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sukhoi.py
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sukhoi.py
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import os
import pandas as pd
import numpy as np
from scipy.integrate import cumtrapz
from scipy.signal import butter, sosfiltfilt
import matplotlib.pyplot as plt
import glob
import time
BASE_FOLDER_PATH = r'ur-path'
ACCEL_FOLDER_PATH = os.path.join(BASE_FOLDER_PATH, '1. Accelerations')
VEL_FOLDER_PATH = os.path.join(BASE_FOLDER_PATH, '2. Velocities')
POS_FOLDER_PATH = os.path.join(BASE_FOLDER_PATH, '3. Positions')
for folder_path in [VEL_FOLDER_PATH, POS_FOLDER_PATH]:
if not os.path.exists(folder_path):
os.makedirs(folder_path)
# i used sos (second-order-sections) cuz it provides numerical stability
# Butterworth filter functions
def butter_lowpass(cutoff, fs, order=5):
nyq = 0.5 * fs
normal_cutoff = cutoff / nyq
sos = butter(order, normal_cutoff, btype='low', analog=False, output='sos')
return sos
def butter_highpass(cutoff, fs, order=5):
nyq = 0.5 * fs
normal_cutoff = cutoff / nyq
sos = butter(order, normal_cutoff, btype='high', analog=False, output='sos')
return sos
def sos_filter(data, sos):
y = sosfiltfilt(sos, data)
return y
def process_acceleration_data(acceleration_file):
acceleration_data = pd.read_csv(acceleration_file)
required_columns = ['Timestamp (IST)', 'X', 'Y', 'Z']
for col in required_columns:
if col not in acceleration_data.columns:
raise ValueError(f"Missing column: {col}")
timestamps = pd.to_datetime(acceleration_data['Timestamp (IST)'])
acc_x = acceleration_data['X'].values
acc_y = acceleration_data['Y'].values
acc_z = acceleration_data['Z'].values
time_sec = (timestamps - timestamps.iloc[0]).dt.total_seconds().values
# Filtering parameters - i got the most accurate value for a 5-m pre-measured distance
fs = 20.0
low_cutoff = 6.90
high_cutoff = 2.5
# Butterworth filter
lowpass_sos = butter_lowpass(low_cutoff, fs)
highpass_sos = butter_highpass(high_cutoff, fs)
# Low-pass filter to remove noise
acc_x_low = sos_filter(acc_x, lowpass_sos)
acc_y_low = sos_filter(acc_y, lowpass_sos)
acc_z_low = sos_filter(acc_z, lowpass_sos)
# High-pass filter to remove drift
acc_x_filtered = sos_filter(acc_x_low, highpass_sos)
acc_y_filtered = sos_filter(acc_y_low, highpass_sos)
acc_z_filtered = sos_filter(acc_z_low, highpass_sos)
# Calculate velocities
vel_x = cumtrapz(acc_x_filtered, time_sec, initial=0)
vel_y = cumtrapz(acc_y_filtered, time_sec, initial=0)
vel_z = cumtrapz(acc_z_filtered, time_sec, initial=0)
# Calculate positions
pos_x = cumtrapz(vel_x, time_sec, initial=0)
pos_y = cumtrapz(vel_y, time_sec, initial=0)
pos_z = cumtrapz(vel_z, time_sec, initial=0)
# High-pass filter to remove drift from positions
pos_x_filtered = sos_filter(pos_x, highpass_sos)
pos_y_filtered = sos_filter(pos_y, highpass_sos)
pos_z_filtered = sos_filter(pos_z, highpass_sos)
return timestamps, vel_x, vel_y, vel_z, pos_x_filtered, pos_y_filtered, pos_z_filtered
def plot_position_data(timestamps, pos_x_filtered, pos_y_filtered, pos_z_filtered):
# Plot the filtered position data
plt.figure()
plt.plot(timestamps, pos_x_filtered, label='X Position')
plt.plot(timestamps, pos_y_filtered, label='Y Position')
plt.plot(timestamps, pos_z_filtered, label='Z Position')
plt.xlabel('Time')
plt.ylabel('Position (m)')
plt.legend()
plt.title(f'Filtered Position Data')
plt.show()
def save_position_data(timestamps, pos_x_filtered, pos_y_filtered, pos_z_filtered, total_distance):
filename = f"position_data_{total_distance:.2f}_meters.csv"
output_file_path = os.path.join(POS_FOLDER_PATH, filename)
filtered_position_data = pd.DataFrame({
'Timestamp (IST)': timestamps,
'Filtered_Pos_X': pos_x_filtered,
'Filtered_Pos_Y': pos_y_filtered,
'Filtered_Pos_Z': pos_z_filtered
})
filtered_position_data.to_csv(output_file_path, index=False)
print(f"Position data saved to {output_file_path}")
def save_velocity_data(timestamps, vel_x, vel_y, vel_z):
current_time = pd.Timestamp.now().strftime("%Y-%m-%d_%H-%M-%S")
filename = f"velocity_data_{current_time}.csv"
output_file_path = os.path.join(VEL_FOLDER_PATH, filename)
# Save velocity data
velocity_data = pd.DataFrame({
'Timestamp (IST)': timestamps,
'Vel_X': vel_x,
'Vel_Y': vel_y,
'Vel_Z': vel_z
})
velocity_data.to_csv(output_file_path, index=False)
print(f"Velocity data saved to {output_file_path}")
def main():
while True:
# Get the most recent acceleration file
latest_acceleration_file = max(glob.glob(os.path.join(ACCEL_FOLDER_PATH, '*.csv')), key=os.path.getctime)
# Process acceleration data
timestamps, vel_x, vel_y, vel_z, pos_x_filtered, pos_y_filtered, pos_z_filtered = process_acceleration_data(latest_acceleration_file)
# Plot position data
plot_position_data(timestamps, pos_x_filtered, pos_y_filtered, pos_z_filtered)
# Calculate total distance covered
total_distance = np.sum(np.sqrt(np.diff(pos_x_filtered)**2 + np.diff(pos_y_filtered)**2 + np.diff(pos_z_filtered)**2))
print(f"Total distance covered: {total_distance:.2f} meters")
# Save position data
save_position_data(timestamps, pos_x_filtered, pos_y_filtered, pos_z_filtered, total_distance)
# Save velocity data
save_velocity_data(timestamps, vel_x, vel_y, vel_z)
break # End the while loop after latest file
if __name__ == "__main__":
main()