grasp.py 文件源码

def butter_highpass(highcut, fs, order):
    nyq = 0.5 * fs
    high = highcut / nyq
    b, a = butter(order, high, btype="highpass")
    return b, a


# Sources for Batch Iterators
#
# These classes load training and test data and perform some basic preprocessing on it.
# They are then passed to factory functions that create the net. There they are used
# as data sources for the batch iterators that feed data to the net.
# All classes band pass or low pass filter their data based on min / max freq using
# a causal filter (lfilter) when the data is first loaded.
# * TrainSource loads a several series of EEG data and events, splices them together into
#   one long stream, then normalizes the EEG data to zero mean and unit standard deviation.
# * TestSource is like TrainSource except that it uses the mean and standard deviation
#   computed for the associated training source to normalize the EEG data.
# * SubmitSource is like TestSource except that it does not load and event data.