from scipy.signal import butter, lfilter


def _butter_lowpass(cutoff, fs, order=5):
    nyq = 0.5 * fs
    normal_cutoff = cutoff / nyq
    b, a = butter(order, normal_cutoff, btype="low", analog=False)
    return b, a


def butter_lowpass_filter(data, cutoff, fs, order=5):
    b, a = _butter_lowpass(cutoff, fs, order=order)
    y = lfilter(b, a, data)
    return y


def identical(x):
    return x

6c00cf392d4b0a4c2b7cf2313bc8cf4aa6dea421
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def identical(x):
    return x

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def butter_lowpass_filter(data, cutoff, fs, order=5):
    b, a = _butter_lowpass(cutoff, fs, order=order)
    y = lfilter(b, a, data)
    return y


def identical(x):
    return x

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def _butter_lowpass(cutoff, fs, order=5):
    nyq = 0.5 * fs
    normal_cutoff = cutoff / nyq
    b, a = butter(order, normal_cutoff, btype='low', analog=False)
    return b, a


def butter_lowpass_filter(data, cutoff, fs, order=5):
    b, a = _butter_lowpass(cutoff, fs, order=order)
    y = lfilter(b, a, data)
    return y


def identical(x):
    return x

from scipy.signal import butter, lfilter


def _butter_lowpass(cutoff, fs, order=5):
    nyq = 0.5 * fs
    normal_cutoff = cutoff / nyq
    b, a = butter(order, normal_cutoff, btype='low', analog=False)
    return b, a


def butter_lowpass_filter(data, cutoff, fs, order=5):
    b, a = _butter_lowpass(cutoff, fs, order=order)
    y = lfilter(b, a, data)
    return y


def identical(x):
    return x

from scipy.signal import butter, lfilter


def _butter_lowpass(cutoff, fs, order=5):
    nyq = 0.5 * fs
    normal_cutoff = cutoff / nyq
    b, a = butter(order, normal_cutoff, btype="low", analog=False)
    return b, a


def butter_lowpass_filter(data, cutoff, fs, order=5):
    b, a = _butter_lowpass(cutoff, fs, order=order)
    y = lfilter(b, a, data)
    return y


def identical(x):
    return x
