81 lines
2.2 KiB
Python
81 lines
2.2 KiB
Python
#!/usr/bin/python
|
||
# -*- coding: UTF-8 -*-
|
||
"""
|
||
@author:Marques
|
||
@file:my_augment.py
|
||
@email:admin@marques22.com
|
||
@email:2021022362@m.scnu.edu.cn
|
||
@time:2022/07/26
|
||
"""
|
||
from utils.Preprocessing import BCG_Operation
|
||
import numpy as np
|
||
from scipy.signal import stft
|
||
|
||
preprocessing = BCG_Operation()
|
||
preprocessing.sample_rate = 100
|
||
|
||
|
||
def my_augment(dataset):
|
||
dataset -= dataset.mean()
|
||
dataset = preprocessing.Iirnotch(dataset)
|
||
dataset = preprocessing.Butterworth(dataset, "lowpass", low_cut=20, order=6)
|
||
dataset_low = preprocessing.Butterworth(dataset, "lowpass", low_cut=0.5, order=4)
|
||
dataset_low = (dataset_low - dataset_low.mean()) / dataset_low.std()
|
||
# dataset_high = preprocessing.Butterworth(dataset, "highpass", high_cut=1, order=6)
|
||
|
||
dataset = {"low": dataset_low}
|
||
# "high": dataset_high}
|
||
return dataset
|
||
|
||
|
||
def get_stft(x, fs, n):
|
||
print(len(x))
|
||
f, t, amp = stft(x, fs, nperseg=n)
|
||
z = np.abs(amp.copy())
|
||
return f, t, z
|
||
|
||
|
||
def my_segment_augment(dataset, SP, EP):
|
||
dataset_low = dataset["low"][int(SP) * 100:int(EP) * 100].copy()
|
||
# dataset_high = dataset["high"][int(SP) * 100:int(EP) * 100].copy()
|
||
|
||
dataset_low = dataset_low[::10]
|
||
|
||
# 获取整段的特征 （3，1）
|
||
# 按照十秒窗获取 （3，3）
|
||
# 按照十秒窗步进两秒获取 （3，21）
|
||
|
||
sub_windows_size = 30
|
||
stride = 1
|
||
|
||
manual_feature = [[], [], []]
|
||
|
||
SP = 0
|
||
EP = sub_windows_size
|
||
while EP <= sub_windows_size:
|
||
# mean
|
||
manual_feature[0].append(abs(dataset_low[SP:EP]).mean())
|
||
# var
|
||
manual_feature[1].append(abs(dataset_low[SP:EP]).var())
|
||
# RMS
|
||
manual_feature[2].append(np.sqrt((dataset_low[SP:EP] ** 2).mean()))
|
||
|
||
SP += stride
|
||
EP += stride
|
||
|
||
dataset_low = dataset_low.reshape(-1, 1)
|
||
manual_feature = np.array(manual_feature)
|
||
manual_feature = manual_feature.reshape(1, -1)
|
||
|
||
# _, _, dataset_high = stft(dataset_high, 100, nperseg=50)
|
||
# dataset_high = dataset_high.astype(np.float).T
|
||
# dataset_high = dataset_high.reshape(dataset_high.shape[0], dataset_high.shape[1])
|
||
|
||
# return dataset_low, dataset_high
|
||
|
||
return dataset_low, manual_feature
|
||
|
||
|
||
if __name__ == '__main__':
|
||
pass
|