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https://github.com/Telecominfraproject/oopt-gnpy.git
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Refactor and simplify network functions
Signed-off-by: EstherLerouzic <esther.lerouzic@orange.com> Change-Id: Ifa0949a8b7739036639e1a4b006ceb08804558ce
This commit is contained in:
EstherLerouzic
committed by
Esther Le Rouzic
Esther Le Rouzic
parent
ac8fd770ab
commit
920ac30aa5
@@ -8,10 +8,10 @@ gnpy.core.network
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Working with networks which consist of network elements
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"""
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from copy import deepcopy
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from operator import attrgetter
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from collections import namedtuple
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from logging import getLogger
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from typing import Tuple, List, Optional
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from gnpy.core import elements
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from gnpy.core.exceptions import ConfigurationError, NetworkTopologyError
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@@ -24,8 +24,25 @@ from gnpy.core.science_utils import RamanSolver
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logger = getLogger(__name__)
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def edfa_nf(gain_target, variety_type, equipment):
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amp_params = equipment['Edfa'][variety_type]
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def edfa_nf(gain_target: float, amp_params) -> float:
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"""Calculates the noise figure (NF) of an EDFA (Erbium-Doped Fiber Amplifier)
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based on the specified gain target and amplifier parameters.
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This function creates an EDFA instance with the given parameters and
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computes its noise figure using the internal calculation method.
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Parameters:
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-----------
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gain_target : float
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The target gain for which the noise figure is to be calculated.
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amp_params : object
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An object containing the amplifier parameters.
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Returns:
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--------
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float
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The calculated noise figure (NF) of the EDFA in dB.
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"""
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amp = elements.Edfa(
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uid='calc_NF',
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params=amp_params.__dict__,
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@@ -40,17 +57,65 @@ def edfa_nf(gain_target, variety_type, equipment):
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return amp._calc_nf(True)
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def select_edfa(raman_allowed, gain_target, power_target, equipment, uid, restrictions=None, verbose=True):
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"""amplifer selection algorithm
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@Orange Jean-Luc Augé
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def select_edfa(raman_allowed: bool, gain_target: float, power_target: float, edfa_eqpt: dict, uid: str,
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target_extended_gain: float, restrictions: Optional[List[str]] = None, verbose: Optional[bool] = True) \
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-> Tuple[str, float]:
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"""Selects an amplifier within a library based on specified parameters.
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This function implements an amplifier selection algorithm that considers
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various constraints, including gain and power targets, as well as
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restrictions on amplifier types. It can also handle Raman amplifiers
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if allowed.
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Parameters:
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-----------
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raman_allowed : bool
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Indicates whether Raman amplifiers are permitted in the selection.
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gain_target : float
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The target gain that the selected amplifier should achieve.
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power_target : float
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The target power level for the amplifier.
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edfa_eqpt : dict
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A dictionary containing available EDFA equipment, where keys are
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amplifier names and values are amplifier objects.
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uid : str
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A unique identifier for the node where the amplifier will be used.
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target_extended_gain : float
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The extended gain target derived from configuration settings.
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restrictions : Optional[List[str]], default=None
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A list of amplifier names that are restricted from selection.
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verbose : Optional[bool], default=True
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If True, enables verbose logging of warnings and information.
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Returns:
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--------
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Tuple[str, float]
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A tuple containing the selected amplifier's variety and the power
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reduction applied (if any).
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Raises:
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-------
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ConfigurationError
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If no amplifiers meet the minimum gain requirement for the specified
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node.
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Notes:
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------
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- The function considers both gain and power limitations when selecting
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an amplifier.
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- If no suitable amplifier is found or if the target gain exceeds the
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capabilities of available amplifiers, a warning is logged.
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Author:
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-------
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Jean-Luc Augé
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"""
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Edfa_list = namedtuple('Edfa_list', 'variety power gain_min nf')
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TARGET_EXTENDED_GAIN = equipment['Span']['default'].target_extended_gain
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# for roadm restriction only: create a dict including not allowed for design amps
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# because main use case is to have specific radm amp which are not allowed for ILA
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# with the auto design
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edfa_dict = {name: amp for (name, amp) in equipment['Edfa'].items()
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edfa_dict = {name: amp for (name, amp) in edfa_eqpt.items()
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if restrictions is None or name in restrictions}
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pin = power_target - gain_target
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@@ -59,13 +124,13 @@ def select_edfa(raman_allowed, gain_target, power_target, equipment, uid, restri
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# edfa list with:
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# extended gain min allowance of 3dB: could be parametrized, but a bit complex
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# extended gain max allowance TARGET_EXTENDED_GAIN is coming from eqpt_config.json
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# extended gain max allowance target_extended_gain is coming from eqpt_config.json
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# power attribut include power AND gain limitations
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edfa_list = [Edfa_list(
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variety=edfa_variety,
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power=min(pin + edfa.gain_flatmax + TARGET_EXTENDED_GAIN, edfa.p_max) - power_target,
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power=min(pin + edfa.gain_flatmax + target_extended_gain, edfa.p_max) - power_target,
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gain_min=gain_target + 3 - edfa.gain_min,
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nf=edfa_nf(gain_target, edfa_variety, equipment))
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nf=edfa_nf(gain_target, edfa_eqpt[edfa_variety]))
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for edfa_variety, edfa in edfa_dict.items()
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if ((edfa.allowed_for_design or restrictions is not None) and not edfa.raman)]
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@@ -73,9 +138,9 @@ def select_edfa(raman_allowed, gain_target, power_target, equipment, uid, restri
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# do not allow extended gain min for Raman
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raman_list = [Edfa_list(
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variety=edfa_variety,
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power=min(pin + edfa.gain_flatmax + TARGET_EXTENDED_GAIN, edfa.p_max) - power_target,
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power=min(pin + edfa.gain_flatmax + target_extended_gain, edfa.p_max) - power_target,
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gain_min=gain_target - edfa.gain_min,
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nf=edfa_nf(gain_target, edfa_variety, equipment))
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nf=edfa_nf(gain_target, edfa_eqpt[edfa_variety]))
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for edfa_variety, edfa in edfa_dict.items()
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if (edfa.allowed_for_design and edfa.raman)] \
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if raman_allowed else []
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@@ -97,7 +162,6 @@ def select_edfa(raman_allowed, gain_target, power_target, equipment, uid, restri
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to satisfy min gain requirement in node {uid} \
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please increase span fiber padding')
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else:
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# TODO: convert to logging
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if verbose:
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logger.warning(f'\n\tWARNING: target gain in node {uid} is below all available amplifiers min gain: '
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+ '\n\tamplifier input padding will be assumed, consider increase span fiber padding '
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@@ -121,7 +185,7 @@ def select_edfa(raman_allowed, gain_target, power_target, equipment, uid, restri
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# =>chose the amp with the best NF among the acceptable ones:
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selected_edfa = min(acceptable_power_list, key=attrgetter('nf')) # filter on NF
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# check what are the gain and power limitations of this amp
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power_reduction = min(selected_edfa.power, 0)
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power_reduction = min(selected_edfa.power, 0.0)
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if power_reduction < -0.5 and verbose:
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logger.warning(f'\n\tWARNING: target gain and power in node {uid}\n'
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+ '\tis beyond all available amplifiers capabilities and/or extended_gain_range:\n'
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@@ -358,8 +422,12 @@ def set_egress_amplifier(network, this_node, equipment, pref_ch_db, pref_total_d
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restrictions = next_node.restrictions['preamp_variety_list']
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else:
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restrictions = None
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edfa_variety, power_reduction = select_edfa(raman_allowed, gain_target, power_target, equipment,
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node.uid, restrictions, verbose)
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edfa_eqpt = {n: a for n, a in equipment['Edfa'].items()}
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edfa_variety, power_reduction = \
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select_edfa(raman_allowed, gain_target, power_target, edfa_eqpt,
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node.uid,
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target_extended_gain=equipment['Span']['default'].target_extended_gain,
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restrictions=restrictions, verbose=verbose)
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extra_params = equipment['Edfa'][edfa_variety]
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node.params.update_params(extra_params.__dict__)
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dp += power_reduction
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@@ -590,8 +658,7 @@ def set_roadm_internal_paths(roadm, network):
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def add_roadm_booster(network, roadm):
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next_nodes = [n for n in network.successors(roadm)
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if not (isinstance(n, elements.Transceiver) or isinstance(n, elements.Fused)
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or isinstance(n, elements.Edfa))]
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if not isinstance(n, (elements.Transceiver, elements.Fused, elements.Edfa))]
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# no amplification for fused spans or TRX
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for next_node in next_nodes:
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network.remove_edge(roadm, next_node)
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@@ -617,7 +684,7 @@ def add_roadm_booster(network, roadm):
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def add_roadm_preamp(network, roadm):
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prev_nodes = [n for n in network.predecessors(roadm)
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if not (isinstance(n, elements.Transceiver) or isinstance(n, elements.Fused) or isinstance(n, elements.Edfa))]
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if not isinstance(n, (elements.Transceiver, elements.Fused, elements.Edfa))]
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# no amplification for fused spans or TRX
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for prev_node in prev_nodes:
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network.remove_edge(prev_node, roadm)
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