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 0x16655b5fb30>"
      ]
     },
     "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 [00:48<00:00,  7.06it/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",
       "</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>-0.615303</td>\n",
       "      <td>974.883176</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>32.534231</td>\n",
       "      <td>-4.136674</td>\n",
       "      <td>-5.779334</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>-1.474517</td>\n",
       "      <td>973.983196</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>32.19775</td>\n",
       "      <td>-3.96521</td>\n",
       "      <td>-5.606724</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>-0.012905</td>\n",
       "      <td>975.37748</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>31.867976</td>\n",
       "      <td>-3.967489</td>\n",
       "      <td>-5.475976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>-0.409821</td>\n",
       "      <td>974.887067</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>31.486513</td>\n",
       "      <td>-3.825637</td>\n",
       "      <td>-5.541731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>0.123006</td>\n",
       "      <td>975.300676</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>31.298818</td>\n",
       "      <td>-3.787043</td>\n",
       "      <td>-5.413442</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>-1.64821</td>\n",
       "      <td>975.202367</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>0.008629</td>\n",
       "      <td>0.047334</td>\n",
       "      <td>-9.72838</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>-0.234923</td>\n",
       "      <td>976.676106</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>-0.090048</td>\n",
       "      <td>0.032271</td>\n",
       "      <td>-9.894719</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>-0.380105</td>\n",
       "      <td>976.591375</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>0.065239</td>\n",
       "      <td>-0.04206</td>\n",
       "      <td>-9.74432</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>-0.659671</td>\n",
       "      <td>976.37226</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>-0.032256</td>\n",
       "      <td>0.028021</td>\n",
       "      <td>-9.75175</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>-0.119963</td>\n",
       "      <td>976.972419</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>-0.039147</td>\n",
       "      <td>0.040512</td>\n",
       "      <td>-9.797138</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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              -0.615303        974.883176               -0.0   \n",
       "1              -1.474517        973.983196               -0.0   \n",
       "2              -0.012905         975.37748               -0.0   \n",
       "3              -0.409821        974.887067                0.0   \n",
       "4               0.123006        975.300676               -0.0   \n",
       "...                  ...               ...                ...   \n",
       "3444            -1.64821        975.202367               -0.0   \n",
       "3445           -0.234923        976.676106               -0.0   \n",
       "3446           -0.380105        976.591375               -0.0   \n",
       "3447           -0.659671         976.37226               -0.0   \n",
       "3448           -0.119963        976.972419               -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              32.534231           -4.136674           -5.779334   \n",
       "1               32.19775            -3.96521           -5.606724   \n",
       "2              31.867976           -3.967489           -5.475976   \n",
       "3              31.486513           -3.825637           -5.541731   \n",
       "4              31.298818           -3.787043           -5.413442   \n",
       "...                  ...                 ...                 ...   \n",
       "3444            0.008629            0.047334            -9.72838   \n",
       "3445           -0.090048            0.032271           -9.894719   \n",
       "3446            0.065239            -0.04206            -9.74432   \n",
       "3447           -0.032256            0.028021            -9.75175   \n",
       "3448           -0.039147            0.040512           -9.797138   \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                0.0             0.0             0.0       319.117737  \n",
       "1                0.0             0.0             0.0       319.467704  \n",
       "2                0.0             0.0             0.0       320.045754  \n",
       "3                0.0             0.0             0.0       320.848534  \n",
       "4                0.0             0.0             0.0       321.872233  \n",
       "...              ...             ...             ...              ...  \n",
       "3444             0.0             0.0             0.0       307.516651  \n",
       "3445             0.0             0.0             0.0         306.9983  \n",
       "3446             0.0             0.0             0.0       306.479948  \n",
       "3447             0.0             0.0             0.0       305.961597  \n",
       "3448             0.0             0.0             0.0       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": 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 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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jv/6Yc/uGbkYmCEKz55MVFhYiKCgIO3fuxPjx4wHYruQMHjwY77zzTr2v+eGHH3Dffffh6tWrCA629Xt+8MEHWLhwIQoLC6FSqbBw4UJs3rwZx48fF1/36KOPoqSkBKmpqQCAuLg4jBgxAu+99x4AwGq1IiIiAi+88AIWLVrUqPoNBgO0Wi30ej00Gk1zTwMREbmorEslmLZqj3j/yNJJ0Hq5/iKA9fnV6j04XLP56LFlk+DnwosdNvb3u0VjcvR6PQCgU6dODo9//vnnCAwMxIABA7B48WJUVFSIz2VkZCA2NlYMOACQmJgIg8GAEydOiG0SEhIc3jMxMREZGRkAAJPJhMzMTIc2crkcCQkJYpv6GI1GGAwGhxsREXVcP9XpuhkZ1cltAw7gOC7n5zNF0hXSjpTNfaHVasW8efMwZswYDBgwQHz88ccfR/fu3REWFoajR49i4cKFyMnJwTfffAMA0Ol0DgEHgHhfp9M12MZgMKCyshI3btyAxWKpt012dvZta16+fDlefvnl5n5lIiJyM47jcdyzq8rurjobdv4r4yKmxoZKWE37aHbISU5OxvHjx7F7926Hx5999lnxODY2FqGhoZg4cSLOnTuHnj17Nr/SVrB48WKkpKSI9w0GAyIiIiSsiIiIpHKpuALZulLx/oQY196Q8078vWtXP844fx1Wq+CWg6zralZ31dy5c7Fp0yZs374d4eHhDbaNi4sDAJw9exYAEBISgvx8x5Hd9vshISENttFoNPDy8kJgYCAUCkW9bezvUR+1Wg2NRuNwIyKijmlbdu2A41CtJ/oE+0lYTfuYn9BbPD5yuUS6QtpJk0KOIAiYO3cuNm7ciG3btiEqKuqOr8nKygIAhIbaLovFx8fj2LFjDrOg0tLSoNFo0K9fP7FNenq6w/ukpaUhPj4eAKBSqTBs2DCHNlarFenp6WIbIiKihuyoswDg+F5dIJO591UNAJjUv3aYR+oJnYSVtI8mhZzk5GT8+9//xrp16+Dn5wedTgedTofKykoAwLlz5/Dqq68iMzMTFy5cwHfffYcnn3wS48ePx8CBAwEAkyZNQr9+/TBjxgwcOXIEW7duxUsvvYTk5GSo1WoAwHPPPYfz58/jxRdfRHZ2NlavXo2vvvoK8+fPF2tJSUnBRx99hLVr1+LUqVOYM2cOysvLMWvWrNY6N0RE5Kaqqi3IOF+7Iee43oESVtN+YkJqr1b9Y+d5CStpH00ak/P+++8DsE0Tr+uzzz7DU089BZVKhZ9++gnvvPMOysvLERERgYcffhgvvfSS2FahUGDTpk2YM2cO4uPj4ePjg5kzZ+KVV14R20RFRWHz5s2YP38+Vq5cifDwcHz88cdITEwU20yfPh2FhYVYsmQJdDodBg8ejNTU1FsGIxMREd3sQG4xqqqtAACZDBjTs2OEHJlMhntigsSuuhvlJoedyt1Ni9bJcXVcJ4eIqGN65fuT+HRPLgBgULgW/507VuKK2k/ayXw888+DAICVjw7Gg4O7SlxR07XLOjlERESuaOfpOuNxerv3rKqbje7ZWTz+V8ZFCStpeww5RETUoVwqrsC5wtrtiMb16lghx0ddO1Ll4MUbsFrdt0OHIYeIiDqUnacLxWMflQJDuvlLV4xE/jCxl3h8Sue+q/8z5BARUYeyI6c25MT3DISHouP9FCb0rZ2ks+2U+25s3fH+myUiog7LZLZi77nafZvG9eoYs6pu1j+sdrDuO+lnJKykbTHkEBFRh3HwQjEqTBbx/pjozg20dl9yuQwDw7UAAItVQIXJLHFFbYMhh4iIOoy643GC/NTo2cVXwmqk9Zu47uLxgdxiCStpOww5RETUYdQdjzO6Z+cOsZXD7dRd5Xnz0WsSVtJ2GHKIiKhDuKavRE5+7a7jozvIKse3E6r1Eo83ZF6WsJK2w5BDREQdws46V3EAIL5nxxyPU9e0wWHicVGZUcJK2gZDDhERdQh1u6oiOnkhopO3hNU4hweH1G7psOdsUQMtXRNDDhERuT2zxYo9daaOj+7Rsbuq7OKiOonHn+/Pk7CStsGQQ0REbu/YFT1Kq2qnSY/uoFPHb+atqt3i4UBuMdxtz26GHCIicns3d8VwPE6tuROixeOL1yskrKT1MeQQEZHb+/lMbcjpFeSLID9PCatxLhP7BonHu84UNtDS9TDkEBGRW6swmXEo74Z4fzSv4jiI7aoVj/+x87yElbQ+hhwiInJrB3KLUW2pHWsyOpqDjutSKuQI9FUBAK6UVMJidZ9xOQw5RETk1uqOx5HJgFFRvJJzs9+N7ykeH7+il7CS1sWQQ0REbm332evi8YAwLbTeHhJW45zqbvGwPadAwkpaF0MOERG5rcJSI05dM4j3OR6nfn2C/cTjVdvPSlhJ62LIISIit7X3nOPUcY7HqZ9MJkO/UA0AoNoioNJkkbii1sGQQ0REbqvueBylXIYRkQESVuPcfjOqu3hcdzaaK2PIISIityQIAnbXWR9nSDd/hxV+ydGYOqtAb8t2j3E5DDlEROSWLlyvwFV9lXh/VA+Ox2lItzobln6yO1fCSloPQw4REbml3Tdt5TAistNtWhJgG5czqkftOaowmRto7RoYcoiIyC3trrNFgVwGDO3O8Th38uiIbuLxoYsl0hXSShhyiIjI7VisAvaeq10fp3+YFr5qjse5k7pT7H84fk3CSloHQw4REbmd41f0KK2q7W5hV1XjBGlqNy79fH+ehJW0DoYcIiJyO3Wv4gDAyCiGnMaaGFO7K7mrj8thyCEiIreTcd4x5HB9nMb7f8MjxOODF1x7vRyGHCIicismsxW/5BaL93t28UFnX7WEFbmWujOsNh29KmElLceQQ0REbuXo5RJUVtduS8Cuqqbx91aJx18dvCxhJS3HkENERG4l49zNXVUMOU01qV+weFxudN1xOQw5RETkVm4edMyQ03QPDwsXjw9edN1xOQw5RETkNqqqLciss7lkqNYT4QFeElbkmuLqdPFtduFxOQw5RETkNg7nlcBktor3R0R2gkwmk7Ai1+Qu43IYcoiIyG1knLtpvyoOOm62uuNyylx0XA5DDhERuY2b18eJY8hptoeG1hmXc6G4gZbOiyGHiIjcQoXJjKxLJeJ9f28PRHfxla4gF1c3IG466pr7WDHkEBGRWzh44QaqLYJ4f3j3TpDLOR6nuQJ8asflfJ3pmuNyGHKIiMgt3NxVNTKKWzm0VGJ/114vhyGHiIjcAhcBbH2/GlI7LudQnuutl8OQQ0RELq+0qhrHrujF+14eCgzoqpWwIvdQd1zOD8d1ElbSPAw5RETk8n65UAyLtXY8zpBu/vBQ8CeupeqOy1m3P0/CSpqHfwFEROTy2FXVdsb37iIeG82WBlo6nyaFnOXLl2PEiBHw8/NDUFAQpk2bhpycHIc2VVVVSE5ORufOneHr64uHH34Y+fn5Dm3y8vKQlJQEb29vBAUFYcGCBTCbHQc07dixA0OHDoVarUZ0dDTWrFlzSz2rVq1CZGQkPD09ERcXhwMHDjTl6xARkZu4eb8qro/Teh4fGSEeH7usb6Cl82lSyNm5cyeSk5Oxb98+pKWlobq6GpMmTUJ5ebnYZv78+fj++++xYcMG7Ny5E1evXsVDDz0kPm+xWJCUlASTyYS9e/di7dq1WLNmDZYsWSK2yc3NRVJSEiZMmICsrCzMmzcPTz/9NLZu3Sq2+fLLL5GSkoKlS5fi0KFDGDRoEBITE1FQUNCS80FERC6mpMKEk9cM4n2lXIYh3TizqrXERXUWj7ccc61xOTJBEIQ7N6tfYWEhgoKCsHPnTowfPx56vR5dunTBunXr8Otf/xoAkJ2djb59+yIjIwOjRo3CDz/8gPvuuw9Xr15FcLBtatoHH3yAhQsXorCwECqVCgsXLsTmzZtx/Phx8bMeffRRlJSUIDU1FQAQFxeHESNG4L333gMAWK1WRERE4IUXXsCiRYsaVb/BYIBWq4Ver4dGo2nuaSAiIgmlHtfhuX9nivcHR/jj2+QxElbkfiIXbRaPL7yRJGElNo39/W7RmBy93nbZqlMn22XBzMxMVFdXIyEhQWwTExODbt26ISMjAwCQkZGB2NhYMeAAQGJiIgwGA06cOCG2qfse9jb29zCZTMjMzHRoI5fLkZCQILapj9FohMFgcLgREZFr23fL+jjsqmptdc9p3QHezq7ZIcdqtWLevHkYM2YMBgwYAADQ6XRQqVTw9/d3aBscHAydTie2qRtw7M/bn2uojcFgQGVlJYqKimCxWOptY3+P+ixfvhxarVa8RURE3LYtERG5Bg46bntPjY4Uj09dc50LBM0OOcnJyTh+/DjWr1/fmvW0qcWLF0Ov14u3S5cuSV0SERG1wPUyI3LySx0eG96d43FaW3yP2nE5Pxx3nX2smhVy5s6di02bNmH79u0ID69dDTEkJAQmkwklJSUO7fPz8xESEiK2uXm2lf3+ndpoNBp4eXkhMDAQCoWi3jb296iPWq2GRqNxuBERkevad95xd+zewb4Oa7tQ66h7TldtPydhJU3TpJAjCALmzp2LjRs3Ytu2bYiKinJ4ftiwYfDw8EB6err4WE5ODvLy8hAfHw8AiI+Px7FjxxxmQaWlpUGj0aBfv35im7rvYW9jfw+VSoVhw4Y5tLFarUhPTxfbEBGR+7t5PA67qtrOwPDaFaRbMGepXTUp5CQnJ+Pf//431q1bBz8/P+h0Ouh0OlRWVgIAtFotZs+ejZSUFGzfvh2ZmZmYNWsW4uPjMWrUKADApEmT0K9fP8yYMQNHjhzB1q1b8dJLLyE5ORlqtRoA8Nxzz+H8+fN48cUXkZ2djdWrV+Orr77C/PnzxVpSUlLw0UcfYe3atTh16hTmzJmD8vJyzJo1q7XODREROblbN+VkyGkrz4zrIR6fKyxvoKXzUDal8fvvvw8AuPvuux0e/+yzz/DUU08BAN5++23I5XI8/PDDMBqNSExMxOrVq8W2CoUCmzZtwpw5cxAfHw8fHx/MnDkTr7zyitgmKioKmzdvxvz587Fy5UqEh4fj448/RmJiothm+vTpKCwsxJIlS6DT6TB48GCkpqbeMhiZiIjcU0FpFc4WlDk8xis5bWdsdKB4vPWEDtFB0RJW0zgtWifH1XGdHCIi1/X9kat44YvD4v3wAC/sXniPhBW5P/t6OZ4ecmS/OkWyOtplnRwiIiKp3NJVxas4bS46yBcAUFVtlbiSxmHIISIil7Tv5vVxOB6nzT13V0/x+PKNCgkraRyGHCIicjn5hiqcL3Ic/MrxOG3v7j61O5L/eCK/gZbOgSGHiIhczs1Txzv7qNCzi49E1XQcgb5q8XjV9rMSVtI4DDlERORybt7KYXhkAGQymUTVdCxhWk8AwPVyk8SV3BlDDhERuRwuAiid5Htqp44XlRklrOTOGHKIiMilXNNX4sJ126BX+8UbLgLYfu7tV7seXfop5x6Xw5BDREQuxd5VpVLKIQiAj0qBfqFc66y9BPl5isfvOfm4HIYcIiJyKfaQo6i5jDO0ewCUCv6ctSeNp23DhEvFlRJX0jD+VRARkUvZl2sLOUq5LeRwEcD29/uJvcTj0qpqCStpGEMOERG5jMs3KnCpuBIKuQz2PYm4CGD7u39QmHi8PadQwkoaxpBDREQuw95V5e/lgTKjGR4KGQZH+EtbVAcUrKkdl7PaicflMOQQEZHL2He+GACgqOmqGhjuD08PhZQldVj27sJsXanEldweQw4REbkEQRDE9XHsP7BcH0c6L9xTOy6nqtoiYSW3x5BDREQu4VJxJa6UVEIpl8Ei2EbkjIwKkLiqjuuREeHi8e4zRRJWcnsMOURE5BLsV3HC/L2QbzBCJgOGdeeVHKmEar3E49U7nHNcDkMOERG5hAx7V5XC1lUVE6KB1stDypKoxqG8EqlLqBdDDhEROT1BEG5ZBHBkJLuqpPa78T3EY7PFKmEl9WPIISIip3fxegV0hip4KGrH43B9HOn9ZlR38fhAbrGEldSPIYeIiJyevauqZxdf5BaVA+BKx84gopO3ePz+znMSVlI/hhwiInJ69q4qdc2mnN07eyOozoJ0JL2fnXCGFUMOERE5tbrr4yi4Po7T+c2obuKx1So00LL9MeQQEZFTO19UjoJSI1RKOcw1P6LsqnIevx0TJR4fvaKXsJJbMeQQEZFTs3dVDQjTIPuabQsBDjp2HlGBPuLx+062Xg5DDhEROTV7V5W3SgmTxYoufmpEdva+w6uovchqpvQDwNYT+RJWciuGHCIiclq28Ti2qclyuX19nE4OP6wkvV8N6SoeC4LzjMthyCEiIqd1tqAMRWVGqJVyVJtti82N4CKATue5u3qKx2cKyiSsxBFDDhEROS37+jhDuvnjWM2gVo7HcT69g33F4w+caL0chhwiInJa9vE4/l4qlBnN8FMrEROikbgqulnd7sNvDl2RsBJHDDlEROSUrNba8TiKmk05h0UGiGvlkHNJ7B8sdQm3YMghIiKndLqgFMXlJnh5KGCsto/HYVeVs3r+7mjx+FJxhYSV1GLIISIip7SvZn2c4ZEByLp0AwAwkuNxnNbAcK14/OGu8xJWUoshh4iInJJ90HGIxhNFZSaolHKHH1JyLnXH5fxr30UJK6nFkENERE7HahWwP7dmPE7NGJzBEf5QKxVSlkV3MDY6UOoSHDDkEBGR08nWlaKkohreKgUqqy0AuF+VK5h7T+24nILSKgkrsWHIISIip2PvqhoR2QmH8mzjcbg+jvOLq/Pf0Sc/50pYiQ1DDhEROR37ppxRgT64VFwJuQwY2s1f2qLojuqOy/mHEww+ZsghIiKnYrEKOJBrCznKmvE4/cI08PP0kLIsaqTBEf5SlyBiyCEiIqdy6poBhiozfNVKVNSMx+H6OK7j9xNrx+XoK6slrIQhh4iInIy9q2pkVCcculizPg5DjsuY0CdIPF6794J0hYAhh4iInIx9v6p+oRrk5JcCAIYz5LiMuuNy3t12RsJKGHKIiMiJmC1WHKizPo4gAD27+KCLn1riyqgpegXZdiWvtgiS1sGQQ0RETuPEVQNKjWZoPJUoM5oBACOjOktcFTXVCxN7icdGs0WyOhhyiIjIadi7qkZGdcbBC7YrOnFcH8flJMWGAgA6+6jgIZcuaigl+2QiIqKb2BcBHBiuxcr0AgDclNMVKeQynPnzFJgtAuRy2Z1f0EaaHK927dqF+++/H2FhYZDJZPj2228dnn/qqacgk8kcbpMnT3ZoU1xcjCeeeAIajQb+/v6YPXs2ysrKHNocPXoU48aNg6enJyIiIrBixYpbatmwYQNiYmLg6emJ2NhYbNmypalfh4iInES1xYpfasbjqJRyWKwCwgO8EObvJXFl1BweCjm8VNLuNdbkkFNeXo5BgwZh1apVt20zefJkXLt2Tbx98cUXDs8/8cQTOHHiBNLS0rBp0ybs2rULzz77rPi8wWDApEmT0L17d2RmZuLNN9/EsmXL8OGHH4pt9u7di8ceewyzZ8/G4cOHMW3aNEybNg3Hjx9v6lciIiIncOyKHuUmC7ReHjDUrK/CqzjUEk3urpoyZQqmTJnSYBu1Wo2QkJB6nzt16hRSU1Pxyy+/YPjw4QCAd999F1OnTsVf//pXhIWF4fPPP4fJZMKnn34KlUqF/v37IysrC3/729/EMLRy5UpMnjwZCxYsAAC8+uqrSEtLw3vvvYcPPvigqV+LiIgkZh+PExfVCQcv2NbHGcVBx9QCbTIaaMeOHQgKCkKfPn0wZ84cXL9+XXwuIyMD/v7+YsABgISEBMjlcuzfv19sM378eKhUKrFNYmIicnJycOPGDbFNQkKCw+cmJiYiIyPjtnUZjUYYDAaHGxEROQf7IoBDuwcg61IJAF7JoZZp9ZAzefJk/POf/0R6ejr+8pe/YOfOnZgyZQosFtsUMp1Oh6CgIIfXKJVKdOrUCTqdTmwTHBzs0MZ+/05t7M/XZ/ny5dBqteItIiKiZV+WiIhahclsFa/eeHkoYLJYEeSnRvfO3hJXRq6s1WdXPfroo+JxbGwsBg4ciJ49e2LHjh2YOHFia39ckyxevBgpKSnifYPBwKBDROQEjl0pQWW1BQHeHrhRYQJgu4pTd/VcoqZq88nrPXr0QGBgIM6ePQsACAkJQUFBgUMbs9mM4uJicRxPSEgI8vPzHdrY79+pze3GAgG2sUIajcbhRkRE0rN3VY3q0Vm8osP1cail2jzkXL58GdevX0doqG1hoPj4eJSUlCAzM1Nss23bNlitVsTFxYltdu3aherq2t1L09LS0KdPHwQEBIht0tPTHT4rLS0N8fHxbf2ViIioldnXxxke2QmZ9k05OeiYWqjJIaesrAxZWVnIysoCAOTm5iIrKwt5eXkoKyvDggULsG/fPly4cAHp6el48MEHER0djcTERABA3759MXnyZDzzzDM4cOAA9uzZg7lz5+LRRx9FWFgYAODxxx+HSqXC7NmzceLECXz55ZdYuXKlQ1fTH/7wB6SmpuKtt95CdnY2li1bhoMHD2Lu3LmtcFqIiKi9GM0WMdj4qZWorLbA39tD3P+IqLmaHHIOHjyIIUOGYMiQIQCAlJQUDBkyBEuWLIFCocDRo0fxwAMPoHfv3pg9ezaGDRuGn3/+GWp17eZqn3/+OWJiYjBx4kRMnToVY8eOdVgDR6vV4scff0Rubi6GDRuG//mf/8GSJUsc1tIZPXo01q1bhw8//BCDBg3C119/jW+//RYDBgxoyfkgIqJ2duSSHlXVVgT6qlBcMx5nRGQnSVfKJfcgEwRB2i1CJWQwGKDVaqHX6zk+h4hIIit/OoO3fzqNpIGhqDRZsC27AC8l9cXT43pIXRo5qcb+fnODTiIiklTdRQB/ETfl5HgcajmGHCIikkxVtQWZebbxOP7eKpRWmeGrVqJvqJ/ElZE7YMghIiLJHMq7AZPZii5+ahSVGgEAw7oHQKngzxO1HP+KiIhIMnvOFgEAxvTsLHZVcSsHai0MOUREJJndZ2pCTnQgDuTax+Mw5FDrYMghIiJJlFSYcPSKHgAQovXE9XIT1Eo5YsO1EldG7oIhh4iIJLH33HUIAtAryBe5ReUAgOGRAVArFRJXRu6CIYeIiCTxc01X1dhegeLeVfE9OHWcWg9DDhERSWL32UIAwJiegeJaOfE9GXKo9TDkEBFRu8u7XoFLxZVQymUI8PHAjYpqeKsUGBjuL3Vp5EYYcoiIqN39XHMVZ2i3ABy5ZBt8PDyyEzy4Pg61Iv41ERFRu9tddzxOTVfVqB6cOk6tiyGHiIjalcUqYG/NQOMx0Z2x/zwHHVPbYMghIqJ2deyKHvrKavh5KqGUy2Go2a8qtivXx6HWxZBDRETtyr6VQ3yP2q0cRkRyvypqffyLIiKidvXzGdug43F118fh1HFqAww5RETUbipMZmRevAEAiO9Zu19VfI9AKcsiN8WQQ0RE7WZ/bjGqLQK6+nuh3GhGqdEMjacS/cI0UpdGboghh4iI2o196vi4OlPHR0Z1hkIuk7IsclMMOURE1G5217dfFcfjUBthyCEionZRUFqFnPxSyGTAyKhO4swqLgJIbYUhh4iI2oV96nj/MA0u36hEhckCf28P9A3heBxqGww5RETULnbm2KeOdxG7quKiOkHO8TjURhhyiIiozVmtAnbVjMe5u3cX7ONWDtQOGHKIiKjNHbuiR3G5CX5qJWLDtTh4oXatHKK2wpBDRERtbudpW1fVmOhAHLusR2W1BZ19VOgd7CtxZeTOGHKIiKjN7cgpAADc1acLdtcMQB4THQiZjONxqO0w5BARUZsqqTAh61IJAOCu3l3EsTlje7GritoWQw4REbWpn88UwSoAvYN94aNS4tjlEgC2VY+J2hJDDhERtSn7eJy7+wRh7zlb4OnZxQehWi+JKyN3x5BDRERtxmoVxJBzV+8u+Pmsfe+qLlKWRR0EQw4REbWZUzoDCkuN8FYpMDwywGGDTqK2xpBDRERtZkfNKseje3ZGvt6IvOIKKOUyxHERQGoHDDlERNRmHLuqbMdDuwXAV62UsizqIBhyiIioTRiqqpF50bay8V29g/DzaU4dp/bFkENERG1iz5kiWKwCegT6oGuAF/aeY8ih9sWQQ0REbSI927bK8T0xQTh6uQSGKjP8PJUY2FUrcWXUUTDkEBFRq7NYBWy3h5y+QeKsqtE9O0Op4E8PtQ/+pRERUas7crkE12t2HR8R2Ynr45AkGHKIiKjVbTtlu4ozvk8XmMxWHM6zDUDm+jjUnhhyiIio1f10Kh8AkNA3CPtzr6PaIiCikxe6d/aRuDLqSBhyiIioVV0pqUS2rhRymW3q+C771PFodlVR+2LIISKiVrWtZsDx0G4B6OSjwu6z3MqBpMGQQ0RErSq9pqtqYt9g6PRVOFtQBpnMNrOKqD0x5BARUaupMJmx99x1AMDEvkHYkWO7qjMw3B/+3iopS6MOqMkhZ9euXbj//vsRFhYGmUyGb7/91uF5QRCwZMkShIaGwsvLCwkJCThz5oxDm+LiYjzxxBPQaDTw9/fH7NmzUVZW5tDm6NGjGDduHDw9PREREYEVK1bcUsuGDRsQExMDT09PxMbGYsuWLU39OkRE1Ir2nL0Ok9mK8AAv9AryFbuu7ukTJHFl1BE1OeSUl5dj0KBBWLVqVb3Pr1ixAn//+9/xwQcfYP/+/fDx8UFiYiKqqqrENk888QROnDiBtLQ0bNq0Cbt27cKzzz4rPm8wGDBp0iR0794dmZmZePPNN7Fs2TJ8+OGHYpu9e/fisccew+zZs3H48GFMmzYN06ZNw/Hjx5v6lYiIqJWki7OqgmGyWMXxOPfEMOSQBIQWACBs3LhRvG+1WoWQkBDhzTffFB8rKSkR1Gq18MUXXwiCIAgnT54UAAi//PKL2OaHH34QZDKZcOXKFUEQBGH16tVCQECAYDQaxTYLFy4U+vTpI95/5JFHhKSkJId64uLihN/97neNrl+v1wsABL1e3+jXEBFR/SwWqzDitTSh+8JNws6cAmHX6QKh+8JNwvDX0gSLxSp1eeRGGvv73apjcnJzc6HT6ZCQkCA+ptVqERcXh4yMDABARkYG/P39MXz4cLFNQkIC5HI59u/fL7YZP348VKra/tvExETk5OTgxo0bYpu6n2NvY/+c+hiNRhgMBocbERG1jiOXS1BQaoSPSoG4Hp2wPbsQADChTxfI5TKJq6OOqFVDjk6nAwAEBwc7PB4cHCw+p9PpEBTkeNlSqVSiU6dODm3qe4+6n3G7Nvbn67N8+XJotVrxFhER0dSvSEREt7H1hK2r6u6YIKiVCmzPqd2gk0gKHWp21eLFi6HX68XbpUuXpC6JiMht/HjS9n8yE/uHILeoHLlF5fBQyDAmmuvjkDRaNeSEhIQAAPLz8x0ez8/PF58LCQlBQUGBw/NmsxnFxcUObep7j7qfcbs29ufro1arodFoHG5ERNRyZwtKcb6wHCqFHBP6dBFnVY2I7AQ/Tw+Jq6OOqlVDTlRUFEJCQpCeni4+ZjAYsH//fsTHxwMA4uPjUVJSgszMTLHNtm3bYLVaERcXJ7bZtWsXqqurxTZpaWno06cPAgICxDZ1P8fexv45RETUfuxdVaOjO8PP0wPbs9lVRdJrcsgpKytDVlYWsrKyANgGG2dlZSEvLw8ymQzz5s3Da6+9hu+++w7Hjh3Dk08+ibCwMEybNg0A0LdvX0yePBnPPPMMDhw4gD179mDu3Ll49NFHERYWBgB4/PHHoVKpMHv2bJw4cQJffvklVq5ciZSUFLGOP/zhD0hNTcVbb72F7OxsLFu2DAcPHsTcuXNbflaIiKhJtp6o7aoqN5qxP9e2IOAEhhySUlOnbW3fvl0AcMtt5syZgiDYppH/6U9/EoKDgwW1Wi1MnDhRyMnJcXiP69evC4899pjg6+sraDQaYdasWUJpaalDmyNHjghjx44V1Gq10LVrV+GNN964pZavvvpK6N27t6BSqYT+/fsLmzdvbtJ34RRyIqKWu3KjQui+cJMQuWiTUGCoElKPXxO6L9wkjF+xTbBaOXWcWl9jf79lgiAIEmYsSRkMBmi1Wuj1eo7PISJqpjV7crHs+5MYERmADc+NxqL/HMX6Xy7hqdGRWPZAf6nLIzfU2N/vDjW7ioiIWp99PE5i/xAIgsCp4+Q0GHKIiKjZbpSbcOBCMQBbyDlx1YB8gxFeHrYFAYmkxJBDRETN9tOpfFisAvqGahDRyVucVTUmOhBqpULi6qijY8ghIqJmq51VZVuBnl1V5EwYcoiIqFkMVdXYddq2y/jkASEoLjfh8KUSAMCEmC4SVkZkw5BDRETNknYiHyaLFdFBvugT7Ift2QUQBKBvqAahWi+pyyNiyCEioubZfOwaAOC+gaGQyWTi3lX39gtu6GVE7YYhh4iImkxfUY2fzxQCsIWcSpMFO0/b7k9iyCEnwZBDRERNtvWkDtUWATEhfogO8sPPZwpRVW1FV38v9A/j4qrkHBhyiIioyTYdtXVVJcWGAgB+PGlbEHBS/2DIZDLJ6iKqiyGHiIia5Ea5CXvO2mZVJQ0MhdliRfqpmpDTL0TK0ogcMOQQEVGTpJ7QwWIV0C9Ugx5dfPHLhRu4UVGNAG8PjIgMkLo8IhFDDhERNcnmmq6q+wbZu6pss6om9g2GUsGfFXIe/GskIqJGu15mxN5ztq6q+2LDIAgCfqzZoJNTx8nZMOQQEVGjbTmug1UABoZr0a2zN45c1uNKSSW8VQrc1ZurHJNzYcghIqJG23joMgDggUFhAIAtNQsC3hMTBE8PbshJzoUhh4iIGuVCUTkO5ZVALrOFHEEQxPE59qnkRM6EIYeIiBpl4+ErAICxvbogSOOJozVdVV4eCtzdh7uOk/NhyCEiojsSBAHfZtlCzkNDugIAthyv6arqGwQvFbuqyPkw5BAR0R0dyruBi9cr4K1SYFL/YAiCII7HmTqAXVXknBhyiIjojr45ZLuKM3lACLxVShy/YsCl4kp4esgxIYazqsg5MeQQEVGDjGaLuFfVQ0PCAQCbjl4FYJtV5a1SSlYbUUMYcoiIqEHbswuhr6xGsEaN+J6dYbUK+O6ILeTYp5ITOSOGHCIiatDGw7a1caYN7gqFXIaDF2/gmr4KfmolZ1WRU2PIISKi2youN2FbdgEA4FdDbbOq/ptVOz6HCwCSM2PIISKi2/rm0GVUWwTEdtUiJkSDaotVnFX1wGB2VZFzY8ghIqJ6CYKAL3+5BACYPiICALD7TBFuVFQj0FeN+B6dpSyP6I4YcoiIqF6H8kpwpqAMXh4KPFhz1cbeVXXfwFAoFfwJIefGv1AiIqrXl7/kAQCSBobCz9MD5UYzfjyZD4BdVeQaGHKIiOgWpVXV+P6IbezNozVdVanHdagwWRAV6IMhEf4SVkfUOAw5RER0i++PXENltQXRQb4Y1j0AAPCfQ7ap5A8N6QqZTCZleUSNwpBDRES3sHdVPToiAjKZDFdKKpFx/jqA2qnkRM6OIYeIiBycvGrAkct6eChk+FXNjuMbD12GIADxPTojPMBb4gqJGochh4iIHKw7cBEAMKlfCDr7qiEIAv5Ts0Hnw8PCpSyNqEkYcoiISGSoqhZ3HH9iVDcAwKG8G8gtKoe3SoEpA0KkLI+oSRhyiIhItOHgZVSYLOgT7Ccu9rfhoG3A8eQBIfBRc8dxch0MOUREBACwWgX8K+MCAODJ0d0hk8lQZjSLO45PHx4hYXVETceQQ0REAICdpwtx4XoF/DyV4oDj77KuosJkQY8uPhgZ1UniComahiGHiIgAAGtrruI8MjwC3ipbt9T6mqnkj43oxrVxyOUw5BAREXKLyrEjpxAyGfBkfHcAwPErehy9rIdKIeesKnJJDDlERIR/1lzFmdAnCN07+wCovYozqX8wOvmopCqNqNkYcoiIOjhDVTW+rplBNXN0JACg3GjGfw/bBhw/PrKbVKURtQhDDhFRB7dufx5KjWZEB/liXHQgAGDj4SsoNZoRFeiDUTVTyYlcDUMOEVEHZjRb8OnuXADA78b3gFwugyAIYvfVjFHdIZdzwDG5JoYcIqIO7NvDV1BQakSIxhMPDrZNG884fx2n88vgrVLg18M54JhcV6uHnGXLlkEmkzncYmJixOerqqqQnJyMzp07w9fXFw8//DDy8/Md3iMvLw9JSUnw9vZGUFAQFixYALPZ7NBmx44dGDp0KNRqNaKjo7FmzZrW/ipERG7NahXwj13nAQCzx0ZBpbT9JKzdewEA8NDQrtB4ekhVHlGLtcmVnP79++PatWvibffu3eJz8+fPx/fff48NGzZg586duHr1Kh566CHxeYvFgqSkJJhMJuzduxdr167FmjVrsGTJErFNbm4ukpKSMGHCBGRlZWHevHl4+umnsXXr1rb4OkREbunHk/k4X1gOjacSj8XZBhdfvlGBtJO2/+M5Mz5SwuqIWq5NNiFRKpUICbl1Eze9Xo9PPvkE69atwz333AMA+Oyzz9C3b1/s27cPo0aNwo8//oiTJ0/ip59+QnBwMAYPHoxXX30VCxcuxLJly6BSqfDBBx8gKioKb731FgCgb9++2L17N95++20kJia2xVciInIrgiDgg53nAAAz4rvDt2ZPqn/tuwirAIyJ7oxewX5SlkjUYm1yJefMmTMICwtDjx498MQTTyAvz7bWQmZmJqqrq5GQkCC2jYmJQbdu3ZCRkQEAyMjIQGxsLIKDg8U2iYmJMBgMOHHihNim7nvY29jf43aMRiMMBoPDjYioI9qfW4ysSyVQKeV4anQUAKDMaMa6/bZ/X/MqDrmDVg85cXFxWLNmDVJTU/H+++8jNzcX48aNQ2lpKXQ6HVQqFfz9/R1eExwcDJ1OBwDQ6XQOAcf+vP25htoYDAZUVlbetrbly5dDq9WKt4gIbjZHRB3TOz+dBgD8v2Hh6OKnBgCsP5CH0iozenTxQULf4IZeTuQSWr27asqUKeLxwIEDERcXh+7du+Orr76Cl5dXa39ckyxevBgpKSnifYPBwKBDRB3O3nNF2He+GCqFHM9PiAYAVFus4lTyZ8b14LRxcgttPoXc398fvXv3xtmzZxESEgKTyYSSkhKHNvn5+eIYnpCQkFtmW9nv36mNRqNpMEip1WpoNBqHGxFRRyIIAt5JOwMAeHRkBLr62/6dufnoNVzVVyHQVy3uQE7k6to85JSVleHcuXMIDQ3FsGHD4OHhgfT0dPH5nJwc5OXlIT4+HgAQHx+PY8eOoaCgQGyTlpYGjUaDfv36iW3qvoe9jf09iIiofnvPXceBC8VQKeV4/m7bVRxBqJ1K/tTo7vD0UEhZIlGrafWQ87//+7/YuXMnLly4gL179+JXv/oVFAoFHnvsMWi1WsyePRspKSnYvn07MjMzMWvWLMTHx2PUqFEAgEmTJqFfv36YMWMGjhw5gq1bt+Kll15CcnIy1Gpbv/Fzzz2H8+fP48UXX0R2djZWr16Nr776CvPnz2/tr0NE5DYEQcDf0mxjcR4f2Q0hWk8AwM7ThTh1zQBvlQK/GdVdyhKJWlWrj8m5fPkyHnvsMVy/fh1dunTB2LFjsW/fPnTp0gUA8Pbbb0Mul+Phhx+G0WhEYmIiVq9eLb5eoVBg06ZNmDNnDuLj4+Hj44OZM2filVdeEdtERUVh8+bNmD9/PlauXInw8HB8/PHHnD5ORNSAXWeKkHnxBtRKOZ6/uycAW/BZmW7rvnp8ZDf4e3O3cXIfMkEQBKmLkIrBYIBWq4Ver+f4HCJya1argGmr9+DoZT1mj43Cn+6zdf/vPlOE33yyH2qlHD8vnIAgP0+JKyW6s8b+fnPvKiKiDuC7I1dx9LIevmol5jhcxbF1Xz02shsDDrkdhhwiIjdXVW3Bm1tzAABz7u6JQF/b+MZ954vxy4UbUCnkeO6unlKWSNQmGHKIiNzcp3tycaWkEmFaT8wea1vdWBAEcUHA6SMixEHIRO6EIYeIyI1dLzNi9XbbHlULJvcRp4fvPF2I/bm2qeT27isid8OQQ0Tkxt756QzKjGbEdtXiwUG2Rf6sVgErUm3dVzPjuyPMX9rV6InaCkMOEZGbOnXNgHUHbBtu/t/UvuJWDZuOXcPJawb4qZXigoBE7oghh4jIDVmtAv648RgsVgFTY0MQ37MzAMBktuKtH21XcZ4d3wMBPlwXh9wXQw4RkRv66uAlHMorgY9KgSX39Rcf/3z/RVy8XoFAXzV+WzMImchdMeQQEbmZ4nIT3kjNBgDMv7e3OHOquNyEt2u2dZh/by/4qFt90Xsip8KQQ0TkZpZvOYWSimr0DdXgqdGR4uNv/ZgDQ5UZ/UI1eHREN+kKJGonDDlERG5k77kibMi8DAB4bdoAKBW2f82fvGrAFzWDkJfe3w+KmkHIRO6MIYeIyE2UGc1YsOEoAOCJuG4Y1j0AgG3hv5e/PwGrACQNDEVcj85SlknUbhhyiIjcxJ83n8SVkkpEdPLC4ql9xcc3ZF7G/txieHrIsXhKjIQVErUvhhwiIjewPacAXxy4BAB489eD4FszqLiozIg/bz4FAEi5tzfCA7wlq5GovTHkEBG5OH1FNRb9x9ZN9dsxURhVpzvq1U0noa+sRr9QDX47hlPGqWNhyCEicmGCIGDhf44i32BEjy4+eHFyH/G57dkF+G/WVchlwBsPx4qDkIk6Cv7FExG5sDV7LyD1hA4eChnemT5Y3IDzRrkJL9Zc3Zk1JgoDw/0lrJJIGgw5REQu6silEry+xTbe5o9T+4pBRhAEvPTtcRSWGtGziw8WJPZp4F2I3BdDDhGRC9JXViN53SFUWwRM7h+CmXUW/ftv1lVsPnYNSrkMb9e5ukPU0TDkEBG5GItVwLz1h3H5hm26+F9+PRAymW1xv7zrFfjTf48DAH4/sRe7qahDY8ghInIxr285he05hVAr5Vj9+DBovTwAAFXVFjy/LhOlVWYM7eaP5+/uKXGlRNJiyCEiciHrD+Thk925AIC3HhmE2HCt+Nyrm07i+BUDArw98N7jQzmbijo8/i+AiMhFZJy7jpe+tXVFzU/ojfsGhonPbTx8GZ/vz4NMBrw9fTDC/L2kKpPIaTDkEBG5gONX9Hj2nwdhtgq4f1AYfj8xWnzuUN4NLPzPMQDA3AnRuLtPkFRlEjkVhhwiIid3tqAMT356AKVGM+KiOuHNOgONr5RU4tl/ZsJktiKhbzDmJfSWuFoi58GQQ0TkxK6UVOLJT/ajuNyE2K5afDxzuDgl3FBVjdlrfkFRmRF9QzVY+ehgKOQyiSsmch5KqQsgIqL6XSquwOMf78NVfRV6dvHBmlkj4OdZO5Pq6bUHka0rRaCvGh/PHA4fNf+VTlQX/xdBROSEcovK8fhH+3BNX4Xunb3xr9lx6OyrBgBUW6xI/vwQDuQWw0+txJpZI9CVA42JbsGQQ0TkZE7nl+KJj/eL2zKse2YUgjWeAGwBZ/6XWUjPLoBaKcfHM4djQFftHd6RqGNiyCEiciJ7zxbhd/+2LegXE+KHfz8dh8CaKzgmsxW//+IwUk/ooJTLsPqJoYjr0VniiomcF0MOEZGT+DrzMhb95yjMVgHDuwfgoyeHI8BHBcA2Bif580NIzy6ASiHH6ieGYmLfYIkrJnJuDDlERBIzW6x4c2sO/rHrPADg/kFhePPXA8VZVMXlJjzzz4PIvHgDaqUcHz05HON7d5GyZCKXwJBDRCShAkMV5n5xGAdyiwEAz9/dE/87qQ/kNVPBc4vKMeuzA7hwvQIaTyU+fHI4RrGLiqhRGHKIiCSy63Qh/mfDERSWGuGrVmLFrwdiamyo+Py27HzMW58FQ5UZ4QFeWDNrBKKD/CSsmMi1MOQQEbWzCpMZr285hX/vywMA9An2w/u/GYoeXXwBABargJU/ncbft50FAAyO8MdHTw5HFz+1ZDUTuSKGHCKidrTzdCGW/Pc4Ll6vAAA8NToSCyfHwEtlG39zqbgC//PVERy4YOu+ejK+O15K6geVkgvUEzUVQw4RUTu4UlKJ1zadxA/HdQCAUK0n3vz1IIztFQgAEAQBX/5yCa9uOolykwU+KgX+/KtYTBvSVcqyiVwaQw4RURsqM5rx6e5cvL/jHCqrLVDIZZgZH4l59/aCpmaLhhxdKV769hh+uXADADAyshPeemQQIjp5S1k6kctjyCEiagNV1Rb8K+Mi3t95DsXlJgDAiMgAvPLgAPQN1QAArpcZ8d72s/hXxkWYrQK8PBRIubc3fjs2ihttErUChhwiolZUUmHCugN5WLPnAgpKjQCAqEAfzEvohQcGhUEmk6G0qhpr9lzAP3adR5nRDABI7B+MJff35x5URK2IIYeIqBWcyS/F2owL+E/mFVRWWwAA4QFe+MPEXvjVkK5QKuS4XmbEmr0XsGbvBZRW2cJN/zANFk2JwbheXNyPqLUx5BARNVNxuQnfH7mK/xy6jKOX9eLjfUM1mD02Cg8MCoOHQoYjl/X4Z8YFbDp6DSazFQDQs4sPXrjHdnVHzq4pojbBkENE1ASFpUZsy85H2sl87MgphNkqAACUchkmxARh1phIxPfojGv6Kny6JxffHLqM0/ll4usHhmsx566eSOwfwnBD1MYYcoiIGlBtseLoZT0yzhUhPbsAWZdKIAi1z8d21eKhoV1x38AwFJYasfN0If6SmoMjl0rENmqlHEmxoXhydCQGR/i3+3cg6qgYcoiI6rheZsSxK3ocu6zHgQvFOHjhhjjGxm5guBYTY4IRE+qHwlIjMs5dx7vbzoqzqABAJgNGdO+Eh4Z2xdSBoeJ0cSJqPy4fclatWoU333wTOp0OgwYNwrvvvouRI0dKXRYROblyoxnnC8txrrAM5wvLkJNfiuNXDLhSUnlLW62XB0K1nvD39kCwxhNFZUZ8tjcXJRXVDu28VQrERXVCQr9g3NsvGEF+nu31dYioHi4dcr788kukpKTggw8+QFxcHN555x0kJiYiJycHQUFBUpdHRBKxWgUYqqqhM1ThWkkVruorHf6ZW1QOnaGq0e9XYTIjW1d6y+MqhRxDuvljdM9AjInujEER/vBQcPsFImchE4S6vcuuJS4uDiNGjMB7770HALBarYiIiMALL7yARYsW3fH1BoMBWq0Wer0eGo2mrcslojuwWAWYzFYYzZaaf1pRZjSj3Giu+acF5SbbfdtjFugrq1FYWoUrJVW4WlIJfWX1nT+oGTw95IgJ0WBAVw36h2kxIEyL3iG+UCsVbfJ5RHR7jf39dtkrOSaTCZmZmVi8eLH4mFwuR0JCAjIyMup9jdFohNFoFO8bDIY2qW3WZwewPaewTd6biNpOJx8VgjWeCNN6IjLQB5GBPojq7IPunb0R5u/FVYiJXIzLhpyioiJYLBYEBwc7PB4cHIzs7Ox6X7N8+XK8/PLLbV4bAw6R9Lr4qeHnqYS3SgF/LxW03h7w9/KA1ssD/t4e4mOda4JNkEbNqzJEbsZlQ05zLF68GCkpKeJ9g8GAiIiIVv+cBwaF4bsjV1v9fTsKWSP+z3Jj/v+07A5vJBPbNdSmdYppXL13eo87v4v9PWSo/f6ymv+QAZDLZbc8J6tpIJPV3pc53JeJ713fc/b3VcplUNS5Od6XQyGD7Z9yQCmXQyGXwVulgJdKAW+VAt4qZc0/FfBSKaFWysX3sLdXKmSQy2zv7aGQw0ulgJeHAp4ecnh6KKBWyu/43zsRdRwuG3ICAwOhUCiQn5/v8Hh+fj5CQkLqfY1arYZarW7z2v7+2BD8/bEhbf45REREdHsuOw1ApVJh2LBhSE9PFx+zWq1IT09HfHy8hJURERGRM3DZKzkAkJKSgpkzZ2L48OEYOXIk3nnnHZSXl2PWrFlSl0ZEREQSc+mQM336dBQWFmLJkiXQ6XQYPHgwUlNTbxmMTERERB2PS6+T01JcJ4eIiMj1NPb322XH5BARERE1hCGHiIiI3BJDDhEREbklhhwiIiJySww5RERE5JYYcoiIiMgtMeQQERGRW2LIISIiIrfEkENERERuyaW3dWgp+2LPBoNB4kqIiIiosey/23fatKFDh5zS0lIAQEREhMSVEBERUVOVlpZCq9Xe9vkOvXeV1WrF1atX4efnB5lM1qz3MBgMiIiIwKVLl7j/VTPw/LUMz1/L8Py1DM9fy/D8NZ8gCCgtLUVYWBjk8tuPvOnQV3LkcjnCw8Nb5b00Gg3/SFuA569leP5ahuevZXj+Wobnr3kauoJjx4HHRERE5JYYcoiIiMgtMeS0kFqtxtKlS6FWq6UuxSXx/LUMz1/L8Py1DM9fy/D8tb0OPfCYiIiI3Bev5BAREZFbYsghIiIit8SQQ0RERG6JIYeIiIjcEkNOI6xatQqRkZHw9PREXFwcDhw40GD7DRs2ICYmBp6enoiNjcWWLVvaqVLn1JTz99FHH2HcuHEICAhAQEAAEhIS7ni+3V1T//7s1q9fD5lMhmnTprVtgU6sqeeupKQEycnJCA0NhVqtRu/evTv0/36bev7eeecd9OnTB15eXoiIiMD8+fNRVVXVTtU6l127duH+++9HWFgYZDIZvv322zu+ZseOHRg6dCjUajWio6OxZs2aNq/T7QnUoPXr1wsqlUr49NNPhRMnTgjPPPOM4O/vL+Tn59fbfs+ePYJCoRBWrFghnDx5UnjppZcEDw8P4dixY+1cuXNo6vl7/PHHhVWrVgmHDx8WTp06JTz11FOCVqsVLl++3M6VO4emnj+73NxcoWvXrsK4ceOEBx98sH2KdTJNPXdGo1EYPny4MHXqVGH37t1Cbm6usGPHDiErK6udK3cOTT1/n3/+uaBWq4XPP/9cyM3NFbZu3SqEhoYK8+fPb+fKncOWLVuEP/7xj8I333wjABA2btzYYPvz588L3t7eQkpKinDy5Enh3XffFRQKhZCamto+Bbsphpw7GDlypJCcnCzet1gsQlhYmLB8+fJ62z/yyCNCUlKSw2NxcXHC7373uzat01k19fzdzGw2C35+fsLatWvbqkSn1pzzZzabhdGjRwsff/yxMHPmzA4bcpp67t5//32hR48egslkaq8SnVpTz19ycrJwzz33ODyWkpIijBkzpk3rdAWNCTkvvvii0L9/f4fHpk+fLiQmJrZhZe6P3VUNMJlMyMzMREJCgviYXC5HQkICMjIy6n1NRkaGQ3sASExMvG17d9ac83eziooKVFdXo1OnTm1VptNq7vl75ZVXEBQUhNmzZ7dHmU6pOefuu+++Q3x8PJKTkxEcHIwBAwbg9ddfh8Viaa+ynUZzzt/o0aORmZkpdmmdP38eW7ZswdSpU9ulZlfH34620aE36LyToqIiWCwWBAcHOzweHByM7Ozsel+j0+nqba/T6dqsTmfVnPN3s4ULFyIsLOyW//F3BM05f7t378Ynn3yCrKysdqjQeTXn3J0/fx7btm3DE088gS1btuDs2bN4/vnnUV1djaVLl7ZH2U6jOefv8ccfR1FREcaOHQtBEGA2m/Hcc8/h//7v/9qjZJd3u98Og8GAyspKeHl5SVSZa+OVHHJab7zxBtavX4+NGzfC09NT6nKcXmlpKWbMmIGPPvoIgYGBUpfjcqxWK4KCgvDhhx9i2LBhmD59Ov74xz/igw8+kLo0l7Bjxw68/vrrWL16NQ4dOoRvvvkGmzdvxquvvip1adSB8UpOAwIDA6FQKJCfn+/weH5+PkJCQup9TUhISJPau7PmnD+7v/71r3jjjTfw008/YeDAgW1ZptNq6vk7d+4cLly4gPvvv198zGq1AgCUSiVycnLQs2fPti3aSTTnby80NBQeHh5QKBTiY3379oVOp4PJZIJKpWrTmp1Jc87fn/70J8yYMQNPP/00ACA2Nhbl5eV49tln8cc//hFyOf8/dUNu99uh0Wh4FacF+FfXAJVKhWHDhiE9PV18zGq1Ij09HfHx8fW+Jj4+3qE9AKSlpd22vTtrzvkDgBUrVuDVV19Famoqhg8f3h6lOqWmnr+YmBgcO3YMWVlZ4u2BBx7AhAkTkJWVhYiIiPYsX1LN+dsbM2YMzp49KwZDADh9+jRCQ0M7VMABmnf+Kioqbgky9sAocIvEO+JvRxuReuSzs1u/fr2gVquFNWvWCCdPnhSeffZZwd/fX9DpdIIgCMKMGTOERYsWie337NkjKJVK4a9//atw6tQpYenSpR1+CnlTzt8bb7whqFQq4euvvxauXbsm3kpLS6X6CpJq6vm7WUeeXdXUc5eXlyf4+fkJc+fOFXJycoRNmzYJQUFBwmuvvSbVV5BUU8/f0qVLBT8/P+GLL74Qzp8/L/z4449Cz549hUceeUSqryCp0tJS4fDhw8Lhw4cFAMLf/vY34fDhw8LFixcFQRCERYsWCTNmzBDb26eQL1iwQDh16pSwatUqTiFvBQw5jfDuu+8K3bp1E1QqlTBy5Ehh37594nN33XWXMHPmTIf2X331ldC7d29BpVIJ/fv3FzZv3tzOFTuXppy/7t27CwBuuS1durT9C3cSTf37q6sjhxxBaPq527t3rxAXFyeo1WqhR48ewp///GfBbDa3c9XOoynnr7q6Wli2bJnQs2dPwdPTU4iIiBCef/554caNG+1fuBPYvn17vf8us5+zmTNnCnfdddctrxk8eLCgUqmEHj16CJ999lm71+1uZILA64hERETkfjgmh4iIiNwSQw4RERG5JYYcIiIicksMOUREROSWGHKIiIjILTHkEBERkVtiyCEiIiK3xJBDREREbokhh4iIiNwSQw4RERG5JYYcIiIicksMOUREROSW/j9wV6VJWrgwxQAAAABJRU5ErkJggg==",
      "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": 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['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 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": []
  }
 ],
 "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": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}