def test_channel(channel):
"""
create a test waveform and plot both it and the output of
passing the waveform through the channel.
"""
name = channel.__name__
message = [1, 0, 1, 0, 1, 0, 1, 0]
inp = lab2_1.transmit(message, 10, 100)
out = channel(inp)
upper = max(max(inp), max(out))
lower = min(min(inp), min(out))
upper = upper + abs(upper) * 0.1 # leave some space at the edges, unless that edge is 0
lower = lower - abs(lower) * 0.1
p.figure()
p.suptitle(name, fontsize=20, fontweight="bold")
p.subplot(211)
p.axis([0, len(inp), lower, upper]) # custom axis scaling
p.plot(inp, "g-")
p.title("Input")
p.grid(True)
p.ylabel("Voltage")
p.subplot(212)
p.axis([0, len(inp), lower, upper]) # make the axes the same
p.plot(out, "b-")
p.title("Output")
p.grid(True)
p.ylabel("Voltage")
p.xlabel("Sample Number")
p.savefig(name + ".png")
p.clf() # clears the figure so we can start blank again
def testDeconvolver(channel,noise=0.):
# generates a random 8 bit message
message = lab2_5.randomMessage(100)
# generates samples from the bit sequence
inp = numpy.array(lab2_1.transmit(message,0,100))
# passes the samples through channel
out = channel(inp,noise)
# uses the deconvolver to reconstruct the input
dMessage = deconvolver(out,lab2_3.unit_sample_response(channel))
p.figure() # plot
p.suptitle('Random Message through '+channel.__name__,fontsize = 20,fontweight='bold')
plot_samples(311, inp, len(inp), -.1, 1.1, 'Input')
plot_samples(312, out, len(out), -.1, 1.1, 'Output', 'g-')
plot_samples(313, dMessage, len(dMessage), -.1, 1.1, 'Deonvolved Result', 'r-')
p.subplots_adjust(hspace=0.5)
if noise == 0.:
p.savefig(channel.__name__+'Deconvolution.png')
else:
p.savefig(channel.__name__+'Noise'+str(noise)+'Deconvolution.png')
p.clf()
def transmitMessage(channel): # compute the output of the channel when transmitting the message # 10101010 with a 10 sample preamble and 100 sample postamble message = [1,0,1,0,1,0,1,0] inp = lab2_1.transmit(message,10,100) return channel(inp)
def predictChannelOutput(channel): message = [1,0,1,0,1,0,1,0] return convolutionSum(lab2_1.transmit(message,10,100),lab2_3.unit_sample_response(channel))
def transmitMessage(message,channel,samples_per_bit): inp = lab2_1.transmit(message,0,0,samples_per_bit) return channel(inp)
