一维信号的时频分析（Python）

代码较为简单，很容易读懂。

Importing the required libraries

import os
import numpy as np
import pywt
import pandas as pd
import pickle as pkl
from matplotlib import pyplot as plt

Parameters or Required Variables

DATA_POINTS_PER_FILE = 2560
TIME_PER_REC = 0.1
SAMPLING_FREQ = 25600 # 25.6 KHz
SAMPLING_PERIOD = 1.0/SAMPLING_FREQWIN_SIZE = 20
WAVELET_TYPE = 'morl'

Helper Functions

def load_df(pkz_file):with open(pkz_file, 'rb') as f:df=pkl.load(f)return df

# perform CWT on 1d signals and return 2d feature image
def extract_feature_image(ind, feature_name='horiz accel'):data_range = df_row_ind_to_data_range(ind)data = df[feature_name].values[data_range[0]:data_range[1]]# use window to process(= prepare, develop) 1d signaldata = np.array([np.mean(data[i:i+WIN_SIZE]) for i in range(0, DATA_POINTS_PER_FILE, WIN_SIZE)])  # perform cwt on 1d datacoef, _ = pywt.cwt(data, np.linspace(1,128,128), WAVELET_TYPE)  # transform to power and apply logarithm ?!coef = np.log2(coef**2+0.001) # normalize coefcoef = (coef - coef.min())/(coef.max() - coef.min()) return coef

main_dir = ''

pkz_file=main_dir+'bearing1_3.pkz'
df=load_df(pkz_file)
df.head()

Printing total data points and total no. of data files present in a pickle(.pkz) file

no_of_rows = df.shape[0]
no_of_files = int(no_of_rows / DATA_POINTS_PER_FILE)
print(no_of_rows, no_of_files)

Plotting 1D vibration signals(both horiz accel and vert accel)

plt.plot(range(no_of_rows), df['horiz accel'])
plt.show()
plt.plot(range(no_of_rows), df['vert accel'], 'r')
plt.show()

signal processing = Extracting Time-Frequency Domain feature images

no_of_samples=5
fig, ax = plt.subplots(2, no_of_samples, figsize=[20,8])
ax[0,0].set_ylabel('horiz accel features image')
ax[1,0].set_ylabel('vert accel features image')
'''
dividing the feature images into 5 samples
'''
for i, p in enumerate(np.linspace(0,1,no_of_samples)):ind = int((no_of_files-1)*p)#extracting and plotting horizontal acceleration feature images (horiz accel feature images) for 5 samples (0.00, 0.25, 0.50, 0.75, 1.00).#horiz accel => horizontal acceleration vibration signalcoef = extract_feature_image(ind, feature_name='horiz accel')ax[0,i].set_title('{0:.2f}'.format(p))im = ax[0,i].imshow(coef, cmap='coolwarm')fig.colorbar(im, ax=ax[0,i], fraction=0.046, pad=0.04)#extracting and plotting vertical acceleration feature images (vert accel feature images) for 5 samples (0.00, 0.25, 0.50, 0.75, 1.00). #vert accel => vertical acceleration vibration signalcoef = extract_feature_image(ind, feature_name='vert accel')ax[1,i].set_title('{0:.2f}'.format(p))im = ax[1,i].imshow(coef, cmap='coolwarm')fig.colorbar(im, ax=ax[1,i], fraction=0.046, pad=0.04)'''
The tight_layout() function in pyplot module of matplotlib library is used to automatically adjust subplot parameters to give specified padding.
padding = adding space (adding required white space)
'''
plt.tight_layout()
'''
The show() function in pyplot module of matplotlib library is used to display all figures.
'''
plt.show()