Graph with values of 2 in 2dB

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As I can by these values in the graph of 2 in 2dB, you are leaving one by one 

import matplotlib.pyplot as plt 
#============Ber teórico e Simulado sem repetição==============================
ber_teorico=[ 0.158655253931, 0.130927296756, 0.104028637085, 0.0788958719817, 0.0564953017494, 0.0376789881475, 0.0230071388779, 0.0125870331221, 0.00600438640016, 0.00241331041963, 0.000782701129001, 0.000193985472058, 3.43026238664e-05]
ber_simulado=[ 0.15865525,  0.1309273 ,  0.10402864,  0.07889587,  0.0564953 , 0.03767899,  0.02300714,  0.01258703,  0.00600439,  0.00241331]
#============Ber teórico e Simulado com repetição3==============================
ber_teorico_rep3=[0.0746773727417, 0.0329520675659, 0.00971763153076, 0.00165344696045, 7.73620605469e-05]
ber_simulado_rep3=[0.0580078125, 0.031015625, 0.0126171875, 0.003515625, 0.0004296875]
##============Ber teórico e Simulado com repetição5==============================
ber_teorico_rep5=[0.039536809082, 0.0121407412833, 0.00168153762639, 0.000117503027916, 2.13478624821e-06]
ber_simulado_rep5=[0.031171875, 0.011953125, 0.0040234375, 0.0007421875, 3.90625e-05]
#============Ber teórico e Simulado com repetição7==============================
ber_teorico_rep7=[0.0214306183185, 0.00435647205124, 0.000411447771569, 9.5440267396e-06, 3.40762921493e-08]
ber_simulado_rep7=[0.0180078125, 0.0054296875, 0.00140625, 3.90625e-05, 0.0]

plt.figure(1)
plt.plot(ber_teorico, 'b-', ber_simulado, 'ko', ber_teorico_rep3, 'g-', ber_simulado_rep3, 'bo', ber_teorico_rep5, 'r-', ber_simulado_rep5, 'go', ber_teorico_rep7,'y-', ber_simulado_rep7, 'mo' )
plt.axis([0, 8, 1e-8, 1e0])
plt.xscale('linear')
plt.yscale('log')
plt.xlabel('EbNo(dB)')
plt.ylabel('BER')
plt.grid(True)
plt.title('BER - FSK Coerente com M=2')
    
asked by anonymous 09.10.2017 / 22:35

1 answer

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What about: 

import matplotlib.pyplot as plt

ber_teorico=[ 0.158655253931, 0.130927296756, 0.104028637085, 0.0788958719817, 0.0564953017494, 0.0376789881475, 0.0230071388779, 0.0125870331221, 0.00600438640016, 0.00241331041963, 0.000782701129001, 0.000193985472058, 3.43026238664e-05]
ber_simulado=[ 0.15865525, 0.1309273 ,  0.10402864,  0.07889587,  0.0564953 , 0.03767899,  0.02300714,  0.01258703,  0.00600439,  0.00241331]

ber_teorico_rep3=[0.0746773727417, 0.0329520675659, 0.00971763153076, 0.00165344696045, 7.73620605469e-05]
ber_simulado_rep3=[0.0580078125, 0.031015625, 0.0126171875, 0.003515625, 0.0004296875]

ber_teorico_rep5=[0.039536809082, 0.0121407412833, 0.00168153762639, 0.000117503027916, 2.13478624821e-06]
ber_simulado_rep5=[0.031171875, 0.011953125, 0.0040234375, 0.0007421875, 3.90625e-05]

ber_teorico_rep7=[0.0214306183185, 0.00435647205124, 0.000411447771569, 9.5440267396e-06, 3.40762921493e-08]
ber_simulado_rep7=[0.0180078125, 0.0054296875, 0.00140625, 3.90625e-05, 0 ]

plt.plot(ber_teorico, 'k-', label="teorico", linewidth=2 )
plt.plot(ber_simulado, 'ko--', label="simulado", linewidth=2 )
plt.plot(ber_teorico_rep3, 'r-', label="teorico REP3", linewidth=2 )
plt.plot(ber_simulado_rep3, 'ro--', label="simulado REP3", linewidth=2)
plt.plot(ber_teorico_rep5, 'g-', label="teorico REP5", linewidth=2 )
plt.plot(ber_simulado_rep5, 'go--', label="simulado REP5", linewidth=2 )
plt.plot(ber_teorico_rep7, 'b-', label="teorico REP7", linewidth=2 )
plt.plot(ber_simulado_rep7, 'bo--', label="simulado REP7", linewidth=2 )

plt.figure(1)
plt.legend(loc='lower left')
plt.axis([0, 4, 1e-8, 1e0])
plt.xscale('linear')
plt.yscale('log')
plt.xlabel('EbNo(dB)')
plt.ylabel('BER')
plt.grid(True)
plt.title('BER - FSK Coerente com M=2')
plt.show()

Output:

    
09.10.2017 / 23:14
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