This program will detect these events during circuits at YSCN: take-off, landing, touch-and-go

detect_circuits_patterns_v1.py

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import sys

def analyze_flight_data(file_path):
    # Load the GPS data
    gps_data = pd.read_csv(file_path)
    gps_data['datetime'] = pd.to_datetime(gps_data['datetime'])

    # Define criteria for touch-and-go, full-stop landing, and take-off
    touch_and_go_speed_threshold = 60
    low_speed_threshold = 45
    full_stop_threshold = 30
    full_stop_intervals = 30  # seconds

    # Define airport elevation
    airport_elevation_m = 57

    # Calculate the rolling minimum speed over a 10-second window
    window_size = 10  # seconds
    gps_data['rolling_min_speed'] = gps_data['speed (kn)'].rolling(window=window_size, min_periods=1).min()

    # Identify touch-and-go and full-stop landing points
    events = []
    for i in range(window_size, len(gps_data) - window_size):
        if gps_data['speed (kn)'][i] < touch_and_go_speed_threshold and gps_data['speed (kn)'][i - 1] >= touch_and_go_speed_threshold:
            idx = gps_data[gps_data['datetime'] == gps_data['datetime'][i]].index[0]
            if gps_data['rolling_min_speed'][i - window_size:i + window_size].iloc[-1] < gps_data['speed (kn)'][i]:
                if gps_data['speed (kn)'][idx:idx + full_stop_intervals].min() < full_stop_threshold:
                    events.append((gps_data['datetime'][i], "Full-Stop Landing", "black"))
                else:
                    events.append((gps_data['datetime'][i], "Touch-and-Go", "red"))

    # Identify initial take-off as the first intersection of speed with the low-speed threshold (45 knots)
    initial_take_off_time = None
    for i in range(1, len(gps_data)):
        if gps_data['speed (kn)'][i] >= low_speed_threshold and gps_data['speed (kn)'][i - 1] < low_speed_threshold:
            initial_take_off_time = gps_data['datetime'][i]
            break

    # Add the initial take-off event
    if initial_take_off_time:
        events.append((initial_take_off_time, "Initial Take-Off", "yellow"))

    # Sort the list by timestamps
    events.sort()

    # Format timestamps as HH:MM:SS
    formatted_timestamps = [
        (timestamp.strftime("%H:%M:%S"), event, color)
        for timestamp, event, color in events
    ]

    # Plot the speed and altitude data with vertical lines and circle markings, and mark the initial take-off
    fig, ax1 = plt.subplots(figsize=(12, 6))

    # Plot speed
    ax1.set_xlabel('Time')
    ax1.set_ylabel('Speed (knots)', color='tab:blue')
    ax1.plot(gps_data['datetime'], gps_data['speed (kn)'], label='Speed (kn)', color='tab:blue')
    ax1.axhline(y=touch_and_go_speed_threshold, color='r', linestyle='--', label='Touch-and-go speed threshold (60 knots)')
    ax1.axhline(y=low_speed_threshold, color='orange', linestyle='--', label='Low speed threshold (45 knots)')
    ax1.tick_params(axis='y', labelcolor='tab:blue')

    # Add event circles
    for timestamp, event, color in events:
        ax1.plot(timestamp, gps_data[gps_data['datetime'] == timestamp]['speed (kn)'].values[0], 'o', color=color)

    # Create a second y-axis for altitude
    ax2 = ax1.twinx()
    ax2.set_ylabel('Altitude (m)', color='tab:green')
    ax2.plot(gps_data['datetime'], gps_data['alt (m)'], label='Altitude (m)', color='tab:green')
    ax2.axhline(y=airport_elevation_m, color='purple', linestyle='--', label='Airport elevation')
    ax2.tick_params(axis='y', labelcolor='tab:green')

    # Set x-axis major ticks to 5-minute intervals
    ax1.xaxis.set_major_locator(mdates.MinuteLocator(interval=5))
    ax1.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S'))

    # Rotate x-axis labels for better readability
    plt.setp(ax1.xaxis.get_majorticklabels(), rotation=45)

    # Add text box with the list of events and their colors at the bottom left
    events_text = "\n".join([f"{timestamp} - {event} ({color})" for timestamp, event, color in formatted_timestamps])
    plt.gcf().text(0.08, 0.3, events_text, fontsize=10, bbox=dict(facecolor='white', alpha=0.5))

    # Add legends
    fig.tight_layout()
    fig.legend(loc='upper left', bbox_to_anchor=(0.1, 0.9))

    plt.title('Speed and Altitude Over Time')
    plt.grid(True)
    plt.show()

    return events

if __name__ == "__main__":
    if len(sys.argv) != 2:
        print("Usage: python script_name.py <input_csv_file>")
        sys.exit(1)

    file_path = sys.argv[1]
    events = analyze_flight_data(file_path)
    for timestamp, event in events:
        print(f"{timestamp} - {event}")