import numpy as np import matplotlib.pyplot as plt from matplotlib.animation import FuncAnimation def wave(t, frequency, amplitude, phase, speed): x = np.linspace(0, 4 * np.pi, 1000) y = amplitude * np.sin(frequency * (x - speed * t) + phase) return x, y def update(t, wave1_params, wave2_params): x1, y1 = wave(t, *wave1_params) x2, y2 = wave(t, *wave2_params) line1.set_data(x1, y1) line2.set_data(x2, y2) line_sum.set_data(x1, y1 + y2) return line1, line2, line_sum # 初始波形参数 wave1_params = [2, 1, 0, 1] # 频率、振幅、相位、速度 wave2_params = [3, 1, 0, 1] fig, ax = plt.subplots() ax.set_xlim(0, 4 * np.pi) ax.set_ylim(-2, 2) line1, = ax.plot([], [], label='Wave 1') line2, = ax.plot([], [], label='Wave 2') line_sum, = ax.plot([], [], label='Sum', linestyle='--') plt.legend() ani = FuncAnimation(fig, update, frames=np.arange(0, 10, 0.1), fargs=(wave1_params, wave2_params), blit=True) plt.show() 可以研究的十个物理问题

import numpy as np import matplotlib.pyplot as plt import math

import matplotlib.pyplot as plt import math # 解决图标题中文乱码问题 import matplotlib as mpl mpl.rcParams['font.sans-serif'] = ['SimHei'] # 指定默认字体 mpl.rcParams['axes.unicode_minus'] = False #...

import numpy as np(2).py

matplotlib.pyplot绘图显示控制方法

在使用Python库时，常常会用到matplotlib....import matplotlib.pyplot as plt x = np.arange(0, 2*np.pi, .001) y = np.sin(2 * np.pi * x) plt.clf() plt.plot(x, y) l = plt.axhline(linewidth=1, color='black')

优化这段代码import scipy.io.wavfile as wavfile import numpy as np import matplotlib.pyplot as plt # 读取WAV文件 sample_rate, data = wavfile.read('test.wav') # 将音频数据转换为一维数组或二维数组 if data.ndim == 1: # 单声道音频数据转换为一维数组 audio_data = data else: # 双声道音频数据转换为二维数组 audio_data = data.sum(axis=1) / 2 # 显示音频波形图 plt.plot(audio_data) plt.show()

import matplotlib.pyplot as plt try: import scipy.io.wavfile as wavfile except ImportError: print('scipy module not found') def read_wav_file(file_path): try: # 读取 WAV 文件，指定编码为 'utf-8'...

详细解释一下import numpy as np import matplotlib.pyplot as plt def ricker(f, length=0.128, dt=0.001): t = np.linspace(-length/2, (length-dt)/2, int(length/dt)) y = (1-2*(np.pi**2)*(f**2)*(t**2))*np.exp(-(np.pi**2)*(f**2)*(t**2)) return t, y f = 20 # 设置Ricker子波频率为20Hz t, wavelet = ricker(f) # 生成20Hz Ricker子波 plt.figure() plt.plot(t, wavelet) plt.xlabel("Time (s)") plt.ylabel("Amplitude") plt.title("Ricker Wavelet (f = %d Hz)" % f) plt.show()

首先，通过import numpy as np和import matplotlib.pyplot as plt引入NumPy和Matplotlib库。接下来，定义了一个名为ricker的函数，用于生成Ricker子波。该函数的输入参数为频率f、长度length和时间间隔dt...

import librosa import librosa.display import matplotlib.pyplot as plt import numpy as np # 读取音频文件 audio_file = 'D:\360se6\bishe\古筝\gz1.wav' y, sr = librosa.load(audio_file, sr=None) # 计算CQT变换 C = librosa.cqt(y, sr=sr) # 可视化CQT plt.figure(figsize=(10, 4)) librosa.display.specshow(librosa.amplitude_to_db(np.abs(C), ref=np.max), sr=sr, x_axis='time', y_axis='cqt_note') plt.colorbar(format='%.0f') plt.title('CQT') plt.tight_layout() plt.show()

4. 使用Matplotlib库绘制CQT图像，其中使用librosa.amplitude_to_db()函数将CQT系数矩阵转换为以分贝为单位的矩阵，使用librosa.display.specshow()函数进行图像绘制，使用plt.colorbar()函数添加颜色条。...

import librosa import matplotlib.pyplot as plt import numpy as np import librosa.display # 1. Get the file path to the included audio example filepath = 'D:\\360se6\\bishe\\古筝\\' filename = filepath + 'gz1.wav' # 2. Load the audio as a waveform y # Store the sampling rate as sr y, sr = librosa.load(filename, sr=None) plt.figure(figsize=(12, 8)) amplitude_to_db(np.abs(S)) plt.subplot(4, 2, 1) librosa.display.specshow(D, y_axis='linear') plt.colorbar(format='%+2.0f dB') plt.title('Linear-frequency power spectrogram')

这段代码中在第8行的amplitude_to_db(np.abs(S))这一行缺少了一个赋值表达式。如果你想要将信号变换为分贝单位的幅度谱，应该将其赋值给一个变量，例如： S_db = librosa.amplitude_to_db(np.abs(S)) ...

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 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_name] data_test = [] ...

import wfdbimport numpy as npimport matplotlib.pyplot as pltfrom scipy import signal# 读取心电数据record = wfdb.rdrecord('D:/下载/ptb-xl-a-large-publicly-available-electrocardiography-dataset-1.0.3/records100/00000/00003_lr')# 获取心电信号ecg_signal = record.p_signal[:, 0]# 获取采样频率fs = record.fs# 获取时间轴t = np.arange(ecg_signal.size) / fs# 去除基线漂移ecg_detrend = signal.detrend(ecg_signal)# 定义滤波器b, a = signal.butter(4, 0.2, 'low')# 进行滤波ecg_filt = signal.filtfilt(b, a, ecg_detrend)# 绘制原始信号，滤波后的信号以及去除基线漂移后的信号plt.figure(figsize=(12, 6))plt.subplot(3, 1, 1)plt.plot(t, ecg_signal, 'b')plt.title('Original Signal')plt.xlabel('Time (s)')plt.ylabel('Amplitude (mV)')plt.subplot(3, 1, 2)plt.plot(t, ecg_filt, 'r')plt.title('Filtered Signal')plt.xlabel('Time (s)')plt.ylabel('Amplitude (mV)')plt.subplot(3, 1, 3)plt.plot(t, ecg_detrend, 'g')plt.title('Detrended Signal')plt.xlabel('Time (s)')plt.ylabel('Amplitude (mV)')plt.tight_layout()plt.show()请在这段代码的基础上加上绘制去除基线漂移并滤波后的图像

import matplotlib.pyplot as plt from scipy import signal # 读取心电数据 record = wfdb.rdrecord('D:/下载/ptb-xl-a-large-publicly-available-electrocardiography-dataset-1.0.3/records100/00000/00003_lr')...

import librosa as librosa import pycharts import numpy as np from scipy.fft import rfft, irfft from scipy.io import wavfile import matplotlib.pyplot as plt # 读取音频文件 fs, audio = wavfile.read("C:/Users/H-XUE49/Desktop/语音信号/实验一/雷声.wav") # 将音频转换为单声道信号 audio = np.mean(audio, axis=1) # 计算时间轴 time = np.arange(len(audio)) / fs # 绘制时间波形图 pycharts.plot(time, audio, xlabel="Time (s)", ylabel="Amplitude", title="Time domain waveform of audio signal") # 计算音频信号的自相关函数 autocorr = np.correlate(audio, audio, mode="full") # 取自相关函数的一半作为倒谱 r = autocorr[len(autocorr)//2:] # 计算倒谱 cepstrum = irfft(np.log(np.abs(r))) # 绘制倒谱图 pycharts.plot(np.arange(len(cepstrum)) / fs, cepstrum, xlabel="Time (s)", ylabel="Cepstrum", title="Cepstrum of audio signal")为什么显示ValueError: File format b'ID3\x03' not understood. Only 'RIFF' and 'RIFX' supported.请修改错误

import matplotlib.pyplot as plt # 读取音频文件 audio, fs = librosa.load("C:/Users/H-XUE49/Desktop/语音信号/实验一/雷声.mp3", sr=None, mono=True) # 计算时间轴 time = np.arange(len(audio)) / fs # ...

以一串代码为例完成 2、设计一个模拟Butterworth低通滤波器，指标要求：fp=56Hz，fs=250Hz，Rp=2.5dB，As=47dB，并利用signal.freqs绘制幅频响应曲线(频率单位：rad/s，幅度响应单位:dB)。，代码如下：import matplotlib.pyplot as plt import numpy as np from scipy import signal b, a = signal.butter(4, 100, 'low', analog=True) w, h = signal.freqs(b, a) plt.semilogx(w, 20 * np.log10(abs(h))) plt.title('Butterworth filter frequency response') plt.xlabel('Frequency [rad/s]') plt.ylabel('Amplitude [dB]') plt.margins(0, 0.1) plt.grid(which='both', axis='both') plt.axvline(100, color='green') # cutoff frequency plt.show()

4. 使用Matplotlib库中的plt.semilogx函数来绘制幅频响应曲线，其中w表示x轴数组，20 * np.log10(abs(h))表示y轴数组，plt.title表示图表标题，plt.xlabel表示x轴标签，plt.ylabel表示y轴标签，plt.margins表示图表...

如何在这个函数中输出图片# -- coding: utf-8 -- """ Created on Fri May 20 10:01:20 2023 @author: Rayquaza """ #%%In[1] import numpy as np import matplotlib.pyplot as plt #%%In[2] def ricker(f, length, dt): t = np.arange(-length/2,(length-dt)/2, dt) y = (1.0 - 2.0*(np.pi**2)*(f**2)*(t**2)) * np.exp(-(np.pi**2)*(f**2)*(t**2)) return t,y i = 0 ; ax = {} Frequency = [5,10,20,30];length = 0.128;dt = 0.001 fig = plt.figure(num=1, figsize=(20,15),dpi=300) for f in Frequency: t, w = ricker(f, length, dt) ax[i] = fig.add_subplot(2, 2, i+1) ax[i].plot(t,w) ax[i].set_title(str(f)+"Hz ricker wavelet",fontsize = 16) ax[i].set_xlabel("time",fontsize =12) ax[i].set_ylabel("amplitude",fontsize = 12) i=i+1 plt.tight_layout() plt.show()

你可以在 plt.show() 之前调用 plt.savefig() 方法来保存图片，例如： python fig = plt.figure(num=1, figsize=(20,15),dpi=300) for f in Frequency: t, w = ricker(f, length, dt) ax[i] = fig.add_...

import librosa import matplotlib.pyplot as plt filepath = 'D:\\360se6\\bishe\\古筝\\' filename = filepath + 'gz1.wav' y, sr = librosa.load(filename,sr = None ) CQT = librosa.amplitude_to_db(librosa.cqt(y, sr = 16000 ), ref = np. max ) plt.subplot( 4 , 2 , 3 ) librosa.display.specshow(CQT, y_axis = 'cqt_note' ) plt.colorbar( format = '%+2.0f dB' ) plt.title( 'Constant-Q power spectrogram (note)' )

这段代码使用了Python的librosa库对一个音频文件进行了处理，并使用matplotlib库绘制了CQT表示的音频频谱图。具体来说： - 第1行导入了librosa库；...因此，需要在代码开头添加import numpy as np语句，以便正确运行。

优化这段import numpy as np import matplotlib.pyplot as plt %config InlineBackend.figure_format='retina' # 输入信号 def inputVoltageSignal_func(t_vec, A, phi, noise, freq): Omega = 2np.pifreq return Anp.sin(Omegat_vec + phi) + noise * (2np.random.random(t_vec.size)-1) # 锁相测量部分 def LockinMeasurement_func(inputVoltageSignal, t_vec, ref_freq): # 生成参考信号 sin_ref = 2np.sin(2 * np.pi * ref_freq * t_vec) cos_ref = 2np.cos(2 np.pi * ref_freq * t_vec) # 混频信号 signal_0 = inputVoltageSignal * sin_ref signal_1 = inputVoltageSignal * cos_ref # 低通滤波 X = np.mean(signal_0) Y = np.mean(signal_1) # 计算振幅和相位 A = np.sqrt(X2 + Y2) phi = np.arctan2(Y, X) return A, phi # 参数 A = 1 phi = 0 noise = 1 ref_freq = 100 t_vec = np.linspace(0, 0.2, 1001) # 列表来保存幅值和相位数据 amplitude_list = [] phase_list = [] freq_list = np.arange(1, 1001) # 循环计算不同频率下的幅值和相位 for freq in freq_list: # 生成原始信号 Vin_vec = inputVoltageSignal_func(t_vec, A, phi, noise, freq=freq) # 锁相测量 A, phi = LockinMeasurement_func(Vin_vec, t_vec, ref_freq=freq) # 保存幅值和相位数据 amplitude_list.append(A) phase_list.append(phi) #绘图 # 幅值与频率的关系图 plt.figure(figsize=(10, 6)) plt.subplot(2,1,1) plt.plot(freq_list, amplitude_list) plt.xlabel('freq (Hz)') plt.ylabel('A') plt.title('relationship between A and freq') plt.show() # 相位与频率的关系图 plt.figure(figsize=(10, 6)) plt.subplot(2,1,2) plt.plot(freq_list, phase_list) plt.xlabel('freq (Hz)') plt.ylabel('Phi') plt.title('relationship between Phi and freq') plt.show()使用while循环

import matplotlib.pyplot as plt %config InlineBackend.figure_format='retina' # 输入信号 def inputVoltageSignal_func(t_vec, A, phi, noise, freq): Omega = 2 * np.pi * freq return A * np.sin(Omega * ...

import librosa import librosa.display import matplotlib.pyplot as plt # 读取音频文件 audio_file = 'example.wav' y, sr = librosa.load(audio_file, sr=None) # 计算CQT变换 C = librosa.cqt(y, sr=sr) # 可视化CQT plt.figure(figsize=(10, 4)) librosa.display.specshow(librosa.amplitude_to_db(C, ref=np.max), sr=sr, x_axis='time', y_axis='cqt_note') plt.colorbar(format='%+2.0f dB') plt.title('CQT') plt.tight_layout() plt.show()

你需要在代码文件的开头部分加入以下语句： ...import numpy as np 因为代码中使用了np.max，需要导入numpy库才能使用np。加入这句话之后，代码应该可以正确运行并绘制出CQT变换的可视化图像了。

优化这段python代码import numpy as np import matplotlib.pyplot as plt %config InlineBackend.figure_format='retina' # 输入信号 def inputVoltageSignal_func(t_vec, A, phi, noise, freq): Omega = 2np.pifreq return Anp.sin(Omegat_vec + phi) + noise * (2np.random.random(t_vec.size)-1) # 锁相测量部分 def LockinMeasurement_func(inputVoltageSignal, t_vec, ref_freq): # 生成参考信号 sin_ref = 2np.sin(2 * np.pi * ref_freq * t_vec) cos_ref = 2np.cos(2 np.pi * ref_freq * t_vec) # 混频信号 signal_0 = inputVoltageSignal * sin_ref signal_1 = inputVoltageSignal * cos_ref # 低通滤波 X = np.mean(signal_0) Y = np.mean(signal_1) # 计算振幅和相位 A = np.sqrt(X2 + Y2) phi = np.arctan2(Y, X) return A, phi # 振幅和相位 A = 1 phi = 0 # 参考频率 ref_freq = 17.77777 # 加入噪声 noise = 0.1 #可通过调节参数控制噪声大小 # 时间 t_vec = np.linspace(0, 0.2, 1001) # 生成原始信号 Vin_vec = inputVoltageSignal_func(t_vec, A, phi, noise, freq=ref_freq) # 锁相测量 A, phi = LockinMeasurement_func(Vin_vec, t_vec, ref_freq) print('Result: A=%.3f, phi=%.3f'%(A,phi)),使freq从1增加到1000，最后画出两张图，一张是输出信号幅值A与频率freq的关系，第二张是输出信号相位phi与频率freq的关系

import matplotlib.pyplot as plt %config InlineBackend.figure_format='retina' # 输入信号 def inputVoltageSignal_func(t_vec, A, phi, noise, freq): Omega = 2*np.pi*freq return A*np.sin(Omega*t_vec + ...

python 3.74 运行import numpy as np 报错lib\site-packages\numpy\init.py

import numpy as np import os,sys #获取当前文件夹，并根据文件名 def path(fileName): p=sys.path[0]+'\\'+fileName return p #读文件 def readFile(fileName): f=open(path(fileName)) str=f.read() ...

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numpy numpy_numpy教程之Matplotlib

优化这段pythonimport numpy as np import matplotlib.pyplot as plt import math # 待测信号 freq = 17.77777 # 信号频率 t = np.linspace(0, 0.2, 1001) Omega =2 * np.pi * freq phi = np.pi A=1 x = A * np.sin(Omega * t + phi) # 加入噪声 noise = 0.2 * np.random.randn(len(t)) x_noi

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优化这段pythonimport numpy as np import matplotlib.pyplot as plt import math # 待测信号 freq = 17.77777 # 信号频率 t = np.linspace(0, 0.2, 1001) Omega =2 * np.pi * freq phi = np.pi A=1 x = A * np.sin(Omega * t + phi) # 加入噪声 noise = 0.2 * np.random.randn(len(t)) x_noi

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