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ffc7dbc241
The idea behind this change is to reproduce the exact same behaviour as with the scalar, but accounting for variable levels of powers. - delete the neq_ch: equivalent channel count in dB because with mixed rates and power such a value has limited utility - instead creates a vector that records the 'user defined' distribution of power. This vector is used as a reference for channel equalization out of the ROADM. If target_power_per_channel has some channels power above input power, then the whole target is reduced. For example, if user specifies delta_pdb_per_channel: freq1: 1dB, freq2: 3dB, freq3: -3dB, and target is -20dBm out of the ROADM, then the target power for each channel uses the specified delta_pdb_per_channel. target_power_per_channel[f1, f2, f3] = -19, -17, -23 However if input_signal = -23, -16, -26, then the target can not be applied, because -23 < -19dBm and -26 < -23dBm, and a reduction must be applied (ROADM can not amplify). Then the target is only applied to signals whose power is above the threshold. others are left unchanged and unequalized. the new target is [-23, -17, -26] and the attenuation to apply is [-23, -16, -26] - [-23, -17, -26] = [0, 1, 0] Important note: This changes the previous behaviour that equalized all identical channels based on the one that had the min power !! TODO: in coming refactor where transmission and design will be properly separated, the initial behaviour may be set again as a design choice. This change corresponds to a discussion held during coders call. Please look at this document for a reference: https://telecominfraproject.atlassian.net/wiki/spaces/OOPT/pages/669679645/PSE+Meeting+Minutes - in amplifier: the saturation is computed based on this vector delta_pdb_per_channel, instead of the nb of channels. The target of the future refactor will be to use the effective carrier's power. I prefer to have this first step, because this is how it is implemented today (ie based on the noiseless reference), and I would like first to add more behaviour tests before doing this refactor (would it be needed). - in spectralInfo class, change pref to a Pref object to enable both p_span0 and p_spani to be conveyed during propagation of spectral_information in elements. No refactor of them at this point. Signed-off-by: EstherLerouzic <esther.lerouzic@orange.com> Change-Id: I591027cdd08e89098330c7d77d6f50212f4d4724
133 lines
6.2 KiB
Python
133 lines
6.2 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Checks that RamanFiber propagates properly the spectral information. In this way, also the RamanSolver and the NliSolver
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are tested.
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"""
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from pathlib import Path
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from pandas import read_csv
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from numpy.testing import assert_allclose
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from numpy import array, genfromtxt
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import pytest
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from gnpy.core.info import create_input_spectral_information, create_arbitrary_spectral_information, Pref
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from gnpy.core.elements import Fiber, RamanFiber
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from gnpy.core.parameters import SimParams
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from gnpy.tools.json_io import load_json
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from gnpy.core.exceptions import NetworkTopologyError
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from gnpy.core.science_utils import RamanSolver
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TEST_DIR = Path(__file__).parent
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def test_fiber():
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""" Test the accuracy of propagating the Fiber."""
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fiber = Fiber(**load_json(TEST_DIR / 'data' / 'test_science_utils_fiber_config.json'))
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# fix grid spectral information generation
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spectral_info_input = create_input_spectral_information(f_min=191.3e12, f_max=196.1e12, roll_off=0.15,
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baud_rate=32e9, power=1e-3, spacing=50e9)
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# propagation
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spectral_info_out = fiber(spectral_info_input)
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p_signal = spectral_info_out.signal
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p_nli = spectral_info_out.nli
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expected_results = read_csv(TEST_DIR / 'data' / 'test_fiber_fix_expected_results.csv')
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assert_allclose(p_signal, expected_results['signal'], rtol=1e-3)
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assert_allclose(p_nli, expected_results['nli'], rtol=1e-3)
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# flex grid spectral information generation
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frequency = 191e12 + array([0, 50e9, 150e9, 225e9, 275e9])
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slot_width = array([37.5e9, 50e9, 75e9, 50e9, 37.5e9])
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baud_rate = array([32e9, 42e9, 64e9, 42e9, 32e9])
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signal = 1e-3 + array([0, -1e-4, 3e-4, -2e-4, +2e-4])
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delta_pdb_per_channel = [0, 0, 0, 0, 0]
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pref = Pref(p_span0=0, p_spani=0)
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spectral_info_input = create_arbitrary_spectral_information(frequency=frequency, slot_width=slot_width,
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signal=signal, baud_rate=baud_rate, roll_off=0.15,
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delta_pdb_per_channel=delta_pdb_per_channel,
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ref_power=pref)
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# propagation
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spectral_info_out = fiber(spectral_info_input)
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p_signal = spectral_info_out.signal
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p_nli = spectral_info_out.nli
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expected_results = read_csv(TEST_DIR / 'data' / 'test_fiber_flex_expected_results.csv')
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assert_allclose(p_signal, expected_results['signal'], rtol=1e-3)
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assert_allclose(p_nli, expected_results['nli'], rtol=1e-3)
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@pytest.mark.usefixtures('set_sim_params')
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def test_raman_fiber():
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""" Test the accuracy of propagating the RamanFiber."""
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# spectral information generation
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spectral_info_input = create_input_spectral_information(f_min=191.3e12, f_max=196.1e12, roll_off=0.15,
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baud_rate=32e9, power=1e-3, spacing=50e9)
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SimParams.set_params(load_json(TEST_DIR / 'data' / 'sim_params.json'))
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fiber = RamanFiber(**load_json(TEST_DIR / 'data' / 'test_science_utils_fiber_config.json'))
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# propagation
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spectral_info_out = fiber(spectral_info_input)
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p_signal = spectral_info_out.signal
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p_ase = spectral_info_out.ase
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p_nli = spectral_info_out.nli
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expected_results = read_csv(TEST_DIR / 'data' / 'test_raman_fiber_expected_results.csv')
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assert_allclose(p_signal, expected_results['signal'], rtol=1e-3)
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assert_allclose(p_ase, expected_results['ase'], rtol=1e-3)
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assert_allclose(p_nli, expected_results['nli'], rtol=1e-3)
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@pytest.mark.parametrize(
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"loss, position, errmsg",
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((0.5, -2, "Lumped loss positions must be between 0 and the fiber length (80.0 km), boundaries excluded."),
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(0.5, 81, "Lumped loss positions must be between 0 and the fiber length (80.0 km), boundaries excluded.")))
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@pytest.mark.usefixtures('set_sim_params')
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def test_fiber_lumped_losses(loss, position, errmsg, set_sim_params):
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""" Lumped losses length sanity checking."""
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SimParams.set_params(load_json(TEST_DIR / 'data' / 'sim_params.json'))
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fiber_dict = load_json(TEST_DIR / 'data' / 'test_lumped_losses_raman_fiber_config.json')
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fiber_dict['params']['lumped_losses'] = [{'position': position, 'loss': loss}]
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with pytest.raises(NetworkTopologyError) as e:
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Fiber(**fiber_dict)
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assert str(e.value) == errmsg
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@pytest.mark.usefixtures('set_sim_params')
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def test_fiber_lumped_losses_srs(set_sim_params):
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""" Test the accuracy of Fiber with lumped losses propagation."""
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# spectral information generation
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spectral_info_input = create_input_spectral_information(f_min=191.3e12, f_max=196.1e12, roll_off=0.15,
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baud_rate=32e9, power=1e-3, spacing=50e9)
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SimParams.set_params(load_json(TEST_DIR / 'data' / 'sim_params.json'))
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fiber = Fiber(**load_json(TEST_DIR / 'data' / 'test_lumped_losses_raman_fiber_config.json'))
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raman_fiber = RamanFiber(**load_json(TEST_DIR / 'data' / 'test_lumped_losses_raman_fiber_config.json'))
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# propagation
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# without Raman pumps
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stimulated_raman_scattering = RamanSolver.calculate_stimulated_raman_scattering(
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spectral_info_input, fiber)
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power_profile = stimulated_raman_scattering.power_profile
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expected_power_profile = genfromtxt(TEST_DIR / 'data' / 'test_lumped_losses_fiber_no_pumps.csv', delimiter=',')
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assert_allclose(power_profile, expected_power_profile, rtol=1e-3)
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# with Raman pumps
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expected_power_profile = genfromtxt(TEST_DIR / 'data' / 'test_lumped_losses_raman_fiber.csv', delimiter=',')
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stimulated_raman_scattering = RamanSolver.calculate_stimulated_raman_scattering(
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spectral_info_input, raman_fiber)
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power_profile = stimulated_raman_scattering.power_profile
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assert_allclose(power_profile, expected_power_profile, rtol=1e-3)
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# without Stimulated Raman Scattering
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expected_power_profile = genfromtxt(TEST_DIR / 'data' / 'test_lumped_losses_fiber_no_raman.csv', delimiter=',')
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stimulated_raman_scattering = RamanSolver.calculate_attenuation_profile(spectral_info_input, fiber)
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power_profile = stimulated_raman_scattering.power_profile
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assert_allclose(power_profile, expected_power_profile, rtol=1e-3)
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