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 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))
