mirror of
https://github.com/Telecominfraproject/oopt-gnpy.git
synced 2025-10-29 17:22:42 +00:00
7a1b15a916
Signed-off-by: EstherLerouzic <esther.lerouzic@orange.com> Change-Id: Ifdd6a566fda74c5b7d417f9d61c51d4d3da07bfd
175 lines
8.2 KiB
Python
175 lines
8.2 KiB
Python
#!/usr/bin/env python3
|
|
# -*- coding: utf-8 -*-
|
|
|
|
# SPDX-License-Identifier: BSD-3-Clause
|
|
# test_science_utils
|
|
# Copyright (C) 2025 Telecom Infra Project and GNPy contributors
|
|
# see AUTHORS.rst for a list of contributors
|
|
|
|
"""
|
|
Checks that RamanFiber propagates properly the spectral information. In this way, also the RamanSolver and the NliSolver
|
|
are tested.
|
|
"""
|
|
|
|
from pathlib import Path
|
|
from pandas import read_csv
|
|
from numpy.testing import assert_allclose
|
|
from numpy import array
|
|
from copy import deepcopy
|
|
import pytest
|
|
|
|
from gnpy.core.info import create_input_spectral_information, create_arbitrary_spectral_information
|
|
from gnpy.core.elements import Fiber, RamanFiber
|
|
from gnpy.core.parameters import SimParams
|
|
from gnpy.tools.json_io import load_json
|
|
from gnpy.core.exceptions import NetworkTopologyError
|
|
from gnpy.core.science_utils import RamanSolver
|
|
|
|
TEST_DIR = Path(__file__).parent
|
|
|
|
|
|
@pytest.mark.usefixtures('set_sim_params')
|
|
def test_fiber():
|
|
"""Test the accuracy of propagating the Fiber."""
|
|
fiber = Fiber(**load_json(TEST_DIR / 'data' / 'test_science_utils_fiber_config.json'))
|
|
fiber.ref_pch_in_dbm = 0.0
|
|
# fix grid spectral information generation
|
|
spectral_info_input = create_input_spectral_information(f_min=191.3e12, f_max=196.1e12, roll_off=0.15,
|
|
baud_rate=32e9, spacing=50e9, tx_osnr=40.0,
|
|
tx_power=1e-3)
|
|
# propagation
|
|
spectral_info_out = fiber(spectral_info_input)
|
|
|
|
p_signal = spectral_info_out.signal
|
|
p_nli = spectral_info_out.nli
|
|
|
|
expected_results = read_csv(TEST_DIR / 'data' / 'test_fiber_fix_expected_results.csv')
|
|
assert_allclose(p_signal, expected_results['signal'], rtol=1e-3)
|
|
assert_allclose(p_nli, expected_results['nli'], rtol=1e-3)
|
|
|
|
# flex grid spectral information generation
|
|
frequency = 191e12 + array([0, 50e9, 150e9, 225e9, 275e9])
|
|
slot_width = array([37.5e9, 50e9, 75e9, 50e9, 37.5e9])
|
|
baud_rate = array([32e9, 42e9, 64e9, 42e9, 32e9])
|
|
signal = 1e-3 + array([0, -1e-4, 3e-4, -2e-4, +2e-4])
|
|
delta_pdb_per_channel = [0, 0, 0, 0, 0]
|
|
spectral_info_input = create_arbitrary_spectral_information(frequency=frequency, slot_width=slot_width,
|
|
signal=signal, baud_rate=baud_rate, roll_off=0.15,
|
|
delta_pdb_per_channel=delta_pdb_per_channel,
|
|
tx_osnr=40.0, tx_power=1e-3)
|
|
# propagation without Raman
|
|
spectral_info_out = fiber(spectral_info_input)
|
|
|
|
p_signal = spectral_info_out.signal
|
|
p_nli = spectral_info_out.nli
|
|
|
|
expected_results = read_csv(TEST_DIR / 'data' / 'test_fiber_flex_expected_results.csv')
|
|
assert_allclose(p_signal, expected_results['signal'], rtol=1e-3)
|
|
assert_allclose(p_nli, expected_results['nli'], rtol=1e-3)
|
|
|
|
# propagation with Raman
|
|
SimParams.set_params({'raman_params': {'flag': True}})
|
|
spectral_info_out = fiber(spectral_info_input)
|
|
|
|
p_signal = spectral_info_out.signal
|
|
p_nli = spectral_info_out.nli
|
|
|
|
expected_results = read_csv(TEST_DIR / 'data' / 'test_fiber_flex_expected_with_raman_results.csv')
|
|
assert_allclose(p_signal, expected_results['signal'], rtol=1e-3)
|
|
assert_allclose(p_nli, expected_results['nli'], rtol=1e-3)
|
|
|
|
|
|
@pytest.mark.usefixtures('set_sim_params')
|
|
def test_raman_fiber():
|
|
"""Test the accuracy of propagating the RamanFiber."""
|
|
# spectral information generation
|
|
spectral_info_input = create_input_spectral_information(f_min=191.3e12, f_max=196.1e12, roll_off=0.15,
|
|
baud_rate=32e9, spacing=50e9, tx_osnr=40.0,
|
|
tx_power=1e-3)
|
|
SimParams.set_params(load_json(TEST_DIR / 'data' / 'sim_params.json'))
|
|
SimParams().raman_params.solver_spatial_resolution = 5
|
|
fiber = RamanFiber(**load_json(TEST_DIR / 'data' / 'test_science_utils_fiber_config.json'))
|
|
fiber.ref_pch_in_dbm = 0.0
|
|
# propagation
|
|
spectral_info_out = fiber(spectral_info_input)
|
|
|
|
p_signal = spectral_info_out.signal
|
|
p_ase = spectral_info_out.ase
|
|
p_nli = spectral_info_out.nli
|
|
|
|
expected_results = read_csv(TEST_DIR / 'data' / 'test_raman_fiber_expected_results.csv')
|
|
assert_allclose(p_signal, expected_results['signal'], rtol=1e-3)
|
|
assert_allclose(p_ase, expected_results['ase'], rtol=1e-3)
|
|
assert_allclose(p_nli, expected_results['nli'], rtol=1e-3)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"loss, position, errmsg",
|
|
((0.5, -2, "Lumped loss positions must be between 0 and the fiber length (80.0 km), boundaries excluded."),
|
|
(0.5, 81, "Lumped loss positions must be between 0 and the fiber length (80.0 km), boundaries excluded.")))
|
|
@pytest.mark.usefixtures('set_sim_params')
|
|
def test_fiber_lumped_losses(loss, position, errmsg, set_sim_params):
|
|
"""Lumped losses length sanity checking."""
|
|
SimParams.set_params(load_json(TEST_DIR / 'data' / 'sim_params.json'))
|
|
fiber_dict = load_json(TEST_DIR / 'data' / 'test_lumped_losses_raman_fiber_config.json')
|
|
fiber_dict['params']['lumped_losses'] = [{'position': position, 'loss': loss}]
|
|
with pytest.raises(NetworkTopologyError) as e:
|
|
Fiber(**fiber_dict)
|
|
assert str(e.value) == errmsg
|
|
|
|
|
|
@pytest.mark.usefixtures('set_sim_params')
|
|
def test_fiber_lumped_losses_srs(set_sim_params):
|
|
"""Test the accuracy of Fiber with lumped losses propagation."""
|
|
# spectral information generation
|
|
spectral_info_input = create_input_spectral_information(f_min=191.3e12, f_max=196.1e12, roll_off=0.15,
|
|
baud_rate=32e9, spacing=50e9, tx_osnr=40.0,
|
|
tx_power=1e-3)
|
|
|
|
fiber = Fiber(**load_json(TEST_DIR / 'data' / 'test_lumped_losses_raman_fiber_config.json'))
|
|
raman_fiber = RamanFiber(**load_json(TEST_DIR / 'data' / 'test_lumped_losses_raman_fiber_config.json'))
|
|
|
|
# propagation
|
|
# without Raman pumps
|
|
SimParams.set_params({'raman_params': {'flag': True, 'order': 2}})
|
|
stimulated_raman_scattering = RamanSolver.calculate_stimulated_raman_scattering(spectral_info_input, fiber)
|
|
power_profile = stimulated_raman_scattering.power_profile
|
|
expected_power_profile = read_csv(TEST_DIR / 'data' / 'test_lumped_losses_fiber_no_pumps.csv').values
|
|
assert_allclose(power_profile, expected_power_profile, rtol=1e-3)
|
|
|
|
# with Raman pumps
|
|
SimParams.set_params({'raman_params': {'flag': True, 'order': 1, 'solver_spatial_resolution': 5}})
|
|
stimulated_raman_scattering = RamanSolver.calculate_stimulated_raman_scattering(spectral_info_input, raman_fiber)
|
|
power_profile = stimulated_raman_scattering.power_profile
|
|
expected_power_profile = read_csv(TEST_DIR / 'data' / 'test_lumped_losses_raman_fiber.csv').values
|
|
assert_allclose(power_profile, expected_power_profile, rtol=1e-3)
|
|
|
|
# without Stimulated Raman Scattering
|
|
expected_power_profile = read_csv(TEST_DIR / 'data' / 'test_lumped_losses_fiber_no_raman.csv', header=None)
|
|
stimulated_raman_scattering = RamanSolver.calculate_attenuation_profile(spectral_info_input, fiber)
|
|
power_profile = stimulated_raman_scattering.power_profile
|
|
assert_allclose(power_profile, expected_power_profile, rtol=1e-3)
|
|
|
|
|
|
@pytest.mark.usefixtures('set_sim_params')
|
|
def test_nli_solver():
|
|
"""Test the accuracy of NLI solver."""
|
|
fiber = Fiber(**load_json(TEST_DIR / 'data' / 'test_science_utils_fiber_config.json'))
|
|
fiber.ref_pch_in_dbm = 0.0
|
|
# fix grid spectral information generation
|
|
spectral_info_input = create_input_spectral_information(f_min=191.3e12, f_max=196.1e12, roll_off=0.15,
|
|
baud_rate=32e9, spacing=50e9, tx_osnr=40.0,
|
|
tx_power=1e-3)
|
|
|
|
SimParams.set_params(load_json(TEST_DIR / 'data' / 'sim_params.json'))
|
|
sim_params = SimParams()
|
|
|
|
spectral_info_output = fiber(deepcopy(spectral_info_input))
|
|
|
|
sim_params.nli_params.method = 'ggn_approx'
|
|
sim_params.raman_params.result_spatial_resolution = 10e3
|
|
spectral_info_output_approx = fiber(deepcopy(spectral_info_input))
|
|
|
|
assert_allclose(spectral_info_output.nli, spectral_info_output_approx.nli, rtol=1e-1)
|
|
|