Python中滑动平均算法（Moving Average）方案：

#!/usr/bin/env python# -*- coding: utf-8 -*-import numpy as np# 等同于MATLAB中的smooth函数，但是平滑窗口必须为奇数。# yy = smooth(y) smooths the data in the column vector y ..# The first few elements of yy are given by# yy(1) = y(1)# yy(2) = (y(1) + y(2) + y(3))/3# yy(3) = (y(1) + y(2) + y(3) + y(4) + y(5))/5# yy(4) = (y(2) + y(3) + y(4) + y(5) + y(6))/5# ...def smooth(a,WSZ): # a:原始数据，NumPy 1-D array containing the data to be smoothed # 必须是1-D的，如果不是，请使用 np.ravel()或者np.squeeze()转化 # WSZ: smoothing window size needs, which must be odd number, # as in the original MATLAB implementation out0 = np.convolve(a,np.ones(WSZ,dtype=int),'valid')/WSZ r = np.arange(1,WSZ-1,2) start = np.cumsum(a[:WSZ-1])[::2]/r stop = (np.cumsum(a[:-WSZ:-1])[::2]/r)[::-1] return np.concatenate(( start , out0, stop ))# another one，边缘处理的不好"""def movingaverage(data, window_size): window = np.ones(int(window_size))/float(window_size) return np.convolve(data, window, 'same')"""# another one，速度更快# 输出结果 不与原始数据等长，假设原数据为m，平滑步长为t，则输出数据为m-t+1"""def movingaverage(data, window_size): cumsum_vec = np.cumsum(np.insert(data, 0, 0)) ma_vec = (cumsum_vec[window_size:] - cumsum_vec[:-window_size]) / window_size return ma_vec"""

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