mirror of
https://github.com/Telecominfraproject/oopt-gnpy.git
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a3b1157e38
Fiber latency evaluated during propagation. The speed of ligth in fiber is evaluated as the vacuum speed of ligth divided by the core reflective index n1. The latency in the transceiver is evaluated in ms. Change-Id: I7a3638c49f346aecaf1d4897cecf96d345fdb26c
1058 lines
50 KiB
Python
1058 lines
50 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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gnpy.core.elements
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==================
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Standard network elements which propagate optical spectrum
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A network element is a Python callable. It takes a :class:`.info.SpectralInformation`
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object and returns a copy with appropriate fields affected. This structure
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represents spectral information that is "propogated" by this network element.
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Network elements must have only a local "view" of the network and propogate
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:class:`.info.SpectralInformation` using only this information. They should be independent and
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self-contained.
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Network elements MUST implement two attributes :py:attr:`uid` and :py:attr:`name` representing a
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unique identifier and a printable name, and provide the :py:meth:`__call__` method taking a
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:class:`SpectralInformation` as an input and returning another :class:`SpectralInformation`
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instance as a result.
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"""
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from numpy import abs, array, errstate, ones, interp, mean, pi, polyfit, polyval, sum, sqrt, log10, exp, asarray, full,\
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squeeze, zeros, append, flip, outer, ndarray
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from scipy.constants import h, c
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from scipy.interpolate import interp1d
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from collections import namedtuple
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from typing import Union
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from gnpy.core.utils import lin2db, db2lin, arrange_frequencies, snr_sum, per_label_average, pretty_summary_print, \
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watt2dbm, psd2powerdbm
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from gnpy.core.parameters import RoadmParams, FusedParams, FiberParams, PumpParams, EdfaParams, EdfaOperational
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from gnpy.core.science_utils import NliSolver, RamanSolver
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from gnpy.core.info import SpectralInformation, ReferenceCarrier
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from gnpy.core.exceptions import NetworkTopologyError, SpectrumError, ParametersError
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class Location(namedtuple('Location', 'latitude longitude city region')):
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def __new__(cls, latitude=0, longitude=0, city=None, region=None):
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return super().__new__(cls, latitude, longitude, city, region)
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class _Node:
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"""Convenience class for providing common functionality of all network elements
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This class is just an internal implementation detail; do **not** assume that all network elements
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inherit from :class:`_Node`.
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"""
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def __init__(self, uid, name=None, params=None, metadata=None, operational=None, type_variety=None):
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if name is None:
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name = uid
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self.uid, self.name = uid, name
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if metadata is None:
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metadata = {'location': {}}
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if metadata and not isinstance(metadata.get('location'), Location):
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metadata['location'] = Location(**metadata.pop('location', {}))
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self.params, self.metadata, self.operational = params, metadata, operational
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if type_variety:
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self.type_variety = type_variety
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@property
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def location(self):
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return self.metadata['location']
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loc = location
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@property
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def longitude(self):
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return self.location.longitude
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lng = longitude
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@property
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def latitude(self):
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return self.location.latitude
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lat = latitude
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class Transceiver(_Node):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.osnr_ase_01nm = None
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self.osnr_ase = None
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self.osnr_nli = None
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self.snr = None
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self.passive = False
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self.baud_rate = None
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self.chromatic_dispersion = None
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self.pmd = None
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self.pdl = None
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self.latency = None
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self.penalties = {}
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self.total_penalty = 0
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self.propagated_labels = [""]
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def _calc_cd(self, spectral_info):
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"""Updates the Transceiver property with the CD of the received channels. CD in ps/nm.
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"""
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self.chromatic_dispersion = spectral_info.chromatic_dispersion * 1e3
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def _calc_pmd(self, spectral_info):
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"""Updates the Transceiver property with the PMD of the received channels. PMD in ps.
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"""
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self.pmd = spectral_info.pmd * 1e12
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def _calc_pdl(self, spectral_info):
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"""Updates the Transceiver property with the PDL of the received channels. PDL in dB.
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"""
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self.pdl = spectral_info.pdl
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def _calc_latency(self, spectral_info):
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"""Updates the Transceiver property with the latency of the received channels. Latency in ms.
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"""
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self.latency = spectral_info.latency * 1e3
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def _calc_penalty(self, impairment_value, boundary_list):
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return interp(impairment_value, boundary_list['up_to_boundary'], boundary_list['penalty_value'],
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left=float('inf'), right=float('inf'))
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def calc_penalties(self, penalties):
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"""Updates the Transceiver property with penalties (CD, PMD, etc.) of the received channels in dB.
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Penalties are linearly interpolated between given points and set to 'inf' outside interval.
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"""
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self.penalties = {impairment: self._calc_penalty(getattr(self, impairment), boundary_list)
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for impairment, boundary_list in penalties.items()}
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self.total_penalty = sum(list(self.penalties.values()), axis=0)
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def _calc_snr(self, spectral_info):
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with errstate(divide='ignore'):
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self.propagated_labels = spectral_info.label
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self.baud_rate = spectral_info.baud_rate
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ratio_01nm = lin2db(12.5e9 / self.baud_rate)
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# set raw values to record original calculation, before update_snr()
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self.raw_osnr_ase = lin2db(spectral_info.signal / spectral_info.ase)
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self.raw_osnr_ase_01nm = self.raw_osnr_ase - ratio_01nm
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self.raw_osnr_nli = lin2db(spectral_info.signal / spectral_info.nli)
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self.raw_snr = lin2db(spectral_info.signal / (spectral_info.ase + spectral_info.nli))
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self.raw_snr_01nm = self.raw_snr - ratio_01nm
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self.osnr_ase = self.raw_osnr_ase
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self.osnr_ase_01nm = self.raw_osnr_ase_01nm
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self.osnr_nli = self.raw_osnr_nli
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self.snr = self.raw_snr
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self.snr_01nm = self.raw_snr_01nm
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def update_snr(self, *args):
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"""
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snr_added in 0.1nm
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compute SNR penalties such as transponder Tx_osnr or Roadm add_drop_osnr
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only applied in request.py / propagate on the last Trasceiver node of the path
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all penalties are added in a single call because to avoid uncontrolled cumul
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"""
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# use raw_values so that the added SNR penalties are not cumulated
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snr_added = 0
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for s in args:
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snr_added += db2lin(-s)
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snr_added = -lin2db(snr_added)
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self.osnr_ase = snr_sum(self.raw_osnr_ase, self.baud_rate, snr_added)
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self.snr = snr_sum(self.raw_snr, self.baud_rate, snr_added)
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self.osnr_ase_01nm = snr_sum(self.raw_osnr_ase_01nm, 12.5e9, snr_added)
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self.snr_01nm = snr_sum(self.raw_snr_01nm, 12.5e9, snr_added)
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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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'type': type(self).__name__,
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'metadata': {
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'location': self.metadata['location']._asdict()
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}
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}
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def __repr__(self):
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return (f'{type(self).__name__}('
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f'uid={self.uid!r}, '
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f'osnr_ase_01nm={self.osnr_ase_01nm!r}, '
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f'osnr_ase={self.osnr_ase!r}, '
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f'osnr_nli={self.osnr_nli!r}, '
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f'snr={self.snr!r}, '
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f'chromatic_dispersion={self.chromatic_dispersion!r}, '
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f'pmd={self.pmd!r}, '
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f'pdl={self.pdl!r}, '
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f'latency={self.latency!r}, '
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f'penalties={self.penalties!r})')
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def __str__(self):
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if self.snr is None or self.osnr_ase is None:
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return f'{type(self).__name__} {self.uid}'
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snr = per_label_average(self.snr, self.propagated_labels)
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osnr_ase = per_label_average(self.osnr_ase, self.propagated_labels)
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osnr_ase_01nm = per_label_average(self.osnr_ase_01nm, self.propagated_labels)
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snr_01nm = per_label_average(self.snr_01nm, self.propagated_labels)
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cd = mean(self.chromatic_dispersion)
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pmd = mean(self.pmd)
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pdl = mean(self.pdl)
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latency = mean(self.latency)
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result = '\n'.join([f'{type(self).__name__} {self.uid}',
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f' GSNR (0.1nm, dB): {pretty_summary_print(snr_01nm)}',
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f' GSNR (signal bw, dB): {pretty_summary_print(snr)}',
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f' OSNR ASE (0.1nm, dB): {pretty_summary_print(osnr_ase_01nm)}',
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f' OSNR ASE (signal bw, dB): {pretty_summary_print(osnr_ase)}',
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f' CD (ps/nm): {cd:.2f}',
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f' PMD (ps): {pmd:.2f}',
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f' PDL (dB): {pdl:.2f}',
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f' Latency (ms): {latency:.2f}'])
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cd_penalty = self.penalties.get('chromatic_dispersion')
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if cd_penalty is not None:
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result += f'\n CD penalty (dB): {mean(cd_penalty):.2f}'
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pmd_penalty = self.penalties.get('pmd')
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if pmd_penalty is not None:
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result += f'\n PMD penalty (dB): {mean(pmd_penalty):.2f}'
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pdl_penalty = self.penalties.get('pdl')
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if pdl_penalty is not None:
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result += f'\n PDL penalty (dB): {mean(pdl_penalty):.2f}'
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return result
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def __call__(self, spectral_info):
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self._calc_snr(spectral_info)
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self._calc_cd(spectral_info)
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self._calc_pmd(spectral_info)
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self._calc_pdl(spectral_info)
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self._calc_latency(spectral_info)
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return spectral_info
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class Roadm(_Node):
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def __init__(self, *args, params=None, **kwargs):
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if not params:
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params = {}
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try:
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super().__init__(*args, params=RoadmParams(**params), **kwargs)
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except ParametersError as e:
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raise ParametersError(f'Config error in {kwargs["uid"]}: {e}') from e
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# Target output power for the reference carrier, can only be computed on the fly, because it depends
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# on the path, since it depends on the equalization definition on the degree.
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self.ref_pch_out_dbm = None
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self.loss = 0 # auto-design interest
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# Optical power of carriers are equalized by the ROADM, so that the experienced loss is not the same for
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# different carriers. The ref_effective_loss records the loss for a reference carrier.
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self.ref_effective_loss = None
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self.passive = True
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self.restrictions = self.params.restrictions
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self.propagated_labels = [""]
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# element contains the two types of equalisation parameters, but only one is not None or empty
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# target for equalization for the ROADM only one must be not None
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self.target_pch_out_dbm = self.params.target_pch_out_db
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self.target_psd_out_mWperGHz = self.params.target_psd_out_mWperGHz
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self.target_out_mWperSlotWidth = self.params.target_out_mWperSlotWidth
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self.per_degree_pch_out_dbm = self.params.per_degree_pch_out_db
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self.per_degree_pch_psd = self.params.per_degree_pch_psd
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self.per_degree_pch_psw = self.params.per_degree_pch_psw
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@property
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def to_json(self):
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if self.target_pch_out_dbm is not None:
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equalisation, value = 'target_pch_out_db', self.target_pch_out_dbm
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elif self.target_psd_out_mWperGHz is not None:
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equalisation, value = 'target_psd_out_mWperGHz', self.target_psd_out_mWperGHz
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elif self.target_out_mWperSlotWidth is not None:
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equalisation, value = 'target_out_mWperSlotWidth', self.target_out_mWperSlotWidth
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else:
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assert False, 'There must be one default equalization defined in ROADM'
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to_json = {
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'uid': self.uid,
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'type': type(self).__name__,
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'params': {
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equalisation: value,
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'restrictions': self.restrictions,
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},
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'metadata': {
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'location': self.metadata['location']._asdict()
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}
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}
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# several per_degree equalization may coexist on different degrees
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if self.per_degree_pch_out_dbm:
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to_json['params']['per_degree_pch_out_db'] = self.per_degree_pch_out_dbm
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if self.per_degree_pch_psd:
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to_json['params']['per_degree_psd_out_mWperGHz'] = self.per_degree_pch_psd
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if self.per_degree_pch_psw:
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to_json['params']['per_degree_psd_out_mWperSlotWidth'] = self.per_degree_pch_psw
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return to_json
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def __repr__(self):
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return f'{type(self).__name__}(uid={self.uid!r}, loss={self.loss!r})'
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def __str__(self):
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if self.ref_effective_loss is None:
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return f'{type(self).__name__} {self.uid}'
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total_pch = pretty_summary_print(per_label_average(self.pch_out_dbm, self.propagated_labels))
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return '\n'.join([f'{type(self).__name__} {self.uid}',
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f' effective loss (dB): {self.ref_effective_loss:.2f}',
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f' reference pch out (dBm): {self.ref_pch_out_dbm:.2f}',
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f' actual pch out (dBm): {total_pch}'])
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def get_roadm_target_power(self, ref_carrier: ReferenceCarrier = None,
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spectral_info: SpectralInformation = None) -> Union[float, ndarray]:
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"""Computes the power in dBm for a reference carrier or for a spectral information.
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power is computed based on equalization target.
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if spectral_info baud_rate is baud_rate = [32e9, 42e9, 64e9, 42e9, 32e9], and
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target_pch_out_dbm is defined to -20 dbm, then the function returns an array of powers
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[-20, -20, -20, -20, -20]
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if target_psd_out_mWperGHz is defined instead with 3.125e-4mW/GHz then it returns
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[-20, -18.819, -16.9897, -18.819, -20]
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if instead a reference_baud_rate is defined, the functions computes the result for a
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single reference carrier whose baud_rate is reference_baudrate
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"""
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if spectral_info:
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if self.target_pch_out_dbm is not None:
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return full(len(spectral_info.channel_number), self.target_pch_out_dbm)
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if self.target_psd_out_mWperGHz is not None:
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return psd2powerdbm(self.target_psd_out_mWperGHz, spectral_info.baud_rate)
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if self.target_out_mWperSlotWidth is not None:
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return psd2powerdbm(self.target_out_mWperSlotWidth, spectral_info.slot_width)
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else:
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if self.target_pch_out_dbm is not None:
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return self.target_pch_out_dbm
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if self.target_psd_out_mWperGHz is not None:
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return psd2powerdbm(self.target_psd_out_mWperGHz, ref_carrier.baud_rate)
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if self.target_out_mWperSlotWidth is not None:
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return psd2powerdbm(self.target_out_mWperSlotWidth, ref_carrier.slot_width)
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return None
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def get_per_degree_ref_power(self, degree, ref_carrier):
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"""Get the target power in dBm out of ROADM degree for the reference bandwidth
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If no equalization is defined on this degree use the ROADM level one.
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"""
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if degree in self.per_degree_pch_out_dbm:
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return self.per_degree_pch_out_dbm[degree]
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elif degree in self.per_degree_pch_psd:
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return psd2powerdbm(self.per_degree_pch_psd[degree], ref_carrier.baud_rate)
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elif degree in self.per_degree_pch_psw:
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return psd2powerdbm(self.per_degree_pch_psw[degree], ref_carrier.slot_width)
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return self.get_roadm_target_power(ref_carrier)
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def get_per_degree_power(self, degree, spectral_info):
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"""Get the target power in dBm out of ROADM degree for the spectral information
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If no equalization is defined on this degree use the ROADM level one.
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"""
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if degree in self.per_degree_pch_out_dbm:
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return self.per_degree_pch_out_dbm[degree]
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elif degree in self.per_degree_pch_psd:
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return psd2powerdbm(self.per_degree_pch_psd[degree], spectral_info.baud_rate)
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elif degree in self.per_degree_pch_psw:
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return psd2powerdbm(self.per_degree_pch_psw[degree], spectral_info.slot_width)
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return self.get_roadm_target_power(spectral_info=spectral_info)
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def propagate(self, spectral_info, degree):
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"""Equalization targets are read from topology file if defined and completed with default
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definition of the library.
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If the input power is lower than the target one, use the input power instead because
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a ROADM doesn't amplify, it can only attenuate.
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There is no difference for add or express : the same target is applied. For the moment
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propagates operates with spectral info carriers all having the same source or destination.
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"""
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# TODO maybe add a minimum loss for the ROADM
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# find the target power for the reference carrier
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ref_per_degree_pch = self.get_per_degree_ref_power(degree, spectral_info.pref.ref_carrier)
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# find the target powers for each signal carrier
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per_degree_pch = self.get_per_degree_power(degree, spectral_info=spectral_info)
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# Definition of ref_pch_out_dbm for the reference channel:
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# Depending on propagation upstream from this ROADM, the input power (p_spani) might be smaller than
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# the target power out configured for this ROADM degree's egress. Since ROADM does not amplify,
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# the power out of the ROADM for the ref channel is the min value between target power and input power.
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# (TODO add a minimum loss for the ROADM crossing)
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self.ref_pch_out_dbm = min(spectral_info.pref.p_spani, ref_per_degree_pch)
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# Definition of effective_loss:
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# Optical power of carriers are equalized by the ROADM, so that the experienced loss is not the same for
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# different carriers. effective_loss records the loss for the reference carrier.
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self.ref_effective_loss = spectral_info.pref.p_spani - self.ref_pch_out_dbm
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input_power = spectral_info.signal + spectral_info.nli + spectral_info.ase
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target_power_per_channel = per_degree_pch + spectral_info.delta_pdb_per_channel
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# Computation of the per channel target power according to equalization policy
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# If target_power_per_channel has some channels power above input power, then the whole target is reduced.
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# For example, if user specifies delta_pdb_per_channel:
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# freq1: 1dB, freq2: 3dB, freq3: -3dB, and target is -20dBm out of the ROADM,
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# then the target power for each channel uses the specified delta_pdb_per_channel.
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# 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. 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]
|
|
# note that this changes the previous behaviour that equalized all identical channels based on the one
|
|
# that had the min power.
|
|
# 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
|
|
correction = (abs(watt2dbm(input_power) - target_power_per_channel)
|
|
- (watt2dbm(input_power) - target_power_per_channel)) / 2
|
|
new_target = target_power_per_channel - correction
|
|
delta_power = watt2dbm(input_power) - new_target
|
|
|
|
spectral_info.apply_attenuation_db(delta_power)
|
|
spectral_info.pmd = sqrt(spectral_info.pmd ** 2 + self.params.pmd ** 2)
|
|
spectral_info.pdl = sqrt(spectral_info.pdl ** 2 + self.params.pdl ** 2)
|
|
self.pch_out_dbm = watt2dbm(spectral_info.signal + spectral_info.nli + spectral_info.ase)
|
|
self.propagated_labels = spectral_info.label
|
|
|
|
def update_pref(self, spectral_info):
|
|
"""Update Reference power
|
|
|
|
This modifies the spectral info in-place. Only the `pref` is updated with new p_spani,
|
|
while p_span0 is not changed.
|
|
"""
|
|
spectral_info.pref = spectral_info.pref._replace(p_spani=self.ref_pch_out_dbm)
|
|
|
|
def __call__(self, spectral_info, degree):
|
|
self.propagate(spectral_info, degree=degree)
|
|
self.update_pref(spectral_info)
|
|
return spectral_info
|
|
|
|
|
|
class Fused(_Node):
|
|
def __init__(self, *args, params=None, **kwargs):
|
|
if not params:
|
|
params = {}
|
|
super().__init__(*args, params=FusedParams(**params), **kwargs)
|
|
self.loss = self.params.loss
|
|
self.passive = True
|
|
|
|
@property
|
|
def to_json(self):
|
|
return {'uid': self.uid,
|
|
'type': type(self).__name__,
|
|
'params': {
|
|
'loss': self.loss
|
|
},
|
|
'metadata': {
|
|
'location': self.metadata['location']._asdict()
|
|
}
|
|
}
|
|
|
|
def __repr__(self):
|
|
return f'{type(self).__name__}(uid={self.uid!r}, loss={self.loss!r})'
|
|
|
|
def __str__(self):
|
|
return '\n'.join([f'{type(self).__name__} {self.uid}',
|
|
f' loss (dB): {self.loss:.2f}'])
|
|
|
|
def propagate(self, spectral_info):
|
|
spectral_info.apply_attenuation_db(self.loss)
|
|
|
|
def update_pref(self, spectral_info):
|
|
spectral_info.pref = spectral_info.pref._replace(p_span0=spectral_info.pref.p_span0,
|
|
p_spani=spectral_info.pref.p_spani - self.loss)
|
|
|
|
def __call__(self, spectral_info):
|
|
self.propagate(spectral_info)
|
|
self.update_pref(spectral_info)
|
|
return spectral_info
|
|
|
|
|
|
class Fiber(_Node):
|
|
def __init__(self, *args, params=None, **kwargs):
|
|
if not params:
|
|
params = {}
|
|
super().__init__(*args, params=FiberParams(**params), **kwargs)
|
|
self.pch_out_db = None
|
|
self.passive = True
|
|
self.propagated_labels = [""]
|
|
# Raman efficiency matrix function of the delta frequency constructed such that each row is related to a
|
|
# fixed frequency: positive elements represent a gain (from higher frequency) and negative elements represent
|
|
# a loss (to lower frequency)
|
|
if self.params.raman_efficiency:
|
|
frequency_offset = self.params.raman_efficiency['frequency_offset']
|
|
frequency_offset = append(-flip(frequency_offset[1:]), frequency_offset)
|
|
cr = self.params.raman_efficiency['cr']
|
|
cr = append(- flip(cr[1:]), cr)
|
|
self._cr_function = lambda frequency: interp(frequency, frequency_offset, cr)
|
|
else:
|
|
self._cr_function = lambda frequency: zeros(squeeze(frequency).shape)
|
|
|
|
# Lumped losses
|
|
z_lumped_losses = array([lumped['position'] for lumped in self.params.lumped_losses]) # km
|
|
lumped_losses_power = array([lumped['loss'] for lumped in self.params.lumped_losses]) # dB
|
|
if not ((z_lumped_losses > 0) * (z_lumped_losses < 1e-3 * self.params.length)).all():
|
|
raise NetworkTopologyError("Lumped loss positions must be between 0 and the fiber length "
|
|
f"({1e-3 * self.params.length} km), boundaries excluded.")
|
|
self.lumped_losses = db2lin(- lumped_losses_power) # [linear units]
|
|
self.z_lumped_losses = array(z_lumped_losses) * 1e3 # [m]
|
|
|
|
@property
|
|
def to_json(self):
|
|
return {'uid': self.uid,
|
|
'type': type(self).__name__,
|
|
'type_variety': self.type_variety,
|
|
'params': {
|
|
# have to specify each because namedtupple cannot be updated :(
|
|
'length': round(self.params.length * 1e-3, 6),
|
|
'loss_coef': round(self.params.loss_coef * 1e3, 6),
|
|
'length_units': 'km',
|
|
'att_in': self.params.att_in,
|
|
'con_in': self.params.con_in,
|
|
'con_out': self.params.con_out
|
|
},
|
|
'metadata': {
|
|
'location': self.metadata['location']._asdict()
|
|
}
|
|
}
|
|
|
|
def __repr__(self):
|
|
return f'{type(self).__name__}(uid={self.uid!r}, ' \
|
|
f'length={round(self.params.length * 1e-3,1)!r}km, ' \
|
|
f'loss={round(self.loss,1)!r}dB)'
|
|
|
|
def __str__(self):
|
|
if self.pch_out_db is None:
|
|
return f'{type(self).__name__} {self.uid}'
|
|
|
|
total_pch = pretty_summary_print(per_label_average(self.pch_out_dbm, self.propagated_labels))
|
|
return '\n'.join([f'{type(self).__name__} {self.uid}',
|
|
f' type_variety: {self.type_variety}',
|
|
f' length (km): {self.params.length * 1e-3:.2f}',
|
|
f' pad att_in (dB): {self.params.att_in:.2f}',
|
|
f' total loss (dB): {self.loss:.2f}',
|
|
f' (includes conn loss (dB) in: {self.params.con_in:.2f} out: {self.params.con_out:.2f})',
|
|
f' (conn loss out includes EOL margin defined in eqpt_config.json)',
|
|
f' reference pch out (dBm): {self.pch_out_db:.2f}',
|
|
f' actual pch out (dBm): {total_pch}'])
|
|
|
|
def loss_coef_func(self, frequency):
|
|
frequency = asarray(frequency)
|
|
if self.params.loss_coef.size > 1:
|
|
try:
|
|
loss_coef = interp1d(self.params.f_loss_ref, self.params.loss_coef)(frequency)
|
|
except ValueError:
|
|
raise SpectrumError('The spectrum bandwidth exceeds the frequency interval used to define the fiber '
|
|
f'loss coefficient in "{type(self).__name__} {self.uid}".'
|
|
f'\nSpectrum f_min-f_max: {round(frequency[0]*1e-12,2)}-'
|
|
f'{round(frequency[-1]*1e-12,2)}'
|
|
f'\nLoss coefficient f_min-f_max: {round(self.params.f_loss_ref[0]*1e-12,2)}-'
|
|
f'{round(self.params.f_loss_ref[-1]*1e-12,2)}')
|
|
else:
|
|
loss_coef = full(frequency.size, self.params.loss_coef)
|
|
return squeeze(loss_coef)
|
|
|
|
|
|
@property
|
|
def loss(self):
|
|
"""total loss including padding att_in: useful for polymorphism with roadm loss"""
|
|
return self.loss_coef_func(self.params.ref_frequency) * self.params.length + \
|
|
self.params.con_in + self.params.con_out + self.params.att_in + sum(lin2db(1 / self.lumped_losses))
|
|
|
|
def alpha(self, frequency):
|
|
"""Returns the linear exponent attenuation coefficient such that
|
|
:math: `lin_attenuation = e^{- alpha length}`
|
|
|
|
:param frequency: the frequency at which alpha is computed [Hz]
|
|
:return: alpha: power attenuation coefficient for f in frequency [Neper/m]
|
|
"""
|
|
return self.loss_coef_func(frequency) / (10 * log10(exp(1)))
|
|
|
|
def cr(self, frequency):
|
|
"""Returns the raman efficiency matrix including the vibrational loss
|
|
|
|
:param frequency: the frequency at which cr is computed [Hz]
|
|
:return: cr: raman efficiency matrix [1 / (W m)]
|
|
"""
|
|
df = outer(ones(frequency.shape), frequency) - outer(frequency, ones(frequency.shape))
|
|
cr = self._cr_function(df)
|
|
vibrational_loss = outer(frequency, ones(frequency.shape)) / outer(ones(frequency.shape), frequency)
|
|
return cr * (cr >= 0) + cr * (cr < 0) * vibrational_loss # Raman efficiency [1/(W m)]
|
|
|
|
def chromatic_dispersion(self, freq=None):
|
|
"""Returns accumulated chromatic dispersion (CD).
|
|
|
|
:param freq: the frequency at which the chromatic dispersion is computed
|
|
:return: chromatic dispersion: the accumulated dispersion [s/m]
|
|
"""
|
|
freq = self.params.ref_frequency if freq is None else freq
|
|
beta2 = self.params.beta2
|
|
beta3 = self.params.beta3
|
|
ref_f = self.params.ref_frequency
|
|
length = self.params.length
|
|
beta = beta2 + 2 * pi * beta3 * (freq - ref_f)
|
|
dispersion = -beta * 2 * pi * ref_f**2 / c
|
|
return dispersion * length
|
|
|
|
@property
|
|
def pmd(self):
|
|
"""differential group delay (PMD) [s]"""
|
|
return self.params.pmd_coef * sqrt(self.params.length)
|
|
|
|
def propagate(self, spectral_info: SpectralInformation):
|
|
"""Modifies the spectral information computing the attenuation, the non-linear interference generation,
|
|
the CD and PMD accumulation.
|
|
"""
|
|
# apply the attenuation due to the input connector loss
|
|
attenuation_in_db = self.params.con_in + self.params.att_in
|
|
spectral_info.apply_attenuation_db(attenuation_in_db)
|
|
|
|
# inter channels Raman effect
|
|
stimulated_raman_scattering = RamanSolver.calculate_stimulated_raman_scattering(spectral_info, self)
|
|
|
|
# NLI noise evaluated at the fiber input
|
|
spectral_info.nli += NliSolver.compute_nli(spectral_info, stimulated_raman_scattering, self)
|
|
|
|
# chromatic dispersion and pmd variations
|
|
spectral_info.chromatic_dispersion += self.chromatic_dispersion(spectral_info.frequency)
|
|
spectral_info.pmd = sqrt(spectral_info.pmd ** 2 + self.pmd ** 2)
|
|
|
|
# latency
|
|
spectral_info.latency += self.params.latency
|
|
|
|
# apply the attenuation due to the fiber losses
|
|
attenuation_fiber = stimulated_raman_scattering.loss_profile[:, -1]
|
|
spectral_info.apply_attenuation_lin(attenuation_fiber)
|
|
|
|
# apply the attenuation due to the output connector loss
|
|
attenuation_out_db = self.params.con_out
|
|
spectral_info.apply_attenuation_db(attenuation_out_db)
|
|
self.pch_out_dbm = watt2dbm(spectral_info.signal + spectral_info.nli + spectral_info.ase)
|
|
self.propagated_labels = spectral_info.label
|
|
|
|
def update_pref(self, spectral_info):
|
|
# in case of Raman, the resulting loss of the fiber is not equivalent to self.loss
|
|
# because of Raman gain. In order to correctly update pref, we need the resulting loss:
|
|
# power_out - power_in. We use the total signal power (sum on all channels) to compute
|
|
# this loss, because pref is a noiseless reference.
|
|
loss = round(lin2db(self._psig_in / sum(spectral_info.signal)), 2)
|
|
self.pch_out_db = spectral_info.pref.p_spani - loss
|
|
spectral_info.pref = spectral_info.pref._replace(p_span0=spectral_info.pref.p_span0,
|
|
p_spani=self.pch_out_db)
|
|
|
|
def __call__(self, spectral_info):
|
|
# _psig_in records the total signal power of the spectral information before propagation.
|
|
self._psig_in = sum(spectral_info.signal)
|
|
self.propagate(spectral_info)
|
|
self.update_pref(spectral_info)
|
|
return spectral_info
|
|
|
|
|
|
class RamanFiber(Fiber):
|
|
def __init__(self, *args, params=None, **kwargs):
|
|
super().__init__(*args, params=params, **kwargs)
|
|
if not self.operational:
|
|
raise NetworkTopologyError(f'Fiber element uid:{self.uid} '
|
|
'defined as RamanFiber without operational parameters')
|
|
|
|
if 'raman_pumps' not in self.operational:
|
|
raise NetworkTopologyError(f'Fiber element uid:{self.uid} '
|
|
'defined as RamanFiber without raman pumps description in operational')
|
|
|
|
if 'temperature' not in self.operational:
|
|
raise NetworkTopologyError(f'Fiber element uid:{self.uid} '
|
|
'defined as RamanFiber without temperature in operational')
|
|
|
|
pump_loss = db2lin(self.params.con_out)
|
|
self.raman_pumps = tuple(PumpParams(p['power'] / pump_loss, p['frequency'], p['propagation_direction'])
|
|
for p in self.operational['raman_pumps'])
|
|
self.temperature = self.operational['temperature']
|
|
|
|
@property
|
|
def to_json(self):
|
|
return dict(super().to_json, operational=self.operational)
|
|
|
|
def propagate(self, spectral_info: SpectralInformation):
|
|
"""Modifies the spectral information computing the attenuation, the non-linear interference generation,
|
|
the CD and PMD accumulation.
|
|
"""
|
|
# apply the attenuation due to the input connector loss
|
|
attenuation_in_db = self.params.con_in + self.params.att_in
|
|
spectral_info.apply_attenuation_db(attenuation_in_db)
|
|
|
|
# Raman pumps and inter channel Raman effect
|
|
stimulated_raman_scattering = RamanSolver.calculate_stimulated_raman_scattering(spectral_info, self)
|
|
spontaneous_raman_scattering = \
|
|
RamanSolver.calculate_spontaneous_raman_scattering(spectral_info, stimulated_raman_scattering, self)
|
|
|
|
# nli and ase noise evaluated at the fiber input
|
|
spectral_info.nli += NliSolver.compute_nli(spectral_info, stimulated_raman_scattering, self)
|
|
spectral_info.ase += spontaneous_raman_scattering
|
|
|
|
# chromatic dispersion and pmd variations
|
|
spectral_info.chromatic_dispersion += self.chromatic_dispersion(spectral_info.frequency)
|
|
spectral_info.pmd = sqrt(spectral_info.pmd ** 2 + self.pmd ** 2)
|
|
|
|
# latency
|
|
spectral_info.latency += self.params.latency
|
|
|
|
# apply the attenuation due to the fiber losses
|
|
attenuation_fiber = stimulated_raman_scattering.loss_profile[:spectral_info.number_of_channels, -1]
|
|
|
|
spectral_info.apply_attenuation_lin(attenuation_fiber)
|
|
|
|
# apply the attenuation due to the output connector loss
|
|
attenuation_out_db = self.params.con_out
|
|
spectral_info.apply_attenuation_db(attenuation_out_db)
|
|
self.pch_out_dbm = watt2dbm(spectral_info.signal + spectral_info.nli + spectral_info.ase)
|
|
self.propagated_labels = spectral_info.label
|
|
|
|
|
|
class Edfa(_Node):
|
|
def __init__(self, *args, params=None, operational=None, **kwargs):
|
|
if params is None:
|
|
params = {}
|
|
if operational is None:
|
|
operational = {}
|
|
self.variety_list = kwargs.pop('variety_list', None)
|
|
super().__init__(*args, params=EdfaParams(**params), operational=EdfaOperational(**operational), **kwargs)
|
|
self.interpol_dgt = None # interpolated dynamic gain tilt
|
|
self.interpol_gain_ripple = None # gain ripple
|
|
self.interpol_nf_ripple = None # nf_ripple
|
|
self.channel_freq = None # SI channel frequencies
|
|
# nf, gprofile, pin and pout attributes are set by interpol_params
|
|
self.nf = None # dB edfa nf at operational.gain_target
|
|
self.gprofile = None
|
|
self.pin_db = None
|
|
self.nch = None
|
|
self.pout_db = None
|
|
self.target_pch_out_db = None
|
|
self.effective_pch_out_db = None
|
|
self.passive = False
|
|
self.att_in = None
|
|
self.effective_gain = self.operational.gain_target
|
|
self.delta_p = self.operational.delta_p # delta P with Pref (power swwep) in power mode
|
|
self.tilt_target = self.operational.tilt_target
|
|
self.out_voa = self.operational.out_voa
|
|
self.propagated_labels = [""]
|
|
|
|
@property
|
|
def to_json(self):
|
|
return {'uid': self.uid,
|
|
'type': type(self).__name__,
|
|
'type_variety': self.params.type_variety,
|
|
'operational': {
|
|
'gain_target': round(self.effective_gain, 6) if self.effective_gain else None,
|
|
'delta_p': self.delta_p,
|
|
'tilt_target': self.tilt_target,
|
|
'out_voa': self.out_voa
|
|
},
|
|
'metadata': {
|
|
'location': self.metadata['location']._asdict()
|
|
}
|
|
}
|
|
|
|
def __repr__(self):
|
|
return (f'{type(self).__name__}(uid={self.uid!r}, '
|
|
f'type_variety={self.params.type_variety!r}, '
|
|
f'interpol_dgt={self.interpol_dgt!r}, '
|
|
f'interpol_gain_ripple={self.interpol_gain_ripple!r}, '
|
|
f'interpol_nf_ripple={self.interpol_nf_ripple!r}, '
|
|
f'channel_freq={self.channel_freq!r}, '
|
|
f'nf={self.nf!r}, '
|
|
f'gprofile={self.gprofile!r}, '
|
|
f'pin_db={self.pin_db!r}, '
|
|
f'pout_db={self.pout_db!r})')
|
|
|
|
def __str__(self):
|
|
if self.pin_db is None or self.pout_db is None:
|
|
return f'{type(self).__name__} {self.uid}'
|
|
nf = mean(self.nf)
|
|
total_pch = pretty_summary_print(per_label_average(self.pch_out_dbm, self.propagated_labels))
|
|
return '\n'.join([f'{type(self).__name__} {self.uid}',
|
|
f' type_variety: {self.params.type_variety}',
|
|
f' effective gain(dB): {self.effective_gain:.2f}',
|
|
f' (before att_in and before output VOA)',
|
|
f' noise figure (dB): {nf:.2f}',
|
|
f' (including att_in)',
|
|
f' pad att_in (dB): {self.att_in:.2f}',
|
|
f' Power In (dBm): {self.pin_db:.2f}',
|
|
f' Power Out (dBm): {self.pout_db:.2f}',
|
|
f' Delta_P (dB): ' + (f'{self.delta_p:.2f}' if self.delta_p is not None else 'None'),
|
|
f' target pch (dBm): ' + (f'{self.target_pch_out_db:.2f}' if self.target_pch_out_db is not None else 'None'),
|
|
f' effective pch (dBm): {self.effective_pch_out_db:.2f}',
|
|
f' actual pch out (dBm): {total_pch}',
|
|
f' output VOA (dB): {self.out_voa:.2f}'])
|
|
|
|
def interpol_params(self, spectral_info):
|
|
"""interpolate SI channel frequencies with the edfa dgt and gain_ripple frquencies from JSON
|
|
:param spectral_info: instance of gnpy.core.info.SpectralInformation
|
|
:return: None
|
|
"""
|
|
# TODO|jla: read amplifier actual frequencies from additional params in json
|
|
|
|
self.channel_freq = spectral_info.frequency
|
|
amplifier_freq = arrange_frequencies(len(self.params.dgt), self.params.f_min, self.params.f_max) # Hz
|
|
self.interpol_dgt = interp(spectral_info.frequency, amplifier_freq, self.params.dgt)
|
|
|
|
amplifier_freq = arrange_frequencies(len(self.params.gain_ripple), self.params.f_min, self.params.f_max) # Hz
|
|
self.interpol_gain_ripple = interp(spectral_info.frequency, amplifier_freq, self.params.gain_ripple)
|
|
|
|
amplifier_freq = arrange_frequencies(len(self.params.nf_ripple), self.params.f_min, self.params.f_max) # Hz
|
|
self.interpol_nf_ripple = interp(spectral_info.frequency, amplifier_freq, self.params.nf_ripple)
|
|
|
|
self.nch = spectral_info.number_of_channels
|
|
pin = spectral_info.signal + spectral_info.ase + spectral_info.nli
|
|
self.pin_db = watt2dbm(sum(pin))
|
|
# The following should be changed when we have the new spectral information including slot widths.
|
|
# For now, with homogeneous spectrum, we can calculate it as the difference between neighbouring channels.
|
|
self.slot_width = self.channel_freq[1] - self.channel_freq[0]
|
|
|
|
"""in power mode: delta_p is defined and can be used to calculate the power target
|
|
This power target is used calculate the amplifier gain"""
|
|
pref = spectral_info.pref
|
|
if self.delta_p is not None:
|
|
self.target_pch_out_db = round(self.delta_p + pref.p_span0, 2)
|
|
self.effective_gain = self.target_pch_out_db - pref.p_spani
|
|
|
|
"""check power saturation and correct effective gain & power accordingly:"""
|
|
# Compute the saturation accounting for actual power at the input of the amp
|
|
self.effective_gain = min(
|
|
self.effective_gain,
|
|
self.params.p_max - self.pin_db
|
|
)
|
|
self.effective_pch_out_db = round(pref.p_spani + self.effective_gain, 2)
|
|
|
|
"""check power saturation and correct target_gain accordingly:"""
|
|
self.nf = self._calc_nf()
|
|
self.gprofile = self._gain_profile(pin)
|
|
|
|
pout = (pin + self.noise_profile(spectral_info)) * db2lin(self.gprofile)
|
|
self.pout_db = lin2db(sum(pout * 1e3))
|
|
# ase & nli are only calculated in signal bandwidth
|
|
# pout_db is not the absolute full output power (negligible if sufficient channels)
|
|
|
|
def _nf(self, type_def, nf_model, nf_fit_coeff, gain_min, gain_flatmax, gain_target):
|
|
# if hybrid raman, use edfa_gain_flatmax attribute, else use gain_flatmax
|
|
#gain_flatmax = getattr(params, 'edfa_gain_flatmax', params.gain_flatmax)
|
|
pad = max(gain_min - gain_target, 0)
|
|
gain_target += pad
|
|
dg = max(gain_flatmax - gain_target, 0)
|
|
if type_def == 'variable_gain':
|
|
g1a = gain_target - nf_model.delta_p - dg
|
|
nf_avg = lin2db(db2lin(nf_model.nf1) + db2lin(nf_model.nf2) / db2lin(g1a))
|
|
elif type_def == 'fixed_gain':
|
|
nf_avg = nf_model.nf0
|
|
elif type_def == 'openroadm':
|
|
# OpenROADM specifies OSNR vs. input power per channel for 50 GHz slot width so we
|
|
# scale it to 50 GHz based on actual slot width.
|
|
pin_ch_50GHz = self.pin_db - lin2db(self.nch) + lin2db(50e9 / self.slot_width)
|
|
# model OSNR = f(Pin per 50 GHz channel)
|
|
nf_avg = pin_ch_50GHz - polyval(nf_model.nf_coef, pin_ch_50GHz) + 58
|
|
elif type_def == 'openroadm_preamp':
|
|
# OpenROADM specifies OSNR vs. input power per channel for 50 GHz slot width so we
|
|
# scale it to 50 GHz based on actual slot width.
|
|
pin_ch_50GHz = self.pin_db - lin2db(self.nch) + lin2db(50e9 / self.slot_width)
|
|
# model OSNR = f(Pin per 50 GHz channel)
|
|
nf_avg = pin_ch_50GHz - min((4 * pin_ch_50GHz + 275) / 7, 33) + 58
|
|
elif type_def == 'openroadm_booster':
|
|
# model a zero-noise amp with "infinitely negative" (in dB) NF
|
|
nf_avg = float('-inf')
|
|
elif type_def == 'advanced_model':
|
|
nf_avg = polyval(nf_fit_coeff, -dg)
|
|
return nf_avg + pad, pad
|
|
|
|
def _calc_nf(self, avg=False):
|
|
"""nf calculation based on 2 models: self.params.nf_model.enabled from json import:
|
|
True => 2 stages amp modelling based on precalculated nf1, nf2 and delta_p in build_OA_json
|
|
False => polynomial fit based on self.params.nf_fit_coeff"""
|
|
# gain_min > gain_target TBD:
|
|
if self.params.type_def == 'dual_stage':
|
|
g1 = self.params.preamp_gain_flatmax
|
|
g2 = self.effective_gain - g1
|
|
nf1_avg, pad = self._nf(self.params.preamp_type_def,
|
|
self.params.preamp_nf_model,
|
|
self.params.preamp_nf_fit_coeff,
|
|
self.params.preamp_gain_min,
|
|
self.params.preamp_gain_flatmax,
|
|
g1)
|
|
# no padding expected for the 1stage because g1 = gain_max
|
|
nf2_avg, pad = self._nf(self.params.booster_type_def,
|
|
self.params.booster_nf_model,
|
|
self.params.booster_nf_fit_coeff,
|
|
self.params.booster_gain_min,
|
|
self.params.booster_gain_flatmax,
|
|
g2)
|
|
nf_avg = lin2db(db2lin(nf1_avg) + db2lin(nf2_avg - g1))
|
|
# no padding expected for the 1stage because g1 = gain_max
|
|
pad = 0
|
|
else:
|
|
nf_avg, pad = self._nf(self.params.type_def,
|
|
self.params.nf_model,
|
|
self.params.nf_fit_coeff,
|
|
self.params.gain_min,
|
|
self.params.gain_flatmax,
|
|
self.effective_gain)
|
|
|
|
self.att_in = pad # not used to attenuate carriers, only used in _repr_ and _str_
|
|
if avg:
|
|
return nf_avg
|
|
else:
|
|
return self.interpol_nf_ripple + nf_avg # input VOA = 1 for 1 NF degradation
|
|
|
|
def noise_profile(self, spectral_info: SpectralInformation):
|
|
"""Computes amplifier ASE noise integrated over the signal bandwidth. This is calculated at amplifier input.
|
|
|
|
:return: the asepower in W in the signal bandwidth bw for 96 channels
|
|
:return type: numpy array of float
|
|
|
|
ASE power using per channel gain profile inputs:
|
|
|
|
NF_dB - Noise figure in dB, vector of length number of channels or
|
|
spectral slices
|
|
G_dB - Actual gain calculated for the EDFA, vector of length number of
|
|
channels or spectral slices
|
|
ffs - Center frequency grid of the channels or spectral slices in
|
|
THz, vector of length number of channels or spectral slices
|
|
dF - width of each channel or spectral slice in THz,
|
|
vector of length number of channels or spectral slices
|
|
|
|
OUTPUT:
|
|
|
|
ase_dBm - ase in dBm per channel or spectral slice
|
|
|
|
NOTE:
|
|
|
|
The output is the total ASE in the channel or spectral slice. For
|
|
50GHz channels the ASE BW is effectively 0.4nm. To get to noise power
|
|
in 0.1nm, subtract 6dB.
|
|
|
|
ONSR is usually quoted as channel power divided by
|
|
the ASE power in 0.1nm RBW, regardless of the width of the actual
|
|
channel. This is a historical convention from the days when optical
|
|
signals were much smaller (155Mbps, 2.5Gbps, ... 10Gbps) than the
|
|
resolution of the OSAs that were used to measure spectral power which
|
|
were set to 0.1nm resolution for convenience. Moving forward into
|
|
flexible grid and high baud rate signals, it may be convenient to begin
|
|
quoting power spectral density in the same BW for both signal and ASE,
|
|
e.g. 12.5GHz."""
|
|
|
|
ase = h * spectral_info.baud_rate * spectral_info.frequency * db2lin(self.nf) # W
|
|
return ase # in W at amplifier input
|
|
|
|
def _gain_profile(self, pin, err_tolerance=1.0e-11, simple_opt=True):
|
|
"""
|
|
Pin : input power / channel in W
|
|
|
|
:param gain_ripple: design flat gain
|
|
:param dgt: design gain tilt
|
|
:param Pin: total input power in W
|
|
:param gp: Average gain setpoint in dB units (provisioned gain)
|
|
:param gtp: gain tilt setting (provisioned tilt)
|
|
:type gain_ripple: numpy.ndarray
|
|
:type dgt: numpy.ndarray
|
|
:type Pin: numpy.ndarray
|
|
:type gp: float
|
|
:type gtp: float
|
|
:return: gain profile in dBm, per channel or spectral slice
|
|
:rtype: numpy.ndarray
|
|
|
|
Checking of output power clamping is implemented in interpol_params().
|
|
|
|
|
|
Based on:
|
|
|
|
R. di Muro, "The Er3+ fiber gain coefficient derived from a dynamic
|
|
gain tilt technique", Journal of Lightwave Technology, Vol. 18,
|
|
Iss. 3, Pp. 343-347, 2000.
|
|
|
|
Ported from Matlab version written by David Boerges at Ciena.
|
|
"""
|
|
|
|
# TODO|jla: check what param should be used (currently length(dgt))
|
|
if len(self.interpol_dgt) == 1:
|
|
return array([self.effective_gain])
|
|
|
|
# TODO|jla: find a way to use these or lose them. Primarily we should have
|
|
# a way to determine if exceeding the gain or output power of the amp
|
|
tot_in_power_db = self.pin_db # Pin in W
|
|
|
|
# linear fit to get the
|
|
p = polyfit(self.channel_freq, self.interpol_dgt, 1)
|
|
dgt_slope = p[0]
|
|
|
|
# Calculate the target slope
|
|
targ_slope = -self.tilt_target / (self.params.f_max - self.params.f_min)
|
|
|
|
# first estimate of DGT scaling
|
|
dgts1 = targ_slope / dgt_slope if dgt_slope != 0. else 0.
|
|
|
|
# when simple_opt is true, make 2 attempts to compute gain and
|
|
# the internal voa value. This is currently here to provide direct
|
|
# comparison with original Matlab code. Will be removed.
|
|
# TODO|jla: replace with loop
|
|
|
|
if not simple_opt:
|
|
return
|
|
|
|
# first estimate of Er gain & VOA loss
|
|
g1st = array(self.interpol_gain_ripple) + self.params.gain_flatmax \
|
|
+ array(self.interpol_dgt) * dgts1
|
|
voa = lin2db(mean(db2lin(g1st))) - self.effective_gain
|
|
|
|
# second estimate of amp ch gain using the channel input profile
|
|
g2nd = g1st - voa
|
|
|
|
pout_db = lin2db(sum(pin * 1e3 * db2lin(g2nd)))
|
|
dgts2 = self.effective_gain - (pout_db - tot_in_power_db)
|
|
|
|
# center estimate of amp ch gain
|
|
xcent = dgts2
|
|
gcent = g1st - voa + array(self.interpol_dgt) * xcent
|
|
pout_db = lin2db(sum(pin * 1e3 * db2lin(gcent)))
|
|
gavg_cent = pout_db - tot_in_power_db
|
|
|
|
# Lower estimate of amp ch gain
|
|
deltax = max(g1st) - min(g1st)
|
|
# if no ripple deltax = 0 and xlow = xcent: div 0
|
|
# TODO|jla: add check for flat gain response
|
|
if abs(deltax) <= 0.05: # not enough ripple to consider calculation
|
|
return g1st - voa
|
|
|
|
xlow = dgts2 - deltax
|
|
glow = g1st - voa + array(self.interpol_dgt) * xlow
|
|
pout_db = lin2db(sum(pin * 1e3 * db2lin(glow)))
|
|
gavg_low = pout_db - tot_in_power_db
|
|
|
|
# upper gain estimate
|
|
xhigh = dgts2 + deltax
|
|
ghigh = g1st - voa + array(self.interpol_dgt) * xhigh
|
|
pout_db = lin2db(sum(pin * 1e3 * db2lin(ghigh)))
|
|
gavg_high = pout_db - tot_in_power_db
|
|
|
|
# compute slope
|
|
slope1 = (gavg_low - gavg_cent) / (xlow - xcent)
|
|
slope2 = (gavg_cent - gavg_high) / (xcent - xhigh)
|
|
|
|
if abs(self.effective_gain - gavg_cent) <= err_tolerance:
|
|
dgts3 = xcent
|
|
elif self.effective_gain < gavg_cent:
|
|
dgts3 = xcent - (gavg_cent - self.effective_gain) / slope1
|
|
else:
|
|
dgts3 = xcent + (-gavg_cent + self.effective_gain) / slope2
|
|
|
|
return g1st - voa + array(self.interpol_dgt) * dgts3
|
|
|
|
def propagate(self, spectral_info):
|
|
"""add ASE noise to the propagating carriers of :class:`.info.SpectralInformation`"""
|
|
# interpolate the amplifier vectors with the carriers freq, calculate nf & gain profile
|
|
self.interpol_params(spectral_info)
|
|
|
|
ase = self.noise_profile(spectral_info)
|
|
spectral_info.ase += ase
|
|
|
|
spectral_info.apply_gain_db(self.gprofile - self.out_voa)
|
|
spectral_info.pmd = sqrt(spectral_info.pmd ** 2 + self.params.pmd ** 2)
|
|
spectral_info.pdl = sqrt(spectral_info.pdl ** 2 + self.params.pdl ** 2)
|
|
self.pch_out_dbm = watt2dbm(spectral_info.signal + spectral_info.nli + spectral_info.ase)
|
|
self.propagated_labels = spectral_info.label
|
|
|
|
def update_pref(self, spectral_info):
|
|
spectral_info.pref = \
|
|
spectral_info.pref._replace(p_span0=spectral_info.pref.p_span0,
|
|
p_spani=spectral_info.pref.p_spani + self.effective_gain - self.out_voa)
|
|
|
|
def __call__(self, spectral_info):
|
|
self.propagate(spectral_info)
|
|
self.update_pref(spectral_info)
|
|
return spectral_info
|