junction21_aud.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Challenge Category 1\n",
    "<p>For the first challenge, we want to solve a real-world problem of indoor device mapping. The floorplans we have provided you with already contain mapped devices, meaning they have defined locations on a floorplan. In reality, this takes a lot of time. We physically need to identify each device inside the building and place them on a floorplan. The objective of this exercise is to come up with ways to speed up this process. We have written down some ideas below, however feel free to be creative! You can come up with entirely new ideas of your own.</p>\n",
    "\n",
    "#### Example Aproach:\n",
    "<p>Let's assume the floorplan contains 500 devices. We can delete 400 devices, and use the occupancy events data to identify neighbours. Once the neighbours have been identified, we can simply use the data to locate the missing 400 devices! This means our engineers only need to locate 100 devices, and let the system run for N days and then find the rest.</p> \n",
    "<p>The Machine Learning aspect of the challenge comes from: 1. increasing the number of deleted devices, and 2. the smallest possible value of N that gives the best mappings. You can use Euclidean distance between the predicted location of the object and the actual location from our provided floorplans to determine accuracy of your algorithm. We have provided 5 different sites with a variety of device configurations and data, so be sure to properly create Training and Test Datasets!</p>\n",
    "\n",
    "#### Ideas:\n",
    "* Build a graph with most correlated sensors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Challenge Category 2\n",
    "<p>The second challenge is more about providing value to the client. We have provided data from real-world buildings. These buildings are occupied according to certain predictable patterns. For example a school might only observe occupancy during the morning. A hospital or a shopping mall might observe occupancy throughout the day. The objective of this exercise is to determine how people move through buildings by combining spatial and temporal data. </p>\n",
    "\n",
    "#### Example Aproach:\n",
    "<p>We can define a graph network of devices, with the edge weights representing the distance between devices. Then depending on the proximity of devices and the correlation of events within a certain time-window, say 15 minutes, we can cluster the most frequently visited spaces in a building. By creating similar sequences across the day, week or month we can determine patterns that show us how the building is used at different times of the day, or different days of the week etc. We can also try to figure out which paths are dominant and which are used least frequently. Such information is extremely valuable to building owners and tenants.</p> \n",
    "<p>You can experiment with different windowing approaches. Try to think about what sort of metrics are important. Is it really worth investigaing how the occupancy changes at midnight, or is it worth understanding how people move through the building at 9AM? Also, does the pattern change during the week, for example do more people visit on Monday or Friday?</p>\n",
    "<p>We would love to see your ideas on what is the best way to present the results of this type of data analysis to the customers. UI/UX experts, we're looking at you!</p>\n",
    "\n",
    "#### Ideas:\n",
    "* where to place ads\n",
    "* where to place coffee machines\n",
    "* flow of people\n",
    "* evacuation plans \n",
    "* elevators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Challenge Category 3\n",
    "<p>The last challenge is related to a type of sensor that is not common in smart buildings at the moment: audio sensors. We have availabe motion sensors that generate occupancy data, but we decided to augmnet that with audio data. We want to explore the utility of incorporating more data sources in a smart building.</p>\n",
    "<p>The data collection is done at a garage, and contains 4 audio sensors placed in a rectangular grid. There is a lot of activity happening in the garage: people are walking, driving their car, or bicycles. The objective would be to identify these events by combining the audio streams from all the 4 sensors and by incorporating motion data to pinpoint where the activity was taking place.</p>\n",
    "\n",
    "#### Example Aproach:\n",
    "<p>This is an extremely open ended challenge. There are numerous ways this can be tackled. Clever audio signal processing, or using deep learning to detect events. The choice is yours.</p> \n",
    "\n",
    "#### Ideas:\n",
    "* identify events\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "id": "5iV0mFTt-TI9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from matplotlib import pyplot as plt\n",
    "import librosa\n",
    "import librosa.display\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import base64\n",
    "import imageio as iio\n",
    "from plotting import Plotting\n",
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [],
   "source": [
    "import soundfile as sf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example working with an audio file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {
    "id": "WoI87pIi-3ad"
   },
   "outputs": [],
   "source": [
    "aud_sensor_id = 1\n",
    "#test_aud_file = \"audio_demo/junction_test_bike.wav\"\n",
    "#test_aud_file = \"audio_data/audio_files/audio_sensor_1/audio0.wav\"\n",
    "test_aud_file = \"audio_data/audio_sensor_\"+ str(aud_sensor_id) + \".wav\"\n",
    "aud, SR = librosa.load(test_aud_file, sr=22050)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 367
    },
    "id": "Q6IFIj66CeeA",
    "outputId": "f682d945-3ecc-4d5f-ea80-d9a3569ace97"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Recorded waveform')"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %% Plot signal waves \n",
    "plt.figure(figsize=(12,5))\n",
    "librosa.display.waveplot(aud, sr=SR)\n",
    "plt.ylabel(\"Signal amplitude\")\n",
    "plt.title(\"Recorded waveform\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try to split audio file in segments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split a given long audio file on silent parts.\n",
    "# Accepts audio numpy array audio_data, window length w and hop length h, threshold_level, tolerence\n",
    "# threshold_level: Silence threshold\n",
    "# Higher tolence to prevent small silence parts from splitting the audio.\n",
    "# Returns array containing arrays of [start, end] points of resulting audio clips\n",
    "def split_audio(audio_data, w, h, threshold_level, tolerence=10):\n",
    "    split_map = []\n",
    "    start = 0\n",
    "    data = np.abs(audio_data)\n",
    "    threshold = threshold_level*np.mean(data[:])\n",
    "    inside_sound = False\n",
    "    near = 0\n",
    "    for i in range(0,len(data)-w, h):\n",
    "        win_mean = np.mean(data[i:i+w])\n",
    "        if(win_mean>threshold and not(inside_sound)):\n",
    "            inside_sound = True\n",
    "            start = i\n",
    "        if(win_mean<=threshold and inside_sound and near>tolerence):\n",
    "            inside_sound = False\n",
    "            near = 0\n",
    "            split_map.append([start, i])\n",
    "        if(inside_sound and win_mean<=threshold):\n",
    "            near += 1\n",
    "    return split_map\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_aud = aud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [],
   "source": [
    "sound_clips = split_audio(selected_aud, 10000, 2500, 1, 5)\n",
    "#for intvl in sound_clips:\n",
    "#    clip, index = librosa.effects.trim(aud[intvl[0]:intvl[1]],top_db=20, frame_length=512, hop_length=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0, 400000],\n",
       " [1872500, 1897500],\n",
       " [1917500, 1955000],\n",
       " [1962500, 2500000],\n",
       " [2502500, 2855000],\n",
       " [2920000, 2985000],\n",
       " [3400000, 3425000],\n",
       " [3460000, 3505000],\n",
       " [3585000, 3652500],\n",
       " [3715000, 3840000],\n",
       " [4137500, 4177500],\n",
       " [4180000, 4247500],\n",
       " [4260000, 4292500],\n",
       " [5015000, 5047500],\n",
       " [6695000, 6730000],\n",
       " [7115000, 7150000],\n",
       " [7342500, 7375000],\n",
       " [8527500, 8562500],\n",
       " [11112500, 11145000],\n",
       " [12522500, 12557500],\n",
       " [14097500, 14132500],\n",
       " [14472500, 14517500],\n",
       " [14642500, 14680000],\n",
       " [15145000, 15187500],\n",
       " [15720000, 15755000],\n",
       " [17537500, 17575000],\n",
       " [17670000, 17705000],\n",
       " [19345000, 19377500],\n",
       " [20320000, 20352500],\n",
       " [20500000, 20560000],\n",
       " [20575000, 20782500],\n",
       " [20790000, 20837500],\n",
       " [20887500, 20972500],\n",
       " [21005000, 21065000],\n",
       " [21067500, 21115000],\n",
       " [21122500, 21177500],\n",
       " [21215000, 21335000],\n",
       " [21342500, 21367500],\n",
       " [21412500, 21590000],\n",
       " [21650000, 21765000],\n",
       " [21800000, 22100000],\n",
       " [22107500, 22170000],\n",
       " [22180000, 22237500],\n",
       " [22242500, 23835000],\n",
       " [23865000, 23920000],\n",
       " [24272500, 24320000],\n",
       " [24402500, 24437500],\n",
       " [24532500, 24552500],\n",
       " [24622500, 24645000],\n",
       " [24647500, 24722500],\n",
       " [24725000, 24842500],\n",
       " [24847500, 24890000],\n",
       " [24895000, 24985000],\n",
       " [25067500, 25112500],\n",
       " [25160000, 25382500],\n",
       " [25477500, 25537500],\n",
       " [25960000, 25992500],\n",
       " [27710000, 27742500],\n",
       " [28055000, 30570000],\n",
       " [30572500, 30812500],\n",
       " [30815000, 30867500],\n",
       " [30905000, 31217500],\n",
       " [31300000, 31360000],\n",
       " [31497500, 31530000],\n",
       " [31560000, 31585000],\n",
       " [31807500, 31840000],\n",
       " [33877500, 33912500],\n",
       " [35500000, 35537500],\n",
       " [36142500, 36160000],\n",
       " [36412500, 36447500],\n",
       " [36677500, 36712500],\n",
       " [37122500, 37157500],\n",
       " [39647500, 39677500],\n",
       " [40217500, 40252500],\n",
       " [40790000, 40842500],\n",
       " [41500000, 44192500],\n",
       " [44275000, 44347500],\n",
       " [44717500, 44750000],\n",
       " [44865000, 44885000],\n",
       " [44992500, 45027500],\n",
       " [45942500, 45965000]]"
      ]
     },
     "execution_count": 404,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sound_clips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45977735"
      ]
     },
     "execution_count": 392,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(aud)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39"
      ]
     },
     "execution_count": 393,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sound_clips)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 550,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Recorded waveform')"
      ]
     },
     "execution_count": 550,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "\n",
    "for item in sound_clips:\n",
    "    if (item[0]<5000000):\n",
    "        plt.axvline(item[0], ymin=-1, ymax=1, color='green')\n",
    "        plt.axvline(item[1], ymin=-1, ymax=1, color='red')\n",
    "    \n",
    "plt.plot(selected_aud[:5000000])\n",
    "\n",
    "plt.ylabel(\"Signal amplitude\")\n",
    "\n",
    "plt.title(\"Recorded waveform\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Recorded waveform')"
      ]
     },
     "execution_count": 405,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "\n",
    "for item in sound_clips:\n",
    "        plt.axvline(item[0], ymin=-1, ymax=1, color='green')\n",
    "        plt.axvline(item[1], ymin=-1, ymax=1, color='red')\n",
    "    \n",
    "plt.plot(selected_aud)\n",
    "\n",
    "plt.ylabel(\"Signal amplitude\")\n",
    "plt.title(\"Recorded waveform\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [],
   "source": [
    "import IPython.display as ipd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" >\n",
       "                    <source 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\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "execution_count": 548,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipd.Audio(selected_aud[sound_clips[13][0]:sound_clips[13][1]], rate=22050)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x140749f40>]"
      ]
     },
     "execution_count": 440,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the sound of the door\n",
    "door1 = selected_aud[sound_clips[13][0]:sound_clips[13][1]]\n",
    "door2 = selected_aud[sound_clips[14][0]:sound_clips[14][1]]\n",
    "door3 = selected_aud[sound_clips[17][0]:sound_clips[17][1]]\n",
    "fig, axs = plt.subplots(3, figsize=(12,10), sharey=True, sharex=True)\n",
    "fig.suptitle('The sound of the garage door')   \n",
    "axs[0].plot(door1)\n",
    "axs[1].plot(door2)\n",
    "axs[2].plot(door3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "metadata": {},
   "outputs": [],
   "source": [
    "sf.write('visual/audio_sensor_1_door_1.wav', selected_aud[sound_clips[13][0]:sound_clips[13][1]], 22050)\n",
    "sf.write('visual/audio_sensor_1_door_2.wav', selected_aud[sound_clips[14][0]:sound_clips[14][1]], 22050)\n",
    "sf.write('visual/audio_sensor_1_door_3.wav', selected_aud[sound_clips[17][0]:sound_clips[17][1]], 22050)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1411ba3a0>]"
      ]
     },
     "execution_count": 532,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the sound of something being dragged (?)\n",
    "dragged = selected_aud[sound_clips[16][0]:sound_clips[16][1]]\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.title(\"The sounds of something being dragged(?)\")\n",
    "plt.plot(dragged)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "metadata": {},
   "outputs": [],
   "source": [
    "sf.write('visual/audio_sensor_1_dragged.wav', selected_aud[sound_clips[16][0]:sound_clips[16][1]], 22050)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x140b42940>]"
      ]
     },
     "execution_count": 470,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the sound of a car engine\n",
    "carengine1 = selected_aud[sound_clips[2][0]:sound_clips[2][1]]\n",
    "carengine2 = selected_aud[sound_clips[31][0]:sound_clips[31][1]]\n",
    "fig, axs = plt.subplots(2, figsize=(12,5), sharey=True,)\n",
    "fig.suptitle('The sound of the car engine')   \n",
    "axs[0].plot(carengine1)\n",
    "axs[1].plot(carengine2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "metadata": {},
   "outputs": [],
   "source": [
    "sf.write('visual/audio_sensor_1_carengine_1.wav', selected_aud[sound_clips[2][0]:sound_clips[2][1]], 22050)\n",
    "sf.write('visual/audio_sensor_1_carengine_2.wav', selected_aud[sound_clips[31][0]:sound_clips[31][1]], 22050)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13e331730>]"
      ]
     },
     "execution_count": 551,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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72r5iySLGdb8nOLnR7+eYvGp3vsb3jl4cMN7fnbjG1fZeE/x2nIQzDQRBEETBgIxlIgyvk/t74pAotnDz/pj34QY7abt/lmxHx2HjMWH5Tpz/7n/o8tKEuB571vp9AIAVO9yV3T56Iv5FVNyGprz8z4q4H9uOdbuPeFZg8RpWce+I+Z62JwiCIAgBGctEGLHUIIlW+za/JHPtvJEiFnboyMUA4l8EJcV60n6bv0W7PlsJq/hoirMM34sXNvfUBrehKe+59O7GiyNRDAymrvamSR3tPcY5x7yN+0jXmSAI4hQmLsYyY6wvY2wFY2w1Y2ywZn1XxthcxlguY+yieByTKHjMWr8Pi7d4T8C6J5+8fnZyY2KQsGlvfMMvBMu3BzzKJiPvnhHei6xc2rZWTG0qKERjh37iQtNbxo2soY6RC7bi/Hf/S7quN+ccE1fsJKOdIAgiCcRsLDPGUgC8A6AfgCYALmeMNVE22wjgOgDfxHo8IrF4VaeQDdCl2w7i7Lem4HhOwSz8kEw749Bxe+/pn4uiS6arUbYoTqtaKqrvFhTyCrABuNaKx18bQ1x+PBgxaxOu+3QWXh+7KqntSDSb9h7FqIX2yacEQRD5TTw8y+0ArOacr+WcZwP4DsBAeQPO+XrO+UIApN9UyFimkZl7btSyJLTEme0HzTG7ahhEvEmUR7Ba6aIoUzQtIfvOL1ZudxfHnQzEVcvNT/FxDZusiotvjPNmLL/013IMG708EU1KCOe9MxW3fzM32c0gCIIIIx7GcnUAm6S/N1vLPMMYG8QYm80Ym71rFxUPOBnQhfbaGaUmJqzwJlkXDXZG/AcJ1h32msDmlvmb92PaWm/xuwWNh34ySwDKyAOOe3s1TFRzwlhkJZ++M8FdHPc3MzYmpJR4tGOtdyeuwfv/Orf93hHz8eJfyTWqOefYcySQMGxXlIcgCCK/KVAJfpzz4ZzzLM55VsWK0Zf0JWLAY4LfviORahirdx7G0q36wialDV7Q18es9HbgKDiR6y08JJ5JfscSFJqSaI+4WzbuOYr7vp+f0Pb8Nj8UN1wsPSVi/WtjVmJGnAcOh10mHx44loNxy3bg0V8W4cqPZsS1DQASOiA6cDQHv8zbYpvYOXLBVuzVPOvxRI4rzyVjmSCIAkQ8jOUtAGpKf9ewlhGnAIc0xsS63UfQ/83JEcuXbj1oLFdsV2TDZHh75USON0Pui2nr43Lc/MBLwRMv/Ldmt6vtHvllIX6euwUz1+31tP+dmlkIU8jK2l2hcuC6OOc3xq3CpcOnuzruml2HXYXGFEtPdbW/qz+egRs+nw0A2J+AMuzzNu4Pft60N76e600OnvCt+4/hrm/n4Q4X4RErdxxC5uBRyBw8SjuQtiMnL3Q97JJxCYIg8pt4GMuzADRgjNVhjKUDuAzAyDjslwCweuehApsB7/VlOGfjvuDnGY/2DFtn9xPX74lPclW2R23ep35fGiwPHU8ScT0XbzEPKDjnro1elV/muhv3pvgCXUksFRkFJo++vHTY6OX4wEV4ARA5pT9nwz70fOVffDg5MvTm9q/nYsgvi4J/6zzYOuQy3KYBYbzwOiBxQlR1NCFmRbYdcA6v+nr6huDntR6LDcmTWuRZJgiiIBGzscw5zwVwB4C/ASwD8D3nfAlj7GnG2LkAwBhryxjbDOBiAB8wxpbEetzCxvjlOyI0eKeu3o1er07C97M3Gb4VMISu/3QmJsYp5tdLFMaPczZ72rdP2nnlUhlh6+yKdXi1LU3TxdEYqTuiiL92ItpS1QAwbY1+Ot7OE/fdrE244sPoQgN+cHmNJ60M5Bhc/9ksT+dZt6VJHUNd/ILLxLW9R8Pvh48sI3nG2nCjc8/hExi1aBu+nrExuKxomjtjWWXM0h0Ryyas2BkXr3A0Wuh2McDy86IbqIjr6fWwYl+cc7w1bhU2OAx65d+1/2hiQz6SzZMjl+DBHxYkuxkEQbgkLjHLnPM/OecNOef1OOfPWcuGcs5HWp9ncc5rcM6Lc87Lc86bxuO4hYn/+2w27v5uftiyNda0s85rOG/jPgz9bTGy8/yYsGIXbvpidsxtyPNzLNWoW5j4aa5XYzm6iidca1KZafPMGO3yaCTK3E4H/zx3M274bJbn/etI8TFULZ2hXTdv0z7t8nLF0yOWff7femQOHoVHfl6k+Ubi8DIYmK6JxTUJT0QzNZ+T58dlSmiG2M8+xSB7+o+lEd/PcOlZVvl3ZeTg9fpPZ8WlMuTb41d7/s4wm+S9XYdDyae6GQhhPx/PycPrY1fazh7IV0gU4tl56AReGbMS131q/3zI8eGP/rLYdtv8YOWOQ9h92D4xd+fB455zG/YeycZn/613PRAlCCL5FKgEv4LCviPZaPvc2KgKbOiYvnZPWPLKviPZGPjOVG3ykNtjXjp8Or6YtiGYUKX21zsOHvc8Jf7GuFX4dOp619sv9yj5lRKtsRynGdlo1L/mbNAbpzJTV+/Gfd8vwLjl7rz7Ttclz8+NU96mc6F7YT8x0jyBUzLDXSxuNLg1lv9avA27DkUaIyZvfjSDnRZP/hMRSvP3koDXd64UBwyEX5d3JgQM0mg9y6k+d11rdq4fmYNH4ScPhpPX8AYAGG6j9nJQChvR2X3vWudi64HjeH3sKtuZLhkxOF5ntddJa/yWr+YEP+ti2fOb3q9NQtazY43rs3P9aPf8ONR79E9P+y3sXnOCKIyQsaxh8urd2HXoBN77dw3uGzEfmYNHYfKqXcgcPAqrbMIFTFw2fDq6/29i8O++b0zCgk37cbYmCW6PJoRAF78nQhqEkZTn59huGVjHsvPQ/vlxYbGXAHD9pzNx4+dmD/SbHjVcPRNlKe0lMSb49WlaGUB0WrnbHeI0mwz9K0z9wI3k1aLN4QOieBRx0RnLKT79CWcsYLjowiXmbXQeHDgxYtZG2/Wcczzzx1Lc8tVcPKuR85ONJpm8vMj2OsUH26mQNKseXsxFLjzy8t8rAsd0cT11Ayq3BpEw5O/P5yn5RZsPBO9VOfdAd8v8Oj+8eqFdfPxc6f4Rp07MuOw9Yu+ltaueuWL7Ibz41/KIe3b/0WzH8I5YyRw8Srvcq7qOwO0Mydb9xwrEoIEgCDKWtcgxej/PC8QRj7ReGHJW+uRVu9Bp2HhXxk52rh+rdwYMbaG5u2nfMVw2fBo6vjAuuN3d30WWPf52Zsj4OHQ8B5mDR+G4pewgv8xfGB0wPISB8P3szXjlnxXB9RNW7MLYZZGxlE6si8KTpUMNw+jSoIKr77nRiXVz3JU7vCfrdWkQKWG4dtdhLN8eMBjU5Cid9+yYso1qwDV+/C/X7TEZ4zpvtcnQq1a6KADgJ03y3u8LvFdPUw2Y5/+0jyX+eMo6fDzFXK7ai2d5ydboZ3/UmQ51puREbh4++2+9434ufO+/iGWqgSlL6t361ZygUTlRE66RaGat34tz3p6C4ZPXYt+RbKzfE4qj1lXwbFmjdNjfG/ea+wPZkBZG4RHr/vcSraBe6ss/nI73Jq7BwWPhz1evVyfhzJcnut9xlPyzJLLCZrTSkhe8G3m/6Og4bDzaPT8Ooxdtw/xN+6M6FkEQ8YGMZRumSwlAwW5Repc888dSbNl/DBv2uEvauf/7SO/R9LV7sVXyXjrJTqlld+0ScgDgrSjiG1Wu+SQyOSyajH/1NXxZ21phf7spTHLVGbXC4nndeJVGLw696BZ4fOms2hk5k9DjlX/R9/XIWQFAb9At2Bx+zBkxqBmYXs//rnRfxEeEBG3UnLvGVUq63s/xnDzsPnwCU1Z7U9rQeZNlTFrNew5HemuFwRuNB27NriM4eNx8H3d8YbznfZrYKoVhjV68Hbd9FZBhe+mvFaavJIwt+wJtmbNhH1o/MyZsAK3zLF+UVTPs76mr9Umm6rnU9U0rXc7MqXkKudZgUF0uYornbNiLL6att72esTDoyzkRoTI5mpkONxx0CEdRufXruTjvnakJ17kmCMIMGcs2yMkdwgaSvaPiXeA2FFc1pOy+diI3D9/NjIwNVEMJ5H2K/aleuzw/x5wNkQbaQz8uwJUfmXVpK5QoAgA4eiLSc66GeESD+uK73iEBCACePa85Lm0bennbTQkDwAVtwotJPjsqMonLjtfHegtNmaCJW1YTzHQlwgXf39wBdSsWN643ebPcxpqfXrts0CA6ofFGe0mmvO7Tmch6dqyneFs3HDFImY1csDViWWpK4Mc8/ps+IUw+lwNbVQtbd/hELlo8+Y+xHbqQqGhRZQu9Jq2qPPvHUmQOHmXr3ZR1qWWEx1en2KHb3eO/Rp5bNfRk2baDEedSve8B4Nd57qQI1d/FrE5WjodfI/2+C9+bhqG/LUGnYeYBTk6eP2JQ+dHktZi8KnKgqZvBuf+HBWE5JXIbv5mxEV9KsnluOeTBuG/zzJgCKyNKEIUdMpZdIoo+yAau6LgOHMvB0Wxnb4EaMmunJTr01yVaZQq1ZHOu5N1gjGHP4RN4V6nE9fb41bjwvWlhyw4czcH3szcbvURAaLCgm+JXlQTcoA4q4tHvZ+fZh8C0yyyHhpVLxH4gl9z/wwJMdfC0nla1lHFduzrlkG7JCOhiIsULWhi8ZzWp7Kl9jauUDMYyf/BvbCW+xcyLGnKQnwh952OGgjPyPXZrt3oO+4oyqN4F13w8M+xvL+XPdQbxR9aA2C5Z1BTrbzcToO7P5Kn9eka4YfifQdJQRe2bTJj6xpu/DMWzz1cSNAH7JMI3xq7CtZ/MDFP8eHbUMlytXBvAHOsulzI/ki2rdyzSDipU1PPr5jsyugEjQRCJh4xlDTojTrwM5CR3sdnF709Dk6F/a/eVK3WOXqSvZime4OM5eXj+z2URGfzyS4UBuPiDcKMYAF4bG15K+qEfF6Dl0+FeILtqdSZPnxOqF0Q1lqOt0tW5fijW2akqn48xdJXijjfGufqZDicZPztlAiAUOyumy2XETIIw7G7oXAcAcKkyVW7i6xkbbQdppkuSneuPSyJivBH3WLYh2Uo2TlINxrAwRrs3ioxPd2LnIXfhH9s1YSJqUR+dpxcARi82x5FzHrg213wyM0JJx/R8/WxTaEY15kyedzW2Od4ez77Nqmj3P3/TfqzeeRgdXhjnOSlShB9t2x+4FnbJuG+O188ozV4f8qjr8kucUMPXvA40nRSIlm8/qL0W63YfIRUOgogBMpY98sU0qULVLn287MHjOcFpylfGhAxVL1Jr6mv92VFLtUZWrvJyM7VJ5vvZkdPmQ38zy4zpPFu6RCAVWS7P7X7dkJVZLvjZyYDz+ViYx9CNR++X2zq6aod67gVOFdHckp4a+XiKZEHxm2qXLwYAaFWrjOv9mvSu52zYh8EaTeYDR3PQ8LHRaPz4X3ErfhMv/EFjWX8twu8x/e8WWs/R3I9Ocmh2nPXav2F/3/TF7LDYZsGHk83JkBNX7ETHYeMwaeUuXKIMlN3GB8u4jcN1W1zF7TldryQR5+SGf0+2/3q9+q+raoIyE1bsxHgrREqEcrwnJQ5vVPJO5khGsUzJjLTgZ6cQMB2xjinSNAO+PD/H62NXYtyyHej7+mRtKEr3/03EgDenxHZwgjiFIWNZg1084TzN1J/KNR/PxIXv/YelWw/iPZfTjpFtCGf7Ab2RF432rEqiPIZOSm1ebZOXLmwRscypLC6D9+n1SqX0BUFUFkWpw12rXDHb9fUrBcJGdJdWqDOIxDYRsmEyFlVqly9mHOb87+9QstnNXesGP1/5cSj29G+NKoCOeGiUr9t9BJmDR9mGtYjxiqmUuXx/HNHomgMhtRk7O7FRZX3io92d5VSwYrcmYVEbzmXzjN/69dzgftRB2jsTvPc9uoRWHarWs8mL7UauMSfPj26StCYAfDJVybuIsZ+7/tNZQa+uCG+S45e7vhxeLMYUr168iL3udp6f23qs3aq3mDz1qSmRr+ypq3fj9bGrcIMlC7rVMJBwcl4QBGGGjGUNnzkkS+0xVHVavOUAbvpidlDm58nfo6vqvXLHIbPsgUK03lkZJ4MzWtQX6NfTw/V3dS8V07kFgD7K1Czg3Hafz5ux/FDfRqhmqJ6nIsdPekEOBdF5p2+zYmvtrm0Vq43C++zWWP79zs5Gz7IcYtS7aehcyx60I5pkTx1nvxW7F0vEln5uI98mzpHp98tFT0yJuCI23y6UQGf05eT5bT3Lb45fhXqP/qktvGImspGta5V1/e3f5m/Bb/PdJdHpkBNa7c5HWkp4O03PoZv+yU2FydwolSd0BNtks0uTXOYr/6zULhfUe/RP9H59knG9rorhn4siw2xMHn6dMX7NJ5Ex1zKUFEgQsUPGsoYFm+1H/6c/O1YrVH/2W1PC4g51htBrY+w7WyDg6VUNTZNXTE7SirZLfOhH7wUR3GTzq79htpJB30mjs/yiUpZXfjHojN5ho+01fX2MuSqzXbZYGq7pUBu3dasfzLwXyNex5ysTg593ejKCwhEvsIkrQt6tq8+oDSD0O+28acOvyQIgGcsuqzWWykjDLWfW1a6Tz5PwbqtEWYQxKoZYJY//WbrDaHQ5Gcsyxw3x7cIOtjPs1mjCm/q8PgkD35lq/M5X1uCw7XPmKnCRRLbBi5zf3d/Nx93fzfdwPDN2du5Fp9cI+9t0q6oa42fULRexjVPS2vGcPNf3NxAqm71i+yFtKIqTdKGdqoVdkRuBWjHSidu+nuv6OG+MW4V7v5/vuE/Zg+3l3BEEoYeM5QSiJuMBgc7OiYvemxZhiE1bq882l1800RZo+HORu6l1r5iME0H1MkWxftiAsGVqqeS1u0MvHtlO+27QGa7akJGWgh6NKzlut+9ojjEcRRhK45bt0BpN0SCSJr+Tqt09c14zAKGkxTU2L11RWCSknOH+hdi/RVXtcnmQYHLGH/YQo2uK6dbx1uWtMXNIT/O+DNP5YkCR4aIsdami+hLfYh9OOraqR89NfoBXdh2KbINQtUigWIcWuxAKdQCqDmRTfAwta5TGwz+Fe41l7frsXD9y8/yOAx0vRXuAUGJsn9cnofdrZi+vCa8KFU6s3nnIsW/OzfPjrXGrgoa+ztss+M1FUuATUg7K8WwylgkiVshYLoBk5/mjShKLpkKdW1Svm/oe1SkN9NeU8/ZKqhQbUERKeJOPpibnyPQ6rTJa1ixjewwRE6tLfARCHvKHflzo0NoQmYNHRSgL3HJmSLpMnE+dgSakvZ7/M9IDdmbDiiiWnoKi6QHjkDGG9BSf1uBI18Q3AuYEP3m6tkQRvWE5TqMjbUKdmt916ISxdHCDyiVQqaQ5/EW+3xY/1Sf4+T/rXHVrFBgQpaf6cOh4TkTFsxcvbI7GVfSSfZdkBbyk1csUNR4f8DYgURnQXD9AAYCe0mBu8M/h99jaXYeDns5vb3I3QAQiPbqAWQbOhJ2nvXyJ9LC/1Vmk9nXKIS3Fh2lrIuPN9x/Nxk9zNqPhY6NRf8hoT21ygxuVHc65fcB5HOn16iQMeHMKDtgUnBq9eDteGbMSL/+1HIeO5+Dp373pwavM3rAPt1pl40VlV4IgoicuxjJjrC9jbAVjbDVjbLBmfRHG2Ahr/QzGWGY8jkskjtmP9Qr7e41S4ED1OsVrel59z6VJBrKc3LJLim02eW36N6/iKl7ZqZSsaJPXIhV7DmeHVRi8ukPt4GfhpdTNPti2BUADJdksPVVvLIMF5NCmPdIjbHHZYiFDR/amy8aRGooSDarRctnwSFlDgUnWTZBnMOQ/sBRiSmYElmXn+nHDZ7Nx3jtTkZvnR+3yxTCwVTVcqlSLlBHnQ6c+IqMbvKhMf0TvHVefHxl5ALJhz9EwSTk5jKCKy1h6ADhtaKQ31klmEQgUIhJGtTrY6XVaJUx5uDuAgHqOrIihPrc+xrB29xFtsFaen+PpP9wZg3bJciZeH+sc6lb30T8xM4pKmhe0ru68kQGdbrrgzm8DMnR7j+ag+ZP/hIVhNKlaCl/f2F77vd6KoorM6MXbcd+I+fhuVmRxK4IgvBGzscwYSwHwDoB+AJoAuJwx1kTZ7AYA+zjn9QG8BuDFWI+bKNyUTz4VUE2Xgw7lrd1IyUWDKTllkRRXbjLsZK+0oLVGYs3JUItWD9rPOeZuDMVpy0Zerp+HKUaULRaSpBI/R3fU7Ny8MA87EKhip5sy55yjcdVSqFq6KKY83B2THwoYOhVLFgluI5dXV40jWRHDDXf1bIA20vlV92cXwiKu1TUdaqOMdC4ETolislE1xzrnfh64Bk4x6+L6OsmmuTFWTdt4kY1s/cyY4OdbvgrFs+qUELwgbnNd3LBg9+ETuG9EIIfhe8XI+ujatmEDLTlUQDVqp6zejb1HsrUzZLl+HqE3bCLHhZqGyt9LduCVf0LKLrrZDK+PdM1ygVmHWGYX3Bzyd038tp9zNKtWOmyZmDlwmk38WaqY+P5VbVy0gCAIHfHwLLcDsJpzvpZzng3gOwADlW0GAvjc+vwjgJ4sHq6rBBCLbmphQjUwqipT1Krt4uVqysaaiprsY3pXNpKSnuRjj1oYeoHrEod04QUvWEmCpjLT0SaTH8/JC/uuHBaR5+dYYTCgQlX6Ik/qiVx/pLHsYxGG6e7DJ5CTx4MSczXKFkNNjWSdXG43ItTG5ofLxoigefXSKF8idG29KBiIstWpPp/2e2rb1KqM8u8X7fZzDr/fHHYS2nfg/3bV8ACgRln7MI1E4zSoM/HCBc0BhAw9p0HB2GWBJGU5Ee58y6OaKqlgyHuRr7sTbpVbcvL8mOayMiAADD075KN5a/xq198TqEViAOCRfo0BAH/d3RUAMMoaIIh4fKeZKzluXz6fj5+t+pPM3N69fth5B4B/lurzTFrZhJwVTdeHVhEE4Uw8jOXqAGQXxGZrmXYbznkugAMAysfh2HEnmjLOhREfY7i8XagqnPpKUA0pL8by+1ed7npbk8HWsV5ISUOOWXYqxKAaXbLn+pF+p3lqgxNFlKQz+YWXx3lYaIGcDGk3jpy3cX9EGeNUnw95igGU9WxAgWHWevupZlnrV/X21Shr1oPWGSPdG1VEG0nmzIusYZo1kDB5ydUqdmrpal1IAOcBbVmnKnt5Qc+yvRFnqrCXX/gYw5uXt/b8vZ/mBGLxxeDRS+KlQBhhaZrZGiBQOc4tTudZ5Bi8OW6V65Ls1csU9Vz6XWWXRrby5jPrYf2wASiuDLLFwOO2bvVwSVYN3HdWw+C6jLTQOZJl8WSvsWlgrrLquX44p2W1SGN5SeS9eHfPBrYFlXSDAYIg3FGgEvwYY4MYY7MZY7N37drl/IUEcPXH9pqVpwrMB3SSykqrBqNqP57bshqAQEKdE6pn1A6TfJrs0JGnap1exP+t2YOcPD8451i3+0jY75D3Kat0iDZc0d4c96p6OoHA1LTs8UyRjGC/opfavHpomlWcy7aZen1dNSkwReNZFsxwiMu8/MNQwRFV8uoSlyW0BakpPnSR5AC9GMvCa5rqY1rPspCRE2TVDoQSiPtNnm4WiBjRyasik8yeOrdp8LO4Drl5HA0rl0DV0hm4oE1ovC8u2x8LtxlnA2SukWLT3XC+yzjYFB9DZZtZGRPiftm8LzCgyNac33euiJyiv6lLneBncQ580kMiPztyZVMncvI4ruuYadPegNG6ce9R1/fQ1ME9IgxKrwgvcfnigVCToTbeX2Esly+ejpcuahk2W3Y8xx8MUflhjj5pOMeldz30XIT3mfUUaceH+zbGvWc1tB1ok4QcQURPPIzlLQDkt2oNa5l2G8ZYKoDSACLm1zjnwznnWZzzrIoVK8ahaUS0MIS/DFWPrWw8v39VGzx/fnPMe/wsfHRtFq71aCzYYYpZll8KshHqZor3RK4fn0xdj+7/m4ivZoRe8qbp+oVWAuA3MzZq1wPA6bUjDdst+4/hqKSPLRsaeX6OZdtC3rhPrm8b/Ny1YcDgnLQy3MgzV/XSe2NjRShueEFWScjO9cPv59h+4LhWy1s+ZyIeV4SUmH6rMLKEcaI774JWT4+JWCYUL6pKscWHTuTi/X/XYOb6vahcKgPTHumJh/o0Dq6/q0eD4Of9LmaeGhqq/ekYMegMvHpJS9zRvb7jtimMhd1DbnnYCiXo1yygyKF6lqcO7oEBGjnBNClsSDfANc24vHxRZKVNmT6vTwpWotQh8h+YzTEA4JWLW4b97bVSp4pQVhGJvP/XuY52O855cLZCzB6VLhoeZ3/b13NtDX03es1AqJ9Tf1uL6qXD+jpZO/2uHvp7SQzCCYLwTjyM5VkAGjDG6jDG0gFcBmCkss1IANdany8CMJ5TWaECDUd4TGJlpQT08m0h47lvs6pITfGhrOWRcYpf9BLW4MYZItu4H01Z57w9gLlWgZShkh6pySnjJrFL92J8dcxKYwz8NzM34nPJG1dMCtkQsc3bD4aHD5jKa6sDm1iZ+EA3z9/5n2W4VC0diuvt+vIE9Hz1Xwz5ZZFWlq9uhdBUdHowDCPw/47DxtseL1i4xcUgQZbsE8VW0lJ9GHvfmQACurqiuI1oh3zN5UGDuMzyQEflinbmGQiV9nXLgzGGHqc5a4Ezn3e1sy4NKgQ12ydYqhs5eX5UsZ7ntplljZJ5cjJb/UqRAwDdPT9zSE9cbJiR0BnkOsS1zfVz25ki9ei6hF4vPP6bfcVVEWrh58BNXwRKS+88GDi3/TTVRXccNIf/nN2iGl660H5QYcfwyWvR8LGQ7J7sPLivdyPtd9xokRMEoSdmY9mKQb4DwN8AlgH4nnO+hDH2NGPsXGuzjwGUZ4ytBnAfgAh5OaJg4Ve8e6oxZjelp8b3Rexb2dczA5vqN4Q5rEJeXs1BH1eHrgKhybOc5kKFQBcGMXPdXrxiqNg4fFJ4LKZ8aNNU6mFDFUfGWNTVG3VkSkZs14bOMzzrhw2IqOgmWLf7CHI05+bcltXC2iwMJBEKsu2A3tAQcdViNsFNqXY5hlT8nppli6GYxnOu0yIuKhkZ4tL0e8OsIR6N97eNUtJ60spd+FC5R1KiyImuXb4YqpcJGMYfTVmHO7+dh5w8HkxW7CDF/pdUnts/FgZibMfdf6bWg68zlkMJpeHP5MwhPVHJZQhJanAgxG2rqaqD7lg9yzK6JOSQEe/Hpr2BwievWTJ1jDGMubdr2PamAd/7V52OFB/DJW1rYtVz/Vy3afkzfXF3z8Ash5PsXSyGOEEQkcQlZplz/ifnvCHnvB7n/Dlr2VDO+Ujr83HO+cWc8/qc83acc3dZGwWMt6JIrjlZyfPzsMxq2SgRsY8mvL6yru6QaVw39LdArKqcbAgApaRpz9rl9MkyciyoXNCBQ++JNRnLTnHQgHs9WDk2WcaNOIzJSx3wLCdmoubNy1rhfxe3xPphAzCwVTXULm9O+jMhEosqSCEad/VsEGZQCUPEqRiPkDD0+Rh8zF1ctGxk/l+nTMx4tCfqVyqhNa5mrd8XsSzMWHY8Wny45pOZeE7RdfYx5lnPfNPeY2hevUzw798XbMWuwydQv1IJTHqwO+7pGQoxkeNgs54dix2W17ReRX3pcz/n2HXoBE6TKuwJ7+7bSgx0pZIZOKOuu5xucV1GL96OK23yBMCBMsXS0L95FevY8bk6pTJStUVkRLteH6uvwqrqn5voK3mhvbQ5Iy0F57ZyF0pRrni680YEQbimQCX4FWTev6oNzklwzJc8XZxsSmSkonb54vji/9oBAF6QXtziJWpCjlv9b83uiDLStTQSZiZE0Y4KSmiHLAEXlApTDKdqZUJT6R3qhV7UOw4ex+jFkdJL6izuH3d2BgC8Niby5fhA74Zhfz/SX6+kodKveeR0rVvUBLwgzKzhKn5DtJQplh70Gqf4GHYePBGmca3T7FUNHBE+Iitv1K9UIixOV5gMIl7bRFNpsJHq8yHXz3E0Oxcta+gHIQCwUjpvjLFgSJGTpJwgI132LCdP8TJwaG/Hb1mzTESZ7OxcP1JTGGqVLxYRRy/YrVGGUPl57hbM37Q/LP5WPEO66pFuzndm+WJhgxg7mcmszLKYP7Q33r0yoK4TD89yTp4fB4/namfOhGH73sQ1wWX/KN5kVcvYLqYesL+fdHrjJjUSFbc61gRBuIOMZRc0rlISfZu5i7eLhcH9GjtvlE8USQ0YCOIFIasqXPjef7bflRNJrvhwBu74Zl7Y+mi8HnZxztd+OhP/rd6Nuo/+6WpfqvSaQH2Zi4puK3YcijDEr+8UnvyjxnSbiMX7pSYRCRhgtJabVtOXeI6GfUeycSwnD+e8PSW4TDeA3LzvmKv9pab4gsVYxKl3ijuVY5RTfAzLtx1Ek6F/207Xm2JH3RpXsuFnZ+81ruI+sS8aGPNeKfPOHvW1Ruq3MyOrupli4k1s2X8M2w6EX2txTnXttGv6mHu7YvkzffHPvWeGPSNCGeUuyQMOBJQ66ioeb6dny6RScrEUQtTAKr+tS+bV3S9qMqf6npizYV9YoR4v6MK/dIofTapGPuNqvoN6/giC8AYZywZkKbA/7+qSxJYkl2iqhjHGghWvgECRgyOGeFu3yJXDVPYfzcHEle6lBk0hC6pRIf+1UDEkMtJSPEuEAUBKDElIpupha3YdwahF24K/a/Kq0Lkwea7a1TFXcTMxYUXkOdbF0S7YvN/1Pn+9vRNeuqhFsJ1OBo9cUCPVx7RtivyO/ry5HbeIUtoAMEWRoatYsgh+vKUDKpRIx882GrfqfnQ0qKQPdxD4mLc6mVVLZyAtxac1lnXhK27PhzybMFRJihPHktVG7rTUGUy3/k+3dkSDyiWRkZaC9FRfmFGak+dHRpovTMe4X7MquPeshhH7cRr8PNCnkTZ5tVsj5+RKIPqB7oY99mFrJnRH0xnQ6swdgIikzTiGcxPEKQkZywYYGF64oDk+uibLMWHn+5s7BD//X6c6YZ6KkxF56jDaCrvqNOwH/64xbOkOpxeVzia8+oxM7bamQh12hzjvnakR2z42wH0Vrs+tcJZYpGB1L0UZEeawxhSuIXGtFSeuGnBuk7AEumdDTVazo3b54mF6zk5aubJcVoqyrcnYNMkJuvUsZ6SlBGdD3hgXHpLDAGRllsPsx85CMYcKaU5T8l/e0N52PYN9WILKe1bxH7fjszXP98f1nTIdt1PjkWXEc19GGtyKc6dTY1g/bEDEefmfJAmXnecPGogXtK6O81pVw3tXna49104hMkVSfcHZIhmTlrvKvqPRhTbsiWMxkDTN87F2d2QZ+WZKboSX0DeCICIhY9nAFe1r4fJ2tdDLRVUoYWBc3ykTQ89pgpcvbomZQ3oG18ufnUh0XLQb6hsSemLhTYfys3Iy4byNkUlWuhehrCeq+txKZaQaJd++mq7XS/YSj8oY0754AX1hFlHoQJek9/z5zY3HCTMOLeOucim9wSQUPtJTnSWiBrSoilQfC1O7KJmRiv6axCY7dJ5l9UXtaX+OnuXQ+VAHUKbv7jqkj78tmaEPa9G1Sa46uWV/KPTAdMvo7oGGlUuihU1sdZXSGbhf4zGV21GjbLGwgio6OliJdEIbWedZvqdX5LQ8Y8y2XLLAbpZHN3i6sn3tsHY5IYdX5OT5gwb4q5e2wuuXRZ9knZ7i014vXXKurhLeHhdx3PFEZ8LrZvpu0OhB169UAj/c0gHPnd8MX97QznXhG4Ig9JCxHCM/39YRmRWKY9RdnfGolORVqWQGRt7RCSMGnYFKJSONNlNZUl3H5wa1DK7dS9kJU5UuJ8+mzJpdkd4O22NKL7Hz37WPiRbIEnXvK57raJJ9oi1rfaZlcH5/cweMv//MsMpnArHrscsiy9TqNFoF862CKECoot9T5zbTbium1t3OBtSpUDzMUPD7ue150xk7Ok/wbd3MiarNq5fGlIe7G9erMctyRUDRZoHa1uWG6no7DcayWxjC788Za0P1lExJax9dmxVWBRIItN1JvaNjfX2C4+i7uwQHcz0tTeZ6hpLJr1/WCg/2aRSModa1saphIOlGq9h0j6iDl4kPdMOrl7QMDiqjSY48nuOPuTKfgDGG8sUjB5o6+UFdYRm3OsVLnurjqV1TB/fAoK51nTeEfobt4b76XJe2meVwZfva6NKgYlITUwmiMEDGsgGTh+3Ny1vj6YFNsfDJ3lg/bEBwyrlptdIR8WQtapRBe8vAuFtJsGitTFWLOECTZ6ddZihOUCc/dnbzqniobyO8fmkrTHqwOzobXrpuuFFj7AFAt5cn4kdD+VYV04s8ntj1/26VDmRUtQk3u1j9XD98el2g+l67OuVQt2KJ4DWXEV5fnQa1XZjP85IKyb4jgWlgnT4wAOTkCmPZ3WO9aufhMFWQPG5vLOtitEtofo+dUVGldAZqlDVPCf86P7z4pxqn/X9SYmWsRSickJ8z+V647/sFoeUe9lck1WdbMh3QT7MD4QO5GmWLYfkzfXFjl4CBdVeP+pj0YGAA0rpWGVQulYHbu9cPGki6+9h07g5pdKbd8s6V4eEZmRWK44I2sYWkHTmR60rn3C3qbNBz5zfTDpK1WvEuL3bxIqkRsz/iug7RqOZUL1MUj/Y/LaJturG7WklxwdDexhkugiDiBz1lBkyxgee2rIZrOmSilMspXMG1VpleINBBA+FJVm0lY7iaxuvTXkqq0b38fD6G27rVx3mtq6NW+WLBqVK7gh8mTLqq2w8exwM/LNCuU/Hqo1UHAOOXR3pgvRCNJ8VkhKrIHsPUFJ/W2O3RODxpSLz4Xr2kVcS2ulLCAtmzfKNVNewHw4Bl2tpA8tmfi7YZ92fH8Ry/7SBDNwWs/k4nTMagQJW8KqdM+cvn2u3sgeyNVvnmRnOcsFy4xuQQ3mooniK4rG0oHrt7o0q4sn1tjL47kDCsu+4mI1aumAkEBiQXn14Dj/RrjNu610et8sWw9vn++PnWyBkr3TU1eWvVTUWsvcoFbSKn9csY1FpiYdraPVoZOjs61XcX7tG4SslAiIjLzuqgcm/+fU9Xw5YIVocEgNcvbYVbuwVCxkpr5OAEjSK82ZENY4xh8kOhmRm7/REEET/IWM4nyhVPxx3d6+Pi02sEY/i6W1nYjauUDPM8y56CKpIk2cIne2PRk71dHe/6Tpl48cLmuEwqvTvyjk7Bz4+f7S45Lb9qkg9RkuV+XxBu8OmlqMzGUjTOqMgBUPRTlzcr06riPOriqL2Wof19wVbt8od/WoRRC7dhvFXW2AtrdgW86l/P2GDcRjcF7HVQ4uQNVj3JzW3CiXTtkVVYBE1s5PNq2xjSAsaci6WYuM/S4z6nZbVgOfjTqpbC21e0xpzHz4rY3jSY0KmXpKb4cPOZ9YL3j8/HtNdD53ncc1ifdKYKh5xpqOD4ipSEJ/BSSfPJc5pg+TN9HbfbfzRHm8BmYunTffDZ9SED/8dbOhi3FSExbgt9qImrjWykAuV4+FJFU3Fbt3p44pwmuNDG0/7Z9YEZKnG9TFFhanVEgiASDxnLCt/c2B4jBp3hvGEUPNCnEV6WXjLiZd+pfoUwr51cQONyydgtlZGGkhlpEWobUwf3iDhWaooPl7atFWZQtKhRJvhZrqZmR35FukVOQYa/KZxkt1SeHqiP6/VCLJrIqtESzwp7du0a9lcobONWm9hhlbcslQdTlUAgMlxEjSd2w0iDoS8YoBRtKV88PaLwg0DnWT6eE6l8YeeZTLM5l2JfGWkpUYc1VSqZEZDHU8oPn92imjaExeQtrxlnNYMJK/QDKlnH2g71/h4x6AxPbbyuUx3Pg0Q3FEtPDQvbEPesbqawiFUG3W07TCXdTfS0Zl3y/IFjXN+pju1sSPkSRfDRNVnB2QFTj8EYQ/s65fDB1acbtiAIIt6QsazQsX4FbcxpIihhGYAZaeGXQYRBtDTEL1/SNrz0s6qpKWPn+XtXiTHUUd9B+9VErKVn/TzcwBzYMnLa187D0tuFiokT0ZR2Fqg/32U1bFeoRrDs5ZMLgnQzeAUF7eqUQynrHvx1vr0RC0Qa/I/0c1e10AuqJNjcjfuMBoZuuU75wm4WRd2HrK8uEhVrlC1qbIOboi+tapZBUZchPtHE2rvhLOV5MMUBt6rpXvZPpmrp+Ho7oxmI6eAceO3SlmHhKW9ZydCPS7NZsgqJSfpT7ktXPtvP8djCUPeSONyrSeVgf27XhY64uQP6NI2+GihBEN4gYzmJXHx6DTzYpxHu7BGe/Cfijbs2qBBMFKkkhWMUkaTBdAkjdohYQx9jrmTCytjIRNlh8tbYyaTJcABfTQ+FBOjigrsYjMG6FYprBwl9mnozoGPJII/8qv6F+ZWDtq5MpmW8y6E1ALDnSMhAlN/LTsUQapQtilIe4kxVJYciafHvPtQqeLl+rpWnAyLjbk2zD3YVI9U47I+vbRv8fEGbGlg/bIBRP/nBPo0wKs4Fi+JRslmHLH0HACsMyiF2YS92qJrXJq4+ozZ6neYc5+62nzAhJBFTfAznt64R5vU+p2U1rB82IMwpcmfPBlj+TF88NuA0DFNmAXS4SaoT961Oms6OMsXScGu3evj6xsTMcBIE4R1vc9tEXElN8eH27vUjlp/fujqOZOfikqyaSE/xoVTRNPQ1eBFucik5JKaRHx/QBOWKpaOvjVyZF0wSdaap76pl3JWF9nOO9ybaFzLRTWMDQBuH4g9G4minRIZh6Lfr7MKDxjkHYyzodVRnEgwF6pCWav+D0lN8EQU7vrxBn9AFREpsmYzYWFA1mkct3IZ7e+m1h1XVj7oVituWvdYhz4AUT0/xFEpQJgHJVbpT6jXBTYdqhD/Ut1HM+5RxO5P0zHnuwqPK2gxw3PDqJS0xYtYmtPRg/GekpQQVRky4ibMWDDqzLias2ImszMh4czsYY0Y5OIIgkgN5lgsgPh/DNR0ykZGWAp8v4AFWPatOpXFlJj/UHR9ekwUg8BJ67OwmwWnYr29sj8vbhcI6VL1mJ0zKGbHaUX4/d1QaMGEy4kxGZSJQWxBLGIaInTUlOnVrpPewZzgUJ0lL8WHnoROYICUE2iUtqZ5lu2s8f+hZ+OPOzrbHd8M1HWq71vV94tymnqtnyt5pr6XdezaOPdRHRReGcYNBytErsjJOdxsVk3H3n4kOdcs7hjKJPgWIv0e8WIzxzBVKFAmTz4sXGWkprmOc29QqixXP9vNUdZEgiIJJTJ5lxlg5ACMAZAJYD+ASznlE+TXG2F8AzgAwhXN+dizHJAL8fmdnnNAkM+mw85Z1ql8Be45k49uZm3B2i6o412MFQdO7yGQcun11HYxB79Vk8K3f461QSiyoxQ+8FolpUKkEVlm6z6cN/ctYtQ8IqCvocLITxFTy9Z/NCn3H5gq5ldYDAuE7uhCeB/vYezRVL+odPRrgmEGJQrXPqpX2FlYChKtzqLJ1TpgqRMaCTt+3uIfzbsfVHTLx+G9LANh7q+tVLIFvXSQ5y/d0rDkKKrJzQM3pIAiCyG9i7YUGAxjHOW8AYJz1t46XAVwd47EIiYy0lHzT2NTpPgtMxpUpqcWtp2fq6j3OGyEQf6i+p03xhGrRkZIZqfjjzs640aqa2ERjdJ7dwlv5Z0EtKTnwk+uyPGf+v3dVePLljoPxL7V7TFORsbSNsakqQtgZ1gLVkHdSNZGNpDmP9ULpomnIM9xLs9aHj8urlM4I3gs3uqyEmagY4WjRnf9EqEbEI7RDThJM5Hl0c58RBEEkklh7zIEAPrc+fw7gPN1GnPNxAPQZJUTSES/O4oZEJjs1gas1Vd3siPdrz8civdhOhS8E7TLLoVn10njs7CZYP2wAKpeKHBS0lOT2okXoaQvchCd49ZDqcErCH78sUj7MLnFJDVPgLlS4hYKGqEDpVO5ZRmjViqupG8yoCGPbriqiipD4Kqj6tfFUmsiy4vm9nB8T8r2SyGqKXkPDCIIg4k2sPVxlzrmoHrEdQPyD+IiEc1aTynigd0M8drZeWWOZIXN++TN9jeW5z2sVKfUGRJcUZScjpa1OZnhxq/aBrGdtQvX0zdMUkjBxaVYgFlz1prvxFFYskfg4x+0HvceE/3RrqMiDG4+f0KatbhmiXoxlYYxVK1MUQ89ugo+vy3L4Ruh+8BKq+pQVy1tQjeVWtcrEbV+f/V+7sOpysZAWFu8df+/vlIe7Y8ETvSNk7wiCIPIbR2OZMTaWMbZY82+gvB0PuJBiUpNljA1ijM1mjM3etWtXLLsiPJDiY7ijR4OwqlMyJzTT9YC90XeNwePcIgpPbRWNx1ege0Wb4oNVo1XVq9YhFyJ46cIWnrL0X7yoRVhpbEEpFwVWGGMxa822ViqOxYPTa4cy+yvZxFELbuhcB2ue749H+5+G3k0q4+Is53Ou4/8617H1sP5+R8BbL2JnhSHvxmiuUbYYXru0Jd690r7IQzSl4+OBXTl0r5Qokhq1drqKLGGZCGWUGmWL2YYFEQRB5BeOb23OeS/TOsbYDsZYVc75NsZYVQDe6+yGH2s4gOEAkJWVlV+VlgkHdIlarR28XbrY5LJRxljbFmrQrGpQWZ/gl8IY8qTxXFEXHl55utqNce2GSqUy8GCfRo6GwDoPZX51JCIBDQgU4ji/dXVXHnLGGFJYoILa8GucPcMA8OddXbD7sLcYbaEPLO4VcdncGprnt3ZW0YhH6IIXzmtVDVmZ5VAhH2YZoqVmuaLYtPdYvp8bgiCI/CRWneWRAK4FMMz6/28xt4gocNQsF+nRM8U3y/x2eycMfGdq8G9TRUIn7F7EP8/d4no/pYulhVV4M1Uxyw90+toqXkIWVOzUM2Il3oU4VJq4qIonI3vghbGc4mO4u2eDuOmJA4nxntpxf+9GcS9zHW9+urUjVu047LwhQRDESUys1sIwAGcxxlYB6GX9DcZYFmPsI7ERY2wygB8A9GSMbWaM9YnxuEQ+ovOA1qtY3PF7Xo3jn6SStDJebBS75DmvlbQEP93aES9f5FzVK97oZMRMDFKK07j57tTBPTy3qSAizxCIuONqZYri3rMaGmX1okEetLXNjH+Ii0pBN5QBoFLJDHSqH5/S1ARBEAWVmDzLnPM9AHpqls8GcKP0d2JdUURCUSXDAGDIALNChgknm/f02mWxftgAZA4eFba8WTX3GsVqBTiZYkVSIKSW1Sp4Tu06PdqqgDHgJVbVVM3QDi/noCAjyxRe0KY6ypVIRzdDKfRYEJ7lkkVSMWJQB4etCYIgiMICqb0TjjDGUFIxxuwkxkxc0yEzquOf31qvrOGVPk1CU/Jj7usal30mEjehLgJVJu6lC/PfE54suknSfIwxdG9UKe6V24CQlrDPxyhGlyAI4hSCjGXCFfHItrQrsWtHvCRcb5JCFYp5MESTxSP9G7veVi0Ck5VZzrBl4ePK9rXy5Tj5ZSCPva8rfryFPNcEQRAFhYJvMRAFAu5U4SKB2KphSHR1mHrXFR0pyDS3CSlRaVcnOuP41m718N7ENQBOvrCMr25ojwWb9yfEi6wjvxL86lfSq7kQBEEQyYGMZcIV0QozVChRxLMMmIpbE8WNfvHJhFoxz45ok6waVg5p7upUTwoynRtUQOcYtai9ILz3B47l5NsxCYIgiORDYRiEK44ZCpM48X+dM2M+tlvP8gsXNI/5WKcafn/g/2WLpeH9q+yLcpzq7Iii4iFBEARx8lO4XHFEgaN2OWeJOSfczn7LFcVMPHd+M7Sumf/KFvnJ1Wfoqyfq6NKwAkoWScWXN7TXFp8hCIIgiFMd8iwTrohG/QIA2tQuE/Ox3cakutnsyva1PRe9SCZrnu/v+Ts3danrvJFFpZIZWPRUH1vJPYIgCII4lSHPMuGK8sXTse1AYBr6ljPruf5e1dKBONgu+RBbWhjFvFI8KDDMGtILY5buQK3yBb+YxclILBUVCYIgiJMXMpYJV4wY1AETVuzEtR0zPX937uNnoXgR5xAJQdeGFTFp5S7Px8kvVYRk8nBfs5xcxZJFcEU+yaidiqzddSTZTSAIgiCSAIVhEK6oVb5YVIYyAJQrnu4qnljwYO9Gwc9OkmjntaoW/FxYTeVMyVPcplaZ5DXkFCcZVRwJgiCI5EPGMlGgWbPzsO36tpIxXVgdy61qlgl+bl+3fPIacopzQZv4VJIkCIIgTi7IWCYKHLLRm+dQDKVURhoAYEDzqqdEGAaRPLzoXhMEQRCFB+r9iQLN/qNUAOLR/qehYskieO78ZsluCkEQBEGccsSU4McYKwdgBIBMAOsBXMI536ds0wrAewBKAcgD8BznfEQsxyVOHSqUIO3fSqUyMGtIr2Q3gwDw3pVtUKFkkWQ3gyAIgshHYvUsDwYwjnPeAMA462+VowCu4Zw3BdAXwOuMsTIxHpc4RXj90tbuNqQIDCIf6Ne8Ktpm2iedEgRBEIWLWI3lgQA+tz5/DuA8dQPO+UrO+Srr81YAOwFUjPG4RCFGDj0u5iA5J7YlW5kgCIIgiEQQq85yZc75NuvzdgCV7TZmjLUDkA5gTYzHJQoxPsla9jkk7fVuUgVXtK+Fe3s1THSzCIIgCII4BXE0lhljYwFU0awaIv/BOeeMMaN0AWOsKoAvAVzLOfcbthkEYBAA1KpFxRVOVepVLBH87OQxTk/14fnzmye2QQRBEARBnLI4Gsucc2NmEWNsB2OsKud8m2UM7zRsVwrAKABDOOfTbY41HMBwAMjKyqLasqco6amh6CBSgyMIgiAIIpnEGrM8EsC11udrAfymbsAYSwfwC4AvOOc/xng84hTj8IncZDeBIAiCIIhTmFiN5WEAzmKMrQLQy/objLEsxthH1jaXAOgK4DrG2HzrX6sYj0ucIhw8RsYyQRAEQRDJI6YEP875HgA9NctnA7jR+vwVgK9iOQ5x6rL3SHaym0AQBEEQxCkMVfAjCjR7Dp9IdhMIgiAIgjiFIWOZKNCkptAtShAEQRBE8iBLhCjQtK5VJtlNIAiCIAjiFIaMZaJA074OlRYmCIIgCCJ5kLFMFGgYCS0TBEEQBJFEyFgmCIIgCIIgCANkLBMEQRAEQRCEATKWCYIgCIIgCMIAGcsEQRAEQRAEYYCMZYIgCIIgCIIwQMYyQRAEQRAEQRhITXYDCELHm5e3RokiKcluBkEQBEEQpzhkLBMFknNbVkt2EwiCIAiCICgMgyAIgiAIgiBMkLFMEARBEARBEAbIWCYIgiAIgiAIA2QsEwRBEARBEIQBxjlPdhu0MMZ2AdiQpMNXALA7SccmAtA1KBjQdUg+dA2SD12DggFdh+RTmK9Bbc55Rd2KAmssJxPG2GzOeVay23EqQ9egYEDXIfnQNUg+dA0KBnQdks+peg0oDIMgCIIgCIIgDJCxTBAEQRAEQRAGyFjWMzzZDSDoGhQQ6DokH7oGyYeuQcGArkPyOSWvAcUsEwRBEARBEIQB8iwTBEEQBEEQhAEylgmCIAiCIAjCABnLBEEQBEEQBGGAjGWCIAiCIAiCMEDGMkEQBEEQBEEYIGOZIAiCIAiCIAyQsUwQBEEQBEEQBshYJgiCIAiCIAgDZCwTBEEQBEEQhAEylgmCIAiCIAjCABnLBEEQBEEQBGGAjGWCIAiCIAiCMEDGMkEQBEEQBEEYIGOZIAiCIAiCIAyQsUwQBEEQBEEQBshYJgiCIAiCIAgDZCwTBEEQBEEQhAEylgmCIAiCIAjCQGqyG2CiQoUKPDMzM9nNIAiCIAiCIAo5c+bM2c05r6hbV2CN5czMTMyePTvZzSAIgiAIgiAKOYyxDaZ1FIZBEARBEARBEAbIWCYIgiAIgiAIA2QsEwRBEARBEIQBMpYJgiAIgiAIwgAZywRBEARBEARhgIxlgiAIgiAIgjAQF2OZMdaXMbaCMbaaMTZYs/4+xthSxthCxtg4xljteByXIAiCIAiCIBJJzMYyYywFwDsA+gFoAuByxlgTZbN5ALI45y0A/AjgpViPSxAEQRAEQRCJJh6e5XYAVnPO13LOswF8B2CgvAHnfALn/Kj153QANeJwXIIgCIIgCIJIKPEwlqsD2CT9vdlaZuIGAKPjcFyCIAiCIAiCSCj5Wu6aMXYVgCwAZxrWDwIwCABq1aqVjy0jCIIgCIIgiEji4VneAqCm9HcNa1kYjLFeAIYAOJdzfkK3I875cM55Fuc8q2LFinFoGkEQBEEQBEFETzyM5VkAGjDG6jDG0gFcBmCkvAFjrDWADxAwlHfG4ZgEQRAEQRAEkXBiNpY557kA7gDwN4BlAL7nnC9hjD3NGDvX2uxlACUA/MAYm88YG2nYHUEQBEEQBEEUGOISs8w5/xPAn8qyodLnXvE4DkEQBEEQBEHkJ1TBjyAsnhy5BJmDRyW7GQRBEARBFCDIWCYIi8/+W5/sJhAEQRAEUcAgY5kgCIIgCIIgDJCxTBAEQRAEQRAGyFgmCIJIMlv2H8O4ZTuS3QyCIAhCAxnLRNy4+P3/8P6/a5LdDIJIOOt3H8HmfUfjtr8Bb07GDZ/Pjtv+CIIgiPhBxvIpRO/X/sVF7/2XsP3PWr8Pw0YvT9j+CaKg0O1/E9H5xQlx29/+ozlx25cTB4/nYMGm/fl2PIIgiJMdMpZPIVbuOIzZG/YluxkEQSSRSz+YjoHvTMXR7NxkN4Uo5Hw/axPOe2dqsptBEDFDxjJRYOCcY8OeI8luBlGAycnz47/Vu5PdjJOaZdsOAgB+mbclyS0hCjsP/bQQ82kWgygEkLGcBDjneOmv5Viz63Cym1Kg+Oy/9Tjz5YlYuHl/Utvx6piVOJadl9Q2JJvsXD++nrEBfj9PdlPCaDBkNK74aAbm0AwJgMDgIerv5kb/XYIgiFMJMpZjZM/hE8jzaFBs2X8M705cg+s/nZWgVp2czFy3FwCwae+xpLbjzXGr8Ob4VUltgx3XfDITd307L6HHaPjYaAz5ZTF+nLs5oceJlt2HTyTt2AVlILV8+0E0GDIafy/ZHtX3n/x9aZxbRBAEUTghYzkG9h7JxunPjsXLf6/w9L2j1st24974ZdMXBg4dD8RQpqawJLek4BhEOiat3IWRC7bmy7HENSms5Pk5vpy2HtkevKzvJVDxxYsnf+GmAwCAsUtDknNb9x/D6p2H4t4uouCS5+dYvOVAsptBEIUaMpZjYO+RbADAP0u9eXae+G1JIpoTF7q9PAE3fJYcj/cUKxZ14x4aRBD28DhFh/wwexMe/20Jhk9ybwCfyInvQGrr/tBMij+KHyZ/o+Ow8ej16iRsP3A8Di07Nfh13hZ8OW19spsRNe9OWI2z35pCCicEkUDIWI4HHt9v09buSUw74sD6PUcxbvlO4/qf525Gz1cmJrQN8+Mcs7z/aDa+nLYe3IMhEo3REk+e/3MZnv0j+dPksiFXkJiqSfJbvv1gcADrlgPHcsL+74o4T3x0HDY++DnXS0iX1Q7drTrkl0Uxtirx7Dp0Ai/9tdxzGFu8uWfEfDxegB0YTiyyvMrbaIBEEAkjLsYyY6wvY2wFY2w1Y2ywZn1XxthcxlguY+yieByzIMDEyyq5zcg3Nu09ivu+X4A1u45ExEnGMxEs3kEY1346C4//tiT4UnGDJ6NFYev+Y8gcPAozYhgUDZ+0Fh9NWRex3Eu4QDxI9qDBxJfTN0Qs6/v6ZPR/Y7J2+4PHc/Dnom0Ry8Wv8zH3d52Xbb3i5XyLVnBND2Tay61fzfHeqATx9B9L8e7ENZi0aleymxI39h7JzncPb8F8Qk8OcvP8OJFbcEPuiIJDzMYyYywFwDsA+gFoAuByxlgTZbONAK4D8E2sxytIBF9WBdSgiDdTJG/ezV+Gv3T/73P3oRu5ef4IxYvjcZ7alhEvLy/KAd/M2Oi4TbMn/sbHGoN2umUkfzszsA/OOT6Zsg4Hj8deeEJnJAq27D+G3BjUEXQwm6HLuGU78JVNe5LB9oPHtZX17huxALd9PRfrdodLEwYVITzYv74EhtR78bIKo/3nuZEScOMNs0OjF0eXDJgIjlk6z3l5haf/PO+dqRiYz7rCIl7Zi0Tb0exc7WzKhBU7o1IjWrT5AD5QYvn3SbM8Xt6R2w4EnA2Zg0dhS4Jnti4dPh2NHvsroccgCgfx8Cy3A7Cac76Wc54N4DsAA+UNOOfrOecLARQqrSKWQA9TQeSgzVT1xBXuvUOvjV2Jc9+eiiVbA5389gPH0fjxUIeVkkhrxGL34RP4fvYm19tnDh6FB39YELbs8IlcPKMJlRD2jjBmpq3dg6f/WIoWT/4TfYMtRs7Xa+NuO3AMnYaNx8v/eEs2jYUbPp+Nx35d7Ljdhj1H0Oix0VibQKlEWR3jg3/XRqzfZCXTqoOyV8asBADsPew+fMNuABErfg895LE4DzAnrNhpDLuZsGJnAjTQA+exoM5eRINI2l669aDn73LOo8rXEOEX45btcNgyRLeXJ6LlU5H90fWfzsK5b3s39s95ewpeUKq3yuF0JzzMiH00OeSA8PKbTPj9HB9OWovDJyKTlQuaBCXnPKiDThQs4mEsVwcgWx2brWWFHvHKLGBStAnDbUzc0N8WazsmwcLNASN5t2Wg/K4oO9SpUDzKFjoRMnJu/WoOHvpxYdCIcsMPc9zJqAkvygnLy5sTg+dM9f6kpegf2R0HA8bitDXeQj9y8vwY+M5U/LcmcYU+fpu/FSdy/Rgxy/3gxCs7D4aMZd0YVoQqmMa3XmKWZRWbAzGWqd6jSOBle5gZiHe3EzCUphjXnfnyRO26vq9Pwk82z0Zunl/rMd92IGCYy2vy/Nx2duTA0Rw8+8fShIQjXfrBtLBB1/6j2Wg69K+gpKUX+r+pDwey4+sZG9H15QmYtzE6A86L72bnocDvnLIqvs+9afbOyzmUZ+uGamLJdx06gVnr9fsbs3RHxIB43PKdeO7PZXhulLcckHtHzMdZr/7r6Tux8v3sTej3xmRMXGHOGyKSQ4FK8GOMDWKMzWaMzd61q+DHsY2wPJOnigTcHwvdyZV9MW1DxJQcEHhpnvH8OEy2OmjhQFan2mqVKxZbQw3IL5MNlgcnHnq9H04K92QKR9mohYEY2ZQYZiBU74/J6y6mPL3G027ZdwwLNu3HIz/rE8Lc7G7wTwtt14smfzAp0uMbL3YcDA3ktuyL9I6Ka2LyCuvO2+f/rccjP0f+Nlm2b73kbeWcY8nWA/h7yXajh/Dqj2fg3Ymrg3//Oj/8mfr8v/Xa7yWC6mWKRizb7cHDLli+/RDuV2ZdZOoPGY2L3v8vYvkSy/sqT9FfNnwa6g8ZrZ2xAYBhfy3HR1PWue6LvDBj3d6w8Kt5m/bjSHYe3pmw2uZb8WO2ZQCuj9KDH42D/qqPZ7iSnTuek4fbvp6jDXGSK2re89187ffz4jh7cP67U3Hx+9Milr8zYTVu+mI2rvhwethyYTwfPOZNBvOXeVuwaqd+NmzF9kNxCatTETMS63cnt5Lt8Zw8ZA4eFdZXnerEw1jeAqCm9HcNa5lnOOfDOedZnPOsihUrxqFp0XPj57Nw7Sczbbd5b2Li9FbdkpPnx+CfFjpOkcYjrlp9kR7NNnc+Om/q/E37sV0yaoThckbd8mHb1S6fIGNZ+iw8K6+Pja74iHw+n/tzGT6dGvKGqNPKPukpiyWmmHOOGQYPjdD+VeMWH/hhgXbgIhBtNdnEbkzv7xw8xtGEKx0+kRuReGMXxiEbNLo4x1Ain/77Pk1P+MTIJfh2pv1vkxNBf567BQPenIKbv5yDs17Te6Qmr9qNl/4KhcrkKXEXmzTGSKI4p2U1AIEX419RxjLLz4FdTsC8jfvD/pYTguXHZdb6gFf14ynrsPdIdsS1FPeEzlN94+ezcPvXc123XUeiI8B2HjqOzMGjUOeRUXGP91cfszkb9rqa0t+vmR3p+/qksL8nLN+JPxdt1w5iZGfRKDmJVrpE8UwA36wZDAMI1juYq9xrwiEySpPgK7B7Px7RzJL2eX0SLv1gumbr2Ph8WuCeWLMrucayCMf6dOr6pLajIBEPY3kWgAaMsTqMsXQAlwEYGYf9JpWxy3bi35UF37t95zfz8N2sTRjwZmD69IfZm9DxhXERD/+7CTDs7zZ4EYBAbK3ahjuVqnPixVQkNfw2TFQIo66/9mrHibANtY1PSdXQBiteWtlrWX/IaG8HREgBwy6+zjR1++OczRGxhII8P8cTI5dEtNFEbp4fv2muayJo9sTfOOet8JCAHq+Yp0RnO8QeBgcFhp8ZbRyybLTJCbBuYzTVapWqCsvvC7Yic/AorW5ytdIZtvv+aLK9J1+ck2Gjl+MWSSXDSxiAfCs0GDI6woh9cqRekm2mNI1+q8HAbfPMGHSSZPWAgNEGRIagbN1/DGOX7bQ1iNwQNrCzDjI5SrUOnZE13zLkOEdYvP/R7NzgLEO0j1eu5KD4c9E2XPjeNPQzqMM4sXx7eGGboPKTpm2mkLt50sB9j2HGgnOOn+Zsjqpsu9t+6CkXlSp/tAkjMskwJjK2OBFeay/0jfK+KczEbCxzznMB3AHgbwDLAHzPOV/CGHuaMXYuADDG2jLGNgO4GMAHjLGTV9TSJet3H4kqycMroiMXHdZDPy3E1gPHIwzDNYbpJBNPu+hg7Kbvth44HpH0FxHzHJTeC2+szqjdvO9ozBI/us7Vq4nU5aUJgX3F1BJ71DjYtbsD1+6QTRx4NHHRCzbvD4bEyCfCFL/7waS1uPu7+Z6rB8r2x/GcPNcvgpU73N2z6n2hjfsNnh6jtWzkH5ty0vJMwS/z9BNqv87bgnu+05cnV2PQxf4458jz8+AAc4FGoUCovKiDTcGzo5bZxmILb586tX7+u5EhE8Z9KM+UfK52HTqBzwxhJdHGHO+zng21P5Ml8TIHj8KqHdFVMRTt33XoBP5nJcuanKJ+P7ftk3ThG58bip+8PT60bbTGci1pRu4JaZCyfHv4e0g1bp3Gyat3Hgoaz7qmPTtqWdjfQlZUVtX4wFD0Z/Ti7bj/hwV4a5y7GT65OmU89blVb7TMJoMnO5F0axSaWf96xgac/27+KqxE83z6/TzpRn4iiUvMMuf8T855Q855Pc75c9ayoZzzkdbnWZzzGpzz4pzz8pzzpvE4bqKQH8jsXL9tuIGOZ/5Yim7/mxhVkodX1Clu8deqnYfCXuQ5HjuWT6aui+hkB7SoGva3U6fu9OCIKe7tB8LjhmWj9kRuHgZ9MRudX5xgjIdziy7e18mjOm7ZDmQOHhW2bOrq3Z68q28qLwKdxyls/4ZkO3n6HgidJ7UtK3ccQubgUWEzI7rOT/6afBYGSPetfHp2WiE0Xgt/yMfp98ZkT6ogbqSjznxpYtjfazVTmKIJ8u8R0n6A/X0wUTqPahn0T13EGN8zYj5+nb81LK5akJYaflzxyN73/QLUe/TP4PKHNXHhb453jifUKR4IhO643a2890h2mNGzT7n2ardyXDIedW0WxBrDqsa/H1Wuy1mvBcIIFm0+gKxnx0a028QCK/n4mk9mBmOqAf1A++k/lqLRY38ZQwx0s3lTV+sTcH+TYtejPTNtM8sFP+86FOpT1X7j4R/Dr4uTw6DXq5OC4WqmxDqZH6xcHrk9p9cuq912tTXomedC9s7v5+j1aig85D+Pycx2GPKmASRHMeNETqi/HvLL4ogwpvxCvo+ceHP8KrR48p+IpOXCQoFK8CsoyFOoF7w3FU2G/u3p+zrtXcH+o+ZOe/SibUGDBAh4fHq+MjHiJZub58cF7wYUDNSOThjPfV+fHDb9niP9JrfTrH1fDxhNczbsRebgUahayn7aV2X88p3IHDwKKw1enonLd2LHweN4VJnmkt89709ci3+W7gjuT0d2rj/svG7ed1Rr0H0/O3KqbdzynUaN53cmrA6TMRJMXb3b0wtN7dTFIGLmusB5XSFNeS7bdjAiYVSECKjTfiL+u/v/JoYtF5nnP8+1V++QvWLyoMsUEyi80F7GXZxzvCHFhas6x4LcPH9QW/WJ30LT024Ku2zXGKEyDR8bHTyunEAnJzWqiiwy8hjrLsVDLLy7bqZk2z8/LmJZRmpK2N8ihln1Uu8/mhN36T0R/253OXu8MjFMSqz1M2OCKhZAZCXFnNzQ3g4f1w8KF27ej3keDBDTwNSNks27E1dj9+ETmLZ2D9buOuxaiky9nrp7/gvLS7xypzsvtp0snDwo1P3e4zl5jjOVpvOkhpFEhJUYrGXdIEAX36wiZjPkZ+r72Zu110vEbU92ocrxr9Lu0YtDITdqmJLp3jAN9E33qg67kI148aFDCFUy8fu5VnVJJLSf/dYUTypTJwtkLDuweIu7UIo7vnGXWHL9Z/riHcdz8nDr13NxxUczgsu+nL4Ba3YdwU+K0bPtwHHM3bgfD/4Q6bmRp6ZkL4A8Nf3HQm9xfV9YSQdqHJsTwlsyepF+Gjs7z699qOROf/+xUOcmpny/n7UJmYNHBY3OQV/ORqunxwS36/ziBLR/fiwAhFUaNMlbyRrPMi//vUJbmtxkXLidulq/O/Cbr/s0kEDa5/VJeG3MSvw2fwv6vTEZw5QYY6dp0vXKS1i077f59uESN38Rmrp2k9i01jI4vbxY1u85qg2LyBw8KkzKSS6xLpJc4oV8XcS9rFaglBn49hRcNjyUbS+Hg4xZGm5sicfNKWbaRJG08C54+0GzV2aDdJ1lD7cXDVsddrMkOuNIDqdSp/TlmOuZBi/kuW9PdeUVF5im2+XiH6ZfIJ6dpVsPoscr/+KGz2eHt9dlrKxOC1o0q+/rk11V7Zuxzp0ndOv+yMHfgz8uRP83JyPr2bEYLoU0yCE0pqqjaojWQeX5XWUIdzIpQbw9fpXtPSOSNNX3hQhhEyzeciCYaO2GXOV3yMm3Z7wQPhD1GTq0H+foE3ZVVRo7HrBRfokXXhL8fpu/BcMnrQl69GUOn8jVzvBOWLETl7w/LWJAtMqalbTjs//W4/IPp2PsUv3Ac9uB4xHvMDf4/Vw7+1ZQIGNZg5tkH3Uk69YANU2niL5HNh7VdvT430S0e25s2LLjLuN4Za+svNe7vp0XLA5iSmgU209RvEi6Mrs6fpmnN1Jz/dygiQtc8sE0ZA4eFZaNm5PH8eqYlfhoSmDUPXXVbizfflBbEEW8IORKg8Jo08lOeZGi4lw/df3vyl24+cvZkSsUxAtOnjp+Y9wq24RJHbEWx5BjoE0Gl+53fjtzI253OTi040PJax9N/GEsGfZqgRmZBZsPYPrakKFnpxErPMGPuyjOoiNdmf+tUqqIcdubvgjdW7qkqjUOnmeTTKLXsyjfE6oRuc9m5izaxFBTyIabcCDh5XzbIP/WQUkgNLZBudfU2blrP7VXTgLc5xW8NnZlxDLhpd19+ASe/zNgiJzIzUPnF0MGqCk+3AkR36yGPK3brb+f/vfPymDseLRMWbUbZ78VqeltF7rnRaLVNPj3MtCPli37AxUI7bTHoyFz8Citas3d383H838ux4M/LsQ/S7bjkHQOmz3xtzbs7Y6v52Lm+r04qsyquhn0i35GnmECwgdXXvTiBe9OXI32z4+LqjBPfkDGsgY3RuC9I+Yn5Ngncv3Gl8ra3Uew89CJ4Mtqy/5jYR3waCUT3GRKfTRlHTjnGLN0B0Yu2IoBb07BjLV7tFJ5dpJSO2y8YDKml13gd0a20s+50UCR439v/XpuMFQEAHq+MjHseyavkZAYkpm93sO0sPWfSqqP4e8lkaPtVKXnNoUimLAziXX3ynsaw8DpnhZeS/Wc5XGO2ev3hhln2w8eD0652cE5D8YkOmGUrrNW6CrLrYgyiSuexGo0lMpIC/v7/DY1jNvKnkN1kLlm12HbCpuAOXHT60DlrfGhZ1CdTREDb1242SwPz5iMGxvbdP/sdOij3MZkqoMC1etqCk+Qwz5yvZRndIE6wBVtiHZQMl9x5GTbGPex/haTIX6fzXvVpL2t4+vpG7XLW9cKxU6bwu9i4b7v5wcVXO7/YYFtPzl97R5kDh4VNA7d5EbJqjU6Bn05J0JBRsXv5zhiOWouUfSq3dQECOVK22+7bNtBrN55CJxzfDZ1XfD3ncjNCwspm7U+EI74v38Cg8StB/I/odINZCxHiW56XoccLqDjDCuOUe6MhRdYeILUWDWT4WOSYNJ54A6dyMV26aa8dLheM3JaHCq7mfpVDv3z5vTuNr0L1uw6glekcs+TV+vbrvMueEni3LjnKBo9Fhm6oUsgPHAsJyJ5LF4yfqa+aqtGZuzIiTxXRpEqe7f/aA4uen8amj3hLW4fCGh0yt5QHZNW7sLkVbuMv2Xuhv0AEBbXfTLh9EIuVTQ17G+3ho5qvC3ecsBY3TG0b/1yN/GiMnal7YVm+iKNUk60xomb+9a0ha7Ax6EoMvZV6bOHfjQnL8rc8PnsYP9vCtOyCwkCzPf+p1PWa5dPiFP1N7uwsvcnxhZTa/Kyx0tf2DSTII+q5CQ6FfU5tLt3v5+1KZgM//Pc8FwDuxm4y6x3rojFVjXd3fR5uiIxapiNyh+SU22pEpsvhz3qmLp6d7CmwyKNQo9g8ZYD6PfGZPR6dRL6vTEZT/6+FI//GpjBePTnxejxyr/BAbVaYEatFVBQIGM5H+j+8kRMNxjXIjlJfvkJD5BIJPhj4bYwT43b6TbhecgxWKtOL1cgPpWXxLSUDp2N5GQw2MlhHZGM3us/jYwP33HwOPa4TP4zMdrgbdcZy/eNmB/VlJRb3O67zTNjwnRdTZ3/r0pimVsDp+nQvyJeDDqDSeWaT2bi6o/NU9hfWglAunOrJpcVROQkJB1FlAS/x35xF86hMyDTDRJy0ZCbZ57hEhw6noMHFaOxXPF0APoQoWi9kbp4YbkNAIwzGDqjLBqVoi4vTcBrYyLDI9wgnBUmI8ZJirGPUiBEoAvXAJy96SbU61PeupY6PpGKMKmcVrWU47GeNniJ413GXDUm86T74UebBGj1lrPz6D7008KwGU43yFVfDxzNxs6DxzF+efispMnDum73EfR/YzL2H82OUOeR+dOgOf7NjPCcENmZJkJ8TFz50Yygosv3szfj6o9naBMe5bwGEbsucq/+sxxwRwxt/96hyFWyIGNZgyl+84+FgQIBTtXyVPYcyQ6OIk3I7z5dPKLcobqtqiNu0jU7I9s7f+N+Y+lktV3RVGBzAzfs28k+95IUoqKTFYsXL/0V2dGMMyh4eMHudEzwsP+f5mzGweM5mLB8J67+eEbYOvGCUxNj3E7RH8nOw6iF23Du21Pw1O8BD0I8i5fco5me1Q16BNsPHMeb48yJSMfiNAX7UN9Gtut1pbcFfj+PSPi109KWWbQ5fCByNDvPKOsVOgfur8eRE3nGhDFTG4BQmJSuy4hGCxywn2lK1ZVedEAUgvHq6X7DRgu4YklzrLlAlZAULLdRUmkyVJ98bEe0T52aT1NNUw7d1fGjeO7LFAuEI0VTnMQONVTwIUnO0C5PRY29F7Mp6m+Tw8NMxVl0fDF9ffAz50C758dFyAqaHGzvTFiNpdsO4p+lO2yvtVxZVZ7dlvMxgMhiYSrCmNa9Cyav2u054VF0DZxz7b0Sz0F/PCmYrUoy72gytfceycYd3wRuKpNnUcTrRYV0z+j6mqG/RV/HRedJueaTma7E1v1+/Q0dL3RmeCJiyfKDBRrjwcS7E92rAdidfq9xoHd+Mw/XfzYr4nsliqRgz+ETEZ6dOhWLe9r/ws0HgoM5L1Oq63abkzry/FybzGU31rvz27l4dczKiGlGQbSGm0rRtBTt8rSUQOPsDHpVCssLg74M93S9MXYV/jNo+Ip3nOp9tTt/E1fudFR40DkVhHKIztDWyU25Ya5N0pFbhQkd8Rw4lymahvWGXAS7ASfn3DjDt2nv0Qj9aDfI+uECN0Va1BnLeBb9EJiMYRGqZucIiaYUuepMkPsRVe9+zoaQEWkKTVJzeDpK8cE7PSg5yIO8VwwzFqbnRSR7btl3zHbWJVtK/r/k/WlGY96p6qUoOvNfHEIyf5u/JRgmOHPdXtR55M+IbdzMeCeDgtmqJKPTmWUAWtYsAwCoV7GE9nsD3pziWopIRb7pl28/FLeOyi5hwk3sntcCFPHAFHtdmFALBUTD30u222p6q2Tn+Y16wNm5fpz+7NiI5R/8G31sopswDMGLGq+8wPSCtVMDEZ5jtZx0LOgGjabHdEDzQAGfDTaZ3fGs8Jmd5ze+9ERfcstX4c+VXRez53A2JhnUcQR2iaovaqSj7Cp+cm6u/mWXJOpG99eEnaHhlY17j2Kh4ffZ9eV+bvakRTO4mLB8JxZqBu1OiWE6dFUjVXT5MG0MBUgA4G5DJUs375li6amO28jM3bjPNn5brRB64XvTgve06d6wG/zqEscBYMSsjUHliNw8P7KeHeMqydtUn0AMUj+avFY7uyOQHQLLtx9Csyf+NoYw2sVHi/AJu3A5t8ihIfd9r/dIRzMoyg/IWNagy7L3MYYtVvyT3QNoKpzhhNwxfTtzY0Txg2j52VCCF3A3gkucTznAEY/VEZ1w0sX+waCzWZDRJXEA0c02mDw3Xrzi+Y3J2BBTtzqEp8rrtK5d+WKd8WKadRFL7foDORk1VuwMv2iMwlnr9zrKSJniToHIxCEAOKdlNeP2fu6sC67DrXxlxPc4j7qstA47FSO7vA+/wbMcbTiCScdfzPJ4ccI88vMixyIuunyY4zbecFVy1Qtecz8+mbIurAqjjEmebIQVumA6TXazrKYZ54d/WoRrPp6JHQeP4+d5W7D7sDsH1HGbBEQgEPqm5gzI6CoPmmY/THHxQOBefHKk/bvGfWKy8zaJtjmihYxll3Dw4E3+zQy9LA1g1ip24jolGc2NNFeslC5qNjYETlq2fyzcit/mmw1yJ27+0rvHIxbUbOWTAbWQwsmAU6lzL6jFCARNDIlEZzasGExy0hmKfQ0vhiVbD+DOb8yD1L0aOTST8bF8m/O0d7STR7py6XbPaTSzVMdy8uJaThgAGlcpaVzn5xx5BmPIziAetyw654Sfx9ezHNinfn+m+xcIhFjpckecVGSixWuS5VO/28u16X6bnYPGLbpz4jX5z3TWdx46jq4vT9Cue//fNcjO9RuvZbTP7Kqdh9H++XGuVVQShVzIxy1Hs/McRQXUYk0m3DxyCYz6jAkylhVMD+TXNgayTLw74ETiZjTo5xx/2Ugb3fHNPPzgQUlC5VA+iMQT+U8sHiQVry/4f1fuCqrM6Aq9mCpR3vT57GBpdZWNe45qp/xNL89EakA/osj7AfYvGDvjxVQxy1TVLRZ056pZ9VLWOu6YUKjjj4XbtIoANcraJ6fl+s0GUbRFEUy3qZ9zYxLVoi0HInTYAXt5vvxENzCTsRsI6HC6xOJcXNm+lut9Gt9jhsXtnossOy/z4l/LsU8TbrF65+G4JyAWJGLJTTruciAz1kW5+QTpCcQMGcsKsw0Z5aZ4JBXTtE9BxE3BiJU7DjtOB6mV/Qginpg8o/k5LO368gTt8ZIxOJ6oCQOza8eybQeNElOmqmhqNTcn/r6nq+M2upexCJv6b80eRw+4KT5z9obIPluXdyKT5+fGJE+dNrMbTC/5XD8PJmVFHGv3Ec+xuNESTeiJXYwuYJYlNeGkodujcSUAwLlKyI6dIWe2laN7NlfuOITqmsFW79f+DSa7JZI2tcoEP+tCKdyw85B3Z0UsXdnX0zc4b5QP7UgkcTGWGWN9GWMrGGOrGWODNeuLMMZGWOtnMMYy43HcRBDrdbKbKvposj5ZKlki3L+6iBEsqNV0TjWi6fySiam0cjR8aHhudAZdw8r65Fs3OD37ozRSU5zzqH9r28xQIlQ5G01blSIaBQ47O5NzboybjVcisRsZSrvY3ZplizqWMz7n7cjyyIA58cjOwMr187AZi/eubBP87OTZMiVxVymdoV1uFyLDAXSuX96wzv7aeLlngMA9YkqkizaUzuRZNmlHO7W5dvliKJaegqzMcmHLv5hmNsbi2dcI7tCEYyVAHESLLNl34Xv/RbWPa6JIxoulpsIMQ8XdaIh2kJNoYjaWGWMpAN4B0A9AEwCXM8aaKJvdAGAf57w+gNcAvBjrcRNFNFOBMttspp9No9I7vy246g8FdZR3qjFrXXQehmRxxYcznDdyia6EOAC8rZF4TKRGp6xP2rFewMDxc6DrS/r4R6dKajXLFgt+fvzs04KfnaZDdSWa/ZyjbgW9zJ/fbzaKnXISdNQuXyximS6UIOJYmkO9fFELAIFkY1Oom93Md4USZn1ju9OYl8dRskgoZ0PWSVYrbqqYZg9NoSt27xR1RqBCiZAxaScb9+o/K1Ddow7yidy8MKkzGbcxpyqmsISnpVjnvk2rBD83rVYKM2yq3+b6eUTJ5R0Hj2vL3Qu6/2+idvnJ+u46q0nlmPdhCjWzwxSSld/UrRC9wyORxOPN0g7Aas75Ws55NoDvAAxUthkI4HPr848AerJEVbqIkVgrCNlVlzMRT3kronBiVza1sGPyOFYpnYFLlFKp6TFodHp5uZ7XqjqAgLFjMmp0FSRN1JFeENGM1/2c47pOmQCAljVKh63jMGulj5jtXR1G5000eVXVNqoIrfe3NAMfQZ4y1V+vYnGcUTfgeRSDFrfHE+T6OR77LVQtUR5k2b2Z1u46bFz/hEExQJRC1sE5D7veXRtUDH62S657c/xqR2nGxwacFva3GnM/6q7OAAKa4P9KMdLq9+wQxYIuyaoRtlz29sqzDowxPPyTPsntaHYuRi3cFlGYh7HwYj0zH+0Ztt5UCe5kNZZbWRK1+Y1TcZL8okk15wqQySAexnJ1AHKPu9lapt2Gc54L4AAAcy+XROJdbpMgiPjywgXNAQAXnV4DM5UcAzf6pSaimf6bYihe4MQPszeFGWjVJGPTLjTC5GGTv/LMec3C1nFu1lEdvcicvGtCF/4iG5D7FdWQ327vFGij5nftsGbiJisFWmSv6d4jOWHG/ppdR/DEOU0BAF0bVoQJu6u572h2WNEVuUiEnX73mKU7jPHQGWn61+m9I0J6smoRmzx/yKgfMeiMiAqasXBhm3ADVlU3aVqtNK5oXwuli6aHGaP1Krnz7HHOgzG1W/cfxx3d62u3e+LcJmHfMc2+Hs3O00pb+v3ACSlvpqwUymE3C7N6V8jT/2Af+0qbMnJRrAtaq6ZM4klNUlEOtYJjsiiog5wCleDHGBvEGJvNGJu9a1dysoGz807O6nEEcarQplYg1ldnVuyLoUiFWhrXDmHgOGkRm3jwx4VhUmqVSrkzlk1FTiqUSA++ZNT44U37juLS4dODfw+zBhtAYIAghyDYaSHLqE4Feepc1foV7fHzyOqcPmmdzNTBPYLSlp9MXRcxu1AyI5AUZxdGMlOJo2xUOXS+e78Wkg+sVLJIsOIiYO9Z/mfpDtxmKJqkq2ioooar+DnHr1a8cLPqpeOaMJqhGOafaAoYpTAW4bnv3qiSq/3/KsU57z58Ard0q6fdrlLJ0L3Nufk8mZbvOHg8LNwjLcWHm7rUAWCvUiUnsIs+Q8d3g84I+1uubpqRrq/QmUjUMJRTjUIbswxgC4Ca0t81rGXabRhjqQBKA4gIXOKcD+ecZ3HOsypWNHsMEkmJIs7aw4LL2tZ03sjA+PvPjPq7xMlJA5cem8JI/Tj99vvOahis8BSPLnXKw92j+p7p2HaFUlRM5WftFAZeGK3Pezi7RbWgoSV7SYHwWGu1jTl5HGmSAWenhSwokuqLCDeTvWGyh+qty1sH5SF/mL0JjR//K+x74tA6o/c2yfhSC3cIA9wuKenKj8Lj5k3JZT1PqxweKqCsl+OI7dQJRFPsQoHUEIM8Pw8qgvgYi9qrpktsTUthuDQr9I7SFdxJ8bGokzwXbAqFgdSvVMLGHx9iw17zzM9oQwXKFB+LiI3+cHLA8H/s18W6r0QgD4ZU7MIe+kjx1vmFr0C5MPOfwuxZngWgAWOsDmMsHcBlAEYq24wEcK31+SIA43kson4JxK4kq8q4KKv1AUBdQ8lsovCienpOJZpXL+28kQs4D8Q9Bj7H3oXUKBuZrOYGIXGlMvPRXq738b9/VmqX22nXqqWMRSjHx1PWBWNc3ShTyFSWQkCKuEiQrF6mqNb7ekX7WhEJdxyh0BGdhrWd0Ssve1qJ3xXet50eZgNSDQaTj4UPMGQvPABMf6Sn+hVb2tYxezFVZEPV54tOinDRk70x8o5A/HHlUqHzn+JjYb9ZVPGTicVYlgtVpPiY9hrK3nzAPj/nD0MhrrPfmuLKa69DxO83dDEI1OGk150InBJM48W9vRrmy3EKCzEby1YM8h0A/gawDMD3nPMljLGnGWPnWpt9DKA8Y2w1gPsARMjLFRTeGLfK9ba6rHSZXqe5m84iTg0ub+deaL+wEa/u38950Bupekzzk8ql9AltJjWO27rVw0uW8oOJwf0aAzBLk+nQGTpOxk+K4rqat3E/KpYsgnevbGO8R30M+PCaLDSqXBJrdx/RHsPH9AMYk5Ea+A6LaLPwKNcpH1L3UAsjCePptbHhAw672FTT7IaPMaM37+fbOiI1xYfujdzPdGbVLme7fv2wAcHPsnydjzHMsLmnP7u+rXZ5yYw0ZKSloHGVkmFeUsYYWtYoo/2OwGTkeiXFx7Qe9cvbBTzbI+/o5LgPOwlV2btvhwjPECywBpcZqWZHhZ2SS6WSZrWVRJFfxnLxIgXTefO3TRG0ZBIXhz/n/E/OeUPOeT3O+XPWsqGc85HW5+Oc84s55/U55+0453rh1EKGk4cnljAO4uTDjbxWoSVOP93HWPBl8kscyurmFw/1bYxLsuyf9xJFAnG4OZbheE+vBgCAq8+obfyO7rSKeF4Tuvtw16ET6N+8KooX0X/3gjY1cFaTysHKhC/+tTxiGx9jEZ7RRpVL2vaD4lr6/RytrWIMF54eSEzr3KCC9jvVSmdoQyrObVkNtxuSzL65qT0e6Xea1iOuepZlRKyr6by8IMV/C9x49i+yfuOrY0LGfgpjwcqTOk4zlHcXMMYiZPYuMbxjRLiNybP8511dbI+lksIYMtJS0K9ZFRSX4nxFeE6Zot40oVVM519lyABVtTaAnf2ZmuLD8mf6ateVzHAfVhUv8iNmObN8MVzTIdO4/u0rWke136ouVHGcUGfPCgqneHRM7NhlyzpNuydj1Eqc/Nx3VuKnzzrUja9YTbwGCjd2qWP0vHiJF46FeD+3n14X8BgKk+WwFeObluLD+mEDItQtZHSnQo3vVencoELw+hZzkcD0YJ9GeOrcpmHLVkrlvIVx6GORhlejKiVtpfDSUgPfzfXzYKyz0Is2XeePr2urXWd3j3WsVwHpqT48pjGmUlN8jgauyeOuW+7mThflnOXqqD4fCw6YdDjZUD6mD+PQzXC2rxPwfqdorhlgTrIyeYjFAKdiySJhpY/FNYk1cbFijM+c0zUpKCFyl7erCRaFVXbzmXU9be/zMVtN+rNbVPNUclzwy22dYpLvDO5n3uaY9xFvyFiOkVcvbWVc51TGdK5LqZbiScjIJeJPvLJ8m+aDDmWLmvGJMRacXtt9HKcdxYukGo2G+UN7G7/Xokb8fs+NylRvrHS34p9/nBN4Qdxl6Z06VSarUbYoGlSKjMUUKhI3dta3My3FFyws4saIuaZD7QjPnkhKA4A1z/cHEDDmdLsrV8zsVRQJVHJ5YScJ/tOqltLeA/9n+L0yuoHO9Z0yHY3lBh4qQ5YqmhZRrlnF5MluafPc2UnaAYHBha4Aim5Go5R1j8xct1c7mFGvY7dGFfHHnZ3RwhDWIc7rX4u3K3HYzomYbog1UsR0fcsmcIA9+SHvycM3dqlr9CybwnAA4JF+p9kOqlX6N6vquI2QZ/RC5VJFHAd1bvhpTsGbNSRj2Yb7z2qID6/JMq5/eqD9zWSXgQu4r7IjOrbCwLKn+4bF7BHe8ZKQE+25bpdpH3cp6GKYKldxetF7IRot2trl9dXtosFDSLEnRLyvCHX4dOp62+0HtKiKD6+N7J/SUwMe6as04RuPnx3wrIpzaJdMKDAZdiq7D2eHqT3c1TMQRmJ3uVpZxtdaQ/KZCd0LWRj0doVKdPusVDJDu7ykNEDQnQNT0mqrmmXQyVDCOrg/w7vB7j51uu0XbTmASSsjJVera5LUOtcPPLeqTrlALTLTs3ElNLN+bxsrXEZHNyu2++wWAWOsb7PAYEjXZ/37YDc80Ns8SybHmL8zwVy0xg2mAVgiVQai6afqVSxhnFFxSja0C9dSEbOTqmyeTDTVUBlj2mfT5Cwplp6CT67LwqfXtcUn14X6sgWb93s+dqIhY9mGO3s2sC092biKvYfvrCaVcXt3vfYk4L6m/Zk2wvvJIJYp+mRJSDarntyqQPGUCjR5A90oGbil52nuSq6WdjuQi+N1j2ZX0YZOyOoCgnNaOntlTNipgtglIZXSxCGn+phtyWOtAWjtZ1CXwLStm6lWuwQ9md8XbA37W7RZV5GrdNE0fHVDe1uDwm5q3G7wZRfbqwtRSU/1aWfv7uwZin/Wncu3LtfHdbasWQaXZNXEjEfNKhqmkJGhZ0eGifS23kFFogwVWL87Upu7vaEPF55WVdXEbaGMcsWLID3FF1S2EAMOXbhP7fLFcUePBhHFUwSykoaokvnPvV0BBGJu44FbTWkvVCpZBNd3ykSZKJ1cprGp6PbtQnXcHyNw/52huQ++vKFdTPvWPZs3dq6D586P9Hz/c29X9GhcGd0bV0KPxqF3jpCbLEiQsWzgxQsjEzdU7KSrhvQ/DV0aVMSDfRpHdIB3Wx4XN7xxWSsMbJX/VYRE1S0dH12bhbt6RCbSmF4eMiZj2STFBcB2wOK202xWLb5hBV6Jp1SgybOZjIGIW0knp2lpL0TzOxmik6/TJfiUNGixuwk1sZttKmKoAAfovTyqB+qi02uEGc92hmg5S13ATdEF2bDzopctvKRlNGEYg7rWNSbwuUF3D6y1KrbZGfcmVQudMSjHXuqM5cwKZi8wY8yomAKYwxJ0A4Q3LmuN0Xd3QYkiqfj+5g7GfZrwEi9s8ubLy3XhgyLkMMUX/tuER9dOolGu8CeTrenoxDV598rTI9ZFc26GuXjPe/WwzhzSC0+c09QxKXHiA9208eQmz7JwzqtJdHbvx2iQnYDROGB0zW9XpxzqVojsO6KV7kwGZCwb6NLA2ZtrNxt+U9dQwH0HZVrwXpsErXUv9MeiJ0Oxl5zHr1a82/isfs2q2BoWxYukorHGe+MmE9bUEdxyptkDb9fXf3+Luw4ymvireBMvZXGTl7xq6fzXBK1rYzAIJj7QLa4JNNGcR5+PRTUtqk2Wi2LAJ7C7Rk6JeSrqi2zOhn1hLyrdzxWLhAGsVuJT6Vy/QtgUturV6hnlIDda+jcPTOvr+pFgBUNlXTfJQPZSSlj2UMdbyebICXOlWDW+vmh6SrAt7eq4C4+S8dJ2U8iN0z5E2IYuydMJU4zueRon0TGrAqROSk43g2HHixc2RxGb2RzBgqG9I+573UyPDt3MlCCzQnG8fUWbiOWm8yFyXtR7f4ykX6464t68vDX+vqcrnj/feVAgOJYdujffuMy7Koau9eVLRMYy24XzFETIWDbgZjTuNmHLtKvyGvkjxliYN2vZtoPBm4yx2KbaZz92ljaGVdUmLZZuTqISyWW6l5Up+UPGZCynp/qM3mxTx3RB6+qoVDLDtkMSFNV40PIjUU5GdxsM00hPOWGKa+ztwTh5sE8jXHy6fvpTSJapPKOJ0X+ob2P0a2auclW9TFFbD5wdpnjraIooBCTn3G/fNrMsBnWt6+n6NHPhubbzZNnnOESuU1/063YfCXu+dP2OMHyFUeRkLKueRjXTvYRLoyFeiPbrzpT4uWqb378q0gvpBrk/81roxQm7fjwaAwUwx2p7GSCY+n23vz8ajWDTvge0iAx3Es++7jteixTVVLya1RRnz/tXBQzZoukpmDf0rLB1Tkmoghs761UqxKBP50QwDeqFJLfdoUUBIMG5LauhUZWSuMIm3GrFs+Gyedl5IWNZfvd+fWP74OeStqot+gbKSx8/uwm+uKG9djugYNaoIGPZgE3F2SBun02TpNV9SnLDo/0bR2wjaw76GAsm6ESDqVNStUkz0nzGG154nHW7Sk/14btBZ2iNKoGpz61SKgMta5bBrd0iPcxPnKvfnzCAx9/fzXi8O3vUN5bwTVb8tMxlNoVK7OJs5Y5LoN6Odgkht3evj2qGeFddTNz6YQNwdYdM/HRrx7DlKT6G92yMkQvbRHqHRNxhqC31cFnbmsECBk5EI0PlY0APD/GJP9zSEY/2Pw0ta5aJMNoPHc/RfkfIcdkZvXa6rXaeZd1z06955CBF3k53llTPslMizb9KwtguJc/CbfKfimmg5oQwxnTPrvAsqufRq8deIB8j3tq3druLtljEf2v2aJdXKxNuBMqawqpetSnkTz6HdsmL0QwqvHxHtENnZIr73RQDraK+465UEuTkGHH1HnJK4AwdQ79cF0bihOj37Az1aAYr6qBbLlokHytsdsHmMN0MoU5NJWdCnQrFbGOvK9mEMCULMpYNZNjEDwrcvrNNRkl7ZUptUNdIQ1GWSQqI5zs/DHUrxpb5bzc1JTopU1zgGXXL42obsXPTgy6m8XRTt6WLpmmn3YpabbGLDbu/dyP8dU9X7bqLDJ1q4yolIxJc4oHIQHfi9u71cFrVUpg5xFw6uZPLfdlxYZsaER6Ch/o2shWrl+Ny37isVfBzFUPndqcmPr9h5ZJhL+yWNcpg2IUt8MIF9hXuBLpZAnFbCXkl1cs2qGtd7UAsGkzGl3g2htso6Nhxf29z9TmdooGI92ubGbom6/eEkrl0eqfBvsTqR2QJOIGdyo8aUuZmVkdHtC9D0Xvo+hGRpKfOPETrFJaNuGhCeFQWDO2NtUJmz8baSHMYgAiPp1tOUxLRZUPzpi7hns+HDBUQZeUmu1hsnXSdE3YDkVrlwr2/jSzHh67PF+9jUxXHeY+He4fVHIGIZFebn/LqJa0w9j7npG3Zo+tUVOanW+1DCutXKoHa5Yvh8QGnGbfpaeORdXvfyDaGetuLQYIu/KqeZXc8b83GqWGfJYqkBnMGnGynbgVM1AAgY9mIrjNXYxLFSG/B0N64rmOm52PY9SsTH+iG5tVL48E+jYI3FgPDea2r45Is88j55jPr4q+79cahW4qm62+Lbo0q4gGrI3I7DeUVdTDwiFUCWI1f696ooq1x4YbrOtXRLn//qtNjFtHXUbNcsTCDTcizyZI5APBgn8YYfbe3ClrvXtkmbBry9NplHcsr1ypfDIue6hO27LZu9V0ntJQvHjKU/hvcQ7uNbFhe26E2HrM6+mg8UGKatFLJyGdTGN/dGlXC4qf64JubwiWRyhRLj1sZWaf4a3V61y2ql69v05DnWB2MyDJRpt+lK+Tg5hTYDRQfUJ65/CiQI2OnDCRuf/XZteur7BKE3d4vvdwqxxRLCxrddrtOcVAfkY0uk3fuvFahhFo7Q1+d9TRtO2NtyGtt1+++OW6VcZ0J9ZjyTKBpFkeH6FNU2TthPJdVni9ZbUPXDhMVSqQjIy0F9SuVwCsXtwwWFdIh11q4vmMm+jStHJRUFIhBbVMpCf11pX5DVu2yyEhLwb8PdkfH+hWMYRW9m5pD4vo2q4prO9R2LPAi35stpXypamWK4rPr22HRk721DjHhDCpZJBW3d6+HrzSzn+JZdHq9OimNJQMylj3w1LlNw0pUCxmm0sXSbBPUTNhNQ2RWKI7f7+wc7kVjgRf1Sxe1NH6vXoUSUekjypgkrP53cctgmxMVwaAaBaJowSP9TwuTYvr0+nZaD+Nvt3cyhl3oaK0kGfx0a0dkVijuXhLNBbJXvKFU3ECUopUlc5y432Cc5Pp5WAf0060d0bFe7N5nO+QXrZsXzVMDm+FGy5MVTcLUKJsSvPJsiPpc3Wkpt5gMlNoeZaic743wN4HbxFr1nDzaP+RBUjVU5cGGl4FHNRdJoCdyzclnailtL/GwArdT2DrOsVFVEWfdrewhADw90FzIwe1pvVOjDOSEaqjJOHmWZSP+ob56w3XOxn2u2uG2jLQs/abLtfFKExsva7TvL9P71FQCvagy6FXPuyknSTaALzy9RrCokBPntqqGD67OihhgNrcSOuXn/7zW1cMSPdXZrOfPb44LrBA39R1mx1MDm2GWzYwlEN6Xyo6BmuWKIS3Fh5IZaWEhgqLZd/cK/C7GGB7s0zjM+BeI2fQa5ez7oYIQIqlCxrLC2Pu6RsRUCsoVTw8LqWhdq6x2OzvkqVFTeIZKkVQf+jevgs8MI1h59HexjddZIE+dfntTpCh5M0O1s/1HQ6P8RIm5m6ZnU3whKaazNYkfs4b0wvJn+qJlzTLBTsQOofmoPpMizEAN+7inVwO8e6W36U8d8otOLujhpuwwYE6oyvP7tZqZADDqrs4eWugeNaFNxLO76ehkb598L5mmUIFIz5DASUNZeMJUD6OYSlSlzfprYoG9UK54eHt+uc0swyijGr2yh7GjEnYj30dejGWTvq5MS5tEXTtvq+651BFtOdw2tcq4MqS8qAfZTY27nT2LZpbEbnDpVtcaMA/c3MZYu02Kk6sjxsOQsUvS7d88dB+Vtan+GCvqNVCdL6ZTY2ec/u9isyPLdJ98cl1b/HRrx4iBp7y1LrziUqsyo8nx8Med0fX7quf5mxvba+0EIOAIEJfSqQgbEAiHm/xQd0fPsVMBlmRAxrJC/Uol0VAZ9V/Wtia6NKiA4kVSjcHrbjuQqDRiGcO7V54e8cIUnC4Z7W46+I+vDRndagziq5e0NMYLVYoiPtEpTssJ9ecsf6avNlu8YskiwVGwiAe8wab8rXihmjowtTO/p1fDsE4cCIyS3by85U5XHm3LnhC3L1yTHFf/5lXRy7CuabXSrgYQJm7uWtd5IwA/3NIB84ee5TqWX/db8rMAjzjnrygvuDt7RMZZN7D0hZ8yJJvKqOEUbtVA1JeenSEjv1fjFV4isNMEt7tN7eQmb5JKhA+70F1suoou/j0aZCUKXS6EG2SDPx7nX74HnWZd5NuioiFkxm1IgdsCF/I9rf5et7Nwl0vJzKrxt+CJkFyqXCjEzazPd4POwJSHu7tqw4QHutmul0+bmrsjtOJ1/YMYYF+kSVwV4QmmAUzpomlajXZZr1r3LhP9l2ncYRdb7oWO9StE2Anip8hedjeJtIwx1CxnvqZCxz1RYZ6xkL+6PycpcufeokYZFEtPCVYUEjh1mC9f1AIP/rgQ10YR2+wGL2WN7Qy8C2wyidVpKzfEW5/UjV6vK8+mZVCb9ufGeP3w2ixkPTs2bNnkh7qjy0sTjN8xFXU4r1V1fDl9g+MxTSLuTnqhL17YAj/P3eK4fx1uZ0Ay0lJi1lOORrnA1PnWLFfU1jMlDAXV4NAN8P68uwtO5PqNxoWp7LeXRFH1nrMbdMj9jdtH7NoO7svhCtTKdnYvsTrSoMDuuY/XS7xOheJYtztUJtutR1YupBPtS3n8A6HkLruoidOqlsKybZFJlCqNq4YcNE5typWkmlQjRuDWs+xGGxxQkh2VfasxuCbke0kdFMsza3LMeZcGFTFhRWQJbyBghE5ZvRutapZx3e/UcTlwfahvo4hwoRcuaI7zW1fX9uFvX94GuZfqH9gPrj4dW/cf85wk2qZW2WACru6eEDNtjQxhh/Go9OeFeMgr/nRLR+w4dDwOrYk/MXmWGWPlGGNjGGOrrP9r4xIYY38xxvYzxv6I5XgFhVlDemGhVDgEcPZQXHR6Dbx/VRvbaeZocav3LIjmlv79js5hhswZdcqHxd+aeOY8c0xgorEzNspb18v0YjJ99+G+gYTDomkpKJWRFhGPUq1MUax4tm9Y5+/mvRXPUtU6opXPAqKTavOCvHsvU9CCj6/Vq09MfqgHRt6hn4p8sE+jkCShi0OmpfhsXz66AgqAc3yubGSr96LdeZe33bTvmHE7mQf7RkpTOuHFayqHpXkx/N3Spmb46yVL8sZ9fWP7wPNo8WwU/Y5pwKNy85l1wwat8jm6UfEAmtQlVLw8n2LWq17F4sb+a9XOw6725TrUxGZwdlpV9zkiQCDvQg0HkgdX8qyeaSYXAD6+LguTH+quNZTtYttfvqiFMUHuh1s64qozauHWM+tFnJviRVKN8ck+HzM6oYoXSUUDmxh1E5dYYRam+O56FUvgx1s64Ilz9HKyupweE7py73aIcyP3UfFwjJUulhYxs19QiPUNPRjAOM55AwDjrL91vAzg6hiPVWAoXiQ1rGMGwjsdnUQLYwx9m1WNyWjxwvWdMsP+lmVu3JaYlL0Oap9aND0F/9zrLJ3TIooSwzJ2EkvG74iMW5tBhJN3y+ndfk3HgJfus/8LjyP3sYCX995eoSSOGwzC9PIxonFwiSxsoRjiFlUBwGmqUzV0Pr2uLW5zIcPm1Hnq1kbT4erKKTtxe/f6wenjeMz4RetVGX61WWZODQWS4/gaVQkNVFe7NIzsjH1TgvKhE7mu9q2iGtnilzzQ25t6xv9JijWllURJeWClSim6iZ9UsUs6AwKzRj/d2gGP9AuX7rKLHz+zYUWUKJIalleiI5rBcqk4JiE7Ib/fGGO4Q0qay80L3acXtDaHezWwnCsNNZ7QsBwG6baXQ4LUc1QkNcU4q9TQpiz7xVk1jRXtTq9dFs+e17xAhQGULW6+zlmZ5VxVIXTCbZKiQJwdzkMhKKZzFs2zWBCJ1U8/EEA36/PnACYCeFjdiHM+jjHWTV1eWOnbzF2iSzzo1qiivpDHOU1Rs2wxPP3HUgDh0/9uR5wXnV4D45fvBBAojuKmQplKMvoc+UF2u62KKV5UNcA71quA1y9thXtGzA/sz/rBN3Wti+f+XAbAXdKlE0+c0yQirq1740qewm8EqgfE6RKpZ6J740quOtd4VvNKJNEesV+zKhi9eHtgH4adnN3C7OEC7J9F9byPv78bNu49guM5/rhXn4wmdne+UtXMzQyXzgvYqHJJrNhxSLM1ULW0eVBrd3/ZrUvxGUoyO9wINcsV0xpn8tS+KMccbIePYbEiz6hDrcp5VpPKGLN0R1BeUqZ+pRJ4sE8j18U3oqV6maLYYlWEU5/LB/o0wtsTVgMA6knvFrtQg0uyaqJh5ZKOifF5hr63gYuZTEEBsnWjpmm1Uni4b2NtHLQd/9zbFWt3uRtAC7xWRRXnl4Pjjcta47nz9IPqsfediVJFC0e0b6xuzsqc823W5+0A3Gv2EADMxRycEIVHPru+nVZ3FgjIzwDuEzBU5IS27QejiyOKdYQeXUJkTIe0Rda8FtSzSYgCzHGrGZLR6nSeru9Ux1U58ZlDemKmJLGnQ425czq21zKyopBAV4dkPbFeLqJj8pJEm4ilI16ejqHS9KfpGqtSa0BkWV0TdZX4yvRUH+pXKolm1UvH3fMVjVyX6tGXDWHVaBT9nO48tZOKM6nKJnaDJzfGsu4erG94XoulRfdSl9v4xTTnvAM3CCO5uUaZiDGG27vXj9ATlstDezWwdMhJwXYx0H7J0LK7KxljrhSkTIabl24oHkVkkg1jDLd2q+eoi6zSsHJJzw47VZrSCTFL1b1RJaSl+IxKRfUrlTDaJycbjj0kY2wsY2yx5t9AeTseeKPGFJnGGBvEGJvNGJu9a5c+qL+wMe5+51AGHd/f3AFf2dRWB2QPa+RlebR/Y7wsFa0YeUcn2wpCZzb0ptk7oHl8vOte4q4EInPb1MnI3hrTO0CcsZIZqVqpIPl7XismikTJREz1VSqZ4Vgd7UVFjcDUCp1Xyw1C59rphX1l+1qY81ivsBg1Ueq3l5UtL6bt43U/vXJxy4iKjlEUHQMAVJU0i00VFXUhLnbXXVQgdNrOxLKn+zpvpKFdneiutYwamibzf53r4J0r2mBgq0hPu1x4Qr1n7E6B3TqRpDlQE7tqyhu5+Ux3qi/5gVCL8GL0yiXErzIYP3beaDVmWz69dudaNm5jMQBEfHJNgwavF2P5uo6Z6NG4kmNVPCJAMY/JgCUz0vDf4B5JzUnKbxzPEOfcqGDNGNvBGKvKOd/GGKsKYGcsjeGcDwcwHACysrISm1VUQBCi8F49ZxVKFEHnBvYjTuFd0Z1ItbS2k9eydFFv7Xv9slZBLWMdMx7tifbPj7Pdx6+3d4qq7LSQ+DEZWF/c0C74uUPd8pioybYWHfPn/9cObSRviIhrbCZJwJmE/Sc/pJcz+u2OThi3bKfWa5aIBFDBTV3q4MvpG1DSxqiROadVNcxcv9ezZ+PmM+ti4opdYWWYdTDGUF65vsXSUzH67i5BI1N4L6MpfqHjQo3xIZe2j1YDWOWnWztg/qYDYQa1wE49IdbklmgGl4C5YtZrl5p1Y72Q4mNhnk8TpgISOpppih4ImlQrhQVDe0fEOQNAu7r6gUGsSi4A8IxNqXAv1K1YIqoQK4EpOfR+m5jxiBLGLrW85QHhWU0q48c5m122MpzPrm+nXf7HnZ1x9ltTjMofOoqlp+ITm8p6RDjRJHG7VUkqLMT6ZhgJ4Frr87UAfotxf6cky5/pi6mGcsExEXQtx76rrfvdZdwL0lJ8QUNHeMvkxDI38lFeigvIMMYwsFV1o4ElvxQHudQQFnRvXAmTHuzu6sVvinFsWLmkNs4cSGzM7pABTbD8mX4Ry8WARM08vzSrJl69pGWEvrQTp9cuh5XP9YswhN1yWtVSQf3Oi7Nq4KYudXBvAssqF0lNCXpk46X8cXrtckadb7sp7WjUQBJJtDrpqta0HQMlJRF14Cn+1lXJc8oF0BnKQIKVZzzMBlzerqbzRh5oIsWxtzI4P+wSaK9Uq0S6/C3yDEhvg9Z7LDSrXhr/PtjNcyIz4Z4aluHrNRzjVCLWyOthAL5njN0AYAOASwCAMZYF4BbO+Y3W35MBNAZQgjG2GcANnPO/Yzx2gWPB0N5Rxf7Fw6NhSxzev6aCJH/e1QX935xsGxddy0aEXIesJJFoGGPo2bgSxi0PnxQRJpPu1NXyWB45FiY/1B0b9x5N6DGE5/hGRbUjPdVnq7udHxRJTcGQAXpppHgintt4aQDbYRdeITzb8Sq3fl3HTHz233rXWrjxwouqiV0Y04VtauDgsRxtWIE4j3alo3XEa/ZAh9f4/ngix4aaYnbt7j1VqSmzQqifkxUv7GCMYfjVp2PQl3Ncbe8WNQGSiC+VSmVg5bP9Co1yRSKIyVjmnO8BEJFNxDmfDeBG6e8usRznZMHkyTiZee3Slhjyy2JjEpsIH4lGEk8I9qteqLt7JebFfvUZtTFr/d6I5doXiPXSyy8JoYGtqmH4pLURnhmTdzqePHd+MzSpVgodPUxzFjZSfAxvXNYKWVHGaXuhe6NKWLd7nTYWXsyGxOu2EzMVJfO5QIHbqoWAfUGdFB/DjV3Msz/RhCok4pnu0qACJq/aHZbsVhDx8tNlxRsv4T3dGnmTISMKBtE4+k4lCoemB2FPDP33+a1r4PzWZu9i0ANr0wmHtgnf6MULm+Pct6eiWpn8yZY1JSPce1YDjF22A2WlwU5+v/KaVisdU4xiLJQplu4pVrSwMtBQWMQOtYqcG4YMOA29mlTSlrgtUSQV9/Rq4Dn0xUQoEit+d/S4+8901HZ+U1OS3kRBfEl7DQGrV7EEJq/a7eks39ilLr6duQkP9U1cnoKKl2GCHIbhJW8hPdWH1y9thTPqnrqDb6LwQcZyIaZEkVTUq1gcD/ROfGfsphNWtxEJiFL11qQgSiLL4TAhiTiCMDPhgW6ev5PiY+hYz6wuc08cw5CCeqgebeWGlUtg5Q69QVyvYglHuURV1syOZMVp28UMezX0BnWtiyVbDxgrOeqoF2MSn44vb2iHEzmRHWqrmmUwf9N+TwV8xKyEW6lDmfNsipMQxMkIGcuFmBQfw7j7uyW7GUZOq1oKV7avZTvNmh8Ig0IOJRGeuMIgbl+YKVMIQ5/iicgX8GK8AoEqnyt3HEZGHKqDOZFmJw+SIExGaqf65TF19R50NkgBmqhWpih+uKVjPJoWE10a6LXNf7q1I45m53pKIM5IS8Hz5zc3nos/7uyME7l52nUEUdggY5mICTceqzrli+PydrUiSnCn+BieM5QdzU+qlMrAXT3q43wpma159dJYvOUgyniUzCPyF500GxHiyva1UbNcMZzpUCBG5fXLWmHKqt2eYo+jpSApgIhiQ/FSRSkopPiYa8lImSva1zKui6aiK0GcrJCxTMQFOw+sz8fwwgXJN4pNMMZwnxKq8uS5TXFp21r5qnxBeKerx2I5pxo+HzMmXN3YuQ4+mrJOu65URlrc4qadcCtRlh8UoKYQBFGAIGOZIDQUSU2JWueZyD/uS6D+cmGngpW0lUgpNTcUxNLEhc2zbMdvt3dCsSiL2RDEqQIZy0RMxDPLniC8UpC8ksnGqWKiSm5eIBHsxi764inR8Nz5zXD4eK7n7z15ThN09BgnnAjsqp4WVlqSU4AgHCFjmYgLjHQjiCSQXzrYJwN2esU6SlvKCF7LmdtxZfvoKoBd1yl+Bnss+ILqIaeSuUwQhBNkLBNEnPBS4peIjVQfQ66f0xANwJkNK+Lflbtsk7F0XNGuFoqmpeB8kvkK0ql+BUxYsQs1ylKuAkEQIchYJog4sOCJ3lQqNB/57Y5O+GfJjgIZ75rfiHhTr87QFB/DRacnt5x5QeOGznXQv3lVVCtDKisEQYQgY5mICZqtDFC6KOn95idNq5VG02okXQWE9MFz8pJc3acQwBgjQ5kgiAgKXp1R4qRCGImXZJGHiiCSgTCWs8lYJgiCSAjkWSZioniRVKx4tm/S5acI4lQlPTUQipKbR9M8BEEQiYCMZSJmvGbhEwQRP1J9FIZBEASRSGJyBzLGyjHGxjDGVln/jxD6ZIy1YoxNY4wtYYwtZIxdGssxCYIgiBCDutZF02qlcHaL/Km4RxAEcaoR69z5YADjOOcNAIyz/lY5CuAaznlTAH0BvM4YKxPjcQmCIAgANcsVw6i7uqB8ifjpJRMEQRAhYjWWBwL43Pr8OYDz1A045ys556usz1sB7ARQMcbjEgRBEARBEETCidVYrsw532Z93g6gst3GjLF2ANIBrInxuARBEARBEASRcBwT/BhjYwFU0awaIv/BOeeMMWM6NmOsKoAvAVzLOddmojDGBgEYBAC1anmrRkUQBEEQBEEQ8cbRWOac9zKtY4ztYIxV5Zxvs4zhnYbtSgEYBWAI53y6zbGGAxgOAFlZWaSDRBAEQRAEQSSVWMMwRgK41vp8LYDf1A0YY+kAfgHwBef8xxiPRxAEQRAEQRD5RqzG8jAAZzHGVgHoZf0NxlgWY+wja5tLAHQFcB1jbL71r1WMxyUIgiAIgiCIhMM4L5jRDllZWXz27NnJbgZBEARBEARRyGGMzeGcZ+nWUY1igiAIgiAIgjBAxjJBEARBEARBGCBjmSAIgiAIgiAMkLFMEARBEARBEAbIWCYIgiAIgiAIA2QsEwRBEARBEIQBMpYJgiAIgiAIwgAZywRBEARBEARhgIxlgiAIgiAIgjBAxjJBEARBEARBGCBjmSAIgiAIgiAMkLFMEARBEARBEAbIWCYIgiAIgiAIA2QsEwRBEARBEIQBMpYJgiAIgiAIwgAZywRBEARBEARhgHHOk90GLYyxXQA2JOnwFQDsTtKxiQB0DQoGdB2SD12D5EPXoGBA1yH5FOZrUJtzXlG3osAay8mEMTabc56V7HacytA1KBjQdUg+dA2SD12DggFdh+Rzql4DCsMgCIIgCIIgCANkLBMEQRAEQRCEATKW9QxPdgMIugYFBLoOyYeuQfKha1AwoOuQfE7Ja0AxywRBEARBEARhgDzLBEEQBEEQBGGAjGUJxlhfxtgKxthqxtjgZLenMMAY+4QxtpMxtlhaVo4xNoYxtsr6f1lrOWOMvWmd/4WMsTbSd661tl/FGLtWWn46Y2yR9Z03GWMsf39hwYcxVpMxNoExtpQxtoQxdre1nK5DPsEYy2CMzWSMLbCuwVPW8jqMsRnWeRvBGEu3lhex/l5trc+U9vWItXwFY6yPtJz6LxcwxlIYY/MYY39Yf9M1yGcYY+ut/mI+Y2y2tYz6o3yEMVaGMfYjY2w5Y2wZY6wDXQMbOOf0LxCKkgJgDYC6ANIBLADQJNntOtn/AegKoA2AxdKylwAMtj4PBvCi9bk/gNEAGIAzAMywlpcDsNb6f1nrc1lr3UxrW2Z9t1+yf3NB+wegKoA21ueSAFYCaELXIV+vAQNQwvqcBmCGdb6+B3CZtfx9ALdan28D8L71+TIAI6zPTay+qQiAOlaflUL9l6drcR+AbwD8Yf1N1yD/r8F6ABWUZdQf5e81+BzAjdbndABl6BqY/5FnOUQ7AKs552s559kAvgMwMMltOunhnE8CsFdZPBCBBxXW/8+Tln/BA0wHUIYxVhVAHwBjOOd7Oef7AIwB0NdaV4pzPp0Hns4vpH0RFpzzbZzzudbnQwCWAagOug75hnUuD1t/pln/OIAeAH60lqvXQFybHwH0tDwzAwF8xzk/wTlfB2A1An0X9V8uYIzVADAAwEfW3wx0DQoK1B/lE4yx0gg4sj4GAM55Nud8P+gaGCFjOUR1AJukvzdby4j4U5lzvs36vB1AZeuz6RrYLd+sWU4YsKaSWyPg2aTrkI9Y0//zAexE4KWyBsB+znmutYl83oLn2lp/AEB5eL82RDivA3gIgN/6uzzoGiQDDuAfxtgcxtggaxn1R/lHHQC7AHxqhSR9xBgrDroGRshYJpKKNeokSZZ8gDFWAsBPAO7hnB+U19F1SDyc8zzOeSsANRDwQjZObotOLRhjZwPYyTmfk+y2EOjMOW8DoB+A2xljXeWV1B8lnFQEwiPf45y3BnAEgbCLIHQNwiFjOcQWADWlv2tYy4j4s8OapoH1/53WctM1sFteQ7OcUGCMpSFgKH/NOf/ZWkzXIQlY050TAHRAYDoz1Voln7fgubbWlwawB96vDRGiE4BzGWPrEQiR6AHgDdA1yHc451us/+8E8AsCg0fqj/KPzQA2c85nWH//iIDxTNfAABnLIWYBaGBlRqcjkNAxMsltKqyMBCCyZq8F8Ju0/Bor8/YMAAesKaG/AfRmjJW1snN7A/jbWneQMXaGFUt4jbQvwsI6Nx8DWMY5f1VaRdchn2CMVWSMlbE+FwVwFgKx4xMAXGRtpl4DcW0uAjDe8vSMBHAZCyg11AHQAIFEGuq/HOCcP8I5r8E5z0Tg/IznnF8Jugb5CmOsOGOspPiMQD+yGNQf5Ruc8+0ANjHGGlmLegJYCroGZhKVOXgy/kMg43MlArGEQ5LdnsLwD8C3ALYByEFgNHsDAnF/4wCsAjAWQDlrWwbgHev8LwKQJe3n/xBIpFkN4HppeRYCHe0aAG/DKrRD/8KuQWcEptMWAphv/etP1yFfr0ELAPOsa7AYwFBreV0EDK3VAH4AUMRanmH9vdpaX1fa1xDrPK+AlGFO/Zen69ENITUMugb5e+7rIqAUsgDAEnGeqD/K9+vQCsBsq0/6FQE1C7oGhn9UwY8gCIIgCIIgDFAYBkEQBEEQBEEYIGOZIAiCIAiCIAyQsUwQBEEQBEEQBshYJgiCIAiCIAgDZCwTBEEQBEEQhAEylgmCIAiCIAjCABnLBEEQBEEQBGGAjGWCIAiCIAiCMPD/1gY3gLlJn1cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the sound of people talking\n",
    "peopletalking1 = selected_aud[sound_clips[21][0]:sound_clips[21][1]]\n",
    "peopletalking2 = selected_aud[sound_clips[41][0]:sound_clips[41][1]]\n",
    "fig, axs = plt.subplots(2, figsize=(12,5), sharey=True,sharex=True)\n",
    "fig.suptitle('The sound of people talking')   \n",
    "axs[0].plot(peopletalking1)\n",
    "axs[1].plot(peopletalking2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14111c130>]"
      ]
     },
     "execution_count": 523,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# car door closing\n",
    "cardoor1 = selected_aud[sound_clips[55][0]:sound_clips[55][1]]\n",
    "cardoor2 = selected_aud[sound_clips[62][0]:sound_clips[62][1]]\n",
    "cardoor3 = selected_aud[sound_clips[74][0]:sound_clips[74][1]]\n",
    "fig, axs = plt.subplots(3, figsize=(12,10), sharey=True,)\n",
    "fig.suptitle('The sound of car door closing')   \n",
    "axs[0].plot(cardoor1)\n",
    "axs[1].plot(cardoor2)\n",
    "axs[2].plot(cardoor3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "metadata": {},
   "outputs": [],
   "source": [
    "sf.write('visual/audio_sensor_1_cardoor_1.wav', selected_aud[sound_clips[55][0]:sound_clips[55][1]], 22050)\n",
    "sf.write('visual/audio_sensor_1_cardoor_2.wav', selected_aud[sound_clips[62][0]:sound_clips[62][1]], 22050)\n",
    "sf.write('visual/audio_sensor_1_cardoor_3.wav', selected_aud[sound_clips[74][0]:sound_clips[74][1]], 22050)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,item in enumerate(sound_clips):\n",
    "    sf.write('audio_data/audio_segments/audio_sensor_'+str(aud_sensor_id)+'_sample_'+str(i)+'.wav', selected_aud[item[0]:item[1]] , 22050)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>audio_sensor</th>\n",
       "      <th>file</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>550000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_0.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1875000</td>\n",
       "      <td>2900000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_1.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2925000</td>\n",
       "      <td>3125000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_2.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3400000</td>\n",
       "      <td>3675000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_3.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3725000</td>\n",
       "      <td>3975000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_4.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4175000</td>\n",
       "      <td>4400000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_5.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5025000</td>\n",
       "      <td>5200000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_6.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6700000</td>\n",
       "      <td>6875000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_7.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7125000</td>\n",
       "      <td>7300000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_8.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7350000</td>\n",
       "      <td>7525000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_9.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11125000</td>\n",
       "      <td>11300000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_10.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12525000</td>\n",
       "      <td>12700000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_11.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14100000</td>\n",
       "      <td>14275000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_12.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14475000</td>\n",
       "      <td>14700000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_13.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15150000</td>\n",
       "      <td>15325000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_14.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15725000</td>\n",
       "      <td>15900000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_15.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17550000</td>\n",
       "      <td>17750000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_16.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>19350000</td>\n",
       "      <td>19525000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_17.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20325000</td>\n",
       "      <td>20550000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_18.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20575000</td>\n",
       "      <td>21175000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_19.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21225000</td>\n",
       "      <td>21775000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_20.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21800000</td>\n",
       "      <td>23850000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_21.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23900000</td>\n",
       "      <td>24075000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_22.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24275000</td>\n",
       "      <td>24475000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_23.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24625000</td>\n",
       "      <td>25150000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_24.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25175000</td>\n",
       "      <td>25550000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_25.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>25975000</td>\n",
       "      <td>26150000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_26.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27725000</td>\n",
       "      <td>27900000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_27.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28075000</td>\n",
       "      <td>31275000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_28.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>31300000</td>\n",
       "      <td>31525000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_29.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>35500000</td>\n",
       "      <td>35675000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_30.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>36425000</td>\n",
       "      <td>36600000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_31.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>37125000</td>\n",
       "      <td>37300000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_32.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>39650000</td>\n",
       "      <td>39825000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_33.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>40225000</td>\n",
       "      <td>40400000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_34.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>40800000</td>\n",
       "      <td>41000000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_35.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>41500000</td>\n",
       "      <td>44425000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_36.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>44725000</td>\n",
       "      <td>44900000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_37.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>45000000</td>\n",
       "      <td>45175000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_38.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>500000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_0.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000000</td>\n",
       "      <td>3075000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_1.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3400000</td>\n",
       "      <td>3650000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_2.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3725000</td>\n",
       "      <td>3975000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_3.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4175000</td>\n",
       "      <td>4375000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_4.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5025000</td>\n",
       "      <td>5200000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_5.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6700000</td>\n",
       "      <td>6875000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_6.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7125000</td>\n",
       "      <td>7300000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_7.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8525000</td>\n",
       "      <td>8700000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_8.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11125000</td>\n",
       "      <td>11300000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_9.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12525000</td>\n",
       "      <td>12700000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_10.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14100000</td>\n",
       "      <td>14275000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_11.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14475000</td>\n",
       "      <td>14675000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_12.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15150000</td>\n",
       "      <td>15325000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_13.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15725000</td>\n",
       "      <td>15900000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_14.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>17550000</td>\n",
       "      <td>17750000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_15.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>19350000</td>\n",
       "      <td>19525000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_16.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20325000</td>\n",
       "      <td>20550000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_17.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20575000</td>\n",
       "      <td>23975000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_18.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>24125000</td>\n",
       "      <td>24350000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_19.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>24600000</td>\n",
       "      <td>24975000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_20.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>25200000</td>\n",
       "      <td>25550000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_21.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>27725000</td>\n",
       "      <td>27900000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_22.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>28075000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_23.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>30925000</td>\n",
       "      <td>31400000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_24.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>33875000</td>\n",
       "      <td>34050000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_25.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>35500000</td>\n",
       "      <td>35675000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_26.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>36150000</td>\n",
       "      <td>36325000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_27.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>36425000</td>\n",
       "      <td>36600000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_28.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>37125000</td>\n",
       "      <td>37300000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_29.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>39650000</td>\n",
       "      <td>39825000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_30.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>40225000</td>\n",
       "      <td>40400000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_31.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>40800000</td>\n",
       "      <td>40975000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_32.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>41500000</td>\n",
       "      <td>44400000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_33.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>44725000</td>\n",
       "      <td>44900000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_34.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>45000000</td>\n",
       "      <td>45175000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_35.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>550000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_0.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000000</td>\n",
       "      <td>3075000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_1.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3400000</td>\n",
       "      <td>3975000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_2.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4150000</td>\n",
       "      <td>4475000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_3.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5025000</td>\n",
       "      <td>5200000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_4.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6700000</td>\n",
       "      <td>6875000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_5.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7125000</td>\n",
       "      <td>7300000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_6.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8525000</td>\n",
       "      <td>8700000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_7.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11125000</td>\n",
       "      <td>11300000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_8.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12525000</td>\n",
       "      <td>12700000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_9.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14100000</td>\n",
       "      <td>14275000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_10.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14475000</td>\n",
       "      <td>14700000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_11.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15150000</td>\n",
       "      <td>15325000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_12.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15725000</td>\n",
       "      <td>15900000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_13.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>17550000</td>\n",
       "      <td>17750000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_14.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>19350000</td>\n",
       "      <td>19525000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_15.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>20325000</td>\n",
       "      <td>20550000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_16.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20600000</td>\n",
       "      <td>21100000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_17.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>21125000</td>\n",
       "      <td>21775000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_18.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>21800000</td>\n",
       "      <td>23850000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_19.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>23900000</td>\n",
       "      <td>24075000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_20.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>24275000</td>\n",
       "      <td>24475000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_21.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>24675000</td>\n",
       "      <td>25150000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_22.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>25200000</td>\n",
       "      <td>25525000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_23.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25975000</td>\n",
       "      <td>26150000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_24.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>27725000</td>\n",
       "      <td>27900000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_25.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>28075000</td>\n",
       "      <td>31275000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_26.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>31300000</td>\n",
       "      <td>31500000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_27.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>31825000</td>\n",
       "      <td>32000000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_28.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>33875000</td>\n",
       "      <td>34050000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_29.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>35500000</td>\n",
       "      <td>35675000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_30.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>36425000</td>\n",
       "      <td>36600000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_31.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>37125000</td>\n",
       "      <td>37300000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_32.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>39650000</td>\n",
       "      <td>39825000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_33.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>40225000</td>\n",
       "      <td>40400000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_34.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>40600000</td>\n",
       "      <td>40775000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_35.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>40800000</td>\n",
       "      <td>41000000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_36.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>41500000</td>\n",
       "      <td>44400000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_37.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>44725000</td>\n",
       "      <td>44900000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_38.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>45000000</td>\n",
       "      <td>45175000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_39.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>575000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_0.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1975000</td>\n",
       "      <td>3075000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_1.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3400000</td>\n",
       "      <td>3950000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_2.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4150000</td>\n",
       "      <td>4425000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_3.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5025000</td>\n",
       "      <td>5200000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_4.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6700000</td>\n",
       "      <td>6875000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_5.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7125000</td>\n",
       "      <td>7300000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_6.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7350000</td>\n",
       "      <td>7525000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_7.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11125000</td>\n",
       "      <td>11300000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_8.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12525000</td>\n",
       "      <td>12700000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_9.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14100000</td>\n",
       "      <td>14275000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_10.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14475000</td>\n",
       "      <td>14700000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_11.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15150000</td>\n",
       "      <td>15325000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_12.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15725000</td>\n",
       "      <td>15900000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_13.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>17550000</td>\n",
       "      <td>17750000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_14.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>19350000</td>\n",
       "      <td>19525000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_15.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>20325000</td>\n",
       "      <td>20575000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_16.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20600000</td>\n",
       "      <td>23850000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_17.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>23875000</td>\n",
       "      <td>24075000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_18.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>24125000</td>\n",
       "      <td>24325000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_19.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>24700000</td>\n",
       "      <td>25425000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_20.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>25500000</td>\n",
       "      <td>25675000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_21.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>25975000</td>\n",
       "      <td>26150000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_22.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>27725000</td>\n",
       "      <td>27900000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_23.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>28075000</td>\n",
       "      <td>31300000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_24.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>31325000</td>\n",
       "      <td>31550000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_25.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>31825000</td>\n",
       "      <td>32000000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_26.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>35500000</td>\n",
       "      <td>35675000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_27.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>36425000</td>\n",
       "      <td>36600000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_28.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>37125000</td>\n",
       "      <td>37300000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_29.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>39650000</td>\n",
       "      <td>39825000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_30.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>40225000</td>\n",
       "      <td>40400000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_31.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>40800000</td>\n",
       "      <td>41000000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_32.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>41500000</td>\n",
       "      <td>44400000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_33.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>44725000</td>\n",
       "      <td>44900000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_34.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>45000000</td>\n",
       "      <td>45175000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_35.wav</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       start       end  audio_sensor                          file\n",
       "0          0    550000             1   audio_sensor_1_sample_0.wav\n",
       "1    1875000   2900000             1   audio_sensor_1_sample_1.wav\n",
       "2    2925000   3125000             1   audio_sensor_1_sample_2.wav\n",
       "3    3400000   3675000             1   audio_sensor_1_sample_3.wav\n",
       "4    3725000   3975000             1   audio_sensor_1_sample_4.wav\n",
       "5    4175000   4400000             1   audio_sensor_1_sample_5.wav\n",
       "6    5025000   5200000             1   audio_sensor_1_sample_6.wav\n",
       "7    6700000   6875000             1   audio_sensor_1_sample_7.wav\n",
       "8    7125000   7300000             1   audio_sensor_1_sample_8.wav\n",
       "9    7350000   7525000             1   audio_sensor_1_sample_9.wav\n",
       "10  11125000  11300000             1  audio_sensor_1_sample_10.wav\n",
       "11  12525000  12700000             1  audio_sensor_1_sample_11.wav\n",
       "12  14100000  14275000             1  audio_sensor_1_sample_12.wav\n",
       "13  14475000  14700000             1  audio_sensor_1_sample_13.wav\n",
       "14  15150000  15325000             1  audio_sensor_1_sample_14.wav\n",
       "15  15725000  15900000             1  audio_sensor_1_sample_15.wav\n",
       "16  17550000  17750000             1  audio_sensor_1_sample_16.wav\n",
       "17  19350000  19525000             1  audio_sensor_1_sample_17.wav\n",
       "18  20325000  20550000             1  audio_sensor_1_sample_18.wav\n",
       "19  20575000  21175000             1  audio_sensor_1_sample_19.wav\n",
       "20  21225000  21775000             1  audio_sensor_1_sample_20.wav\n",
       "21  21800000  23850000             1  audio_sensor_1_sample_21.wav\n",
       "22  23900000  24075000             1  audio_sensor_1_sample_22.wav\n",
       "23  24275000  24475000             1  audio_sensor_1_sample_23.wav\n",
       "24  24625000  25150000             1  audio_sensor_1_sample_24.wav\n",
       "25  25175000  25550000             1  audio_sensor_1_sample_25.wav\n",
       "26  25975000  26150000             1  audio_sensor_1_sample_26.wav\n",
       "27  27725000  27900000             1  audio_sensor_1_sample_27.wav\n",
       "28  28075000  31275000             1  audio_sensor_1_sample_28.wav\n",
       "29  31300000  31525000             1  audio_sensor_1_sample_29.wav\n",
       "30  35500000  35675000             1  audio_sensor_1_sample_30.wav\n",
       "31  36425000  36600000             1  audio_sensor_1_sample_31.wav\n",
       "32  37125000  37300000             1  audio_sensor_1_sample_32.wav\n",
       "33  39650000  39825000             1  audio_sensor_1_sample_33.wav\n",
       "34  40225000  40400000             1  audio_sensor_1_sample_34.wav\n",
       "35  40800000  41000000             1  audio_sensor_1_sample_35.wav\n",
       "36  41500000  44425000             1  audio_sensor_1_sample_36.wav\n",
       "37  44725000  44900000             1  audio_sensor_1_sample_37.wav\n",
       "38  45000000  45175000             1  audio_sensor_1_sample_38.wav\n",
       "0          0    500000             2   audio_sensor_2_sample_0.wav\n",
       "1    2000000   3075000             2   audio_sensor_2_sample_1.wav\n",
       "2    3400000   3650000             2   audio_sensor_2_sample_2.wav\n",
       "3    3725000   3975000             2   audio_sensor_2_sample_3.wav\n",
       "4    4175000   4375000             2   audio_sensor_2_sample_4.wav\n",
       "5    5025000   5200000             2   audio_sensor_2_sample_5.wav\n",
       "6    6700000   6875000             2   audio_sensor_2_sample_6.wav\n",
       "7    7125000   7300000             2   audio_sensor_2_sample_7.wav\n",
       "8    8525000   8700000             2   audio_sensor_2_sample_8.wav\n",
       "9   11125000  11300000             2   audio_sensor_2_sample_9.wav\n",
       "10  12525000  12700000             2  audio_sensor_2_sample_10.wav\n",
       "11  14100000  14275000             2  audio_sensor_2_sample_11.wav\n",
       "12  14475000  14675000             2  audio_sensor_2_sample_12.wav\n",
       "13  15150000  15325000             2  audio_sensor_2_sample_13.wav\n",
       "14  15725000  15900000             2  audio_sensor_2_sample_14.wav\n",
       "15  17550000  17750000             2  audio_sensor_2_sample_15.wav\n",
       "16  19350000  19525000             2  audio_sensor_2_sample_16.wav\n",
       "17  20325000  20550000             2  audio_sensor_2_sample_17.wav\n",
       "18  20575000  23975000             2  audio_sensor_2_sample_18.wav\n",
       "19  24125000  24350000             2  audio_sensor_2_sample_19.wav\n",
       "20  24600000  24975000             2  audio_sensor_2_sample_20.wav\n",
       "21  25200000  25550000             2  audio_sensor_2_sample_21.wav\n",
       "22  27725000  27900000             2  audio_sensor_2_sample_22.wav\n",
       "23  28075000  30900000             2  audio_sensor_2_sample_23.wav\n",
       "24  30925000  31400000             2  audio_sensor_2_sample_24.wav\n",
       "25  33875000  34050000             2  audio_sensor_2_sample_25.wav\n",
       "26  35500000  35675000             2  audio_sensor_2_sample_26.wav\n",
       "27  36150000  36325000             2  audio_sensor_2_sample_27.wav\n",
       "28  36425000  36600000             2  audio_sensor_2_sample_28.wav\n",
       "29  37125000  37300000             2  audio_sensor_2_sample_29.wav\n",
       "30  39650000  39825000             2  audio_sensor_2_sample_30.wav\n",
       "31  40225000  40400000             2  audio_sensor_2_sample_31.wav\n",
       "32  40800000  40975000             2  audio_sensor_2_sample_32.wav\n",
       "33  41500000  44400000             2  audio_sensor_2_sample_33.wav\n",
       "34  44725000  44900000             2  audio_sensor_2_sample_34.wav\n",
       "35  45000000  45175000             2  audio_sensor_2_sample_35.wav\n",
       "0          0    550000             3   audio_sensor_3_sample_0.wav\n",
       "1    2000000   3075000             3   audio_sensor_3_sample_1.wav\n",
       "2    3400000   3975000             3   audio_sensor_3_sample_2.wav\n",
       "3    4150000   4475000             3   audio_sensor_3_sample_3.wav\n",
       "4    5025000   5200000             3   audio_sensor_3_sample_4.wav\n",
       "5    6700000   6875000             3   audio_sensor_3_sample_5.wav\n",
       "6    7125000   7300000             3   audio_sensor_3_sample_6.wav\n",
       "7    8525000   8700000             3   audio_sensor_3_sample_7.wav\n",
       "8   11125000  11300000             3   audio_sensor_3_sample_8.wav\n",
       "9   12525000  12700000             3   audio_sensor_3_sample_9.wav\n",
       "10  14100000  14275000             3  audio_sensor_3_sample_10.wav\n",
       "11  14475000  14700000             3  audio_sensor_3_sample_11.wav\n",
       "12  15150000  15325000             3  audio_sensor_3_sample_12.wav\n",
       "13  15725000  15900000             3  audio_sensor_3_sample_13.wav\n",
       "14  17550000  17750000             3  audio_sensor_3_sample_14.wav\n",
       "15  19350000  19525000             3  audio_sensor_3_sample_15.wav\n",
       "16  20325000  20550000             3  audio_sensor_3_sample_16.wav\n",
       "17  20600000  21100000             3  audio_sensor_3_sample_17.wav\n",
       "18  21125000  21775000             3  audio_sensor_3_sample_18.wav\n",
       "19  21800000  23850000             3  audio_sensor_3_sample_19.wav\n",
       "20  23900000  24075000             3  audio_sensor_3_sample_20.wav\n",
       "21  24275000  24475000             3  audio_sensor_3_sample_21.wav\n",
       "22  24675000  25150000             3  audio_sensor_3_sample_22.wav\n",
       "23  25200000  25525000             3  audio_sensor_3_sample_23.wav\n",
       "24  25975000  26150000             3  audio_sensor_3_sample_24.wav\n",
       "25  27725000  27900000             3  audio_sensor_3_sample_25.wav\n",
       "26  28075000  31275000             3  audio_sensor_3_sample_26.wav\n",
       "27  31300000  31500000             3  audio_sensor_3_sample_27.wav\n",
       "28  31825000  32000000             3  audio_sensor_3_sample_28.wav\n",
       "29  33875000  34050000             3  audio_sensor_3_sample_29.wav\n",
       "30  35500000  35675000             3  audio_sensor_3_sample_30.wav\n",
       "31  36425000  36600000             3  audio_sensor_3_sample_31.wav\n",
       "32  37125000  37300000             3  audio_sensor_3_sample_32.wav\n",
       "33  39650000  39825000             3  audio_sensor_3_sample_33.wav\n",
       "34  40225000  40400000             3  audio_sensor_3_sample_34.wav\n",
       "35  40600000  40775000             3  audio_sensor_3_sample_35.wav\n",
       "36  40800000  41000000             3  audio_sensor_3_sample_36.wav\n",
       "37  41500000  44400000             3  audio_sensor_3_sample_37.wav\n",
       "38  44725000  44900000             3  audio_sensor_3_sample_38.wav\n",
       "39  45000000  45175000             3  audio_sensor_3_sample_39.wav\n",
       "0          0    575000             4   audio_sensor_4_sample_0.wav\n",
       "1    1975000   3075000             4   audio_sensor_4_sample_1.wav\n",
       "2    3400000   3950000             4   audio_sensor_4_sample_2.wav\n",
       "3    4150000   4425000             4   audio_sensor_4_sample_3.wav\n",
       "4    5025000   5200000             4   audio_sensor_4_sample_4.wav\n",
       "5    6700000   6875000             4   audio_sensor_4_sample_5.wav\n",
       "6    7125000   7300000             4   audio_sensor_4_sample_6.wav\n",
       "7    7350000   7525000             4   audio_sensor_4_sample_7.wav\n",
       "8   11125000  11300000             4   audio_sensor_4_sample_8.wav\n",
       "9   12525000  12700000             4   audio_sensor_4_sample_9.wav\n",
       "10  14100000  14275000             4  audio_sensor_4_sample_10.wav\n",
       "11  14475000  14700000             4  audio_sensor_4_sample_11.wav\n",
       "12  15150000  15325000             4  audio_sensor_4_sample_12.wav\n",
       "13  15725000  15900000             4  audio_sensor_4_sample_13.wav\n",
       "14  17550000  17750000             4  audio_sensor_4_sample_14.wav\n",
       "15  19350000  19525000             4  audio_sensor_4_sample_15.wav\n",
       "16  20325000  20575000             4  audio_sensor_4_sample_16.wav\n",
       "17  20600000  23850000             4  audio_sensor_4_sample_17.wav\n",
       "18  23875000  24075000             4  audio_sensor_4_sample_18.wav\n",
       "19  24125000  24325000             4  audio_sensor_4_sample_19.wav\n",
       "20  24700000  25425000             4  audio_sensor_4_sample_20.wav\n",
       "21  25500000  25675000             4  audio_sensor_4_sample_21.wav\n",
       "22  25975000  26150000             4  audio_sensor_4_sample_22.wav\n",
       "23  27725000  27900000             4  audio_sensor_4_sample_23.wav\n",
       "24  28075000  31300000             4  audio_sensor_4_sample_24.wav\n",
       "25  31325000  31550000             4  audio_sensor_4_sample_25.wav\n",
       "26  31825000  32000000             4  audio_sensor_4_sample_26.wav\n",
       "27  35500000  35675000             4  audio_sensor_4_sample_27.wav\n",
       "28  36425000  36600000             4  audio_sensor_4_sample_28.wav\n",
       "29  37125000  37300000             4  audio_sensor_4_sample_29.wav\n",
       "30  39650000  39825000             4  audio_sensor_4_sample_30.wav\n",
       "31  40225000  40400000             4  audio_sensor_4_sample_31.wav\n",
       "32  40800000  41000000             4  audio_sensor_4_sample_32.wav\n",
       "33  41500000  44400000             4  audio_sensor_4_sample_33.wav\n",
       "34  44725000  44900000             4  audio_sensor_4_sample_34.wav\n",
       "35  45000000  45175000             4  audio_sensor_4_sample_35.wav"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if df is None:\n",
    "    df = pd.DataFrame(data=sound_clips, columns=[\"start\",\"end\"])\n",
    "    df.loc[:, 'audio_sensor'] = aud_sensor_id\n",
    "    df.loc[:, 'file'] = ['audio_sensor_'+str(aud_sensor_id)+'_sample_'+str(i)+'.wav' for i in range(len(sound_clips))]\n",
    "else:\n",
    "    df_add = pd.DataFrame(data=sound_clips, columns=[\"start\",\"end\"])\n",
    "    df_add.loc[:, 'audio_sensor'] = aud_sensor_id\n",
    "    df_add.loc[:, 'file'] = ['audio_sensor_'+str(aud_sensor_id)+'_sample_'+str(i)+'.wav' for i in range(len(sound_clips))]\n",
    "    df = pd.concat([df, df_add])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>audio_sensor</th>\n",
       "      <th>file</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>550000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_0.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1875000</td>\n",
       "      <td>2900000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_1.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2925000</td>\n",
       "      <td>3125000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_2.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3400000</td>\n",
       "      <td>3675000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_3.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3725000</td>\n",
       "      <td>3975000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_4.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4175000</td>\n",
       "      <td>4400000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_5.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5025000</td>\n",
       "      <td>5200000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_6.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6700000</td>\n",
       "      <td>6875000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_7.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7125000</td>\n",
       "      <td>7300000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_8.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7350000</td>\n",
       "      <td>7525000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_9.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11125000</td>\n",
       "      <td>11300000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_10.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12525000</td>\n",
       "      <td>12700000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_11.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14100000</td>\n",
       "      <td>14275000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_12.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14475000</td>\n",
       "      <td>14700000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_13.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15150000</td>\n",
       "      <td>15325000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_14.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15725000</td>\n",
       "      <td>15900000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_15.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17550000</td>\n",
       "      <td>17750000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_16.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>19350000</td>\n",
       "      <td>19525000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_17.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20325000</td>\n",
       "      <td>20550000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_18.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20575000</td>\n",
       "      <td>21175000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_19.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21225000</td>\n",
       "      <td>21775000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_20.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21800000</td>\n",
       "      <td>23850000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_21.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23900000</td>\n",
       "      <td>24075000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_22.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24275000</td>\n",
       "      <td>24475000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_23.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24625000</td>\n",
       "      <td>25150000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_24.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25175000</td>\n",
       "      <td>25550000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_25.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>25975000</td>\n",
       "      <td>26150000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_26.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27725000</td>\n",
       "      <td>27900000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_27.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28075000</td>\n",
       "      <td>31275000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_28.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>31300000</td>\n",
       "      <td>31525000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_29.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>35500000</td>\n",
       "      <td>35675000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_30.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>36425000</td>\n",
       "      <td>36600000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_31.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>37125000</td>\n",
       "      <td>37300000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_32.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>39650000</td>\n",
       "      <td>39825000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_33.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>40225000</td>\n",
       "      <td>40400000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_34.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>40800000</td>\n",
       "      <td>41000000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_35.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>41500000</td>\n",
       "      <td>44425000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_36.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>44725000</td>\n",
       "      <td>44900000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_37.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>45000000</td>\n",
       "      <td>45175000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_38.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>500000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_0.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000000</td>\n",
       "      <td>3075000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_1.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3400000</td>\n",
       "      <td>3650000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_2.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3725000</td>\n",
       "      <td>3975000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_3.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4175000</td>\n",
       "      <td>4375000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_4.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5025000</td>\n",
       "      <td>5200000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_5.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6700000</td>\n",
       "      <td>6875000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_6.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7125000</td>\n",
       "      <td>7300000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_7.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8525000</td>\n",
       "      <td>8700000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_8.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11125000</td>\n",
       "      <td>11300000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_9.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12525000</td>\n",
       "      <td>12700000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_10.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14100000</td>\n",
       "      <td>14275000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_11.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14475000</td>\n",
       "      <td>14675000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_12.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15150000</td>\n",
       "      <td>15325000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_13.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15725000</td>\n",
       "      <td>15900000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_14.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>17550000</td>\n",
       "      <td>17750000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_15.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>19350000</td>\n",
       "      <td>19525000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_16.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20325000</td>\n",
       "      <td>20550000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_17.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20575000</td>\n",
       "      <td>23975000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_18.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>24125000</td>\n",
       "      <td>24350000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_19.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>24600000</td>\n",
       "      <td>24975000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_20.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>25200000</td>\n",
       "      <td>25550000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_21.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>27725000</td>\n",
       "      <td>27900000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_22.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>28075000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_23.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>30925000</td>\n",
       "      <td>31400000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_24.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>33875000</td>\n",
       "      <td>34050000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_25.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>35500000</td>\n",
       "      <td>35675000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_26.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>36150000</td>\n",
       "      <td>36325000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_27.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>36425000</td>\n",
       "      <td>36600000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_28.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>37125000</td>\n",
       "      <td>37300000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_29.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>39650000</td>\n",
       "      <td>39825000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_30.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>40225000</td>\n",
       "      <td>40400000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_31.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>40800000</td>\n",
       "      <td>40975000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_32.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>41500000</td>\n",
       "      <td>44400000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_33.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>44725000</td>\n",
       "      <td>44900000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_34.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>45000000</td>\n",
       "      <td>45175000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_35.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>550000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_0.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000000</td>\n",
       "      <td>3075000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_1.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3400000</td>\n",
       "      <td>3975000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_2.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4150000</td>\n",
       "      <td>4475000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_3.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5025000</td>\n",
       "      <td>5200000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_4.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6700000</td>\n",
       "      <td>6875000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_5.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7125000</td>\n",
       "      <td>7300000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_6.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8525000</td>\n",
       "      <td>8700000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_7.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11125000</td>\n",
       "      <td>11300000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_8.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12525000</td>\n",
       "      <td>12700000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_9.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14100000</td>\n",
       "      <td>14275000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_10.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14475000</td>\n",
       "      <td>14700000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_11.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15150000</td>\n",
       "      <td>15325000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_12.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15725000</td>\n",
       "      <td>15900000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_13.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>17550000</td>\n",
       "      <td>17750000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_14.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>19350000</td>\n",
       "      <td>19525000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_15.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>20325000</td>\n",
       "      <td>20550000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_16.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20600000</td>\n",
       "      <td>21100000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_17.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>21125000</td>\n",
       "      <td>21775000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_18.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>21800000</td>\n",
       "      <td>23850000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_19.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>23900000</td>\n",
       "      <td>24075000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_20.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>24275000</td>\n",
       "      <td>24475000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_21.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>24675000</td>\n",
       "      <td>25150000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_22.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>25200000</td>\n",
       "      <td>25525000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_23.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25975000</td>\n",
       "      <td>26150000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_24.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>27725000</td>\n",
       "      <td>27900000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_25.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>28075000</td>\n",
       "      <td>31275000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_26.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>31300000</td>\n",
       "      <td>31500000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_27.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>31825000</td>\n",
       "      <td>32000000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_28.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>33875000</td>\n",
       "      <td>34050000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_29.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>35500000</td>\n",
       "      <td>35675000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_30.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>36425000</td>\n",
       "      <td>36600000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_31.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>37125000</td>\n",
       "      <td>37300000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_32.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>39650000</td>\n",
       "      <td>39825000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_33.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>40225000</td>\n",
       "      <td>40400000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_34.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>40600000</td>\n",
       "      <td>40775000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_35.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>40800000</td>\n",
       "      <td>41000000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_36.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>41500000</td>\n",
       "      <td>44400000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_37.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>44725000</td>\n",
       "      <td>44900000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_38.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>45000000</td>\n",
       "      <td>45175000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_39.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>575000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_0.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1975000</td>\n",
       "      <td>3075000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_1.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3400000</td>\n",
       "      <td>3950000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_2.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4150000</td>\n",
       "      <td>4425000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_3.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5025000</td>\n",
       "      <td>5200000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_4.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6700000</td>\n",
       "      <td>6875000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_5.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7125000</td>\n",
       "      <td>7300000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_6.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7350000</td>\n",
       "      <td>7525000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_7.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11125000</td>\n",
       "      <td>11300000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_8.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12525000</td>\n",
       "      <td>12700000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_9.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14100000</td>\n",
       "      <td>14275000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_10.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14475000</td>\n",
       "      <td>14700000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_11.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15150000</td>\n",
       "      <td>15325000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_12.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15725000</td>\n",
       "      <td>15900000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_13.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>17550000</td>\n",
       "      <td>17750000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_14.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>19350000</td>\n",
       "      <td>19525000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_15.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>20325000</td>\n",
       "      <td>20575000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_16.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20600000</td>\n",
       "      <td>23850000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_17.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>23875000</td>\n",
       "      <td>24075000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_18.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>24125000</td>\n",
       "      <td>24325000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_19.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>24700000</td>\n",
       "      <td>25425000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_20.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>25500000</td>\n",
       "      <td>25675000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_21.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>25975000</td>\n",
       "      <td>26150000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_22.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>27725000</td>\n",
       "      <td>27900000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_23.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>28075000</td>\n",
       "      <td>31300000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_24.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>31325000</td>\n",
       "      <td>31550000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_25.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>31825000</td>\n",
       "      <td>32000000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_26.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>35500000</td>\n",
       "      <td>35675000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_27.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>36425000</td>\n",
       "      <td>36600000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_28.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>37125000</td>\n",
       "      <td>37300000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_29.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>39650000</td>\n",
       "      <td>39825000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_30.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>40225000</td>\n",
       "      <td>40400000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_31.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>40800000</td>\n",
       "      <td>41000000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_32.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>41500000</td>\n",
       "      <td>44400000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_33.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>44725000</td>\n",
       "      <td>44900000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_34.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>45000000</td>\n",
       "      <td>45175000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_35.wav</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       start       end  audio_sensor                          file\n",
       "0          0    550000             1   audio_sensor_1_sample_0.wav\n",
       "1    1875000   2900000             1   audio_sensor_1_sample_1.wav\n",
       "2    2925000   3125000             1   audio_sensor_1_sample_2.wav\n",
       "3    3400000   3675000             1   audio_sensor_1_sample_3.wav\n",
       "4    3725000   3975000             1   audio_sensor_1_sample_4.wav\n",
       "5    4175000   4400000             1   audio_sensor_1_sample_5.wav\n",
       "6    5025000   5200000             1   audio_sensor_1_sample_6.wav\n",
       "7    6700000   6875000             1   audio_sensor_1_sample_7.wav\n",
       "8    7125000   7300000             1   audio_sensor_1_sample_8.wav\n",
       "9    7350000   7525000             1   audio_sensor_1_sample_9.wav\n",
       "10  11125000  11300000             1  audio_sensor_1_sample_10.wav\n",
       "11  12525000  12700000             1  audio_sensor_1_sample_11.wav\n",
       "12  14100000  14275000             1  audio_sensor_1_sample_12.wav\n",
       "13  14475000  14700000             1  audio_sensor_1_sample_13.wav\n",
       "14  15150000  15325000             1  audio_sensor_1_sample_14.wav\n",
       "15  15725000  15900000             1  audio_sensor_1_sample_15.wav\n",
       "16  17550000  17750000             1  audio_sensor_1_sample_16.wav\n",
       "17  19350000  19525000             1  audio_sensor_1_sample_17.wav\n",
       "18  20325000  20550000             1  audio_sensor_1_sample_18.wav\n",
       "19  20575000  21175000             1  audio_sensor_1_sample_19.wav\n",
       "20  21225000  21775000             1  audio_sensor_1_sample_20.wav\n",
       "21  21800000  23850000             1  audio_sensor_1_sample_21.wav\n",
       "22  23900000  24075000             1  audio_sensor_1_sample_22.wav\n",
       "23  24275000  24475000             1  audio_sensor_1_sample_23.wav\n",
       "24  24625000  25150000             1  audio_sensor_1_sample_24.wav\n",
       "25  25175000  25550000             1  audio_sensor_1_sample_25.wav\n",
       "26  25975000  26150000             1  audio_sensor_1_sample_26.wav\n",
       "27  27725000  27900000             1  audio_sensor_1_sample_27.wav\n",
       "28  28075000  31275000             1  audio_sensor_1_sample_28.wav\n",
       "29  31300000  31525000             1  audio_sensor_1_sample_29.wav\n",
       "30  35500000  35675000             1  audio_sensor_1_sample_30.wav\n",
       "31  36425000  36600000             1  audio_sensor_1_sample_31.wav\n",
       "32  37125000  37300000             1  audio_sensor_1_sample_32.wav\n",
       "33  39650000  39825000             1  audio_sensor_1_sample_33.wav\n",
       "34  40225000  40400000             1  audio_sensor_1_sample_34.wav\n",
       "35  40800000  41000000             1  audio_sensor_1_sample_35.wav\n",
       "36  41500000  44425000             1  audio_sensor_1_sample_36.wav\n",
       "37  44725000  44900000             1  audio_sensor_1_sample_37.wav\n",
       "38  45000000  45175000             1  audio_sensor_1_sample_38.wav\n",
       "0          0    500000             2   audio_sensor_2_sample_0.wav\n",
       "1    2000000   3075000             2   audio_sensor_2_sample_1.wav\n",
       "2    3400000   3650000             2   audio_sensor_2_sample_2.wav\n",
       "3    3725000   3975000             2   audio_sensor_2_sample_3.wav\n",
       "4    4175000   4375000             2   audio_sensor_2_sample_4.wav\n",
       "5    5025000   5200000             2   audio_sensor_2_sample_5.wav\n",
       "6    6700000   6875000             2   audio_sensor_2_sample_6.wav\n",
       "7    7125000   7300000             2   audio_sensor_2_sample_7.wav\n",
       "8    8525000   8700000             2   audio_sensor_2_sample_8.wav\n",
       "9   11125000  11300000             2   audio_sensor_2_sample_9.wav\n",
       "10  12525000  12700000             2  audio_sensor_2_sample_10.wav\n",
       "11  14100000  14275000             2  audio_sensor_2_sample_11.wav\n",
       "12  14475000  14675000             2  audio_sensor_2_sample_12.wav\n",
       "13  15150000  15325000             2  audio_sensor_2_sample_13.wav\n",
       "14  15725000  15900000             2  audio_sensor_2_sample_14.wav\n",
       "15  17550000  17750000             2  audio_sensor_2_sample_15.wav\n",
       "16  19350000  19525000             2  audio_sensor_2_sample_16.wav\n",
       "17  20325000  20550000             2  audio_sensor_2_sample_17.wav\n",
       "18  20575000  23975000             2  audio_sensor_2_sample_18.wav\n",
       "19  24125000  24350000             2  audio_sensor_2_sample_19.wav\n",
       "20  24600000  24975000             2  audio_sensor_2_sample_20.wav\n",
       "21  25200000  25550000             2  audio_sensor_2_sample_21.wav\n",
       "22  27725000  27900000             2  audio_sensor_2_sample_22.wav\n",
       "23  28075000  30900000             2  audio_sensor_2_sample_23.wav\n",
       "24  30925000  31400000             2  audio_sensor_2_sample_24.wav\n",
       "25  33875000  34050000             2  audio_sensor_2_sample_25.wav\n",
       "26  35500000  35675000             2  audio_sensor_2_sample_26.wav\n",
       "27  36150000  36325000             2  audio_sensor_2_sample_27.wav\n",
       "28  36425000  36600000             2  audio_sensor_2_sample_28.wav\n",
       "29  37125000  37300000             2  audio_sensor_2_sample_29.wav\n",
       "30  39650000  39825000             2  audio_sensor_2_sample_30.wav\n",
       "31  40225000  40400000             2  audio_sensor_2_sample_31.wav\n",
       "32  40800000  40975000             2  audio_sensor_2_sample_32.wav\n",
       "33  41500000  44400000             2  audio_sensor_2_sample_33.wav\n",
       "34  44725000  44900000             2  audio_sensor_2_sample_34.wav\n",
       "35  45000000  45175000             2  audio_sensor_2_sample_35.wav\n",
       "0          0    550000             3   audio_sensor_3_sample_0.wav\n",
       "1    2000000   3075000             3   audio_sensor_3_sample_1.wav\n",
       "2    3400000   3975000             3   audio_sensor_3_sample_2.wav\n",
       "3    4150000   4475000             3   audio_sensor_3_sample_3.wav\n",
       "4    5025000   5200000             3   audio_sensor_3_sample_4.wav\n",
       "5    6700000   6875000             3   audio_sensor_3_sample_5.wav\n",
       "6    7125000   7300000             3   audio_sensor_3_sample_6.wav\n",
       "7    8525000   8700000             3   audio_sensor_3_sample_7.wav\n",
       "8   11125000  11300000             3   audio_sensor_3_sample_8.wav\n",
       "9   12525000  12700000             3   audio_sensor_3_sample_9.wav\n",
       "10  14100000  14275000             3  audio_sensor_3_sample_10.wav\n",
       "11  14475000  14700000             3  audio_sensor_3_sample_11.wav\n",
       "12  15150000  15325000             3  audio_sensor_3_sample_12.wav\n",
       "13  15725000  15900000             3  audio_sensor_3_sample_13.wav\n",
       "14  17550000  17750000             3  audio_sensor_3_sample_14.wav\n",
       "15  19350000  19525000             3  audio_sensor_3_sample_15.wav\n",
       "16  20325000  20550000             3  audio_sensor_3_sample_16.wav\n",
       "17  20600000  21100000             3  audio_sensor_3_sample_17.wav\n",
       "18  21125000  21775000             3  audio_sensor_3_sample_18.wav\n",
       "19  21800000  23850000             3  audio_sensor_3_sample_19.wav\n",
       "20  23900000  24075000             3  audio_sensor_3_sample_20.wav\n",
       "21  24275000  24475000             3  audio_sensor_3_sample_21.wav\n",
       "22  24675000  25150000             3  audio_sensor_3_sample_22.wav\n",
       "23  25200000  25525000             3  audio_sensor_3_sample_23.wav\n",
       "24  25975000  26150000             3  audio_sensor_3_sample_24.wav\n",
       "25  27725000  27900000             3  audio_sensor_3_sample_25.wav\n",
       "26  28075000  31275000             3  audio_sensor_3_sample_26.wav\n",
       "27  31300000  31500000             3  audio_sensor_3_sample_27.wav\n",
       "28  31825000  32000000             3  audio_sensor_3_sample_28.wav\n",
       "29  33875000  34050000             3  audio_sensor_3_sample_29.wav\n",
       "30  35500000  35675000             3  audio_sensor_3_sample_30.wav\n",
       "31  36425000  36600000             3  audio_sensor_3_sample_31.wav\n",
       "32  37125000  37300000             3  audio_sensor_3_sample_32.wav\n",
       "33  39650000  39825000             3  audio_sensor_3_sample_33.wav\n",
       "34  40225000  40400000             3  audio_sensor_3_sample_34.wav\n",
       "35  40600000  40775000             3  audio_sensor_3_sample_35.wav\n",
       "36  40800000  41000000             3  audio_sensor_3_sample_36.wav\n",
       "37  41500000  44400000             3  audio_sensor_3_sample_37.wav\n",
       "38  44725000  44900000             3  audio_sensor_3_sample_38.wav\n",
       "39  45000000  45175000             3  audio_sensor_3_sample_39.wav\n",
       "0          0    575000             4   audio_sensor_4_sample_0.wav\n",
       "1    1975000   3075000             4   audio_sensor_4_sample_1.wav\n",
       "2    3400000   3950000             4   audio_sensor_4_sample_2.wav\n",
       "3    4150000   4425000             4   audio_sensor_4_sample_3.wav\n",
       "4    5025000   5200000             4   audio_sensor_4_sample_4.wav\n",
       "5    6700000   6875000             4   audio_sensor_4_sample_5.wav\n",
       "6    7125000   7300000             4   audio_sensor_4_sample_6.wav\n",
       "7    7350000   7525000             4   audio_sensor_4_sample_7.wav\n",
       "8   11125000  11300000             4   audio_sensor_4_sample_8.wav\n",
       "9   12525000  12700000             4   audio_sensor_4_sample_9.wav\n",
       "10  14100000  14275000             4  audio_sensor_4_sample_10.wav\n",
       "11  14475000  14700000             4  audio_sensor_4_sample_11.wav\n",
       "12  15150000  15325000             4  audio_sensor_4_sample_12.wav\n",
       "13  15725000  15900000             4  audio_sensor_4_sample_13.wav\n",
       "14  17550000  17750000             4  audio_sensor_4_sample_14.wav\n",
       "15  19350000  19525000             4  audio_sensor_4_sample_15.wav\n",
       "16  20325000  20575000             4  audio_sensor_4_sample_16.wav\n",
       "17  20600000  23850000             4  audio_sensor_4_sample_17.wav\n",
       "18  23875000  24075000             4  audio_sensor_4_sample_18.wav\n",
       "19  24125000  24325000             4  audio_sensor_4_sample_19.wav\n",
       "20  24700000  25425000             4  audio_sensor_4_sample_20.wav\n",
       "21  25500000  25675000             4  audio_sensor_4_sample_21.wav\n",
       "22  25975000  26150000             4  audio_sensor_4_sample_22.wav\n",
       "23  27725000  27900000             4  audio_sensor_4_sample_23.wav\n",
       "24  28075000  31300000             4  audio_sensor_4_sample_24.wav\n",
       "25  31325000  31550000             4  audio_sensor_4_sample_25.wav\n",
       "26  31825000  32000000             4  audio_sensor_4_sample_26.wav\n",
       "27  35500000  35675000             4  audio_sensor_4_sample_27.wav\n",
       "28  36425000  36600000             4  audio_sensor_4_sample_28.wav\n",
       "29  37125000  37300000             4  audio_sensor_4_sample_29.wav\n",
       "30  39650000  39825000             4  audio_sensor_4_sample_30.wav\n",
       "31  40225000  40400000             4  audio_sensor_4_sample_31.wav\n",
       "32  40800000  41000000             4  audio_sensor_4_sample_32.wav\n",
       "33  41500000  44400000             4  audio_sensor_4_sample_33.wav\n",
       "34  44725000  44900000             4  audio_sensor_4_sample_34.wav\n",
       "35  45000000  45175000             4  audio_sensor_4_sample_35.wav"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('audio_segments.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>7125000</td>\n",
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       "      <td>audio_sensor_4_sample_6.wav</td>\n",
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       "      <td>audio_sensor_4_sample_7.wav</td>\n",
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       "      <td>8525000</td>\n",
       "      <td>8700000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_8.wav</td>\n",
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       "      <td>8525000</td>\n",
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       "      <td>audio_sensor_3_sample_7.wav</td>\n",
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       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_10.wav</td>\n",
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       "      <td>11125000</td>\n",
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       "      <td>audio_sensor_3_sample_8.wav</td>\n",
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       "      <td>11300000</td>\n",
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       "      <td>audio_sensor_4_sample_8.wav</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12525000</td>\n",
       "      <td>12700000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_11.wav</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12525000</td>\n",
       "      <td>12700000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_10.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12525000</td>\n",
       "      <td>12700000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_9.wav</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12525000</td>\n",
       "      <td>12700000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_9.wav</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14100000</td>\n",
       "      <td>14275000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_12.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14100000</td>\n",
       "      <td>14275000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_11.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14100000</td>\n",
       "      <td>14275000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_10.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14100000</td>\n",
       "      <td>14275000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_10.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>14475000</td>\n",
       "      <td>14675000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_12.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14475000</td>\n",
       "      <td>14700000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_13.wav</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14475000</td>\n",
       "      <td>14700000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_11.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14475000</td>\n",
       "      <td>14700000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_11.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15150000</td>\n",
       "      <td>15325000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_14.wav</td>\n",
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       "    <tr>\n",
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       "      <td>15150000</td>\n",
       "      <td>15325000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_13.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15150000</td>\n",
       "      <td>15325000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_12.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15150000</td>\n",
       "      <td>15325000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_12.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15725000</td>\n",
       "      <td>15900000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_15.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15725000</td>\n",
       "      <td>15900000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_14.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15725000</td>\n",
       "      <td>15900000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_13.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15725000</td>\n",
       "      <td>15900000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_13.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17550000</td>\n",
       "      <td>17750000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_16.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>17550000</td>\n",
       "      <td>17750000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_15.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>17550000</td>\n",
       "      <td>17750000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_14.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>17550000</td>\n",
       "      <td>17750000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_14.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>19350000</td>\n",
       "      <td>19525000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_17.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>19350000</td>\n",
       "      <td>19525000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_16.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>19350000</td>\n",
       "      <td>19525000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_15.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>19350000</td>\n",
       "      <td>19525000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_15.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20325000</td>\n",
       "      <td>20550000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_18.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20325000</td>\n",
       "      <td>20550000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_17.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>20325000</td>\n",
       "      <td>20550000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_16.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>20325000</td>\n",
       "      <td>20575000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_16.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20575000</td>\n",
       "      <td>21175000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_19.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20575000</td>\n",
       "      <td>23975000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_18.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20600000</td>\n",
       "      <td>21100000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_17.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20600000</td>\n",
       "      <td>23850000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_17.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>21125000</td>\n",
       "      <td>21775000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_18.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21225000</td>\n",
       "      <td>21775000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_20.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21800000</td>\n",
       "      <td>23850000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_21.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>21800000</td>\n",
       "      <td>23850000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_19.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>23875000</td>\n",
       "      <td>24075000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_18.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23900000</td>\n",
       "      <td>24075000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_22.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>23900000</td>\n",
       "      <td>24075000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_20.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>24125000</td>\n",
       "      <td>24325000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_19.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>24125000</td>\n",
       "      <td>24350000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_19.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24275000</td>\n",
       "      <td>24475000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_23.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>24275000</td>\n",
       "      <td>24475000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_21.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>24600000</td>\n",
       "      <td>24975000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_20.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24625000</td>\n",
       "      <td>25150000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_24.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>24675000</td>\n",
       "      <td>25150000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_22.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>24700000</td>\n",
       "      <td>25425000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_20.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25175000</td>\n",
       "      <td>25550000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_25.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>25200000</td>\n",
       "      <td>25525000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_23.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>25200000</td>\n",
       "      <td>25550000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_21.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>25500000</td>\n",
       "      <td>25675000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_21.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>25975000</td>\n",
       "      <td>26150000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_26.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25975000</td>\n",
       "      <td>26150000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_24.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>25975000</td>\n",
       "      <td>26150000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_22.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27725000</td>\n",
       "      <td>27900000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_27.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>27725000</td>\n",
       "      <td>27900000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_22.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>27725000</td>\n",
       "      <td>27900000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_25.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>27725000</td>\n",
       "      <td>27900000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_23.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>28075000</td>\n",
       "      <td>30900000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_23.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28075000</td>\n",
       "      <td>31275000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_28.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>28075000</td>\n",
       "      <td>31275000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_26.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>28075000</td>\n",
       "      <td>31300000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_24.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>30925000</td>\n",
       "      <td>31400000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_24.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>31300000</td>\n",
       "      <td>31500000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_27.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>31300000</td>\n",
       "      <td>31525000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_29.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>31325000</td>\n",
       "      <td>31550000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_25.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>31825000</td>\n",
       "      <td>32000000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_28.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>31825000</td>\n",
       "      <td>32000000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_26.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>33875000</td>\n",
       "      <td>34050000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_25.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>33875000</td>\n",
       "      <td>34050000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_29.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>35500000</td>\n",
       "      <td>35675000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_30.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>35500000</td>\n",
       "      <td>35675000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_26.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>35500000</td>\n",
       "      <td>35675000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_30.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>35500000</td>\n",
       "      <td>35675000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_27.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>36150000</td>\n",
       "      <td>36325000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_27.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>36425000</td>\n",
       "      <td>36600000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_31.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>36425000</td>\n",
       "      <td>36600000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_28.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>36425000</td>\n",
       "      <td>36600000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_31.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>36425000</td>\n",
       "      <td>36600000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_28.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>37125000</td>\n",
       "      <td>37300000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_32.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>37125000</td>\n",
       "      <td>37300000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_29.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>37125000</td>\n",
       "      <td>37300000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_32.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>37125000</td>\n",
       "      <td>37300000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_29.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>39650000</td>\n",
       "      <td>39825000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_33.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>39650000</td>\n",
       "      <td>39825000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_30.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>39650000</td>\n",
       "      <td>39825000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_33.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>39650000</td>\n",
       "      <td>39825000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_30.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>40225000</td>\n",
       "      <td>40400000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_34.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>40225000</td>\n",
       "      <td>40400000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_31.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>40225000</td>\n",
       "      <td>40400000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_34.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>40225000</td>\n",
       "      <td>40400000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_31.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>40600000</td>\n",
       "      <td>40775000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_35.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>40800000</td>\n",
       "      <td>40975000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_32.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>40800000</td>\n",
       "      <td>41000000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_35.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>40800000</td>\n",
       "      <td>41000000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_36.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>40800000</td>\n",
       "      <td>41000000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_32.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>41500000</td>\n",
       "      <td>44400000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_33.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>41500000</td>\n",
       "      <td>44400000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_37.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>41500000</td>\n",
       "      <td>44400000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_33.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>41500000</td>\n",
       "      <td>44425000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_36.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>44725000</td>\n",
       "      <td>44900000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_37.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>44725000</td>\n",
       "      <td>44900000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_34.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>44725000</td>\n",
       "      <td>44900000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_38.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>44725000</td>\n",
       "      <td>44900000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_34.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>45000000</td>\n",
       "      <td>45175000</td>\n",
       "      <td>1</td>\n",
       "      <td>audio_sensor_1_sample_38.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>45000000</td>\n",
       "      <td>45175000</td>\n",
       "      <td>2</td>\n",
       "      <td>audio_sensor_2_sample_35.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>45000000</td>\n",
       "      <td>45175000</td>\n",
       "      <td>3</td>\n",
       "      <td>audio_sensor_3_sample_39.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>45000000</td>\n",
       "      <td>45175000</td>\n",
       "      <td>4</td>\n",
       "      <td>audio_sensor_4_sample_35.wav</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       start       end  audio_sensor                          file\n",
       "0          0    500000             2   audio_sensor_2_sample_0.wav\n",
       "0          0    550000             1   audio_sensor_1_sample_0.wav\n",
       "0          0    550000             3   audio_sensor_3_sample_0.wav\n",
       "0          0    575000             4   audio_sensor_4_sample_0.wav\n",
       "1    1875000   2900000             1   audio_sensor_1_sample_1.wav\n",
       "1    1975000   3075000             4   audio_sensor_4_sample_1.wav\n",
       "1    2000000   3075000             2   audio_sensor_2_sample_1.wav\n",
       "1    2000000   3075000             3   audio_sensor_3_sample_1.wav\n",
       "2    2925000   3125000             1   audio_sensor_1_sample_2.wav\n",
       "2    3400000   3650000             2   audio_sensor_2_sample_2.wav\n",
       "3    3400000   3675000             1   audio_sensor_1_sample_3.wav\n",
       "2    3400000   3950000             4   audio_sensor_4_sample_2.wav\n",
       "2    3400000   3975000             3   audio_sensor_3_sample_2.wav\n",
       "4    3725000   3975000             1   audio_sensor_1_sample_4.wav\n",
       "3    3725000   3975000             2   audio_sensor_2_sample_3.wav\n",
       "3    4150000   4425000             4   audio_sensor_4_sample_3.wav\n",
       "3    4150000   4475000             3   audio_sensor_3_sample_3.wav\n",
       "4    4175000   4375000             2   audio_sensor_2_sample_4.wav\n",
       "5    4175000   4400000             1   audio_sensor_1_sample_5.wav\n",
       "6    5025000   5200000             1   audio_sensor_1_sample_6.wav\n",
       "5    5025000   5200000             2   audio_sensor_2_sample_5.wav\n",
       "4    5025000   5200000             3   audio_sensor_3_sample_4.wav\n",
       "4    5025000   5200000             4   audio_sensor_4_sample_4.wav\n",
       "7    6700000   6875000             1   audio_sensor_1_sample_7.wav\n",
       "6    6700000   6875000             2   audio_sensor_2_sample_6.wav\n",
       "5    6700000   6875000             3   audio_sensor_3_sample_5.wav\n",
       "5    6700000   6875000             4   audio_sensor_4_sample_5.wav\n",
       "8    7125000   7300000             1   audio_sensor_1_sample_8.wav\n",
       "7    7125000   7300000             2   audio_sensor_2_sample_7.wav\n",
       "6    7125000   7300000             3   audio_sensor_3_sample_6.wav\n",
       "6    7125000   7300000             4   audio_sensor_4_sample_6.wav\n",
       "9    7350000   7525000             1   audio_sensor_1_sample_9.wav\n",
       "7    7350000   7525000             4   audio_sensor_4_sample_7.wav\n",
       "8    8525000   8700000             2   audio_sensor_2_sample_8.wav\n",
       "7    8525000   8700000             3   audio_sensor_3_sample_7.wav\n",
       "10  11125000  11300000             1  audio_sensor_1_sample_10.wav\n",
       "9   11125000  11300000             2   audio_sensor_2_sample_9.wav\n",
       "8   11125000  11300000             3   audio_sensor_3_sample_8.wav\n",
       "8   11125000  11300000             4   audio_sensor_4_sample_8.wav\n",
       "11  12525000  12700000             1  audio_sensor_1_sample_11.wav\n",
       "10  12525000  12700000             2  audio_sensor_2_sample_10.wav\n",
       "9   12525000  12700000             3   audio_sensor_3_sample_9.wav\n",
       "9   12525000  12700000             4   audio_sensor_4_sample_9.wav\n",
       "12  14100000  14275000             1  audio_sensor_1_sample_12.wav\n",
       "11  14100000  14275000             2  audio_sensor_2_sample_11.wav\n",
       "10  14100000  14275000             3  audio_sensor_3_sample_10.wav\n",
       "10  14100000  14275000             4  audio_sensor_4_sample_10.wav\n",
       "12  14475000  14675000             2  audio_sensor_2_sample_12.wav\n",
       "13  14475000  14700000             1  audio_sensor_1_sample_13.wav\n",
       "11  14475000  14700000             3  audio_sensor_3_sample_11.wav\n",
       "11  14475000  14700000             4  audio_sensor_4_sample_11.wav\n",
       "14  15150000  15325000             1  audio_sensor_1_sample_14.wav\n",
       "13  15150000  15325000             2  audio_sensor_2_sample_13.wav\n",
       "12  15150000  15325000             3  audio_sensor_3_sample_12.wav\n",
       "12  15150000  15325000             4  audio_sensor_4_sample_12.wav\n",
       "15  15725000  15900000             1  audio_sensor_1_sample_15.wav\n",
       "14  15725000  15900000             2  audio_sensor_2_sample_14.wav\n",
       "13  15725000  15900000             3  audio_sensor_3_sample_13.wav\n",
       "13  15725000  15900000             4  audio_sensor_4_sample_13.wav\n",
       "16  17550000  17750000             1  audio_sensor_1_sample_16.wav\n",
       "15  17550000  17750000             2  audio_sensor_2_sample_15.wav\n",
       "14  17550000  17750000             3  audio_sensor_3_sample_14.wav\n",
       "14  17550000  17750000             4  audio_sensor_4_sample_14.wav\n",
       "17  19350000  19525000             1  audio_sensor_1_sample_17.wav\n",
       "16  19350000  19525000             2  audio_sensor_2_sample_16.wav\n",
       "15  19350000  19525000             3  audio_sensor_3_sample_15.wav\n",
       "15  19350000  19525000             4  audio_sensor_4_sample_15.wav\n",
       "18  20325000  20550000             1  audio_sensor_1_sample_18.wav\n",
       "17  20325000  20550000             2  audio_sensor_2_sample_17.wav\n",
       "16  20325000  20550000             3  audio_sensor_3_sample_16.wav\n",
       "16  20325000  20575000             4  audio_sensor_4_sample_16.wav\n",
       "19  20575000  21175000             1  audio_sensor_1_sample_19.wav\n",
       "18  20575000  23975000             2  audio_sensor_2_sample_18.wav\n",
       "17  20600000  21100000             3  audio_sensor_3_sample_17.wav\n",
       "17  20600000  23850000             4  audio_sensor_4_sample_17.wav\n",
       "18  21125000  21775000             3  audio_sensor_3_sample_18.wav\n",
       "20  21225000  21775000             1  audio_sensor_1_sample_20.wav\n",
       "21  21800000  23850000             1  audio_sensor_1_sample_21.wav\n",
       "19  21800000  23850000             3  audio_sensor_3_sample_19.wav\n",
       "18  23875000  24075000             4  audio_sensor_4_sample_18.wav\n",
       "22  23900000  24075000             1  audio_sensor_1_sample_22.wav\n",
       "20  23900000  24075000             3  audio_sensor_3_sample_20.wav\n",
       "19  24125000  24325000             4  audio_sensor_4_sample_19.wav\n",
       "19  24125000  24350000             2  audio_sensor_2_sample_19.wav\n",
       "23  24275000  24475000             1  audio_sensor_1_sample_23.wav\n",
       "21  24275000  24475000             3  audio_sensor_3_sample_21.wav\n",
       "20  24600000  24975000             2  audio_sensor_2_sample_20.wav\n",
       "24  24625000  25150000             1  audio_sensor_1_sample_24.wav\n",
       "22  24675000  25150000             3  audio_sensor_3_sample_22.wav\n",
       "20  24700000  25425000             4  audio_sensor_4_sample_20.wav\n",
       "25  25175000  25550000             1  audio_sensor_1_sample_25.wav\n",
       "23  25200000  25525000             3  audio_sensor_3_sample_23.wav\n",
       "21  25200000  25550000             2  audio_sensor_2_sample_21.wav\n",
       "21  25500000  25675000             4  audio_sensor_4_sample_21.wav\n",
       "26  25975000  26150000             1  audio_sensor_1_sample_26.wav\n",
       "24  25975000  26150000             3  audio_sensor_3_sample_24.wav\n",
       "22  25975000  26150000             4  audio_sensor_4_sample_22.wav\n",
       "27  27725000  27900000             1  audio_sensor_1_sample_27.wav\n",
       "22  27725000  27900000             2  audio_sensor_2_sample_22.wav\n",
       "25  27725000  27900000             3  audio_sensor_3_sample_25.wav\n",
       "23  27725000  27900000             4  audio_sensor_4_sample_23.wav\n",
       "23  28075000  30900000             2  audio_sensor_2_sample_23.wav\n",
       "28  28075000  31275000             1  audio_sensor_1_sample_28.wav\n",
       "26  28075000  31275000             3  audio_sensor_3_sample_26.wav\n",
       "24  28075000  31300000             4  audio_sensor_4_sample_24.wav\n",
       "24  30925000  31400000             2  audio_sensor_2_sample_24.wav\n",
       "27  31300000  31500000             3  audio_sensor_3_sample_27.wav\n",
       "29  31300000  31525000             1  audio_sensor_1_sample_29.wav\n",
       "25  31325000  31550000             4  audio_sensor_4_sample_25.wav\n",
       "28  31825000  32000000             3  audio_sensor_3_sample_28.wav\n",
       "26  31825000  32000000             4  audio_sensor_4_sample_26.wav\n",
       "25  33875000  34050000             2  audio_sensor_2_sample_25.wav\n",
       "29  33875000  34050000             3  audio_sensor_3_sample_29.wav\n",
       "30  35500000  35675000             1  audio_sensor_1_sample_30.wav\n",
       "26  35500000  35675000             2  audio_sensor_2_sample_26.wav\n",
       "30  35500000  35675000             3  audio_sensor_3_sample_30.wav\n",
       "27  35500000  35675000             4  audio_sensor_4_sample_27.wav\n",
       "27  36150000  36325000             2  audio_sensor_2_sample_27.wav\n",
       "31  36425000  36600000             1  audio_sensor_1_sample_31.wav\n",
       "28  36425000  36600000             2  audio_sensor_2_sample_28.wav\n",
       "31  36425000  36600000             3  audio_sensor_3_sample_31.wav\n",
       "28  36425000  36600000             4  audio_sensor_4_sample_28.wav\n",
       "32  37125000  37300000             1  audio_sensor_1_sample_32.wav\n",
       "29  37125000  37300000             2  audio_sensor_2_sample_29.wav\n",
       "32  37125000  37300000             3  audio_sensor_3_sample_32.wav\n",
       "29  37125000  37300000             4  audio_sensor_4_sample_29.wav\n",
       "33  39650000  39825000             1  audio_sensor_1_sample_33.wav\n",
       "30  39650000  39825000             2  audio_sensor_2_sample_30.wav\n",
       "33  39650000  39825000             3  audio_sensor_3_sample_33.wav\n",
       "30  39650000  39825000             4  audio_sensor_4_sample_30.wav\n",
       "34  40225000  40400000             1  audio_sensor_1_sample_34.wav\n",
       "31  40225000  40400000             2  audio_sensor_2_sample_31.wav\n",
       "34  40225000  40400000             3  audio_sensor_3_sample_34.wav\n",
       "31  40225000  40400000             4  audio_sensor_4_sample_31.wav\n",
       "35  40600000  40775000             3  audio_sensor_3_sample_35.wav\n",
       "32  40800000  40975000             2  audio_sensor_2_sample_32.wav\n",
       "35  40800000  41000000             1  audio_sensor_1_sample_35.wav\n",
       "36  40800000  41000000             3  audio_sensor_3_sample_36.wav\n",
       "32  40800000  41000000             4  audio_sensor_4_sample_32.wav\n",
       "33  41500000  44400000             2  audio_sensor_2_sample_33.wav\n",
       "37  41500000  44400000             3  audio_sensor_3_sample_37.wav\n",
       "33  41500000  44400000             4  audio_sensor_4_sample_33.wav\n",
       "36  41500000  44425000             1  audio_sensor_1_sample_36.wav\n",
       "37  44725000  44900000             1  audio_sensor_1_sample_37.wav\n",
       "34  44725000  44900000             2  audio_sensor_2_sample_34.wav\n",
       "38  44725000  44900000             3  audio_sensor_3_sample_38.wav\n",
       "34  44725000  44900000             4  audio_sensor_4_sample_34.wav\n",
       "38  45000000  45175000             1  audio_sensor_1_sample_38.wav\n",
       "35  45000000  45175000             2  audio_sensor_2_sample_35.wav\n",
       "39  45000000  45175000             3  audio_sensor_3_sample_39.wav\n",
       "35  45000000  45175000             4  audio_sensor_4_sample_35.wav"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=['start','end'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load site_6 (garage) sensor data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ol>\n",
    "<li>First load the pickle file and convert the timestamps to Helsinki timezone</li>\n",
    "<li>Next load the json file containing deviceids</li>\n",
    "<li>Finally load the png file as both a numpy array and a base64 encoded image. We need the later for plotting in Plotly helper functions</li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_events = pd.read_pickle(f'./audio_data/site_6.pkl', compression='gzip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_events.loc[:, 'timestamp'] = (pd.to_datetime(df_events['timestamp'], utc=True)\n",
    "                                 .dt.tz_convert('Europe/Helsinki')\n",
    "                                 .dt.tz_localize(None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>deviceid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-10-28 18:25:03.738</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-10-28 18:25:03.989</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-10-28 18:25:04.144</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-10-28 18:25:04.261</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-10-28 18:25:04.425</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                timestamp  deviceid\n",
       "0 2021-10-28 18:25:03.738         5\n",
       "1 2021-10-28 18:25:03.989         5\n",
       "2 2021-10-28 18:25:04.144        74\n",
       "3 2021-10-28 18:25:04.261        34\n",
       "4 2021-10-28 18:25:04.425        34"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_events.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_devices = pd.read_json(f'./audio_data/site_6.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>deviceid</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>151</td>\n",
       "      <td>1012.132805</td>\n",
       "      <td>532.820374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>audio_sensor_4</td>\n",
       "      <td>903.212359</td>\n",
       "      <td>1065.803693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>audio_sensor_2</td>\n",
       "      <td>1325.483386</td>\n",
       "      <td>672.277047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>audio_sensor_1</td>\n",
       "      <td>895.412256</td>\n",
       "      <td>677.966359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>audio_sensor_3</td>\n",
       "      <td>1334.579512</td>\n",
       "      <td>1059.567587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           deviceid            x            y\n",
       "151             151  1012.132805   532.820374\n",
       "152  audio_sensor_4   903.212359  1065.803693\n",
       "153  audio_sensor_2  1325.483386   672.277047\n",
       "154  audio_sensor_1   895.412256   677.966359\n",
       "155  audio_sensor_3  1334.579512  1059.567587"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_devices.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(f'./audio_data/site_6.png', \"rb\") as image_file:\n",
    "    encoded_string = base64.b64encode(image_file.read()).decode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1317, 2635, 4)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = iio.imread(f'./audio_data/site_6.png')\n",
    "img.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now load the floorplan just to get a feel of what the devices look like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaling_factor = 2 # Set to 1 for highest resolution\n",
    "plotting_obj = Plotting(bg_img=encoded_string, \n",
    "                        dims=(img.shape[1], img.shape[0]), \n",
    "                        df_devices=df_devices, \n",
    "                        scaling_factor=scaling_factor)\n",
    "plotting_obj.run(renderer='browser') # Switch to iframe if you would like to view it here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Animate Data\n",
    "\n",
    "<p>The plotting helper script contains an animation engine utilising Plotly. It is extremely simple to use. Here is an example where we aggregate data into 5 minute bins, and then visualize how motion sensors are triggered through 1 day. You can choose different time intervals, but please remember that higher time granularities can end up rendering a lot of frames and might lead to performance issues. In this example, the total number of frames is (60//5) * 24 = 288</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "freq_window = '1s'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-10-28 18:25:03</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-10-28 18:25:03</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-10-28 18:25:04</td>\n",
       "      <td>74</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-10-28 18:25:04</td>\n",
       "      <td>34</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-10-28 18:25:04</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>2021-10-28 19:04:52</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>2021-10-28 19:04:52</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>2021-10-28 19:04:56</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>2021-10-28 19:04:57</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>2021-10-28 19:04:58</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>913 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              timestamp  deviceid\n",
       "0   2021-10-28 18:25:03         5\n",
       "1   2021-10-28 18:25:03         5\n",
       "2   2021-10-28 18:25:04        74\n",
       "3   2021-10-28 18:25:04        34\n",
       "4   2021-10-28 18:25:04        34\n",
       "..                  ...       ...\n",
       "163 2021-10-28 19:04:52        93\n",
       "164 2021-10-28 19:04:52        92\n",
       "165 2021-10-28 19:04:56        23\n",
       "166 2021-10-28 19:04:57        25\n",
       "167 2021-10-28 19:04:58        93\n",
       "\n",
       "[913 rows x 2 columns]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_events_day = df_events[df_events.timestamp.dt.date.astype(str) == \"2021-10-28\"].copy()\n",
    "df_events_day.timestamp = df_events_day.timestamp.dt.floor(freq_window)\n",
    "df_events_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-28 19:04:57</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2021-10-28 19:04:58</th>\n",
       "      <td>NaN</td>\n",
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       "</table>\n",
       "<p>459 rows × 85 columns</p>\n",
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      ],
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       "deviceid             1    2    3    4    5    9    12   13   19   20   ...  \\\n",
       "timestamp                                                              ...   \n",
       "2021-10-28 18:25:03  NaN  NaN  NaN  NaN  2.0  NaN  NaN  NaN  NaN  NaN  ...   \n",
       "2021-10-28 18:25:04  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...   \n",
       "2021-10-28 18:25:05  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  1.0  ...   \n",
       "2021-10-28 18:25:06  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...   \n",
       "2021-10-28 18:25:07  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...   \n",
       "...                  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "2021-10-28 19:04:49  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...   \n",
       "2021-10-28 19:04:52  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...   \n",
       "2021-10-28 19:04:56  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...   \n",
       "2021-10-28 19:04:57  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...   \n",
       "2021-10-28 19:04:58  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...   \n",
       "\n",
       "deviceid             135  136  138  139  140  142  146  147  150  151  \n",
       "timestamp                                                              \n",
       "2021-10-28 18:25:03  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "2021-10-28 18:25:04  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "2021-10-28 18:25:05  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "2021-10-28 18:25:06  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "2021-10-28 18:25:07  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "...                  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  \n",
       "2021-10-28 19:04:49  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
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     },
     "execution_count": 168,
     "metadata": {},
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    }
   ],
   "source": [
    "df_events_day.loc[:, 'b'] = 1\n",
    "df_events_day = df_events_day.groupby(['deviceid', 'timestamp']).sum()\n",
    "df_events_day = df_events_day.pivot_table(index='timestamp', columns='deviceid', values='b')\n",
    "df_events_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
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       "deviceid             1    2    3    4    5    9    12   13   19   20   ...  \\\n",
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     },
     "execution_count": 172,
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   ],
   "source": [
    "df_events_day = df_events_day.reindex(pd.date_range(df_events_day.index.min(), df_events_day.index.max(), \n",
    "                                                    freq=freq_window, closed='left')).fillna(0)\n",
    "df_events_day"
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  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
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       "\n",
       "deviceid             135  136  138  139  140  142  146  147  150  151  \n",
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       "\n",
       "[2357 rows x 85 columns]"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offset_index = 37\n",
    "df_events_day.iloc[offset_index:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_events_aligned = df_events_day.iloc[offset_index:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
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       "</table>\n",
       "<p>2357 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "deviceid             1    2    3    4    5    9    12   13   19   20   ...  \\\n",
       "2021-10-28 18:25:40  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "2021-10-28 18:25:41  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "2021-10-28 18:25:42  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "2021-10-28 18:25:43  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "2021-10-28 18:25:44  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "...                  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "2021-10-28 19:04:52  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "2021-10-28 19:04:53  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "2021-10-28 19:04:54  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "2021-10-28 19:04:55  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "2021-10-28 19:04:56  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   \n",
       "\n",
       "deviceid             135  136  138  139  140  142  146  147  150  151  \n",
       "2021-10-28 18:25:40  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2021-10-28 18:25:41  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2021-10-28 18:25:42  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2021-10-28 18:25:43  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2021-10-28 18:25:44  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "...                  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  \n",
       "2021-10-28 19:04:52  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2021-10-28 19:04:53  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2021-10-28 19:04:54  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2021-10-28 19:04:55  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2021-10-28 19:04:56  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[2357 rows x 85 columns]"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_events_aligned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = 80\n",
    "end = start + 120\n",
    "frames = df_events_aligned.iloc[start:end].to_dict(orient='records')\n",
    "ts = df_events_aligned.iloc[start:end].reset_index()[['index']].astype(str).to_dict(orient='records')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaling_factor = 2 # Set to 1 for highest resolution\n",
    "plotting_obj = Plotting(bg_img=encoded_string, \n",
    "                        dims=(img.shape[1], img.shape[0]), \n",
    "                        df_devices=df_devices, \n",
    "                        scaling_factor=scaling_factor,\n",
    "                       speed=1000)\n",
    "plotting_obj.populate_data(frames, ts)\n",
    "plotting_obj.run(renderer='browser')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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