rypUkelOTFVfVBhxfZ5Y9RrNfruUeA\nrckxo5BlRiDHLN2wb8Pu7hccuvm7VfWbSV6X/QL93Xc+cObMmVx77bXvW3jq1KmcOnUq6/X6yJ2y\n9/b2jtwZ0Hrr516/Wq0uqXmst/581h8+7lKYx3rrz3f99Ji557He+vNdf5Rdmt/6Za6//vrrc8MN\nN9zlseOq7t76SXZFVf1qkt/v7qceuq+X9BoAAAAsSVWlu+u4x438Nuw/p6quSvLRSd469ywAAABc\n2oYty1X1L6vqiVX18Kr6pCT/Kcm9kjxn5tEAAAC4xA17zXKSD03yY0nun+TtSX4zySd395tmnQoA\nAIBL3qKuWT7Ker3uoy4Yh12zt7d35CYHsEvkmFHIMiOQY0bhmuXz5A8ARuGHPoxAjhmFLDMCOWbp\nFl+WAQAAYEpZBgAAgAllGQAAACaUZQAAAJhYfFne29ubewQ4Eev1eu4RYGtyzChkmRHIMUu3+I+O\nqqpe+msAAAAwKh8dBQAAACdEWQYAAIAJZRkAAAAmlGUAAACYWHxZXq1Wc48AJ8LO7oxAjhmFLDMC\nOWbplGVlmUFsNpu5R4CtyTGjkGVGIMcs3eLLMgAAAEwpywAAADChLAMAAMCEsgwAAAATiy/Ldvlj\nFOv1eu4RYGtyzChkmRHIMUtX3T33DLOqql76awAAADCqqkp313GPW/yZZQAAAJhSlgEAAGBCWQYA\nAIAJZRkAAAAmFl+WV6vV3CPAibCzOyOQY0Yhy4xAjlk6ZVlZZhCbzWbuEWBrcswoZJkRyDFLt/iy\nDAAAAFPKMgAAAEwoywAAADChLAMAAMDE4suyXf4YxXq9nnsE2JocMwpZZgRyzNJVd889w6yqqpf+\nGgAAAIyqqtLdddzjFn9mGQAAAKaUZQAAAJhQlgEAAGBCWQYAAICJxZfl1Wo19whwIuzszgjkmFHI\nMiOQY5ZOWVaWGcRms5l7BNiaHDMKWWYEcszSLb4sAwAAwJSyDAAAABPKMgAAAEwoywAAADCx+LJs\nlz9GsV6v5x4BtibHjEKWGYEcs3TV3XPPMKuq6qW/BgAAAKOqqnR3Hfe4xZ9ZBgAAgCllGQAAACaU\nZQAAAJhQlgEAAGBi8WV5tVrNPQKcCDu7MwI5ZhSyzAjkmKVTlpVlBrHZbOYeAbYmx4xClhmBHLN0\niy/LAAAAMKUsAwAAwISyDAAAABPKMgAAAEwsvizb5Y9RrNfruUeArckxo5BlRiDHLF1199wzzKqq\neumvAQAAwKiqKt1dxz1u8WeWAQAAYEpZBgAAgAllGQAAACaUZQAAAJhYfFlerVZzjwAnws7ujECO\nGYUsMwI5ZumUZWWZQWw2m7lHgK3JMaOQZUYgxyzd4ssyAAAATCnLAAAAMHHFuS6sqr+RpI/5/C/o\n7luOeQwAAADM6pzLcpKfOOZzd5JHJvnjYx4HAAAAszpOWU6SB3f3jeeysKpuPo95Ljq7/DGK9Xo9\n9wiwNTlmFLLMCOSYpTvONcvPTXKct1T/v0ku+cKsLDMKO7szAjlmFLLMCOSYpavu416GPJaq6qW/\nBgAAAKOqqnR3Hfc4u2EDAADAhLIMAAAAE8oyAAAATCy+LNu4gFHYrI4RyDGjkGVGIMcs3dBluaq+\nqqpeV1W3VtVvV9WnTtcoy4xis9nMPQJsTY4ZhSwzAjlm6c7pc5ar6n9LcirJA5PckeTtSV7S3a+5\ngLNtpao+P8kzk3xlkl9P8tQkz6+qj+nuN806HAAAAJe0s55Zrqp/kOTrk7xfkrcleUeSD0zytKr6\n5gs73lb+YZIf6e5/391/0N1fm+St2S/PAAAAcLfO5czym7v7mUc9UFV/64TnORFVtUryCUn++eSh\nX0ryhIs/EQAAALvkXMryo6vqEUl+J8l7s/827PdL8qgkfyHJT1y48c7b/ZNcnuTGyf03JXnQxR8H\nAACAXXLWstzd11XVZyb55CQfnP23bt+U5DeTfO+FHe/CO336dP6fz/7s991+/CMekSc88pFZVWVV\ndZf1e93Z677L/dZbf07rz5zZXz957CSef687m4NfL9j81u/e+jNn9m8cevySnf/06Zx5yEPSn/u5\nqfU6e3t7R24us16vj9yc8WKv7zvuyJn3vCer227LFbfdln7Pe3Lmve9N33Zb+rbbsrnlluxtNunT\np9O3356cOZN0Z3XZZfu/3+l/w2fO7P//dQH+fLD+4q/f604fOm6r57/z14O1l+Lv1/ox1ydJX3bX\nqzZ3Zf4lr7/6SU/K5R/0QfvrL/G/Ty/E+uuvvz433HDDXR47ruq7+Q/jLgurPinJy7r79Dmu/8Qk\nr+rui77n/MHbsN+b5Au6+ycP3f/9ST6muz/90H19rq8BABfW3qtelVuf//xc+ahH5aprrsllV189\n2yzdnTNvf3tOv/GNOf361+eOm25KTh/6K/Cyy3LZB3xALrvPfVL3vvf+r1dfnbrqqj/7Wq2SK69M\nrVapyy+f7fcCAEtWVenuu/7k4WzHHaMs35Hkwd190zmuvznJx3X3Hx93qJNQVb+V5L9395cfuu8P\nk/xEd3/zofuUZYBLSHfn9le+Mre96EW57P73z70+53Ny+X3ve8G/5x1ve1tuf9Wrcvsf/dH7SvFl\nD3hArnjoQ3PFQx6Syx/4wP3yCwDslItRls8keVaSW85leZIvS/LRM5blz0vyvCRfleTFSb4iyRcn\nedThj45SlgEuXadf//rcev31OfM//2dWj31srvq0TzuxM7R9xx3Ze+Urs/eSl+TMLbfkigc/OFd+\n7Mfmykc+UikGgIFcjLL8oiTn2irrYO2Tuvstxx3qpFTVVyb5hiQPTvKqJF/X3b8+WaMsA1zi+syZ\nbF7ykmxuuCFXftRH5V6f+Zmpq646r+c6/cY35rYXvSh3vP3tWX3sx2b9hCfM+nZvAODCuuBleVTK\nMsDu6O7c/upX59Zf+qVc/oAH7L9F+373O6djb3/ta3PLz/xMrvjQD81V11yTyx/kwxEAYAmU5fO0\nXq/7qN3VYNfs7e0duSMg7JLj5Pj0m9+cW3/xF9O33ZarPv3Tc+WjH506YtfQO268Me/98R/P5fe/\nf+711/5aLnu/9zvpseEu/JnMCOSYUSjL5+k+97lP33zzzXOPAVu7+eabc5/73GfuMWAr55Pjvu22\n3PrLv5zbX/OarD7u4/ava16v09257Vd+Jbe/+tW591Oekss+8AMv0NRwV/5MZgRyzCiU5fOkLDMK\nf6Exgm1y3N3Ze9nLsrnhhlx23/vmzHvekys/6qNy1V/6S0eecYYLyZ/JjECOGcX5luUrLsQwAHCx\nVVXWj31s1o99bO648cakKpd/8AfPPRYAsKO2KstV9bAk9+7u3z24fXmSD+7ut24/GgCcn8sf+MC5\nRwAAdtxl2xzc3a9Pcv+q+rqqelh335HkC05kMgAAAJjJVmW5qq5K8oQkT0zyX6vqBUn+8kkMdrHs\n7e3NPQKciPV6PfcIsDU5ZhSyzAjkmKXbaoOvqvo3Sf5Xkj9KcjrJI5L8u4MzzjvB5ywDAACMa64N\nvn6nu39oMsiXJHnWls8LAAAAs9nqbdhJ3u+I+zZbPicAAADMatuyfLqqvqGqrjx031EFGgAAAHbG\ntm/D/r6Drz+pqhcmuSXJW7aeCgAAAGa01QZf73uSqk9M8unZL8o/tks7Zq3X695svHOc3be3t5fV\najX3GLAVOWYUsswI5JhRzLXBV5Kku1+W5GUn8VwXmz8AGMVms5Fndp4cMwpZZgRyzNJte80yAAAA\nDEdZBgAAgAllGQAAACaUZQAAAJhYfFne29ubewQ4Eev1eu4RYGtyzChkmRHIMUt3Ih8dtcuqapc+\n6QoAAIBjON+Pjlr8mWUAAACYUpYBAABgQlkGAACACWUZAAAAJhZfller1dwjwImwszsjkGNGIcuM\nQI5ZOmVZWWYQm81m7hFga3LMKGSZEcgxS7f4sgwAAABTyjIAAABMKMsAAAAwoSwDAADAxOLLsl3+\nGMV6vZ57BNiaHDMKWWYEcszSVXfPPcOsqqqX/hoAAACMqqrS3XXc4xZ/ZhkAAACmlGUAAACYUJYB\nAABgQlkGAACAicWX5dVqNfcIcCLs7M4I5JhRyDIjkGOWTllWlhnEZrOZewTYmhwzCllmBHLM0i2+\nLAMAAMCUsgwAAAATyjIAAABMKMsAAAAwsfiybJc/RrFer+ceAbYmx4xClhmBHLN01d1zzzCrquql\nvwYAAACjqqp0dx33uMWfWQYAAIApZRkAAAAmlGUAAACYUJYBAABgYvFlebVazT0CnAg7uzMCOWYU\nsswI5JilU5aVZQax2WzmHgG2JseMQpYZgRyzdIsvywAAADClLAMAAMCEsgwAAAATyjIAAABMLL4s\n2+WPUazX67lHgK3JMaOQZUYgxyxddffcM8yqqnrprwEAAMCoqirdXcc9bvFnlgEAAGBKWQYAAIAJ\nZRkAAAAmlGUAAACYWHxZXq1Wc48AJ8LO7oxAjhmFLDMCOWbplGVlmUFsNpu5R4CtyTGjkGVGIMcs\n3eLLMgAAAEwpywAAADChLAMAAMCEsgwAAAATiy/LdvljFOv1eu4RYGtyzChkmRHIMUtX3T33DLOq\nql76awAAADCqqkp313GPW/yZZQAAAJhSlgEAAGBCWQYAAIAJZRkAAAAmFl+WV6vV3CPAibCzOyOQ\nY0Yhy4xAjlm6IctyVb2oqs5Mvn70qLXKMqPYbDZzjwBbk2NGIcuMQI5ZuivmHuAC6STPSvK0Q/fd\nOtMsAAAA7JhRy3KS3NrdN809BAAAALtnyLdhH/iCqnp7Vf1uVf2Lqrr33AMBAACwG0Y9s/yjSV6f\n5C1JHp3kO5N8bJLPmnEmAAAAdsTOlOWqekb+/DXIR7mmu3+tu3/40H2vrqrXJnlpVX18d7/i8AGn\nT5/Otdde+77bp06dyqlTp7Jer4/c/Gtvb+/IzQ6st37u9ev1+pKax3rrz2f9er2+pOax3vrzXX84\ny5fCPNZbfz7rq+ou9+3S/NYvd/3111+fG2644S6PHVd199ZPcjFU1f2S3O8sy97U3XfZyKuqLkuy\nSfJF3f0Tk8d6V14DAAAAjqeq0t1H//TnHuzMmeXufkeSd5zn4f97ksuTvPXkJgIAAGBUO3Nm+VxV\n1YcneXKSX8h+uf6YJP8qyXuTPG56GtmZZQAAgHENf2b5GPaSfEaSr01y7yRvSvKfk3yrVgwAAMC5\nGO7M8nE5swwAADCu8z2zPPLnLJ+To3ZQg120t7c39wiwNTlmFLLMCOSYpVOWlWUGcdSW+rBr5JhR\nyDIjkGOWbvFlGQAAAKaUZQAAAJhQlgEAAGBCWQYAAICJxZdlu/wxivV6PfcIsDU5ZhSyzAjkmKXz\nOcs+ZxkAAGBYPmcZAAAAToiyDAAAABPKMgAAAEwoywAAADCx+LK8Wq3mHgFOhJ3dGYEcMwpZZgRy\nzNIpy8oUwNGeAAARv0lEQVQyg9hsNnOPAFuTY0Yhy4xAjlm6xZdlAAAAmFKWAQAAYEJZBgAAgAll\nGQAAACYWX5bt8sco1uv13CPA1uSYUcgyI5Bjlq66e+4ZZlVVvfTXAAAAYFRVle6u4x63+DPLAAAA\nMKUsAwAAwISyDAAAABPKMgAAAEwsviyvVqu5R4ATYWd3RiDHjEKWGYEcs3TKsrLMIDabzdwjwNbk\nmFHIMiOQY5Zu8WUZAAAAppRlAAAAmFCWAQAAYEJZBgAAgInFl2W7/DGK9Xo99wiwNTlmFLLMCOSY\npavunnuGWVVVL/01AAAAGFVVpbvruMct/swyAAAATCnLAAAAMKEsAwAAwISyDAAAABOLL8ur1Wru\nEeBE2NmdEcgxo5BlRiDHLJ2yrCwziM1mM/cIsDU5ZhSyzAjkmKVbfFkGAACAKWUZAAAAJpRlAAAA\nmFCWAQAAYGLxZdkuf4xivV7PPQJsTY4ZhSwzAjlm6aq7555hVlXVS38NAAAARlVV6e467nGLP7MM\nAAAAU8oyAAAATCjLAAAAMKEsAwAAwMTiy/JqtZp7BDgRdnZnBHLMKGSZEcgxS6csK8sMYrPZzD0C\nbE2OGYUsMwI5ZukWX5YBAABgSlkGAACACWUZAAAAJpRlAAAAmFh8WbbLH6NYr9dzjwBbk2NGIcuM\nQI5ZuuruuWeYVVX10l8DAACAUVVVuruOe9zizywDAADAlLIMAAAAE8oyAAAATCjLAAAAMLH4srxa\nreYeAU6End0ZgRwzCllmBHLM0inLyjKD2Gw2c48AW5NjRiHLjECOWbrFl2UAAACYUpYBAABgQlkG\nAACACWUZAAAAJhZflu3yxyjW6/XcI8DW5JhRyDIjkGOWrrp77hlmVVW99NcAAABgVFWV7q7jHrf4\nM8sAAAAwpSwDAADAhLIMAAAAE8oyAAAATCy+LK9Wq7lHgBNhZ3dGIMeMQpYZgRyzdDtXlqvqy6rq\nhVX1rqo6U1UPOWLNfavqeQdr3lVVz62qDzjq+ZRlRrHZbOYeAbYmx4xClhmBHLN0O1eWk9wryQuS\nPP0e1vxoksck+awkn53kE5I878KPBgAAwAiumHuA4+ru70mSqnrsUY9X1UdnvyR/Sne/5OC+L09y\nQ1V9RHf/4UUbFgAAgJ20i2eWz+bxSd7T3b956L4XJ3nvwWMAAABwj0Ysyw9K8vbDd3R3J7np4DEA\nAAC4R5fE27Cr6hlJnnaWZdd096+d9Pc+ffp0rr322vfdPnXqVE6dOpX1en3k5l97e3tHbnZgvfVz\nr1+v15fUPNZbfz7r1+v1JTWP9daf7/rDWb4U5rHe+vNZX1V3uW+X5rd+ueuvv/763HDDDXd57Lhq\n/6TrvKrqfknud5Zlb+ruWw8d89gkL03ysO5+46H7vyTJM7v7/Q/dV0n+V5Kv7u7nTL53XwqvAQAA\nACevqtLdR//05x5cEmeWu/sdSd5xQk/3m0nuXVWPP3Td8uOTXJ39a5cBAADgHl0SZfk4qupB2b/2\n+CMO7npUVX1Qkjd09zu7+/er6gVJfqiqvixJJfmhJD/f3X80z9QAAADskl3c4Osrkrw8yX9I0kl+\nIcnLkvzVQ2u+KMl/T/Jfsv+ZzK9I8rcv7pgAAADsqkvimuU5uWYZAABgXOd7zfIunlk+UUftoAa7\naG9vb+4RYGtyzChkmRHIMUunLCvLDOKoLfVh18gxo5BlRiDHLN3iyzIAAABMKcsAAAAwoSwDAADA\nhLIMAAAAE4svy3b5YxTr9XruEWBrcswoZJkRyDFL53OWfc4yAADAsHzOMgAAAJwQZRkAAAAmlGUA\nAACYUJYBAABgYvFlebVazT0CnAg7uzMCOWYUsswI5JilU5aVZQax2WzmHgG2JseMQpYZgRyzdIsv\nywAAADClLAMAAMCEsgwAAAATyjIAAABMLL4s2+WPUazX67lHgK3JMaOQZUYgxyxddffcM8yqqnrp\nrwEAAMCoqirdXcc9bvFnlgEAAGBKWQYAAIAJZRkAAAAmlGUAAACYWHxZXq1Wc48AJ8LO7oxAjhmF\nLDMCOWbplGVlmUFsNpu5R4CtyTGjkGVGIMcs3eLLMgAAAEwpywAAADChLAMAAMCEsgwAAAATiy/L\ndvljFOv1eu4RYGtyzChkmRHIMUtX3T33DLOqql76awAAADCqqkp313GPW/yZZQAAAJhSlgEAAGBC\nWQYAAIAJZRkAAAAmFl+WV6vV3CPAibCzOyOQY0Yhy4xAjlk6ZVlZZhCbzWbuEWBrcswoZJkRyDFL\nt/iyDAAAAFPKMgAAAEwoywAAADChLAMAAMDE4suyXf4YxXq9nnsE2JocMwpZZgRyzNJVd889w6yq\nqpf+GgAAAIyqqtLdddzjFn9mGQAAAKaUZQAAAJhQlgEAAGBCWQYAAICJxZfl1Wo19whwIuzszgjk\nmFHIMiOQY5ZOWVaWGcRms5l7BNiaHDMKWWYEcszSLb4sAwAAwJSyDAAAABPKMgAAAEwoywAAADCx\n+LJslz9GsV6v5x4BtibHjEKWGYEcs3TV3XPPMKuq6qW/BgAAAKOqqnR3Hfe4xZ9ZBgAAgCllGQAA\nACaUZQAAAJhQlgEAAGBi8WV5tVrNPQKcCDu7MwI5ZhSyzAjkmKVTlpVlBrHZbOYeAbYmx4xClhmB\nHLN0iy/LAAAAMKUsAwAAwISyDAAAABPKMgAAAEwsvizb5Y9RrNfruUeArckxo5BlRiDHLF1199wz\nzKqqeumvAQAAwKiqKt1dxz1u8WeWAQAAYEpZBgAAgAllGQAAACaUZQAAAJjYubJcVV9WVS+sqndV\n1ZmqesgRa15/8Njhr+846vlWq9WFHxouAju7MwI5ZhSyzAjkmKXbubKc5F5JXpDk6fewppN8a5IH\nHfr69qMWKsuMYrPZzD0CbE2OGYUsMwI5Zul2rix39/d09z9L8htnWfqe7r7p0Nd7j1p0+vTpkx8S\nZnDDDTfMPQJsTY4ZhSwzAjlm6XauLB/D11fVn1bVK6rqaVV15VGL7rjjjos9F1wQ/kJjBHLMKGSZ\nEcgxS3fF3ANcIP86ycuTvCPJJyX5p0kenuRL5xwKAACA3XBJlOWqekaSp51l2TXd/Wvn8nzd/d2H\nbv5uVb07yY9X1Td09zvPd04AAACWobp77hlSVfdLcr+zLHtTd9966JjHJnlpkod19xvP8vwPTfK6\nJJ/U3f9t8tj8LwAAAAAXTHfXcY+5JM4sd/c7sv+W6QvlMQe/vvWI733sFw0AAICxXRJl+Tiq6s6P\ngvqIg7seVVUflOQN3f3OqvrkJI9P8sIk707yuCTfleRnu/vNc8wMAADAbrkk3oZ9HFV1XZJ/fHCz\nk9x5ZvjvdPdzq+rjk/xAko9Ksk7yhiQ/luSfd/dtF3lcAAAAdtDOlWUAAAC40Eb+nOWzqqqvqqrX\nVdWtVfXbVfWpc88E56qqrq2q/1ZV766qm6rq56rqUXPPBds6yPaZqvreuWeB46iqB1fVcw7+TL61\nql5dVU+cey44jqq6oqq+o6r++CDHf1xV/6SqLp97Nrg7VfXEg38Lv/ng3xBPOWLNdVX1J1V1S1W9\nsKo+5mzPu9iyXFWfn+SZSZ6R/Q3AXpzk+VX1F2YdDM7dpyX5vuxfo/8ZSU4nub6q7jvrVLCFg30n\nvjTJK7N/qQ3shKr6wCS/kf3cfk72Lwf76iQ3zTkXnIenJfnyJF+T5COT/P0kX5Xk2jmHgrO4Ovv/\ndvj7SW7N5N8QVfWNSf5h9v9cflz2/2z+5aq69z096WLfhl1VL0nyO9395Yfu+8Mk/6m7z/aZz3DJ\nqaqrs7+p3V/r7l+Yex44rqr6gCQvS/J3k1yX5FXd/bWzDgXnqKq+I8mp7j419yywjar6+SR/2t1f\nfOi+5yS5b3f/X/NNBuemqm5O8tTufu7B7UryliT/uru/8+C+q7JfmL++u//t3T3XIs8sV9UqySck\n+aXJQ7+U5AkXfyI4Ee+f/f+m3zn3IHCe/m2Sn+ju/5o/27wRdsX/neSlVfUfq+rGqnpFVT117qHg\nPDw/yWdU1UcmycFbVT89yS/OOhWcv4cneWAOdb+DjZ9/LWfpfjv30VEn5P5JLk9y4+T+m7L/sVSw\ni74nySuS/Obcg8BxVdWXJvnwJF90cNcy3/bELvvw7L9V9buSfEeSj0/yvVWV7v7+WSeDY+juH6iq\nD0vy+1V1Ovt94Rnd/YMzjwbn685+d1T3+5B7OnCpZRmGUlXflf2fjH1qL/XaCnbWwdmLb89+fu+4\n8+44u8xuuSzJS7v7mw9u//eqemSSpyZRltkZVfW1Sb44yRckeXX2f/DzPVX1+u5+1qzDwcm7x383\nL7Us/2mSO7J/Ov6wByZ568UfB85fVX13ks9L8und/fqZx4Hz8fjsv+Pn1fuXFSXZf/fPqar68iRX\nd/ftcw0H5+gtSX5vct9rkjxkhllgG9+c/TPJP35w+9VV9dDsb/ClLLOL3nbw6wOTvPnQ/Q889NiR\nFnnNcnfvZX8Tmc+cPPSXs78rNuyEqvqeJJ+f5DO6+w/nngfO008neXSSjzv4ekyS307yY0keoyiz\nI34j+ztgH/YRSV5/8UeBrVSSM5P7zsS7fdhdr8t+KX5f9zvY4OtTc5but9Qzy8n+NUXPq6qXZv9F\n+orsv5/d9RjshKr6/iRPzv6mMu+uqjuvx7i5u98732RwPN397uzv5P4+VXVLknd29/RMHVyqvjvJ\ni6vqaUl+PPtvXf2a+Lgdds/PJPmmqnpd9t8t8fFJvi7Jc2adCu7BwafCPPLg5mVJHlpVj0nyju5+\nU1U9M8nTquo1Sf4oybckuTnJj97j8y758saq+sok35DkwUleleTruvvX550Kzk1Vncn+dRbTn/Re\n193fNsNIcGKq6oXx0VHsmKr6nOxv7vWRSd6Q5Pu6+/vmnQqO56B0fGuSv5E/u0Txx5J828G7M+GS\nU1XXJPnVg5uH/3387O7+koM1T8/+Z4jfN8lvZf/jpe7xh/KLLssAAABwlEVeswwAAAD3RFkGAACA\nCWUZAAAAJpRlAAAAmFCWAQAAYEJZBgAAgAllGQAAACaUZQAAAJhQlgEAAGBCWQaAHVBVz66qn5/p\ne7+oqs4cfP3Fc1j/7EPr/8bFmBEATpqyDAAzO1Qs7+7rWUm+JsmTZhqxkzwryYOSvPwc1n9Nkgcf\nOhYAds4Vcw8AAORBh/73X03yw5P7bu3umy/uSHdxS3ffdC4LD2a9uaou8EgAcOE4swwAM+vum+78\nSvLu6X3dffP0bdgHb43+gar6V1X1jqq6qaq+tqquqqofrKp3VdUbquoLp9+vqr6hqv5HVd1SVa+s\nqrnOWAPAJUtZBoDd0LnrW5qflP1y/ReT/NMkz0zys0leneQTkjwnybOq6oF3HlBV357ki5N8VZKP\nTvKdSX6oqj7nQv8GAGCXeBs2AOyGOvg67He7+9sO/vd3VdU3Zf8t29+bJFX1bUm+McmnJPmpqro6\nydcl+cvd/RsHx72hqj4pyVOT/OKxBqr6K0kenuQ9Sbq7n3Mevy8AuCQpywCwmzrJKyf33ZTkVe9b\n0H26qt6Z5IMP7vqYJFcl+S9Vdfgs9ZVJXnecb15V90ny7d39mKq6f5LnZv9MNgAMQVkGgN11++R2\n3819d152deevn5vkjWd5rnP53h9QVb+T5JeT/J1jHg8AlzTXLAPAcvxekk2Sh3X3H0++3nTM59pk\n/5rnp2f/+uh/cMKzAsCsnFkGgN101DXM9/hZTQe7av/LJP+y9j/X6YYk907yyUnu6O4fPqdvXPWw\nJL+d5MHd/bNVdWX+7K3eADAEZRkALj3TXa/vvK/v4fbdHffnF3T/o6q6McnXJ/k3Sf5Xklck+efH\nmO/tB+s/r6reL8nV3f3MYxwPAJe86j7r36sAwIJV1YuSvKq7v+aYx51J8je7+6cuyGAAcAG5ZhkA\nOJtO8mVVdXNVfeLZFlfVD1bVzTmHM90AcKlyZhkAuEdV9SHZ/8ipJHlzd++dZf0Dktzn4ObbuvuW\nCzkfAFwIyjIAAABMeBs2AAAATCjLAAAAMKEsAwAAwISyDAAAABPKMgAAAEwoywAAADDx/wMjlg0C\npghXEQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fbb6ebd7fd0>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = utils.Plot(r\"Roll Rate\", \"Time [$s$]\", r\"Roll Rate [${}^0/s$]\")\n",
      "fig.plot(adis_time, roll_rate)\n",
      "fig.ylim([-400, 400])\n",
      "fig.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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azbvfjo9YV9U3VdUbq+o9VXX32bbvraqv2+lrAsBudfhTn8rGhRcmp56a/T/0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\nkiRJktTC/wMMlD+5AWX73gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fbb6e8fe6d0>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": []
    }
   ],
   "metadata": {}
  }
 ]
}