Files
oopt-gnpy/tests/test_propagation.py
EstherLerouzic 87211b35e9 Use design delta_p and gains instead of p_spani
Remove the visualisation of the effective_pch in amp because actual
and target are the relevant ones. effective_pch was artificially
related to a mix of reference channel and noisy channel (mixed between
on the fly redesign but using actual ROADM equalisation which includes noise
in its actual loss).

the change does no more rely on the target power (which is rounded)
but on the designed gain, which is not rounded.

Propagations are slightly changed for openroadm simulations because of that.
(I verified)

The gain of amp was estimated on the fly with p_spni also in case of
RamanFiber preceding elements. removing p_spani requies that an estimation
of Raman gain be done during design.

This commit also adds this estimation.

Signed-off-by: EstherLerouzic <esther.lerouzic@orange.com>
Change-Id: I960b85e99f85a7d168ac5349e325c4928fa5673b
2023-11-20 17:07:46 +01:00

137 lines
5.2 KiB
Python

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# @Author: Jean-Luc Auge
# @Date: 2018-02-02 14:06:55
import pytest
from gnpy.core.elements import Transceiver, Fiber, Edfa, Roadm
from gnpy.core.utils import db2lin
from gnpy.core.info import create_input_spectral_information, ReferenceCarrier
from gnpy.core.network import build_network
from gnpy.tools.json_io import load_network, load_equipment
from pathlib import Path
from networkx import dijkstra_path
from numpy import mean, sqrt, ones
network_file_name = Path(__file__).parent.parent / 'tests/LinkforTest.json'
eqpt_library_name = Path(__file__).parent.parent / 'tests/data/eqpt_config.json'
@pytest.fixture(params=[(96, 0.05e12), (60, 0.075e12), (45, 0.1e12), (2, 0.1e12)],
ids=['50GHz spacing', '75GHz spacing', '100GHz spacing', '2 channels'])
# TODO in elements.py code: pytests doesn't pass with 1 channel: interpolate fail
def nch_and_spacing(request):
"""parametrize channel count vs channel spacing (Hz)"""
yield request.param
def propagation(input_power, con_in, con_out, dest):
equipment = load_equipment(eqpt_library_name)
network = load_network(network_file_name, equipment)
# parametrize the network elements with the con losses and adapt gain
# (assumes all spans are identical)
for e in network.nodes():
if isinstance(e, Fiber):
loss = e.params.loss_coef * e.params.length
e.params.con_in = con_in
e.params.con_out = con_out
if isinstance(e, Edfa):
e.operational.gain_target = loss + con_in + con_out
build_network(network, equipment, 0, 20)
transceivers = {n.uid: n for n in network.nodes() if isinstance(n, Transceiver)}
p = input_power
p = db2lin(p) * 1e-3
spacing = 50e9 # THz
si = create_input_spectral_information(f_min=191.3e12, f_max=191.3e12 + 79 * spacing, roll_off=0.15,
baud_rate=32e9, power=p, spacing=spacing, tx_osnr=None,
ref_carrier=ReferenceCarrier(baud_rate=32e9, slot_width=50e9))
source = next(transceivers[uid] for uid in transceivers if uid == 'trx A')
sink = next(transceivers[uid] for uid in transceivers if uid == dest)
path = dijkstra_path(network, source, sink)
for el in path:
si = el(si)
print(el) # remove this line when sweeping across several powers
edfa_sample = next(el for el in path if isinstance(el, Edfa))
nf = mean(edfa_sample.nf)
print(f'pw: {input_power} conn in: {con_in} con out: {con_out}',
f'OSNR@0.1nm: {round(mean(sink.osnr_ase_01nm),2)}',
f'SNR@bandwitdth: {round(mean(sink.snr),2)}')
return sink, nf, path
test = {'a': (-1, 1, 0), 'b': (-1, 1, 1), 'c': (0, 1, 0), 'd': (1, 1, 1)}
expected = {'a': (-2, 0, 0), 'b': (-2, 0, 1), 'c': (-1, 0, 0), 'd': (0, 0, 1)}
@pytest.mark.parametrize("dest", ['trx B', 'trx F'])
@pytest.mark.parametrize("osnr_test", ['a', 'b', 'c', 'd'])
def test_snr(osnr_test, dest):
pw = test[osnr_test][0]
conn_in = test[osnr_test][1]
conn_out = test[osnr_test][2]
sink, nf, _ = propagation(pw, conn_in, conn_out, dest)
osnr = round(mean(sink.osnr_ase), 3)
nli = 1.0 / db2lin(round(mean(sink.snr), 3)) - 1.0 / db2lin(osnr)
pw = expected[osnr_test][0]
conn_in = expected[osnr_test][1]
conn_out = expected[osnr_test][2]
sink, exp_nf, _ = propagation(pw, conn_in, conn_out, dest)
expected_osnr = round(mean(sink.osnr_ase), 3)
expected_nli = 1.0 / db2lin(round(mean(sink.snr), 3)) - 1.0 / db2lin(expected_osnr)
# compare OSNR taking into account nf change of amps
osnr_diff = abs(osnr - expected_osnr + nf - exp_nf)
nli_diff = abs((nli - expected_nli) / nli)
assert osnr_diff < 0.01 and nli_diff < 0.01
@pytest.mark.parametrize("dest", ['trx B', 'trx F'])
@pytest.mark.parametrize("cd_test", ['a', 'b', 'c', 'd'])
def test_chromatic_dispersion(cd_test, dest):
pw = test[cd_test][0]
conn_in = test[cd_test][1]
conn_out = test[cd_test][2]
sink, _, path = propagation(pw, conn_in, conn_out, dest)
chromatic_dispersion = sink.chromatic_dispersion
num_ch = len(chromatic_dispersion)
expected_cd = 0
for el in path:
expected_cd += el.params.dispersion * el.params.length if isinstance(el, Fiber) else 0
expected_cd = expected_cd * ones(num_ch) * 1e3
assert chromatic_dispersion == pytest.approx(expected_cd)
@pytest.mark.parametrize("dest", ['trx B', 'trx F'])
@pytest.mark.parametrize("dgd_test", ['a', 'b', 'c', 'd'])
def test_dgd(dgd_test, dest):
pw = test[dgd_test][0]
conn_in = test[dgd_test][1]
conn_out = test[dgd_test][2]
sink, _, path = propagation(pw, conn_in, conn_out, dest)
pmd = sink.pmd
num_ch = len(pmd)
expected_pmd = 0
for el in path:
expected_pmd += el.params.pmd_coef**2 * el.params.length if isinstance(el, Fiber) else 0
expected_pmd += el.params.pmd**2 if isinstance(el, Roadm) else 0
expected_pmd = sqrt(expected_pmd) * ones(num_ch) * 1e12
assert pmd == pytest.approx(expected_pmd)
if __name__ == '__main__':
from logging import getLogger, basicConfig, INFO
logger = getLogger(__name__)
basicConfig(level=INFO)
for a in test:
test_snr(a, 'trx F')
print('\n')