In [12]: 
import scipy.signal

def plotspec(x, Ts):
    fig = figure()
    ax1 = fig.add_subplot(211)
    ax1.plot(x)
    
    q = fft.fft(x)
    ax2 = fig.add_subplot(212)
    ax2.plot(fft.fftfreq(len(x), Ts), abs(q))

time = 0.3
Ts = 1.0/10000.0
t = linspace(Ts, time, time/Ts-1)
lent = len(t)
fc = 1000.0
c = cos(2*pi*fc*t)

fm = 20.0

w = 5.0/lent*(linspace(1, lent, lent))+cos(2*pi*fm*t)

v = c*w
gamma = 0
phi = 0
c2 = cos(2*pi*(fc+gamma)*t+phi)

x = v*c2
fbe = [0, 0.05, 0.1, 0.5]
damps = [1,0]
fl = 100
b = scipy.signal.remez(fl,fbe,damps)
m = 2*scipy.signal.lfilter(b,1,x)

plot(w)
figure()
plot(v)
figure()
plot(m)
Out[12]:
[<matplotlib.lines.Line2D at 0x1112566d0>]

5.5. Plot the spectra of w,v,x, and m(t).

In [15]: 
def modAM(w, fc, phi, Ts):
    time = Ts*len(w)
    t = linspace(0, time, len(w))
    
    c = cos(2*pi*fc*t+phi)
    
    v = c*w
    
    return v

def demodAM(v, fc, Ts):
    time = Ts*len(w)
    t = linspace(0, time, len(w))
    
    c2 = cos(2*pi*fc*t+phi)
    
    x = v * c2
    
    fbe = [0, 0.05, 0.1, 0.5]
    damps = [1,0]
    fl = 100
    
    b = scipy.signal.remez(fl, fbe, damps)
    
    m = 2*scipy.signal.lfilter(b,1,x)
    
    return m
    
    
    
Ts = 1.0/10000.0
    
w = 5.0/lent*(linspace(1, lent, lent))+cos(2*pi*fm*t)
v = modAM(w, 1000.0, 0.0, Ts)

m = demodAM(v, 1000.0, Ts)

plotspec(w, Ts)
plotspec(v, Ts)
plotspec(m, Ts)

5.6. Try different phase offsets. How well does the recovered message match? What does ths spectrum of m look like?

In [19]: 
Ts = 1.0/10000.0
    
w = 5.0/lent*(linspace(1, lent, lent))+cos(2*pi*fm*t)


for phi in [-pi, -pi/2, -pi/3, -pi/6, 0, pi/6, pi/3, pi/2, pi]:
    v = modAM(w, 1000.0, phi, Ts)
    m = demodAM(v, 1000.0, Ts)

    plotspec(m, Ts)

5.7. Try different frequency offsets: gamma = [0.01, 0.1, 1.0, 10]. How well does the recovery match?

In [22]: 
Ts = 1.0/10000.0
    
w = 5.0/lent*(linspace(1, lent, lent))+cos(2*pi*fm*t)


for gamma in [0.01, 0.1, 1.0, 10.0]:
    v = modAM(w, 1000.0+gamma, 0.0, Ts)
    m = demodAM(v, 1000.0, Ts)

    plotspec(m, Ts)
    xlim((-100,100))

5.9. Create a routine to implement the square-law mixing modulator of Figure 5.8.

In []: 
def modSquareLaw(w, a0, fc, bw, Ts):
    t = linspace(0, len(w)/Ts, Ts)
    c = a0*cos(2*pi*fc*t)
    
    x = w+c
    y = x*x
    
    Fs = 1.0/Ts
    
    bpe = [0, (fc-bw-0.1*bw)/Fs, (fc-bw)/Fs, (fc+bw)/Fs, (fc+bw+0.1*bw)/Fs, 0.5]
    damps = [0,1,0]
    
    b = scipy.signal.remez(100, bpe, damps)