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Management of lumped losses along a fiber span
The lumped losses are used in the computation of the loss/gain profile through the fiber whether the Raman effect is considered or not. The computed power profile is used to calculate the related NLI impairment. Using the 'gn_model_analytic' method, the lumped losses are taken into account as the contribution of an additional total loss at the end of the fiber span. In case the 'ggn_spectrally_separated' is selected, the method uses the computed power profile according to the specified z and frequency arrays. The lumped losses are so considered within the NLI power evolution along the fiber. Change-Id: I73a6baa321aca4d041cafa180f47afed824ce267 Signed-off-by: Jan Kundrát <jan.kundrat@telecominfraproject.com>
This commit is contained in:
Giacomo Borraccini
committed by
Jan Kundrát
Jan Kundrát
parent
5e50ffbbf6
commit
aaf0480e9c
@@ -90,6 +90,11 @@ The fiber library currently describes SSMF and NZDF but additional fiber types c
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| | | coefficient. In |
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| | | :math:`s\times\sqrt{m}^{-1}`. |
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+----------------------+-----------+------------------------------------------+
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| ``lumped_losses`` | (array) | Places along the fiber length with extra |
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| | | losses. Specified as a loss in dB at |
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| | | each relevant position (in km): |
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| | | ``{"position": 10, "loss": 1.5}``) |
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+----------------------+-----------+------------------------------------------+
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.. _Corning whitepaper on MFD/EA: https://www.corning.com/microsites/coc/oem/documents/specialty-fiber/WP7071-Mode-Field-Diam-and-Eff-Area.pdf
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@@ -365,6 +365,15 @@ class Fiber(_Node):
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else:
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self._cr_function = lambda frequency: zeros(squeeze(frequency).shape)
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# Lumped losses
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z_lumped_losses = array([lumped['position'] for lumped in self.params.lumped_losses]) # km
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lumped_losses_power = array([lumped['loss'] for lumped in self.params.lumped_losses]) # dB
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if not ((z_lumped_losses > 0) * (z_lumped_losses < 1e-3 * self.params.length)).all():
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raise NetworkTopologyError("Lumped loss positions must be between 0 and the fiber length "
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f"({1e-3 * self.params.length} km), boundaries excluded.")
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self.lumped_losses = db2lin(- lumped_losses_power) # [linear units]
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self.z_lumped_losses = array(z_lumped_losses) * 1e3 # [m]
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@property
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def to_json(self):
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return {'uid': self.uid,
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@@ -156,6 +156,7 @@ class FiberParams(Parameters):
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else:
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self._loss_coef = asarray(kwargs['loss_coef']) * 1e-3 # lineic loss dB/m
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self._f_loss_ref = asarray(self._ref_frequency) # Hz
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self._lumped_losses = kwargs['lumped_losses'] if 'lumped_losses' in kwargs else []
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except KeyError as e:
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raise ParametersError(f'Fiber configurations json must include {e}. Configuration: {kwargs}')
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@@ -188,6 +189,10 @@ class FiberParams(Parameters):
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def con_out(self):
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return self._con_out
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@property
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def lumped_losses(self):
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return self._lumped_losses
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@con_out.setter
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def con_out(self, con_out):
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self._con_out = con_out
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@@ -240,6 +245,8 @@ class FiberParams(Parameters):
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dictionary = super().asdict()
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dictionary['loss_coef'] = self.loss_coef * 1e3
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dictionary['length_units'] = 'm'
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if not self.lumped_losses:
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dictionary.pop('lumped_losses')
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if not self.raman_efficiency:
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dictionary.pop('raman_efficiency')
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return dictionary
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@@ -11,7 +11,8 @@ The solvers take as input instances of the spectral information, the fiber and t
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"""
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from numpy import interp, pi, zeros, shape, where, cos, array, append, ones, exp, arange, sqrt, empty, trapz, arcsinh, \
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clip, abs, sum, concatenate, flip, outer, inner, transpose, max, format_float_scientific, diag
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clip, abs, sum, concatenate, flip, outer, inner, transpose, max, format_float_scientific, diag, prod, argwhere, \
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unique, argsort, cumprod
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from logging import getLogger
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from scipy.constants import k, h
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from scipy.interpolate import interp1d
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@@ -69,6 +70,15 @@ class StimulatedRamanScattering:
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class RamanSolver:
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"""This class contains the methods to calculate the Raman scattering effect."""
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@staticmethod
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def _create_lumped_losses(z, lumped_losses, z_lumped_losses):
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lumped_losses = concatenate((lumped_losses, ones(z.size)))
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z, unique_indices = unique(concatenate((z_lumped_losses, z)), return_index=True)
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order = argsort(z)
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lumped_losses = (lumped_losses[unique_indices])[order]
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z = z[order]
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return z, lumped_losses
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@staticmethod
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def calculate_attenuation_profile(spectral_info: SpectralInformation, fiber):
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"""Evaluates the attenuation profile along the z axis for all the frequency propagating in the
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@@ -76,12 +86,16 @@ class RamanSolver:
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"""
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# z array definition
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z = array([0, fiber.params.length])
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# Lumped losses array definition
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z, lumped_losses = RamanSolver._create_lumped_losses(z, fiber.lumped_losses, fiber.z_lumped_losses)
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lumped_loss_acc = cumprod(lumped_losses)
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frequency = spectral_info.frequency
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alpha = fiber.alpha(frequency)
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loss_profile = exp(- outer(alpha, z))
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loss_profile = exp(- outer(alpha, z)) * lumped_loss_acc
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power_profile = outer(spectral_info.signal, ones(z.size)) * loss_profile
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stimulated_raman_scattering = StimulatedRamanScattering(power_profile, loss_profile, frequency, z)
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return stimulated_raman_scattering
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return StimulatedRamanScattering(power_profile, loss_profile, spectral_info.frequency, z)
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@staticmethod
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def calculate_stimulated_raman_scattering(spectral_info: SpectralInformation, fiber):
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@@ -90,13 +104,16 @@ class RamanSolver:
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"""
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logger.debug('Start computing fiber Stimulated Raman Scattering')
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# Raman parameters
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z_resolution = sim_params.raman_params.result_spatial_resolution
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z_step = sim_params.raman_params.solver_spatial_resolution
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z = append(arange(0, fiber.params.length, z_step), fiber.params.length)
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z_final = append(arange(0, fiber.params.length, z_resolution), fiber.params.length)
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if sim_params.raman_params.flag:
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# Raman parameters
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z_resolution = sim_params.raman_params.result_spatial_resolution
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z_step = sim_params.raman_params.solver_spatial_resolution
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z = append(arange(0, fiber.params.length, z_step), fiber.params.length)
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z_final = append(arange(0, fiber.params.length, z_resolution), fiber.params.length)
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# Lumped losses array definition
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z, lumped_losses = RamanSolver._create_lumped_losses(z, fiber.lumped_losses, fiber.z_lumped_losses)
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if hasattr(fiber, 'raman_pumps'):
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# TODO: verify co-propagating pumps computation and in general unsorted frequency
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# Co-propagating spectrum definition
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@@ -119,19 +136,20 @@ class RamanSolver:
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co_cr = fiber.cr(co_frequency)
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co_alpha = fiber.alpha(co_frequency)
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co_power_profile = \
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RamanSolver.first_order_derivative_solution(co_power, co_alpha, co_cr, z)
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RamanSolver.first_order_derivative_solution(co_power, co_alpha, co_cr, z, lumped_losses)
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# Counter-propagating profile initialization
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cnt_power_profile = empty([co_frequency.size, z.size])
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if cnt_frequency.size:
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cnt_cr = fiber.cr(cnt_frequency)
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cnt_alpha = fiber.alpha(cnt_frequency)
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cnt_power_profile = \
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flip(RamanSolver.first_order_derivative_solution(cnt_power, cnt_alpha, cnt_cr, z[-1] - flip(z)))
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flip(RamanSolver.first_order_derivative_solution(cnt_power, cnt_alpha, cnt_cr,
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z[-1] - flip(z), flip(lumped_losses)))
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# Co-propagating and Counter-propagating Profile Computation
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if co_frequency.size and cnt_frequency.size:
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co_power_profile, cnt_power_profile = \
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RamanSolver.iterative_algorithm(co_power_profile, cnt_power_profile, co_frequency, cnt_frequency,
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z, fiber)
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RamanSolver.iterative_algorithm(co_power_profile, cnt_power_profile,
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co_frequency, cnt_frequency, z, fiber, lumped_losses)
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# Complete Power Profile
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power_profile = concatenate((co_power_profile, cnt_power_profile), axis=0)
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# Complete Loss Profile
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@@ -146,7 +164,7 @@ class RamanSolver:
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cr = fiber.cr(spectral_info.frequency)
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# Power profile
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power_profile = \
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RamanSolver.first_order_derivative_solution(spectral_info.signal, alpha, cr, z)
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RamanSolver.first_order_derivative_solution(spectral_info.signal, alpha, cr, z, lumped_losses)
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# Loss profile
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loss_profile = power_profile / outer(spectral_info.signal, ones(z.size))
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frequency = spectral_info.frequency
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@@ -154,7 +172,8 @@ class RamanSolver:
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loss_profile = interp1d(z, loss_profile, axis=1)(z_final)
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stimulated_raman_scattering = StimulatedRamanScattering(power_profile, loss_profile, frequency, z_final)
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else:
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stimulated_raman_scattering = RamanSolver.calculate_attenuation_profile(spectral_info, fiber)
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stimulated_raman_scattering = \
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RamanSolver.calculate_attenuation_profile(spectral_info, fiber)
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return stimulated_raman_scattering
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@staticmethod
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@@ -181,23 +200,26 @@ class RamanSolver:
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return ase
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@staticmethod
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def first_order_derivative_solution(power_in, alpha, cr, z):
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def first_order_derivative_solution(power_in, alpha, cr, z, lumped_losses):
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"""Solves the Raman first order derivative equation
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:param power_in: launch power array
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:param alpha: loss coefficient array
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:param cr: Raman efficiency coefficients matrix
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:param z: z position array
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:param lumped_losses: concentrated losses array along the fiber span
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:return: power profile matrix
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"""
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dz = z[1:] - z[:-1]
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power = outer(power_in, ones(z.size))
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for i in range(1, z.size):
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power[:, i] = power[:, i - 1] * (1 + (- alpha + sum(cr * power[:, i - 1], 1)) * dz[i - 1])
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power[:, i] = \
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power[:, i - 1] * (1 + (- alpha + sum(cr * power[:, i - 1], 1)) * dz[i - 1]) * lumped_losses[i - 1]
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return power
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@staticmethod
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def iterative_algorithm(co_initial_guess_power, cnt_initial_guess_power, co_frequency, cnt_frequency, z, fiber):
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def iterative_algorithm(co_initial_guess_power, cnt_initial_guess_power, co_frequency, cnt_frequency, z, fiber,
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lumped_losses):
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"""Solves the Raman first order derivative equation in case of both co- and counter-propagating
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frequencies
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@@ -207,6 +229,7 @@ class RamanSolver:
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:param cnt_frequency: counter-propagationg frequencies
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:param z: z position array
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:param fiber: instance of gnpy.core.elements.Fiber or gnpy.core.elements.RamanFiber
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:param lumped_losses: concentrated losses array along the fiber span
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:return: co- and counter-propagatng power profile matrix
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"""
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logger.debug(' Start iterative algorithm')
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@@ -227,15 +250,18 @@ class RamanSolver:
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for i in range(1, z.size):
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dpdz = - alpha + sum(cr * next_power[:, i - 1], 1)
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next_power[:co_frequency.size, i] = \
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next_power[:co_frequency.size, i - 1] * (1 + dpdz[:co_frequency.size] * dz[i - 1])
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next_power[:co_frequency.size, i - 1] * (1 + dpdz[:co_frequency.size] * dz[i - 1]) * \
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lumped_losses[i - 1]
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for i in range(1, z.size):
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dpdz = - alpha + sum(cr * next_power[:, -i], 1)
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next_power[co_frequency.size:, -i - 1] = \
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next_power[co_frequency.size:, -i] * (1 + dpdz[co_frequency.size:] * dz[-i])
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next_power[co_frequency.size:, -i] * (1 + dpdz[co_frequency.size:] * dz[-i]) * \
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lumped_losses[-i]
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dpdz_num = (next_power[:co_frequency.size, 1:] - next_power[:co_frequency.size, :-1]) / dz
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dpdz_exp = next_power[:co_frequency.size, :-1] * \
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(- outer(alpha, ones(z.size)) + inner(cr, transpose(next_power)))[:co_frequency.size, :-1]
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(- outer(alpha, ones(z.size)) + inner(cr, transpose(next_power)))[:co_frequency.size, :-1] * \
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lumped_losses[:-1]
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residue = max(abs((next_power - prev_power) / next_power))
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accuracy = max(abs((dpdz_exp - dpdz_num) / dpdz_exp))
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96
tests/data/test_lumped_losses_fiber_no_pumps.csv
Normal file
96
tests/data/test_lumped_losses_fiber_no_pumps.csv
Normal file
@@ -0,0 +1,96 @@
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||||
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||||
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||||
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||||
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||||
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||||
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96
tests/data/test_lumped_losses_fiber_no_raman.csv
Normal file
96
tests/data/test_lumped_losses_fiber_no_raman.csv
Normal file
@@ -0,0 +1,96 @@
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98
tests/data/test_lumped_losses_raman_fiber.csv
Normal file
98
tests/data/test_lumped_losses_raman_fiber.csv
Normal file
@@ -0,0 +1,98 @@
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1.000000000000000021e-03,5.453277270883248422e-04,3.435968835063559607e-04,2.224987019123045480e-04,1.493989071766017103e-04,1.058939742146122178e-04,8.185283432112488435e-05,7.320083692872619591e-05,8.480120525436834187e-05
|
||||
1.000000000000000021e-03,5.447550756300442850e-04,3.429826158632724655e-04,2.219507707465509202e-04,1.489150144895505707e-04,1.054368625228528346e-04,8.136171358212484941e-05,7.255437084770181432e-05,8.362138199130290570e-05
|
||||
1.000000000000000021e-03,5.441892122953419404e-04,3.423756544954533452e-04,2.214091954721005766e-04,1.484365007267826825e-04,1.049846526338566485e-04,8.087593753205775274e-05,7.191586230794716592e-05,8.246071125248328093e-05
|
||||
1.000000000000000021e-03,5.436257869834306257e-04,3.417717090501741430e-04,2.208705585733095858e-04,1.479607869104501987e-04,1.045353737048878776e-04,8.039389156994272538e-05,7.128377081045688692e-05,8.131715986317171246e-05
|
||||
1.000000000000000021e-03,5.430647907230928004e-04,3.411707635569704330e-04,2.203348411819341208e-04,1.474878525008424264e-04,1.040890016129762040e-04,7.991554062444546221e-05,7.065802258502740848e-05,8.019045690600123673e-05
|
||||
1.000000000000000021e-03,5.425062145452035425e-04,3.405728020832017003e-04,2.198020245085192449e-04,1.470176770797095381e-04,1.036455124224165388e-04,7.944084996986793814e-05,7.003854478529189314e-05,7.908033605497744685e-05
|
||||
1.000000000000000021e-03,5.419500494783734655e-04,3.399778087230425684e-04,2.192720898259863408e-04,1.465502403299397984e-04,1.032048823605461293e-04,7.896978519468913008e-05,6.942526543493823701e-05,7.798653539704048551e-05
|
||||
1.000000000000000021e-03,5.413941311127808435e-04,3.393835756573481376e-04,2.187432334857416756e-04,1.460841531649274296e-04,1.027660304092932323e-04,7.850144313387483587e-05,6.881729624423716459e-05,7.690779566810491681e-05
|
||||
1.000000000000000021e-03,5.408384570747234218e-04,3.387900992487282684e-04,2.182154508640741402e-04,1.456194096560940569e-04,1.023289477092565169e-04,7.803580657419308998e-05,6.821458972979072498e-05,7.584390730667668775e-05
|
||||
1.000000000000000021e-03,5.402830249660243826e-04,3.381973758275843189e-04,2.176887373150658610e-04,1.451560038709627043e-04,1.018936254270842219e-04,7.757285840469518032e-05,6.761709884283491779e-05,7.479466373034050742e-05
|
||||
1.000000000000000021e-03,5.397131798668998448e-04,3.375789411508863902e-04,2.171273238640323091e-04,1.446496280566233409e-04,1.014059885387841374e-04,7.704415358894297196e-05,6.692941074150365747e-05,7.359880640935691578e-05
|
||||
1.000000000000000021e-03,5.391436022217508317e-04,3.369613465901344558e-04,2.165671485747253328e-04,1.441448734692186333e-04,1.009205829600201576e-04,7.651897498112265308e-05,6.624864922679121694e-05,7.242198835523922060e-05
|
||||
1.223599761281599736e-03,2.870574988594920204e-03,5.410558860144323162e-03,9.740321680379944794e-03,1.718226620812626088e-02,3.032327552654904765e-02,5.460894525232919822e-02,1.025522470341594938e-01,2.059999999999999887e-01
|
||||
4.083745694274921144e-03,8.369042825128880300e-03,1.439530071705045523e-02,2.397223043565064812e-02,3.897486419880552555e-02,6.202545653148217042e-02,9.626510461383173956e-02,1.437785279168776742e-01,2.000000000000000111e-01
|
||||
|
36
tests/data/test_lumped_losses_raman_fiber_config.json
Normal file
36
tests/data/test_lumped_losses_raman_fiber_config.json
Normal file
@@ -0,0 +1,36 @@
|
||||
{
|
||||
"uid": "Span1",
|
||||
"params": {
|
||||
"length": 80,
|
||||
"loss_coef": 0.2,
|
||||
"length_units": "km",
|
||||
"att_in": 0,
|
||||
"con_in": 0.5,
|
||||
"con_out": 0.0,
|
||||
"lumped_losses": [
|
||||
{
|
||||
"position": 7,
|
||||
"loss": 0.5
|
||||
}
|
||||
],
|
||||
"type_variety": "SSMF",
|
||||
"dispersion": 0.0000167,
|
||||
"effective_area": 83e-12,
|
||||
"pmd_coef": 1.265e-15
|
||||
},
|
||||
"operational": {
|
||||
"temperature": 283,
|
||||
"raman_pumps": [
|
||||
{
|
||||
"power": 0.2,
|
||||
"frequency": 205000000000000,
|
||||
"propagation_direction": "counterprop"
|
||||
},
|
||||
{
|
||||
"power": 0.206,
|
||||
"frequency": 201000000000000,
|
||||
"propagation_direction": "counterprop"
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
@@ -9,13 +9,15 @@ are tested.
|
||||
from pathlib import Path
|
||||
from pandas import read_csv
|
||||
from numpy.testing import assert_allclose
|
||||
from numpy import array
|
||||
from numpy import array, genfromtxt
|
||||
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
|
||||
|
||||
@@ -76,3 +78,51 @@ def test_raman_fiber():
|
||||
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, power=1e-3, spacing=50e9)
|
||||
|
||||
SimParams.set_params(load_json(TEST_DIR / 'data' / 'sim_params.json'))
|
||||
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
|
||||
stimulated_raman_scattering = RamanSolver.calculate_stimulated_raman_scattering(
|
||||
spectral_info_input, fiber)
|
||||
power_profile = stimulated_raman_scattering.power_profile
|
||||
expected_power_profile = genfromtxt(TEST_DIR / 'data' / 'test_lumped_losses_fiber_no_pumps.csv', delimiter=',')
|
||||
assert_allclose(power_profile, expected_power_profile, rtol=1e-3)
|
||||
|
||||
# with Raman pumps
|
||||
expected_power_profile = genfromtxt(TEST_DIR / 'data' / 'test_lumped_losses_raman_fiber.csv', delimiter=',')
|
||||
stimulated_raman_scattering = RamanSolver.calculate_stimulated_raman_scattering(
|
||||
spectral_info_input, raman_fiber)
|
||||
power_profile = stimulated_raman_scattering.power_profile
|
||||
assert_allclose(power_profile, expected_power_profile, rtol=1e-3)
|
||||
|
||||
# without Stimulated Raman Scattering
|
||||
expected_power_profile = genfromtxt(TEST_DIR / 'data' / 'test_lumped_losses_fiber_no_raman.csv', delimiter=',')
|
||||
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)
|
||||
|
||||
Reference in New Issue
Block a user