69 lines
1.9 KiB
Python
69 lines
1.9 KiB
Python
#!/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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if 1000 < dataset.mean() < 2000:
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dataset[dataset == 0] = 1825
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dataset[dataset < 1200] = 1225
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dataset[dataset > 2400] = 2425
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elif 2000 < dataset.mean() < 3000:
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dataset[dataset == 0] = 2375
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dataset[dataset < 1775] = 1775
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dataset[dataset > 2975] = 2975
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dataset -= 550
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else:
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print("something wrong")
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# select_dataset = preprocessing.Butterworth(select_dataset, "lowpass", low_cut=0.7, order=3)
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dataset -= dataset.mean()
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dataset = dataset.dot(1 / 600)
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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=0.7, order=6)
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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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dataset_low = dataset_low.reshape(1, dataset_low.shape[0])
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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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if __name__ == '__main__':
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pass
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