mirror of
https://git.intern.spaceteamaachen.de/ALPAKA/SPATZ.git
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735 lines
76 KiB
Plaintext
735 lines
76 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Preprocess the data"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import shutil\n",
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"\n",
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"from spatz.utils.preprocess import preprocess_file\n",
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"\n",
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"\n",
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"PATH = 'data/simulations/'\n",
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"\n",
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"# Delete the old folder of preprocessed files.\n",
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"if os.path.isdir(PATH + 'temp/'):\n",
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" shutil.rmtree(PATH + 'temp/')\n",
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"\n",
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"# Create the folder again.\n",
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"os.mkdir(PATH + 'temp/')\n",
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"\n",
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"# Preprocess the files.\n",
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"for file in os.listdir(PATH):\n",
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" if not os.path.isdir(PATH + file) and '.txt' in file:\n",
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" df = preprocess_file(PATH + file)\n",
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" df.to_csv(PATH + 'temp/' + file.replace('.txt', '.csv'))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Setup the simulation"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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",
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"\n",
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from spatz.simulation import Simulation, UniformTimeSteps\n",
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"\n",
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"# Construct a time model.\n",
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"timesteps = UniformTimeSteps(0.1, mu=0, sigma=0, delay_only=True)\n",
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"\n",
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"# Construct a simulation instance with the time model.\n",
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"simulation = Simulation(timesteps)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"from spatz.sensors.imu.wsen_isds import WSEN_ISDS_ACC, WSEN_ISDS_GYRO\n",
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"from spatz.sensors.pressure.ms5611_01ba03 import MS5611_01BA03\n",
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"\n",
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"press_sensor = simulation.add_sensor(MS5611_01BA03)\n",
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"\n",
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"# Use the offset argument to change the position of the imu in relation to the rocket's center of gravity.\n",
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"accelerometer = simulation.add_sensor(WSEN_ISDS_ACC, offset=0)\n",
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"gyro = simulation.add_sensor(WSEN_ISDS_GYRO, offset=0)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Run the simulation"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"With everything set up, we can load the dataset we want to explore."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<spatz.simulation.Simulation at 0x24b58fc8f10>"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"simulation.load(PATH + 'temp/' + '7km.csv')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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",
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"\n",
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|█████████▉| 344.9000000000099/345.0 [01:51<00:00, 3.11it/s] \n"
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]
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}
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],
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"source": [
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"logger = simulation.get_logger()\n",
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"\n",
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"# Set verbose to False to disable the progress bar\n",
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"for step, t, dt in simulation.run(verbose=True):\n",
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" # Get the sensor data for the current time\n",
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" press = press_sensor()\n",
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" acc = accelerometer()\n",
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" rot_rate = gyro()\n",
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"\n",
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" # TODO: Add your computation here."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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" <td>0.0</td>\n",
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||
" <td>9.811295</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>9.811276</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>-0.040433</td>\n",
|
||
" <td>0.036262</td>\n",
|
||
" <td>-9.835422</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3448</th>\n",
|
||
" <td>344.9</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.015284</td>\n",
|
||
" <td>339.119808</td>\n",
|
||
" <td>0.051062</td>\n",
|
||
" <td>977.143444</td>\n",
|
||
" <td>-0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>9.811295</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>9.811276</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.056069</td>\n",
|
||
" <td>-0.069178</td>\n",
|
||
" <td>-9.789629</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>3449 rows × 22 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"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 -0.211031 975.287447 -0.0 \n",
|
||
"1 0.916733 976.374447 -0.0 \n",
|
||
"2 1.899961 977.290347 -0.0 \n",
|
||
"3 -2.359224 972.937664 0.0 \n",
|
||
"4 0.077 975.25467 -0.0 \n",
|
||
"... ... ... ... \n",
|
||
"3444 -2.041576 974.809001 -0.0 \n",
|
||
"3445 -2.824255 974.086774 -0.0 \n",
|
||
"3446 2.670845 979.642325 -0.0 \n",
|
||
"3447 -0.448349 976.583582 -0.0 \n",
|
||
"3448 0.051062 977.143444 -0.0 \n",
|
||
"\n",
|
||
" WSEN_ISDS_ACC/FL_y WSEN_ISDS_ACC/FL_z ... WSEN_ISDS_ACC/B_z \\\n",
|
||
"0 4.044397 33.066113 ... 5.665419 \n",
|
||
"1 3.97431 32.663091 ... 5.596367 \n",
|
||
"2 3.903998 32.258775 ... 5.527094 \n",
|
||
"3 3.83641 31.870123 ... 5.460504 \n",
|
||
"4 3.808092 31.70728 ... 5.432604 \n",
|
||
"... ... ... ... ... \n",
|
||
"3444 0.0 9.811295 ... 9.811276 \n",
|
||
"3445 0.0 9.811295 ... 9.811276 \n",
|
||
"3446 0.0 9.811295 ... 9.811276 \n",
|
||
"3447 0.0 9.811295 ... 9.811276 \n",
|
||
"3448 0.0 9.811295 ... 9.811276 \n",
|
||
"\n",
|
||
" WSEN_ISDS_ACC/acc_x_noise WSEN_ISDS_ACC/acc_y_noise \\\n",
|
||
"0 0.0 0.0 \n",
|
||
"1 0.0 0.0 \n",
|
||
"2 0.0 0.0 \n",
|
||
"3 0.0 0.0 \n",
|
||
"4 0.0 0.0 \n",
|
||
"... ... ... \n",
|
||
"3444 0.0 0.0 \n",
|
||
"3445 0.0 0.0 \n",
|
||
"3446 0.0 0.0 \n",
|
||
"3447 0.0 0.0 \n",
|
||
"3448 0.0 0.0 \n",
|
||
"\n",
|
||
" WSEN_ISDS_ACC/acc_z_noise WSEN_ISDS_ACC/out_0 WSEN_ISDS_ACC/out_1 \\\n",
|
||
"0 0.0 32.608105 -4.096233 \n",
|
||
"1 0.0 32.256931 -4.030458 \n",
|
||
"2 0.0 31.836485 -3.877788 \n",
|
||
"3 0.0 31.425801 -3.897955 \n",
|
||
"4 0.0 31.240602 -3.84319 \n",
|
||
"... ... ... ... \n",
|
||
"3444 0.0 0.086998 0.038977 \n",
|
||
"3445 0.0 0.015518 0.009905 \n",
|
||
"3446 0.0 -0.090768 0.031445 \n",
|
||
"3447 0.0 -0.040433 0.036262 \n",
|
||
"3448 0.0 0.056069 -0.069178 \n",
|
||
"\n",
|
||
" WSEN_ISDS_ACC/out_2 WSEN_ISDS_GYRO/out_0 WSEN_ISDS_GYRO/out_1 \\\n",
|
||
"0 -5.632976 0.0 0.0 \n",
|
||
"1 -5.624039 0.0 0.0 \n",
|
||
"2 -5.505637 0.0 0.0 \n",
|
||
"3 -5.482794 0.0 0.0 \n",
|
||
"4 -5.405694 0.0 0.0 \n",
|
||
"... ... ... ... \n",
|
||
"3444 -9.853244 0.0 0.0 \n",
|
||
"3445 -9.91929 0.0 0.0 \n",
|
||
"3446 -9.793827 0.0 0.0 \n",
|
||
"3447 -9.835422 0.0 0.0 \n",
|
||
"3448 -9.789629 0.0 0.0 \n",
|
||
"\n",
|
||
" WSEN_ISDS_GYRO/out_2 \n",
|
||
"0 0.0 \n",
|
||
"1 0.0 \n",
|
||
"2 0.0 \n",
|
||
"3 0.0 \n",
|
||
"4 0.0 \n",
|
||
"... ... \n",
|
||
"3444 0.0 \n",
|
||
"3445 0.0 \n",
|
||
"3446 0.0 \n",
|
||
"3447 0.0 \n",
|
||
"3448 0.0 \n",
|
||
"\n",
|
||
"[3449 rows x 22 columns]"
|
||
]
|
||
},
|
||
"execution_count": 6,
|
||
"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": 7,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"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": 9,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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WPKV0RhQtQXEiN04bDsBz7+/s9HOHlAzMLJ5AIvizc+5vXnGR18WD97NxpKcAGB60+zCv7ETlw5ooF5EuyjlHhjd4HBtjnHNKf58j6hzfmjkWgCcztnX6uUOZTWTAs8Bm59xjQR8tBhpnBM0DFgWV3+LNKpoOHPK6k5YCl5lZX2/g+DJgqfdZuZlN9851S9CxRKQL2lZyhPwDlQCcOaIPyUnRtQRFc4b3+2RcpKGhc5emCKVlcB7wFeBiM1vvva4AHgEuNbOtwCXee4AlwHYgD/gd8C0A51wZ8BNgjff6sVeGV+cZb59twOvtcG0iEqG60pTS4/XrkQBA5q4DnXreuJYqOOf+CTQ3739WE/UdcFczx5oPzG+iPBOY1FIsItI1ZHwqGXSN8YJGD155Kvct/Jifv5HDy988t9POG52384lIxDpcXceHOwKdBr2S4jgjSpegaM4Vpw8GOr9loGQgImHlvbz91NYH+svPT0shNqZrLUgQvIppRVVtp51XyUBEwkrweEFXuL+gKVecPgiABR/uaaFm+1EyEJGw4Zz71P0F53ex8YJG9106HoCHl2zutHMqGYhI2MgprKCwPHD37ZjUHgzrG91LUDRn7ICenX5OJQMRCRsZ6iI6pnGsJLewolPOp2QgImHjrS64BEVzfnjVRAAeXdo5q5gqGYhIWDhUWcva3YHplPGxxvQxXWMJiuZ8+azA6j3LNxe3ULN9KBmISFh4d2sJ9d4SDGeO6EuPxBbviY1qwVNMO+PpZ0oGIhIWPv3g+649XtDoXG+Bvr+v6/i1O5UMRMR3DQ2Ot7d03SUomvP9y08F4CevZnf4ubp2O0xEwsLGgkPsP1xDbIzRKymOSUN6+x1SWJg0tBcAFdV1HX4utQxExHcZucWYQYzB+WmpxHSxJSiaE1jVP6DgYGWHnkvJQER8l5FTTLf4WGrrnbqIjnPPJWkAPPbmlg49j5KBiPiqpKKaj/MPEee1BpQMPu1rM8YA8NeP8luo2TZKBiLiq7e3BGYRxcXGkDagJ4N7d/M5ovDSM2iKbeBxMR1DyUBEfJWRW0yvpDgOV9d1uaeahWrCoGQAVnTgDWhKBiLim7r6Bt7ZUkKPxDhq6hqYMU5dRE158MrA0hQ/ea3jppgqGYiIb9buOkBFVR2xMUZCbAxnj+7nd0hh6byxgZvPdpUe7bBzKBmIiG8yckuIizGcg/RRfemeoFufmhI8xfRQZcc8/UzJQER881ZuMSP7d6fgYKWWoGjBzdNHAPDrt/I65PhKBiLii4KDleQUVhAfG/hv6PyxGi84kXsuGQfAb9/e3iHHVzIQEV80Pus4xoyUnolMHNzL54jCW0rPxA49vpKBiPgiI6eEIb2T2HeokgvGpWgJihBc97lhQMfcb9BiMjCz+WZWbGabgsp+ZGYFZrbee10R9Nn3zSzPzHLNbHZQ+RyvLM/MHggqH21mq73yF80soT0vUETCT1VtPe/l7SclOZEDR2u5UOMFIfmfL01m239f8akB5fYSSsvgD8CcJsp/6Zyb4r2WAJjZROAG4DRvn1+bWayZxQJPApcDE4EbvboAP/OONRY4ANzelgsSkfD34Y4yKmvriTHDDN1sdhJiO6gF1WIycM69A5SFeLy5wALnXLVzbgeQB0zzXnnOue3OuRpgATDXAuntYuBlb//ngGtO7hJEJNJk5BaTGBdDdV0DZwzrQ78e6hDwW1vGDO42sw1eN1Jfr2wosCeoTr5X1lx5f+Cgc67uuPImmdmdZpZpZpklJSXNVRORMJeRU8zEIb3ILSxXF1GYaG0yeAo4BZgC7AN+0V4BnYhz7mnnXLpzLj01VX+BRCLRjv1H2Fl6lPjYGBocSgZholW3+znnihq3zex3wKve2wJgeFDVYV4ZzZSXAn3MLM5rHQTXF5EotDInMKXUgN7d4pk8TE81CwetahmY2eCgt9cCjTONFgM3mFmimY0G0oAPgTVAmjdzKIHAIPNiF5gflQFc5+0/D1jUmphEJDIsyy5k3MCe7Nh/hPPTUoiL1Qz3cNBiy8DM/gLMBFLMLB94CJhpZlMAB+wEvg7gnMsys4VANlAH3OWcq/eOczewFIgF5jvnsrxTfA9YYGY/BdYBz7bXxYlIeDl4tIY1Ow8wc1wqW4qK1UUURlpMBs65G5sobvY/bOfcw8DDTZQvAZY0Ub6dwGwjEYlyGbnF1Dc4EuICrQElg/Ch9pmIdJpl2UUMSE6k7EgNEwYlM7BXkt8hiUfJQEQ6RXVdPW/nljB9TH8+2n2AC8erVRBOlAxEpFN8sK2UIzX19EiMo7beqYsozCgZiEinWL65iO4JsdTVN9A9IZb0kXqqWThRMhCRDuecY3l2MTPSUvhgeynnnpJybBBZwoO+DRHpcJsKyiksr2LsgJ7kH6jUeEEYUjIQkQ63LLuQGINYb+nlC7VKadhRMhCRDvdmdhHpI/uxbs9BxqT2YET/7n6HJMdRMhCRDrWn7Cg5hRVMP6U/q7aXcsmpA/0OSZqgZCAiHWr55sC6lknxMdTWO2ZNGOBzRNIUJQMR6VDLsosYO6An24qP0LtbPJ8b2bflnaTTKRmISIcpO1LD6h1lXHLqQN7KLWbm+FStUhqm9K2ISIdZll1IfYNjaJ8kSo/UcLG6iMKWkoGIdJglGwsZ0a87+w5VERtjzBynZBCulAxEpEMcOlrL+9v2c/npg1iZU8xZo/rSu3u832FJM5QMRKRDLN9cRG2944yhfcgprGDWBE0pDWdKBiLSIV7ftI8hvZMoqagCYNap6iIKZ0oGItLuKqpqeWfLfi4/fTArc0sYndKDMak9/Q5LTkDJQETa3cqcYmrqG5iRlsKqbaW60SwCKBmISLt7fWMhA3slcvBoLTX1DcyZNMjvkKQFSgYi0q6OVNeRkVvMnNMG8WZ2IanJiZw5QncdhzslAxFpVytziqmua+CiCQPIyClh9mkDiYkxv8OSFigZiEi7WrR+L4N6JVFVW09lbT1zThvsd0gSAiUDEWk3B4/W8PaWYj4/ZQhLs4ro0z2es8foWceRoMVkYGbzzazYzDYFlfUzs2VmttX72dcrNzN7wszyzGyDmZ0ZtM88r/5WM5sXVP45M9vo7fOEmak9KRKhlmwspLbecfmkQSzfXMQlpw4kXgvTRYRQvqU/AHOOK3sAWOGcSwNWeO8BLgfSvNedwFMQSB7AQ8DZwDTgocYE4tW5I2i/488lIhFi0foCTkntwaHKWiqq6rhcs4giRovJwDn3DlB2XPFc4Dlv+zngmqDy513AKqCPmQ0GZgPLnHNlzrkDwDJgjvdZL+fcKuecA54POpaIRJC9Byv5cGcZc6cMZWlWIT0SYjlvbIrfYUmIWtt+G+ic2+dtFwKNi44MBfYE1cv3yk5Unt9EeZPM7E4zyzSzzJKSklaGLiId4dUNe3EOrjpjMG9mFXHRhAEkxcf6HZaEqM2ded5v9K4dYgnlXE8759Kdc+mpqamdcUoRCdGi9XuZMrwPxRXVlB6p4fJJmkUUSVqbDIq8Lh68n8VeeQEwPKjeMK/sROXDmigXkQiSV1xB1t5y5k4Zwqsb9pIUH8PM8fqFLZK0NhksBhpnBM0DFgWV3+LNKpoOHPK6k5YCl5lZX2/g+DJgqfdZuZlN92YR3RJ0LBGJEIvW7yXGYPZpg3htwz4unTiIHolxfoclJ6HFb8vM/gLMBFLMLJ/ArKBHgIVmdjuwC7jeq74EuALIA44CtwE458rM7CfAGq/ej51zjYPS3yIwY6kb8Lr3EpEIUd/g+OvafM5PSyW3sIIDR2uZO3mI32HJSWoxGTjnbmzmo1lN1HXAXc0cZz4wv4nyTGBSS3GISHh6L28/ew9V8YMrJ7JofQG9u8VzwTh1EUUa3Q0iIm2yMHMPfbrHc/7YFN7MLuKK0weTEKf/WiKNvjERabWDR2t4M6uIa6YM5e2tJRytqefz6iKKSEoGItJqi9bvpaa+gevTh7PYW6Bu2mitRRSJlAxEpNUWZu5h0tBeDOmTxNtbirl68mBitVx1RFIyEJFW2VRwiKy95VyfPpzXNwUWqJs7pdkFBCTMKRmISKu8lLmHhLgYPj95CH9fV8CY1B6cNqSX32FJKykZiMhJq6yp55V1Bcw+bRAHj9ayekcZX5g6FK1AH7mUDETkpC1aX0B5VR03nz2ChZl7iDH4UvrwlneUsKVkICInxTnH8x/sYsKgZM4c2ZeX1uZz8YQBDOyV5Hdo0gZKBiJyUtbuOkD2vnJuOWcUb+WWUFJRzfVqFUQ8JQMROSnPfbCL5KQ4rpk6hBfX7CY1OZGLJgzwOyxpIyUDEQlZcXkVr2/cx/XpwymvrGNlTjHXfW6YnnMcBfQNikjIXvhwN3UNjq9MH8lfP8qnwaEuoiihZCAiIamtb+CF1buZOT6VEf268+KaPUwf04/RKT38Dk3agZKBiITkHx/vpbiimnnnjuK9bfvZXXaUL5+lVkG0UDIQkRY55/jt29sZPzCZmeNS+f17O0npmaDnHEcRJQMRadFbuSXkFlXw9QvHsH3/EVbmFPMvZ48kKT7W79CknSgZiEiLfvP2Nob0TuLqyUP4w3s7SYiN4ebpI/0OS9qRkoGInNC63QdYvaOM22eM4Uh1HS+vzWfulCGkJif6HZq0IyUDETmhJzPy6N0tni+fNZy/fLiHytp6bjtvtN9hSTtTMhCRZm3IP8jyzcXcMWM0iXExPP/BTs49pT8TtVR11FEyEJFmPb58K326xzPv3FG8vqmQfYequP18tQqikZKBiDRpQ/5BVuQUc8eMMfRMjON372xndEoPLhqvdYiikZKBiDTpl8u20Kd7PLecM5K3tpSwseAQ37hwDDF6xnFUalMyMLOdZrbRzNabWaZX1s/MlpnZVu9nX6/czOwJM8szsw1mdmbQceZ59bea2by2XZKItNX7efvJyC3hGxeeQs/EOH61YitD+3Tj2qnD/A5NOkh7tAwucs5Ncc6le+8fAFY459KAFd57gMuBNO91J/AUBJIH8BBwNjANeKgxgYhI52tocPz0tc0M7dONW88dxQfbSvlo90G+ceEYEuLUmRCtOuKbnQs8520/B1wTVP68C1gF9DGzwcBsYJlzrsw5dwBYBszpgLhEJASvrCsge185/z5nPIlxMfzPm7kM6pWkx1pGubYmAwe8aWZrzexOr2ygc26ft10IDPS2hwJ7gvbN98qaK/8MM7vTzDLNLLOkpKSNoYvI8Spr6nl0aS6Th/Xm6jOGkJFbzEe7D/LtWWlaeiLKxbVx//OdcwVmNgBYZmY5wR8655yZuTaeI/h4TwNPA6Snp7fbcUUk4Nl/bqewvIrHb5gCwKNLtzCyf3e+lK6xgmjXppaBc67A+1kMvEKgz7/I6/7B+1nsVS8AgtuZw7yy5spFpBPlHzjKkxnbuGziQM4e05+/ry9g875y7rt0nJ5k1gW0+hs2sx5mlty4DVwGbAIWA40zguYBi7ztxcAt3qyi6cAhrztpKXCZmfX1Bo4v88pEpJM45/jhoiwAfnj1RI7W1PHzN3I5w+sukujXlm6igcArZtZ4nBecc2+Y2RpgoZndDuwCrvfqLwGuAPKAo8BtAM65MjP7CbDGq/dj51xZG+ISkZO0ZGMhK3OKefDKUxnWtzuPL99KYXkVv7ppqu4r6CJanQycc9uByU2UlwKzmih3wF3NHGs+ML+1sYhI6x2qrOVH/8hi0tBe3HruKPIPHOU3b2/jitMHcdaofn6HJ52krQPIIhLhfvZGDqWHq5k/7yziYmP4r39kA/CDKyf6HJl0Jo0KiXRhK3OKeGH1br563mhOH9ab5dlFLMsu4juXpDG0Tze/w5NOpGQg0kUVV1Tx3Zc2MGFQMv82ezzlVbX856JNjBvYk6/qeQVdjpKBSBfU0OD47ksbOFxdx69unEpSfCwPv7qZovIqfn7dZC070QXpGxfpgn7//k7e3lLCg1eeStrAZDJyi3kxcw93XnAKU4b38Ts88YGSgUgX8/62/fy/JZu5dOJAbp4+0usu+pjxA5O555I0v8MTn2g2kUgXsqfsKHf9+SNGpfTgsesn4xzcv/BjKqrqeOGO6Vp/qAtTy0CkizhcXcfXnsukwcEzt6STnBTP4yu28u7W/fzw6omMG5jsd4jiIyUDkS6gpq6Bu1/4iK3FFfzfTVMZldKDZdlFPL5iK188cxg3TRvhd4jiM3UTiUS5+gbHvS+u563cEh75wunMSEsle2859yxYx6ShvXj42kl4y8pIF6aWgUgUa2hwfP9vG3ht4z5+cMWp3DBtBPsOVfLVP6yhV7d4nrnlLI0TCKCWgUjUqqtv4D9e2cjCzHy+ffFY7rhgDPsPV3PzM6s5XF3HS984h0G9k/wOU8KEkoFIFKqqrec7C9axNKuIb188lnsvHceBIzXc/MxqCg5W8txt0zh1cC+/w5QwomQgEmUOHKnhm39ey6rtZTx09URuO280ReVVfOXZ1ewsPcr8eWdx9pj+focpYUbJQCSKZO8t5+t/yqToUDX/++UpXDN1KHnFh7ntDx9SdriG526bxjmnKBHIZykZiEQB5xx/+6iAH/x9I326JfDi16czdURf3t1awrf+/BGJcTG8cMd0JmupCWmGkoFIhCs9XM0PXtnEG1mFTBvdjydvOpP+PRJ4YsVWfrl8C+MGJPPsrekM69vd71AljCkZiESohgbH39cX8N9LNlNeWccDl0/gjhlj2HuwkpueWcWq7WVcO3UoP71mEj0S9U9dTkx/Q0Qi0Md7DvJf/8jio90HmTy8D3/62umMTunB797dzq9WbMXM+Pl1Z/Clzw3TDWUSEiUDkQjy8Z6DPL5iKytziknpmcDPrzuD684cxtKsQu54PpM9ZZVcPGEAP557mrqF5KQoGYiEubr6BlbmFPPHVbt4d+t+eneL5/5Lx/GVc0by/rZSvvDU+6zfc5DxA5P54+3TmJGW6nfIEoGUDETCkHOO3KIKlmzYx0tr89l3qIqBvRL57uzxXHXGYJZlF3HVr/5J/oFKRvTrzn9fezrXpw8jLlYrzEjrKBmIhImq2nrW7T7Iu1tLeGNTIdv3HyHG4LyxKdxzSRr1DfD6pn08tmwL9Q2Os0b15cErJ3LpxIHExmhcQNpGyUDEB845isqrydp7iE0F5azeUcraXQeormsgNsaYMCiZyyYOJDkpntyich7420acgxH9uvONC8dw1RlDtJyEtKuwSQZmNgd4HIgFnnHOPeJzSCJt4pyjorqOfQer2FV6hN1lR9lVepSdpUfI3ltO6ZGaz+wTF2N0T4gla285WXvLiY81zhzRl3svGcfM8amcPrS3ZgdJhwiLZGBmscCTwKVAPrDGzBY757L9jUy6EucczkFNfQPVdQ3U1DVQXVfv/Ww49vNITR0HjtRQ1sSrqKKKPWWVrTp/jMHwft05bUgvTh/am0lDezN1RB+6J4TFP1OJcuHyt2wakOec2w5gZguAuUC7J4NRD7zW3ocUCdnYAT0Z2CuRAclJDPB+juzXnVEpPRjerxuJcXq2gPgjXJLBUGBP0Pt84OzjK5nZncCdACNGtO4xfSP6dWd32dFW7StdS1yMkRgXQ0JcDN3iY+mZFEePxDh6JsbRt3sC/XoEXn17JNAtPpb4WCMhNoaeSYE6yUnxJHvb3RNi1b0jYS1ckkFInHNPA08DpKenu9Yc451/v6hdYxIRiQbhMim5ABge9H6YVyYiIp0gXJLBGiDNzEabWQJwA7DY55hERLqMsOgmcs7VmdndwFICU0vnO+eyfA5LRKTLCItkAOCcWwIs8TsOEZGuKFy6iURExEdKBiIiomQgIiJKBiIiAphzrbp3y3dmVgLsCrF6CrC/A8Pxi64rsui6Iku0XtdI59xnnoAUscngZJhZpnMu3e842puuK7LouiJLtF5Xc9RNJCIiSgYiItJ1ksHTfgfQQXRdkUXXFVmi9bqa1CXGDERE5MS6SstAREROQMlARESiKxmY2RwzyzWzPDN7oInPE83sRe/z1WY2yocwT1oI13WfmWWb2QYzW2FmI/2I82S1dF1B9b5oZs7MImKaXyjXZWbXe99Zlpm90NkxtkYIfw9HmFmGma3z/i5e4UecJ8PM5ptZsZltauZzM7MnvGveYGZndnaMnSbwEPDIfxFY+nobMAZIAD4GJh5X51vAb7ztG4AX/Y67na7rIqC7t/3NaLkur14y8A6wCkj3O+52+r7SgHVAX+/9AL/jbqfrehr4prc9Edjpd9whXNcFwJnApmY+vwJ4HTBgOrDa75g76hVNLYNpQJ5zbrtzrgZYAMw9rs5c4Dlv+2VgloX/g2lbvC7nXIZzrvHBzqsIPCku3IXyfQH8BPgZUNWZwbVBKNd1B/Ckc+4AgHOuuJNjbI1QrssBvbzt3sDeToyvVZxz7wBlJ6gyF3jeBawC+pjZ4M6JrnNFUzIYCuwJep/vlTVZxzlXBxwC+ndKdK0XynUFu53AbzLhrsXr8prkw51zr3VmYG0Uyvc1DhhnZu+Z2Sozm9Np0bVeKNf1I+BmM8sn8GySf+2c0DrUyf77i1hh83AbaTszuxlIBy70O5a2MrMY4DHgVp9D6QhxBLqKZhJoxb1jZqc75w76GVQ7uBH4g3PuF2Z2DvBHM5vknGvwOzBpWTS1DAqA4UHvh3llTdYxszgCTdnSTomu9UK5LszsEuAHwOedc9WdFFtbtHRdycAk4C0z20mgv3ZxBAwih/J95QOLnXO1zrkdwBYCySGchXJdtwMLAZxzHwBJBBZ7i2Qh/fuLBtGUDNYAaWY22swSCAwQLz6uzmJgnrd9HbDSeaNEYazF6zKzqcBvCSSCSOh/hhauyzl3yDmX4pwb5ZwbRWAs5PPOuUx/wg1ZKH8P/06gVYCZpRDoNtreiTG2RijXtRuYBWBmpxJIBiWdGmX7Wwzc4s0qmg4ccs7t8zuojhA13UTOuTozuxtYSmDmw3znXJaZ/RjIdM4tBp4l0HTNIzBodIN/EYcmxOt6FOgJvOSNh+92zn3et6BDEOJ1RZwQr2spcJmZZQP1wHedc2HdQg3xuu4Hfmdm9xIYTL413H/ZMrO/EEjMKd5Yx0NAPIBz7jcExj6uAPKAo8Bt/kTa8bQchYiIRFU3kYiItJKSgYiIKBmIiIiSgYiIoGQgIiIoGYiICEoGIiIC/H/tg0sZytmlhwAAAABJRU5ErkJggg==",
|
||
"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": 10,
|
||
"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": 12,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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th0dOaBqK3kIeYO6Xoj9/+/+ged/Q1yEikoBCPk5nu4YQRR4/Cyct5E0/LD+4Ef5pDGxfPrSB+wr5o2bAhT+O3n75n4Y2vohILxTycWLtmsMmQrE3wGeP/iwA144fhwV45Ex4+MzBz+ojfYQ8RHfAnvQVWPU4vPDVIdUuIpKIQj6OMYaR3nYOeixjfMVMLJnI+VPOB2B27ST2ezxQtzw6q1/73MAH7n5Zg0ROuTH6c9XjsOSuIf4GIiJdKeS7udv3Exo9XkZ7CwG4a8GRwD1tchU/G1kandX/8kvwnVFw/7z+2zjdryefSFk13Lgsevsvd8P7/zP0X0JExOFzu4Bsc2DkOpq8YygMRg+j9Hv8rL1mLWc/dza7Du/ih+Wj+WH5aG5paOSqQ0146z+ItnHilU6A2ZfDcVdCqA1e+9fo8r5CHqDiaPi71XDf8fCLq6LL/vFA/68TEemFZvLd3DO6HID2Ccd1Wf7HS//Ig2c+2Hn/7vLRzKmdxFerajjk6RbCTTvhjXvhgZPgodMGV8CYKbDwW0fuf7cM3n9R3wcrIkOS9pA3xpxrjNlgjNlkjLkt3etL1tT26IebG+Z/s8djCyYuYO01a3ly8ZOdy171R1gwuZpjaydxbO0kfjTv/9Ayuia5Ik77e7h1C/hGRO//4vPRsH/zATjcoMAXkQEzNo2BYYzxAh8AZwF1wHLgSmvtukTPnzt3rl2xYkXa6hmIK/5zNmGPj2f/76p+n9sSbOGkp07q8znn1ZzHN+Z+A58dzdodB/l45UgqRxUSiVg8cZ8ArLXsa2rnqJGFXQfY/Bo8/uneV1AwCs7/EYybBR4ftB+K3jae6FFA3gB4s7ArF4lETwaLY63FDLA1Ff/cwbxuoNo6QhR6Sbjt2oJhCnweDETbcf4RfY4VDEfwGIM3/u87HMJ0NMOIss5lne8JawfXonOe3/09FYlYjKH3bdPLerqPk0i/Y/ejr3WEw2G8Xu/gt0O8gb42wfM6QhH8XtPr+yuWmQP63WNny3v9fdaV7HvYGLPSWjs30WPp/tc/H9hkrd3sFPI0cBGQMOSzQauxjLR9HAUTp8hfxNpr1gJQ11THQ2se5lebftnlOS9teYmXtrxEqGUyrVtvAIbyF/kUx5uNXO97kfO83Xbyth+E568bwpjZZzBbxvRyO1UKh/hYIv4EyxLVnOzH6u6vH+p4A3ndQMfeFqlgkqfnSX59vX5g//rSJ9Dtfve/q3S83wzws1kP8zeXXZbysdMd8hOB7XH364AuU19jzPXA9QCTJk1Kczn9CxqLLzKwt1lbMMzWhhbOufd1Z8k85w94R2zBN3INgTH/C4CvaCsmUI/tqBhSXavtNL4S/Do4EwNDhNt8P+fLvt8OaTyRTEgU8JLYgvByYPiFfL+stQ8BD0G0XeNyOQQNeG3f85RQOMKTS7fxjy+8l/DxL39qCoX+aVx8/BeYUFbIxsYPuPJ3l3PfNeVc+LHzB1RHJBLdFH1/bL5wQGMBXT4mhiMWr6Hnx8ZIhDCmc6YS+zjeFgxT6PNgOfIR1YZDGI+XiHVqjISjLSJjEn/0dNZvraU9FMHvMXi9nm6rtzR3hCgt8HV9fUdL9DDUQHG0KGuxkTDGWV9sXGwEEw6CvxDr1GNizw8HMb5A9HUeb2eNNhLBhDuiF4/zHplz1ze3U1Lgo9DngVAb1lfobJMBfNTu/P6Abst62zaDEAxHsBYC7fuhqHxAH/2ttZhgS3T7AbTsh6Ixne+xsLWdZ3v3qC/YCuEOKBzVcx1xv+OAfq8UtCpizw21NeMtKO7RUunRxju8D4qdiZUxhMIRjNM6s6F2jK+gx+8SE2sptQXDFPq9PWrAWgi1Y30FPdYd/ztbG8F0vzih837F443+PbQdZEpR2YC2wWClO+R3ANVx96ucZVkrZMD0EfKb9zWz6EevdVl2y9nT+dvTp/YayB8vn06Rr4h39r3DhR8bWDD31xMdtLg3n7e3sT2ehB+VY2/wLi0Sp1fdOVTcmzjhP1hnmTGmyz+Yrqs3jCxM0NwIFPUYy3TrlRtjoiebOXV0+UdlDMYX6LI8VqPxeMDTswEztqTgyB3/iD4/ovf4ffv5/ZMRC2N8YwdcjzHmSMADFI0BjrzHPHG/XY/6/CMS7nNIuI7+9PKcwWyT2HN9hSUJl8ePZYyBkqO6PM8XN7EwvoI+1x/bPt3fr53PNwb8hXGTosR/7ybRSZCx92tsPWkKeEh/yC8HphljaomG+xXA59K8zqR0GOjogENtwR6B88yK7XzzuTWd91+95XRqxhZ3H6IHr8fL7IrZrN6bgi8gEREZhLQeQmmtDQE3AX8A3geesdYm7nFkiQ4Dxnq5/+VNXZbft2RjZ8BfPGcCH/3L4gEFfMzxRx3PxsaNNHc0p7ReEZG+pL0nb639HZDkBdkzw1pLhwFPxNvl4/l/vb6Ze/70AQArvnVm14/yA3TCuBOwWFbuWclp1YM8QUpEZIh0xmuckA0RMQast7Mft3JrI9//3fsA/OpvTx1SwAOccNQJFHgLWLp7acrqFRHpj+tH12STjlD0ejUBT4CDrdFjFX/w0noAfv+1TzKjcuSQxw54A3x8zMdZs29N/08WEUmR3JnJP/sF+OnADk/sTUewBYhelOxQa5D65naWbdnPWTPHJRXwMSeOO5H36t9TX15EMiY3Qn7HSnjvV7D1rxDq6P/5vWgPHgbA78zkf7tmFwC3nH10SspcMHEBIRvikXcfScl4IiL9yY2Qj/+mpsNDP8OuoyMa8gFvgKa2EEvW72XK2GKOrixNtkIA5lTMYdroaTy89mFuXHJjSsYUEelLboR89Xz4rHNlyMN7hzxMu9Ou8XkKaG4PsXRzA5+aPrTLECTi9/p54rwnAHi97nXqmupSNraISCI5EfLWWo5ddjvH1k7CNu0Z8jgdsXaNKWDT3mbaQxHmVJelqMqoIn8R95x+DwCX/c9lhAf7fbEiIoOQEyH//MbnO2+/veN/hzxObCYf8B65Dt2sCcnvcO3urMlnsbh2Mc3BZs587sz+XyAiMkQ5EfILJi7gm3P/HoAVe4Z+Pfr2kBPyzsWoCnweagdxVutgfG/B9wCob63nvfqsPglYRIaxnAj5yuJK/mbW1UwzhbzVMvQ+d2wm73dCfkZlaZcLGqWS3+vnzSvfpKygjJtevikt6xARyYmQjzmptJaV3ggHD2wd9Gt3H97NV1ffDUCpN9qiScWx8X0pCZRw2fTLqG+t55znzknrukQkP+VUyJ8+ZTFhY/jz6v8c9GvP/eW5nbcL7GgAJpUX9fb0lPniMV8EYOfhnSzZuiTt6xOR/JJTIX/irCvxWsurO/4yqNfVNdURtmFKfSP447Yd7Dkc/TKFiWV9f3dnKpQGSnnxkhcBuHfVvWlfn4jkl5wKeZ+vgDMDR/FmqJHm1sYBv+6Btx8A4KlZNzE+HGZqdSVAyg+f7M3kkZO5ff7tbDm0hWMfO5amjqaMrFdEcl9OhTzAxVMuoN0Ynlt+z4Ceb63l1bpXAaj59VcBOP/k2az69lmDul58si6eenHn7QU/X6Dj50UkJXIu5Bccfz2z24P86KNfDygoNzRuoKmjiX8KHgl0z6iJjCnu/p3t6VXkL2LN1Ws4t+ZcLJY5P5vDT97+SUZrEJHck3OXGjYFJVxeXMuaUB0vbHiGSz5+ZZ/Pf73udQA+uWsDTJwLFz/Q63dRppsxhn/55L+w5dAW1u9fzwPvPMAD70RbSWdOOpMvHvNFqkurGV0Y3TFsre3y2mS/JLo3ERvBYLqM3R5up8A7uGvrhyNhLBafp+fbriXYgt/jx+98mXZ7uJ2AJ9Dn7xWrC478/q2hVkb4RvR4fsRGAPAYT6/jhSNhGtsbqW+tB8BrvFQWV/LhgQ+ZPno6HzR+QNiGeW37a3xi4icAmFI2hfrWesaOGEtpoJSmjiaC4SDNwWYMhlV7VzFjzAzKCsoAaA21ErZh9rftZ2b5TA60H6At1EZZQRnF/mIa2xpZvXc1sytmM754PEt3LWVm+Uwa2xvxe/wEvAHaQ+20hdvwGA97W/YysWQiTR1N7Dq8iymjpjChZAJv7nqT6aOns65hHZNLJ2OxHGw/SGuolWAkyNGjj8bv9bNs9zJOPOpEWkOt+Dw+xhSOoS3Uxlu732Jm+Uw2H9jMcRXHsaN5B2UFZWxo3MD+tv2MHTGW6tJqCr2FFHgLqCiqoLGtkWJ/MQfbD3I4eJiwDVPsL6asoAyLZeuhrYwdMZZgJEixv5j9rfupGVVDe7id5o5miv3FVBRV0BHu4MMDH1LiL6El1MKWQ1sYXTCaSSMnsa9lHwW+Aupb6/EZH+NLxnOg7QDrGtZx8oSTWbVnFaMLRxPwBFixZwUlgRJmj53NqIJRhG2Y1XtXM7FkIhUjKqhvredw8DCFvkKCkSBe42X57uXMq5xH7ahadjbvZPLIyXSEO9jTsoeqkipaw600dzQzrngcI3wjWLZ7GeWF5VhraQm1sLN5JyePP5lgJIjf66fIV8Th4GF2NO+gobWBkkAJ44vHs2H/BuZVzqM11Mqelj00tjVywZQL8Hb/wu8UMPFB4ba5c+faFSuGfjJTTGTrmyxc8iX2e72suGpFn2F01e+uInRoB0+vXwVffw9GVSW9/lQ4HDzM6b84nbZwm9ulZCWfx0coEhry6ycUT+DFS17E7/Uz94m5tIfbU1idDNUNx93Ag+886HYZrlhYvZD7Ft03pNcaY1Zaa+cmeizn2jUAnsmn8FmiV46c+0TC3xuAxrZG1uxbw6fag3DUrKwJeIBifzHLr1rOyqtW8g8n/QMjfOk/0mc4SXafxc7DO9nWtI0dzTuSCvjpo6cnVYd0NZwCfmH1wpSOd+Oc9FyZNufaNTE3TDqfn+x4FoCPDn5E7ajaHs95Y+cbWCyf3PUhnHBdpksckIA3wJUzruTKGX23nTrCHXiMp7MVEgvB+I9/sTZFxEbwmMT/v7cEWyjyFw2p9dPXuH1JV5upNy9ve5mbX7mZjnBH5zd1Pf/p55k2elrGapCuntnwDHe9dRcA3z31u3xm2mdcrih35ORMHsBz0vUs2baD0nCEG5fcSGuotcdzXtn2ChX+Uma1tcC0s1yoMnUC3kCXXrfX4+3R34sFaV9BXOQv6vLcwRhKwA91XcmIbadQJMTy3csZUziGqWVTM1qDdOX3+Dtvx96Dkho5G/KUVnLUlDO5o2E/25u2M//J+V2CviPcwV93/JXTPCPxBEqg+mQXi5VMigVKMBJk2e5lzKucl/H/aKSr2A53iH7HsqRO7oY8wKWPcH6kgPmR6Jtm/pPzO4+mWbprKS2hFhbV74Da08CnN1a+iM3kNx/czJ6WPcyvnO9yRRI/k4+/1LckL2d78gAUlMI5/8wjv/4KXz92IX9u/rDH1+6dVL8NTv66SwWKG2KBsn7/egBmlc9ysxyhW8hrJp9SuT2TB5h9BVSfxL+vfYWzKk/p8tAniycRAJiqL+7IJ7FAibXvCp1LS4t7NJNPn9yeyQN4PLD43+A/P8U9y39Dy99/yGs7/8KafWu4af3/wtijoWyS21VKBsXaNS2x7w+ICxhxR3ywx/fnJXm5P5MHGH8cnHIThNooevYazqs9j1vnfJXirW8O+6NqZPA6Qz6kkM8WatekT36EPMDZ34PyabDpz/CdUbD+txBuh6lnuF2ZZFj3do1mju5TuyZ98ifkjYHrXz1y/3nn5KfJC1wpR9wTm8l3hrxm8q6L/49Wfx+plT8hD1BQAt+uh5LKI8t8g7vIlgx/6slnH83k0yf3d7x25/XDLRuit7Po4mySObFA6ezJq13juvg+vP7TTa38msl3p7Mc81L3do3P5N9cJ9t0OeNVM/mUyu+Ql7wU367xe/y6pEEWiL/uko6uSS2FvOSdWDvAYtUayBLxfw/p+OKMfKaQl7zjNUdCRP347KD/bNNHIS95xxjT2R5QayA76D/b9FHIS16KzRw1g8wO2vmdPgp5yUuxmbxmkNlBO7/TRyEveUkzeckXSYW8MebfjDHrjTFrjDG/MsaUxT12uzFmkzFmgzHmnKQrFUmhzpm8Ql5yXLIz+T8Bx1hrZwMfALcDGGNmAlcAs4BzgQeMMTouSrJG50xe7RrJcUmFvLX2j9bakHP3LaDKuX0R8LS1tt1a+xGwCdB3rEnW0Exe8kUqe/LXAi85tycC2+Meq3OW9WCMud4Ys8IYs2Lfvn0pLEekd+rJS77o97glY8yfgcoED91hrf2N85w7gBDw5GALsNY+BDwEMHfuXF0xTDJCM3nJF/2GvLW2zy9ANcZ8AbgAOMPazss67gCq455W5SwTyQqx47IV8tnjicVPUOIvcbuMnJPUGQjGmHOBbwKnWWtb4h56AXjKGHMPMAGYBixLZl0iqRTb4aodr9njuIrj3C4hJyV7mtn9QAHwJ+dkhrestTdYa98zxjwDrCPaxrnRWhtOcl0iKaPLGki+SCrkrbVT+3js+8D3kxlfJF062zWayUuO0xmvkpc62zXqyUuOU8hLXtKOV8kXCnnJSzqEUvKFQl7yko6ukXyhkJe85HHe+prJS65TyEteskTP21PIS65TyEteU8hLrlPIS17qnMmrJy85TiEveSliI4Bm8pL7FPKS1wJeXdZAcptCXvKTc71Ur76wTHKcQl7yUqwnbzAuVyKSXgp5yUth56KoHo/+CUhu0ztc8lJsx6vaNZLrFPKSl2IzeYW85DqFvOSlcEQhL/lBIS95qcBbAOg4ecl9yX79n8iwdMfJd1A7qpaTxp/kdikiaaWQl7w0dsRY/u6Ev3O7DJG0U7tGRCSHKeRFRHKYQl5EJIcp5EVEcphCXkQkhynkRURymEJeRCSHKeRFRHKYsda6XUMnY8w+YOsQXz4WqE9hOek23OqF4Vez6k0v1Zteg6l3srW2ItEDWRXyyTDGrLDWznW7joEabvXC8KtZ9aaX6k2vVNWrdo2ISA5TyIuI5LBcCvmH3C5gkIZbvTD8ala96aV60ysl9eZMT15ERHrKpZm8iIh0o5AXEclhORHyxphzjTEbjDGbjDG3uV1PIsaYLcaYtcaYt40xK5xlY4wxfzLGbHR+jnaxvv82xuw1xrwbtyxhfSbqPmd7rzHGnJAl9X7HGLPD2cZvG2MWxz12u1PvBmPMOS7UW22MecUYs84Y854x5mZneVZu4z7qzeZtXGiMWWaMecep+bvO8lpjzFKntl8YYwLO8gLn/ibn8ZosqfdRY8xHcdt4jrN8aO8Ja+2w/gN4gQ+BKUAAeAeY6XZdCercAozttuyHwG3O7duAH7hY36eAE4B3+6sPWAy8BBjgZGBpltT7HeCWBM+d6bwvCoBa5/3izXC944ETnNulwAdOXVm5jfuoN5u3sQFKnNt+YKmz7Z4BrnCWPwh8xbn9t8CDzu0rgF9kSb2PApcmeP6Q3hO5MJOfD2yy1m621nYATwMXuVzTQF0EPObcfgy42K1CrLWvA/u7Le6tvouAx23UW0CZMWZ8Rgp19FJvby4CnrbWtltrPwI2EX3fZIy1dpe1dpVzuwl4H5hIlm7jPurtTTZsY2utbXbu+p0/FlgEPOcs776NY9v+OeAMY4zJTLV91tubIb0nciHkJwLb4+7X0feb0S0W+KMxZqUx5npn2Thr7S7n9m5gnDul9aq3+rJ5m9/kfJT977j2V1bV67QFjic6c8v6bdytXsjibWyM8Rpj3gb2An8i+onigLU2lKCuzpqdxw8C5W7Wa62NbePvO9v4340xBd3rdQxoG+dCyA8Xn7DWngCcB9xojPlU/IM2+nksa49nzfb6HD8BPgbMAXYBP3K1mgSMMSXAL4GvWWsPxT+Wjds4Qb1ZvY2ttWFr7RygiugniRnuVtS37vUaY44Bbida9zxgDHBrMuvIhZDfAVTH3a9ylmUVa+0O5+de4FdE34B7Yh+3nJ973aswod7qy8ptbq3d4/yjiQD/xZF2QVbUa4zxEw3MJ621zzuLs3YbJ6o327dxjLX2APAKcArRtoYvQV2dNTuPjwIaMltpVFy95zqtMmutbQd+SpLbOBdCfjkwzdmDHiC6A+UFl2vqwhhTbIwpjd0GzgbeJVrnNc7TrgF+406FveqtvheAq529/ScDB+NaDq7p1p+8hOg2hmi9VzhHU9QC04BlGa7NAI8A71tr74l7KCu3cW/1Zvk2rjDGlDm3RwBnEd2X8ApwqfO07ts4tu0vBV52Pk25We/6uP/0DdH9B/HbePDviUzuTU7XH6J7nT8g2n+7w+16EtQ3heiRB+8A78VqJNr/WwJsBP4MjHGxxp8T/fgdJNrr+1Jv9RHdu/8fzvZeC8zNknp/5tSzxvkHMT7u+Xc49W4AznOh3k8QbcWsAd52/izO1m3cR73ZvI1nA6ud2t4F7nSWTyH6H84m4FmgwFle6Nzf5Dw+JUvqfdnZxu8CT3DkCJwhvSd0WQMRkRyWC+0aERHphUJeRCSHKeRFRHKYQl5EJIcp5EVEcphCXkQkhynkRURy2P8HuFzgwR95pbAAAAAASUVORK5CYII=",
|
||
"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": "3.10.4"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|