{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![book header](pictures/header.png)\n",
    "\n",
    "[Table of Contents](0_Table_of_Contents.ipynb)\n",
    "\n",
    "# Chapter 4: Module 2 - Reading KITT Sensor Data\n",
    "\n",
    "**Contents:**\n",
    "* [Distance Sensor](#distance-sensors)\n",
    "* [The Microphones](#the-microphones)\n",
    "* [FAQ](#faq)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-28T08:43:34.403324Z",
     "start_time": "2025-11-28T08:43:33.090082Z"
    }
   },
   "source": [
    "# Import necessary libraries\n",
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import csv\n",
    "\n",
    "# Uncomment one of the following lines depending on your setup\n",
    "\n",
    "# If you are using the real car, uncomment the next lines and comment the simulator lines\n",
    "from serial import Serial\n",
    "import sounddevice\n",
    "\n",
    "# If you are using the simulator, uncomment the next lines and comment the real car lines\n",
    "# from KITT_Simulator.serial_simulator import Serial\n",
    "# from KITT_Simulator.sounddevice_simulator import sounddevice\n",
    "\n",
    "# Note: After changing the import statement, you need to restart the kernel for changes to take effect."
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "KITT relies on its sensors to drive autonomously. It is equipped with:\n",
    "1. Two front-mounted distance sensors.\n",
    "2. Five microphones positioned around the field to record audio signals from KITT's beacon and relay them to the soundcard, after which they can be read by your PC.\n",
    "\n",
    "This task focuses on reading data from the distance sensors to avoid obstacles and processing the microphone data from the field.\n",
    "\n",
    "**Preparation**\n",
    "- Ensure KITT is operational and properly set up.\n",
    "- Reserve a time slot for testing on a field equipped with microphones and an audio card.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distance Sensors\n",
    "\n",
    "KITT’s front distance sensors use ultrasonic technology. Two SRF02 modules, mounted on the left and right sides, measure the distance to obstacles. These \"parking sensors\" work by emitting a 40 kHz pulse and measuring the time it takes for the echo to return. This time is converted into a distance measurement.\n",
    "\n",
    "- Each sensor requires a minimum of 66 ms between readings, as specified in the SRF02 datasheet (available on Brightspace or at `Files/Datasheets/srf02.pdf`).\n",
    "- The system is configured with a 70 ms cycle time; the left and right sensors take turns recording measurements.\n",
    "- These measurements are stored in a buffer on KITT's microcontroller, with each new reading overwriting the previous one. When you request the KITT status, you will obtain a copy of the current buffer values.\n",
    "\n",
    "<img src=\"pictures/srf02-ultrasonic-sensor.jpg\" alt=\"Ultrasonic Sensor\" width=\"400\" height=\"240\">\n",
    "\n",
    "### Step 0: Characteristics of the Distance Sensors\n",
    "\n",
    "Using the readings on the car display, report on the following questions:\n",
    "\n",
    "1. What is the accuracy of the distance sensors? Does this change with distance?\n",
    "2. What are the minimum and maximum distances the sensors can measure?\n",
    "3. What is the field of view of the distance sensors (beam angle)?\n",
    "\n",
    "To measure this field of view, move an obstacle from left to right over a line, at about 1 m distance from the sensors, and observe when the sensors start to 'see' the object.\n",
    "\n",
    "The field of view is important when making recordings: you should realize that the distance sensors may detect chairs, bags, etc., and then make false readings. This happens even if these objects are not straight in front of the sensors. (The field of view does depend on distance.)\n",
    "\n",
    "**Note:** Do not copy the questions into your report, but naturally include the information in your report as part of your discussion.\n",
    "\n",
    "### Step 1: Status Command\n",
    "\n",
    "To ensure you can experiment at home, we have added the status command to the simulator. The simulator will accurately simulate the sensor distances, but not its behavior. Make sure to test on the real car frequently.\n",
    "\n",
    "As you have learned in the previous module, you can ask KITT to capture a status command by writing `\"S\\n\"` to the serial port. Then you have to read the message using `read_until`; this will generate a binary message that you need to decode. KITT always ends its message with the end-of-transmission character (0x04). The response contains three sections:\n",
    "1. Audio beacon status and settings\n",
    "2. PWM values for the motors\n",
    "3. Sensor readings"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T08:40:49.777160Z",
     "start_time": "2025-11-21T08:40:48.574633Z"
    }
   },
   "source": [
    "### Student Version ###\n",
    "\n",
    "serial = Serial('COM4', 115200)\n",
    "serial.write(b'Sd\\n')\n",
    "status = serial.read_until(b\"\\x04\")\n",
    "status = status.decode('utf-8')\n",
    "print(f\"Car status is:\\n\\n{status}\")\n",
    "\n",
    "serial.close()"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Car status is:\n",
      "\n",
      "USL99\n",
      "USR51\n",
      "\u0004\n"
     ]
    }
   ],
   "execution_count": 45
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T08:52:18.034321Z",
     "start_time": "2025-11-21T08:52:18.022808Z"
    }
   },
   "cell_type": "code",
   "source": "serial.close()",
   "outputs": [],
   "execution_count": 51
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you only need distance measurement information, you can request it separately:\n",
    "\n",
    "```python\n",
    "serial.write(b'Sd\\n')\n",
    "```\n",
    "\n",
    "This returns only the left and right distance sensor values, filtering out the rest of the status report.\n",
    "\n",
    "### Step 2: Extracting and Isolating Distance Data\n",
    "\n",
    "Assuming you have received the full status information from KITT, you can extract and isolate the distance sensor readings (left and right) from the status report.\n",
    "\n",
    "After sending the status command (`b'S\\n'`), the response will contain a variety of information, including the distance measurements. Now write a Python function to extract the distance data from the status report.\n",
    "\n",
    "1. **Extract the distance measurements**:\n",
    "\n",
    "The distance values are typically embedded in the `Sensors` section of the status response. You can process the `status` output to isolate just the left (`L`) and right (`R`) distance sensor values. Write a function to extract these values.\n",
    "\n",
    "*Hints:*\n",
    "\n",
    "- Use `decode('utf-8')` to convert bytes to a string.\n",
    "- Use `splitlines()` to separate the status message into individual lines.\n",
    "- Look for the line that contains `\"Dist.\"` to find the distance measurements.\n",
    "- Use `split()` to break the line into individual words.\n",
    "- Be cautious of the positions of the distance values in the list; adjust indices as necessary.\n",
    "\n",
    "**Note:** If you use the `Sd` status command, you retrieve less info and can write a faster function! The parsing of the status string is also easier."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T08:49:45.232118Z",
     "start_time": "2025-11-21T08:49:45.222261Z"
    }
   },
   "source": [
    "### Student Version ###\n",
    "\n",
    "def extract_dis ():\n",
    "    serial = Serial('COM4', 115200)\n",
    "    serial.write(b'S\\n')\n",
    "    _status = serial.read_until(b'\\x04')\n",
    "    _status = _status.decode('utf-8')\n",
    "\n",
    "    lines = _status.splitlines()\n",
    "\n",
    "    # Initialize variables to hold distance values\n",
    "    dist_L = None\n",
    "    dist_R = None\n",
    "\n",
    "    # Iterate over each line to find distance data\n",
    "    for line in lines:\n",
    "        if \"Dist.\" in line:\n",
    "            words = line.split()\n",
    "            # Extract distance values based on their positions\n",
    "\n",
    "            # Assign dist_L and dist_R accordingly\n",
    "            dist_L = words[3]\n",
    "            dist_R = words[5]\n",
    "            break  # Exit the loop after finding the distances\n",
    "\n",
    "    # Print the extracted distance values\n",
    "    print(f\"Left Distance: {dist_L}\")\n",
    "    print(f\"Right Distance: {dist_R}\")\n",
    "\n",
    "    serial.close()\n",
    "\n",
    "    return dist_L, dist_R"
   ],
   "outputs": [],
   "execution_count": 46
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T08:38:36.110680Z",
     "start_time": "2025-11-21T08:38:34.392005Z"
    }
   },
   "cell_type": "code",
   "source": "extract_dis()",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Left Distance: 99\n",
      "Right Distance: 51\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('99', '51')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 44
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. **Determine how fast you can read out (and process) your distance data** by writing a script that requests the status 100 times. You can calculate the average delay for this operation (and its standard deviation); you could also present the results in a histogram. To measure delays, you will need to keep track of time:\n",
    "\n",
    "```python\n",
    "start_time = time.time()                    # Initialize\n",
    "current_time = time.time() - start_time     # Find current time since initialization\n",
    "```\n",
    "\n",
    "If you can read out the sensors faster than 70 ms (or is it 140 ms?), reason if you will obtain duplicate values from the buffer.\n",
    "\n",
    "**Student Task:**\n",
    "\n",
    "- Write a script that sends the status command 100 times, recording the time taken for each read.\n",
    "- Store the time intervals in a list.\n",
    "- After collecting the data, calculate the average delay and standard deviation.\n",
    "- Plot a histogram of the delays."
   ]
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T08:54:21.237164Z",
     "start_time": "2025-11-21T08:54:21.222343Z"
    }
   },
   "cell_type": "code",
   "source": "serial.close()",
   "outputs": [],
   "execution_count": 55
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T09:22:30.754889Z",
     "start_time": "2025-11-21T09:22:21.720051Z"
    }
   },
   "source": [
    "### Student Version ###\n",
    "times = []\n",
    "new_times = []\n",
    "total_time = 0\n",
    "summed_times = 0\n",
    "serial = Serial('COM4', 115200)\n",
    "\n",
    "for i in range(100):\n",
    "    start_time = time.time()\n",
    "    serial.write(b'S\\n')\n",
    "    _status = serial.read_until(b'\\x04')\n",
    "    _status = _status.decode('utf-8')\n",
    "    current_time = time.time() - start_time\n",
    "    times.append(current_time)\n",
    "\n",
    "serial.close()\n",
    "#print(times)\n",
    "\n",
    "for j in range(100):\n",
    "    total_time += times[j]\n",
    "\n",
    "average = total_time / 100\n",
    "\n",
    "for k in range(100):\n",
    "    new_times.append((times[k] - average) ** 2)\n",
    "    summed_times += new_times[k]\n",
    "\n",
    "variance = summed_times / 100\n",
    "standard_deviation = (variance) ** 0.5\n",
    "\n",
    "print(f'The average time of a distance measurement is: {average:.3f} [s]')\n",
    "print(f'The standard deviantion of a distance measurement is: {standard_deviation:.3f} [s]')\n",
    "\n",
    "plt.hist(times)\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average time of a distance measurement is: 0.079 [s]\n",
      "The standard deviantion of a distance measurement is: 0.035 [s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 63
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "<font color=#6698FF> As the ultrasonic sensors refresh every 70 ms taking turns, every 140 ms will give new data. So we need to measure every 140 ms to get new data and not data stored in the buffer."
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Using Distance Values to Model the Car\n",
    "\n",
    "Ultrasonic sensors are not just used for detecting obstacles; they play a crucial role in modeling the car's behavior during autonomous driving. To control the car effectively, we need to understand how it responds to drive and steering commands, similar to how a human driver knows how much acceleration or steering input affects the car's movement.\n",
    "\n",
    "However, while KITT doesn’t have an accelerometer to measure acceleration directly, we can use the ultrasonic sensors to estimate how the car moves over time. By measuring the distance to a cardboard-box wall, we can derive its speed and acceleration.\n",
    "\n",
    "#### Understanding Speed and Acceleration\n",
    "\n",
    "- **Velocity** is the rate of change of position over time:\n",
    "\n",
    "  $$\n",
    "  v(t) = \\frac{{\\mathrm d} x}{{\\mathrm d} t}(t) \\,.\n",
    "  $$\n",
    "\n",
    "- **Acceleration** is the change in speed over time:\n",
    "\n",
    "  $$\n",
    "  a(t) = \\frac{{\\mathrm d} v}{{\\mathrm d} t}(t) \\,.\n",
    "  $$\n",
    "\n",
    "Note that $x(t)$, $v(t)$ and $a(t)$ are time varying. To implement the differentials, in practice we will subtract two subsequent samples $x(t_1)$ and $x(t_2)$. We will then have an estimate of $v(t)$ for $t = (t_1+t_2)/2$:\n",
    "\n",
    "$$\n",
    "v\\left(\\frac{t_1+t_2}{2}\\right) \\approx \\frac{x(t_2) - x(t_1)}{t_2-t_1} \\,.\n",
    "$$\n",
    "\n",
    "In theory, this approximation gets better for $t_2$ close to $t_1$, but at the same time the division of two small numbers will make the result very sensitive to noise, so in practice there is a trade-off.\n",
    "\n",
    "#### Plotting KITT's Motion Towards a Wall\n",
    "\n",
    "To understand how KITT moves, make recordings of the distance sensor values as KITT drives towards a wall. Do this for multiple motor commands, and store them in a `.csv` file. (You can use the `Files/Recordings` folder to organize your data). You can then later import the data into Python. Next to the sensor values, you should also store the time stamp of each sample.\n",
    "\n",
    "You will see KITT speed up, and then reach a constant speed. To do this experiment, please let KITT drive towards the supplied cardboard wall. **Turn off KITT's motors once the distance is less than 40 cm to ensure KITT does not crash into the wall.**  Note that you may have to discard the first few readings as they may be inaccurate.\n",
    "\n",
    "*Hints:*\n",
    "\n",
    "- Choose an appropriate motor speed value for `motor_speed_value`.\n",
    "- Ensure that you stop the car if it gets too close to the wall to prevent collisions.\n",
    "- Use `time.time()` to keep track of elapsed time.\n",
    "- Store the data in a list with the format `[current_time, dist_L, dist_R]`.\n",
    "- Write the data to a CSV file for later analysis.\n",
    "- Also document the motor speed setting (e.g. use this as part of your file name)."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T11:13:56.871532Z",
     "start_time": "2025-11-21T11:13:52.882260Z"
    }
   },
   "source": [
    "### Student Version ###\n",
    "from pathlib import Path\n",
    "\n",
    "serial = Serial('/dev/rfcomm3', 115200)\n",
    "serial.write(b'D150\\n')\n",
    "serial.write(b'M165\\n')\n",
    "\n",
    "# Initialize a list to store recorded data\n",
    "data = []\n",
    "\n",
    "# Record data for a specified duration (e.g., 10 seconds)\n",
    "recording_duration = 10  # in seconds\n",
    "start_time = time.time()\n",
    "\n",
    "while time.time() - start_time < recording_duration:\n",
    "    # Send the status command to get the distance readings\n",
    "    serial.write(b'S\\n')\n",
    "    \n",
    "    # Read the status response\n",
    "    status = serial.read_until(b'\\x04').decode('utf-8')\n",
    "    \n",
    "    dist_L = None\n",
    "    dist_R = None\n",
    "\n",
    "    lines = status.splitlines()\n",
    "\n",
    "    for line in lines:\n",
    "        if \"Dist.\" in line:\n",
    "            words = line.split()\n",
    "            # Extract distance values based on their positions\n",
    "\n",
    "            # Assign dist_L and dist_R accordingly\n",
    "            dist_L = int(words[3])\n",
    "            dist_R = int(words[5])\n",
    "            break\n",
    "\n",
    "    current_time = time.time() - start_time\n",
    "    data.append([current_time, dist_L, dist_R])\n",
    "    \n",
    "    # Check if KITT is too close to the wall and stop if necessary\n",
    "    if dist_L < 130 or dist_R < 130:\n",
    "        serial.write(b'M135\\n')\n",
    "        time.sleep(0.75)\n",
    "        serial.write(b'M150\\n')  # Stop the car\n",
    "        print(\"Stopping KITT to avoid collision.\")\n",
    "        break  # Exit the loop\n",
    "        # Note: you can also add a small loop here and still read the stopping data\n",
    "    \n",
    "    time.sleep(0.1)  # Wait before the next reading\n",
    "\n",
    "# Close the serial connection\n",
    "serial.close()\n",
    "\n",
    "filepath = Path('./kitt_wall_data_165.csv')\n",
    "df = pd.DataFrame(data,columns = [\"Time\",\"Distance_L\",\"Distance_R\"])\n",
    "df.to_csv(filepath,index=False)\n",
    "!pwd\n",
    "# Recommeded file output: Files/Recordings/kitt_distance_data_{speed}.csv"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stopping KITT to avoid collision.\n",
      "/home/nano/Documents/EE/Y2/IP3/A.K.03/Manual\r\n"
     ]
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-21T11:12:41.634004Z",
     "start_time": "2025-11-21T11:12:41.624576Z"
    }
   },
   "cell_type": "code",
   "source": "df.to_csv(Path(\"./kitt_wall_data_165.csv\"))",
   "outputs": [],
   "execution_count": 16
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Processing the Recorded Data\n",
    "\n",
    "Read your `.csv` data into Python (see template script below), and plot the distance values over time to visualize KITT's motion. Use a single plot with separate colors for the L and R sensors. You should notice a 'staircase' shape! Explain this in your report.\n",
    "\n",
    "Next, to derive velocity, first merge the L and R sensor data into a single position estimate: remove the duplicate values (keep only the first value of a duplicate reading), then merge the remaining values into a single (time, position) array. Plot the result in your distance plot to see if you did this correctly.\n",
    "\n",
    "After that, estimate the velocity of KITT as function of time. Obviously, you will use $ v(t) = \\Delta x / \\Delta t \\ $, but what time $t$ do you associate with each of these estimates?\n",
    "\n",
    "Make a plot of the resulting velocity estimates over time.\n",
    "\n",
    "*Hints:*\n",
    "\n",
    "- When merging the L and R distance measurements, don't simply average them. Read the above paragraph again.\n",
    "- Be aware that the sensors alternate readings every 70 ms, leading to a 'staircase' effect.\n",
    "- To calculate velocity, use the differences in distance and time (`diff()` function).\n",
    "- Since KITT is moving towards the wall, the distance decreases; yet, the estimated velocity should be positive when moving forward.\n",
    "- For the time associated with each velocity estimate, use the midpoint between consecutive time stamps.\n",
    "- Remove any NaN values resulting from the `diff()` operation."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-26T14:37:36.226400Z",
     "start_time": "2025-11-26T14:37:33.263943Z"
    }
   },
   "source": [
    "### Student Version ###\n",
    "\n",
    "motor_speed_value = 160  # Use the same motor speed as during recording\n",
    "\n",
    "# Load the recorded data from the CSV file\n",
    "csv_filename = f'./kitt_wall_data_160.csv'\n",
    "data = pd.read_csv(csv_filename)\n",
    "\n",
    "# Consider discarding the first few readings (inaccurate readings)\n",
    "data = data[2:]\n",
    "\n",
    "# Create a new DataFrame to hold your processed data\n",
    "merged_data = []\n",
    "plotting_data = []\n",
    "\n",
    "# Iterate over the data\n",
    "for index, row in data.iterrows():\n",
    "    # Extract time and distances\n",
    "    time_stamp = row['Time']\n",
    "    dist_L = row['Distance_L']\n",
    "    dist_R = row['Distance_R']\n",
    "    \n",
    "    distance = min(dist_L,dist_R)\n",
    "\n",
    "    merged_data.append([time_stamp, distance])\n",
    "    plotting_data.append([time_stamp, dist_L, dist_R])\n",
    "\n",
    "# Convert merged data to DataFrame\n",
    "merged_df = pd.DataFrame(merged_data, columns=['Time', 'Distance'])\n",
    "plotting_df = pd.DataFrame(plotting_data, columns=['Time', 'Dist_L', 'Dist_R'])\n",
    "\n",
    "# Plotting the distance measured by the left and the right sensor\n",
    "plt.figure()\n",
    "plt.plot(plotting_df['Time'], plotting_df['Dist_L'], color='blue', label='Left sensor')\n",
    "plt.plot(plotting_df['Time'], plotting_df['Dist_R'], color='red', label='Right sensor')\n",
    "plt.xlabel('Time (s)')\n",
    "plt.ylabel('Distance (cm)')\n",
    "plt.title('Distance from the left and right sensor')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "merged_df['Velocity'] = -merged_df['Distance'].diff().div(merged_df['Time'].diff())\n",
    "\n",
    "# Note: Use a negative sign because distance to the wall decreases as KITT moves forward\n",
    "\n",
    "# Calculate the time corresponding to each velocity estimate\n",
    "# It's common to use the midpoint of the time intervals\n",
    "merged_df['Velocity_Time'] = merged_df['Time'] - merged_df['Time'].diff() / 2\n",
    "\n",
    "# Plotting Distance\n",
    "plt.figure()\n",
    "plt.plot(merged_df['Time'], merged_df['Distance'], label='Distance to Wall')\n",
    "plt.xlabel('Time (s)')\n",
    "plt.ylabel('Distance (cm)')\n",
    "plt.title('Distance to Wall Over Time')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Plotting Velocity\n",
    "plt.figure()\n",
    "plt.plot(merged_df['Velocity_Time'], merged_df['Velocity'], label='Velocity (cm/s)')\n",
    "plt.xlabel('Time (s)')\n",
    "plt.ylabel('Velocity (cm/s)')\n",
    "plt.title('Velocity of KITT Over Time')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "print(merged_df)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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nz4vkYq7Sasj9999P+/fvF8ccAAAcB8GOCs2YMYPWrl1Lly5dqnPb4sWLqXfv3tS5c2e771dYWJhNKtouXLhAf/zxB917771kD7/++isNHz5clKM3hJPa77zzTpo/f75d9gsAwBwVlVXioiUIdlRowoQJIrBYsmRJje08ErF8+XK67777xPfbtm2jwYMHi1EKXkPoySefpMLCwgaDikmTJok3ee6Yftttt1FaWlqN+/z+++/Up08fUaodGhpKN910k8lpLL7O+HYe4eHvz507J8q79+7dW+Mx+WdatmxZ76KOP/zwA3Xr1o2aNWtWY/v27dtp2LBhIsAKCgqisWPH0pUrV8RtvJ1HgmbNmiVui4iIoM8++0y8fj4+PELUrl07+uuvv0wGOzfeeKO4vmnTJurbty/5+PiIBQIHDhwoRn6qj7L99ttvVFxcXO9xBQCwlyuFZdT/rQ109xe7xXpfWoFgpzGLwZVVOORi7onJ7Q3uueceEexU/xkOdLiU/o477hArQo8fP55uueUWOnz4MH3//fci+HniiSdMPiYHGhzoZGdn0+bNm8XI0ZkzZ+j222833ufPP/8UwQs/7oEDB2j9+vUiEKhvSksaaeJFG/l7DnhGjRoltlXH3/OoTX3r3PA0EY9WVXfw4EEaOXIkderUiXbu3CleGwce1Vs+fPXVVyIg2717twh8Hn30UZoyZQoNGDBATD+NGTOGpk+fLtZZkuTk5IjH4mCH12CaPHkyDR06VBxDfh6epqteYcX7xffbtWtXvb8vAAB7WX88nTLyS2nnmSw6eDFHMwceiwpaqLi8kjq9tJoc4egrY8nb3bxfGeeL/Oc//xGBCY9iSEEDBzcBAQE0b948McXCIxssNjZWTLfwG/fChQvFyEx1HLgcOXJEBEk8CsSWLl1K8fHxIlDh0ZzXX3+dpk6dSnPnzjX+HI+4mMIjT9XbJUgeeOABeuSRR+i9994jDw8PEXTw8/JoSn14JKV2sPPOO++IbR9//LFxG+9rdbxvL774ojHn56233hLBz4MPPii2vfTSS+JYJCYmGvdx5cqV1LVrV7HIJAd+ubm5YiStbdu24vaOHTvWeA4eVeLjXX20BwDAUTad0K/azn7Ye4l6tAjSxC8DIzsqFRcXJ0YovvzyS/H96dOnxQgI5/MwTlDmkQ2ekpIuPM3DIzgc0NR27NgxEeRIgQ7jURMOVvi26qMpTcEjJZxA/csvv4jveXSK82OkaS9TeIqodnBmzr5w0CLh5wwJCaEuXboYt/HUFsvIyDA5hRUcHCxGnPi48ajRBx98IEapauNpwuqjQwAgX7nF5fTP5YdoR1ImqU1FZRVtPXX1df1xKJmKy7TRDBojOxbycnMRIyyOem5LcGDD0zMLFiwQozo8+sAjNzy1xbkpPOXy1FNP1fm5Fi1aNKn9QVNwUi9PwfH+3nzzzfTtt9+KIKIhPBoj5eJYsi/cdbw6nn6qvk2ajpKmArltxKpVq+j555833of3k3OdeDtPBfJIEU/xXXfddcb78AiQNJIFAPL2v7/P04/7LlHC5VxaNWsIqcmhSzkimAvwciN/L1e6mF1MqxJT6KYezUntMLJjIX4D5KkkR1wsXW2XE4g5z4UDBp5y4qkt6TF4VINHZDgJt/bFVGsMnp7hzt98kRw9elTksPAIj/SYPN1lLg4squfQVJ/KWrdunZiC4nwXDnoa0qNHD7Ev1Vm6L+bgZGROZq49NcfPz9NgO3bsEFVufLwlSUlJVFJSIu4DAPK3/pi+6OJ4aj6l5ZWQmmw8rh+lHhwbSrf21I/SL99bt2pXjRDsqBhPTXECMb8R8/RK9dJsHtHhN2dOSOYpn1OnTokpmvoSlDlxmKd4pk2bJvJoOKmXR2B4pEjKl5kzZw5999134isHUpxr8/bbb9e7fzw1xQFJampqjZEZDqx4ZOT//u//RDL1tUZpeBqJk4OrB078mjmX6LHHHhPJw8ePHxf5N5mZjR+a5qoqaQqL8XQfPw8/N+fkrFmzRhzH6nk7PHXYpk0bY04PAMhXVkEpHaiWtLv55NUpbDXYdFKfrzOsQzjd0qsZ8WffHUlZdDFb/dPsCHZUjqeyOJDggICTaiU8ArFx40Y6efKkKD/nkQdOyK1+n+p4RIiDIR7ZGDJkiAh++E2cp24knAjNFV8cFHTv3p1GjBghgqL6vPvuu2LKh/OAao988H7ztBGPRl3LuHHjRAUajwZJ2rdvL4KPQ4cOiYqw/v37i/3n+1kr2OHkYw6iOOmbn4+nBR9//HF6+OGHjffh4E9KeAYAedt0IoOqF72qKdhJzy+hhMt54vrQ9mHUPMibBrYNFd/ztJ3aIWdH5fhNvr6Sda6g4oCgPrzuTe1cnoaqohhPOdU37VT78Tipt752CpcvXxYjSbyP18IBDOfRcAUXB3USHnXitXbqm5K61v4xHi3Ky8sTo1n8lR+zegKzlEhtCldx8agZrwMEAPK3/rh+CmtQu1DadjqTtp7MEEm9ri7KHxfYclI/qt25mT+F+XmI61N6Nxevk4Odp0bGkrOzZakSSqL83yCoCi98yJViH330kUiuNhePpvCIk616Y3Hu0IcfflgnqbkhPHXIuVJceg4A8lZWUWUMCJ4e3V4k8eaVVIikXjWVnA9rH27cNjY+kvw8XelyTrFYd0fNEOyArHDOUK9evcSUmDlTWNVHd1544QWb9cbiqbC7777bop/hqb7qI00AIF97zmVTQWkFhfp6UI+YQJHEK01tqankfFiHq5Whnm4uNKm7PnXhh71Xi0/UCMEOyAqvq1NaWipygXjtGwAAe1h/TD/yMSIuTEzncF6LWvJ2DlUrOe8eE1jjtim99FVZqxJSxX3UCsEOAABoGuc1Svk6I+L0i4lKwc7hS7mUWVBKSrbJMDrFo1W184+6Ng+g9hG+VFpRRb8fSia1QrBjJi01TAP7wXkF4HhJGYV0PquI3F2caZBh+irc35M6RfmL61tPKXt0Z6OUr9Phar5O9Urb23ob1txRcVUWgp1rkKZSuAwawNqkNhKWJD4DgHVtMIzq9GsTTL4eV4uUpfyWzQrO2+GmnwnVSs5NmdyjGbk6O9Ghizl0Ms02RR6OhtLzax0gV1exngr3R+I3pPo6bysJ97/i4I1X9lXD61HiseIRHQ500tPTRX8x5CcBOD5fZ1RH/RSWhIODjzcl0ZZTmVRVpVNkafbW03VLzmvjpOwRceG05mgaLd97kV64Qb8qvpog2LkGHuKLiooSq+WqpXM1v9Fy80xemdjSFhRaY+tjVbvrOwDYV25ROe09r1/Bnd/wq+vZMoj8PFwpu7CMjlzOpW61knuVYMtJfUl59ZJzU6b0jhHBzi8HLtOz18eRmwrWFqoOwY4ZuFdUbGysaqayysvLacuWLWJdGkyfOO5Y8eNhRAfA8S0UKqt0Ikk3Jti7xm38hj+wXSitSkwVVVlKC3YqdUTbkuqWnJvCt/MIDydjbzyeTmPi1fUhDMGOmXgKw9PTk9SA32B5kTx+PQh2cKwAtGzDcankvOYUlmRohzAR7PCifE+OjCUluVBAlFtcQf6ernVKzmvjwO7mns3o0y1nRKKy2oIddY1TAQAAWLDYnlSWPaqj6WkeKan34MUcyilS1uj+0Sv6t/gh7cPMankxpVdzYwDIic1qgmAHAAA0af8F/WJ7gd5u1KNFkMn7RAd6iSmuKh2XoOunhJTiWI5TvSXnpsRG+IkRIJ7WW3HgMqkJgh0AANCk9cf0JefDO4STSwOVVlKwoKTVlHlk5mKhU4Ml56ZIa+5w+wg1rQOGYAcAADRpvTFfp+GRj+qtI5QSAEgl5/HRfvWWnJsyoVsUebo506n0Ajp0KZfUAsEOAABozvmsQjqdXiAW0+Oclob0bhVE3u4uYrTkaIp+gT6llJwPMawIbS5/Tzca1zlKdc1BEewAAIBmq7D6tAoWDTIb4uHqQgPahihmKosTr6WS86EWBjvVE5V/P5hMxWWVpAYIdgAAQLPBzsh6qrDqm8qSqrfk3+W8grxcdNSteYDFP39dmxBqHuRF+aUVtDoxldQAwQ4AAGhKfkk5/X0my6x8HclQwwrE+89fobyScpIzKSCLC9SZVXJeG7fFuNUwurN8nzqmshDsAACApmw7lUnllTpqHepDbcJ8zfqZFiHe1CbUhyqqdLTjtD5Qknuw0ymw8cnUt/ZqTtwhZ/vpLLqYrW9YrGQIdgAAQJNVWCPNHNWRSInMm0/qf16OOIma+3hJIzuN1TzI25in9NP+S6R0CHYAAEAzuHs5935iI8zM15FI/aU2n5BvCbqUQM0l5/7uTXus2wxr7izfe0kcNyVDsAMAAJpx8FIOZRWWiW7mXIllaeKuh6szJeeWiHVo5Ih7eDWm5NyUsfGR5OfpSpdzio05TkqFYAcAADRjwzFDMNAhTDS/tISnm4sIeKTRHTmWnEstLRpTcm7q9U7sFq2KNXcQ7AAAgObydepr/Gl2CboM83b0Jeflost5Y0rOG5rK+ishVfZVaA1BsAMAAJqQnFNMx1LyiNtgSaXklhpqyNvZc/YKFZZWkByrsAab2eXcHBw0xYb7UmlFFf1+KJmUCsEOAABoaiHBni2CKNincdm7XH4eE+xFZZVVsstjkYIdbmxqLU5OTjUSlZUKwQ4AAGiqy7mlVVi13/zluJpy9ZJzS7qcm2Nyj2aiK/zBizl0Ki2flAjBDgAAqF5RWQVtT9KPxIyMi2jSYw0zTIFx3o5cStC3GErOOzfzt6jLuTn48aSVppfvU+boDoIdAABQPV71uKyiSvR8ah9h3qrJ9enfNoTcXJzoYnYxnc0sJDnYaCg5lwIxa5tiaB/x8/5LVF5ZZZPnsCUEOwAAoKlVk3kqqil8qq3RI4cu6NVLzqWFD61teFw4hfq6U2ZBmaym78yFYAcAAFSNp5o2HJfydZo2hVVnNWUZBDvVS867xwTa5DncXJzpph7NFLvmDoIdAABQtcTkPErLKyVvdxfq19qyVZPrI5Wu70zKopLySlJbybkpUwxVWdxugxOilQTBDgAAqNp6w6rJg2NDxarA1sB5P1EBnmL9mV1ns0kOwc4wK1dh1dY+wo+6xQSKzu8rDlwmJUGwAwAAqiZNYTW1Cqv+EvR0eZSc2yhfp7rbejc3TmXJpRLNHAh2AABAtdLzS+jQJX0wMCzOusGAFOw4Mm+nesl5uJ+nzZ9vYrdo0QyVG6FKx1UJHBrsbNmyhSZOnEjR0dEiSl6xYkWN2zlqfOmllygqKoq8vLxo1KhRdOrUqRr3yc7OpmnTppG/vz8FBgbSjBkzqKBAnt1oAQDAvji/RGp7YO1gYGBsqFhs70xGIV3MLiI1lpzX5u/pRuM6R4rryxWUqOzQYKewsJC6detGCxYsMHn7O++8Q/Pnz6dFixbRrl27yMfHh8aOHUslJSXG+3Cgk5iYSGvXrqU//vhDBFAPPfSQHV8FAADIPV9npJWqsGq/8fdqESSub3LA6I49Ss4bSlT+7VCyw5OzFRHsjBs3jl577TW66aab6tzGozrvv/8+vfjiizRp0iTq2rUrLV26lJKTk40jQMeOHaNVq1bR559/Tv369aNBgwbRhx9+SMuWLRP3AwAA7eI34m2n9cGAtAKwtUl5MpsdsPaMPUrOTenfJoSaBXpRfkkFrU5MJSWQbc7O2bNnKTU1VUxdSQICAkRQs3PnTvE9f+Wpq969exvvw/d3dnYWI0EAAKBdXCVVVFZJEf4eFB/tb5PnkPJ2diRlUmlFpSpLzmtzdnaiWw0rKiulOagryRQHOiwioubQI38v3cZfw8NrRuuurq4UHBxsvI8ppaWl4iLJy8sTX8vLy8VF7aTXqIXX2lQ4VjheOL+U+/9xbWKKsSS7oqLCJvsUG+plXFl4V1KGGPWwdz7S4LbBdY6Jrf92Te4WSR+sP0XbkzLpXEaeGOlxBHNfn2yDHVt68803ae7cuXW2r1mzhry9vUkrOM8JcKxwbjke/i9a/3hxVfTKg7ymjhP555+nlSvPka208XKmzAJnWrJ6D11paZ++UXllRAnJ+rfw8guHaGXqIbufW7H+znQqz5ne/n4TXR/jmDL0oqIiZQc7kZH6bO+0tDRRjSXh77t37268T3p6zfUNOHrnCi3p502ZPXs2PfPMMzVGdmJiYmjMmDGiqkvtOBLm/wCjR48mNzc3R++OrOFY4Xjh/FLm/8eTafmU/fdOUSY987aR5OVuncUETak6nEK7lx+hSxX+NH78ALKHXw4kE+1LoPhoP5o6ub9D/naVN0uhf/54hI4U+ND71w8W01v2Js3MKDbYad26tQhY1q9fbwxu+EVxLs6jjz4qvu/fvz/l5OTQvn37qFevXmLbhg0bqKqqSuT21MfDw0NcauMTQktv/lp7vU2BY4XjhfNLWf8fN5/Wr2o8sF0o+fvYdv2ZYXGR5Ox0hE6mF1BmUQVFBdh+Smdrkv71De8Q0eCxsOXfrhu6NqO5vx+jSzkltO9SHg1oG0r2Zu5rc2iCMq+Hc/DgQXGRkpL5+oULF8S6O7NmzRLVWr/99hsdOXKE7rnnHrEmz+TJk8X9O3bsSNdffz09+OCDtHv3btq+fTs98cQTNHXqVHE/AADQpg2GknNbVWFVF+TjLtoo2Ksqi0vOpcUE7VlyXhuPlk3oFq2IRGWHBjt79+6lHj16iAvjqSW+zgsJsmeffZZmzpwp1s3p06ePCI641NzT82qU/s0331BcXByNHDmSxo8fL8rPP/30U4e9JgAAcKzswjLaf+GK3YIde6+m7KiS84baR/yVkEJ5JfItenHoNNawYcMa7K3BozuvvPKKuNSHK6++/fZbG+0hAAAoDfeqqtIRdYryp2g7VQkN6xBO7687RdtOZVJ5ZRW52bAU3FEl56ZwsNUu3JdOpxfQH4dS6M5+LUiOZLvODgAAQGOsN5Rkj+xon1Ed1qVZAAV5u1F+aQUduJCjii7n5uBBCWl0Z/k++baPQLADAACqwaMqWwzBgL2msBj3yBpinMpKV02Xc3NM7tFMvH4O8k6n55McIdgBAADV2HM2W4yuhHDScHP75rNIeTvSyIsaupybg/djeIdwWScqI9gBAADVTWENjwu3+7ovg2P1wU5ich6l519tWG1NUsNRe3U5N9cUw1TWT/svi9E1uUGwAwAAqrHBEOyMsmO+jiTMz0Pk7rAtJ/UNSK2pskoni5JzU3jKUN82o9QhTVGvBcEOAACowpmMAjqbWUhuLk40yDDKYm+2LEE/ePGKbErOa+Pqs8ndm4nrP+yVX6Iygh0AAFDVqM51bULI18MxK6tIIy5bT2WIkRi1lpybMqV3jPH3wCM8ciK/owUAANAI646l2b0KqzYeceGRl5yicrH4n1pLzk3pEOlH3ZoHUEWVjlYcuExygmAHAAAUj6d39pzTr5o8Mi7CYfvBIy5SorI1c1fkWHLe0OgOT2U1tGiwvSHYAQAAxePEXZ42ig33pRYh3g7dF2MJuhXzdqTE5Pho+ZScmzKxW7ToNH8yrYAOX9IHZ3KAYAcAAFSTrzPCAVVYtUmLCx6+lCP6dFmDFDhJ69nIVYCXG13fOVJ2Kyoj2AEAAEXjLuAbT6Q7fApLEhngSXGRfsSzOJyo3FQ8YiU9jtxKzk2Z0ks/lfXrwWQqKa8kOUCwAwAAinbgYo5ICA70dqOeLeRRki3l1Vgjb+eg4fXJseTclAFtQ6hZoBfll1TQ6sRUkgMEOwAAoGjrj6Ubq5TkUpItrXC85VQGVTWxBJ27uMu55Lw2Xrn6ll7NZdU+Qv5HDQAAoAEbjhtKzjs6fgpL0qtlEPm4u1BmQZloH6HmknNTphiCne1JmXTpShE5GoIdAABQrIvZRaLyh7tuD3XQqsmmuLs604B2oU3ugq6UkvPaYoK9qX+bEJG39NM+x6+5g2AHAAAUa71hIcHeLYMowNuN5ERKJm5K6willJybclsf/ejOj/svNnkqr6kQ7AAAgOK7nI+S0RRW7fV29l/IEYseNqnLuYJGdSTXx0eRn4crXcwupl1ns8mREOwAAIAiFZRW0K4z2bJZX6e25kHe1C7cV5SObz+d2aSSc7mvr2OKl7sLTegWJa4vd3BzUAQ7AACgSNtOZVJZZRW1CvGmNqE+JEfG1ZQNFVVqLjlvqH3EyoQUyi9p3OiWNSDYAQAARefrjIiLICcnJ5JzsMN5O5b2itqssJJzU3rEBFLbMH0gKiVaO4Krw54ZAACgkTjhVVo1eZQMp7AkfVsHk6ebM6XlldLx1HzqGOVv9s9uVGDJeW0chH50Z09qHuRFfp6OSyBXZqgIAACadvhyrljDhhNge7cKJrnydHMRJdiWVmUpteTcFA7wHBnoMAQ7AACgOBsMU1jcdJPXtJGzYYbkYktaRyi55FyO5H2GAAAANFByPiJOvlNYtfN29p7PFhVkai85lyMEOwAAoCgpuSWiBQPnJCshGGgV6kMtQ7ypvFJHO8woQa/Z5Vz+wZwSINgBAABFkUY9erYIohBfD1ICKclY2ndzS865mgmaDsEOAAAoilSlpIQpLImUZMx5O9cqQVdDybnc4CgCAIBilFUS7UjSr5o8UsYl57Vd1yZEJFJfzimmpIxC8/J1FFxyLjcIdgAAQDFO5jlRaUUVNQv0og4RfqQU3u6u1K918DVXU+aS88OX1FFyLicIdgAAQDESrzgZR3XkumqyOasp1wcl57aBYAcAABSBc12kYEdJ+ToSqXKMO4AX83ycCSg5tw0EOwAAoAjHUvMpt8yJvNycRQ6M0rQN8xXTb2UVVfT3maw6t6Pk3HYQ7AAAgCJsPKFfo2Zg2xDRhkFpeNrNWJVlYioLJee2g2AHAAAUYYMhsXdEnHITd6W8HVNJyig5tx0EOwAAIHv6KqW8GgGDEg1oG0Kuzk50LquIzmXWLEFHybntuNrwsQEAAJqUkHwsJV9M+axKSBHbYnx0FO6njFWTTeHu371bBdHfZ7LF6+JWEgwl57aFYAcAAGQjr6Sctp3KFNM8HAyk5ZXWuP268CpSuqHtw43BzvQBrcQ2lJzbFoIdAABw6OjN0ZQ82nQiQ7RS2HfhiqhKkni5uYipHy7bHtg2iI7s3KT43xa/lrdXHaedSVlUUl4pkq1Rcm5bCHYAAMCucotrjt6k59ccvWkb5iO6fXNQ0KdVsLHyqry8nI6o4HcVF+knpuL4de85l00D2oaiy7mNIdgBAADbLwaYnCcCGw5w9l/IqTN6M7BdCA3lAKd9GMUEe6v6NyJK0NuH0fJ9l8RoFreSQJdz20KwAwAAVpdbVE5bT3NwkyGCHE7Ara5duK8IbHgEp0/rIPJwVd66OU3Br5uDHZ6+8nbXv3Z0ObcdBDsAANBkVVVS7k26CHD2X7hC1QZvxBs6T9fw1NRQDYzeXMugdqHk7ER0Or2Aftp/WWxDl3PbQbADAACNklNURltF7o1+9CazoOboTSyP3nTQj95wubXWRm8aEuDtRj1bBNHe81fock6x2IYu57aDYAcAACySnFNMT39/UCTX1h69Gdju6uhN8yBtj95cCx8jDnZYfLQ/hft5OnqXVAvBDgAAWOSXA5dF527WPoJHb/SJxb1bBZO7KxbmNxcft3fXnjRcV+6q0EqAYAcAACzCuTnsn2Pa0xMjYnH0GolHcyL8PcTCiSM7RuA42hCCHQAAK8guLBOr4PKIx6iO4ap+8zqWrA92ujYPdPSuKJqzsxN9Mb0PXcwuEvk7YDsIdgAAmrB2zMbj6aIb98GLOaQz5K9wRZJag52isgo6m6VvYNkxyt/Ru6N4nZsFiAvYFoIdAAAz5ZeU0/bTmbThuL68uvbKv7wy7vHUfErJLaGsglIK8VVuw8r68OvjoC7Mz0NcAJQAwQ4AQAOjN0kZBbTxeIYIcLj6qKJa+RFXH/F6KcPj9K0NogK8aOh/NtL5rCLRrXtQrPqCgaOGKaxOGNUBBUGwAwBQDTdm5AaNG0+ki8vFbP0aKJI2ofq+TSPiTK/8y0GAPtjJo0Gxoao7tvy6WKdoTGGBciDYAQDN4wRRzrPh0ZsdSVlUWlFlPCbuLs7Ur02wCG6GdwinVqE+DR4vzmP5KyHVGBSotRIL+TqgJAh2AEBzyiurxJQU591wgMNL9lcXFeAppqY4uOEGldyo0VzS9I4UFKgJN+88npIvrmMaC5QEwQ4AaEJxWSX9fjhZVE9tO5VJ+aUVxttcnJ2oV4sgfYATF0YdIvxEZ+rG6GiY3uEAqrSiUlUtEs5nFVJxeSV5ujlT62uMcAHICYIdAFC9vJJyuvvzXXToUq5xW4iPu+hFxKM3Q2LDRK8ia4gO8CR/T1fKK6kQAU98tHrKiqXRqg6R/iJABFAKBDsAoGq8Lsz9i/eIQCfI243u7t9K5N90bRYgFnWzNh4R4uTdv89ki8olVQU7qMQChUKwAwCqrqx64Ku9otkij7Z8PaOfXRZw4+RdDna4/FxNUIkFSoWObQCgSpwv88j/9onqKh93F/rq/r52W6n2apLy1WkzNU1jdYryc/SuAKgn2KmsrKR///vf1Lp1a/Ly8qK2bdvSq6++Khb6kvD1l156iaKiosR9Ro0aRadOnXLofgOA46utnvzugKi24mTaL+/tQz3s2HtIKsvmkZ3qf6+UjFeE5oaVnLfNOTsASiLrYOftt9+mhQsX0kcffUTHjh0T37/zzjv04YcfGu/D38+fP58WLVpEu3btIh8fHxo7diyVlJQ4dN8BwHHl0f/44RCtTkwjd1dn+uye3tSvTYhd9yE2wpdcnZ0ot7hctI5QA2lKrlWID/l6IAMClEXWwc6OHTto0qRJdMMNN1CrVq3o1ltvpTFjxtDu3bvF7fyJ6f3336cXX3xR3K9r1660dOlSSk5OphUrVjh69wHAzqqqdPTcT4fpt0PJIthYOK0nDY4Ns/vvgcvN24X71kjqVTppSq4jprBAgWQdng8YMIA+/fRTOnnyJLVv354OHTpE27Zto/fee0/cfvbsWUpNTRVTV5KAgADq168f7dy5k6ZOnWrycUtLS8VFkpen/2NUXl4uLmonvUYtvNamwrFSzvHiDz9z/zhOy/ddIi6yem9KFxrSLthh53mHCF/RNDPhcg4NjQ1W/PmVYCjb7xDu67D9VdLxcjStHKtyM1+frIOd5557TgQicXFx5OLiInJ4Xn/9dZo2bZq4nQMdFhERUePn+HvpNlPefPNNmjt3bp3ta9asIW9vb9KKtWvXOnoXFAPHSt7Hi9Nifj3vTBtTnMmJdHRn2yrSXdhPKy+Q4+RwWbsLbTxwkloXHVf8+bX7FC+O6ESFl0/QypUNvx5bU8Lxkgu1H6uioiLbBDs8IsK5MefPnxdPEhYWRj169BBJxNb2ww8/0DfffEPffvstxcfH08GDB2nWrFkUHR1N06dPb/Tjzp49m5555hnj9xxQxcTEiCkyf3/1J95xJMz/AUaPHk1ubtZZSE2tcKyUcbw+WH+aNqacEddfnRRPt/duTo4WkJRFvy7ZRznkS+PHD1L0+VVaXknP7NrAYSXdPXE4Rfp7OmQ/lHK85EArxyrPMDNjtWBn+/bt9MEHH9Dvv/8uDiJPF3H1U3Z2tgiA2rRpQw899BA98sgj5OdnnbLEf/3rX2J0R5qO6tKliwiyeGSGg53IyEixPS0tTVRjSfj77t271/u4Hh4e4lIbnxBqPim0/nqbAsdKvsfr402n6aNN+kDn5Ymd6K7+1v/g1Rhdmuurvy5cKaLSKqcGk3rlfn4dTysSid+8KGPzYN9Gt9KwFrkfLzlR+7FyM/O1mZWgfOONN9Ltt98ukoR5qic/P5+ysrLo0qVLYnSHS705SXj9+vUit8Zaw2b82M7ONXeRp7OqqvQdiXk0iQMeft7qUR6PPPXv398q+wAA8vXltrP0zqoT4vpz4+Lo3oHyCHRYiK8HRfh7iCm2E6l5qkhO5pWhHR3oADSGWSM7XA31008/1RtB8agOX3i05ejRo5SSkkLWMHHiRJGj06JFCzGNdeDAAZGcfP/994vb+T8dT2u99tprFBsbK4IfXpeHp7kmT55slX0AAHn6dtcFeuWPo+L6UyNj6ZGhbUlueL2dtLwMOpqST71amk5SVlLZOTqdg6qDnYcfftjsB+zUqZO4WAOvp8PBy2OPPUbp6ekiiOF94UUEJc8++ywVFhaKKbScnBwaNGgQrVq1ijw9HTOnDAC29/P+S/TCiiPi+sND29CsUbGyPOwcHPDChkovP5f2X1osEUBpLE5QvnjxohhRad5cnwDIa95wAjEHOBxwWBPn/vA6OnypD+/LK6+8Ii4AoH5/Hk6hfy4/JKaHpvdvSc9dHyfbqZWrKykrN9jhkn70xALNLSp455130saNG8V1Lu/mTG8OeF544QUEHABgU+uOptFTyw5QlY5oap8YmjMxXraBTvVg50RqvkjwVaJLV4opv7SC3F2cqW2YfqFEANUHOwkJCdS3b19jaXjnzp3FSsdcIr5kyRJb7CMAAG05mUGPfbOfKqp0NLl7NL1+Uxdy5tUDZax1qI/ozVVcXknnsgpJiRINU1jcAsPNRdaL7gPUy+Izl8vOpbLtdevWiUotxgv/WSsxGQCgur/PZNFDX++lssoqGtc5kv47pRu5yDzQYbyPUtNMpU5lXe10jnwd0FCww1VR3HRz69atosT8+uuvF9u5H1VIiH2b7QGA+u2/cIVmLNlDJeVVNCIunD6Y2oNcFTTC0MnQS0qpScrI1wE1sPgvBnce/+STT2jYsGF0xx13ULdu3cT23377zTi9BQBgDQmXc2n6l7upsKySBrULpY+n9RSdzJVEGhFR7MgOKrFAS9VYvMAf943iICczM1Ms3hcUpF8hlHEllpb6SgGAbXFS791f7KL8kgrq2yqYPr2nF3m6cX8mZblakaVfq0ZJcovK6XJOsbiOsnNQMrM/IoWGhtKECRNEF3IOdqoHOoxXVw4PD7fFPgKAxpzJKKBpn++iK0Xl1C0mkL64tzd5u8u6b3G94gzBTmpeCWUXlpGSHDOs/Nw8yIsCvNTbcgDUz+xg5/jx4zR27FhRgdWyZUvq16+fWN34yBH9wl4AANZwMbuI7vxsF2UWlIopoKX39SU/T+W+0XJPrJYh3oqcysIUFmgu2OGWDTNnzhQVWNxok9s0cKAzePBg0SqCv9+wYQNVVlbado8BQLVScovpjs/+FqMgseG+9PWMvhTgrdxAR9LRUJGltCRlVGKBWjQq0487nnNy8rJlyygjI0NUZ3GQc99991FYWJhYcwcAwBLp+SU07bNdYhG7ViHe9M0D/UQzTTXgBppKHtmR9h9AqZo8Cc7NQceMGSMu3MuKm3VWVFRYZ+8AQBM4l+Wuz3fRmcxCahboRd88eB2F+6unv52U3CuNlChBWUUVnU4vENexxg5oMtgpKSmhw4cPi+acVVVVxu28bDt3KgcAMFducbmoujqZVkAR/h707YP9RMCjJtLICAcPpRWV5OEq/6qypIwCsYijn4erSFAG0FSwwx3F77nnHlGRVRsHO8jZAQBzlZRX0v1L9oiWBKG+7vTNA9dRyxAf1R3A6ABP8vd0pbySChHwxEcHkGKSk6P9Zd1/DMAmOTucpDxlyhTRGoJHdapfEOgAgLmqqnT09PcHad/5K6Ks+esZ/ahduDobTXKwoLT1dowrJ6NNBGgx2OFKrGeeeYYiIiJss0cAoAlvrTpOfyWkim7an97dS/WL1klTWUqpyEIlFmg62Ln11ltp06ZNttkbANCEr3eeo0+3nBHX/zOlK/Vro/6+eldHduQf7Oh0uqvBDiqxQIs5Ox999JGYxuJGoF26dBHVWNU9+eST1tw/AFCZ9cfSaM5vieL6v8Z2oEndm5EWSNNBHERwMCHnPBhe5yinqJxcnZ1UO7UI2mJxsPPdd9/RmjVryNPTU4zwVP8Py9cR7ABAfY5cyqUnvj1AVTqiqX1i6LFhbTVzsGIjfEXwwNVnKbklFC3jijNpqq1tmK8i+5EBNDnYeeGFF2ju3Ln03HPPkbOzsroPA4DjXLpSRPd/tYeKyytpcGwovTq5s6xHN6yNy805eDiRli+mspQQ7GAKC9TC4milrKyMbr/9dgQ6AGA2Hs3gEvOM/FKKi/Sjj6f1JDcX7X1YUkqSstQAFJVYoBYW/7WZPn06ff/997bZGwBQHV6J99H/7TMuGvjlvX0U3dizKTpG+dUIJuQKDUCBtD6NxWvpvPPOO7R69Wrq2rVrnQTl9957z5r7BwAKxom4s38+QjuSssjH3UUEOnKevrG1TlEBsh/ZKSitoHNZRTWCMwDNBTvc6bxHjx7iekJCQo3btDT/DgDXNn/9afpp/yVycXaij6b1VMTKwbYkBQ/ns4uosLSC3GU4k3fCMOoU6e+pmkasABYHOxs3bsRRA4Br+mnfJZq37qS4/uqkzjS8Q7jmjxoHD+F+HpSeX0rHU/Opa7SvjKewMKoD6mHx54rc3FzKzs6us5235eXJd2gWAOxn55kseu7nw+L6I0Pb0p39WuDw105SluniglhMENTI4mBn6tSptGzZsjrbf/jhB3EbAGhbahHR498dovJKHU3oGkXPju3g6F2SFbmvpHzU0LtLyi8C0GSws2vXLho+fHid7cOGDRO3AYB2cWn5J8ddKL+kgnq3DKL/TulGzs7I5TO5krIMk5QrKqvouCEIwzQWaDrYKS0tpYqKijrby8vLqbi42Fr7BQAKU1RWQQ/97wBllzpRqxBv+vSe3lh9t4GRnROp+VTJS0nLyLmsQiqtqCJvdxdqGeLj6N0BcFyw07dvX/r000/rbF+0aBH16tXLWvsFAArCb9pPfneAEpLzyMdVR5/f3ZOCfdwdvVuy1DrUhzzdnMVK0hey9SXecpFoGG3ihR+5gg5As9VYr732Go0aNYoOHTpEI0eOFNvWr19Pe/bsET2zAEB7a+m88nsirTuWTu6uzvRgXBm1DPF29G7JFgcRHSL96dDFHDpmyI+RC2l/0CYCSOsjOwMHDqSdO3dSTEyMSEr+/fffqV27dnT48GEaPHiwbfYSAGTri21n6aud58X1/97SmVqjYvmaOhnKurn8XI6VWNJUG4BmR3ZY9+7d6ZtvvrH+3gCAoqxKSKHXVx4T158fH0fjOkfSyguO3iv5k4KJo6n5FBdC8msAimAHtDiyU1hYaNGDWnp/AFCeAxeu0FPLDpJOR3T3dS3pwcFtHL1LiiEFE3Ia2UnPL6HMglLiVJ24SIzsgAaDHZ6meuuttyglJaXBefu1a9fSuHHjaP78+dbcRwCQmfNZhfTAV3tF5c6IuHCaM7ET2sVYIM4Q7KTllVJBOckqX6dVqA95ubs4encA7D+NtWnTJnr++efp5Zdfpm7dulHv3r0pOjqaPD096cqVK3T06FGRx+Pq6kqzZ8+mhx9+2Lp7CQCykVNURvct3kNZhWUUH+1PH97Rg1xdZNjkScZ8PVxFEvf5rCK6XCSPqidMYQFpPdjp0KED/fTTT3ThwgVavnw5bd26lXbs2CHW1QkNDRWNQT/77DMxquPigk8EAGpVWlFJDy3dR2cyCyk6wFN0MffxaFTqn+Z1jPQXwU6yTGb9pRWdUYkFamTRX6kWLVrQP/7xD3EBAG2pqtLRv5Yfpt3nssnPw5UW39eXIvw9Hb1bik5SXpWYSpcLZTKyg0osUDGMPQOAWd5de4J+O5RMrs5OtPCuXtQhEjXmTSGNoMhhGqukvJLOZBSI6/GoxAIVQrADANe0bPcFWrAxSVx/8+YuNCg2FEetiaTeU2nFRGUVVQ49nty6gjtXhPq6U5ifh0P3BcAWEOwAQIM2n8ygF1YkiOtPjoylKb1jcMSsoFmgF/l7ulKlzolOG0ZV5DCF5eTk+JEmAGtDsAMADVboPP7NftH76uYezejpUbE4WlbCQQX3oJLDejuoxAK1Q7ADACal5pbQ/Uv2UEFpBV3XJpjeuqUrPvVb2dVgx7EjO6jEArWzONhp1aoVvfLKK6IMHQDUKb+knO5bsodS80qoXbgvfXJXb9HkE2yTtyMFG46qspOeHz2xQK0s/us1a9Ys+vnnn6lNmzY0evRoWrZsGZWWltpm7wDA7sorq+jxbw+IN0BOWF18bx8K8HbDb8IGOlYb2eFV6B3hQnYRFZZVimC2TaiPQ/YBQJbBzsGDB2n37t3UsWNHmjlzJkVFRdETTzxB+/fvt81eAoBd8BvuS78m0JaTGeTp5kxfTO9DMcHeOPo20i7Mh5yddJRTXE4puSUOOc7SqA5PqWElbFCrRo9L9+zZU/TASk5Opjlz5tDnn39Offr0ER3Rv/zyS4d9SgGAxlu4OYm+232RuCBn/tQe1C0mEIfThjzcXCjCsC6jo6aypEosdDoHNWt0sFNeXk4//PAD3XjjjWJFZe6XxQHPLbfcIvpoTZs2zbp7CgA2tf/CFXpn1Qlxfc6ETjQmPhJH3A6ifXSODXaSka8D6mdxUxueqlq8eDF999135OzsTPfccw/NmzeP4uLijPe56aabxCgPACjH1pOZ4uuYThF078DWjt4dzWjuo6N9mVdHWBw2smNY0RlAjSwOdjiI4cTkhQsX0uTJk8nNrW7iYuvWrWnq1KnW2kcAsIOE5FzxtV+bEBxvO4o2pEQdS7H/WjtXCsuMuUJSGTyAGlkc7Jw5c4ZatmzZ4H18fHzE6A8AKEfiZX2w0xmf8O2qmWEa61xWIRWWVti1i7w0ddYi2Jv8PFFxB+plcc7O8OHDKSsrq872nJwcUY4OAMqTXVhGyYZP+JjOsC8/N6JwPw/img57r6SM5GTQCouDnXPnzlFlZWWd7bzWzuXLl621XwBgR4mGKazWoT74hO8AcZG+DklSRr4OaIXZ46W//fab8frq1aspICDA+D0HP+vXrxerKwOA8iRcRpKqI3WM9Kctp7LsnqSMSizQCrODHU5GlprXTZ8+vcZtnKTMgc67775r/T0EALslJ3eOvvohBtQ9slNaUUmn0/U9uTB1CWpndrBTVVVlrLTas2cPhYaG2nK/AMARycnNUH7s0IagKfmiw7yLs5PNn5MDnYoqHQV4uVF0gGFlQwCVsjhn5+zZswh0AFTW9PNcVpG4Ho+RHYfgXCluz1FcXknnswrtPIXlh272oHpmjexwW4iHHnqIPD09xfWGPPnkk9baNwCw45sef7oP9nHHMXcAHsnpEOFHhy7livV22oTpp7XsU4mFqUtQP7OCHV4hmds/cLDD1+vD+TwIdgCUJcEQ7MQ3w5ueI3HeDAc7R1Ny6YauUTZ/Pik/CPk6oAWu5k5dmboOAGpaTBDBjiN1jPK320rK3Ki5+jQWgNo1uhGovfDaPXfddReFhISQl5cXdenShfbu3VvjP+1LL71EUVFR4vZRo0bRqVOnHLrPAEqSaHjTQ3KyPIIdKQixpcs5xZRXUkFuLk4UG45gB9TP4mCHu5q//fbbdba/8847NGXKFLKmK1eu0MCBA0Vp+19//UVHjx4V5e1BQUE1npfziBYtWkS7du0SrSrGjh1LJSX61WABoH7FZZV0Kl0/koDkZHlUZKXmlYieVbYkjR61C/cjd1fZf+YFaDKLz/ItW7bQ+PHj62wfN26cuM2aOKiKiYkRfbb69u0ryt7HjBlDbdu2NY7qvP/++/Tiiy/SpEmTqGvXrrR06VJKTk6mFStWWHVfANToeGoeVemIQn3dKcLfw9G7o2ncm4p7VNljvR1MYYHWWNxxrqCggNzd61Zs8OhLXp51/4Pyqs08SsMjRps3b6ZmzZrRY489Rg8++KAxfyg1NVVMXUl4Zed+/frRzp076+28zq0t+CKR9ru8vFxc1E56jVp4rU2l9mN16OIV8bVTlB9VVFQ0+fHUfrysrfbx4sUFL2QX0ZFLV6hPS9vlUCVcztE/X4Svon5XOL9wrGoz9/y1ONjhnJnvv/9e5MlUt2zZMurUqRNZE3dYX7hwIT3zzDP0/PPPi8UMudqLgy1exZkDHRYREVHj5/h76TZT3nzzTZo7d26d7WvWrCFvb/0nKy1Yu3ato3dBMdR6rFYn8eCuM3kUZdDKlSut9rhqPV62Ih0vlzxeTNCF1u07TpG5R232fPvPuHD9LOWdP0orcxJJaXB+4VhJior0a4RZPdj597//TTfffDMlJSXRiBEjxDbui/Xdd9/R8uXLyZp41ebevXvTG2+8Ib7v0aMHJSQkiPyc2i0rLDF79mwRQFUf2eHpMp4i8/dX/wqyHAnzH4vRo0eLETnQ7rH6bOHf/D+AJg7qTuM6Rzb58dR+vKyt9vFyP5ZOf317kPKd/Wn8+AE2W0Qya+dGcX36pFEU6K2c3xPOLxyr2sydUbI42Jk4caLIh+EA5McffxQVUJwrs27dOho6dChZE1dY1R4t6tixI/3000/iemSk/o9zWlqauK+Ev+/evXu9j+vh4SEutfEfGy39gdba620KNR6rsooqOpmm743UvUWIVV+fGo+XLUnHq0uMvvgiKbOQdE4uNkkePn0p37iIZFiAMkeycX7hWEnM/TtjcbDDbrjhBnGxNa7EOnHiRI1tJ0+epJYtW4rrnLDMAQ+PLEnBDUd5XJX16KOP2nz/AJSMq7DKKqvIz9OVYoK9HL07QETNAr3I39NVlIVz7ypbLPh31ND0FYsJgpY0Kthh+/bto2PHjonr8fHxYorJ2p5++mkaMGCAGEW67bbbaPfu3fTpp5+Ki7Ri86xZs+i1116j2NhYEfzwNFt0dLSxSzsAXGN9negA9EaSCf6bxuvt7DqbLdo52CIgkcrOOxnW9QHQAouDnfT0dFHltGnTJgoMDBTbcnJyaPjw4SJJOSwszGo716dPH/rll19Ejs0rr7wighkuNefWFZJnn32WCgsLRe8u3o9BgwbRqlWrRGsLALj2ysnxNnhDhcaTgh1blZ9LPbGkRQwBtMDiCeGZM2dSfn4+JSYmUnZ2trhw0jBPH9miL9aECRPoyJEjYpFAHkmSys6rfxLiQIirr/g+nDvUvn17q+8HgFp7YnVGTyxZ6WTDlZQrKqvoRJphZAdBLmiIxSM7PGrCAQUnCks4iXjBggWimgkA5K+y6mpvJLSJkBcpCDmWmicWTuUPdNZyJrNQJKb7erhSTJAyk5MB7DKyw+XgprKfeRvfBgDydzazkIrLK8nLzYVah/o6enegmnbhvuTi7EQ5ReWidYQ1SQEut6ZwdrZeEAWgumCH19Z56qmnREuG6s06OZl45MiR1t4/ALCBRENFDne85jdWkA9PNxdqF+Zrk6ksKV8HU1igNRYHOx999JHIz2nVqpXoUcUXThzmbR9++KFt9hIArCrBkJyMfB154iCUWTtJWXo8VGKB1lics8MrDe/fv1/k7Rw/flxs4/yd6v2pAEDeEi5fLTsH+eFKqRUHk41l4tbA+T9XG4CiEgu0pVHr7HDCHC9vzhcAUBZ+05OmseKb4U1PjqRpJmnayRrS80spq7CMeNayQ6R+5AhAK8wKdubPn2/2A9qi/BwArOfSlWKxQq+bixPFhuNNT46kkZdzWYVUWFpBPh6NXv/VSAqc2ob5irwgAC0x63/QvHnzzB7xQbADoIx8Hf50b4veS9B0ob4eFO7nIUZjjqfmU6+W+p5ZTYEpLNAys4Kds2fP2n5PAMAuEgxTWMjXkf/oTnp+hkgqtkqwg0os0LBGf6wrKysTTTorKiqsu0cAYJeeWPFYOVkRU1nWqshCJRZomcXBTlFREc2YMYO8vb1FA9ALFy4Y20i89dZbtthHALBicrI0jYWeWNpJUi4qqxALSTJUYoEWWRzscFPOQ4cOiUag1Zttcun5999/b+39AwAr4hyQzAJ9RU7HSFRiyVknw1o7J1LzRXuPpuC8H52OKMzPQ1wAtMbiFP8VK1aIoOa6666r0bOFR3mSkpKsvX8AYEXSqA63JPByR0WOnLUK8SEPV2cqKquk81mF1MawqnJTkpOxmCBolcUjOxkZGRQeHl5ne2FhoVUb1gGA7fJ1kJwsf64uzqKHFWvq4oLGfB10OgeNsjjY6d27N/3555/G76UA5/PPP6f+/ftbd+8AwKqM+TpITtZUkrKU94N8HdAqi6ex3njjDRo3bhwdPXpUVGJ98MEH4vqOHTto8+bNttlLALBuJRY+4WsmSZnzfY4bRoYwjQVaZfbITkJCgvg6aNAgOnjwoAh0unTpQmvWrBHTWjt37qRevXrZcl8BoAmuFJbR5ZxicR3TGdoZ2eF8n+LySvJ0c6bWoT5W3DsAFY7sdO3alfr06UMPPPAATZ06lT777DPb7hkA2GRUp1WIN/l7uuHoKoCUs5OSWyKC1SAfd4sfQxoV6hDpTy5chgegQWaP7PAUFVdc/eMf/6CoqCi69957aevWrbbdOwCw+srJyNdRDj9PN2oR7N2k0R1UYgFYEOwMHjyYvvzyS0pJSaEPP/xQtJAYOnQotW/fnt5++21KTU3F8QSQMSwmqEwdDevtNDZvB5VYAI2oxvLx8aH77rtPjPScPHmSpkyZQgsWLKAWLVrQjTfeiGMKIFMoO1emTlEBTQp2jD2xDEETgBY1qeVxu3bt6Pnnn6cXX3yR/Pz8apSkA4B85JeUG9sFoBJLmSM7jVlrJ6uglNLySolXCOGcHQCtsrj0XLJlyxYxrfXTTz+Rs7Mz3XbbbaJnFgDIj/RGGR3gSSG+aBegxIqs0+n5VFZRRe6uzhb/3nk1Zl+PRv+5B1A8i87+5ORkWrJkibicPn2aBgwYQPPnzxeBDk9vAYC883U6ReunREA5mgd5kZ+nK+WXVNDp9AKLlg04mpJbY3QIQKvMDnZ4IcF169ZRaGgo3XPPPXT//fdThw4dbLt3AGDVSqzOzTCVoTS8Sj2P7uw+my2SjS0KdtATC8CyYMfNzY1+/PFHmjBhArm4oIEggJJIb3roiaVMnQzBDicb32LBz0nTWFhEErTO7GDnt99+s+2eAIBNlJRX0qn0AnG9M3piKZLU5sGStXb49346o6BGRReAViFjDcDQPyi7sIzS80soI7+U0vNLxddof/emlSzKwPHUfPH6QnzcKcIfyclKTlLmkR2dTmdswNyQU2kF4vce5O2G3ztoHoIdULXiskpjAFM9iKkd1GQVlok3BlOe7Uqq6XRuzpskyE9shK9o9ZBTVE6peSUUFeBldnIyT2Hh9w5ah2AHFKeqSkdXingUpv4AJtPwtaC0wuzH5TggxMeDwvz0l3OZhXQhu4gSryg7QLi6mCCSk5XK082F2ob50Mm0AjGVZU6wY8zXMYwKAWgZgh2QPR62/2TLGfrzcIoIZjILSqminlEYUzxcnSnc34PC/TwpzNdDXDd+9TNs9/MQ0zyuLlcnrf7393l6cUUCHc1R9kRWorESC3kbSsZBCwc7nGw+Ii7C7KR0aQoMQMsQ7IDsLdlxjt7663id7cE+7jWClzBjEFMtqPHzID8P10YN4w+PCxdfz+WTGEkKD1Bep/Dyyio6bviEj5WTlY2DlhUHk81aSZk/IKAnFsBVCHZA1nYmZdFrfx4T1x8f3pbGdIoUQQxPN1mykmxjNAv0ovbhvnQyvYC2nc6im3spb+FMTlItq6wSi9JJ3bNBmTpaUJF16Uox5ZdWkLuLM7UN87XD3gHIm7LH50HVLl0pose/3S8Shyd3j6Z/julA3WICRb6CrQMdydD2oeLr5pOZpOQpLB7VQZKqOoKds1mFVFRWYVaeFic2u1WbmgXQKvwvANlWUT389T5RDs5v1G/e3NUhb9ZSsLPlVGa91Vpyhk7n6iElzut0+uUEzOt0jnwdAIZgB2SH8w1m/3xYvFFzXs4nd/ciL3fHrNrds0Ugeblw9Vc5HbqUQ8otO8ebnhpIwYuUfFwf5OsA1IRgB2Tni21nRSImryvy0Z09qHmQ43JNeAqgQ6B+RGfT8XRSEh6Jkj7ho02EtvJ2UIkFUBOCHZCV7acz6Y2V+oTkF8Z3pAFt9dNIjtTJEOxsPJFBSnJO5HZUkqebM7VBkqoqSN3LGwp2covK6XJOseH+GNEDYAh2QDYuZhfRE9/uJ06NublnM7pvYCuSg46GYOfI5VyxcKHSprB46oNHyUD5pOUDOGeHF9c05ViqPhBqHuRFAV7KWy4BwBYQ7IBsEpIf+nqfyI3p0iyA3ripi2yqh/zdiboYcl42K2h0R0pOjo/GYoJq0SrERyySySN257OLTN4HU1gAdSHYAVkkJD/702ExNB9iSEjm5fHlZGisfjpt44l0xY3sdEZysmrwCt8dIv0aTFJGJRZAXQh2wOE+3XKGfj+UTK7OTvTxtJ4UHXjtvj+OKkHfejJTrEqshAASIzvqrsiqL28HlVgAdSHYAYfacjKD3l6lbwXx0sRO1K9NiCx/Izy1xmXwvCrtvvNXSO54Bd3c4nJyc3Gi9hH6kQBQf0VWWUWVWDWbYY0dgKsQ7IDDnM8qpJnfHRAJybf1bk53X9dStr8NTvAd1j5MMVNZ0srJHOjYa7VpsI9OhiRlabqquqQMQ3sQD1eRoAwAevgrCA5RWFohVkjm0QduAfHKpM6ySUiuzzBDY9CNClhvJ+Ey1tdRqzhDzk5KbgldKSyrcZs02tMR7UEAakCwAw7JJ/nXj4dE+Wyorwd9cpf8EpJNGRIbSlzBfTKtQPTtUsLIDpKT1cfP041igr1MTmVJScuYwgKoCcEO2N3CzUm08kiqyCdZdFdPigzwVMRvIdDbnXq2CBLXN8m8BD1BKjtvhrJzVbeNqB3soCcWgEkIdsCuON/lP6tPiOsv3xhPvVsFK+o3MNwwlbVJxnk76XkllJFfKkahOkZiBV11Jynn1xgxNQY7hrweANBDsAN2czazkJ767oDo2nxH3xia1k++Ccn1Gd5BH+xsP51FJeWVJEcJhimstmG+DmugCvYf2UnNK6GconKxhEO7cF/8CgCqQbADdlFQWkEPLd1LeSUVopM4j+ootTdRhL8HFZdX0q6z2SRHiVJyMqawVD+yczo9X5SbV8/X4SBXCTlwAPaEYAdsjnv4/OOHg3QqvYDC/Txo0V29yMNVmX+MuWJMGt2Ra1WWNLIj9VEC9eGycj9PVyqv1NHp9IKaycn4vQPUgWAHbG7BxtO0OjGN3F2cadHdvSjcXxkJyfUZ1kHeeTtS2Tl6YqkXB921FxeUGoCiEgugLgQ7YFPrj6XRe+tOiuuvTIo3VjMp2aDYUFFJdi6rSOQhyQmvu3I5p1hcxyd8bbWNQANQgPoh2AGb4dVcZy07KBKS77quBU3t20IVR9vXw5X6ttZXkW2Q2VSW1A+rZYg3BXi5OXp3wE5JypwTx8G3lFcGADUh2AGbyC8pFwnJ3EuqT6sgemmCMhOS6zNcplNZxsUEo7G+jtpVn8Y6bhjdifT3pBBfDwfvGYD8INgBmyQkP/39IUrKKBR/fBdM66m6/kxS3s6uM9mi9YXcFhPEFJb6xUb4ip5tV4rKjSOMGNUBME1d70AgCx+sP0XrjqWJAEckJPspOyHZlLZhPtQi2Fs0XdyRlEVykXhZahOBkR214/JyPg/ZLwcui68IcgFMQ7ADVrUmMVUEO+y1yZ2pe0ygKo+wvgQ9TFZ5O5y3ccaQMI2yc21NZXFTUNYpCkEugCkIdsBqeIGzp78/KK5P79+Sbusdo+qjK3VB57wdXqrf0aSqnKgAT9FgFdSvdpk5prEAVBDsvPXWW+IT9axZs4zbSkpK6PHHH6eQkBDy9fWlW265hdLS0hy6n1qUW1xODy7dR4VllaJS6cUJnUjt+rcJIU83Z/Gp+kTa1R5FjpJgmMLCqI72RnaYt7sLtQzRT2sBgEKDnT179tAnn3xCXbt2rbH96aefpt9//52WL19OmzdvpuTkZLr55psdtp/aTUg+KNaciQ7wpI+n9SQ3F8WcWk3KmRjQNlRc33jc8V3QsZigtoOduEg/kbAMAHUp4h2poKCApk2bRp999hkFBV1dlC43N5e++OILeu+992jEiBHUq1cvWrx4Me3YsYP+/vtvh+6zlsxbd1LkrXi4OtMnd/fW1BSKlLcjh9YRxrJzJCdrRpifh7gwJCcD1M+VFICnqW644QYaNWoUvfbaa8bt+/bto/LycrFdEhcXRy1atKCdO3fSddddZ/LxSktLxUWSl6fPdeDH4ovaSa/RGq+V20B8uOG0uP7apE4UF+GtqmN4rWM1sK0++N534Qpl5hU5bCG/0vJK0XuMdQh33O/AmueWFljjeHVt5k/rj2dQ5yg/1R93nF84VrWZe87LPthZtmwZ7d+/X0xj1Zaamkru7u4UGFiz4iciIkLcVp8333yT5s6dW2f7mjVryNvbm7Ri7dq1Tfr55CKieUe4oacTDY2qIvfkg7QyWZ+grKVjFeHlQmnFRB8tX0c9Qh2TqHy+gKiyypV8XHW0f9sGcnJS9rmlNU05XgO9iIJbO5FHyiFaufIQaQHOLxwrSVGRfuVwRQc7Fy9epKeeekqc2J6e1lurZfbs2fTMM8/UGNmJiYmhMWPGkL+/+jtFcyTMx3T06NHk5ubW6BWSJy/8m8qqium61kG0aHovclVhno45x+qw8wn6Yvt5yvWNofHjO5MjfLfnItGRY9SjVSjdcEMvUvK5pSU4XjheOLeaRpqZUXSww9NU6enp1LNnT+O2yspK2rJlC3300Ue0evVqKisro5ycnBqjO1yNFRkZWe/jenh4iEtt/MdZS3+gm/J65/91ki5kF1OzQC9aMK0XeXl6aPZYjewUKYKdracyycXFlZwdkCR6LFW/vk6X5oGyOIe19n+pqXC8cLxwbjWOuX9nZP1RfOTIkXTkyBE6ePCg8dK7d2+RrCxd5xe6fv1648+cOHGCLly4QP3793fovqsZd1deuvOcuP7WLV0034und8tg0Rw0s6CMjhjKv+3/O0FPLAAARY7s+Pn5UefONacFfHx8xJo60vYZM2aIKang4GAxBTVz5kwR6NSXnAxNw4vnzfktgap0ROO7RNLgWH01kpZxW4xB7UJpVWIqbTyRTt3svGp0eWUVHUvVr/ODNXYAABQ2smOOefPm0YQJE8RigkOGDBHTVz///LOjd0u1uAfPnnNXyMvNhV68Qf0LB5preJyhBP2E/dfbOZ1eQGUVVeTn4Sr6dQEAgIJGdkzZtGlTje85cXnBggXiAraVV1JOb6w8Lq7PHNmOogO9cMhrdUE/fCmHMgtK7brWkLRyMq+z4oh8IQAAuVP8yA7Yz7y1J8UbeZtQH3pgUBsc+moi/D3FFBK3yNps59GdxGR9NQIWEwQAMA3BDpjdZHLpzvPi+ss3xos8FahpuGF0h/N2HLFyMvJ1AABMwzsWmJWU/NKvCVRZpaNxnSNpSHskJTeUt7PlZAZVVFbZrS8ZRnYAABqGYAeuacXBaknJGuhm3ljdY4Io0NuN8koq6MDFHLs859msQioqqxTd13l6EQAA6kKwA2YnJT8xop1YRBBM447TQw2jXtwY1R6kUR3ufq3GFawBAKwBfx2hQe+vPUUZ+Yak5MGtcbTMzduxV7BjqMRCvg4AQP0Q7EC9jqfm0VeGlZI5KdnDlZt+QkM4n4mbcB5PzaeU3GKbH6wErJwMAHBNCHag/qTkFYkiKfn6eCQlmyvYx516GFZQ3mTjEnT+HSVcRtk5AMC1INgBk349mEy7z2WLxNd/T0RScmOmsmydt3M5p5hyi8vJzcWJYiN8bfpcAABKhmAH6sgvKafXVx4T12eOiEVSsoWGx+mDne2nM6m0otJmZ5g0qhMb7ocpRgCABiDYgTreX6dPSm6NpORG6RTlT2F+HqIkfM/ZKzZfTLBzM3+bPQcAgBog2IEaTqTm05IdSEpu0n8qZycaZihBt+VqylJPLLSJAABoGIIdqJHw+m/DSsmclCytGQOWG2GYyrJlsCOtsRMfHWCz5wAAUAMEO2D026Fk2n0WScnWMDA2lFydnehMRiGdzyq0+lmWnldC6fmlosy9Y5Sf1R8fAEBNEOyAMSn5tT/1SclPDMdKyU3l7+lGvVsF2WyBQWlUp22YL3m7u1r98QEA1ATBDggfGJKSW4V404ND2uCoWLULeobt8nWikZwMAHAtCHZAJCUvRlKyzfJ2dp7JouIy65ago9M5AID5EOxonFgp2ZCUPDY+goYZRiOg6dqF+4o1isoqqmhHUqZN2kQgORkA4NoQ7GgcJyXvkpKSJ2ClZGtycnKi4XHWL0HPKSqjS1f0fbc6YRoLAOCaEOxoWH5JBb1uSEp+fFg7ah7k7ehdUnEX9AwximbNKawWwd4U4OVmlccEAFAzBDsatmBTkihfRlKy7QxoG0rurs6ij9Xp9AKrPCZWTgYAsAyCHY1KKSJasvOCuD7nxnjydHNx9C6pkpe7C/VvE2LVxqBSTyzk6wAAmAfBjgbxdMqPZ51FUvKYThHGqRawjeEdrJu3czU5GWXnAADmQLCjQX8eSaXTec7k4YqkZHt2Qd977grllZQ36bEKSyvobKZ+RWaM7AAAmAfBjsYUlFbQW6tOiuuPDm1DMcFISra1liE+1CbMhyqqdLT9VNNK0I+l5BHnOUf6e4rO6gAAcG0IdjRm/vpTlJZfSqEeOnpgYEtH744GV1NOt1Knc0xhAQCYC8GOhpxKy6cvt50V129uXUUeSEp2SOuIqqrGl6AnGMrOO6HTOQCA2RDsaGql5EQxlTIqLozig6yz5guYp0/rIPJ2dxH9x46m6AOWxkBPLAAAyyHY0YjfD6eIHk2clPzC+DhH747meLi60MB2oU3qgl5SXmlcq6dzswCr7h8AgJoh2NFIUvLrfx4V1x8fzislezl6lzTdGLSxeTsn0/LFyFywjztFBXhaee8AANQLwY4GfMhJyXml1DLEmx4a0sbRu6NZwwzr7Ry4mEPZhWVNWEzQX/TdAgAA8yDY0UBS8heGpOQ5EzthpWQHigrworhIP1E6vuVkhsU/j07nAACNg2BH5UnJc34zJCV3jKARcRGO3iXNG96EqSypASjKzgEALINgR8X+OJxCO5L0Sck8qgPyydvZfDJDtOswV3lllVhQkHVG2TkAgEUQ7KgUtxV4/c9j4vpjw9phpWSZ6BETSP6erpRTVE4HL14x++eSMgqorKKKfD1cqQVWvQYAsAiCHZWav+EUpeaViDfGh4ciKVkuXF2caUh7Q2PQ4xkWJyd3ivYnZ2ckJwMAWALBjgqdTs+nL7bqk5JfvhFJyWpoHZFo6HSOKSwAAMsh2FF1UnI4kpJlaGiHMOLKcU44TssrMetnEg0jO0hOBgCwHIIdlfnzSAptPy0lJcc7enfAhFBfD+raPFBc33zi2lNZ3EtLGtmJR3IyAIDFEOyoLCn5tT/0ScmPDmuLpGQZG25YYHCDGa0jzmUVUmFZpQhg24b52GHvAADUBcGOCpOSY4K96JGhbR29O2BG3s6205miysqc9XU6RvmLBGcAALAM/nKqxKGLOfSlYaXklyfGY6VkmevSLIBCfd1F37K957PNWjkZ+ToAAI2DYEcFuBP2vYt3U3mljsZ0iqCRHbFSstxx+fjQ9vrRnU3XyNuRkpORrwMA0DgIdhTuck4x3f3FLrpSVE7dYgJp3u3dHb1LYKbhcdfO2+HqOuPIDpKTAQAaBcGOgmUVlIpAJyW3hNqF+9Lie/uQj4ero3cLzDS4XRi5ODuJkbmL2UUm75OcWyJWW3Z1dqL2kb44tgAAjYBgR6E41+PexXvoTEYhNQv0oq9n9KVgH3dH7xZYIMDbjXq1CBLXN9WzwGDCZf2oTvsIP/JwdcHxBQBoBAQ7ClRSXkkPLd1LRy7nigBn6Yy+FBXg5ejdgiZ1QTedt5NoCHbio/1xfAEAGgnBjsJUVFbRU8sOiG7m3BTyq/v6UtswTG8oPW9nR1KmCGJrSzCUnXduFmD3fQMAUAsEOwrCyaov/JJAqxPTyN3VmT67pzd1aY43QSXrEOFHUQGeVFJeRTvPZNXfE6sZRnYAABoLwY6CvLXqOH2/9yJx0+sP7+hB/duGOHqXoImcnJxomGGBwU21qrLS80soLa9U9NHiBQUBAKBxEOwoxKLNSfTJ5jPi+lu3dKWx8ZGO3iWwkhHV8nZ49K72ysltQn3I2x1VdgAAjYVgRwG+33OB3vrruLj+/Pg4uq13jKN3CaxoQNsQcndxpgvZRXQms7BOcjLydQAAmgbBjsytSkil2T8fEde539VDQ9DzSm14baR+bYLF9Y3VprKkkR0sJggA0DQIdmRsx+lMevK7A1SlI7q9dwz93/UdHL1LYCNS3s7GauvtSCsnxyM5GQCgSRDsyNThSzn04NK9VFZZRdfHR9LrN3UWyayg7ryd3WezxYKRuUXldDG7WGyLj0LFHQBAUyDrUbaNPfdQYVklDWwXQh/c0Z1cXRCXqlnrUB9qFeJN57KKaPvpTPIztP2ICfYSKy0DAEDj4R1UZpJziumeL3ZRdmEZdW0eQJ/c3RttArQ2lXU8Hfk6AABWhGBHRjjA4cae3PyxTZgPLbmvr1glGbTWOiJdtAJhqMQCAGg6BDuyauy5m5IyCik6wJP+N6MfGntqTL/WweTl5iIWElx3LE1s64SeWAAA6g523nzzTerTpw/5+flReHg4TZ48mU6cOFHjPiUlJfT4449TSEgI+fr60i233EJpafo3CqUordA39jx8KZeCvN1o6Yx+FB2Ixp5a4+nmInK0WFGZvk8Wys4BAFQe7GzevFkEMn///TetXbuWysvLacyYMVRYeHXhtaeffpp+//13Wr58ubh/cnIy3XzzzaQUlVU6euq7g6Kxp4+7C311f19qF47GnlrP22ER/h4U5ufh0P0BAFADWSeErFq1qsb3S5YsESM8+/btoyFDhlBubi598cUX9O2339KIESPEfRYvXkwdO3YUAdJ1111H8m/seYRWJaaKFXS5sWfX5oGO3i2QQd4Ow6gOAIAGgp3aOLhhwcH61WY56OHRnlGjRhnvExcXRy1atKCdO3fWG+yUlpaKiyQvT79SLT8WX+zlv2tO0bI9+sae703pQn1aBtjl+aXnsOdrVSp7H6twH1dqH+5LJ9MLKC7SV3G/I5xbOF44v+RBK/8Xy818fU666p0HZayqqopuvPFGysnJoW3btoltPKJz33331QhcWN++fWn48OH09ttvm3ysl19+mebOnVtnOz+et7c32cOGZCf69byLuD61TSX1j1DErwHs4ECmE61LdqZ7YyspDKlbAAD1KioqojvvvFMMhvj7+yt/ZIdzdxISEoyBTlPMnj2bnnnmmRojOzExMSIfqKGDZS3L912mX3cmiuv/GhNLDw1uTfaOhDkHavTo0eTmhgXr5HasxhPRC6RMOLdwvHB+yYNW/i/mGWZmrkURwc4TTzxBf/zxB23ZsoWaN29u3B4ZGUllZWVitCcw8GquC1dj8W318fDwEJfa+ISw9UmxOjGVXvxVH+g8PKQNPT6iPTmKPV6vWuBY4Xjh/JIP/H/EsZKY+x4m62osnmHjQOeXX36hDRs2UOvWNUdAevXqJV7o+vXrjdu4NP3ChQvUv39/kpsdSZk009DY87bezem5cXGO3iUAAADVc5X71BXn0fz6669irZ3U1FSxPSAggLy8vMTXGTNmiCkpTlrmKaiZM2eKQEdulVhHLuXSQ0v3UVlFFY3pFEFv3NQFjT0BAAC0HuwsXLhQfB02bFiN7Vxefu+994rr8+bNI2dnZ7GYICcqjx07lj7++GOSk6SMApq+eLdYJbl/mxCaf0cPNPYEAACwE1kHO+YUinl6etKCBQvERa6NPe/+XN/Ys0uzAPr0nl5ipVwAAACwD1nn7KiqsWcoN/bsQ36eSAgGAACwJwQ7NlJYWkH3GRp7RgV40tcP9KMQXyz9DwAAYG8IdmzExdmJIvw9RWPPr2f0pWZo7AkAAOAQss7ZUTLOy/l4Wk+6eKWYWof6OHp3AAAANAsjOzbk6uKMQAcAAMDBEOwAAACAqiHYAQAAAFVDsAMAAACqhmAHAAAAVA3BDgAAAKgagh0AAABQNQQ7AAAAoGoIdgAAAEDVEOwAAACAqiHYAQAAAFVDsAMAAACqhmAHAAAAVA3BDgAAAKiaq6N3QA50Op34mpeXR1pQXl5ORUVF4vW6ubk5endkDccKxwvnl3zg/yOOVW3S+7b0Pl4fBDtElJ+fLw5GTExMgwcLAAAA5Pk+HhAQUO/tTrprhUMaUFVVRcnJyeTn50dOTk6kdhwJc2B38eJF8vf3d/TuyBqOFY4Xzi/5wP9HHKvaOIThQCc6OpqcnevPzMHIDicuOTtT8+bNSWs40EGwg2OFc8vx8H8RxwvnVuM1NKIjQYIyAAAAqBqCHQAAAFA1BDsa5OHhQXPmzBFfAccK5xb+LyoF/nbhWDUWEpQBAABA1TCyAwAAAKqGYAcAAABUDcEOAAAAqBqCHQAAAFA1BDsqs2XLFpo4caJYTZJXg16xYsU1f2bTpk3Us2dPUenQrl07WrJkCWmFpceLjxXfr/YlNTWV1O7NN9+kPn36iJXGw8PDafLkyXTixIlr/tzy5cspLi6OPD09qUuXLrRy5UrSgsYcL/6/V/vc4uOmBQsXLqSuXbsaF1js378//fXXXw3+jFbPLUuP1RINn1cSBDsqU1hYSN26daMFCxaYdf+zZ8/SDTfcQMOHD6eDBw/SrFmz6IEHHqDVq1eTFlh6vCT8ppWSkmK88JuZ2m3evJkef/xx+vvvv2nt2rWiKeOYMWPEMazPjh076I477qAZM2bQgQMHxBs+XxISEkjtGnO8GL95VT+3zp8/T1rAq9i/9dZbtG/fPtq7dy+NGDGCJk2aRImJiSbvr+Vzy9JjpeXzyoh7Y4E68a/3l19+afA+zz77rC4+Pr7Gtttvv103duxYndaYc7w2btwo7nflyhWd1qWnp4tjsXnz5nrvc9ttt+luuOGGGtv69eune/jhh3VaY87xWrx4sS4gIMCu+yVnQUFBus8//9zkbTi3zD9Wi3Fe6TCyo3E7d+6kUaNG1dg2duxYsR3q1717d4qKiqLRo0fT9u3bNXmocnNzxdfg4OB674Pzy7LjxQoKCqhly5aiWe+1Pq2rVWVlJS1btkyMgvEUjSk4t8w/Vkzr5xWCHY3jXJOIiIga2/h77i5cXFzssP2SKw5wFi1aRD/99JO48B+OYcOG0f79+0lLqqqqxJTnwIEDqXPnzhafX1rIcWrM8erQoQN9+eWX9Ouvv9L//vc/8XMDBgygS5cukRYcOXKEfH19Rf7gI488Qr/88gt16tTJ5H21fm5Zcqw6aPy8Yuh6DmAB/qPBFwn/wUhKSqJ58+bR119/rZljybkonBuxbds2R++Kqo4XfzKv/umcz6+OHTvSJ598Qq+++iqpHf/f4txBHgX78ccfafr06SL3qb43cS2z5Fj11/h5xRDsaFxkZCSlpaXV2MbfczKbl5eXw/ZLSfr27aupN/0nnniC/vjjD1HJxomSjTm/eLtWWHK8anNzc6MePXrQ6dOnSQvc3d1FRSjr1asX7dmzhz744APxplyb1s8tS46V1s8rhmksjeNof/369TW2ceVIQ3O/UBN/uuLpLbXjHG5+4+bh8g0bNlDr1q2v+TNaPr8ac7xM5WPwdIUWzi9TeLqltLTU5G1aPrcsPVa1afK8skECPThQfn6+7sCBA+LCv9733ntPXD9//ry4/bnnntPdfffdxvufOXNG5+3trfvXv/6lO3bsmG7BggU6FxcX3apVq3RaYOnxmjdvnm7FihW6U6dO6Y4cOaJ76qmndM7Ozrp169bp1O7RRx8VlUKbNm3SpaSkGC9FRUXG+/Cx4mMm2b59u87V1VX33//+V5xfc+bM0bm5uYljp3aNOV5z587VrV69WpeUlKTbt2+fburUqTpPT09dYmKiTu34OHCl2tmzZ3WHDx8W3zs5OenWrFkjbse51fhjNVfD55UEwY7KSKXRtS/Tp08Xt/PXoUOH1vmZ7t2769zd3XVt2rQRZYpaYenxevvtt3Vt27YVfyiCg4N1w4YN023YsEGnBaaOE1+qny98rKRjJ/nhhx907du3F+cXL3Pw559/6rSgMcdr1qxZuhYtWohjFRERoRs/frxu//79Oi24//77dS1bthSvPSwsTDdy5EjjmzfDudX4YzVLw+eVxIn/cfToEgAAAICtIGcHAAAAVA3BDgAAAKgagh0AAABQNQQ7AAAAoGoIdgAAAEDVEOwAAACAqiHYAQAAAFVDsAMAAACqhmAHAGTn3nvvpcmTJzvs+e+++2564403zLrv1KlT6d1337X5PgFA42EFZQCwKycnpwZvnzNnDj399NOikWZgYCDZ26FDh2jEiBF0/vx58vX1veb9ExISaMiQIXT27FkKCAiwyz4CgGUQ7ACAXaWmphqvf//99/TSSy/RiRMnjNs4wDAnyLCVBx54gFxdXWnRokVm/0yfPn3EaNTjjz9u030DgMbBNBYA2FVkZKTxwiMhPNJTfRsHOrWnsYYNG0YzZ86kWbNmUVBQEEVERNBnn31GhYWFdN9995Gfnx+1a9eO/vrrrzqjLuPGjROPyT/D01OZmZn17ltlZSX9+OOPNHHixBrbP/74Y4qNjSVPT0/xOLfeemuN2/n+y5Yts9oxAgDrQrADAIrw1VdfUWhoKO3evVsEPo8++ihNmTKFBgwYQPv376cxY8aIYKaoqEjcPycnR0xH9ejRg/bu3UurVq2itLQ0uu222+p9jsOHD1Nubi717t3buI1/9sknn6RXXnlFjEDx4/C0VXV9+/YV+1VaWmrDIwAAjYVgBwAUoVu3bvTiiy+KEZbZs2eLURYOfh588EGxjafDsrKyRMDCPvroIxHocKJxXFycuP7ll1/Sxo0b6eTJkyafg/N0XFxcKDw83LjtwoUL5OPjQxMmTKCWLVuKx+Hgp7ro6GgqKyurMUUHAPKBYAcAFKFr167G6xyQhISEUJcuXYzbeHqJpaenGxONObCRcoD4wkEPS0pKMvkcxcXF5OHhUSOJevTo0SLIadOmjRg5+uabb4yjRxIvLy/xtfZ2AJAHBDsAoAhubm41vueApPo2KUCpqqoSXwsKCkQuzcGDB2tcTp06VWcaSsIjRRyw8CiNhPOBeJrsu+++o6ioKDGCxKNMPE0myc7OFl/DwsKs/KoBwBoQ7ACAKvXs2ZMSExOpVatWInm5+oWnpUzp3r27+Hr06NEa27k6a9SoUfTOO++IabJz587Rhg0baiRCN2/eXARLACA/CHYAQJW4DJxHXO644w7as2ePmLpavXq1qN7iqitTeGSGg6Rt27YZt/3xxx80f/58MSrEOT1Lly4Vo0cdOnQw3mfr1q0iQRoA5AnBDgCoEicNb9++XQQ2HIhwfg+XrvNChc7Ozg2us8N5ORK+/88//ywquzp27CjW3+Eprfj4eHF7SUkJrVixQiRKA4A8YVFBAIBaSco8asMLHvbv3/+ax2bhwoX0yy+/0Jo1a3AcAWQKIzsAALUqq3iqqqHFB6vjJOkPP/wQxxBAxjCyAwAAAKqGkR0AAABQNQQ7AAAAoGoIdgAAAEDVEOwAAACAqiHYAQAAAFVDsAMAAACqhmAHAAAAVA3BDgAAAKgagh0AAAAgNft/wS3b3AvTpksAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Time  Distance    Velocity  Velocity_Time\n",
      "0   0.666117     291.0         NaN            NaN\n",
      "1   0.837830     288.0   17.471025       0.751974\n",
      "2   1.030023     283.0   26.015511       0.933927\n",
      "3   1.207749     276.0   39.386623       1.118886\n",
      "4   1.390952     267.0   49.125710       1.299350\n",
      "5   1.591268     257.0   49.921077       1.491110\n",
      "6   1.790691     253.0   20.057907       1.690980\n",
      "7   1.991666     240.0   64.684758       1.891178\n",
      "8   2.179857     226.0   74.392436       2.085761\n",
      "9   2.338029     213.0   82.188818       2.258943\n",
      "10  2.515954     198.0   84.305133       2.426992\n",
      "11  2.714974     190.0   40.196935       2.615464\n",
      "12  2.902802     174.0   85.184397       2.808888\n",
      "13  3.096991     157.0   87.543546       2.999897\n",
      "14  3.247902     140.0  112.649286       3.172447\n",
      "15  3.460058     122.0   84.843396       3.353980\n",
      "16  3.627290     103.0  113.614594       3.543674\n",
      "17  3.832908      84.0   92.404083       3.730099\n"
     ]
    }
   ],
   "execution_count": 40
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Tips to Consider:**\n",
    "\n",
    "**Continuous Measurement** involves data that can be taken at any point in time, with no gaps. For example, a car’s speedometer provides a continuous record of the car’s speed.\n",
    "\n",
    "**Discrete Measurement**, on the other hand, collects data at specific intervals. For instance, KITT’s ultrasonic sensors take distance readings every 70 ms. In between these measurements, we don’t know the exact position of the car. Discrete data can still be useful, but it may miss details about rapid changes in speed or acceleration that occur between measurements. In order to interpret it correctly, you may need to filter or interpolate the data.\n",
    "\n",
    "The following shows the difference between continuous and discrete data:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-26T13:55:32.596911Z",
     "start_time": "2025-11-26T13:55:31.928370Z"
    }
   },
   "source": [
    "# Define time for continuous measurement (smooth, no gaps)\n",
    "time_continuous = np.linspace(0, 10, 1000)  # Time from 0 to 10 seconds, 1000 data points\n",
    "# Define time for discrete measurement (specific intervals)\n",
    "time_discrete = np.linspace(0, 10, 20)  # Time from 0 to 10 seconds, 20 data points\n",
    "# Simulate continuous speed (sinusoidal speed pattern for illustration)\n",
    "speed_continuous = 10 * np.sin(0.5 * np.pi * time_continuous)  # Continuous speed\n",
    "# Simulate discrete speed (sampled at specific intervals)\n",
    "speed_discrete = 10 * np.sin(0.5 * np.pi * time_discrete)  # Discrete speed\n",
    "# Plotting both continuous and discrete measurements\n",
    "plt.figure(figsize=(7, 4))\n",
    "plt.plot(time_continuous, speed_continuous, label=\"Continuous Measurement\", color=\"blue\")\n",
    "plt.scatter(time_discrete, speed_discrete, label=\"Discrete Measurement\", color=\"red\", zorder=5)\n",
    "plt.xlabel('Time (s)')\n",
    "plt.ylabel('Speed (cm/s)')\n",
    "plt.title('Continuous vs Discrete Measurement of Speed')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 38
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5: Implementing the Distance Sensor Reading in Your KITT Class\n",
    "\n",
    "In the previous module, you have created a class for KITT. Add a method to read the distance sensors to your `KITT` class in your 'Student Code' files. You can use the code you have written in the previous steps to do this. Make sure to test your code. It is advisable to store all the old distance data in a list inside the `KITT` class. This will be convenient during the final challenge, where the route planning might need old measurements to determine the position of objects.\n",
    "\n",
    "**Student Task:**\n",
    "\n",
    "- Add a method `read_distance_sensors()` to your `KITT` class.\n",
    "- The method should send the status command to KITT and extract the distance measurements.\n",
    "- Store the readings along with timestamps in an internal list or data structure.\n",
    "\n",
    "### Step 6: Mid-term Assessment 2.1 and Report\n",
    "\n",
    "After you finish this assignment, and ultimately in week 4, showcase the functionality of your script to your assigned TA. After you pass this assessment, you are ready to document your results in your midterm report. For this Module, you would include a chapter that covers the above tasks (using independently-readable text, i.e., don’t refer to “Step 1”).\n",
    "\n",
    "Include plots; for each plot, it should be clear how the plot was made (i.e., the corresponding experimental setup), and you have to describe what is seen in the plot before you discuss results and derive conclusions. Review the guidelines in Chapter 7 for more information. Include the corresponding code in an appendix.\n",
    "\n",
    "Remember to document your code, using comments to define input/output variables of functions and to explain the logic and any modifications made. Your completed script will be crucial for the upcoming challenges, contributing to the overall autonomous driving system."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Microphones\n",
    "\n",
    "The field is equipped with four microphones at its corners, and a fifth microphone positioned at a higher level between two of the edge microphones. These microphones, together with the beacon mounted on KITT, will be used to locate KITT within the field (more details in Chapter 5).\n",
    "\n",
    "<img src=\"pictures/axisdef.png\" alt=\"Microphones Axis Defenition\" width=\"400\" height=\"240\">\n",
    "\n",
    "To use the microphone array, you must ensure that the correct sound card driver is installed. The sound card used in this project is the **Scarlett 18i20 3rd Gen**. Below are instructions on how to configure Sounddevice and the necessary drivers on different platforms.\n",
    "\n",
    "### Simulator\n",
    "For the Sounddevice package another simulator has been made. The simulator will return a realistic audio recording, and change the recordings according to the location of the car. But, it contains only 1 recording, so it will not appear as random as the real car. It also does not adjust to your particular beacon settings. Make sure to test on the real car frequently. Use it in combination with the serial simulator to change locations and test like you would on the real car.\n",
    "\n",
    "### Important: Lab rules for the microphone array\n",
    "\n",
    "When working with the microphone array, please follow these rules to ensure smooth operations and avoid disrupting other groups:\n",
    "\n",
    "1. **Do not rearrange the microphone connectors**. The setup is shared between multiple groups, and changing the connections may lead to incorrect results for other teams.\n",
    "2. **Do not touch the volume settings**. If the volume needs adjustment, contact a TA for assistance.\n",
    "3. **Handle the equipment carefully**. The microphone array and associated hardware are sensitive, and mishandling could cause damage.\n",
    "4. **Start and stop on time**. The lab is shared, and other groups have scheduled time slots. Be respectful of their time.\n",
    "\n",
    "Test time is limited. But by using the simulator and preparing a plan of what you want to test during each scheduled slot, there should be enough time to complete the tasks. \n",
    "\n",
    "### Step 1: Initializing the microphone array\n",
    "\n",
    "Before using the microphone array, it must first be initialized. As part of the initialization process, you will need to specify the sampling frequency (`Fs`) that will be used to record the audio. The sampling frequency will vary based on the test field you are working with, and it will be **48 kHz** or **44.1 kHz**.\n",
    "\n",
    "A typical laptop or PC may have multiple audio devices (e.g., built-in microphones, Bluetooth headsets, external sound cards). To ensure that the correct device is used, you can list all available audio devices using Sounddevice and select the appropriate one. Use the following code snippet to list all audio devices recognized by Sounddevice and find the index of the Scarlett 18i20 or any other relevant device:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-28T08:43:48.894383Z",
     "start_time": "2025-11-28T08:43:48.886035Z"
    }
   },
   "source": [
    "for i, device in enumerate(sounddevice.query_devices()):\n",
    "    print(i, device['name'])\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 HDA Intel PCH: ALC3204 Analog (hw:0,0)\n",
      "1 HDA Intel PCH: HDMI 0 (hw:0,3)\n",
      "2 HDA Intel PCH: HDMI 1 (hw:0,7)\n",
      "3 HDA Intel PCH: HDMI 2 (hw:0,8)\n",
      "4 HDA Intel PCH: HDMI 3 (hw:0,9)\n",
      "5 Scarlett 18i20 USB: Audio (hw:1,0)\n",
      "6 sysdefault\n",
      "7 front\n",
      "8 surround40\n",
      "9 surround51\n",
      "10 surround71\n",
      "11 hdmi\n",
      "12 pipewire\n",
      "13 dmix\n",
      "14 default\n",
      "15 Scarlett 18i20 3rd Gen Direct Scarlett 18i20 USB\n",
      "16 Built-in Audio Analog Stereo\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have identified the index of the microphone array device from the list, you can initialize it by specifying the device index (`device_index`) and the desired sampling frequency (`Fs`).\n",
    "\n",
    "Here’s how you can open the audio stream using Sounddevice:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-28T08:44:01.560544Z",
     "start_time": "2025-11-28T08:44:01.200051Z"
    }
   },
   "source": [
    "import sounddevice as sd\n",
    "\n",
    "device_index = 15      # your input device index\n",
    "Fs = 48000            # sample rate\n",
    "\n",
    "# Open 5‑channel float32 input stream on that device\n",
    "stream = sd.InputStream(\n",
    "    device=device_index,   # or device=(device_index, None) for in/out\n",
    "    channels=5,\n",
    "    samplerate=Fs,\n",
    "    dtype='float32',       # 16‑bit would be 'int16'\n",
    "    # or True, depending on how you use it\n",
    ")\n",
    "\n",
    "stream.start()\n"
   ],
   "outputs": [],
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Recording audio data\n",
    "\n",
    "To make a recording with the microphone array, you must specify the **length of the recording** as the number of **audio frames** to capture. Each audio frame consists of samples from all 5 microphones. Given that we are using 16-bit audio (2 bytes per sample), each frame will contain **10 bytes** (5 microphones × 2 bytes per sample).\n",
    "\n",
    "Thus, recording **N frames** will produce **10N bytes** of data. Note: The simulator returns a fixed length recording at 44.1 kHz. The real car will return a recording of the length you specify.\n",
    "\n",
    "The following command records `N` frames from the microphone array and stores the result as a numpy array:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-28T08:55:26.343774Z",
     "start_time": "2025-11-28T08:55:21.422119Z"
    }
   },
   "source": [
    "Fs = 48000  # Sampling frequency\n",
    "N = 5*Fs  # 2 seconds of audio data\n",
    "samples,_ = stream.read(N)\n",
    "samples_reshaped = np.array(samples)\n",
    "print(samples_reshaped.shape)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(240000, 5)\n"
     ]
    }
   ],
   "execution_count": 22
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, the microphone data is **interleaved**. This means that the first value (`data[0]`) corresponds to the first sample of microphone 0, the second value (`data[1]`) corresponds to the first sample of microphone 1, and so on. For example, `data[5]` contains the second sample of microphone 0, and the pattern continues. To visualize the interleaving of the data, refer to the table below:\n",
    "\n",
    "| data[0] | data[1] | data[2] | data[3] | data[4] | data[5] | data[6] | data[7] | ... |\n",
    "|---------|---------|---------|---------|---------|---------|---------|---------|-----|\n",
    "| mic 0   | mic 1   | mic 2   | mic 3   | mic 4   | mic 0   | mic 1   | mic 2   | ... |\n",
    "| frame 0 | frame 0 | frame 0 | frame 0 | frame 0 | frame 1 | frame 1 | frame 1 | ... |\n",
    "\n",
    "#### Deinterleaving the data\n",
    "\n",
    "To work with the data from each microphone independently, the **interleaved data** must be split into separate streams for each microphone. This process is called **deinterleaving**.\n",
    "\n",
    "Write a function to deinterleave the audio data and store the samples from each microphone in a separate numpy array:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-28T08:48:59.901746Z",
     "start_time": "2025-11-28T08:48:59.896773Z"
    }
   },
   "source": [
    "### Student Version ###\n",
    "# TODO: Reshape the data into a matrix with 5 columns (one for each microphone)"
   ],
   "outputs": [],
   "execution_count": 11
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the audio data\n",
    "\n",
    "Once you've extracted the audio data for each microphone, you can plot it using Python. **Matplotlib** is a commonly used module for creating plots. Plot the audio data from each microphone to visualize the sound captured by the microphone array:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-28T08:55:31.398190Z",
     "start_time": "2025-11-28T08:55:30.120422Z"
    }
   },
   "source": [
    "### Student Version ###\n",
    "fig, ax = plt.subplots(5,1,figsize=(20,30))\n",
    "\n",
    "t = np.arange(0,5,1/Fs)\n",
    "ax[0].plot(t,samples_reshaped[:,0])\n",
    "ax[1].plot(t,samples_reshaped[:,1])\n",
    "ax[2].plot(t,samples_reshaped[:,2])\n",
    "ax[3].plot(t,samples_reshaped[:,3])\n",
    "ax[4].plot(t,samples_reshaped[:,4])\n",
    "fig.show()\n",
    "\n",
    "# TODO: Plot the data for each microphone"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5220/3214262070.py:10: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x3000 with 5 Axes>"
      ],
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     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 23
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Testing the microphone array\n",
    "Below are some experiments you can perform to test the microphone array, and develop the code.\n",
    "\n",
    "**Part 1: Clapping**: With the real microphones connected, have your teammate clap in front of each microphone in turn. Record the audio data and plot the results. Is the order of the microphones what you expected? How does the sound intensity change as you move from one microphone to another?\n",
    "\n",
    "**Part 2: Beacon detection**: Turn on KITT's beacon and record the results. Can you identify where KITT is located just by observing the shift in the recordings? Change the beacons parameters and see how it affects the recordings.\n",
    "\n",
    "**Part 3: Ideal OOK signal**: Compare the waveform of the recording to an ideal OOK of your code.  What can you see, and what do you infer from this? Are some beacon signals better than others? How can you find a good beacon signal? (This point is revisited in Module 3.)\n",
    "\n",
    "**Part 4: Reference recording**: Make some recordings of the beacon at different locations. These recordings will be useful to your teammates working on the localization algorithm. Similarly, make a recording of a single pulse from the beacon close to one of the microphones. Cut out the pulse and save it separately.\n",
    "\n",
    "**Part 5: KITT class**: Add a method to read the microphones to your KITT class in 'Student Code' files. The method should make a stream, turn on the beacon, start the recording, stop the recording, and turn off the beacon. You can choose to return the recording as a result, or store it internally inside the KITT class. Make sure to test your code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Bonus Tasks - Optional*\n",
    "\n",
    "- See if you can automate selecting the correct sounddevice device index. The correct device index changes from one computer to another and can sometimes even change on the same computer after a reboot. So, it is worth your time to make a program that can automatically select the right device index.\n",
    "- Implement start-up sanity checks: some process which you can run after you arrive at the test field, so that you can quickly check the microphone connections and audio levels.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mid-term assessment 2.2 and report\n",
    "\n",
    "After you finish this assignment, and ultimo in week 4, showcase the functionality of your script to your\n",
    "assigned TA. After you pass this assessment, you are ready to document your results in your midterm\n",
    "report.\n",
    "\n",
    "For this Module, you would include a chapter that covers the above tasks (using independently-readable\n",
    "text, i.e., don’t refer to “Task 1”). Include plots; for each plot it should be clear how the plot was made\n",
    "(i.e., the corresponding experimental set-up), and you have to describe what is seen in the plot before\n",
    "you discuss results and derive any conclusions. Be sure to answer the questions posed along with the\n",
    "plots (using independently-readable text).\n",
    "\n",
    "Include the corresponding code in an Appendix. Remember to document your code, using comments\n",
    "to define input/output variables of functions and to explain the logic and any modifications made. Your\n",
    "completed script will be crucial for the upcoming challenges, contributing to the overall autonomous\n",
    "driving system.\n",
    "\n",
    "This concludes the mid-term assignments related to communication with KITT. After the mid-term, you\n",
    "must integrate this module with the localization module created by your colleagues. Take into account\n",
    "that integrating is often harder than originally anticipated, e.g. your code has to run in parallel, and you\n",
    "have to worry about timing aspects. Hopefully, using the KITT class will provide you with a sturdy and\n",
    "flexible framework to continue your work towards the final challenge\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FAQ\n",
    "\n",
    "**What is the beam angle ?**\n",
    "\n",
    "The beam angle of a sensor refers to how wide the sensor's detection area is. It determines how much space the sensor can cover when it sends out signals (like sound or light) to detect objects.\n",
    "\n",
    "To determine the beam angle of ultrasonic sensors mounted in front of the car, you have multiple options: \n",
    "\n",
    "1. **Check the sensor datasheet**: The easiest way or at least a way to get some idea to determine the beam angle is to refer to the manufacturer's datasheet for your specific ultrasonic sensor. The datasheet will typically provide the beam angle, often around 15 to 30 degrees for common ultrasonic sensors. But keep in mind that is for a single sensor and not the current set up! Also, the 'reach' of the sensor is angle dependent: straight ahead, it can see several meters, but at an angle, perhaps just half a meter.  \n",
    "\n",
    "2. **Experimental Determination for KITT**:\n",
    "   - **Measure detection width**: Place a flat object (like a wall) at a fixed distance in front of the sensor (e.g., 1 meter).\n",
    "   - **Move the object**: Move the object left and right to determine the points where the sensor stops detecting the object.\n",
    "   - **Calculate the angle**: Measure the distance between these two points (detection width) and the distance from the sensor to the object. You may use the following formula:\n",
    "\n",
    "   \n",
    "     $$\n",
    "     \\text{Beam Angle} = 2 \\times \\arctan\\left(\\frac{\\text{Detection Width}/2}{\\text{Distance to Object}}\\right)$$\n",
    "     \n",
    "   - This calculation will give you the beam angle in degrees.\n"
   ]
  },
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   "metadata": {},
   "source": [
    "**I see random numbers from sensors for large distances. Is my sensor damaged ?**\n",
    "\n",
    "During experiments, you may occasionally receive random or unexpected data from the sensors. This can occur not only when the sensors are operating outside their effective range but also at times when they are within range. Several factors (consider what they might be?) can cause ultrasonic sensors to produce inaccurate readings. Additionally, since there are two sensors—one on the left and one on the right—they might produce different, completly different readings.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Are the ultrasonic sensor measurements for the left and the right side done at exactly the same time ?**\n",
    "\n",
    "If you closely observe the blinking of the small LEDs on the ultrasonic board on the car, you might notice that they turn on and off alternatingly. This indicates a slight time difference in the sensor measurements. This delay is also noticeable and can be measured using a moving car. (The reason to operate the two sensors alternatingly is that otherwise they might interfere on each other.)"
   ]
  }
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