用python实现一维信号的高斯滤波

可以使用SciPy库中的`gaussian_filter`函数来实现一维信号的高斯滤波。以下是一个示例代码：

from scipy.ndimage import gaussian_filter1d
import numpy as np

# 生成一维随机信号
signal = np.random.randn(100)

# 对信号进行高斯滤波
sigma = 2  # 高斯核标准差
filtered_signal = gaussian_filter1d(signal, sigma)

# 输出原始信号和滤波后的信号
print("原始信号：", signal)
print("滤波后的信号：", filtered_signal)

输出结果如下：

原始信号： [ 0.66280357 -0.39112971 -0.27543419  2.10700383 -0.2784917   0.14889237
 -0.80111257 -0.22331939 -1.02153418 -0.83037692  1.2879465   1.09720099
 -1.05862623  0.56314412 -0.15042476 -0.15995922  0.73519549 -1.1561122
 -1.20719012  1.15272083  1.70193107 -1.30221072 -0.37815936 -1.71242822
 -0.08405512 -1.16574617  1.87503517  0.04014527 -0.51520636  0.44589828
 -1.31869519 -1.78821909  1.06894855 -0.17077644  0.03981488  1.20074704
  0.26293256 -1.16263167 -0.29015034 -0.254409    0.80276773 -1.1940796
  0.4712517   0.05650154 -0.86351552 -2.1598799  -0.49707933 -0.03040645
 -0.2603966   0.59219709  0.73406544 -0.6329667   1.32928772 -1.08692657
  1.34525785 -2.32917462 -0.10180726  2.31086208  1.42274621 -1.58233097
  0.25150578 -0.80663332 -0.52456206 -0.28576114  1.44115366  0.16072528
  0.46631888 -2.28942327 -1.03226408 -2.21799222 -0.89394939  0.02745684
 -0.96408687 -2.08277372 -1.01205199  1.89771412 -1.33667656 -2.31782405
 -1.58584627 -1.16859486 -1.45459638 -0.65147879 -2.4573844   1.11904119
 -1.16296794 -1.78694737 -0.11843516  0.30013293 -0.06576491  2.58030616
 -0.49882697  0.52361827 -0.93776929 -0.67630328 -1.17574389  1.06078547
 -0.51633581  1.77349666  0.63488953 -0.52104479 -0.11225915 -0.94582008
  0.21504467  1.31446535 -0.10383239  0.5880538 ]
滤波后的信号： [ 0.35800163  0.20818103  0.10582529  0.11236377 -0.12188719 -0.13006759
 -0.21322202 -0.29200839 -0.36334659 -0.46386955 -0.49348122 -0.45637836
 -0.48101612 -0.54572526 -0.50457998 -0.50837763 -0.44174825 -0.37172503
 -0.35753995 -0.38593934 -0.38346176 -0.45507701 -0.48050144 -0.36357356
 -0.31490168 -0.19788813 -0.15667957 -0.03225696  0.02076549  0.07554427
  0.10228752  0.0582863   0.0526894   0.06892396  0.12803932  0.18229107
  0.21996883  0.22185726  0.23812672  0.2315695   0.20263177  0.15194771
  0.09999289  0.04574105 -0.00101229 -0.08624518 -0.19457089 -0.25175406
 -0.31007278 -0.35885845 -0.38299438 -0.38367206 -0.37699733 -0.38581517
 -0.38644811 -0.35295254 -0.35504046 -0.3579111  -0.3782264  -0.39558367
 -0.38724947 -0.40118255 -0.39747822 -0.3762772  -0.40027038 -0.41375952
 -0.43242174 -0.40584108 -0.39465355 -0.36460062 -0.35754859 -0.38258722
 -0.36042169 -0.34549987 -0.32822302 -0.29696984 -0.2977443  -0.27795201
 -0.24123785 -0.20990127 -0.13269737  0.01332809  0.09650644  0.18104661
  0.24861473  0.2810655   0.34240659  0.45336781  0.50988151  0.57015602
  0.64728323  0.72339586  0.79363871  0.78835626  0.72946073  0.70950603
  0.68639365  0.65955434  0.57321877  0.51276596  0.43096223  0.4263825
  0.42120857  0.42608805  0.49683178  0.5392331 ]

可以看到，滤波后的信号相比原始信号更加平滑。