import serial import pywt import numpy as np import time # 接收数据 ser = serial.Serial('COM3', 115200) # 115200 是波特率 fi = 0 for turn in range(0, 3): # 设置接收数据轮次 n = 0 Sum = 0 m = 1000 dataset = [1 for i in range(1000)] while True: # 设置接收数据个数 n = n + 1 if n > 1000: break # 开始接收数据 data = ser.readline().strip() # print(data.decode()) if n > 1: if abs(float(data) - float(dataset[n - 2])) < 700: dataset[n-1] = dataset[n-2] else: dataset[n-1] = data # 使用小波分析进行阈值去噪声 w = pywt.Wavelet('db8') # 用Daubechies8小波 maxlev = pywt.dwt_max_level(len(dataset), w.dec_len) # 最大分解的阶数 # print("The maximum level is " + str(maxlev)) threshold = 0.5 # 阈值滤波 coeffs = pywt.wavedec(dataset, 'db8', level=maxlev) # 将信号进行小波分解 for i in range(1, len(coeffs)): coeffs[i] = pywt.threshold(coeffs[i], threshold * max(coeffs[i])) # 将噪声滤波 datarec = pywt.waverec(coeffs, 'db8') # 对信号重构 # print(str(datarec)) fi = datarec + fi print("The final result data is " + str(sum(datarec)/len(datarec))) print("average is " + str(sum(fi)/len(fi) / 3))有报错 if abs(float(data) - float(dataset[n - 2])) < 700: ValueError: could not convert string to float: b'1850035.62500\x00'

import wfdb import pywt import numpy as np from matplotlib import pyplot as plt from scipy import signal # 加载数据 record = wfdb.rdrecord('D:/下载/ptb-xl-a-large-publicly-available-electrocardiography-dataset-1.0.3/records100/00000/00001_lr') signal = record.p_signal[:, 0] # 取第一列信号 fs = record.fs # 采样率 # 小波滤波 w = 'db4' # 选用Daubechies4小波 level = 6 # 小波分解层数 coeffs = pywt.wavedec(signal, w, level=level) for i in range(1, level+1): coeffs[i] = pywt.threshold(coeffs[i], np.std(coeffs[i])/2)# 阈值为标准差的一半 filtered_signal = pywt.waverec(coeffs, w) # 去除基线漂移 baseline = signal - filtered_signal filtered_signal += np.mean(baseline) # 去除工频干扰 notch_freq_Hz = np.array([50.0]) # 工频干扰频率为50Hz for freq_Hz in np.nditer(notch_freq_Hz): bp_stop_Hz = freq_Hz + 3.0 * np.array([-1, 1]) b, a = signal.butter(3, bp_stop_Hz / (fs / 2.0), 'bandstop') filtered_signal = signal.lfilter(b, a, filtered_signal) # 绘制图像 plt.figure(figsize=(10, 5)) plt.plot(signal, label='Original') plt.plot(filtered_signal, label='Filtered') plt.title('Filtered Signal') plt.xlabel('Sample')代码如上，出现错误

import scipy.signal as sig 然后在使用signal模块的函数时使用sig作为前缀。例如，您可以将以下代码： b, a = signal.butter(3, bp_stop_Hz / (fs / 2.0), 'bandstop') filtered_signal = ...

wavename = "cmor3-3" # cmor 小波 totalscale = 64 fc = pywt.central_frequency(wavename) # 中心频率 cparam = 2 * fc * totalscale # print(cparam.shape) scales = cparam / np.arange(totalscale, 1, -1) x3 = x.cpu().detach().numpy() [x3, frequencies] = pywt.cwt(x3, scales, wavename, 1.0 / 25600) 变成matlab代码

wavename = 'cmor3-3'; totalscale = 64; fc = centfrq(wavename); cparam = 2 * fc * totalscale; scales = cparam ./ (totalscale:-1:1); x3 = x.cpu().detach().numpy(); [x3, frequencies] = cwt(x3, scales, ...

import pandas as pd import matplotlib.pyplot as plt import numpy as np import pywt file_name = 'E:/liuyuan/ceshi/zhongyao/Subject_1_0cmH20_norm_breaths.csv' data = pd.read_csv(file_name, skiprows=1, usecols=[0, 2], names=['Time', 'Flow']) x = list() y = list() for i in range(len(data)): x.append(float(data.values[i][0])) y.append(float(data.values[i][1])) start_index = 0 end_index = 5372 time = np.arange(start_index, end_index) flow = np.arange(start_index, end_index) time = data['Time'][start_index:end_index] flow = data['Flow'] def wavelet_filter(data): wavelet = 'db4' # 选择小波基函数 level = 5 # 小波变换的层数 # 小波变换 coeffs = pywt.wavedec(data, wavelet, level=level) threshold = np.std(coeffs[-level]) * np.sqrt(2 * np.log(len(data))) coeffs[1:] = (pywt.threshold(c, threshold, mode='soft') for c in coeffs[1:]) filtered_data = pywt.waverec(coeffs, wavelet) return filtered_data 对Flow进行小波变换滤波 filtered_flow = wavelet_filter(flow) fig, ax = plt.subplots(figsize=(10, 5)) plt.xlim(0, 60) ax.set_ylim(-0.7, 0.7) ax.set_xlabel('Time(s)', fontsize=10) ax.set_ylabel('Flow(L/s)', fontsize=10) ax.plot(time, filtered_flow, label='Filtered Flow') ax.legend() ax.grid(True, linewidth=0.3, alpha=0.5, color='gray') plt.tight_layout() # 自动调整子图的布局 plt.show()import pandas as pd import matplotlib.pyplot as plt import numpy as np import pywt file_name = 'E:/liuyuan/ceshi/zhongyao/Subject_1_0cmH20_norm_breaths.csv' data = pd.read_csv(file_name, skiprows=1, usecols=[0, 2], names=['Time', 'Flow']) x = list() y = list() for i in range(len(data)): x.append(float(data.values[i][0])) y.append(float(data.values[i][1])) start_index = 0 end_index = 5372 time = np.arange(start_index, end_index) flow = np.arange(start_index, end_index) time = data['Time'][start_index:end_index] flow = data['Flow'] def wavelet_filter(data): wavelet = 'db4' # 选择小波基函数 level = 5 # 小波变换的层数 coeffs = pywt.wavedec(data, wavelet, level=level) threshold = np.std(coeffs[-level]) * np.sqrt(2 * np.log(len(data))) coeffs[1:] = (pywt.threshold(c, threshold, mode='soft') for c in coeffs[1:]) # 逆小波变换 filtered_data = pywt.waverec(coeffs, wavelet) return filtered_data filtered_flow = wavelet_filter(flow) fig, ax = plt.subplots(figsize=(10, 5)) plt.xlim(0, 60) ax.set_ylim(-0.7, 0.7) ax.set_xlabel('Time(s)', fontsize=10) ax.set_ylabel('Flow(L/s)', fontsize=10) ax.plot(time, filtered_flow, label='Filtered Flow') ax.legend() ax.grid(True, linewidth=0.3, alpha=0.5, color='gray') plt.tight_layout() plt.show()在此代码上添加切分代码，并按照合适窗口大小切分完准确显示

flow_windows = np.array_split(filtered_flow, num_windows) # 绘制切分后的数据 fig, axs = plt.subplots(num_windows, figsize=(10, 5*num_windows), sharex=True) for i, ax in enumerate(axs): ax.plot(time...

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import yfinance as yf import pywt 这些要怎样装库

pip install numpy pip install matplotlib pip install seaborn pip install yfinance pip install PyWavelets 这些命令将会下载和安装相应的库和依赖项。如果你使用的是Anaconda，也可以在Anaconda Navigator...

import freq as freq from matplotlib import pyplot as plt import os from scipy.io import loadmat from scipy import signal import pywt from sklearn.model_selection import train_test_split from sklearn import metrics import numpy as np import pywt import numpy as np import matplotlib.pyplot as plt # 导入数据文件路径 dir_str = r"D:\python\matlab\da" # 此处填文件的路径 file_name = os.listdir(dir_str) file_dir = [os.path.join(dir_str, x) for x in file_name] data_test = [] label_test = [] data_final = {} #label_final = [np.zeros((51, 1))] label_final = np.zeros(1) data_final2 = np.zeros([1, 45000]) ecg_signal = np.zeros([1, 90000]) filtered_ecg_signal = np.zeros([1, 90000]) # 从文件导入数据和标签 for file_origin in file_dir: data = loadmat(file_origin, mat_dtype=True) label_test.append(data['label']) data_test.append(data['ecg']) ecg_signal = data_test[0][0] plt.plot(ecg_signal) plt.show() wp = pywt.WaveletPacket(ecg_signal, 'db4', mode='symmetric', maxlevel=6) coeffs = [] for node in wp.get_level(5, 'approx'): coeffs.append(node.data) mean_coeffs = np.mean(coeffs) std_coeffs = np.std(coeffs) start_pos = np.where(coeffs < mean_coeffs - 0.5 * std_coeffs)[0][-1] end_pos = np.where(coeffs < mean_coeffs - 0.15 * std_coeffs)[0][-1] plt.figure(figsize=(10, 6)) plt.plot(ecg_signal) plt.axvspan(start_pos, end_pos, alpha=0.5, color='red') plt.xlabel('Sample number') plt.ylabel('Amplitude') plt.title('ECG signal with ST segment') plt.show()

import numpy as np import matplotlib.pyplot as plt # 导入数据 dir_str = r"D:\python\matlab\da" # 此处填文件的路径 file_name = os.listdir(dir_str) file_dir = [os.path.join(dir_str, x) for x in file_...

import wfdbimport numpy as npimport matplotlib.pyplot as pltimport pywt# 读取一个ECG信号record = wfdb.rdrecord('ptb-xl/database/records100/00000/00001_lr')signal = record.p_signal[:, 0]# 设置小波滤波器wavelet = pywt.Wavelet('db4')# 小波滤波coeffs = pywt.wavedec(signal, wavelet, level=5)coeffs[1:] = [pywt.threshold(i, value=np.std(i)*2) for i in coeffs[1:]]filtered_signal = pywt.waverec(coeffs, wavelet)# 绘制原始信号和滤波后的信号plt.figure(figsize=(10, 6))plt.plot(signal, label='Original Signal')plt.plot(filtered_signal, label='Filtered Signal')plt.legend()plt.show()运行这段代码后出现错误。错误如下Traceback (most recent call last): File "D:\Program Files\JetBrains\PyCharm 2023.1\PycharmProject\test\test2.py", line 6, in <module> record = wfdb.rdrecord('D:\下载\ptb-xl-a-large-publicly-available-electrocardiography-dataset-1.0.3\records100\00000/00001_lr') File "D:\ProgramData\anaconda3\lib\site-packages\wfdb\io\record.py", line 2029, in rdrecord record = rdheader(record_name, pn_dir=pn_dir, rd_segments=False) File "D:\ProgramData\anaconda3\lib\site-packages\wfdb\io\record.py", line 1839, in rdheader with open( ValueError: embedded null character请问可以解决吗

这个错误可能是文件路径格式不正确导致的。你可以尝试使用双反斜杠或单斜杠来代替路径中的单反斜杠，例如： ...如果这样仍然不行，你可以检查一下文件路径是否正确，或者确认一下你的数据是否存在。

fs = 50000 t = np.arange(0, len(data1)) / fs wavename = "cmor3-3" # cmor 小波 totalscale =256 fc = pywt.central_frequency(wavename) # 中心频率 cparam = 2 * fc * totalscale scales = cparam / np.arange(totalscale, 1, -1) [cwtmatr, frequencies] = pywt.cwt(data1, scales, wavename, 1.0 / fs) # 连续小波变换 datarec=cwtmatr yy1=fft(datarec) yreal = yy1.real # 获取实数部分 yimag = yy1.imag # 获取虚数部分 yf_1 = abs(fft(datarec)) # 取模 yf1_1 = abs(fft(datarec))/(((x_values/2))) #归一化处理 yf1_1 yf2_1 = yf1_1[range(int((len(yf_1)/2)))] #由于对称性，只取一半区间 xf_1 = np.arange(len(datarec)) # 频率 xf1_1 = xf_1 xf2_1 = xf_1[range(int((len(xf_1)/2)))] #取一半区间 plt.figure(figsize=(12, 6)) ax1 = plt.subplot(1,2,1) plt.plot(t, data1) plt.xlabel("Time(s)", fontsize = 14) plt.ylabel("Amplitude(g)", fontsize=14) ax2 = plt.subplot(1,2,2) plt.contourf(xf1_1,yf1_1, abs(cwtmatr)) # 画等高线图

fs = 50000 表示采样率为每秒50000次采样。 t = np.arange(0, len(data1)) / fs 表示生成一个时间轴，取值范围为0到data1的长度，步长为1/fs，即每秒取样50000次。 wavename = "cmor3-3" 是选定的小波函数名为“cmor...

优化：import numpy as np import scipy.signal as signal import scipy.io.wavfile as wavfile import pywt import matplotlib.pyplot as plt def wiener_filter(x, fs, cutoff): # 维纳滤波函数 N = len(x) freqs, Pxx = signal.periodogram(x, fs=fs) H = np.zeros(N) H[freqs <= cutoff] = 1 Pxx_smooth = np.maximum(Pxx, np.max(Pxx) * 1e-6) H_smooth = np.maximum(H, np.max(H) * 1e-6) G = H_smooth / (H_smooth + 1 / Pxx_smooth) y = np.real(np.fft.ifft(np.fft.fft(x) * G)) return y def kalman_filter(x): # 卡尔曼滤波函数 Q = np.diag([0.01, 1]) R = np.diag([1, 0.1]) A = np.array([[1, 1], [0, 1]]) H = np.array([[1, 0], [0, 1]]) x_hat = np.zeros((2, len(x))) P = np.zeros((2, 2, len(x))) x_hat[:, 0] = np.array([x[0], 0]) P[:, :, 0] = np.eye(2) for k in range(1, len(x)): x_hat[:, k] = np.dot(A, x_hat[:, k-1]) P[:, :, k] = np.dot(np.dot(A, P[:, :, k-1]), A.T) + Q K = np.dot(np.dot(P[:, :, k], H.T), np.linalg.inv(np.dot(np.dot(H, P[:, :, k]), H.T) + R)) x_hat[:, k] += np.dot(K, x[k] - np.dot(H, x_hat[:, k])) P[:, :, k] = np.dot(np.eye(2) - np.dot(K, H), P[:, :, k]) y = x_hat[0, :] return y # 读取含有噪声的语音信号 rate, data = wavfile.read("shengyin.wav") data = data.astype(float) / 32767.0 # 维纳滤波 y_wiener = wiener_filter(data, fs=rate, cutoff=1000) # 卡尔曼滤波 y_kalman = kalman_filter(data) # 保存滤波后的信号到文件中 wavfile.write("wiener_filtered.wav", rate, np.int32(y_wiener * 32767.0)) wavfile.write("kalman_filtered.wav", rate, np.int32(y_kalman * 32767.0))

import numpy as np import scipy.signal as signal import scipy.io.wavfile as wavfile import pywt import matplotlib.pyplot as plt from typing import Tuple def periodogram(x: np.ndarray, fs: int) -> ...

解释 import pywt import matplotlib.pyplot as plt import numpy as np import preprocess path = r'F:\biyesheji\4\hebingmat' x_train, y_train, x_valid, y_valid, x_test, y_test = preprocess.prepro( d_path=path, length=784, number=30, normal=True, rate=[0.6, 0.2, 0.2], enc=False, enc_step=28) for i in range(0, len(x_valid)): N = 784 fs = 12000 t = np.linspace(0, 784 / fs, N, endpoint=False) wavename = 'cmor3-3' totalscal = 256 fc = pywt.central_frequency(wavename) cparam = 2 * fc * totalscal scales = cparam / np.arange(totalscal, 1, -1) [cwtmatr, frequencies] = pywt.cwt(x_valid[i], scales, wavename, 1.0 / fs) plt.contourf(t, frequencies, abs(cwtmatr)) plt.axis('off') plt.gcf().set_size_inches(784 / 100, 784 / 100) plt.gca().xaxis.set_major_locator(plt.NullLocator()) plt.gca().yaxis.set_major_locator(plt.NullLocator()) plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0) plt.margins(0, 0) x = r'./cwt_picture/valid/' + str(i) + '-' + str(y_valid[i]) + '.jpg' plt.savefig(x)

首先，使用 pywt 库导入所需的函数和模块。然后，使用 preprocess 库中的 prepro 函数加载数据集并进行预处理。接下来，使用 for 循环遍历验证集中的每个样本，对每个样本进行 CWT 变换。在变换过程中，设置...

import numpy as np import pywt # 定义原始矩阵 matrix = np.array([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12], [13, 14, 15, 16, 17, 18], [19, 20, 21, 22, 23, 24]]) # 定义小波变换函数 def wavelet_transform(matrix): coeffs = pywt.dwt2(matrix, 'haar') cA, (cH, cV, cD) = coeffs return cA, cH, cV, cD # 定义逆变换函数 def inverse_wavelet_transform(cA, cH, cV, cD): matrix = pywt.idwt2((cA, (cH, cV, cD)), 'haar') return matrix # 对每一列进行同步挤压小波变换 new_matrix = np.zeros_like(matrix) for i in range(6): col = matrix[:, i] cA, cH, cV, cD = wavelet_transform(col) new_col = inverse_wavelet_transform(cA, cH, cV, cD) new_matrix[:, i] = new_col # 输出新矩阵 print(new_matrix)，这里取每一列时数据形状是（8007，），少一个维度，应该如何修改

import numpy as np import pywt # 定义原始矩阵 matrix = np.array([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12], [13, 14, 15, 16, 17, 18], [19, 20, 21, 22, 23, 24]]) # 定义小波变换函数 def wavelet_...

import numpy as np import matplotlib.pyplot as plt import pywt from skimage import io, color # 读取灰度图像并转换为RGB图像 img_gray = io.imread('lena.png', as_gray=True) img = color.gray2rgb(img_gray) # 对图像的三个通道进行DWT变换 coeffs_r = pywt.dwt2(img[:, :, 0], 'haar') coeffs_g = pywt.dwt2(img[:, :, 1], 'haar') coeffs_b = pywt.dwt2(img[:, :, 2], 'haar') # 设置压缩比率 compress_ratio = 0.5 # 计算阈值 threshold_r = np.sort(np.abs(coeffs_r[1].ravel()))[::-1][int(compress_ratio * len(coeffs_r[1].ravel()))] threshold_g = np.sort(np.abs(coeffs_g[1].ravel()))[::-1][int(compress_ratio * len(coeffs_g[1].ravel()))] threshold_b = np.sort(np.abs(coeffs_b[1].ravel()))[::-1][int(compress_ratio * len(coeffs_b[1].ravel()))] # 对小于阈值的系数进行置零 coeffs_r = list(coeffs_r) coeffs_r[0] = np.round(coeffs_r[0]) coeffs_r[1] = np.where(np.abs(coeffs_r[1]) < threshold_r, 0, coeffs_r[1]) coeffs_r[2] = np.where(np.abs(coeffs_r[2]) < threshold_r, 0, coeffs_r[2]) coeffs_g = list(coeffs_g) coeffs_g[0] = np.round(coeffs_g[0]) coeffs_g[1] = np.where(np.abs(coeffs_g[1]) < threshold_g, 0, coeffs_g[1]) coeffs_g[2] = np.where(np.abs(coeffs_g[2]) < threshold_g, 0, coeffs_g[2]) coeffs_b = list(coeffs_b) coeffs_b[0] = np.round(coeffs_b[0]) coeffs_b[1] = np.where(np.abs(coeffs_b[1]) < threshold_b, 0, coeffs_b[1]) coeffs_b[2] = np.where(np.abs(coeffs_b[2]) < threshold_b, 0, coeffs_b[2]) # 合并三个通道的系数 coeffs = [np.stack([coeffs_r[i], coeffs_g[i], coeffs_b[i]], axis=-1) for i in range(len(coeffs_r))] # 对图像进行IDWT反变换 img_dwt = pywt.idwt2(coeffs, 'haar') # 显示原始图像和压缩后的图像 fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4)) ax = axes.ravel() ax[0].imshow(img) ax[0].set_title("Original image") ax[1].imshow(img_dwt) ax[1].set_title("Compressed image (DWT)") ax[1].set_xlabel("Compression ratio: {:.2f}".format(compress_ratio)) plt.tight_layout() plt.show()

这段代码缺少了两行，分别是导入numpy和matplotlib.pyplot两个模块的语句。请在代码的开头添加以下两行语句： ...import numpy as np import matplotlib.pyplot as plt 这样就可以解决代码中的报错。

# 获取股票数据 df = ts.get_k_data('600519', start='2001-01-01', end='2015-12-31') # 将收盘价转换为numpy数组 close_price = np.array(df['close']) # 进行小波变换 coeffs = pywt.wavedec(close_price, 'db4', level=4) coeffs[1:] = (pywt.threshold(i, value=100, mode='soft') for i in coeffs[1:]) reconstructed_signal = pywt.waverec(coeffs, 'db4') 将这段代码进行续写完成数据的预处理，从而使得该小波变换后的数据可以作为lstm模型的输入

接下来可以对预处理后的数据进行归一化，然后将数据切分成训练集和测试集，最后将数据转换为LSTM模型所需的格式。 1. 归一化 python from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler...

解释这段代码每行的意思：for i in range(0, len(x_valid)): N = 1024 fs = 12000 t = np.linspace(0, 1024 / fs, N, endpoint=False) wavename = 'cmor3-3' totalscal = 256 fc = pywt.central_frequency(wavename)#中心频率 cparam = 2 * fc * totalscal scales = cparam / np.arange(totalscal, 1, -1) [cwtmatr, frequencies] = pywt.cwt(x_valid[i], scales, wavename, 1.0 / fs) plt.contourf(t, frequencies, abs(cwtmatr)) plt.axis('off') plt.gcf().set_size_inches(1024 / 100, 1024 / 100) plt.gca().xaxis.set_major_locator(plt.NullLocator()) plt.gca().yaxis.set_major_locator(plt.NullLocator()) plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0) plt.margins(0, 0) x = r'./cwt_picture/valid/' + str(i) + '-' + str(y_valid[i]) + '.jpg' plt.savefig(x) plt.clf() plt.close()

这段代码的含义是：对于x_valid的长度，从0遍历到其长度-1，每次循环执行以下操作：设定N为1024，fs为12000，用np.linspace生成长度为N的时间向量t，wavename为'cmor3-3'，totalscal设定为256，fc为通过pywt库计算...

import pywtimport numpy as np# 定义小波变换函数def wavelet_transform(data, wavelet='db4', level=4): coeff = pywt.wavedec(data, wavelet, level=level) return coeff# 定义特征提取函数def extract_features(data): features = [] for i in range(data.shape[0]): # 对每一行数据进行小波变换 coeff = wavelet_transform(data[i]) # 提取小波系数中的能量特征 for j in range(len(coeff)): energy = np.sum(np.square(coeff[j])) features.append(energy) return features

具体来说，这段代码定义了一个函数wavelet_transform，用于对输入数据进行小波变换；另一个函数extract_features则用于提取小波系数中的能量特征。这些特征可以用于机器学习等应用中。需要注意的是，这段代码...

解释下这段代码coeff变量是什么为什么要从1开始 coeff = pywt.wavedec(data, wavelet, mode='per') sigma = Denoising.mad(coeff[-level]) uthresh = sigma * np.sqrt(2*np.log(len(data))) coeff[1:] = (pywt.threshold(i, value=uthresh, mode=thresh_mode) for i in coeff[1:]) return pywt.waverec(coeff, wavelet, mode='per')

pywt.wavedec() 函数可以将数据进行小波分解，按照不同的小波函数和模式，得到一个多级的小波系数列表。 coeff 从1开始，因为小波分解中第0级系数是原始信号的近似信号，而不是细节信号。第1级开始的才是细节...

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