{"id":1046534,"date":"2024-12-31T13:34:13","date_gmt":"2024-12-31T05:34:13","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1046534.html"},"modified":"2024-12-31T13:34:17","modified_gmt":"2024-12-31T05:34:17","slug":"python%e5%a6%82%e4%bd%95%e5%81%9a%e5%82%85%e9%87%8c%e5%8f%b6%e5%88%86%e6%9e%90","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1046534.html","title":{"rendered":"python\u5982\u4f55\u505a\u5085\u91cc\u53f6\u5206\u6790"},"content":{"rendered":"

\"python\u5982\u4f55\u505a\u5085\u91cc\u53f6\u5206\u6790\"<\/p>\n

Python\u505a\u5085\u91cc\u53f6\u5206\u6790\u7684\u6838\u5fc3\u6b65\u9aa4\u5305\u62ec\uff1a\u4f7f\u7528NumPy\u5e93\u8fdb\u884cFFT\u53d8\u6362\u3001\u5206\u6790\u9891\u8c31\u56fe\u3001\u6ee4\u6ce2\u5668\u8bbe\u8ba1\u3001\u9006\u53d8\u6362\u8fd8\u539f\u65f6\u57df\u4fe1\u53f7\u3002<\/strong>\u5728\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u5c06\u6df1\u5165\u63a2\u8ba8\u8fd9\u4e9b\u6b65\u9aa4\uff0c\u5e2e\u52a9\u4f60\u5168\u9762\u7406\u89e3\u5982\u4f55\u5728Python\u4e2d\u8fdb\u884c\u5085\u91cc\u53f6\u5206\u6790\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5085\u91cc\u53f6\u5206\u6790\u7b80\u4ecb<\/p>\n<\/p>\n

\u5085\u91cc\u53f6\u5206\u6790\u662f\u4e00\u79cd\u5c06\u4fe1\u53f7\u4ece\u65f6\u57df\u8f6c\u6362\u5230\u9891\u57df\u7684\u6570\u5b66\u65b9\u6cd5\u3002\u901a\u8fc7\u5085\u91cc\u53f6\u53d8\u6362\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06\u590d\u6742\u7684\u65f6\u57df\u4fe1\u53f7\u5206\u89e3\u4e3a\u4e0d\u540c\u9891\u7387\u7684\u6b63\u5f26\u6ce2\u548c\u4f59\u5f26\u6ce2\u7684\u53e0\u52a0\uff0c\u4ece\u800c\u5206\u6790\u4fe1\u53f7\u7684\u9891\u8c31\u7279\u6027\u3002\u5085\u91cc\u53f6\u5206\u6790\u5728\u4fe1\u53f7\u5904\u7406\u3001\u56fe\u50cf\u5904\u7406\u3001\u8bed\u97f3\u8bc6\u522b\u3001\u632f\u52a8\u5206\u6790\u7b49\u9886\u57df\u6709\u7740\u5e7f\u6cdb\u7684\u5e94\u7528\u3002<\/p>\n<\/p>\n

\u4e8c\u3001\u4f7f\u7528NumPy\u8fdb\u884cFFT\u53d8\u6362<\/p>\n<\/p>\n

\u5728Python\u4e2d\uff0c\u6700\u5e38\u7528\u7684\u5085\u91cc\u53f6\u53d8\u6362\u5de5\u5177\u662fNumPy\u5e93\u7684fft\u6a21\u5757\u3002NumPy\u5e93\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u5feb\u901f\u5085\u91cc\u53f6\u53d8\u6362\uff08FFT\uff09\u7b97\u6cd5\uff0c\u53ef\u4ee5\u5c06\u65f6\u57df\u4fe1\u53f7\u8f6c\u6362\u4e3a\u9891\u57df\u4fe1\u53f7\u3002\u4e0b\u9762\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528NumPy\u8fdb\u884c\u5085\u91cc\u53f6\u53d8\u6362\u3002<\/p>\n<\/p>\n

1\u3001\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/h3>\n<\/p>\n

import numpy as np<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u751f\u6210\u4e00\u4e2a\u793a\u4f8b\u4fe1\u53f7<\/h3>\n<\/p>\n
\u9996\u5148\uff0c\u6211\u4eec\u751f\u6210\u4e00\u4e2a\u5305\u542b\u591a\u4e2a\u9891\u7387\u6210\u5206\u7684\u793a\u4f8b\u4fe1\u53f7\u3002<\/p>\n<\/p>\n
# \u91c7\u6837\u9891\u7387<\/p>\n
fs = 1000<\/p>\n
\u91c7\u6837\u95f4\u9694<\/strong><\/h2>\n
t = np.arange(0, 1, 1\/fs)<\/p>\n
\u751f\u6210\u5305\u542b\u4e24\u4e2a\u9891\u7387\u6210\u5206\u7684\u4fe1\u53f7<\/strong><\/h2>\n
f1 = 50<\/p>\n
f2 = 120<\/p>\n
signal = np.sin(2 * np.pi * f1 * t) + 0.5 * np.sin(2 * np.pi * f2 * t)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u8ba1\u7b97FFT<\/h3>\n<\/p>\n
\u4f7f\u7528NumPy\u7684fft\u51fd\u6570\u8ba1\u7b97\u4fe1\u53f7\u7684\u5feb\u901f\u5085\u91cc\u53f6\u53d8\u6362\u3002<\/p>\n<\/p>\n
# \u8ba1\u7b97FFT<\/p>\n
fft_signal = np.fft.fft(signal)<\/p>\n
\u8ba1\u7b97\u9891\u7387\u8f74<\/strong><\/h2>\n
freqs = np.fft.fftfreq(len(signal), 1\/fs)<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u53ef\u89c6\u5316\u9891\u8c31<\/h3>\n<\/p>\n
\u4f7f\u7528Matplotlib\u5e93\u7ed8\u5236\u9891\u8c31\u56fe\u3002<\/p>\n<\/p>\n
plt.figure(figsize=(10, 6))<\/p>\n
plt.plot(freqs, np.abs(fft_signal))<\/p>\n
plt.title('Frequency Spectrum')<\/p>\n
plt.xlabel('Frequency (Hz)')<\/p>\n
plt.ylabel('Amplitude')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u751f\u6210\u4e86\u4e00\u4e2a\u5305\u542b\u4e24\u4e2a\u9891\u7387\u6210\u5206\u7684\u793a\u4f8b\u4fe1\u53f7\uff0c\u7136\u540e\u4f7f\u7528NumPy\u7684fft\u51fd\u6570\u8ba1\u7b97\u4e86\u4fe1\u53f7\u7684\u5feb\u901f\u5085\u91cc\u53f6\u53d8\u6362\uff0c\u5e76\u7ed8\u5236\u4e86\u9891\u8c31\u56fe\u3002\u9891\u8c31\u56fe\u5c55\u793a\u4e86\u4fe1\u53f7\u4e2d\u5404\u4e2a\u9891\u7387\u6210\u5206\u7684\u5e45\u5ea6\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u9891\u8c31\u5206\u6790<\/p>\n<\/p>\n
\u9891\u8c31\u5206\u6790\u662f\u5085\u91cc\u53f6\u5206\u6790\u7684\u91cd\u8981\u90e8\u5206\uff0c\u901a\u8fc7\u9891\u8c31\u56fe\u6211\u4eec\u53ef\u4ee5\u89c2\u5bdf\u4fe1\u53f7\u4e2d\u5404\u4e2a\u9891\u7387\u6210\u5206\u7684\u5e45\u5ea6\u548c\u76f8\u4f4d\u3002\u4e0b\u9762\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u8fdb\u884c\u9891\u8c31\u5206\u6790\u3002<\/p>\n<\/p>\n
1\u3001\u8ba1\u7b97\u529f\u7387\u8c31\u5bc6\u5ea6<\/h3>\n<\/p>\n
\u529f\u7387\u8c31\u5bc6\u5ea6\uff08PSD\uff09\u662f\u63cf\u8ff0\u4fe1\u53f7\u9891\u7387\u6210\u5206\u5f3a\u5ea6\u7684\u4e00\u79cd\u5ea6\u91cf\uff0c\u901a\u8fc7PSD\u53ef\u4ee5\u66f4\u76f4\u89c2\u5730\u89c2\u5bdf\u4fe1\u53f7\u7684\u9891\u8c31\u7279\u6027\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528SciPy\u5e93\u4e2d\u7684welch\u51fd\u6570\u8ba1\u7b97\u529f\u7387\u8c31\u5bc6\u5ea6\u3002<\/p>\n<\/p>\n
from scipy.signal import welch<\/p>\n
\u8ba1\u7b97\u529f\u7387\u8c31\u5bc6\u5ea6<\/strong><\/h2>\n
f, Pxx_den = welch(signal, fs, nperseg=1024)<\/p>\n
plt.figure(figsize=(10, 6))<\/p>\n
plt.semilogy(f, Pxx_den)<\/p>\n
plt.title('Power Spectral Density')<\/p>\n
plt.xlabel('Frequency (Hz)')<\/p>\n
plt.ylabel('PSD (V^2\/Hz)')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528SciPy\u5e93\u7684welch\u51fd\u6570\u8ba1\u7b97\u4e86\u4fe1\u53f7\u7684\u529f\u7387\u8c31\u5bc6\u5ea6\uff0c\u5e76\u7ed8\u5236\u4e86\u529f\u7387\u8c31\u5bc6\u5ea6\u56fe\u3002\u529f\u7387\u8c31\u5bc6\u5ea6\u56fe\u5c55\u793a\u4e86\u4fe1\u53f7\u4e2d\u5404\u4e2a\u9891\u7387\u6210\u5206\u7684\u529f\u7387\u5bc6\u5ea6\u3002<\/p>\n<\/p>\n
2\u3001\u6ee4\u6ce2\u5668\u8bbe\u8ba1<\/h3>\n<\/p>\n
\u5728\u4fe1\u53f7\u5904\u7406\u8fc7\u7a0b\u4e2d\uff0c\u6211\u4eec\u7ecf\u5e38\u9700\u8981\u5bf9\u4fe1\u53f7\u8fdb\u884c\u6ee4\u6ce2\uff0c\u4ee5\u53bb\u9664\u566a\u58f0\u6216\u63d0\u53d6\u7279\u5b9a\u9891\u7387\u6210\u5206\u3002\u4e0b\u9762\u6211\u4eec\u5c06\u4ecb\u7ecd\u5982\u4f55\u8bbe\u8ba1\u548c\u5e94\u7528\u6ee4\u6ce2\u5668\u3002<\/p>\n<\/p>\n
\u4f4e\u901a\u6ee4\u6ce2\u5668<\/h4>\n<\/p>\n
\u4f4e\u901a\u6ee4\u6ce2\u5668\u5141\u8bb8\u4f4e\u9891\u4fe1\u53f7\u901a\u8fc7\uff0c\u963b\u6b62\u9ad8\u9891\u4fe1\u53f7\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528SciPy\u5e93\u4e2d\u7684butter\u548clfilter\u51fd\u6570\u8bbe\u8ba1\u548c\u5e94\u7528\u4f4e\u901a\u6ee4\u6ce2\u5668\u3002<\/p>\n<\/p>\n
from scipy.signal import butter, lfilter<\/p>\n
\u8bbe\u8ba1\u4f4e\u901a\u6ee4\u6ce2\u5668<\/strong><\/h2>\n
def lowpass_filter(data, cutoff, fs, order=5):<\/p>\n
    nyq = 0.5 * fs<\/p>\n
    normal_cutoff = cutoff \/ nyq<\/p>\n
    b, a = butter(order, normal_cutoff, btype='low', analog=False)<\/p>\n
    y = lfilter(b, a, data)<\/p>\n
    return y<\/p>\n
\u5e94\u7528\u4f4e\u901a\u6ee4\u6ce2\u5668<\/strong><\/h2>\n
cutoff = 100<\/p>\n
filtered_signal = lowpass_filter(signal, cutoff, fs)<\/p>\n
plt.figure(figsize=(10, 6))<\/p>\n
plt.plot(t, signal, label='Original Signal')<\/p>\n
plt.plot(t, filtered_signal, label='Filtered Signal', linestyle='--')<\/p>\n
plt.title('Signal Filtering')<\/p>\n
plt.xlabel('Time (s)')<\/p>\n
plt.ylabel('Amplitude')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u5b9a\u4e49\u4e86\u4e00\u4e2a\u4f4e\u901a\u6ee4\u6ce2\u5668\u51fd\u6570\uff0c\u7136\u540e\u8bbe\u8ba1\u4e86\u4e00\u4e2a\u622a\u6b62\u9891\u7387\u4e3a100Hz\u7684\u4f4e\u901a\u6ee4\u6ce2\u5668\uff0c\u5e76\u5c06\u5176\u5e94\u7528\u4e8e\u793a\u4f8b\u4fe1\u53f7\u3002\u6700\u540e\uff0c\u6211\u4eec\u7ed8\u5236\u4e86\u539f\u59cb\u4fe1\u53f7\u548c\u6ee4\u6ce2\u540e\u4fe1\u53f7\u7684\u65f6\u57df\u56fe\u3002<\/p>\n<\/p>\n
\u5e26\u901a\u6ee4\u6ce2\u5668<\/h4>\n<\/p>\n
\u5e26\u901a\u6ee4\u6ce2\u5668\u5141\u8bb8\u7279\u5b9a\u9891\u7387\u8303\u56f4\u5185\u7684\u4fe1\u53f7\u901a\u8fc7\uff0c\u963b\u6b62\u5176\u4ed6\u9891\u7387\u4fe1\u53f7\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528SciPy\u5e93\u4e2d\u7684butter\u548clfilter\u51fd\u6570\u8bbe\u8ba1\u548c\u5e94\u7528\u5e26\u901a\u6ee4\u6ce2\u5668\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
    nyq = 0.5 * fs<\/p>\n
    low = lowcut \/ nyq<\/p>\n
    high = highcut \/ nyq<\/p>\n
    b, a = butter(order, [low, high], btype='band')<\/p>\n
    y = lfilter(b, a, data)<\/p>\n
    return y<\/p>\n
\u5e94\u7528\u5e26\u901a\u6ee4\u6ce2\u5668<\/strong><\/h2>\n
lowcut = 40<\/p>\n
highcut = 130<\/p>\n
filtered_signal = bandpass_filter(signal, lowcut, highcut, fs)<\/p>\n
plt.figure(figsize=(10, 6))<\/p>\n
plt.plot(t, signal, label='Original Signal')<\/p>\n
plt.plot(t, filtered_signal, label='Filtered Signal', linestyle='--')<\/p>\n
plt.title('Signal Filtering')<\/p>\n
plt.xlabel('Time (s)')<\/p>\n
plt.ylabel('Amplitude')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u5b9a\u4e49\u4e86\u4e00\u4e2a\u5e26\u901a\u6ee4\u6ce2\u5668\u51fd\u6570\uff0c\u7136\u540e\u8bbe\u8ba1\u4e86\u4e00\u4e2a\u5e26\u901a\u6ee4\u6ce2\u5668\uff0c\u5c06\u5176\u5e94\u7528\u4e8e\u793a\u4f8b\u4fe1\u53f7\uff0c\u5e76\u7ed8\u5236\u4e86\u539f\u59cb\u4fe1\u53f7\u548c\u6ee4\u6ce2\u540e\u4fe1\u53f7\u7684\u65f6\u57df\u56fe\u3002<\/p>\n<\/p>\n
\u56db\u3001\u9006\u53d8\u6362\u8fd8\u539f\u65f6\u57df\u4fe1\u53f7<\/p>\n<\/p>\n
\u5728\u5085\u91cc\u53f6\u53d8\u6362\u540e\uff0c\u5982\u679c\u6211\u4eec\u9700\u8981\u56de\u5230\u65f6\u57df\u4fe1\u53f7\uff0c\u53ef\u4ee5\u4f7f\u7528\u9006\u5085\u91cc\u53f6\u53d8\u6362\uff08IFFT\uff09\u3002NumPy\u5e93\u63d0\u4f9b\u4e86ifft\u51fd\u6570\u6765\u5b9e\u73b0\u8fd9\u4e00\u529f\u80fd\u3002<\/p>\n<\/p>\n
# \u8ba1\u7b97\u9006\u5085\u91cc\u53f6\u53d8\u6362<\/p>\n
recovered_signal = np.fft.ifft(fft_signal)<\/p>\n
plt.figure(figsize=(10, 6))<\/p>\n
plt.plot(t, signal, label='Original Signal')<\/p>\n
plt.plot(t, recovered_signal.real, label='Recovered Signal', linestyle='--')<\/p>\n
plt.title('Signal Recovery')<\/p>\n
plt.xlabel('Time (s)')<\/p>\n
plt.ylabel('Amplitude')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528NumPy\u7684ifft\u51fd\u6570\u8ba1\u7b97\u4e86\u4fe1\u53f7\u7684\u9006\u5085\u91cc\u53f6\u53d8\u6362\uff0c\u5e76\u7ed8\u5236\u4e86\u539f\u59cb\u4fe1\u53f7\u548c\u8fd8\u539f\u4fe1\u53f7\u7684\u65f6\u57df\u56fe\u3002\u53ef\u4ee5\u770b\u5230\uff0c\u9006\u53d8\u6362\u540e\u7684\u4fe1\u53f7\u4e0e\u539f\u59cb\u4fe1\u53f7\u975e\u5e38\u63a5\u8fd1\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u5b9e\u4f8b\u5e94\u7528<\/p>\n<\/p>\n
\u4e3a\u4e86\u66f4\u597d\u5730\u7406\u89e3\u5085\u91cc\u53f6\u5206\u6790\u7684\u5e94\u7528\uff0c\u6211\u4eec\u5c06\u901a\u8fc7\u4e00\u4e2a\u5b9e\u4f8b\u6765\u5c55\u793a\u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u5085\u91cc\u53f6\u5206\u6790\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5305\u542b\u591a\u4e2a\u9891\u7387\u6210\u5206\u548c\u566a\u58f0\u7684\u4fe1\u53f7\uff0c\u6211\u4eec\u5e0c\u671b\u901a\u8fc7\u5085\u91cc\u53f6\u5206\u6790\u6765\u63d0\u53d6\u7279\u5b9a\u9891\u7387\u6210\u5206\uff0c\u5e76\u53bb\u9664\u566a\u58f0\u3002<\/p>\n<\/p>\n
1\u3001\u751f\u6210\u542b\u566a\u58f0\u4fe1\u53f7<\/h3>\n<\/p>\n
# \u751f\u6210\u542b\u566a\u58f0\u4fe1\u53f7<\/p>\n
noise = np.random.normal(0, 0.5, len(t))<\/p>\n
noisy_signal = signal + noise<\/p>\n
plt.figure(figsize=(10, 6))<\/p>\n
plt.plot(t, noisy_signal, label='Noisy Signal')<\/p>\n
plt.title('Noisy Signal')<\/p>\n
plt.xlabel('Time (s)')<\/p>\n
plt.ylabel('Amplitude')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u5085\u91cc\u53f6\u53d8\u6362\u4e0e\u9891\u8c31\u5206\u6790<\/h3>\n<\/p>\n
# \u8ba1\u7b97FFT<\/p>\n
fft_noisy_signal = np.fft.fft(noisy_signal)<\/p>\n
freqs = np.fft.fftfreq(len(noisy_signal), 1\/fs)<\/p>\n
\u7ed8\u5236\u9891\u8c31\u56fe<\/strong><\/h2>\n
plt.figure(figsize=(10, 6))<\/p>\n
plt.plot(freqs, np.abs(fft_noisy_signal))<\/p>\n
plt.title('Frequency Spectrum of Noisy Signal')<\/p>\n
plt.xlabel('Frequency (Hz)')<\/p>\n
plt.ylabel('Amplitude')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u8bbe\u8ba1\u6ee4\u6ce2\u5668<\/h3>\n<\/p>\n
# \u5e94\u7528\u5e26\u901a\u6ee4\u6ce2\u5668<\/p>\n
filtered_signal = bandpass_filter(noisy_signal, lowcut, highcut, fs)<\/p>\n
plt.figure(figsize=(10, 6))<\/p>\n
plt.plot(t, noisy_signal, label='Noisy Signal')<\/p>\n
plt.plot(t, filtered_signal, label='Filtered Signal', linestyle='--')<\/p>\n
plt.title('Signal Filtering')<\/p>\n
plt.xlabel('Time (s)')<\/p>\n
plt.ylabel('Amplitude')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
4\u3001\u9006\u53d8\u6362\u8fd8\u539f\u4fe1\u53f7<\/h3>\n<\/p>\n
# \u8ba1\u7b97\u9006\u5085\u91cc\u53f6\u53d8\u6362<\/p>\n
recovered_signal = np.fft.ifft(fft_noisy_signal)<\/p>\n
plt.figure(figsize=(10, 6))<\/p>\n
plt.plot(t, noisy_signal, label='Noisy Signal')<\/p>\n
plt.plot(t, recovered_signal.real, label='Recovered Signal', linestyle='--')<\/p>\n
plt.title('Signal Recovery')<\/p>\n
plt.xlabel('Time (s)')<\/p>\n
plt.ylabel('Amplitude')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u8fd9\u4e2a\u5b9e\u4f8b\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u5982\u4f55\u4f7f\u7528\u5085\u91cc\u53f6\u5206\u6790\u6765\u5904\u7406\u542b\u566a\u58f0\u4fe1\u53f7\uff0c\u63d0\u53d6\u7279\u5b9a\u9891\u7387\u6210\u5206\uff0c\u5e76\u53bb\u9664\u566a\u58f0\u3002\u5085\u91cc\u53f6\u5206\u6790\u662f\u4e00\u79cd\u5f3a\u5927\u7684\u4fe1\u53f7\u5904\u7406\u5de5\u5177\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u7406\u89e3\u548c\u5206\u6790\u4fe1\u53f7\u7684\u9891\u8c31\u7279\u6027\u3002<\/p>\n<\/p>\n
\u603b\u7ed3<\/p>\n<\/p>\n
\u5728\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u5982\u4f55\u5728Python\u4e2d\u8fdb\u884c\u5085\u91cc\u53f6\u5206\u6790\u3002\u6211\u4eec\u9996\u5148\u4ecb\u7ecd\u4e86\u5085\u91cc\u53f6\u5206\u6790\u7684\u57fa\u672c\u6982\u5ff5\uff0c\u7136\u540e\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528NumPy\u5e93\u8fdb\u884cFFT\u53d8\u6362\u3001\u8fdb\u884c\u9891\u8c31\u5206\u6790\u3001\u8bbe\u8ba1\u548c\u5e94\u7528\u6ee4\u6ce2\u5668\u3001\u4ee5\u53ca\u9006\u53d8\u6362\u8fd8\u539f\u65f6\u57df\u4fe1\u53f7\u3002\u6700\u540e\uff0c\u6211\u4eec\u901a\u8fc7\u4e00\u4e2a\u5b9e\u4f8b\u5c55\u793a\u4e86\u5085\u91cc\u53f6\u5206\u6790\u7684\u5b9e\u9645\u5e94\u7528\u3002<\/p>\n<\/p>\n
\u5e0c\u671b\u8fd9\u7bc7\u6587\u7ae0\u80fd\u591f\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u5085\u91cc\u53f6\u5206\u6790\uff0c\u5e76\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u7075\u6d3b\u8fd0\u7528\u8fd9\u4e9b\u6280\u672f\u3002\u5085\u91cc\u53f6\u5206\u6790\u5728\u4fe1\u53f7\u5904\u7406\u9886\u57df\u6709\u7740\u5e7f\u6cdb\u7684\u5e94\u7528\uff0c\u638c\u63e1\u8fd9\u4e9b\u6280\u672f\u5c06\u4e3a\u4f60\u7684\u7814\u7a76\u548c\u5de5\u4f5c\u5e26\u6765\u6781\u5927\u7684\u5e2e\u52a9\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5085\u91cc\u53f6\u5206\u6790\u5728Python\u4e2d\u6709\u4ec0\u4e48\u5b9e\u9645\u5e94\u7528\uff1f<\/strong>
\u5085\u91cc\u53f6\u5206\u6790\u5728Python\u4e2d\u5e7f\u6cdb\u5e94\u7528\u4e8e\u4fe1\u53f7\u5904\u7406\u3001\u56fe\u50cf\u5206\u6790\u3001\u58f0\u97f3\u5904\u7406\u7b49\u9886\u57df\u3002\u4f8b\u5982\uff0c\u5728\u97f3\u9891\u4fe1\u53f7\u5904\u7406\u4e2d\uff0c\u5085\u91cc\u53f6\u53d8\u6362\u53ef\u4ee5\u5e2e\u52a9\u5206\u6790\u9891\u7387\u6210\u5206\uff0c\u4ece\u800c\u5b9e\u73b0\u97f3\u9891\u538b\u7f29\u3001\u566a\u58f0\u6d88\u9664\u7b49\u529f\u80fd\u3002\u5728\u56fe\u50cf\u5904\u7406\u4e2d\uff0c\u5085\u91cc\u53f6\u53d8\u6362\u53ef\u4ee5\u7528\u4e8e\u56fe\u50cf\u6ee4\u6ce2\u548c\u91cd\u5efa\uff0c\u5e2e\u52a9\u63d0\u9ad8\u56fe\u50cf\u8d28\u91cf\u3002<\/p>\n
\u5982\u4f55\u4f7f\u7528Python\u5e93\u8fdb\u884c\u5085\u91cc\u53f6\u5206\u6790\uff1f<\/strong>
Python\u4e2d\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u5e93\u8fdb\u884c\u5085\u91cc\u53f6\u5206\u6790\uff0c\u6700\u5e38\u7528\u7684\u662fNumPy\u548cSciPy\u3002NumPy\u63d0\u4f9b\u4e86numpy.fft<\/code>\u6a21\u5757\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u8ba1\u7b97\u4e00\u7ef4\u548c\u591a\u7ef4\u5085\u91cc\u53f6\u53d8\u6362\u3002SciPy\u5219\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u529f\u80fd\uff0c\u5305\u62ec\u79bb\u6563\u5085\u91cc\u53f6\u53d8\u6362\u548c\u5feb\u901f\u5085\u91cc\u53f6\u53d8\u6362\uff08FFT\uff09\u3002\u901a\u8fc7\u8fd9\u4e9b\u5e93\uff0c\u7528\u6237\u53ef\u4ee5\u65b9\u4fbf\u5730\u5bf9\u6570\u636e\u8fdb\u884c\u5085\u91cc\u53f6\u53d8\u6362\u3001\u9006\u53d8\u6362\u4ee5\u53ca\u9891\u8c31\u5206\u6790\u3002<\/p>\n
\u5728\u8fdb\u884c\u5085\u91cc\u53f6\u5206\u6790\u65f6\uff0c\u5982\u4f55\u9009\u62e9\u5408\u9002\u7684\u91c7\u6837\u9891\u7387\uff1f<\/strong>
\u9009\u62e9\u5408\u9002\u7684\u91c7\u6837\u9891\u7387\u975e\u5e38\u91cd\u8981\uff0c\u901a\u5e38\u9075\u5faa\u5948\u594e\u65af\u7279\u91c7\u6837\u5b9a\u7406\uff0c\u5373\u91c7\u6837\u9891\u7387\u5e94\u81f3\u5c11\u662f\u4fe1\u53f7\u6700\u9ad8\u9891\u7387\u7684\u4e24\u500d\u3002\u5982\u679c\u91c7\u6837\u9891\u7387\u8fc7\u4f4e\uff0c\u53ef\u80fd\u4f1a\u5bfc\u81f4\u6df7\u53e0\u73b0\u8c61\uff0c\u5f71\u54cd\u5206\u6790\u7ed3\u679c\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u4ee5\u6839\u636e\u4fe1\u53f7\u7684\u7279\u6027\u548c\u5206\u6790\u9700\u6c42\uff0c\u9009\u62e9\u9002\u5f53\u7684\u91c7\u6837\u9891\u7387\uff0c\u4ee5\u786e\u4fdd\u5085\u91cc\u53f6\u5206\u6790\u7684\u51c6\u786e\u6027\u548c\u6709\u6548\u6027\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u505a\u5085\u91cc\u53f6\u5206\u6790\u7684\u6838\u5fc3\u6b65\u9aa4\u5305\u62ec\uff1a\u4f7f\u7528NumPy\u5e93\u8fdb\u884cFFT\u53d8\u6362\u3001\u5206\u6790\u9891\u8c31\u56fe\u3001\u6ee4\u6ce2\u5668\u8bbe\u8ba1\u3001\u9006\u53d8\u6362\u8fd8\u539f\u65f6\u57df […]","protected":false},"author":3,"featured_media":1046549,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1046534"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1046534"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1046534\/revisions"}],"predecessor-version":[{"id":1046551,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1046534\/revisions\/1046551"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1046549"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1046534"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1046534"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1046534"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}