平滑数据噪声的一个简单朴素的做法是，对窗口（样本）求平均，然后仅仅绘制出给定窗口的平均值，而不是所有的数据点。
import matplotlib.pyplot as plt
import numpy as np def moving_average(interval, window_size):
window = np.ones(int(window_size)) / float(window_size)
return np.convolve(interval, window, 'same') # numpy的卷积函数 t = np.linspace(start = -4, stop = 4, num = 100)
y = np.sin(t) + np.random.randn(len(t)) * 0.1
y_av = moving_average(interval = y, window_size = 10)
plt.plot(t, y, "b.-", t, y_av, "r.-") plt.xlabel('Time')
plt.ylabel('Value')
plt.legend(['original data', 'smooth data'])
plt.grid(True)
plt.show()
以下方法是基于信号（数据点）窗口的卷积（函数的总和）
import matplotlib.pyplot as plt
import numpy as np WINDOWS = ['flat', 'hanning', 'hamming', 'bartlett', 'blackman'] def smooth(x, window_len = 11, window = 'hanning'):
if x.ndim != 1:
raise ValueError('smooth only accepts 1 dimension arrays.')
if x.size < window_len:
raise ValueError('Input vector needs to be bigger than window size.')
if window_len < 3:
return x
if not window in WINDOWS:
raise ValueError('Window is one of "flat", "hanning", "hamming", "bartlett", "blackman"')
s = np.r_[x[window_len-1:0:-1], x, x[-1:-window_len:-1]]
if window == 'flat':
w = np.ones(window_len, 'd')
else:
w = eval('np.' + window + '(window_len)')
y = np.convolve(w/w.sum(), s, mode='valid')
return y t = np.linspace(-4, 4, 100)
x = np.sin(t)
xn = x + np.random.randn(len(t))*0.1 y = smooth(x)
ws = 31 plt.figure() plt.subplot(211)
plt.plot(np.ones(ws))
for w in WINDOWS[1:]:
eval('plt.plot(np.' + w + '(ws))')
plt.axis([0, 30, 0, 1.1])
plt.legend(WINDOWS)
plt.title('Smoothing windows') plt.subplot(212)
plt.plot(x)
plt.plot(xn)
for w in WINDOWS:
plt.plot(smooth(xn, 10, w))
l = ['original signal', 'signal with noise']
l.extend(WINDOWS)
plt.legend(l)
plt.title('Smoothed signal') plt.show()
中值过滤，即逐项的遍历信号，并用相邻信号项中的中值替代当前项
import matplotlib.pyplot as plt
import numpy as np
import scipy.signal as signal x = np.linspace(start=0, stop=1, num=51) x[3::5] = 1.5 # 从第4个 数开始，每个5个数，将其值改为 1.5 plt.plot(x, 'k.')
plt.plot(signal.medfilt(volume=x, kernel_size=3), 'b.-') # 在给定大小的邻域内取中值替代数据值，在邻域中没有元素的位置补0
plt.plot(signal.medfilt(volume=x, kernel_size=15), 'r.-')
plt.legend(['original signal', 'length 3', 'length 15'])
plt.show()