{"id":974517,"date":"2024-12-27T06:05:46","date_gmt":"2024-12-26T22:05:46","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/974517.html"},"modified":"2024-12-27T06:05:48","modified_gmt":"2024-12-26T22:05:48","slug":"python%e5%a6%82%e4%bd%95%e5%8e%bb%e9%99%a4%e7%9b%b4%e8%be%be%e6%b3%a2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/974517.html","title":{"rendered":"python\u5982\u4f55\u53bb\u9664\u76f4\u8fbe\u6ce2"},"content":{"rendered":"

\"python\u5982\u4f55\u53bb\u9664\u76f4\u8fbe\u6ce2\"<\/p>\n

Python\u53bb\u9664\u76f4\u8fbe\u6ce2\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528\u6ee4\u6ce2\u5668\u5904\u7406\u4fe1\u53f7\u3001\u5085\u91cc\u53f6\u53d8\u6362\u5206\u6790\u9891\u57df\u7279\u5f81\u3001\u4fe1\u53f7\u7a97\u53e3\u5316\u3001\u91c7\u7528\u81ea\u9002\u5e94\u6ee4\u6ce2\u6280\u672f\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u4f7f\u7528\u6ee4\u6ce2\u5668\u5904\u7406\u4fe1\u53f7\u7684\u65b9\u6cd5\u3002<\/strong><\/p>\n<\/p>\n

\u53bb\u9664\u76f4\u8fbe\u6ce2\u662f\u4fe1\u53f7\u5904\u7406\u4e2d\u7684\u4e00\u4e2a\u5e38\u89c1\u95ee\u9898\uff0c\u7279\u522b\u662f\u5728\u96f7\u8fbe\u3001\u58f0\u7eb3\u548c\u65e0\u7ebf\u901a\u4fe1\u7b49\u9886\u57df\u3002\u76f4\u8fbe\u6ce2\u901a\u5e38\u662f\u6307\u4fe1\u53f7\u4e2d\u4e0d\u9700\u8981\u7684\u6216\u5e72\u6270\u7684\u90e8\u5206\uff0c\u5b83\u4f1a\u5f71\u54cd\u5230\u4fe1\u53f7\u7684\u51c6\u786e\u6027\u548c\u5904\u7406\u6548\u679c\u3002\u901a\u8fc7\u4f7f\u7528\u6ee4\u6ce2\u5668\u5904\u7406\u4fe1\u53f7\u7684\u65b9\u6cd5\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u53bb\u9664\u76f4\u8fbe\u6ce2\u3002<\/p>\n<\/p>\n

\u6ee4\u6ce2\u5668\u662f\u4fe1\u53f7\u5904\u7406\u4e2d\u6700\u5e38\u7528\u7684\u65b9\u6cd5\u4e4b\u4e00\u3002\u6ee4\u6ce2\u5668\u53ef\u4ee5\u5206\u4e3a\u4f4e\u901a\u3001\u9ad8\u901a\u3001\u5e26\u901a\u548c\u5e26\u963b\u6ee4\u6ce2\u5668\u3002\u5177\u4f53\u9009\u62e9\u54ea\u79cd\u6ee4\u6ce2\u5668\uff0c\u53d6\u51b3\u4e8e\u8981\u53bb\u9664\u7684\u76f4\u8fbe\u6ce2\u7684\u9891\u7387\u7279\u6027\u3002<\/p>\n<\/p>\n

\u4f4e\u901a\u6ee4\u6ce2\u5668\u5141\u8bb8\u4f4e\u9891\u4fe1\u53f7\u901a\u8fc7\uff0c\u800c\u963b\u6b62\u9ad8\u9891\u4fe1\u53f7\u3002\u9ad8\u901a\u6ee4\u6ce2\u5668\u5219\u76f8\u53cd\uff0c\u5141\u8bb8\u9ad8\u9891\u4fe1\u53f7\u901a\u8fc7\uff0c\u963b\u6b62\u4f4e\u9891\u4fe1\u53f7\u3002\u5e26\u901a\u6ee4\u6ce2\u5668\u53ea\u5141\u8bb8\u4e00\u5b9a\u9891\u7387\u8303\u56f4\u5185\u7684\u4fe1\u53f7\u901a\u8fc7\uff0c\u800c\u5e26\u963b\u6ee4\u6ce2\u5668\u5219\u963b\u6b62\u7279\u5b9a\u9891\u7387\u8303\u56f4\u7684\u4fe1\u53f7\u901a\u8fc7\u3002<\/p>\n<\/p>\n

\u5728Python\u4e2d\uff0c\u5e38\u7528\u7684\u4fe1\u53f7\u5904\u7406\u5e93\u5982SciPy\u548cNumPy\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6ee4\u6ce2\u5668\u8bbe\u8ba1\u548c\u5e94\u7528\u529f\u80fd\u3002\u5229\u7528\u8fd9\u4e9b\u5e93\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8bbe\u8ba1\u548c\u5e94\u7528\u5404\u79cd\u7c7b\u578b\u7684\u6ee4\u6ce2\u5668\u6765\u53bb\u9664\u76f4\u8fbe\u6ce2\u3002<\/p>\n<\/p>\n

\u4e0b\u9762\u6211\u4eec\u5c06\u6df1\u5165\u63a2\u8ba8Python\u4e2d\u5982\u4f55\u901a\u8fc7\u8fd9\u4e9b\u65b9\u6cd5\u53bb\u9664\u76f4\u8fbe\u6ce2\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u6ee4\u6ce2\u5668\u5904\u7406\u4fe1\u53f7<\/h3>\n<\/p>\n

\u6ee4\u6ce2\u5668\u662f\u4e00\u79cd\u7528\u4e8e\u53bb\u9664\u4fe1\u53f7\u4e2d\u4e0d\u9700\u8981\u6210\u5206\u7684\u5de5\u5177\u3002\u5728Python\u4e2d\uff0c\u4f7f\u7528SciPy\u5e93\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u4e0d\u540c\u7c7b\u578b\u7684\u6ee4\u6ce2\u5668\u3002<\/p>\n<\/p>\n

1. \u4f4e\u901a\u6ee4\u6ce2\u5668<\/h4>\n<\/p>\n

\u4f4e\u901a\u6ee4\u6ce2\u5668\u7528\u4e8e\u5141\u8bb8\u4f4e\u9891\u4fe1\u53f7\u901a\u8fc7\uff0c\u5e76\u963b\u6b62\u9ad8\u9891\u4fe1\u53f7\u3002\u8fd9\u5728\u53bb\u9664\u9ad8\u9891\u566a\u58f0\u65f6\u975e\u5e38\u6709\u7528\u3002<\/p>\n<\/p>\n

SciPy\u5e93\u63d0\u4f9b\u4e86\u4e00\u4e2a\u8bbe\u8ba1\u6ee4\u6ce2\u5668\u7684\u51fd\u6570scipy.signal.butter<\/code>\uff0c\u5b83\u53ef\u4ee5\u7528\u6765\u8bbe\u8ba1Butterworth\u6ee4\u6ce2\u5668\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u4f4e\u901a\u6ee4\u6ce2\u5668\u8bbe\u8ba1\u793a\u4f8b\uff1a<\/p>\n<\/p>\n

from scipy.signal import butter, filtfilt<\/p>\n
\u8bbe\u8ba1\u4f4e\u901a\u6ee4\u6ce2\u5668<\/strong><\/h2>\n
def lowpass_filter(data, cutoff, fs, order=5):<\/p>\n
    nyquist = 0.5 * fs<\/p>\n
    normal_cutoff = cutoff \/ nyquist<\/p>\n
    b, a = butter(order, normal_cutoff, btype='low', analog=False)<\/p>\n
    y = filtfilt(b, a, data)<\/p>\n
    return y<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0ccutoff<\/code>\u662f\u622a\u6b62\u9891\u7387\uff0cfs<\/code>\u662f\u91c7\u6837\u9891\u7387\uff0corder<\/code>\u662f\u6ee4\u6ce2\u5668\u7684\u9636\u6570\u3002filtfilt<\/code>\u51fd\u6570\u7528\u4e8e\u5e94\u7528\u6ee4\u6ce2\u5668\uff0c\u5b83\u80fd\u591f\u6d88\u9664\u76f8\u4f4d\u5931\u771f\u3002<\/p>\n<\/p>\n
2. \u9ad8\u901a\u6ee4\u6ce2\u5668<\/h4>\n<\/p>\n
\u9ad8\u901a\u6ee4\u6ce2\u5668\u5141\u8bb8\u9ad8\u9891\u4fe1\u53f7\u901a\u8fc7\uff0c\u800c\u963b\u6b62\u4f4e\u9891\u4fe1\u53f7\u3002\u5728\u53bb\u9664\u4f4e\u9891\u5e72\u6270\u65f6\uff0c\u9ad8\u901a\u6ee4\u6ce2\u5668\u662f\u7406\u60f3\u7684\u9009\u62e9\u3002<\/p>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528\u4e0e\u4f4e\u901a\u6ee4\u6ce2\u5668\u76f8\u4f3c\u7684\u65b9\u5f0f\u8bbe\u8ba1\u9ad8\u901a\u6ee4\u6ce2\u5668\uff0c\u53ea\u9700\u5c06btype<\/code>\u53c2\u6570\u8bbe\u4e3a'high'<\/code>\u5373\u53ef\uff1a<\/p>\n<\/p>\n
# \u8bbe\u8ba1\u9ad8\u901a\u6ee4\u6ce2\u5668<\/p>\n
def highpass_filter(data, cutoff, fs, order=5):<\/p>\n
    nyquist = 0.5 * fs<\/p>\n
    normal_cutoff = cutoff \/ nyquist<\/p>\n
    b, a = butter(order, normal_cutoff, btype='high', analog=False)<\/p>\n
    y = filtfilt(b, a, data)<\/p>\n
    return y<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u5e26\u901a\u6ee4\u6ce2\u5668<\/h4>\n<\/p>\n
\u5e26\u901a\u6ee4\u6ce2\u5668\u4ec5\u5141\u8bb8\u7279\u5b9a\u9891\u7387\u8303\u56f4\u5185\u7684\u4fe1\u53f7\u901a\u8fc7\uff0c\u53ef\u4ee5\u7528\u4e8e\u53ea\u5173\u6ce8\u67d0\u4e2a\u9891\u7387\u8303\u56f4\u7684\u4fe1\u53f7\u3002<\/p>\n<\/p>\n
# \u8bbe\u8ba1\u5e26\u901a\u6ee4\u6ce2\u5668<\/p>\n
def bandpass_filter(data, lowcut, highcut, fs, order=5):<\/p>\n
    nyquist = 0.5 * fs<\/p>\n
    low = lowcut \/ nyquist<\/p>\n
    high = highcut \/ nyquist<\/p>\n
    b, a = butter(order, [low, high], btype='band')<\/p>\n
    y = filtfilt(b, a, data)<\/p>\n
    return y<\/p>\n
<\/code><\/pre>\n<\/p>\n
4. \u5e26\u963b\u6ee4\u6ce2\u5668<\/h4>\n<\/p>\n
\u5e26\u963b\u6ee4\u6ce2\u5668\u7528\u4e8e\u963b\u6b62\u7279\u5b9a\u9891\u7387\u8303\u56f4\u5185\u7684\u4fe1\u53f7\uff0c\u8fd9\u5728\u53bb\u9664\u7279\u5b9a\u9891\u7387\u7684\u5e72\u6270\u4fe1\u53f7\u65f6\u975e\u5e38\u6709\u6548\u3002<\/p>\n<\/p>\n
# \u8bbe\u8ba1\u5e26\u963b\u6ee4\u6ce2\u5668<\/p>\n
def bandstop_filter(data, lowcut, highcut, fs, order=5):<\/p>\n
    nyquist = 0.5 * fs<\/p>\n
    low = lowcut \/ nyquist<\/p>\n
    high = highcut \/ nyquist<\/p>\n
    b, a = butter(order, [low, high], btype='bandstop')<\/p>\n
    y = filtfilt(b, a, data)<\/p>\n
    return y<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u5085\u91cc\u53f6\u53d8\u6362\u5206\u6790<\/h3>\n<\/p>\n
\u5085\u91cc\u53f6\u53d8\u6362\u7528\u4e8e\u5c06\u4fe1\u53f7\u4ece\u65f6\u57df\u8f6c\u6362\u5230\u9891\u57df\uff0c\u5e2e\u52a9\u8bc6\u522b\u4fe1\u53f7\u4e2d\u7684\u9891\u7387\u6210\u5206\u3002\u5728\u9891\u57df\u4e2d\uff0c\u53ef\u4ee5\u66f4\u5bb9\u6613\u5730\u8bc6\u522b\u548c\u53bb\u9664\u76f4\u8fbe\u6ce2\u3002<\/p>\n<\/p>\n
1. \u5feb\u901f\u5085\u91cc\u53f6\u53d8\u6362\uff08FFT\uff09<\/h4>\n<\/p>\n
Python\u7684NumPy\u5e93\u63d0\u4f9b\u4e86\u5feb\u901f\u5085\u91cc\u53f6\u53d8\u6362\uff08FFT\uff09\u529f\u80fd\uff0c\u53ef\u4ee5\u5feb\u901f\u5c06\u4fe1\u53f7\u4ece\u65f6\u57df\u8f6c\u6362\u5230\u9891\u57df\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
def fft_analysis(data, fs):<\/p>\n
    n = len(data)<\/p>\n
    T = 1.0 \/ fs<\/p>\n
    yf = np.fft.fft(data)<\/p>\n
    xf = np.fft.fftfreq(n, T)[:n\/\/2]<\/p>\n
    return xf, 2.0\/n * np.abs(yf[:n\/\/2])<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u5206\u6790\u9891\u8c31\uff0c\u53ef\u4ee5\u8bc6\u522b\u51fa\u76f4\u8fbe\u6ce2\u7684\u9891\u7387\u6210\u5206\uff0c\u7136\u540e\u4f7f\u7528\u6ee4\u6ce2\u5668\u53bb\u9664\u8fd9\u4e9b\u6210\u5206\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4fe1\u53f7\u7a97\u53e3\u5316<\/h3>\n<\/p>\n
\u4fe1\u53f7\u7a97\u53e3\u5316\u662f\u4e00\u79cd\u5e38\u7528\u7684\u4fe1\u53f7\u5904\u7406\u6280\u672f\uff0c\u7528\u4e8e\u5c06\u4fe1\u53f7\u5206\u5272\u6210\u591a\u4e2a\u5c0f\u6bb5\uff0c\u7136\u540e\u5bf9\u6bcf\u4e2a\u5c0f\u6bb5\u8fdb\u884c\u5904\u7406\u3002\u8fd9\u6709\u52a9\u4e8e\u63d0\u9ad8\u4fe1\u53f7\u5904\u7406\u7684\u7075\u6d3b\u6027\u3002<\/p>\n<\/p>\n
1. \u7a97\u51fd\u6570\u5e94\u7528<\/h4>\n<\/p>\n
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528SciPy\u5e93\u7684\u7a97\u53e3\u51fd\u6570\uff0c\u4f8b\u5982Hamming\u7a97\u3001Hanning\u7a97\u7b49\u3002<\/p>\n<\/p>\n
from scipy.signal import windows<\/p>\n
def apply_window(data, window_type='hamming'):<\/p>\n
    window = windows.get_window(window_type, len(data))<\/p>\n
    return data * window<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7a97\u53e3\u5316\u5904\u7406\u6709\u52a9\u4e8e\u51cf\u5c11\u9891\u8c31\u6cc4\u6f0f\uff0c\u4f7f\u5f97\u4fe1\u53f7\u5904\u7406\u66f4\u52a0\u7cbe\u786e\u3002<\/p>\n<\/p>\n
\u56db\u3001\u81ea\u9002\u5e94\u6ee4\u6ce2\u6280\u672f<\/h3>\n<\/p>\n
\u81ea\u9002\u5e94\u6ee4\u6ce2\u6280\u672f\u662f\u4e00\u79cd\u52a8\u6001\u8c03\u6574\u6ee4\u6ce2\u5668\u53c2\u6570\u7684\u65b9\u6cd5\uff0c\u7528\u4e8e\u53bb\u9664\u4fe1\u53f7\u4e2d\u7684\u4e0d\u89c4\u5219\u5e72\u6270\u3002<\/p>\n<\/p>\n
1. LMS\u7b97\u6cd5<\/h4>\n<\/p>\n
\u6700\u5c0f\u5747\u65b9\u8bef\u5dee\uff08LMS\uff09\u7b97\u6cd5\u662f\u4e00\u79cd\u5e38\u7528\u7684\u81ea\u9002\u5e94\u6ee4\u6ce2\u7b97\u6cd5\u3002\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u81ea\u5b9a\u4e49\u51fd\u6570\u5b9e\u73b0LMS\u6ee4\u6ce2\u5668\u3002<\/p>\n<\/p>\n
def lms_filter(desired, input_signal, mu, order):<\/p>\n
    n = len(input_signal)<\/p>\n
    y = np.zeros(n)<\/p>\n
    e = np.zeros(n)<\/p>\n
    w = np.zeros(order)<\/p>\n
    for i in range(order, n):<\/p>\n
        x = input_signal[i:i-order:-1]<\/p>\n
        y[i] = np.dot(w, x)<\/p>\n
        e[i] = desired[i] - y[i]<\/p>\n
        w = w + 2 * mu * e[i] * x<\/p>\n
    return y, e<\/p>\n
<\/code><\/pre>\n<\/p>\n
LMS\u7b97\u6cd5\u901a\u8fc7\u4e0d\u65ad\u8c03\u6574\u6ee4\u6ce2\u5668\u7684\u6743\u91cd\uff0c\u4ee5\u6700\u5c0f\u5316\u8bef\u5dee\u4fe1\u53f7\uff0c\u4ece\u800c\u81ea\u9002\u5e94\u5730\u53bb\u9664\u76f4\u8fbe\u6ce2\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u53bb\u9664\u76f4\u8fbe\u6ce2\u662f\u4fe1\u53f7\u5904\u7406\u4e2d\u7684\u4e00\u4e2a\u91cd\u8981\u4efb\u52a1\uff0c\u901a\u8fc7\u4f7f\u7528Python\u4e2d\u7684\u6ee4\u6ce2\u5668\u5904\u7406\u4fe1\u53f7\u3001\u5085\u91cc\u53f6\u53d8\u6362\u5206\u6790\u3001\u4fe1\u53f7\u7a97\u53e3\u5316\u3001\u81ea\u9002\u5e94\u6ee4\u6ce2\u6280\u672f\u7b49\u65b9\u6cd5\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u53bb\u9664\u76f4\u8fbe\u6ce2\uff0c\u63d0\u5347\u4fe1\u53f7\u5904\u7406\u7684\u7cbe\u786e\u6027\u3002\u8fd9\u4e9b\u6280\u672f\u5728\u96f7\u8fbe\u3001\u58f0\u7eb3\u548c\u901a\u4fe1\u7cfb\u7edf\u7b49\u9886\u57df\u5177\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\u3002\u5408\u7406\u9009\u62e9\u548c\u5e94\u7528\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u80fd\u591f\u663e\u8457\u6539\u5584\u4fe1\u53f7\u5904\u7406\u7684\u6548\u679c\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u8bc6\u522b\u548c\u5904\u7406\u76f4\u8fbe\u6ce2\uff1f<\/strong>
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