Sliding Window Implementation for 1D Curve Smoothing and Noise Reduction

This program implements sliding window-based smoothing and noise reduction functionality for 1D curves. By selecting appropriate window widths, the system can operate according to the frequency characteristics of useful signals. The sliding window technique, a commonly used data processing method, segments raw data into overlapping windows for sequential processing, achieving effective curve smoothing and noise removal. The implementation employs a moving average algorithm where each data point is replaced by the average of neighboring points within the specified window. Key functions include window_width_selection() for configuring the smoothing intensity and signal_frequency_analysis() for automated parameter optimization. The program provides multiple configurable window width options, enabling users to adjust parameters according to specific application requirements. Additionally, the system offers supplementary features including visualization tools that display both the smoothed curves and quantitative noise reduction metrics. These analytical capabilities help users better understand data variations and processing effectiveness through comparative plots and statistical indicators. The implementation handles edge cases using symmetric padding techniques to maintain data integrity. In summary, this program delivers a straightforward yet powerful tool for 1D curve smoothing and noise reduction operations, featuring an intuitive interface and comprehensive analytical feedback for optimal signal processing outcomes.