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pypho
Commit 8f28018c authored by Arne Striegler's avatar Arne Striegler 😁
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v1.0.0

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examples/example_amplifier_osnr_8.py 0 → 100644
View file @ 8f28018c
##!/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_oamp import pypho_oamp
from pypho_osnr import pypho_osnr
from pypho_functions import *
import numpy as np
import matplotlib.pyplot as plt
# Define network elements
gp = pypho_setup(nos = 128, sps = 128, symbolrate = 10e9)
bitsrc = pypho_bits(glova = gp, nob = gp.nos, pattern = 'random')
esigsrc = pypho_signalsrc(glova = gp, pulseshape = 'gauss_rz' , fwhm = 0.85)
sig_1550 = pypho_lasmod(glova = gp, power = 0, Df = 0, teta = 0.25*np.pi)
SSMF = pypho_fiber(glova = gp, l = 8.0e3, D = 17, S = 0, alpha = 2.0, gamma = 1.0e-12, phi_max = 10.0) # High loss fiber, to speed up the simulation
amp = pypho_oamp(glova = gp, NF = 6)
osnr = pypho_osnr(glova = gp)
# Simulation
bits = bitsrc()
esig = esigsrc(bitsequence = bits)
E = sig_1550(esig = esig)
E = osnr( E = E, OSNR = 58.0 ) # Set initial OSNR to 58 dB
P = 3.0 # Signal power
plt.figure(1); plt.plot(0, osnr( E = E ), 'r*')
for t in range(10): # Loop through 10 spans
E = SSMF(E = E)
E = amp( E = E, Pmean = P) # Set mean power to 3 dBm
plt.plot(t+1, osnr( E = E ), 'r*')
plt.title("OSNR as function of number of spans", loc='left');plt.ylabel('OSNR [dB]');plt.xlabel('Span number'); plt.grid(True)
\ No newline at end of file
examples/example_ber_estimation_10.py 0 → 100644
View file @ 8f28018c
##!/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_fiber_birefringence import pypho_fiber_birefringence
from pypho_arbmod import pypho_arbmod
from pypho_oamp import pypho_oamp
from pypho_osnr import pypho_osnr
from pypho_sample import pypho_sample
from pypho_optfi import pypho_optfi
from pypho_cwlaser import pypho_cwlaser
from pypho_functions import *
import numpy as np
import copy
import matplotlib.pyplot as plt
from matplotlib import colors as mcolors
colors = dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS)
plt.close('all')
# Define network elements
gp = pypho_setup(nos = 512, sps = 128, symbolrate = 10e9)
bitsrc = pypho_bits(glova = gp, nob = gp.nos, pattern = 'ones')
esigsrc = pypho_signalsrc(glova = gp, pulseshape = 'gauss_rz' , fwhm = 0.25)
sig_1550 = pypho_lasmod(glova = gp, power = 0, Df = 0, teta = np.pi/4.0)
SSMF = pypho_fiber(glova = gp, l = 80.0e3, D = 17.0, S = 0, alpha = 0.3, gamma = 1.14, phi_max = 0.4)
DCF = pypho_fiber(glova = gp, l = 1.0e3, D = -SSMF.D*SSMF.l, S = 0, alpha = 0.2e-12, gamma = 1.0e-12, phi_max = 10.0)
amp = pypho_oamp(glova = gp, Pmean = 3.0, NF = 5)
osnr = pypho_osnr(glova = gp)
modulator = pypho_arbmod(glova = gp)
sigsampler = pypho_sample(glova = gp)
cw_src = pypho_cwlaser(glova = gp, power = 3 , Df = 0, teta = np.pi/4.0)
filter_f0 = pypho_optfi(glova = gp, Df = 0, B = 50)
# Simulation
# Create bitpattern
bits_x = np.append(bitsrc(pattern = 'random'), [bitsrc(pattern = 'random'), bitsrc(pattern = 'random'), bitsrc(pattern = 'random')])
bits_y = np.append(bitsrc(pattern = 'random'), [bitsrc(pattern = 'random'), bitsrc(pattern = 'random'), bitsrc(pattern = 'random')])
# Create pulsetrain
onebits = bitsrc(pattern = 'ones')
esig = esigsrc(bitsequence = onebits)
E_Tx = sig_1550(esig = esig)
# Define constellation points: 16-QAM
alpha = np.arctan(np.sqrt(1.0)/3.0)
constpts_x = [( [np.sqrt(3.0**2 + 1.0)]*8 + [np.sqrt(2.0)]*4 + [np.sqrt(2*3.0**2)]*4),
( [2.0*np.pi*x/4.0+alpha for x in range(0,4)] + [2.0*np.pi*x/4.0+np.pi-alpha for x in range(0,4)] + [2.0*np.pi*x/4.0+np.pi/4 for x in range(0,8)] ),
( [colors.values()[x] for x in range(0,16)]),
( [x for x in range(0,16)]),
([(0),(0),(0),(0)], [(0),(0),(0),(1)], [(0),(0),(1),(0)], [(0),(0),(1),(1)], [(0),(1),(0),(0)], [(0),(1),(0),(1)], [(0),(1),(1),(0)], [(0),(1),(1),(1)],
[(1),(1),(1),(1)], [(1),(1),(1),(0)], [(1),(1),(0),(1)], [(1),(1),(0),(0)], [(1),(0),(1),(1)], [(1),(0),(1),(0)], [(1),(0),(0),(1)], [(1),(0),(0),(0)]
)] # codes not optimized!
constpts_y = constpts_x # Constellation
E = modulator( E = E_Tx, constpoints = [constpts_x, constpts_y], bits = [bits_x, bits_y]) # Modulate
P0 = 0.0
E = amp(E = E, Pmean = 0)
LO = cw_src(power = 10.0*np.log10(1e5*np.mean( np.abs( E[0]['E'][0])**2 ) )) # Local oscillator
E = amp(E = E, Pmean = P0)
E = osnr( E = E, OSNR = 15.0 ) # Set initial OSNR to bad 20 dB
for c in range(0, 10):
print('Span: ', c)
fibres = pypho_fiber_birefringence.create_pmd_fibre(SSMF.l, 1e3, 0.00)
E = SSMF(E = E, birefarray = fibres)
E = DCF(E = E, l = 1.0)
E = amp(E = E, Pmean = P0)
print('OSNR = ', osnr( E = E))
############################
# Calculate decision matrix
############################
E = amp(E = E, Pmean = 0)
#Ein = copy.deepcopy(E)
plt.figure(1)
# Get decision matrix
Dec_x, Esx_re_ax, Esx_im_ax, Dec_y, Esy_re_ax, Esy_im_ax = get_decision_matrix(gp, E, [constpts_x, constpts_y], [bits_x, bits_y], LO, filter_f0, sigsampler)
# Plot decision matrix
plt.figure(1)
plt.subplot(2, 1, 1); h = plt.contourf(Esx_re_ax, Esx_im_ax, Dec_x, 32, cmap=plt.cm.bone )
plt.subplot(2, 1, 2); h = plt.contourf(Esy_re_ax, Esy_im_ax, Dec_y, 32, cmap=plt.cm.bone )
############################
# Calculate BER
############################
BER = calc_BER (gp, E, [constpts_x, constpts_y], osnr( E = E), Dec_x, Dec_y, Esx_re_ax, Esx_im_ax, Esy_re_ax, Esy_im_ax, 5, LO, filter_f0, sigsampler)
print(BER)
\ No newline at end of file
examples/example_dispersion_1.py
View file @ 8f28018c
......@@ -30,7 +30,7 @@ E_Tx = sig_1550(esig = esig)
# Define your parameters here
T_0 = 25.0e-12
z = 100.0e3
z = SSMF.l
D = 17.0
beta_2, beta_3 = DS2beta(17.0, 0, gp.lambda0)
......@@ -45,7 +45,7 @@ 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$, t)$')
plt.plot(gp.timeax*1.0e12, np.abs(E[0]['E'][0]), 'g', label= '$E(z='+ SSMF.l/1e3+ 'km, t)')
# Get FWHM of the input signal E_Tx
......@@ -64,11 +64,11 @@ plt.annotate(s='', xy=(r1,np.max(np.abs(E[0]['E'][0]))/2), xytext=(r2,np.max(np.
plt.text(r1+(r2-r1)/2.0, 0.01 + np.max(np.abs(E[0]['E'][0]))/2, '$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')
plt.show()
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'
\ No newline at end of file
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')
\ No newline at end of file
examples/example_dispersion_3.py
View file @ 8f28018c
......@@ -60,7 +60,7 @@ E = copy.deepcopy(E_Tx)
# Fiber transmission
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
E = DCF(E = E,l = 16.8e3, D = -100, S = -0.058/16.8 * 100) # For full dispersion compensation set S = -0.058/16.8 * 100
# Filter out the channels
......
examples/example_filter_9.py 0 → 100644
View file @ 8f28018c
##!/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_oamp import pypho_oamp
from pypho_osnr import pypho_osnr
from pypho_functions import *
from pypho_optfi import *
import numpy as np
import copy
import matplotlib.pyplot as plt
from scipy.interpolate import UnivariateSpline
# Define network elements
gp = pypho_setup(nos = 128, sps = 128, symbolrate = 10e9)
bitsrc = pypho_bits(glova = gp, nob = gp.nos, pattern = 'random')
esigsrc = pypho_signalsrc(glova = gp, pulseshape = 'gauss_rz' , fwhm = 0.85)
sig_1550 = pypho_lasmod(glova = gp, power = 0, Df = 0, teta = 0.25*np.pi)
osnr = pypho_osnr(glova = gp)
opfilter = pypho_optfi(glova = gp, Df = 0, B = 50, filtype = 'cosrolloff', alpha = 0.8, loss = 0.0)
amp = pypho_oamp(glova = gp, NF = 6)
# Simulation
bits = bitsrc()
esig = esigsrc(bitsequence = bits)
E = sig_1550(esig = esig)
E = amp( E = E, Pmean = 0) # Set mean power to 0 dBm
E = osnr( E = E, OSNR = 30.0 ) # Set initial OSNR to 30 dB
E_Tx = copy.deepcopy(E)
plt.figure(1)
plt.plot((gp.freqax-gp.f0)*1.0e-9, 10.0*np.log10(E[0]['noise']*1e3), 'r', label='White gaussian noise')
E = copy.deepcopy(E_Tx)
E = opfilter(E = E, filtype = 'cosrolloff' )
plt.plot((gp.freqax-gp.f0)*1.0e-9, 10.0*np.log10(E[0]['noise']*1e3), 'g', label='cosrolloff')
E = copy.deepcopy(E_Tx)
E = opfilter(E = E, filtype = 'gauss' )
plt.plot((gp.freqax-gp.f0)*1.0e-9, 10.0*np.log10(E[0]['noise']*1e3), 'b', label='gauss')
E = copy.deepcopy(E_Tx)
E = opfilter(E = E, filtype = 'rect' )
plt.plot((gp.freqax-gp.f0)*1.0e-9, 10.0*np.log10(E[0]['noise']*1e3), 'k', label='rect')
E = copy.deepcopy(E_Tx)
E = opfilter(E = E, filtype = 'gaussrolloff' )
plt.plot((gp.freqax-gp.f0)*1.0e-9, 10.0*np.log10(E[0]['noise']*1e3), 'm', label='gaussrolloff')
plt.ylabel("Spectral power density [dBm/( " + str(gp.fres*1.0e-9) + " GHz)]"); plt.xlabel('Frequency deviation in GHz');
plt.grid(True)
plt.legend()
\ No newline at end of file
examples/example_generic_modulator_9.py 0 → 100644
View file @ 8f28018c
##!/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_fiber_birefringence import pypho_fiber_birefringence
from pypho_arbmod import pypho_arbmod
from pypho_oamp import pypho_oamp
from pypho_osnr import pypho_osnr
from pypho_sample import pypho_sample
from pypho_functions import *
import numpy as np
import copy
import matplotlib.pyplot as plt
from matplotlib import colors as mcolors
colors = dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS)
plt.close('all')
# Define network elements
gp = pypho_setup(nos = 4*128, sps = 128, symbolrate = 10e9)
bitsrc = pypho_bits(glova = gp, nob = gp.nos, pattern = 'ones')
esigsrc = pypho_signalsrc(glova = gp, pulseshape = 'gauss_rz' , fwhm = 0.33)
sig_1550 = pypho_lasmod(glova = gp, power = 0, Df = 0, teta = np.pi/4.0)
SSMF = pypho_fiber(glova = gp, l = 80.0e3, D = 17.0, S = 0, alpha = 0.2, gamma = 1.14, phi_max = 0.01)
DCF = pypho_fiber(glova = gp, l = 1.0e3, D = -SSMF.D*SSMF.l*1.0e-3, S = 0, alpha = 0.2e-12, gamma = 1.0e-9, phi_max = 10.0)
amp = pypho_oamp(glova = gp, Pmean = 3.0, NF = 5)
osnr = pypho_osnr(glova = gp)
modulator= pypho_arbmod(glova = gp)
cstdgr = pypho_sample(glova = gp)
# Simulation
# Create bitpattern (enough up to 4 bits per symbol)
bits_x = np.append(bitsrc(pattern = 'random'), [bitsrc(pattern = 'random'), bitsrc(pattern = 'random'), bitsrc(pattern = 'random')])
bits_y = np.append(bitsrc(pattern = 'random'), [bitsrc(pattern = 'random'), bitsrc(pattern = 'random'), bitsrc(pattern = 'random')])
# Create pulsetrain
onebits = bitsrc(pattern = 'ones')
esig = esigsrc(bitsequence = onebits)
E_Tx = sig_1550(esig = esig)
# OOK
constpts_ook = [( [0.001, 1.0]),
( [0.0, 0.0] ),
( [colors.values()[x] for x in range(0,2)]),
( [x for x in range(0,2)]),
( [(0)], [(1)] )]
# 8-PSK
constpts_8psk = [( [1.0]*8),
( [ 2.0*np.pi*x/8.0 for x in range(0,8)] ),
( [colors.values()[x] for x in range(0,8)]),
( [x for x in range(0,8)]),
([(0),(0),(0)], [(0),(0),(1)], [(0),(1),(0)], [(0),(1),(1)], [(1),(1),(1)], [(1),(1),(0)], [(1),(0),(1)], [(1),(0),(0)],
)]
# 16-PSK
constpts_16psk = [( [1.0]*16),
( [ 2.0*np.pi*x/16.0 for x in range(0,16)] ),
( [colors.values()[x] for x in range(0,16)]),
( [x for x in range(0,16)]),
([(0),(0),(0),(0)], [(0),(0),(0),(1)], [(0),(0),(1),(0)], [(0),(0),(1),(1)], [(0),(1),(0),(0)], [(0),(1),(0),(1)], [(0),(1),(1),(0)], [(0),(1),(1),(1)],
[(1),(1),(1),(1)], [(1),(1),(1),(0)], [(1),(1),(0),(1)], [(1),(1),(0),(0)], [(1),(0),(1),(1)], [(1),(0),(1),(0)], [(1),(0),(0),(1)], [(1),(0),(0),(0)]
)]
# 4-QAM
constpts_4qam = [( [1.0]*4),
( [ 2.0*np.pi*x/4.0+np.pi/4 for x in range(0,4)] ),
( [colors.values()[x] for x in range(0,4)]),
( [x for x in range(0,4)]),
( [(0),(0)], [(0),(1)], [(1),(1)], [(1),(0)] )]
# 16-QAM
alpha = np.arctan(np.sqrt(1.0)/3.0)
constpts_16qam = [( [np.sqrt(3.0**2 + 1.0)]*8 + [np.sqrt(2.0)]*4 + [np.sqrt(2*3.0**2)]*4),
( [2.0*np.pi*x/4.0+alpha for x in range(0,4)] + [2.0*np.pi*x/4.0+np.pi-alpha for x in range(0,4)] + [2.0*np.pi*x/4.0+np.pi/4 for x in range(0,8)] ),
( [colors.values()[x] for x in range(0,16)]),
( [x for x in range(0,16)]),
([(0),(0),(0),(0)], [(0),(0),(0),(1)], [(0),(0),(1),(0)], [(0),(0),(1),(1)], [(0),(1),(0),(0)], [(0),(1),(0),(1)], [(0),(1),(1),(0)], [(0),(1),(1),(1)],
[(1),(1),(1),(1)], [(1),(1),(1),(0)], [(1),(1),(0),(1)], [(1),(1),(0),(0)], [(1),(0),(1),(1)], [(1),(0),(1),(0)], [(1),(0),(0),(1)], [(1),(0),(0),(0)]
)] # codes not optimized!
constpts = constpts_4qam
E = modulator( E = E_Tx, constpoints = [constpts, constpts], bits = [bits_x, bits_y]) # Modulate
P0 = 4
E = amp(E = E, Pmean = P0)
E = osnr( E = E, OSNR = 58.0 ) # Set initial OSNR to 58 dB
plt.figure(1)
cstdgr(E = E, constpoints = [constpts, constpts], bits = [bits_x, bits_y], style = 'o,o') # Plot constallation diagramme
E_Tx = copy.deepcopy(E)
n_span = 10
E = amp(E = E, Pmean = P0)
for c in range(0, n_span): # Transmission fiber
print('Span: ', c)
fibres = pypho_fiber_birefringence.create_pmd_fibre(SSMF.l, 1.0e3, 0.00)
E = SSMF(E = E, birefarray = fibres)
E = amp(E = E, Pmean = P0)
for c in range(0, n_span): # Dispersion compensation
E = DCF(E = E)
plt.figure(2)
cstdgr(E = E, sampletime = gp.sps/2, constpoints = [constpts, constpts], bits = [bits_x, bits_y], style = 'o,o') # Plot constallation diagramme
plt.subplot(2, 1, 1); plt.grid(True); plt.title("Output signal", loc='left'); plt.subplot(2, 1, 2); plt.grid(True); plt.show()
# Plot power and phase of the signal
plt.figure(3)
plt.subplot(2, 1, 1)
plt.title("Input signal", loc='left')
plt.plot(gp.timeax*1.0e12, np.abs(E[0]['E'][0])**2, 'r', label='$E_x(0, t)$')
plt.plot(gp.timeax*1.0e12, np.abs(E[0]['E'][1])**2, 'g', label='$E_y(0, t)$')
plt.ylabel('$10log |E_{x,y}|^2$'); plt.xlabel('Time [ps]'); plt.grid(True)
plt.subplot(2, 1, 2)
plt.plot(gp.timeax*1.0e12, np.angle(E[0]['E'][0]), 'r')
plt.plot(gp.timeax*1.0e12, np.angle(E[0]['E'][1]), 'g')
plt.ylabel('$ \phi_{x,y} $'); plt.xlabel('Time [ps]');plt.grid(True); plt.legend(); plt.show()
\ No newline at end of file
examples/example_selfphasemodulation_5.py
View file @ 8f28018c
......@@ -20,8 +20,8 @@ import matplotlib.pyplot as plt
gp = pypho_setup(nos = 16, sps = 1*128, symbolrate = 10e9)
bitsrc = pypho_bits(glova = gp, nob = gp.nos, pattern = 'ones')
esigsrc = pypho_signalsrc(glova = gp, pulseshape = 'gauss_rz' , fwhm = 0.33)
sig = pypho_lasmod(glova = gp, power = 0, Df = 0, teta = np.pi/8)
SSMF = pypho_fiber(glova = gp, l = 80e3, D = 0.0, S = 0.0, alpha = 0.2, gamma = 1.4, phi_max = .1)
sig = pypho_lasmod(glova = gp, power = 0, Df = 0, teta = np.pi/8)
SSMF = pypho_fiber(glova = gp, l = 80e3, D = 0.0, S = 0.0, alpha = 0.2, gamma = 1.4, phi_max = .01)
# Simulation
......
pyclo_functions.py 0 → 100644
View file @ 8f28018c
#!/usr/bin/env pytsftp://104.199.77.123/var/www/html/ul/upload.php
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 2 21:26:04 2017
@author: arne
"""
import MySQLdb
import numpy as np
import pycurl
from StringIO import StringIO
########################################################################
def mysql_get(sql):
"Read data from DB"
db = MySQLdb.connect(host="130.211.56.253", user="root", passwd="pyphodb", db="pypho")
cur = db.cursor()
cur.execute( sql )
db.close()
return cur.fetchall()
########################################################################
def mysql_set(sql):
"Write data to DB"
db = MySQLdb.connect(host="130.211.56.253", user="root", passwd="pyphodb", db="pypho")
cur = db.cursor()
number_of_rows = cur.execute(sql)
db.commit( )
db.close()
return number_of_rows
########################################################################
def cloud_LoadFiles():
"Save, zip and send all files for cloude computing"
import json
Etmp = np.loadtxt('E.txt').view(complex)
with open('glova.json') as json_data:
glova = json.load(json_data)
with open('link.json') as json_data:
link = json.load(json_data)
return Etmp, glova, link
########################################################################
def local_SaveFiles(filename, E):
np.savetxt(filename + '.txt', E[0]['E'].view(float) )
try:
import zlib
import zipfile
compression = zipfile.ZIP_DEFLATED
except:
compression = zipfile.ZIP_STORED
zf = zipfile.ZipFile(filename +".zip", mode='w')
try:
zf.write(filename + '.txt', compress_type=compression)
finally:
zf.close()
########################################################################
def cloud_SaveFiles(cid, filename):
# Send files to cloud
print ('Sending files to master!')
c = pycurl.Curl()
buffer1 = StringIO()
c.setopt(c.URL, 'https://optical-fiber-transmission-simulation.com/ul/upload.php')
c.setopt(c.POST, 1)
c.setopt(c.HTTPPOST, [("sim_ticket", cid), ("filecontents", (c.FORM_FILE, filename))])
c.setopt(c.WRITEDATA, buffer1)
c.perform()
c.close()
body1 = buffer1.getvalue()
return body1[0:74]
\ No newline at end of file
pypho_arbmod.py 0 → 100644
View file @ 8f28018c
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# pypho_arbmod.py
#
# Copyright 2018 Arne Striegler (arne.striegler@hm.edu)
#
#
#
# Modulates in arbitray constellation points
#
#
########################################################################
import numpy as np
import sys
from pypho_functions import *
########################################################################
class pypho_arbmod(object):
def __init__(self, glova = None):
if glova == None:
print ("ERROR: pypho_armod: You must define the global variables")
sys.exit("PyPho stopped!")
self.glova = glova
########################################################################
def __call__(self, glova = None, E = None, constpoints = None, bits = None):
if E == None:
print ("ERROR: pypho_armod: You must define an optical signal E")
sys.exit("PyPho stopped!")
self.set(E, constpoints, bits)
return self.out(E)
########################################################################
def out(self, E = []):
const_dec = [];
for const_bin in np.asarray( self.constpoints[0][4] ) :
const_dec.append( const_bin.dot(2**np.arange(const_bin.size)[::-1]) )
for symbol in range(0, self.glova.nos):
b = self.bits[0][symbol*self.M : symbol*self.M + self.M]
const_num = b.dot(2**np.arange(b.size)[::-1])
E[0]['E'][0][symbol*self.glova.sps:(symbol+1)*self.glova.sps] *= self.constpoints[0][0][const_dec.index(const_num)]*np.exp(1.0j*self.constpoints[0][1][const_dec.index(const_num)])
b = self.bits[1][symbol*self.M : symbol*self.M + self.M]
const_num = b.dot(2**np.arange(b.size)[::-1])
E[0]['E'][1][symbol*self.glova.sps:(symbol+1)*self.glova.sps] *= self.constpoints[1][0][const_dec.index(const_num)]*np.exp(1.0j*self.constpoints[1][1][const_dec.index(const_num)])
return E
########################################################################