CPM-modem/CPM_Spettri.py at master · FForzano/CPM-modem · GitHub

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import numpy as np
from ImpostazioneSistema.generate_impulse import LREC, LRC
from ImpostazioneSistema.generate_fwaveform import fwaveform
from Modulatore.Modulatore import modulate_signal
from scipy.fft import fft, fftfreq
from scipy.signal import lfilter, butter
from matplotlib import pyplot as plt

def lowpass(x, f_cutoff, fs):
    b,a= butter(5, f_cutoff, fs=fs, btype='low', analog=False)
    return lfilter(b,a,x)

def set_parameters(j, m, T_symbol, T_sample, L, f_0, M):
    with open('modulation_parameter.configurations','wt') as fd:
        print('j=',j,file=fd)
        print('m=',m,file=fd)
        print('T_symbol=',T_symbol,file=fd)
        print('T_sample=',T_sample,file=fd)
        print('L=',L,file=fd)
        print('f_0=',f_0,file=fd)
        print('M=',M,file=fd)

    alph = np.arange(1,2*M,2)-M
    with open('symbols.configurations','wt') as fd:
        for el in alph:
            print(el, file=fd)

if __name__ == '__main__':
    dB = False

    # Shared parameters
    n_symbols=1e3
    T_symbol = 1e-2
    f_0 = 1e3
    T_sample = 1/(10*f_0)
    M=4
    windowSize = 200 # filtering window size
    modType = LREC

    # Gli assi vengono calcolati nella prima simulazione

    ###################################################
    # CPFSK
    # h=0.25, L=1
    ###################################################
    j=1
    m=4
    L=1
    set_parameters(j, m, T_symbol, T_sample, L, f_0, M)
    fwaveform(modType)
    _,s = modulate_signal(n_symbols)

    # Assi temporali e delle frequenze
    t = np.arange(len(s))*T_sample
    f = fftfreq(len(t), T_sample)
    window = (f>f_0) & (f<(f_0+3*1/T_symbol))
    # window = [True]*len(f)

    # Spectra computing:
    Sf = fft(s)
    PDS = T_symbol / (2**(int(np.log2(len(s)))+1)) * (np.abs(Sf)**2)
    PDS = np.array(PDS)

    # filtering for a better spectrum --> VBW
    b = (1/windowSize)*np.ones(windowSize)
    a = 1
    PDS = lfilter(b,a,PDS)

    if dB:
        PDS_dB = 10*np.log10(PDS)
        # PDS_dB = PDS_dB - np.max(PDS_dB)
        plt.plot(f[window]*T_symbol,PDS_dB[window])
    else:
        plt.plot(f[window]*T_symbol,PDS[window])


    ###################################################
    # CPFSK
    # h=0.5, L=1
    ###################################################
    j=1
    m=2
    L=1
    set_parameters(j, m, T_symbol, T_sample, L, f_0, M)
    fwaveform(modType)
    _,s = modulate_signal(n_symbols)

    # Spectra computing:
    Sf = fft(s)
    PDS = T_symbol / (2**(int(np.log2(len(s)))+1)) * (np.abs(Sf)**2)
    PDS = np.array(PDS)


    # filtering for a better spectrum

    b = (1/windowSize)*np.ones(windowSize)
    a = 1
    PDS = lfilter(b,a,PDS)

    if dB:
        PDS_dB = 10*np.log10(PDS)
        # PDS_dB = PDS_dB - np.max(PDS_dB)
        plt.plot(f[window]*T_symbol,PDS_dB[window])
    else:
        plt.plot(f[window]*T_symbol,PDS[window])

    ###################################################
    # CPFSK
    # h=0.8, L=1
    ###################################################
    j=4
    m=5
    L=1
    set_parameters(j, m, T_symbol, T_sample, L, f_0, M)
    fwaveform(modType)
    _,s = modulate_signal(n_symbols)

    # Spectra computing:
    Sf = fft(s)
    PDS = T_symbol / (2**(int(np.log2(len(s)))+1)) * (np.abs(Sf)**2)
    PDS = np.array(PDS)

    # filtering for a better spectrum

    b = (1/windowSize)*np.ones(windowSize)
    a = 1
    PDS = lfilter(b,a,PDS)

    if dB:
        PDS_dB = 10*np.log10(PDS)
        # PDS_dB = PDS_dB - np.max(PDS_dB)
        plt.plot(f[window]*T_symbol,PDS_dB[window])
    else:
        plt.plot(f[window]*T_symbol,PDS[window])

    ###################################################
    # CPFSK
    # h=1, L=1
    ###################################################
    j=1
    m=1
    L=1
    set_parameters(j, m, T_symbol, T_sample, L, f_0, M)
    fwaveform(modType)
    _,s = modulate_signal(n_symbols)

    # Spectra computing:
    Sf = fft(s)
    PDS = T_symbol / (2**(int(np.log2(len(s)))+1)) * (np.abs(Sf)**2)

    # filtering for a better spectrum

    b = (1/windowSize)*np.ones(windowSize)
    a = 1
    PDS = lfilter(b,a,PDS)
    # PDS = lowpass(PDS,0.05,1/(f[1]-f[0]))

    if dB:
        PDS_dB = 10*np.log10(PDS)
        # PDS_dB = PDS_dB - np.max(PDS_dB)
        plt.plot(f[window]*T_symbol,PDS_dB[window])
    else:
        plt.plot(f[window]*T_symbol,PDS[window])

    # Visualizzazione grafico
    plt.title('CPM power density spectra')
    plt.xlabel('f T_s')
    plt.ylabel('S(f) [dB]')
    plt.show()