

In [14]:

    
from numpy import sin, linspace, pi
from pylab import plot, show, title, xlabel, ylabel, subplot
from scipy import fft, arange
%matplotlib inline



In [15]:

    
def plotSpectrum(y,Fs):
    """
    Plots a Single-Sided Amplitude Spectrum of y(t)
    source: http://glowingpython.blogspot.com/2011/08/how-to-plot-frequency-spectrum-with.html
    """
    n = len(y) # length of the signal
    k = arange(n)
    T = n/Fs
    frq = k/T # two sides frequency range
    frq = frq[range(n/2)] # one side frequency range

    Y = fft(y)/n # fft computing and normalization
    Y = Y[range(n/2)]

    plot(frq,abs(Y),'r') # plotting the spectrum
    xlabel('Freq (Hz)')
    ylabel('|Y(freq)|')



In [16]:

    
Fs = 150.0;  # sampling rate
Ts = 1.0/Fs; # sampling interval
t = arange(0,1,Ts) # time vector

ff = 5;   # frequency of the signal
y = sin(2*pi*ff*t)



In [17]:

    
subplot(2,1,1)
plot(t,y)
xlabel('Time')
ylabel('Amplitude')
subplot(2,1,2)
plotSpectrum(y,Fs)
show()

Make your own signal by adding two signals together, then plot the spectum

You should see a spike at each ff you added in



In [34]:

    
Fs = 150.0;  # sampling rate
Ts = 1.0/Fs; # sampling interval
t = arange(0,1,Ts) # time vector
y2 = sin(2*pi*8*t)



In [42]:

    
subplot(2,1,1)
plot(t,y2)
xlabel('Time')
ylabel('Amplitude')
subplot(2,1,2)
plotSpectrum(y2,Fs)
show()

Import a sample signal and plot the freq spectrum



In [36]:

    
y3 = y + y2



In [43]:

    
subplot(2,1,1)
plot(t,y3)
xlabel('Time')
ylabel('Amplitude')
subplot(2,1,2)
plotSpectrum(y3,Fs)
show()



In [40]:

    
y4 = y * -1



In [44]:

    
subplot(2,1,1)
plot(t,y4)
xlabel('Time')
ylabel('Amplitude')
subplot(2,1,2)
plotSpectrum(y4,Fs)
show()



In [53]:

    
y5 = sin(2*pi*14*t)



In [54]:

    
subplot(2,1,1)
plot(t,y5)
xlabel('Time')
ylabel('Amplitude')
subplot(2,1,2)
plotSpectrum(y5,Fs)
show()



In [55]:

    
y6 = y5 + y3



In [56]:

    
subplot(2,1,1)
plot(t,y6)
xlabel('Time')
ylabel('Amplitude')
subplot(2,1,2)
plotSpectrum(y6,Fs)
show()



In [ ]: