aero_plot.py

import matplotlib.pyplot as plt
import numpy as np
from aero_csv import read_data

# Assuming read_data is imported from your module that parses the CSV file.
# from your_data_module import read_data

def graph_acceleration(csv_filename: str):
    times = []      # Time stamps in seconds
    acc_x = []      # Accelerometer X values
    acc_y = []      # Accelerometer Y values
    acc_z = []      # Accelerometer Z values
    acc_mag = []    # Computed acceleration magnitude

    # Read data from CSV using your read_data() generator
    for data in read_data(csv_filename):
        # Convert the microsecond timestamp into seconds
        t = data.us_since_launch / 1_000_000.0
        times.append(t)

        # Get acceleration values from each axis;
        # If the data file contains raw counts that need conversion, apply conversion functions.
        x_val = data.x.accelerometer
        y_val = data.y.accelerometer
        z_val = data.z.accelerometer

        acc_x.append(x_val)
        acc_y.append(y_val)
        acc_z.append(z_val)

        # Compute the magnitude of acceleration (Euclidean norm)
        magnitude = np.linalg.norm([x_val, y_val, z_val])
        acc_mag.append(magnitude)

    # Plot each accelerometer axis over time
    plt.figure(figsize=(12, 6))
    plt.plot(times, acc_x, label="Acc X")
    plt.plot(times, acc_y, label="Acc Y")
    plt.plot(times, acc_z, label="Acc Z")
    plt.xlabel("Time (s)")
    plt.ylabel("Acceleration (m/s²)")
    plt.title("Accelerometer Data (per axis)")
    plt.legend()
    plt.grid(True)
    plt.show()

    # Plot acceleration magnitude over time
    plt.figure(figsize=(12, 6))
    plt.plot(times, acc_mag, label="Acc Magnitude", color='magenta')
    plt.xlabel("Time (s)")
    plt.ylabel("Acceleration Magnitude (m/s²)")
    plt.title("Acceleration Magnitude over Time")
    plt.legend()
    plt.grid(True)
    plt.show()

if __name__ == '__main__':
    csv_filename = "data.csv"  # Path to your CSV file
    graph_acceleration(csv_filename)