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(int.from_bytes(data, byteorder='little', signed=False) - int(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(int.from_bytes(data, byteorder='little', signed=False) - int(dataset[n - 2])) < 700: ValueError: invalid literal for int() with base 10: b'1849580.25000\x00'

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 numpy as np import pywt from scipy.fftpack import dct def nsst(image): # 将图像转换为灰度图像 def nsst(image): # 将图像转换为灰度图像 if len(image.shape) == 3: image = np.mean(image, axis=2) # 设置NSST参数 scales = 3 shearlet_system = pywt.ShearletSystem2D(image.shape, scales) # 计算图像的NSST分解系数 coeffss = [] for scale in range(scales): for shear in range(shearlet_system.shear_count): shearlet = shearlet_system.shearlets[scale][shear] shearlet_fft = np.fft.fft2(shearlet, image.shape) shearlet_fft_conj = np.conj(shearlet_fft) image_fft = np.fft.fft2(image) shearlet_coeff = np.fft.ifft2(shearlet_fft_conj * image_fft) coeffss.append(shearlet_coeff) # 将NSST分解系数组合成一个张量 coeffs = np.stack(coeffss, axis=-1) return coeffs coeffs1 = nsst_decomposition(image1, num_scales=4) coeffs2 = nsst_decomposition(image2, num_scales=4)

import numpy as np import pywt from scipy.fftpack import dct def nsst_decomposition(image): # 将图像转换为灰度图像 if len(image.shape) == 3: image = np.mean(image, axis=2) # 设置NSST参数 ...

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 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 = ...

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 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 pywt

import pywt is a Python library that provides support for discrete wavelet transforms (DWT), wavelet packet transforms (WPT), and multilevel wavelet decomposition and reconstruction. It is used for ...

安装完pywavelets后，import pywt还会出错是为什么

如果你已经按照上面的方法成功安装了 pywavelets，但在 Python 程序中仍然无法正常导入 pywt 模块，可能是因为 Python 找不到 pywt 模块的安装路径。你可以尝试以下方法解决该问题： 1. 确认 pywt 已经正确...

import pywt ModuleNotFoundError: No module named 'pywt'

3. 等待安装完成后，你就可以在你的Python脚本中使用import pywt语句来导入pywt模块了。如果你已经安装了pywt模块但仍然遇到该错误，请确保你的Python环境中只有一个版本，并且该版本中已正确安装了pywt模块。 ...

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) -> ...

import pywt报错

这个问题可能是因为您没有安装 pywt 库或者版本不兼容导致的。您可以尝试使用以下命令安装最新版本的 pywt 库： pip install pywt --upgrade 如果您已经安装了 pywt 库，可以尝试卸载并重新安装： ...

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 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...

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...

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