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import numpy as np
import neo
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?
import mne
import matplotlib.pyplot as plt
# 创建任意数据
sfreq = 20 ?# 采样频率
times = np.arange(0, 5, 0.05) ?# Use 10000 samples (10s)
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sin = np.sin(times * 10) ?# 乘以 10 缩短周期
cos = np.cos(times * 10)
sinX2 = sin * 2
cosX2 = cos * 2
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# 数组大小为 4 X 10000.
data = np.array([sin])
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# 定义 channel types and names.
ch_types = ['mag']
ch_names = ['sin']
"""
创建info对象
"""
info = mne.create_info(ch_names=ch_names,
? ? ? ? ? ? ? ? ? ? ? ?sfreq=sfreq,?
? ? ? ? ? ? ? ? ? ? ? ?ch_types=ch_types)
"""
利用mne.io.RawArray创建raw对象
"""
#raw = mne.io.RawArray(data, info)
#picks = mne.pick_types(
# ? ? ? info, meg=False, eeg=True, stim=False,
# ? ? ? ?include=ch_names
# ? ?)
#raw.save("raw.fif", picks=picks, overwrite=True)
print(info)
#print(raw)
#print(data)
"""
对图形进行缩放
对于实际的EEG / MEG数据，应使用不同的比例因子。
对通道mag的数据进行2倍缩小，对grad的数据进行1.7倍缩小
"""
#scalings = {'mag': 2, 'grad':1.7}
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#raw.plot(n_channels=4, scalings=scalings, title='Data from arrays',show=True, block=True)
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"""
可以采用自动缩放比例
只要设置scalings='auto'即可
"""
#scalings = 'auto'
#raw.plot(n_channels=4, scalings=scalings,title='Auto-scaled Data from arrays',show=True, block=True)
#plt.show()
(mne-1.0.0_0) D:\>python raw_test3.py
<Info | 7 non-empty values
?bads: []
?ch_names: sin
?chs: 1 Magnetometers
?custom_ref_applied: False
?highpass: 0.0 Hz
?lowpass: 10.0 Hz
?meas_date: unspecified
?nchan: 1
?projs: []
?sfreq: 20.0 Hz
>
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