SPATZ/demo.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": 1,
   "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": 2,
   "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": 3,
   "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",
    "from spatz.sensors.gps.erinome1 import Erinome_I\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)\n",
    "\n",
    "# Add a GPS module to the simulation which returns the following data: [latitude, longitude, altitude (km)]\n",
    "gps_module = simulation.add_sensor(Erinome_I)"
   ]
  },
  {
   "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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "altitude = simulation.add_observer(['altitude'])"
   ]
  },
  {
   "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<spatz.simulation.Simulation at 0x207b1f987d0>"
      ]
     },
     "execution_count": 5,
     "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": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  1%|          | 3.3000000000000016/345.0 [00:00<00:31, 10.80it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████▉| 344.9000000000099/345.0 [00:47<00:00,  7.33it/s] \n"
     ]
    }
   ],
   "source": [
    "logger = simulation.get_logger()\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",
    "    gps = gps_module()\n",
    "\n",
    "    # Get the correct altitude data.\n",
    "    alt = altitude()\n",
    "\n",
    "    # TODO: Add your computation here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>MS5611_01BA03/ts_effects</th>\n",
       "      <th>mach/mach_no</th>\n",
       "      <th>mach/speedofsound</th>\n",
       "      <th>MS5611_01BA03/noise</th>\n",
       "      <th>MS5611_01BA03/out</th>\n",
       "      <th>WSEN_ISDS_ACC/FL_x</th>\n",
       "      <th>WSEN_ISDS_ACC/FL_y</th>\n",
       "      <th>WSEN_ISDS_ACC/FL_z</th>\n",
       "      <th>WSEN_ISDS_ACC/B_x</th>\n",
       "      <th>...</th>\n",
       "      <th>WSEN_ISDS_ACC/out_0</th>\n",
       "      <th>WSEN_ISDS_ACC/out_1</th>\n",
       "      <th>WSEN_ISDS_ACC/out_2</th>\n",
       "      <th>WSEN_ISDS_GYRO/out_0</th>\n",
       "      <th>WSEN_ISDS_GYRO/out_1</th>\n",
       "      <th>WSEN_ISDS_GYRO/out_2</th>\n",
       "      <th>Erinome-I/out_0</th>\n",
       "      <th>Erinome-I/out_1</th>\n",
       "      <th>Erinome-I/out_2</th>\n",
       "      <th>general/altitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007016</td>\n",
       "      <td>339.067143</td>\n",
       "      <td>-0.018387</td>\n",
       "      <td>975.480091</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>4.044397</td>\n",
       "      <td>23.256113</td>\n",
       "      <td>-32.577163</td>\n",
       "      <td>...</td>\n",
       "      <td>32.574601</td>\n",
       "      <td>-4.081343</td>\n",
       "      <td>-5.725432</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.885478</td>\n",
       "      <td>21.079909</td>\n",
       "      <td>18284.099507</td>\n",
       "      <td>319.117737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.013913</td>\n",
       "      <td>339.065795</td>\n",
       "      <td>0.512119</td>\n",
       "      <td>975.969833</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>3.97431</td>\n",
       "      <td>22.853091</td>\n",
       "      <td>-32.180101</td>\n",
       "      <td>...</td>\n",
       "      <td>32.2092</td>\n",
       "      <td>-3.986376</td>\n",
       "      <td>-5.665523</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.885479</td>\n",
       "      <td>21.079909</td>\n",
       "      <td>18304.151142</td>\n",
       "      <td>319.467704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.020692</td>\n",
       "      <td>339.063569</td>\n",
       "      <td>-2.260561</td>\n",
       "      <td>973.129825</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>3.903998</td>\n",
       "      <td>22.448775</td>\n",
       "      <td>-31.781763</td>\n",
       "      <td>...</td>\n",
       "      <td>31.811645</td>\n",
       "      <td>-3.93001</td>\n",
       "      <td>-5.484266</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.88548</td>\n",
       "      <td>21.079909</td>\n",
       "      <td>18337.270949</td>\n",
       "      <td>320.045754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.027351</td>\n",
       "      <td>339.060477</td>\n",
       "      <td>0.722061</td>\n",
       "      <td>976.018948</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.83641</td>\n",
       "      <td>22.060123</td>\n",
       "      <td>-31.398858</td>\n",
       "      <td>...</td>\n",
       "      <td>31.417342</td>\n",
       "      <td>-3.875468</td>\n",
       "      <td>-5.482977</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.885481</td>\n",
       "      <td>21.079909</td>\n",
       "      <td>18383.266845</td>\n",
       "      <td>320.848534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.033927</td>\n",
       "      <td>339.056534</td>\n",
       "      <td>0.400507</td>\n",
       "      <td>975.578178</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>3.808092</td>\n",
       "      <td>21.89728</td>\n",
       "      <td>-31.238423</td>\n",
       "      <td>...</td>\n",
       "      <td>31.165447</td>\n",
       "      <td>-3.815917</td>\n",
       "      <td>-5.545503</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.885482</td>\n",
       "      <td>21.079909</td>\n",
       "      <td>18441.920488</td>\n",
       "      <td>321.872233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3444</th>\n",
       "      <td>344.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015286</td>\n",
       "      <td>339.111824</td>\n",
       "      <td>-0.531794</td>\n",
       "      <td>976.318784</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001295</td>\n",
       "      <td>-0.012063</td>\n",
       "      <td>...</td>\n",
       "      <td>0.048509</td>\n",
       "      <td>0.059759</td>\n",
       "      <td>-9.807724</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.950884</td>\n",
       "      <td>21.08013</td>\n",
       "      <td>17619.406241</td>\n",
       "      <td>307.516651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3445</th>\n",
       "      <td>344.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015286</td>\n",
       "      <td>339.11382</td>\n",
       "      <td>1.496568</td>\n",
       "      <td>978.407596</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001295</td>\n",
       "      <td>-0.012063</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0775</td>\n",
       "      <td>0.014265</td>\n",
       "      <td>-9.828498</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.950884</td>\n",
       "      <td>21.08013</td>\n",
       "      <td>17589.706895</td>\n",
       "      <td>306.9983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3446</th>\n",
       "      <td>344.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015285</td>\n",
       "      <td>339.115816</td>\n",
       "      <td>-0.387131</td>\n",
       "      <td>976.584349</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001295</td>\n",
       "      <td>-0.012063</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.055745</td>\n",
       "      <td>-0.00512</td>\n",
       "      <td>-9.8085</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.950884</td>\n",
       "      <td>21.08013</td>\n",
       "      <td>17560.007549</td>\n",
       "      <td>306.479948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3447</th>\n",
       "      <td>344.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015285</td>\n",
       "      <td>339.117812</td>\n",
       "      <td>0.392596</td>\n",
       "      <td>977.424527</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001295</td>\n",
       "      <td>-0.012063</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002507</td>\n",
       "      <td>-0.021518</td>\n",
       "      <td>-9.813728</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.950884</td>\n",
       "      <td>21.08013</td>\n",
       "      <td>17530.308203</td>\n",
       "      <td>305.961597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3448</th>\n",
       "      <td>344.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015284</td>\n",
       "      <td>339.119808</td>\n",
       "      <td>-0.013876</td>\n",
       "      <td>977.078506</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001295</td>\n",
       "      <td>-0.012063</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.047244</td>\n",
       "      <td>0.022966</td>\n",
       "      <td>-9.780433</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.950884</td>\n",
       "      <td>21.08013</td>\n",
       "      <td>17500.608856</td>\n",
       "      <td>305.443246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3449 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       time MS5611_01BA03/ts_effects mach/mach_no mach/speedofsound  \\\n",
       "0       0.1                      0.0     0.007016        339.067143   \n",
       "1       0.2                      0.0     0.013913        339.065795   \n",
       "2       0.3                      0.0     0.020692        339.063569   \n",
       "3       0.4                      0.0     0.027351        339.060477   \n",
       "4       0.5                      0.0     0.033927        339.056534   \n",
       "...     ...                      ...          ...               ...   \n",
       "3444  344.5                      0.0     0.015286        339.111824   \n",
       "3445  344.6                      0.0     0.015286         339.11382   \n",
       "3446  344.7                      0.0     0.015285        339.115816   \n",
       "3447  344.8                      0.0     0.015285        339.117812   \n",
       "3448  344.9                      0.0     0.015284        339.119808   \n",
       "\n",
       "     MS5611_01BA03/noise MS5611_01BA03/out WSEN_ISDS_ACC/FL_x  \\\n",
       "0              -0.018387        975.480091               -0.0   \n",
       "1               0.512119        975.969833               -0.0   \n",
       "2              -2.260561        973.129825               -0.0   \n",
       "3               0.722061        976.018948                0.0   \n",
       "4               0.400507        975.578178               -0.0   \n",
       "...                  ...               ...                ...   \n",
       "3444           -0.531794        976.318784               -0.0   \n",
       "3445            1.496568        978.407596               -0.0   \n",
       "3446           -0.387131        976.584349               -0.0   \n",
       "3447            0.392596        977.424527               -0.0   \n",
       "3448           -0.013876        977.078506               -0.0   \n",
       "\n",
       "     WSEN_ISDS_ACC/FL_y WSEN_ISDS_ACC/FL_z WSEN_ISDS_ACC/B_x  ...  \\\n",
       "0              4.044397          23.256113        -32.577163  ...   \n",
       "1               3.97431          22.853091        -32.180101  ...   \n",
       "2              3.903998          22.448775        -31.781763  ...   \n",
       "3               3.83641          22.060123        -31.398858  ...   \n",
       "4              3.808092           21.89728        -31.238423  ...   \n",
       "...                 ...                ...               ...  ...   \n",
       "3444                0.0           0.001295         -0.012063  ...   \n",
       "3445                0.0           0.001295         -0.012063  ...   \n",
       "3446                0.0           0.001295         -0.012063  ...   \n",
       "3447                0.0           0.001295         -0.012063  ...   \n",
       "3448                0.0           0.001295         -0.012063  ...   \n",
       "\n",
       "     WSEN_ISDS_ACC/out_0 WSEN_ISDS_ACC/out_1 WSEN_ISDS_ACC/out_2  \\\n",
       "0              32.574601           -4.081343           -5.725432   \n",
       "1                32.2092           -3.986376           -5.665523   \n",
       "2              31.811645            -3.93001           -5.484266   \n",
       "3              31.417342           -3.875468           -5.482977   \n",
       "4              31.165447           -3.815917           -5.545503   \n",
       "...                  ...                 ...                 ...   \n",
       "3444            0.048509            0.059759           -9.807724   \n",
       "3445              0.0775            0.014265           -9.828498   \n",
       "3446           -0.055745            -0.00512             -9.8085   \n",
       "3447           -0.002507           -0.021518           -9.813728   \n",
       "3448           -0.047244            0.022966           -9.780433   \n",
       "\n",
       "     WSEN_ISDS_GYRO/out_0 WSEN_ISDS_GYRO/out_1 WSEN_ISDS_GYRO/out_2  \\\n",
       "0                     0.0                  0.0                  0.0   \n",
       "1                     0.0                  0.0                  0.0   \n",
       "2                     0.0                  0.0                  0.0   \n",
       "3                     0.0                  0.0                  0.0   \n",
       "4                     0.0                  0.0                  0.0   \n",
       "...                   ...                  ...                  ...   \n",
       "3444                  0.0                  0.0                  0.0   \n",
       "3445                  0.0                  0.0                  0.0   \n",
       "3446                  0.0                  0.0                  0.0   \n",
       "3447                  0.0                  0.0                  0.0   \n",
       "3448                  0.0                  0.0                  0.0   \n",
       "\n",
       "     Erinome-I/out_0 Erinome-I/out_1 Erinome-I/out_2 general/altitude  \n",
       "0          67.885478       21.079909    18284.099507       319.117737  \n",
       "1          67.885479       21.079909    18304.151142       319.467704  \n",
       "2           67.88548       21.079909    18337.270949       320.045754  \n",
       "3          67.885481       21.079909    18383.266845       320.848534  \n",
       "4          67.885482       21.079909    18441.920488       321.872233  \n",
       "...              ...             ...             ...              ...  \n",
       "3444       67.950884        21.08013    17619.406241       307.516651  \n",
       "3445       67.950884        21.08013    17589.706895         306.9983  \n",
       "3446       67.950884        21.08013    17560.007549       306.479948  \n",
       "3447       67.950884        21.08013    17530.308203       305.961597  \n",
       "3448       67.950884        21.08013    17500.608856       305.443246  \n",
       "\n",
       "[3449 rows x 25 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = logger.get_dataframe()\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Do your research"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['time'][1:], df['MS5611_01BA03/out'][1:])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": []
  }
 ],
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