鍦≒ython涓疄鐜版暟鎹檷鍣彲浠ヤ娇鐢ㄥ悇绉嶆柟娉曞拰搴擄紝浠ヤ笅鏄嚑绉嶅父鐢ㄧ殑鏂规硶锛?/p>

1. 绉诲姩骞冲潎婊ゆ尝锛氫娇鐢ㄧЩ鍔ㄥ钩鍧囨护娉㈠彲浠ヨ绠楁暟鎹偣鍛ㄥ洿鐨勫钩鍧囧€硷紝骞剁敤璇ュ钩鍧囧€兼浛浠ｅ綋鍓嶆暟鎹偣銆傝繖绉嶆柟娉曢€傜敤浜庡钩绋崇殑淇″彿銆傚彲浠ヤ娇鐢∟umPy搴撲腑鐨?code>convolve鍑芥暟瀹炵幇绉诲姩骞冲潎婊ゆ尝銆?/li>

import numpy as np

def moving_average(data, window_size):
    window = np.ones(window_size) / window_size
    return np.convolve(data, window, mode='same')

2. 涓€兼护娉細涓€兼护娉娇鐢ㄦ暟鎹偣鍛ㄥ洿鐨勪腑浣嶆暟鏉ヤ唬鏇垮綋鍓嶆暟鎹偣銆傝繖绉嶆柟娉曢€傜敤浜庢湁杈冨鍣０鐨勪俊鍙枫€傚彲浠ヤ娇鐢⊿ciPy搴撲腑鐨?code>medfilt鍑芥暟瀹炵幇涓€兼护娉€?/li>

from scipy.signal import medfilt

def median_filter(data, window_size):
    return medfilt(data, kernel_size=window_size)

3. 灏忔尝鍙樻崲锛氬皬娉㈠彉鎹㈡槸涓€绉嶅灏哄害鍒嗘瀽鏂规硶锛屽彲浠ュ皢淇″彿鍒嗚В涓轰笉鍚岄鐜囩殑瀛愪俊鍙枫€傞€氳繃婊ら櫎楂橀鍣０瀛愪俊鍙凤紝鍙互瀹炵幇鏁版嵁闄嶅櫔銆傚彲浠ヤ娇鐢≒yWavelets搴撳疄鐜板皬娉㈠彉鎹㈠拰闄嶅櫔銆?/li>

import pywt

def wavelet_denoise(data, wavelet='db4', level=1):
    coeffs = pywt.wavedec(data, wavelet, level=level)
    coeffs[1:] = (pywt.threshold(coeff, value=0.5*max(coeff)) for coeff in coeffs[1:])
    return pywt.waverec(coeffs, wavelet)

浠ヤ笂鏄笁绉嶅父瑙佺殑鏁版嵁闄嶅櫔鏂规硶鐨勭ず渚嬩唬鐮侊紝鍏蜂綋鐨勯€夋嫨鍜岃皟鏁村弬鏁伴渶瑕佹牴鎹暟鎹殑鐗规€у拰闇€姹傝繘琛岃皟鏁淬€?/p>