visualize_dcmimu.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lol, err: list index out of range\n",
      "lol, err: list index out of range\n",
      "lol, err: list index out of range\n",
      "lol, err: list index out of range\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "\n",
    "def match_to_tuple(string):\n",
    "    parts = string.split(',')\n",
    "    return (int(parts[0]), int(parts[1]), int(parts[2]))\n",
    "\n",
    "log = []\n",
    "freq = None\n",
    "\n",
    "with open('log_0.txt', 'r', encoding='latin-1') as f:\n",
    "    for line in f:\n",
    "        parts = line.split(';')\n",
    "        try:\n",
    "            # ax,ay,az,gx,gy,gz,dt_s,y,p,r\n",
    "            ax = parts[0]\n",
    "            ay = parts[1]\n",
    "            az = parts[2]\n",
    "            gx = parts[3]\n",
    "            gy = parts[4]\n",
    "            gz = parts[5]\n",
    "            dt = parts[6]\n",
    "            y = parts[7]\n",
    "            p = parts[8]\n",
    "            r = parts[9]\n",
    "            log.append((ax, ay, az, gx, gy, gz, y, p, r, dt))\n",
    "        except Exception as e:\n",
    "            print('lol, err: {}'.format(e))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'log' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-e6e18fe99c7b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mpylab\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'figure.figsize'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0myaws\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mpitches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mrolls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'log' is not defined"
     ]
    }
   ],
   "source": [
    "import matplotlib.pylab as pylab\n",
    "pylab.rcParams['figure.figsize'] = 12, 12\n",
    "\n",
    "yaws = list(map(lambda x: float(x[6]), log))\n",
    "pitches = list(map(lambda x: float(x[7]), log))\n",
    "rolls = list(map(lambda x: float(x[8]), log))\n",
    "ts = list(map(lambda x: float(x[9]), log))\n",
    "\n",
    "f, axarr = plt.subplots(3, sharex=True)\n",
    "\n",
    "axarr[0].plot(np.degrees(yaws))\n",
    "axarr[1].plot(np.degrees(pitches))\n",
    "axarr[2].plot(np.degrees(rolls))\n",
    "\n",
    "axarr[0].set_title('yaw')\n",
    "axarr[1].set_title('pitch')\n",
    "axarr[2].set_title('roll')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "281.82865944356473\n"
     ]
    }
   ],
   "source": [
    "print(1/(np.sum(ts)/len(ts)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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