'analyse.py' ändern

eval_analyse.py

@@ -1,134 +1,134 @@
-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
+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()