Sourcery refactored main branch

jupyter notebooks/CommpyTest_BERvsEbN0_wPulseShaping.py

@@ -68,38 +68,38 @@ def GrayMapping(M, constType):
 
     L   = int(np.sqrt(M)-1)
     bitsSymb = int(np.log2(M))
-    
+
     code = GrayCode(bitsSymb)
     a    = list(code.generate_gray())
-    
+
     if constType == 'qam':
         PAM = np.arange(-L, L+1, 2)
         PAM = np.array([PAM])
 
         # generate complex square M-QAM constellation
         const = repmat(PAM, L+1, 1) + 1j*repmat(np.flip(PAM.T,0), 1, L+1)
         const = const.T
-    
+
         for ind in np.arange(1,L+1,2):
             const[ind] = np.flip(const[ind],0)        
-        
+
     elif constType == 'psk':
         pskPhases = np.arange(0,2*np.pi,2*np.pi/M)
-        
+
         # generate complex M-PSK constellation
         const     = np.exp(1j*pskPhases) 
-    
+
     const    = const.reshape(M,1)
     const_   = np.zeros((M,2),dtype=complex)
-
 
-    for ind in range(0,M):    
+
+    for ind in range(M):    
         const_[ind,0]   = const[ind,0]   # complex constellation symbol
         const_[ind,1]   =  int(a[ind],2) # mapped bit sequence (as integer decimal)
-        
+
     # sort complex symbols column according to their mapped bit sequence (as integer decimal)                 
     const = const_[const_[:,1].real.argsort()] 
-    
+
     return const
 
 
@@ -121,12 +121,10 @@ def modulateGray(bits, M, constType):
 def demodulateGray(symb, M ,constType):
 
     const = GrayMapping(M, constType)
-
-    hdDecision_vec = np.vectorize(hdDecision, excluded = [1])        
-    index_list = hdDecision_vec(symb, const[:,0])     
-    demod_bits = dec2bitarray(index_list, int(np.log2(M)))
-
-    return demod_bits
+
+    hdDecision_vec = np.vectorize(hdDecision, excluded = [1])
+    index_list = hdDecision_vec(symb, const[:,0])
+    return dec2bitarray(index_list, int(np.log2(M)))
 
 def pulseShape(pulseType, SpS=2, N=1024, alpha=0.1, Ts=1):
 
@@ -158,7 +156,7 @@ def pulseShape(pulseType, SpS=2, N=1024, alpha=0.1, Ts=1):
 alpha  = 0.05          # Rolloff do filtro RRC
 N      = 1024          # Número de coeficientes do filtro RRC
 EbN0dB = 20
-   
+
 # generate random bits
 bitsTx   = np.random.randint(2, size=3*2**12)    
 
@@ -176,7 +174,7 @@ def pulseShape(pulseType, SpS=2, N=1024, alpha=0.1, Ts=1):
 # pulse shaping
 #pulseFilter = pulseShape('rrc', SpS, N, alpha, Ts)
 tindex, rrcFilter = rrcosfilter(N, alpha, Ts, Fa)
-sigTx  = filterNoDelay(pulseFilter, symbolsUp)     
+sigTx  = filterNoDelay(pulseFilter, symbolsUp)
 sigTx  = sigTx/np.sqrt(signal_power(sigTx))
 
 # plot eye diagrams
@@ -211,7 +209,7 @@ def pulseShape(pulseType, SpS=2, N=1024, alpha=0.1, Ts=1):
 
 # +
 # matched filter
-sigRx = filterNoDelay(rrcFilter, sigRx)    
+sigRx = filterNoDelay(rrcFilter, sigRx)
 sigRx = sigRx/SpS
 
 # plot eye diagrams
@@ -240,7 +238,7 @@ def pulseShape(pulseType, SpS=2, N=1024, alpha=0.1, Ts=1):
 BERtheory = theoryBER(M, EbN0dB,'qam')
 
 # print results
-print('EbN0: %3.2f dB, EbN0_est: %3.2f dB,\nBERtheory: %3.1e, BER: %3.1e ' %(EbN0dB, EbN0dB_est, BERtheory, BER))   
+print('EbN0: %3.2f dB, EbN0_est: %3.2f dB,\nBERtheory: %3.1e, BER: %3.1e ' %(EbN0dB, EbN0dB_est, BERtheory, BER))
 print('Total of bits counted: ', ERR.size)
 
 # plot constellations
@@ -268,29 +266,30 @@ def pulseShape(pulseType, SpS=2, N=1024, alpha=0.1, Ts=1):
 BER        = np.zeros(EbN0dB_.shape)
 EbN0dB_est = np.zeros(EbN0dB_.shape)
 
+discard = 100
 for indSNR in range(EbN0dB_.size):
 
     EbN0dB = EbN0dB_[indSNR]
-    
+
     # generate random bits
     bitsTx   = np.random.randint(2, size=3*2**18)    
 
     # map bits to constellation symbols
     mod = QAMModem(m=M)
     symbTx = mod.modulate(bitsTx)
     Es = mod.Es;
-    
+
     # Normalize symbols energy to 1
     symbTx = symbTx/np.sqrt(Es)
-    
+
     # Aumenta a taxa de amostragem do sinal para SpS amostras/símbolo
     symbolsUp = upsample(symbTx, SpS)
-                                   
+
     # filtro formatador de pulso 
     tindex, rrcFilter = rrcosfilter(N, alpha, Ts, Fa)
-    symbolsUp  = filterNoDelay(rrcFilter, symbolsUp)     
+    symbolsUp  = filterNoDelay(rrcFilter, symbolsUp)
     symbolsUp  = symbolsUp/np.sqrt(signal_power(symbolsUp))
-    
+
     # AWGN channel
     snrdB    = EbN0dB + 10*np.log10(np.log2(M))
     noiseVar = 1/(10**(snrdB/10))
@@ -300,24 +299,23 @@ def pulseShape(pulseType, SpS=2, N=1024, alpha=0.1, Ts=1):
     noise    = 1/np.sqrt(2)*noise    
 
     symbRx = symbolsUp + noise   
-     
+
     # filtro casado
-    symbRx = filterNoDelay(rrcFilter, symbRx)    
+    symbRx = filterNoDelay(rrcFilter, symbRx)
     symbRx = symbRx/SpS
-    
+
     # Decimação para uma amostra por símbolo
     symbRx = symbRx[0::SpS]
-        
+
     # Demodulate received symbols        
-    bitsRx = mod.demodulate(np.sqrt(Es)*symbRx, demod_type = 'hard') 
-    discard = 100
+    bitsRx = mod.demodulate(np.sqrt(Es)*symbRx, demod_type = 'hard')
     numBits = bitsTx.size;
-    
+
     # BER calculation, EbN0 estimation
     ERR = np.logical_xor(bitsRx[discard:numBits-discard], bitsTx[discard:numBits-discard])
     BER[indSNR] = ERR.sum()/ERR.size
     EbN0dB_est[indSNR] = 10*np.log10(1/(signal_power(symbRx-symbTx)*np.log2(M)))
-    
+
     print('EbN0: %3.2f dB, EbN0_est: %3.2f dB, BER: %3.1e ' %(EbN0dB, EbN0dB_est[indSNR], BER[indSNR]))    
 
 

jupyter notebooks/Detecção de sinais ópticos e ruído.py

@@ -145,11 +145,11 @@
 σ2_s = 2*q*(Ip + Id)*B  # variância
 μ    = 0                # média
 
-σ     = np.sqrt(σ2_s) 
+σ     = np.sqrt(σ2_s)
 Is    = normal(μ, σ, Namostras)  
 
 # plotas as primeiras 1000 amostras
-plt.plot(Is[0:1000],linewidth = 0.8);
+plt.plot(Is[:1000], linewidth = 0.8);
 plt.xlim(0,1000)
 plt.ylabel('Is')
 plt.xlabel('amostra')

jupyter notebooks/Geração de sinais ópticos.py

@@ -301,9 +301,7 @@ def mzm(Ai, Vπ, u, Vb):
     :return Ao: amplitude da portadora modulada
     """
     π  = np.pi
-    Ao = Ai*np.cos(0.5/Vπ*(u+Vb)*π)
-
-    return Ao
+    return Ai*np.cos(0.5/Vπ*(u+Vb)*π)
 
 
 # ### Transmitindo informação na intensidade (potência) da portadora óptica

jupyter notebooks/Sistemas coerentes.py

@@ -298,17 +298,15 @@ def hybrid_2x4_90(E1, E2):
 
     M = Matrix([[C_3dB, zeros(2)], 
                 [zeros(2), C_3dB]])
-    
+
     U = Matrix([[1, 0, 0, 0],
                 [0, 0, 1, 0],
                 [0, 1, 0, 0],
                 [0, 0, 0, j]])
-    
+
     Ei = Matrix([[E1],[0],[0],[E2]]) # vetor 4x1
-
-    Eo = M*U*M*Ei 
-
-    return Eo    
+
+    return M*U*M*Ei    
 
 # fotodetector balanceado
 def bpd(E1, E2, R=1):
@@ -406,19 +404,17 @@ def hybrid_2x4_90deg(E1, E2):
     :return: hybrid outputs
     '''
     assert E1.size == E2.size, 'E1 and E2 need to have the same size'
-    
+
     # optical hybrid transfer matrix    
     T = np.array([[ 1/2,  1j/2,  1j/2, -1/2],
                   [ 1j/2, -1/2,  1/2,  1j/2],
                   [ 1j/2,  1/2, -1j/2, -1/2],
                   [-1/2,  1j/2, -1/2,  1j/2]])
-    
+
     Ei = np.array([E1, np.zeros((E1.size,)), 
                    np.zeros((E1.size,)), E2])    
-
-    Eo = T@Ei
-
-    return Eo
+
+    return T@Ei
 
 def coherentReceiver(Es, Elo, Rd=1):
     '''
@@ -447,12 +443,12 @@ def coherentReceiver(Es, Elo, Rd=1):
 
 def phaseNoise(lw, Nsamples, Ts):
 
-    σ2 = 2*np.pi*lw*Ts    
+    σ2 = 2*np.pi*lw*Ts
     phi = np.zeros(Nsamples)
-    
-    for ind in range(0, Nsamples-1):
+
+    for ind in range(Nsamples-1):
         phi[ind+1] = phi[ind] + normal(0, np.sqrt(σ2))
-        
+
     return phi