EMG-pattern-recognition/digital_processing.py at master · GraphiteSprite/EMG-pattern-recognition · GitHub

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from scipy import signal
import matplotlib.pyplot as plt
import numpy as np
import pyyawt


def notch_filter(x, samplerate, plot=False):
    x = x - np.mean(x)

    high_cutoff_notch = 59 / (samplerate / 2)
    low_cutoff_notch = 61 / (samplerate / 2)

    [b, a] = signal.butter(4, [high_cutoff_notch, low_cutoff_notch], btype='stop')

    x_filt = signal.filtfilt(b, a, x.T)

    if plot:
        t = np.arange(0, len(x) / samplerate, 1 / samplerate)
        plt.plot(t, x)
        plt.plot(t, x_filt.T, 'k')
        plt.autoscale(tight=True)
        plt.xlabel('Time')
        plt.ylabel('Amplitude (mV)')
        plt.show()

    return x_filt


def bp_filter(x, low_f, high_f, samplerate, plot=False):
    # x = x - np.mean(x)

    low_cutoff_bp = low_f / (samplerate / 2)
    high_cutoff_bp = high_f / (samplerate / 2)

    [b, a] = signal.butter(5, [low_cutoff_bp, high_cutoff_bp], btype='bandpass')

    x_filt = signal.filtfilt(b, a, x)

    if plot:
        t = np.arange(0, len(x) / samplerate, 1 / samplerate)
        plt.plot(t, x)
        plt.plot(t, x_filt, 'k')
        plt.autoscale(tight=True)
        plt.xlabel('Time')
        plt.ylabel('Amplitude (mV)')
        plt.show()

    return x_filt


def plot_signal(x, samplerate, chname):
    t = np.arange(0, len(x) / samplerate, 1 / samplerate)
    plt.plot(t, x)
    plt.autoscale(tight=True)
    plt.xlabel('Time')
    plt.ylabel('Amplitude (mV)')
    plt.title(chname)
    plt.show()


def denoisewavelet(x1, level=5):
    xd, cxd, lxd = pyyawt.wden(x1, 'minimaxi', 's', 'mln', level, 'db5')
    return xd