import numpy as npimport matplotlib.pyplot as pltimport cv2import pandas as pdfrom scipy.signal import find_peaksdef load_profile(fn): profile = np.loadtxt(fn, skiprows=3, delimiter=';', usecols=[0, 1])profile[:, 0] = profile[:, 0] / 1000profile[:, 0] = profile[:, 0] - profile[:, 0][np.argmax(profile[:, 1])]profile[:, 1] = profile[:, 1] / profile[:, 1].maxreturn profiledef plot_profile(profile, peaks, x_range):x = profile[:, 0] y = profile[:, 1]# Plotting the cleaned intensity profile with corrected x-axis valuesplt.figure(figsize=(10, 6))plt.plot(x, y, label='Intensity Profile', color='b')plt.plot(x[peaks], y[peaks], 'ro', label='Prominent Peaks')plt.xlim(-1 * x_range, x_range)plt.xlabel('x (mm^-1)')plt.ylabel('Intensity (a.u.)')plt.title('FFT Image Profile Data (Corrected x-axis)')plt.legendplt.grid(True)plt.show# Define the peak detection functiondef detect_prominent_peaks(profile, **kwargs):"""Detect prominent peaks in the given intensity profile.Parameters:profile (numpy array): Array where the first column is x values and the second column is intensity (y) values.prominence (float): Prominence value for peak detection.Returns:peaks (numpy array): Indices of the detected peaks.actual_distances (numpy array): Actual distances between detected peaks."""# Extract x and y values from the profilex = profile[:, 0]y = profile[:, 1]# Perform peak detectionpeaks, properties = find_peaks(y, **kwargs)# If no peaks are detected, return empty arraysif len(peaks) == 0:return np.array(), np.array()# Determine the center peak (closest to the middle of the x range)center_peak = peaks[np.argmax(y[peaks])]# Exclude the center peak to prevent division by zeronon_center_peaks = peaks[peaks != center_peak]# Calculate the distances from the center peak for each detected peak in the spatial domain# Since x is in the frequency domain, the actual distance in the spatial domain is the reciprocal of the frequency differenceactual_distances = 1 / np.abs(x[non_center_peaks] - x[center_peak])return peaks, actual_distancesprofile_list = ['ML2_Blue_Corrected_1_BW_FFT_20241009.csv', 'ML2_Blue_Corrected_2_BW_FFT_20241009.csv', 'ML2_Green_Corrected_1_BW_FFT_20241009.csv', 'ML2_Green_Corrected_2_BW_FFT_20241009.csv']prominence_list = [0.1, 0.1, 0.05, 0.1]for i, f in enumerate(profile_list):profile = load_profile(f'.\\{f}')peaks, actual_distances = detect_prominent_peaks(profile, prominence=prominence_list[i])plot_profile(profile, peaks, 5)print(actual_distances)摘要：import numpy as npimport matplotlib.pyplot as pltimport cv2import pandas as pdfrom scipy.signal import find_peaksdef load_profile(
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https://www.zhihu.com/consult/people/792359672131756032?isMe=1担任《Mechanical System and Signal Processing》《中国电机工程学报》等期刊审稿专家，擅长领域：信号滤波/降噪，机器学习/深度学习，时间序列预分析/预测，设备故障诊断/缺陷检测/异常检测。
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采用8种方法对一维信号进行降噪（Python）
NS: noisy signalS: original siganlmean filter: ws = window sizemedian filter:average filter: ns = number of noisy signal(different)bandpass filter: l = low cut-off frequency, h = high ...threshold filter: r = ratio(max abs(fft) / min ...)wavelet filter: a = thresholdstd filter:NN: neural network来源：小芳课堂
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