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      import matplotlib.pyplot as plt

      from bin16 import *

      from fft import *

      import math

      #These are the first parameters for choosing the time step of our vector

      fs = 98304

      time_step = 1.0/fs

      t_ini = 0

      t_fin = 0.25

      time_vec = np.arange(t_ini, t_fin, time_step)

      #The two frequences we're going to use for our different signals

      f1 = 4000

      f2 = 800

      #This ensures me of getting the higher value of N who's a power of 2 and smaller than the length of the time_vec

      N = int(math.pow(2,int(math.log(len(time_vec),2))))

      x1 = 0.5 * np.cos(2 * np.pi * f1 * time_vec)

      x2 = 0.5 * np.cos(2 * np.pi * f2 * time_vec)

      liminf = -1

      limsup = +1

      #This will take our float-number signals, who were going from -0.5 to 0.5 (they were all sines), and quantize them into integers going from -32768 to 32767

      #The liminf and limsup is what decides the maximum and minimum values for our quantizations

      #quant16 is a function from the bin16 library

      for i in range(len(time_vec)):

      	x1[i] = quant16(x1[i],liminf,limsup)

      	x2[i] = quant16(x2[i],liminf,limsup)	

      #Now we obtain the values for the Fourier Transforms of both signals, with integer values as well (rounded in the function fft_CT)

      X1 = fft_CT(x1[0:N])

      X2 = fft_CT(x2[0:N])

      plt.figure(1)

      plt.plot(abs(X1),'r')

      plt.plot(abs(X2),'g')

      plt.show()

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				import matplotlib.pyplot as plt
				from bin16 import *
				from fft import *
				import math

				#These are the first parameters for choosing the time step of our vector
				fs = 98304
				time_step = 1.0/fs
				t_ini = 0
				t_fin = 0.25
				time_vec = np.arange(t_ini, t_fin, time_step)

				#The two frequences we're going to use for our different signals
				f1 = 4000
				f2 = 800

				#This ensures me of getting the higher value of N who's a power of 2 and smaller than the length of the time_vec
				N = int(math.pow(2,int(math.log(len(time_vec),2))))

				x1 = 0.5 * np.cos(2 * np.pi * f1 * time_vec)
				x2 = 0.5 * np.cos(2 * np.pi * f2 * time_vec)

				liminf = -1
				limsup = +1

				#This will take our float-number signals, who were going from -0.5 to 0.5 (they were all sines), and quantize them into integers going from -32768 to 32767
				#The liminf and limsup is what decides the maximum and minimum values for our quantizations
				#quant16 is a function from the bin16 library
				for i in range(len(time_vec)):
				x1[i] = quant16(x1[i],liminf,limsup)
				x2[i] = quant16(x2[i],liminf,limsup)

				#Now we obtain the values for the Fourier Transforms of both signals, with integer values as well (rounded in the function fft_CT)
				X1 = fft_CT(x1[0:N])
				X2 = fft_CT(x2[0:N])

				plt.figure(1)
				plt.plot(abs(X1),'r')
				plt.plot(abs(X2),'g')
				plt.show()