Bio_Analyse_Toolbox/eval_analyse.py

import customtkinter as ctk
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from tkinter import filedialog

# Function to load data from file
def load_data():
    filename = filedialog.askopenfilename(title="Select CSV file", filetypes=(("CSV files", "*.csv"), ("All files", "*.*")))
    return pd.read_csv(filename)

# Function to update data
def update_data():
    global data
    data = load_data()
    update_plot(start_slider.get(), end_slider.get())

# Function to update plot based on slider values
def update_plot(start_time, end_time):
    filtered_data = data[(data["Timestamp"] >= start_time) & (data["Timestamp"] <= end_time)]
    ax1.clear()
    ax1.plot(filtered_data["Timestamp"], filtered_data["Value"])
    ax1.set_title("Time Domain")
    ax1.set_xlabel("Timestamp")
    ax1.set_ylabel("Value")

    # Remove DC offset
    data_without_dc = filtered_data["Value"] - filtered_data["Value"].mean()

    # Perform Fourier analysis
    fft_values = np.fft.fft(data_without_dc)
    frequencies = np.fft.fftfreq(len(filtered_data), d=1 / sampling_rate)
    amplitudes = np.abs(fft_values)

    # Consider only positive frequencies
    positive_frequencies = frequencies[:len(frequencies)//2]
    positive_amplitudes = amplitudes[:len(frequencies)//2]

    ax2.clear()
    ax2.plot(positive_frequencies, positive_amplitudes)
    ax2.set_title("Frequency Domain")
    ax2.set_xlabel("Frequency (Hz)")
    ax2.set_ylabel("Amplitude")

    canvas.draw()

# Create the main CustomTkinter window
root = ctk.CTk()
root.title("CSV Plot with Two Sliders")

# Set appearance mode (optional)
ctk.set_appearance_mode("dark")  # Choose between "dark", "light", or "system"

# Create matplotlib figure and axes
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 6))
canvas = FigureCanvasTkAgg(fig, master=root)
canvas.get_tk_widget().pack(side=ctk.LEFT, fill=ctk.BOTH, expand=True)

# Function to initialize the plot
def init_plot():
    global data
    global sampling_rate
    data = load_data()

    # Read the CSV file and extract timestamps
    timestamps = data["Timestamp"].tolist()

    # Calculate time differences
    time_diffs = [timestamps[i + 1] - timestamps[i] for i in range(len(timestamps) - 1)]

    # Calculate average time difference
    average_time_diff = sum(time_diffs) / len(time_diffs)

    # Sampling rate is the reciprocal of the average time difference
    sampling_rate = 1 / average_time_diff
    print("Sampling rate:", sampling_rate, "Hz")

    # Load only the last 10000 data points
    data = data.tail(10000)

    # Plot the initial data
    ax1.plot(data["Timestamp"], data["Value"])
    ax1.set_title("Time Domain")
    ax1.set_xlabel("Timestamp")
    ax1.set_ylabel("Value")

    # Remove DC offset
    data_without_dc = data["Value"] - data["Value"].mean()

    # Perform Fourier analysis
    fft_values = np.fft.fft(data_without_dc)
    frequencies = np.fft.fftfreq(len(data), d=1 / sampling_rate)
    amplitudes = np.abs(fft_values)

    # Consider only positive frequencies
    positive_frequencies = frequencies[:len(frequencies)//2]
    positive_amplitudes = amplitudes[:len(frequencies)//2]

    # Plot frequency spectrum
    ax2.plot(positive_frequencies, positive_amplitudes)
    ax2.set_title("Frequency Domain")
    ax2.set_xlabel("Frequency (Hz)")
    ax2.set_ylabel("Amplitude")

# Initialize the plot
init_plot()

# Create sliders
start_slider = ctk.CTkSlider(master=root, from_=data["Timestamp"].min(), to=data["Timestamp"].max(),
                              command=lambda value: update_plot(value, end_slider.get()))
start_slider.pack()

end_slider = ctk.CTkSlider(master=root, from_=data["Timestamp"].min(), to=data["Timestamp"].max(),
                            command=lambda value: update_plot(start_slider.get(), value))
end_slider.pack()

# Create a frame on the right side for displaying relevant information
info_frame = ctk.CTkFrame(master=root)
info_frame.pack(side=ctk.RIGHT, fill=ctk.BOTH, expand=True)

# Labels for displaying information
sampling_rate_label = ctk.CTkLabel(master=info_frame, text=f"Sampling Rate: {sampling_rate} Hz")
sampling_rate_label.pack()

max_frequency_label = ctk.CTkLabel(master=info_frame, text=f"Max Frequency: {sampling_rate / 2} Hz (Nyquist Frequency)")
max_frequency_label.pack()

# Button to change data file
change_file_button = ctk.CTkButton(master=info_frame, text="Change File", command=update_data)
change_file_button.pack()

# Run the CustomTkinter event loop
root.mainloop()