linear scale thingy for gamozo

lol.py

import math, random
import numpy as np
import matplotlib.pyplot as plt
import csv

def read_iq_data(filename):
    i_data = []
    q_data = []
    try:
        with open(filename, 'r') as file:
            # I removed duplicate spaces from the file beforehand btw
            reader = csv.reader(file, delimiter=' ')
            for row in reader:
                filtered = list(filter(bool, row))
                # print(filtered)
                try:
                    i_value = float(filtered[1])
                    q_value = float(filtered[2])
                    i_data.append(i_value)
                    q_data.append(q_value)
                except ValueError:
                    print(f"Warning: Could not convert values in row: {filtered}")
                    continue
    except Exception as e:
        print(f"Error reading the file: {e}")
    return np.array(i_data), np.array(q_data)

def analyze_iq_data(i_data, q_data):
    """
    Perform basic analysis on I/Q data.
    Parameters:
    - i_data: Array of I values
    - q_data: Array of Q values
    Returns:
    - Dictionary of analysis results
    """
    i_mean = np.mean(i_data)
    q_mean = np.mean(q_data)
    i_diff = i_data - i_mean
    q_diff = q_data - q_mean
    # Calculate magnitude and phase

    # Calculate the absolute phase
    abs_phase = np.arctan2(q_diff, i_diff)
    # Use NumPy's unwrap function to remove discontinuities larger than pi
    unwrapped_phase = np.unwrap(abs_phase)
    # If you need the cumulative sum as in your original code
    cums = unwrapped_phase
    dist = cums * (- 1e3 * 20e-6 / (2 * np.pi))

    # Calculate statistics
    analysis = {
        "i_mean": i_mean,
        "q_mean": q_mean,
        "i_std": np.std(i_data),
        "q_std": np.std(q_data),
        "dist_mean": np.mean(dist),
        "dist_std": np.std(dist)
    }
    return analysis, dist

def plot_iq_data(i_data, q_data, cum):
    fig, axs = plt.subplots(2, 1, figsize=(12, 10))
    # I/Q constellation plot
    axs[0].scatter(i_data, q_data, alpha=0.6, s=0.1)
    axs[0].set_title('I/Q Constellation')
    axs[0].set_xlabel('I')
    axs[0].set_ylabel('Q')
    axs[0].grid(True)
    axs[0].axis('equal')
    # Time series of I and Q
    t = np.arange(len(i_data)) / 100000
    # Phase
    axs[1].plot(t, cum)
    axs[1].set_title('Physical distance from start')
    axs[1].set_xlabel('Time [s]')
    axs[1].set_ylabel('Distance [mm]')
    axs[1].grid(True)
    plt.tight_layout()
    plt.show()

if __name__ == "__main__":
    filename = "log.txt"
    i_data, q_data = read_iq_data(filename)
    if len(i_data) > 0:
        print(f"Successfully read {len(i_data)} I/Q samples")

        # Analyze the data
        analysis, dist = analyze_iq_data(i_data, q_data)

        # Print analysis results
        print("\nAnalysis Results:")
        for key, value in analysis.items():
            print(f"{key}: {value}")

        # Plot the data
        start = 0
        length = -1
        plot_iq_data(i_data[start:start+length], q_data[start:start+length], dist[start:start+length])
        print(dist[0])
        print(dist[-1])
    else:
        print("No data was read from the file")

# if __name__ == "__main__":
#     # filename = "log.txt"
#     i_data, q_data = [], []
#     for a in range(100):
#         # i_data.append(float(random.randint(0, 100)) / 100)
#         # q_data.append(float(random.randint(0, 100)) / 100)
#         i_data.append(math.cos(a / 10))
#         q_data.append(math.sin(a / 10))
#     for a in range(100):
#         i_data.append(math.sin(a / 10))
#         q_data.append(math.cos(a / 10))
#     i_data = np.array(i_data)
#     q_data = np.array(q_data)

#     # Analyze the data
#     analysis, cum, phase = analyze_iq_data(i_data, q_data)

#     # Print analysis results
#     print("\nAnalysis Results:")
#     for key, value in analysis.items():
#         print(f"{key}: {value}")

#     # Plot the data
#     # start = 600000
#     # length = 10000
#     start = 0
#     length = 1000
#     plot_iq_data(i_data[start:start+length], q_data[start:start+length], cum[start:start+length], phase[start:start+length])