SPATZ/aquila_simulation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocess the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we need to transform our simulation data into .csv files containing the data we need for our simulations. We can do that using the `preprocess_file` function in the file `preprocess.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "\n",
    "from spatz.utils.preprocess import preprocess_file\n",
    "\n",
    "\n",
    "PATH = 'data/simulations/'\n",
    "\n",
    "# Delete the old folder of preprocessed files.\n",
    "if os.path.isdir(PATH + 'temp/'):\n",
    "    shutil.rmtree(PATH + 'temp/')\n",
    "\n",
    "# Create the folder again.\n",
    "os.mkdir(PATH + 'temp/')\n",
    "\n",
    "# Preprocess the files.\n",
    "for file in os.listdir(PATH):\n",
    "    if not os.path.isdir(PATH + file) and '.txt' in file:\n",
    "        df = preprocess_file(PATH + file)\n",
    "        df.to_csv(PATH + 'temp/' + file.replace('.txt', '.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup the simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we have to create a simulation instance and specify how we want to iterate through the simulation. We choose to sample data every 0.1 seconds.\n",
    "\n",
    "In addition, there is the option to add delays in the sampling by adding Gaussian noise to the sampling rate. In this case data might be sampled after 0.1 + noise seconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from spatz.simulation import Simulation, UniformTimeSteps\n",
    "\n",
    "# Construct a time model.\n",
    "timesteps = UniformTimeSteps(0.1, mu=0, sigma=0, delay_only=True)\n",
    "\n",
    "# Construct a simulation instance with the time model.\n",
    "simulation = Simulation(timesteps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we need to specify the sensors we are using. For this demo we are using the sensors used by Aquila's CAPUT v4. We call `simulation.add_sensor` with the sensor class as an argument to register and create a sensor for the simulation. This allows the sensor to fetch the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from spatz.sensors.imu.wsen_isds import WSEN_ISDS_ACC, WSEN_ISDS_GYRO\n",
    "from spatz.sensors.pressure.ms5611_01ba03 import MS5611_01BA03\n",
    "\n",
    "press_sensor = simulation.add_sensor(MS5611_01BA03)\n",
    "\n",
    "# Use the offset argument to change the position of the imu in relation to the rocket's center of gravity.\n",
    "accelerometer = simulation.add_sensor(WSEN_ISDS_ACC, offset=0)\n",
    "gyro = simulation.add_sensor(WSEN_ISDS_GYRO, offset=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we are not only interested in obtaining sensor measurements but also want certain ground truth values, we need to register so-called `Observer` objects. `Observer`s are simular to sensors but don't add any noise or other transformations to the data. Instead, when called they just return the correct values and write them to the logger.\n",
    "\n",
    "In this demo we will just observe the rocket's altitude in order to compare it with our model's estimation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "altitude = simulation.add_observer(['altitude'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Caput Sensor Models & Kalman Filter\n",
    "Here the sensor models for internal use of the Kalman Fiter are defined to convert sensor readings to usable datapoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class KF:\n",
    "\n",
    "    def __init__(self):\n",
    "\n",
    "        return\n",
    "    \n",
    "    def sensor_model():\n",
    "\n",
    "        return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run the simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With everything set up, we can load the dataset we want to explore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<spatz.simulation.Simulation at 0x1068756f0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simulation.load(PATH + 'temp/' + '7km.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simulation class has a function `run` which allows us to loop through every time step. The returned values are the index of the current step, the time of the current step and the change in time since the last time step.\n",
    "\n",
    "In each iteration we can call the sensors like functions to obtain the measurements at the current time steps. Please note that calling sensors multiple times at the same time steps may result in different measurements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def log_state_vector(_logger, _state):\n",
    "\n",
    "    _logger.write(\"pos_X\", _state[0], 'state')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class KF:\n",
    "\n",
    "    def __init__(self):\n",
    "\n",
    "        self.state = [ 0, #pos_X\n",
    "                        0, #pos_Y\n",
    "                        0, #pos_Z\n",
    "                        0, #vel_X\n",
    "                        0, #vel_Y\n",
    "                        0, #vel_Z\n",
    "                        0, #q1\n",
    "                        0, #q2\n",
    "                        0, #q3\n",
    "                        0  #q4\n",
    "                        ]\n",
    "        \n",
    "\n",
    "    def predict_state(self):\n",
    "        return\n",
    "    \n",
    "    def correct_state_IMU(self, z):\n",
    "        return\n",
    "    \n",
    "    def correct_state_BARO(self, z):\n",
    "        return\n",
    "    \n",
    "    def correct_state_GNSS(self, z):\n",
    "        return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  1%|          | 2.1000000000000005/345.0 [00:00<00:31, 10.80it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████▉| 344.9000000000099/345.0 [00:14<00:00, 24.57it/s] \n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "logger = simulation.get_logger()\n",
    "filter = KF()\n",
    "\n",
    "# Set verbose to False to disable the progress bar\n",
    "for step, t, dt in simulation.run(verbose=True):\n",
    "    # Get the sensor data for the current time\n",
    "    press = press_sensor()\n",
    "    acc = accelerometer()\n",
    "    rot_rate = gyro()\n",
    "\n",
    "    \n",
    "\n",
    "    # Get the correct altitude data.\n",
    "    alt = altitude()\n",
    "\n",
    "    # TODO: Add your computation here.\n",
    "\n",
    "    # preprocess your data here\n",
    "    baro_alt = (288.15/0.0065) * (1 - pow(press/1013.25, 1/5.255))\n",
    "\n",
    "    # TODO: Filter data here\n",
    "\n",
    "\n",
    "    # Save outputs from your computation here\n",
    "    logger.write(\"predicted_altitude\", baro_alt, 'general')\n",
    "    \n",
    "    logger.write(\"pos_X\", 0, 'state')\n",
    "    logger.write(\"pos_Y\", 0, 'state')\n",
    "    logger.write(\"pos_Z\", baro_alt, 'state')\n",
    "\n",
    "    logger.write(\"vel_X\", 0, 'state')\n",
    "    logger.write(\"vel_Y\", 0, 'state')\n",
    "    logger.write(\"vel_Z\", 0, 'state')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "       time    0 MS5611_01BA03/ts_effects mach/mach_no mach/speedofsound  \\\n",
       "0       0.1  NaN                      0.0     0.007016        339.067143   \n",
       "1       0.2  NaN                      0.0     0.013913        339.065795   \n",
       "2       0.3  NaN                      0.0     0.020692        339.063569   \n",
       "3       0.4  NaN                      0.0     0.027351        339.060477   \n",
       "4       0.5  NaN                      0.0     0.033927        339.056534   \n",
       "...     ...  ...                      ...          ...               ...   \n",
       "3444  344.5  NaN                      0.0     0.015286        339.111824   \n",
       "3445  344.6  NaN                      0.0     0.015286         339.11382   \n",
       "3446  344.7  NaN                      0.0     0.015285        339.115816   \n",
       "3447  344.8  NaN                      0.0     0.015285        339.117812   \n",
       "3448  344.9  NaN                      0.0     0.015284        339.119808   \n",
       "\n",
       "     MS5611_01BA03/noise MS5611_01BA03/out WSEN_ISDS_ACC/FL_x  \\\n",
       "0               1.014128        976.512606               -0.0   \n",
       "1               0.010273        975.467987               -0.0   \n",
       "2              -0.794974        974.595411               -0.0   \n",
       "3              -0.778799        974.518089                0.0   \n",
       "4               1.321533        976.499203               -0.0   \n",
       "...                  ...               ...                ...   \n",
       "3444            0.367679        977.218257               -0.0   \n",
       "3445              2.5935        979.504528               -0.0   \n",
       "3446             2.77665         979.74813               -0.0   \n",
       "3447             1.23878         978.27071               -0.0   \n",
       "3448            1.182087        978.274469               -0.0   \n",
       "\n",
       "     WSEN_ISDS_ACC/FL_y WSEN_ISDS_ACC/FL_z  ... WSEN_ISDS_GYRO/out_1  \\\n",
       "0              4.044397          33.066113  ...                  0.0   \n",
       "1               3.97431          32.663091  ...                  0.0   \n",
       "2              3.903998          32.258775  ...                  0.0   \n",
       "3               3.83641          31.870123  ...                  0.0   \n",
       "4              3.808092           31.70728  ...                  0.0   \n",
       "...                 ...                ...  ...                  ...   \n",
       "3444                0.0           9.811295  ...                  0.0   \n",
       "3445                0.0           9.811295  ...                  0.0   \n",
       "3446                0.0           9.811295  ...                  0.0   \n",
       "3447                0.0           9.811295  ...                  0.0   \n",
       "3448                0.0           9.811295  ...                  0.0   \n",
       "\n",
       "     WSEN_ISDS_GYRO/out_2 general/altitude general/predicted_altitude  \\\n",
       "0                     0.0       319.117737                 310.451543   \n",
       "1                     0.0       319.467704                 319.416512   \n",
       "2                     0.0       320.045754                 326.910955   \n",
       "3                     0.0       320.848534                 327.575329   \n",
       "4                     0.0       321.872233                  310.56652   \n",
       "...                   ...              ...                        ...   \n",
       "3444                  0.0       307.516651                  304.40001   \n",
       "3445                  0.0         306.9983                 284.817635   \n",
       "3446                  0.0       306.479948                 282.733321   \n",
       "3447                  0.0       305.961597                 295.380933   \n",
       "3448                  0.0       305.443246                 295.348739   \n",
       "\n",
       "     state/pos_X state/pos_Y state/pos_Z state/vel_X state/vel_Y state/vel_Z  \n",
       "0              0           0  310.451543           0           0           0  \n",
       "1              0           0  319.416512           0           0           0  \n",
       "2              0           0  326.910955           0           0           0  \n",
       "3              0           0  327.575329           0           0           0  \n",
       "4              0           0   310.56652           0           0           0  \n",
       "...          ...         ...         ...         ...         ...         ...  \n",
       "3444           0           0   304.40001           0           0           0  \n",
       "3445           0           0  284.817635           0           0           0  \n",
       "3446           0           0  282.733321           0           0           0  \n",
       "3447           0           0  295.380933           0           0           0  \n",
       "3448           0           0  295.348739           0           0           0  \n",
       "\n",
       "[3449 rows x 30 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = logger.get_dataframe()\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cost function implementation here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "incomplete input (103588742.py, line 4)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Input \u001b[0;32mIn [11]\u001b[0;36m\u001b[0m\n\u001b[0;31m    \u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m incomplete input\n"
     ]
    }
   ],
   "source": [
    "def weighted_least_square_cost_function(_logger):\n",
    "\n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Do your research"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['time'][1:], df['general/predicted_altitude'][1:], label='predicted_altitude')\n",
    "plt.plot(df['time'][1:], df['general/altitude'][1:], label='general/altitude')\n",
    "plt.show()\n",
    "plt.plot(df['time'][1:], df['general/altitude_error'][1:], label='altitude_error')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['time'][1:], df['mach/mach_no'][1:], label='mach number')\n",
    "plt.plot(df['time'][1:], df['MS5611_01BA03/ts_effects'][1:], label='ts effects')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['mach/mach_no'][1:], df['MS5611_01BA03/ts_effects'][1:])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['time'][1:], df['MS5611_01BA03/out'][1:])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+Mzh53IJ4uJAH+OAX3J8/XgKJIj20REQqlkI+R/bsmoRNMTYU65+/rW0t/NNYaHtrdA2PFPIT3g+f+pk7/ch1o2tfRGQYCvkc4ZAhZCBhMtSGq/jVub8C4EuTvCtgf3AsrH30wBsefDHUYEecA43T4ZX/hjcfH0XlIiJDU8gPEgmH6MGhNhxn9tjZnDT1JN6IRfjfcxa6K/ziU/DtObDx+f1vdKSefNb5d7k/f34+vPCjUdUuIjKYQn6QPw//kV1hw7hoHQC3nXob1ZFqHkq3ctTM6fyyrha6d8CdH4Wbx8DSOyDVO3KjQ10MNdi0hXD2be70o9fBphcL8LcRkUqnkB/k8IZf0BEOU5PqA6AuVsezn3q2f/nXmps4auZ0Xqnyxuwf+Qf4xiQ38LN/Nr/03mmX6T73KVD74/jPwqcfdKd/8hH4/W2F+muJSIVSyA/yg3G1AOyZcHj/vOpINasvXc2NJ9zYP++iKZM4auZ0vj92DHs9WuTHi+GWJjfwvz4BVty5/wXMOR2WfNWdfuoWt42W5aP824hIpSt6yBtjzjLGrDXGrDfGXF/s7eVretI9dfK8Iy/Za9kF77+AlRevHDDvx41jmD9zOkfNnM5phx/JU01TsXUT9274lC/vfxGn/gP83Wvvvf7P09yw/6cm6Ova/3ZEpOKZgj+0OrdxY8LAOuB0oAV4Efgra+3rQ62/aNEiu3y5v73WT/3H0TihMA989qUR10ukEzzy9iPc9PxNw65z1oyzOGr8UfzF+/6CdDrCK5s6OGl2E2aksflcTgZumwM9bUMvP/QkOO0m98yc2mYIRUYe9xeRQDLGrLDWLhpyWZFD/oPAzdbaM73XNwBYa//PUOuXQ8if8+OjGGfj3H3l/h/4tNaybvc6zn/4/GHX6du5mGTrmaOqyeBwfvj3fDt6x6jeLyL5WenM5pjQ+v1at8dWUWP6DngbTy7+Nad96MMH/D4YOeSL/YzXqcCmnNctwAm5KxhjrgSuBJg+fXqRy9m3tLGEvbtQ7ou1ljd3dHHGv/7em/Ot7BJC8S1UT72HUKwdgKrxT5Ns+zA4VQdckyXEA5kP80DmwwBUk+CE0Bruiv3zAbclIgdufwMeGFXAA0xZcxeMMuRH4vuDvK21dwB3gNuT97kc0kBoPw5VPLN2B39953C9fcPNZ57B5t2ncM7RU3i392Wue+4L3PO3zZwyzX0wuONY0o4l7Tik0paaqjCRkGFrR4JJDXF6Uxmqo2FSjkMi5dAQj9DZlyYSMqzf0UVz/dn01V6P47gjNNFwiLTj0NOXoS4eIZ2xhI0lFo3gpNPs7mgnXN1APBKie8saIuMOJW2i1MajVIVhx54EnYkkVTZJ89gGqnasIlV/CN3EIVJNY12c7mSGSLqbSKyaUDgK1mKNIdS1jdZdu2iaeAihWA3tezoxIUO8Ko7t2U2opoForAYSHWxr3UF03KE46SRhm6Suu4XOhjnESeBEa4lHgFQP3RuWUzfreKLGIe04ZEyEN3b0MjaSZEJjHVVhQ08yTWtnErpbqZ00mwkNNexs3cr4aJJEfALGZkhuXk2ydgqxxons6nFoivYRDVnSiW6cmnHUR8FE4mQI8e7uBDWpNiwhJqa3kKiZQne4no07e5jVXEdDdZTe3m52J8OMrYliQmHSfb3EquvI9HURr67DmhDRcIitHb1gMzRWVxENOYTJkGrfSio+jp5EH2PHNBLq2oZpnEZfXx8dSUNN1BBLd7IzU4vBEku0EqmfQGLrGqKT59FU5bCnL43t6+adLTtI1U2lMbWNWc317I5MAOtQ07uZRPUUomFLXVWUhBMis3sTW20ThzXXQihCV28vcZKsa0sztzlKpxMlFIrQl0xRG4/iJDoxToZtySiTqy3pdIruZIba2jp6Ug5jQr2kog0YJ0M0GsOmerCRONFYNd07W6iOQiYUIx6vZmcySlXEUOUk2JUMM6GhGtvThhMfixOK0Jd2ME4Gu3UVm/akmXv0CSQdqAqH6Emm2frO60yePodIop1wfTO2t51QdSMZa+nsTUHHuzROOZw9fRnqqiJs37mTmqoYdfEIneko8e4WqKrHqWqATAqb6iWc7iUZqiZWXUON08vbvdVMaYyzuT1BU9xQFzN0d3WQIE59XT0m1YWpqsP2ttPZ3UN1wzgSiT5q4jF2t7XSOOEQtnV0Mz6aotfGaG6sJxwOYZ0MXYkk0b7dEB9DPGTJRKrp7e2h5+1lNM8+lqRjiFXXQ08bR9RNKFiODUgjDdcMtPgn85iTHMsdf/PHIZenMg4nfuspWjsH/rb+3ZdO4f2T6gmF9h4T7033cuIvTuTiIy7m2kXXFqVuEalcfg7XvAjMMcbMBDYDFwAXFnmbeUkZ6EtCXzpDVWTgsM3vVm/lb3/uHpCtioT4+RUnsGjGuH22WR2pZv74+SzdtrQoNYuIDKeop1Baa9PAF4D/B7wB3G+tfW3kd/kraQzGhvnRMxsGzP+Xx9b2B/yiQ8ey5paz9ivgs46ffDxrdq1hT1J3mhSR0in6mLy19nfA74q9nUJJGsCG2d2T7J933YOruG+5e/z4Py5eyJnzJh1wuwsnLsSxDn9s+SNnzzq7UOWKiIxIV7zmcKxDxhiwYapj7lDNto5Ef8D/4K+OGVXAAxwx7ggAXmsr6y8yIhIwCvkcyVQCgJiJ0plwnw71w6fdU6f+4cz3cc7RU0bd9piqMRw+9nDe3P1m/oWKiOyn4IT8qvvh2W/n1UQy1Q1AJBRjT2+a3mSGh1Zu5pPHTOXzi2fnXeL85vms2rmKtJPnM2NFRPZTMEK+ZQX88rPw9Nfzeuh2f8iHY+xJpHjk1a109qU5f9G0gpR53MTj6E51c++aewvSnojIvgQj5BPt70337Bx1M8mkG/JRE6Uzkebpta0011fxwVlNeRboOmnqSQDc+uKt/OrNXxWkTRGRkQQj5GefBn95jzvdtX3UzSRTPQBEQzH29KZ4du0OTpk9fv9vKLYPY6rG8LOz3ee53vT8TexO7C5IuyIiwwlGyAO3bHmCrzQ3QcfmUbeRTLshHwnHeXNHF3sSaU45fHyhSgTg6OajueusuwA49b5TcbJPjRIRKYJAhPwzm57h/s1P80hdLRu2rdzn+sNJ5fTks46cMibf8vaycOLC/ulvLv1mwdsXEckKRMjPGTuHo5uPBuAPO0cf8sm0+6zWaDQOQCwcYsb42vwLHMKKi1ZQH6vnwXUPsqF9w77fICIyCoEI+al1U/nZ2T/jEBvmpc53Rt1OV18HANVhN9gPm1BHNFycXRQLx/jtJ35LdaSaH6z8QVG2ISISiJDPOrZmKkvppW8UZ9g41uHZVvdbQH3UHaI5fGJdQesbbFx8HOcedi5PvPsEy7YuK+q2RKQyBSrkzzjs43SHQix75a4Dfu/Xnv8a921+GoAa654yeWhTcYZqcn3myM8wpXYKlz92OSt3jH6oSURkKIEK+ROOvIhqx/LsO48d0Pvaett4aP1DAFze3sGWbvce+4eOqyl0iXuZVDuJf/vIvwFwySN7PzxcRCQfgQr5qqp6jouO5b7kVlLJnv1+32Mb3V8K9x/5Ja7Z3cHCWRMB+MBhhbkIal8OazyMYyccC8BRdx+l0ypFpGACFfIAh02YD8A9z399v9/zm/W/4fCGmRzx8JcBWDz/MN765tlMbawuSo1D+eFpP+yfPvqnR1PMJ3aJSOUIXMh/6UPuw7R/1/L0fvWIWzpbeLXtVT72zkvvzRw7k/AQj/ErprpYHS9d/F4N8386n5e2vzTCO0RE9i1wIR+pqufrsZmszXTx7MYn9rn+i9vch3Gf2OveZpjz74SQP7slGoqy9MKl1MfqAbj00Us56u6juPaZa3l+y/Ns6driS12VwlpLR18HPakeUo57q2nHOlhrB3yzstbiWIeUkxqwXsbJsLN354D1e1I9bnuZVP/PvkwfGSeDYx06vNN2E+lE//uyf1JOCsc6rN21tn+7uXVkt9mT6sGxDol0gr5MH92pbvf9Gbe2rmQXGSdDykmxO7GbvkwfuxO7SaQT/dvPlcqkyDiZ/jo3dW6iK9lFe6Kd3nRv/11U+zJ97OjZMaCu3Pqzfydwj3v1pHr698/g/d6Z7BwwP1u7tZa0k6Y33bvX+1JOimQmSV+mD8c6ONahrbeNvkwf63avI5lJ0pnsZE9yD4516E339u+H5duWsye5h+3d23mn4x02tG8gmUnSk+rhrfa36Mv00Zns7J8e/O+V/bfJ1tSV7KKls4Vt3dsG/B07k52s3bWWjr4OHOuwqXMTmzo3sSuxq/9z1pfpY+2utfRlBj43ulCK+iDvA1WoB3kn33yMhc//PQCvXPIKITN8aH/xqS+ybusKHn3zNczf/Akmzs17+4Xw8o6XufiRi/0uY1i10Vq6vbt2HozOOPQMvv2hbxMyIb6z/Dvc+dqdfpckwOcXfJ4fvvzDfa8YQOcddh5fP3n/h5lzjfQg78D15AFis0/n497T+0a622N3qpvnNz/PKSmLaX5/2QQ8wIIJC3jhwhd49M8f9buUIR3MAQ/uwfaNezaytWvrsAE/qXZ0TwE7WBlKO0QZNuG95h1MAX9ow6EFbe/SeZcWtL2soj/j1RfG8M15V/HIun/n5j/dzKzGWRwz4Zi9Vlu6dSlJJ8kZ27fAws/5UOjIaqO11EZrWX3p6iGXp5wU0VC0/yttT7qHaCjKrsQuNnRsYNHERUTDUdJOmp09O5lYO5FYOEZ7op2UkyJkQjRUNbCtaxtN1U1UhavI2AwhE6In3cOmPZuYPXY2IRNyhxvSPYyJjSESivTfmTM7dNGZ7KQ+Vk9XqovqSHX/t6dIKNK/XrbmkAn1z09lUkTDUQAyToaMzfBW+1vMapxFVbgKa+2Au4Bmj7MM9e0s+xU6FophjNnrvT2pHmqiNTz57pNc8/Q1JNIJVu909+2D5zzI+8a9b/T/WCUw+O8TJPevvZ9bXrgFgDNnnMltH7rN54qCI5ghD5gFf8XXln+Xm8bEueSRS3jhwheojQ68uOm5zc9RE4pxTG8PzD7dp0pHLxpyw9EYQzQcZUzYvVK3JlrDtPppA9Y7pOGQ/teN8cYB7eQuC+P2rhpiDcwbP29AGzXRva8bMMYQNuH+NsdUDX1Dt2w4xcKxAfOzAQ8QDoUJE+aIpiP2el/WSENv2f0x3Huz9WfXSztplm9bTkOsgTlj5wzbbrkIasDDwH+7j0z/iI+VBE8gh2sAqBnHJ46/lsva3QMlpz94+oCzbay1PLflOU4INxCtaoBDTvCrUimx7LeItE2zfPtyjp147Ii/PKT4cn/ZD+4ISH6C/cle+Bn+LlPHQidCZ7KTxfcv7j8z4N3Od9nctZmTdm+HGSdDOLBfamSQbK9xa9dWNnVuYtHEIY9XSQnl9uQV8oUV7GSLxuHUL/OT3/4dC2ZOZ1diF8fccwwTaib0jxEf174dPvhRnwuVUsr25LPj8UeNP8rPcoRBIR9SyBdSsHvyAAsuIgysevtdJldPAGBHzw5ae1sBmJlKw6En+ViglFo2UDqTnQD91yWIf9STL57gh3wkBgv/GgM89m4Lj5//eP+i/1s9H1M/GcbN8q8+KblsT74nnX0SWHSk1aUEcoM9d3xe8hfs4Zqsc74HW1+BLSuZ9PDfu6ckOg78y+Ew68MQ4LMWZG8R437se7NPAlOo+E7DNcUT/J581mXe7YfX/Bbu/Bhs/CN0t8KcM/2tS0ouG+r9Ia+evO80XFM8lRPykRh87g/u9MY/wt3nuNOzT/OvJvFF/3CN9+B29Rz9l/ttqipc5WMlwVM5IQ8weT7csHngvJpx/tQivtFwTfnJ7cnrm1VhVcaYfK6qOri5A5wMhPa+d4YEXzbUsz15hYr/NFxTPJXVk8+lgK9Y2eGabE8++1r8o5AvnsoNealYucM1ERPRLQ3KQO6Qmb5ZFZY+3VJxsoGStmmNx5eJ3GDXL93C0t6UipPtyYN6jeVC/w7Fo5CXimOM6R+HV7iUB/07FI9CXipSNlQ0XFMedPC7eBTyUpGyQzbqQZaHID8QxW8KealI2R68Ql6CLq+QN8Z82xizxhizyhjzK2NMY86yG4wx640xa40xukGMlJVsT17nZEvQ5duTfxw40lo7H1gH3ABgjJkLXADMA84C/s2YIR7NLuITHXiVSpFXyFtrH7PWpr2XLwDZp0efB9xrre2z1r4NrAeOz2dbIoWk4RqpFIUck78MeMSbngpsylnW4s3bizHmSmPMcmPM8tbW1gKWIzI8HXiVSrHP85aMMU8Ak4ZYdKO19tfeOjcCaeDnB1qAtfYO4A6ARYsW2QN9v8hoZIdrInqAuwTcPj/h1tqPjLTcGPPXwMeB02z26diwGTgkZ7Vp3jyRstB/nrx68mXj2oXXMr56vN9lBE5e3RhjzFnAV4APWWt7chb9BvhvY8x3gCnAHGBZPtsSKaRsT14PDCkfnznyM36XEEj5fle9HagCHvcuZnjBWnuVtfY1Y8z9wOu4wzift9Zm8tyWSMH0n12jK14l4PIKeWvt7BGWfQP4Rj7tixSLhmukUuiKV6lIOk9eKoVCXiqSevJSKRTyUpH6D7zqtgYScAp5qUgarpFKoZCXiqSQl0qhkJeKplMoJegU8lKRHOsA6slL8CnkpaLpsXMSdAp5qUgW9zZLOrtGgk4hLxUpey89DddI0CnkpSJle/JhPbBMAk4hL5XJuym2d2M9kcBSyEtFyvbkDQp5CTaFvFQ0hbwEnUJeKlJ/T17DNRJwCnmpSNmLodSTl6BTyEtFyp5VUxOt8bkSkeLS5X5Ska47/jqm1U/jpCkn+V2KSFEp5KUija8ez9XHXu13GSJFp+EaEZEAU8iLiASYQl5EJMAU8iIiAaaQFxEJMIW8iEiAKeRFRAJMIS8iEmAm+4SccmCMaQU2jvLt44GdBSyn2A62euHgq1n1FpfqLa4DqfdQa23zUAvKKuTzYYxZbq1d5Hcd++tgqxcOvppVb3Gp3uIqVL0arhERCTCFvIhIgAUp5O/wu4ADdLDVCwdfzaq3uFRvcRWk3sCMyYuIyN6C1JMXEZFBFPIiIgEWiJA3xpxljFlrjFlvjLne73qGYox5xxiz2hjzsjFmuTdvnDHmcWPMm97PsT7W91/GmB3GmFdz5g1Zn3F939vfq4wxx5ZJvTcbYzZ7+/hlY8zZOctu8Opda4w504d6DzHGPG2Med0Y85ox5mpvflnu4xHqLed9HDfGLDPGvOLV/I/e/JnGmKVebfcZY2Le/Crv9Xpv+YwyqfcuY8zbOft4gTd/dJ8Ja+1B/QcIA28Bs4AY8Aow1++6hqjzHWD8oHn/DFzvTV8P3OpjfacCxwKv7qs+4GzgEcAAHwCWlkm9NwNfHmLdud7nogqY6X1ewiWudzJwrDddD6zz6irLfTxCveW8jw1Q501HgaXevrsfuMCb/yPgb7zpvwV+5E1fANxXJvXeBZw/xPqj+kwEoSd/PLDeWrvBWpsE7gXO87mm/XUecLc3fTfwZ34VYq39PbBr0Ozh6jsP+Kl1vQA0GmMml6RQzzD1Duc84F5rbZ+19m1gPe7npmSstVuttS95053AG8BUynQfj1DvcMphH1trbZf3Mur9scAS4EFv/uB9nN33DwKnGWNMaaodsd7hjOozEYSQnwpsynndwsgfRr9Y4DFjzApjzJXevInW2q3e9DZgoj+lDWu4+sp5n3/B+yr7XznDX2VVrzcscAxuz63s9/GgeqGM97ExJmyMeRnYATyO+42i3VqbHqKu/pq95R1Ak5/1Wmuz+/gb3j7+V2NM1eB6Pfu1j4MQ8geLk621xwIfBT5vjDk1d6F1v4+V7fms5V6f59+Bw4AFwFbgX3ytZgjGmDrgf4BrrLV7cpeV4z4eot6y3sfW2oy1dgEwDfebxPv9rWhkg+s1xhwJ3IBb93HAOOC6fLYRhJDfDByS83qaN6+sWGs3ez93AL/C/QBuz37d8n7u8K/CIQ1XX1nuc2vtdu8/jQP8mPeGC8qiXmNMFDcwf26t/aU3u2z38VD1lvs+zrLWtgNPAx/EHdaIDFFXf83e8jFAW2krdeXUe5Y3VGattX3AneS5j4MQ8i8Cc7wj6DHcAyi/8bmmAYwxtcaY+uw0cAbwKm6dl3qrXQr82p8KhzVcfb8BLvGO9n8A6MgZcvDNoPHJT+DuY3DrvcA7m2ImMAdYVuLaDPAT4A1r7XdyFpXlPh6u3jLfx83GmEZvuho4HfdYwtPA+d5qg/dxdt+fDzzlfZvys941Ob/0De7xg9x9fOCfiVIeTS7WH9yjzutwx99u9LueIeqbhXvmwSvAa9kaccf/ngTeBJ4AxvlY4y9wv36ncMf6Lh+uPtyj+z/09vdqYFGZ1HuPV88q7z/E5Jz1b/TqXQt81Id6T8YdilkFvOz9Obtc9/EI9ZbzPp4PrPRqexW4yZs/C/cXznrgAaDKmx/3Xq/3ls8qk3qf8vbxq8DPeO8MnFF9JnRbAxGRAAvCcI2IiAxDIS8iEmAKeRGRAFPIi4gEmEJeRCTAFPIiIgGmkBcRCbD/D2iRw5WeAfLlAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['time'], df['WSEN_ISDS_ACC/out_0'], label='x')\n",
    "plt.plot(df['time'], df['WSEN_ISDS_ACC/out_1'], label='y')\n",
    "plt.plot(df['time'], df['WSEN_ISDS_ACC/out_2'], label='z')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.10.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}