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