scipy_filter.py

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


def filtering(values=np.repeat([0., 1., 0.], 10)):
    out = []

    rep = 2

    for z in values:
        for _ in range(rep):
            out.append(z)

    out = np.array(out)

    sos = signal.butter(1,0.1, output='sos')

    return signal.sosfiltfilt(sos, out)

     
    
    
    
    
    
    # real_values = np.real(values)
    # imag_values = np.imag(values)
    # #sig = np.repeat([0., 1., 0.], 100)
    # # print(real_values)
    # # print(imag_values)
    # sig_temp_real = []
    # sig_temp_imag = []
    # dt = 2
    # for x in values:
    #     for _ in range(dt):
    #         sig_temp_real.append(np.real(x))
    #         sig_temp_imag.append(np.imag(x))

    # sig_real = np.array(sig_temp_real)
    # sig_imag = np.array(sig_temp_imag)

    # win = signal.windows.hann(10)
    # filtered_real = signal.convolve(sig_real, win, mode='same') / sum(win)
    # filtered_imag = signal.convolve(sig_imag, win, mode='same') / sum(win)
    # #print(filtered_real)

    # magnitude = []
    # for a, b in zip(filtered_real, filtered_imag):
    #     magnitude.append(np.sqrt(a * a + b * b))
    # #print(magnitude)

    #fft_vals = scipy.fft.fft(magnitude)
    #print(fft_vals)

    # plt.figure()
    # plt.plot(sig_real, 'o')
    # plt.figure()
    # plt.plot(sig_imag, 'o')
    # plt.figure()
    # plt.plot(win, 'o')
    # plt.figure()
    # plt.plot(filtered_imag, 'o')
    # plt.figure()
    # plt.plot(filtered_real, 'o')
    # plt.figure()
    # plt.plot(magnitude, 'o')
    # plt.show()
    # plt.figure()
    # plt.plot(fft_vals, 'o')
    # plt.show()
    # filtered_complex = filtered_real + 1j * filtered_imag
    #print(filtered_complex)

    #wn = 48e6/((2*np.pi)/ (2*duration * 1e-9/ len(filtered_real)))
    # wn = 0.5
    #print(wn)
    #print(len(filtered_real))
    # sos = signal.butter(len(filtered_real), wn, output='sos')
    # #print(sos)
    # y = signal.sosfilt(sos,filtered_complex)
    # print(y)
    # print(filtered_complex)

    # plt.figure()
    # plt.plot(y, 'o')
    # plt.figure()
    # plt.plot(filtered_complex, 'o')
    # plt.show()

    return filtered_complex

if __name__ == "__main__":
    values = np.array([1.0+0j, 0.5+0.2j, 0.7-0.1j, 0.9+0j, 0.8, 0.3-0.3j, 0.1+0.5j])
    filtering(values=values)