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",
    "\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": 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 0x28edd54c340>"
      ]
     },
     "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": [
      "100%|█████████▉| 344.9000000000099/345.0 [00:33<00:00, 10.36it/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",
    "\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": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>0</th>\n",
       "      <th>MS5611_01BA03/ts_effects</th>\n",
       "      <th>mach/mach_no</th>\n",
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       "      <th>WSEN_ISDS_ACC/FL_y</th>\n",
       "      <th>WSEN_ISDS_ACC/FL_z</th>\n",
       "      <th>...</th>\n",
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       "      <th>WSEN_ISDS_ACC/acc_y_noise</th>\n",
       "      <th>WSEN_ISDS_ACC/acc_z_noise</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",
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       "      <td>976.340813</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>4.044397</td>\n",
       "      <td>33.066113</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>32.623429</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.319118</td>\n",
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       "      <td>0.013913</td>\n",
       "      <td>339.065795</td>\n",
       "      <td>-0.998771</td>\n",
       "      <td>974.458942</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>3.97431</td>\n",
       "      <td>32.663091</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.142847</td>\n",
       "      <td>-3.994765</td>\n",
       "      <td>-5.614795</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.319468</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.020692</td>\n",
       "      <td>339.063569</td>\n",
       "      <td>-0.363539</td>\n",
       "      <td>975.026847</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>3.903998</td>\n",
       "      <td>32.258775</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.770757</td>\n",
       "      <td>-3.886765</td>\n",
       "      <td>-5.512629</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.320046</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.027351</td>\n",
       "      <td>339.060477</td>\n",
       "      <td>0.700004</td>\n",
       "      <td>975.996892</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.83641</td>\n",
       "      <td>31.870123</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.523523</td>\n",
       "      <td>-3.759221</td>\n",
       "      <td>-5.532761</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.320849</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.5</td>\n",
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       "      <td>0.033927</td>\n",
       "      <td>339.056534</td>\n",
       "      <td>0.112829</td>\n",
       "      <td>975.290499</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>3.808092</td>\n",
       "      <td>31.70728</td>\n",
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       "      <td>31.345873</td>\n",
       "      <td>-3.834753</td>\n",
       "      <td>-5.383155</td>\n",
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       "    <tr>\n",
       "      <th>3447</th>\n",
       "      <td>344.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015285</td>\n",
       "      <td>339.117812</td>\n",
       "      <td>-2.728543</td>\n",
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       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.811295</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>-0.032622</td>\n",
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       "      <td>-9.912886</td>\n",
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       "      <td>0.305962</td>\n",
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       "    <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>-1.735287</td>\n",
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       "      <td>-0.0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3449 rows × 23 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               0.842335        976.340813               -0.0   \n",
       "1              -0.998771        974.458942               -0.0   \n",
       "2              -0.363539        975.026847               -0.0   \n",
       "3               0.700004        975.996892                0.0   \n",
       "4               0.112829        975.290499               -0.0   \n",
       "...                  ...               ...                ...   \n",
       "3444           -2.321929        974.528648               -0.0   \n",
       "3445            1.090926        978.001954               -0.0   \n",
       "3446           -1.034095        975.937385               -0.0   \n",
       "3447           -2.728543        974.303388               -0.0   \n",
       "3448           -1.735287        975.357095               -0.0   \n",
       "\n",
       "     WSEN_ISDS_ACC/FL_y WSEN_ISDS_ACC/FL_z  ... WSEN_ISDS_ACC/acc_x_noise  \\\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_ACC/acc_y_noise WSEN_ISDS_ACC/acc_z_noise WSEN_ISDS_ACC/out_0  \\\n",
       "0                          0.0                       0.0           32.623429   \n",
       "1                          0.0                       0.0           32.142847   \n",
       "2                          0.0                       0.0           31.770757   \n",
       "3                          0.0                       0.0           31.523523   \n",
       "4                          0.0                       0.0           31.345873   \n",
       "...                        ...                       ...                 ...   \n",
       "3444                       0.0                       0.0            0.076978   \n",
       "3445                       0.0                       0.0            0.023716   \n",
       "3446                       0.0                       0.0            0.065746   \n",
       "3447                       0.0                       0.0           -0.032622   \n",
       "3448                       0.0                       0.0            -0.02422   \n",
       "\n",
       "     WSEN_ISDS_ACC/out_1 WSEN_ISDS_ACC/out_2 WSEN_ISDS_GYRO/out_0  \\\n",
       "0              -4.020263           -5.677741                  0.0   \n",
       "1              -3.994765           -5.614795                  0.0   \n",
       "2              -3.886765           -5.512629                  0.0   \n",
       "3              -3.759221           -5.532761                  0.0   \n",
       "4              -3.834753           -5.383155                  0.0   \n",
       "...                  ...                 ...                  ...   \n",
       "3444            0.017459           -9.739685                  0.0   \n",
       "3445           -0.065154           -9.798154                  0.0   \n",
       "3446            0.113411            -9.76346                  0.0   \n",
       "3447            0.033615           -9.912886                  0.0   \n",
       "3448            0.069812           -9.781453                  0.0   \n",
       "\n",
       "     WSEN_ISDS_GYRO/out_1 WSEN_ISDS_GYRO/out_2 general/altitude  \n",
       "0                     0.0                  0.0         0.319118  \n",
       "1                     0.0                  0.0         0.319468  \n",
       "2                     0.0                  0.0         0.320046  \n",
       "3                     0.0                  0.0         0.320849  \n",
       "4                     0.0                  0.0         0.321872  \n",
       "...                   ...                  ...              ...  \n",
       "3444                  0.0                  0.0         0.307517  \n",
       "3445                  0.0                  0.0         0.306998  \n",
       "3446                  0.0                  0.0          0.30648  \n",
       "3447                  0.0                  0.0         0.305962  \n",
       "3448                  0.0                  0.0         0.305443  \n",
       "\n",
       "[3449 rows x 23 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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 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['mach/mach_no'][1:], df['MS5611_01BA03/ts_effects'][1:])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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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",
      "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
}