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pam4 signal modulation

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This commit is contained in:
Joseph Hopfmüller
2024-11-15 01:33:39 +01:00
parent e35bdf6a1a
commit 7ac4e48c20
2 changed files with 151 additions and 44 deletions
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183
src/single-core-regen/generate_signal.py
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@@ -2,7 +2,9 @@ import configparser
from datetime import datetime
import hashlib
from pathlib import Path
import time
from matplotlib import pyplot as plt
import scipy
import numpy as np
from rich import inspect
@@ -29,14 +31,17 @@ alpha = 0.2
D = 17
S = 0
birefsteps = 1
birefseed = 0xC0FFEE
; birefseed = 0xC0FFEE
[signal]
modulation = "pam"
mod_order = 4
seed = 0xC0FFEE
mod_depth = 0.5
laser_power = 0
; seed = 0xC0FFEE
pulse_shape = "gauss_rz"
fwhm = 0.33
; jitter_seed = 0xC0FFEE
[script]
data_dir = "{str((Path.home()/".pypho"/"data"))}"
@@ -67,6 +72,73 @@ def get_config(config_file=None):
# conf[section][key] = config[section][key].strip('"')
return conf
class pam_generator:
def __init__(self, glova, mod_order=None, mod_depth=0.5, pulse_shape='gauss', fwhm=0.33, seed=None) -> None:
self.glova = glova
self.pulse_shape = pulse_shape
self.modulation_depth = mod_depth
self.mod_order = mod_order
self.fwhm = fwhm
self.seed = seed
def __call__(self, E, symbols, max_jitter=0):
symbols_x = symbols[0]/(self.mod_order or np.max(symbols[0]))
symbols_y = symbols[1]/(self.mod_order or np.max(symbols[1]))
diffs_x = np.diff(symbols_x, prepend=symbols_x[0])
diffs_y = np.diff(symbols_y, prepend=symbols_y[0])
max_jitter = int(round(max_jitter*self.glova.sps))
digital_x = self.generate_digital_signal(diffs_x, max_jitter)
digital_y = self.generate_digital_signal(diffs_y, max_jitter)
digital_x = np.pad(digital_x, (0, self.glova.sps//2), 'constant', constant_values=(0,0))
digital_y = np.pad(digital_y, (0, self.glova.sps//2), 'constant', constant_values=(0,0))
if self.pulse_shape == 'gauss':
wavelet = self.gauss(oversampling=6)
else:
raise ValueError(f"Unknown pulse shape: {self.pulse_shape}")
# create analog signal of diff of symbols
E_x = np.convolve(digital_x, wavelet)
E_y = np.convolve(digital_y, wavelet)
# convert to pam and set modulation depth (scale and move up such that 1 stays at 1)
E_x = np.cumsum(E_x)*self.modulation_depth + (1-self.modulation_depth)
E_y = np.cumsum(E_y)*self.modulation_depth + (1-self.modulation_depth)
# cut off the wavelet tails
E_x = E_x[self.glova.sps//2+len(wavelet)//2-1:-len(wavelet)//2]
E_y = E_y[self.glova.sps//2+len(wavelet)//2-1:-len(wavelet)//2]
# modulate the laser
np.multiply(E[0]['E'][0],E_x, out=E[0]['E'][0])
np.multiply(E[0]['E'][1],E_y, out=E[0]['E'][1])
return E
def generate_digital_signal(self, symbols, max_jitter=0):
rs = np.random.RandomState(self.seed)
signal = np.zeros(self.glova.nos*self.glova.sps)
for index in range(self.glova.nos):
jitter = max_jitter != 0 and rs.randint(-max_jitter, max_jitter)
signal_index = index*self.glova.sps + jitter
if signal_index < 0:
continue
if signal_index >= len(signal):
continue
signal[signal_index] = symbols[index]
return signal
def gauss(self, oversampling=1):
sample_points = np.linspace(-oversampling*self.glova.sps, oversampling*self.glova.sps, oversampling*2*self.glova.sps, endpoint=True)
sigma = self.fwhm/(1*np.sqrt(2*np.log(2)))*self.glova.sps
pulse = 1/(sigma*np.sqrt(2*np.pi))*np.exp(-np.square(sample_points)/(2*np.square(sigma)))
return pulse
def initialize_fiber_and_data(config, input_data_override=None):
py_glova = pypho.setup(
nos = config['glova']['nos'],
@@ -84,23 +156,21 @@ def initialize_fiber_and_data(config, input_data_override=None):
if input_data_override is not None:
c_data.E_in = input_data_override
else:
config['signal']['seed'] = config['signal'].get('seed',(int(time.time()*1000))%2**32)
config['signal']['jitter_seed'] = config['signal'].get('jitter_seed', (int(time.time()*1000))%2**32)
symbolsrc = pypho.symbols(py_glova, py_glova.nos, pattern='ones', seed=config['signal']['seed'])
esigsrc = pypho.signalsrc(py_glova, pulseshape=config['signal']['pulse_shape'], fwhm=config['signal']['fwhm'])
sig = pypho.lasmod(py_glova, power=0, Df=0, theta=np.pi/4)
modulator = pypho.arbmod(py_glova)
laser = pypho.lasmod(py_glova, power=config['signal']['laser_power'], Df=0, theta=np.pi/4)
modulator = pam_generator(py_glova, mod_depth=config['signal']['mod_depth'], pulse_shape=config['signal']['pulse_shape'], fwhm=config['signal']['fwhm'], seed=config['signal']['jitter_seed'])
symbols_x = symbolsrc(pattern='random', p1=config['signal']['mod_order'])
symbols_y = symbolsrc(pattern='random', p1=config['signal']['mod_order'])
symbols_x[:3] = 0
symbols_y[:3] = 0
ones = symbolsrc(pattern='ones')
esig = esigsrc(bitsequence=ones)*np.sqrt(2)
cw = laser()
cw[0]['E']*=np.sqrt(2)
source_signal = sig(esig)
constpts_x = [np.linspace(1/config['signal']['mod_order'], 1, config['signal']['mod_order'], endpoint=True, dtype=np.complex128)]
constpts_y = [np.linspace(1/config['signal']['mod_order'], 1, config['signal']['mod_order'], endpoint=True, dtype=np.complex128)]
source_signal = modulator(E=source_signal, symbols = (symbols_x, symbols_y), constpoints= (constpts_x, constpts_y))
source_signal = modulator(E=cw, symbols=(symbols_x, symbols_y))
c_data.E_in = source_signal[0]['E']
@@ -112,8 +182,10 @@ def initialize_fiber_and_data(config, input_data_override=None):
D=config['fiber']['d'],
S=config['fiber']['s'],
)
py_fiber.birefarray = pypho.birefringence_segment.create_pmd_fibre(py_fiber.l, py_fiber.l/config['fiber']['birefsteps'], 0, config['fiber']['birefseed'])
c_params = pypho.cfiber.ParamsWrapper.from_fiber(py_fiber)
if config['fiber']['birefsteps'] > 0:
config['fiber']['birefseed'] = config['fiber'].get('birefseed', (int(time.time()*1000))%2**32)
py_fiber.birefarray = pypho.birefringence_segment.create_pmd_fibre(py_fiber.l, py_fiber.l/config['fiber']['birefsteps'], 0, config['fiber']['birefseed'])
c_params = pypho.cfiber.ParamsWrapper.from_fiber(py_fiber, max_step=1e3)
c_fiber = pypho.cfiber.FiberWrapper(c_data, c_params, c_glova)
return c_fiber, c_data
@@ -143,14 +215,17 @@ def save_data(data, config):
f"D = {config['fiber']['d']}",
f"S = {config['fiber']['s']}",
f"birefsteps = {config['fiber']['birefsteps']}",
f"birefseed = {config['fiber']['birefseed']}",
f"birefseed = {config['fiber'].get('birefseed', 'not set')}",
"",
"[signal]",
f'modulation = "{config['signal']['modulation']}"',
f"mod_order = {config['signal']['mod_order']}",
f"seed = {config['signal']['seed']}",
f"mod_depth = {config['signal']['mod_depth']}",
f"seed = {config['signal'].get('seed', 'not set')}",
f'pulse_shape = "{config["signal"]["pulse_shape"]}"',
f"fwhm = {config['signal']['fwhm']}",
f"max_jitter = {config['signal']['max_jitter']}",
f"jitter_seed = {config['signal'].get('jitter_seed', 'not set')}",
"",
"[script]",
f'data_dir = "{config["script"]["data_dir"]}"',
@@ -180,37 +255,65 @@ def save_data(data, config):
print("Saved config to", data_dir / lookup_file)
print("Saved data to", save_dir / f"{config_hash}.npy")
def length_loop(config):
# loop over lengths
# lengths = [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000]
ranges = [1, 10, 100, 1000, 10000, 100000]
lengths = [list(range(length_range,10*length_range, length_range)) for length_range in ranges]
lengths = [length for length_range in lengths for length in length_range]
lengths = sorted(lengths)
input_override = None
for lind, length in enumerate(lengths):
# print(f"\nGenerating data for fiber length {length}")
if lind > 0:
# set the length to the difference between the current and previous length -> incremental
length = lengths[lind] - lengths[lind-1]
print(f"\nGenerating data for fiber length {lengths[lind]}m -> {length}m")
config['fiber']['length'] = length
# set the input data to the output data of the previous run
cfiber, cdata = initialize_fiber_and_data(config, input_data_override=input_override)
if lind == 0:
cdata_orig = cdata
energy_in = np.sum(np.abs(cdata.E_in[0])**2 + np.abs(cdata.E_in[1])**2)/(cfiber.glova.symbolrate*cfiber.glova.sps)
print(f"Energy in: {energy_in} J")
cfiber()
energy_out = np.sum(np.abs(cdata.E_out[0])**2 + np.abs(cdata.E_out[1])**2)/(cfiber.glova.symbolrate*cfiber.glova.sps)
print(f"Energy out: {energy_out} J")
input_override = cdata.E_out
cdata.E_in = cdata_orig.E_in
config['fiber']['length'] = lengths[lind]
save_data(cdata, config)
if __name__ == "__main__":
config = get_config()
# loop over lengths
# lengths = [1000, 2000, 4000, 8000, 16000, 32000, 64000, 128000]
lengths = [1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000]
lengths = sorted(lengths)
input_override = None
for lind, length in enumerate(lengths):
print(f"\nGenerating data for fiber length {length}")
# if lind > 0:
# # set the length to the difference between the current and previous length -> incremental
# length = lengths[lind] - lengths[lind-1]
# print(f"\nGenerating data for fiber length {lengths[lind]}m -> {length}m")
config['fiber']['length'] = length
# set the input data to the output data of the previous run
cfiber, cdata = initialize_fiber_and_data(config, input_data_override=input_override)
cfiber()
# input_override = cdata.E_out
save_data(cdata, config)
length_loop(config)
# cfiber, cdata = initialize_fiber_and_data(config)
# cfiber()
# energy_in = np.sum(np.abs(cdata.E_in[0])**2 + np.abs(cdata.E_in[1])**2)/(cfiber.glova.symbolrate*cfiber.glova.sps)
# energy_out = np.sum(np.abs(cdata.E_out[0])**2 + np.abs(cdata.E_out[1])**2)/(cfiber.glova.symbolrate*cfiber.glova.sps)
# print(f"Energy in: {energy_in} J")
# print(f"Energy out: {energy_out} J")
# save_data(cdata, config)
# fig, axs = plt.subplots(2, 2, sharex=True, sharey=True)
# fig, axs = plt.subplots(2, 1, sharex=True, sharey=True)
# xax = np.linspace(0, cfiber.glova.nos, cfiber.glova.nos*cfiber.glova.sps)-0.5
# axs[0,0].plot(xax,np.abs(cdata.E_in[0]))
# axs[1,0].plot(xax,np.abs(cdata.E_in[1]))
# axs[0,1].plot(xax,np.abs(cdata.E_out[0]))
# axs[1,1].plot(xax,np.abs(cdata.E_out[1]))
# axs[0].plot(xax,np.abs(cdata.E_in[0])**2)
# axs[0].plot(xax,np.abs(cdata.E_out[0])**2)
# axs[0].set_title("x")
# axs[0].grid()
# axs[1].plot(xax,np.abs(cdata.E_in[1])**2)
# axs[1].plot(xax,np.abs(cdata.E_out[1])**2)
# axs[1].set_title("y")
# axs[1].grid()
# plt.show()

12
src/single-core-regen/signal_generation.ini
View File

@@ -11,8 +11,8 @@ flags = "FFTW_PATIENT"
nthreads = 32
[fiber]
length = 1000
gamma = 1.14
length = 100000
gamma = 0
alpha = 0.2
D = 17
; 1700 ps/nm/km @ 1 km length is equivalent to 17 ps/nm/km @ 100 km length but the simulation is way faster
@@ -23,9 +23,13 @@ birefsteps = 0
[signal]
modulation = "pam"
mod_order = 4
; seed = 0xC0FFEE
pulse_shape = "gauss_rz"
mod_depth = 1
laser_power = 0
seed = 0xC0FFEE
pulse_shape = "gauss"
fwhm = 0.33
max_jitter=0.02
; jitter_seed = 0xC0FFEE
[script]
data_dir = "/home/suppl/.pypho/data"