Commit 0dac22b0 authored by Striegler, Arne's avatar Striegler, Arne 😁
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Version 0.500

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examples/example_crossphasemodulation_6.py

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##!/usr/bin/env python2

# -*- coding: utf-8 -*-



#Import functions and libraries

import sys

sys.path.append('../')

from pypho_setup import pypho_setup

from pypho_bits import pypho_bits

from pypho_signalsrc import pypho_signalsrc

from pypho_lasmod import pypho_lasmod

from pypho_fiber import pypho_fiber

from pypho_cwlaser import pypho_cwlaser

from pypho_optfi import pypho_optfi

from pypho_functions import *

import numpy as np

import copy

import matplotlib.patches as mpatches

import matplotlib.pyplot as plt







# Define network elements

gp       = pypho_setup(nos = 4*16, sps = 1*128, symbolrate = 10e9)

bitsrc   = pypho_bits(glova = gp, nob = gp.nos, pattern = 'singlepulse') # Set pattern = "singlepulse"

esigsrc  = pypho_signalsrc(glova = gp, pulseshape = 'gauss_rz' , fwhm = 0.33)

sig_f0   = pypho_cwlaser(glova = gp, power = 3, Df = 0,  teta = 1*np.pi/4.0)

sig_f1   = pypho_lasmod(glova = gp, power = 0, Df = 500, teta = 1*np.pi/1.0)

filter_f0 = pypho_optfi(glova = gp, Df = 0, B = 50)

SSMF     = pypho_fiber(glova = gp, l = 80,  D = 9.0,   S = 0.0, alpha = 0.2, gamma = 1.4, phi_max = .1)



# Simulation



# Define wavelength channel



E_f0  = sig_f0()                                              



bits    = bitsrc()

esig    = esigsrc(bitsequence = bits)

E_f1       = sig_f1(esig = esig)  

E = copy.deepcopy(E_f1)



E[0]['E'][0] = E_f0[0]['E'][0] + E_f1[0]['E'][0]  # Multiplex all signals X-Pol

E[0]['E'][1] = E_f0[0]['E'][1] + E_f1[0]['E'][1]  # Multiplex all signals Y-Pol 





E_Tx = copy.deepcopy(E)

# Plot Spectrum Input signals

plt.figure(1)

plt.subplot(2, 1, 1)

plt.plot((gp.freqax-gp.f0)*1e-9, np.log10(abs(fftshift(fft(E[0]['E'][0]   )**2))), 'r')

plt.plot((gp.freqax-gp.f0)*1e-9, np.log10(abs(fftshift(fft(E[0]['E'][1]   )**2))), 'g:')

plt.title("Input spectrum", loc='left')

plt.ylabel('Spec. density');

plt.grid(True)



# Fiber transmission

       

E = SSMF(E = E)    



# Plot Spectrum Output signals

plt.subplot(2, 1, 2)

plt.plot((gp.freqax-gp.f0)*1e-9, np.log10(abs(fftshift(fft(E[0]['E'][0]   )**2))), 'r')

plt.plot((gp.freqax-gp.f0)*1e-9, np.log10(abs(fftshift(fft(E[0]['E'][1]   )**2))), 'g:')

plt.title("Output spectrum", loc='left')

plt.ylabel('Spec. density'); plt.xlabel('Frequency deviation [GHz]');

plt.grid(True)





# Calculate phase shift

L_eff = (1-np.exp(-SSMF.l*SSMF.alpha))/(SSMF.alpha)

phi_x_max = - (np.max(np.abs(E_Tx[0]['E'][0])**2) + 2.0/3.0* np.max(np.abs(E_Tx[0]['E'][1])**2))* L_eff * SSMF.gamma *1e-3

phi_y_max = - (np.max(np.abs(E_Tx[0]['E'][1])**2) + 2.0/3.0* np.max(np.abs(E_Tx[0]['E'][0])**2))* L_eff * SSMF.gamma *1e-3



print 'L_eff = ', L_eff

print 'phi_x_max = ', phi_x_max

print 'phi_y_max = ', phi_y_max



# Plot power of both pol axis as function of transmission distance

plt.figure(2)



plt.subplot(2, 1, 2)

plt.ylabel('$10log |E_y|^2$'); plt.xlabel('Transmission distance [m]');

plt.ylim((-120, 10))

plt.grid(True)

plt.subplot(2, 1, 1)

plt.ylabel('$10log |E_y|^2$');

plt.ylim((-120, 10)) 

plt.grid(True)

plt.legend()

plt.show()
 
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examples/example_dispersion_1.py

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##!/usr/bin/env python2

# -*- coding: utf-8 -*-



#Import functions and libraries

import sys

sys.path.append('../')

from pypho_setup import pypho_setup

from pypho_bits import pypho_bits

from pypho_signalsrc import pypho_signalsrc

from pypho_lasmod import pypho_lasmod

from pypho_fiber import pypho_fiber

from pypho_functions import *

import numpy as np

import copy

import matplotlib.pyplot as plt

from scipy.interpolate import UnivariateSpline



# Define network elements

gp       = pypho_setup(nos = 16, sps = 256, symbolrate = 10e9)

bitsrc   = pypho_bits(glova = gp, nob = gp.nos, pattern = 'singlepulse')

esigsrc  = pypho_signalsrc(glova = gp, pulseshape = 'gauss_rz' , fwhm = 0.85)

sig_1550 = pypho_lasmod(glova = gp, power = 0, Df = 0, teta = 0)

SSMF     = pypho_fiber(glova = gp, l = 80.0e3,  D = 17,   S = 0, alpha = 0.2e-12, gamma = 0, phi_max=10.0)



# Simulation

bits = bitsrc()

esig = esigsrc(bitsequence = bits)

E_Tx = sig_1550(esig = esig)                                                      





# Define your parameters here

T_0 = 25.0e-12

z = 100.0e3

D = 17.0

beta_2, beta_3 = DS2beta(17.0, 0, gp.lambda0)





# Create a single pulse with gaussian shape (not power!)

E_Tx[0]['E'][0] = E_Tx[0]['E'][0]*0 + np.exp(-(gp.timeax-gp.timeax[-1]/2)**2 / (2.0*T_0**2) )

E = copy.deepcopy(E_Tx)



# Fiber trannsmission

E = SSMF(E = E, D = D, l = z) 



# Plot Input and Output signal 

plt.figure(1)

plt.plot(gp.timeax*1.0e12, np.abs(E_Tx[0]['E'][0]), 'r', label='$E(0, t)$')

plt.plot(gp.timeax*1.0e12, np.abs(E[0]['E'][0]), 'g', label='$E(z=100$km$, t)$')





# Get FWHM of the input signal E_Tx

spline = UnivariateSpline(gp.timeax*1.0e12, np.abs(E_Tx[0]['E'][0])-1*np.max(np.abs(E_Tx[0]['E'][0]))/2, s=0)

r1, r2 = spline.roots() # find the roots

plt.annotate(s='', xy=(r1,np.max(np.abs(E_Tx[0]['E'][0]))/2), xytext=(r2,np.max(np.abs(E_Tx[0]['E'][0]))/2), arrowprops=dict(arrowstyle='<->'))

plt.text(r1+(r2-r1)/2.0, 0.01 +np.max(np.abs(E_Tx[0]['E'][0]))/2, '$T_{FWHM,0}$ = ' + str(np.round(r2-r1,2)) + ' ps', fontsize=12, horizontalalignment='center')

T_FWHM_0 = (r2-r1) * 1e-12

T_0_plot = T_FWHM_0 / 2.35482





# Get FWHM of the output signal E

spline = UnivariateSpline(gp.timeax*1.0e12, np.abs(E[0]['E'][0])-1*np.max(np.abs(E[0]['E'][0]))/2, s=0)

r1, r2 = spline.roots() # find the roots

plt.annotate(s='', xy=(r1,np.max(np.abs(E[0]['E'][0]))/2), xytext=(r2,np.max(np.abs(E[0]['E'][0]))/2), arrowprops=dict(arrowstyle='<->'))

plt.text(r1+(r2-r1)/2.0, 0.01 + np.max(np.abs(E[0]['E'][0]))/2, '$T_{FWHM,1}$ = ' + str(np.round(r2-r1,2)) + ' ps', fontsize=12, horizontalalignment='center')

T_FWHM_1 = (r2-r1) * 1e-12

plt.ylabel('$|E|$ a.u.'); plt.xlabel('Time $t$ [ps]'); legend = plt.legend(loc='upper right')



L_D = (T_0_plot)**2  / np.abs(beta_2)

# Print the results

print 'Input signal 1/e-pulse width by definition : T_0 = ' , T_0*1e12, ' ps'

print 'Input signal 1/e-pulse width from plot : T_0 = ' , T_0_plot*1e12, ' ps'

print 'Input signal FWHM-pulse width from plot : T_FWHM,0 = ' , T_FWHM_0*1e12, ' ps'

print 'Output signal FWHM-pulse width from plot : T_FWHM,1 = ' , T_FWHM_1*1e12, ' ps'

print 'Calculated output FWHM-pulse width : T_FWHM,1 = ' , T_FWHM_0 * np.sqrt(1 + (z/L_D)**2)*1e12, ' ps'
 
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examples/example_dispersion_2.py

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##!/usr/bin/env python2

# -*- coding: utf-8 -*-



#Import functions and libraries

import sys

sys.path.append('../')

from pypho_setup import pypho_setup

from pypho_bits import pypho_bits

from pypho_signalsrc import pypho_signalsrc

from pypho_lasmod import pypho_lasmod

from pypho_fiber import pypho_fiber

from pypho_eye import pypho_eye

from pypho_functions import *

import numpy as np

import copy

import matplotlib.patches as mpatches

import matplotlib.pyplot as plt

from scipy.interpolate import UnivariateSpline



# Define some parameter

T_FWHM = 50e-12       # FWHM-pulsewidth of the optical power

T_e = T_FWHM / 1.66510  # 1/e-pulsewidth of the electrical field

z = 29.756e3         # Fiber length

D = 16.8                # Dispersion coefficient

T_FWHM_norm_max = 0.75   # Maximum FWHM pulseiwdth of the 



# Define network elements

gp       = pypho_setup(nos = 4*16, sps = 1*256, symbolrate = 10e9)

bitsrc   = pypho_bits(glova = gp, nob = gp.nos, pattern = 'random') # Set pattern = "singlepulse"

esigsrc  = pypho_signalsrc(glova = gp, pulseshape = 'gauss_rz' , fwhm = T_FWHM*gp.symbolrate)

sig_1550 = pypho_lasmod(glova = gp, power = 0, Df = 0, teta = 0)

SSMF     = pypho_fiber(glova = gp, l = z,  D = D,   S = 0, alpha = 0.2e-12, gamma = 0, phi_max = 10.0)

eye      = pypho_eye(glova = gp, polarisation = 'x,y')



# Simulation

bits = bitsrc()



esig = esigsrc(bitsequence = bits)

E_Tx = sig_1550(esig = esig)                                              



E = copy.deepcopy(E_Tx)



# Fiber trannsmission

E = SSMF(E = E) 

E[0]['E'][0] = E[0]['E'][0] /np.max(np.abs(E[0]['E'][0]))*1e-3

E_Tx[0]['E'][0] = E_Tx[0]['E'][0] /np.max(np.abs(E_Tx[0]['E'][0]))*1e-3

# Plot Input and Output signal

beta_2, beta_3 = DS2beta(D, 0, gp.lambda0)

L_D = (T_e)**2  / np.abs(beta_2)     # Pulse width T_e here of the electrical field

 

plt.figure(1)

eye(E = E_Tx[0]['E'], polarisation = 'x', style="r")

eye(E = E[0]['E'], polarisation = 'x', style="g")



red_patch = mpatches.Patch(color='red', label='Input signal')

green_patch = mpatches.Patch(color='green', label='Output signal')

plt.legend(handles=[red_patch, green_patch], loc=1)

print 'Calculated output FWHM-pulse width : T_FWHM,1 = ' , T_FWHM * np.sqrt(1 + (z/L_D)**2)*1e12, ' ps'



T_FWHM_norm_0 = T_FWHM*1e12 / 100.0

print (  'Calculated max reach : ' , 100e-12**2 *(1/1.665095)**2 * 2.0*np.pi * 299792458 *  T_FWHM_norm_0*np.sqrt(  T_FWHM_norm_max**2 - T_FWHM_norm_0**2) / (16.8e-6 * 1550e-9**2 )  *1e-3 )
 
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examples/example_dispersion_3.py

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##!/usr/bin/env python2

# -*- coding: utf-8 -*-



#Import functions and libraries

import sys

sys.path.append('../')

from pypho_setup import pypho_setup

from pypho_bits import pypho_bits

from pypho_signalsrc import pypho_signalsrc

from pypho_lasmod import pypho_lasmod

from pypho_fiber import pypho_fiber

from pypho_eye import pypho_eye

from pypho_optfi import pypho_optfi

from pypho_functions import *

import numpy as np

import copy

import matplotlib.patches as mpatches

import matplotlib.pyplot as plt



# Define network elements

gp       = pypho_setup(nos = 4*16, sps = 1*256, symbolrate = 40e9)

bitsrc   = pypho_bits(glova = gp, nob = gp.nos, pattern = 'random') # Set pattern = "singlepulse"

esigsrc  = pypho_signalsrc(glova = gp, pulseshape = 'gauss_rz' , fwhm = 0.33)

sig_1550 = pypho_lasmod(glova = gp, power = 0, Df = 0, teta = 0)

sig_1546 = pypho_lasmod(glova = gp, power = 0, Df = +500, teta = 0)

sig_1554 = pypho_lasmod(glova = gp, power = 0, Df = -500, teta = 0)

filter_1550 = pypho_optfi(glova = gp, Df = 0, B = 100)

filter_1546 = pypho_optfi(glova = gp, Df = +500, B = 100)

filter_1554 = pypho_optfi(glova = gp, Df = -500, B = 100)

SSMF     = pypho_fiber(glova = gp, l = 100e3,  D = 16.8,   S = 0.058, alpha = 0.2e-12, gamma = 0, phi_max = 10.0)

DCF      = pypho_fiber(glova = gp, l = 16.8e3,  D = -100.0,   S = -0.058/16.8 * 100.0, alpha = 0.2e-12, gamma = 0, phi_max = 10.0)

eye      = pypho_eye(glova = gp, polarisation = 'x,y')



# Simulation

bits = bitsrc()



esig = esigsrc(bitsequence = bits)

E_1550 = sig_1550(esig = esig)                                              

E_1546 = sig_1546(esig = esig)

E_1554 = sig_1554(esig = esig)





# Plot Input signals

plt.figure(1)

plt.subplot(3, 1, 1)

plt.title("$\lambda$=1546nm", loc='left')

eye(E = E_1546[0]['E'], polarisation = 'x', style="0.5")



plt.subplot(3, 1, 2)

plt.title("$\lambda$=1550nm", loc='left')

eye(E = E_1550[0]['E'], polarisation = 'x', style="0.5")



plt.subplot(3, 1, 3)

plt.title("$\lambda$=1554nm", loc='left')

eye(E = E_1554[0]['E'], polarisation = 'x', style="0.5")



E_Tx = copy.deepcopy(E_1550)

E_Tx[0]['E'][0] = E_1550[0]['E'][0] + E_1546[0]['E'][0] + E_1554[0]['E'][0] # Multiplex all signals 

E = copy.deepcopy(E_Tx)



# Fiber trannsmission

E = SSMF(E = E,l = 100e3,  D = 16.8, S = 0.058) 

E =  DCF(E = E,l = 16.8e3, D = -100, S = -0.058/16.8 * 100) # For full dispersion compenstion set S = -0.058/16.8 * 100





# Filter out the channels

E_1550 = filter_1550(E = copy.deepcopy(E))

E_1546 = filter_1546(E = copy.deepcopy(E))

E_1554 = filter_1554(E = copy.deepcopy(E))



# Plot Output signals

#plt.figure(2)

plt.subplot(3, 1, 1)

eye(E = E_1546[0]['E'], polarisation = 'x', style="r")

red_patch = mpatches.Patch(color='0.5', label='Input signal')

green_patch = mpatches.Patch(color='red', label='Output signal')

plt.legend(handles=[red_patch, green_patch], loc=1)



plt.subplot(3, 1, 2)

eye(E = E_1550[0]['E'], polarisation = 'x', style="r")

plt.subplot(3, 1, 3)

eye(E = E_1554[0]['E'], polarisation = 'x', style="r")

examples/example_fourwavemixing_4.py

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##!/usr/bin/env python2

# -*- coding: utf-8 -*-



#Import functions and libraries

import sys

sys.path.append('../')

from pypho_setup import pypho_setup

from pypho_fiber import pypho_fiber

from pypho_cwlaser import pypho_cwlaser

from pypho_optfi import pypho_optfi

from pypho_functions import *

import numpy as np

import copy

import matplotlib.patches as mpatches

import matplotlib.pyplot as plt



#  Define some paramaters



delta_f = 25       # Frequency difference of the two cw-signals

L = 20e3           # Fiber length in m

l = 0.1e3           # Step fiber length in m

k = 7               # Defines how man channels left and right will be analyzed

chut = np.arange(-k*delta_f, k*delta_f+delta_f, delta_f)        # Channels under test





P_FWM_x = np.zeros((chut.size, int(L/l)))

P_FWM_y = np.zeros((chut.size, int(L/l)))



# Define network elements

gp       = pypho_setup(nos = 4*16, sps = 1*128, symbolrate = 10e9)

sig_f0  = pypho_cwlaser(glova = gp, power = 3, Df = 0, teta = 1*np.pi/4.0)

sig_f   = pypho_cwlaser(glova = gp, power = 3, Df = delta_f, teta = 1*np.pi/2.0)

filter_df = pypho_optfi(glova = gp, Df = 0, B = 10)

SSMF     = pypho_fiber(glova = gp, l = l,  D = 5.0,   S = 0.0, alpha = 0.2, gamma = 1.4, phi_max = .1)



# Simulation



# Define wavelength channel

E_f0  = sig_f0()                                              

E_fm1 = sig_f(Df = +delta_f, teta = np.pi/2)

E_fp1 = sig_f(Df = -delta_f, teta = np.pi/2)

E_fm2 = sig_f(Df = -2*delta_f, teta = np.pi/2)

E_fp2 = sig_f(Df = +2*delta_f, teta = np.pi/2)

E = copy.deepcopy(E_fm1)



E[0]['E'][0] = E_f0[0]['E'][0] + E_fp1[0]['E'][0] + E_fm1[0]['E'][0] + E_fp2[0]['E'][0] + E_fm2[0]['E'][0] # Multiplex all signals X-Pol

E[0]['E'][1] = E_f0[0]['E'][1] + E_fp1[0]['E'][1] + E_fm1[0]['E'][1] + E_fp2[0]['E'][1] + E_fm2[0]['E'][1] # Multiplex all signals Y-Pol 





E_Tx = copy.deepcopy(E)

# Plot Spectrum Input signals

plt.figure(1)

plt.subplot(2, 1, 1)

plt.plot((gp.freqax-gp.f0)*1e-9, np.log10(abs(fftshift(fft(E[0]['E'][0]   )**2))), 'r')

plt.plot((gp.freqax-gp.f0)*1e-9, np.log10(abs(fftshift(fft(E[0]['E'][1]   )**2))), 'g:')

plt.title("Input spectrum", loc='left')

plt.ylabel('Spec. density');

plt.grid(True)



# Fiber transmission

c1 = 0

for z in np.arange(0,L,l):

    print(z)

    c2 = 0

    for ch in chut:         # Loop through wavelengths and save mean power value of both pol axis

        E_filtered = filter_df(copy.deepcopy(E), Df = ch)

        P_FWM_x[c2][c1] = np.mean( np.abs(E_filtered[0]['E'][0])**2 )

        P_FWM_y[c2][c1] = np.mean( np.abs(E_filtered[0]['E'][1])**2 )

        c2 = c2 + 1



    c1 = c1 + 1        

    E = SSMF(E = E)    



# Plot Spectrum Output signals

plt.subplot(2, 1, 2)

plt.plot((gp.freqax-gp.f0)*1e-9, np.log10(abs(fftshift(fft(E[0]['E'][0]   )**2))), 'r')

plt.plot((gp.freqax-gp.f0)*1e-9, np.log10(abs(fftshift(fft(E[0]['E'][1]   )**2))), 'g:')

plt.title("Output spectrum", loc='left')

plt.ylabel('Spec. density'); plt.xlabel('Frequency deviation [GHz]');

plt.grid(True)



# Plot power of both pol axis as function of transmission distance

plt.figure(2)

for i in np.arange(0,chut.size):

    plt.subplot(2, 1, 1)

    plt.plot(np.arange(0,L,l), 10*np.log10(P_FWM_x[i]*1e3), label=str(chut[i]) + ' GHz') 

    plt.subplot(2, 1, 2)

    plt.plot(np.arange(0,L,l), 10*np.log10(P_FWM_y[i]*1e3)) 



plt.subplot(2, 1, 2)

plt.ylabel('$10log |E_y|^2$'); plt.xlabel('Transmission distance [m]');

plt.ylim((-120, 10))

plt.grid(True)

plt.subplot(2, 1, 1)

plt.ylabel('$10log |E_y|^2$');

plt.ylim((-120, 10)) 

plt.grid(True)

plt.legend()

plt.show()
 
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