mirror of
https://github.com/Telecominfraproject/oopt-gnpy.git
synced 2025-10-30 17:47:50 +00:00
273 lines
7.9 KiB
Python
273 lines
7.9 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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gnpy.core.parameters
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====================
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This module contains all parameters to configure standard network elements.
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"""
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from scipy.constants import c, pi
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from numpy import squeeze, log10, exp
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from gnpy.core.utils import db2lin, convert_length
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from gnpy.core.exceptions import ParametersError
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class Parameters:
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def asdict(self):
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class_dict = self.__class__.__dict__
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instance_dict = self.__dict__
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new_dict = {}
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for key in class_dict:
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if isinstance(class_dict[key], property):
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new_dict[key] = instance_dict['_' + key]
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return new_dict
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class PumpParams(Parameters):
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def __init__(self, power, frequency, propagation_direction):
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self._power = power
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self._frequency = frequency
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self._propagation_direction = propagation_direction
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@property
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def power(self):
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return self._power
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@property
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def frequency(self):
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return self._frequency
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@property
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def propagation_direction(self):
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return self._propagation_direction
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class RamanParams(Parameters):
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def __init__(self, **kwargs):
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self._flag_raman = kwargs['flag_raman']
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self._space_resolution = kwargs['space_resolution'] if 'space_resolution' in kwargs else None
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self._tolerance = kwargs['tolerance'] if 'tolerance' in kwargs else None
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@property
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def flag_raman(self):
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return self._flag_raman
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@property
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def space_resolution(self):
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return self._space_resolution
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@property
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def tolerance(self):
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return self._tolerance
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class NLIParams(Parameters):
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def __init__(self, **kwargs):
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self._nli_method_name = kwargs['nli_method_name']
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self._wdm_grid_size = kwargs['wdm_grid_size']
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self._dispersion_tolerance = kwargs['dispersion_tolerance']
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self._phase_shift_tolerance = kwargs['phase_shift_tolerance']
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self._f_cut_resolution = None
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self._f_pump_resolution = None
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self._computed_channels = kwargs['computed_channels'] if 'computed_channels' in kwargs else None
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@property
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def nli_method_name(self):
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return self._nli_method_name
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@property
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def wdm_grid_size(self):
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return self._wdm_grid_size
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@property
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def dispersion_tolerance(self):
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return self._dispersion_tolerance
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@property
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def phase_shift_tolerance(self):
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return self._phase_shift_tolerance
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@property
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def f_cut_resolution(self):
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return self._f_cut_resolution
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@f_cut_resolution.setter
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def f_cut_resolution(self, f_cut_resolution):
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self._f_cut_resolution = f_cut_resolution
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@property
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def f_pump_resolution(self):
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return self._f_pump_resolution
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@f_pump_resolution.setter
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def f_pump_resolution(self, f_pump_resolution):
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self._f_pump_resolution = f_pump_resolution
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@property
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def computed_channels(self):
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return self._computed_channels
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class SimParams(Parameters):
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def __init__(self, **kwargs):
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try:
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if 'nli_parameters' in kwargs:
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self._nli_params = NLIParams(**kwargs['nli_parameters'])
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else:
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self._nli_params = None
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if 'raman_parameters' in kwargs:
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self._raman_params = RamanParams(**kwargs['raman_parameters'])
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else:
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self._raman_params = None
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except KeyError as e:
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raise ParametersError(f'Simulation parameters must include {e}. Configuration: {kwargs}')
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@property
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def nli_params(self):
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return self._nli_params
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@property
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def raman_params(self):
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return self._raman_params
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class FiberParams(Parameters):
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def __init__(self, **kwargs):
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try:
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self._length = convert_length(kwargs['length'], kwargs['length_units'])
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# fixed attenuator for padding
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self._att_in = kwargs['att_in'] if 'att_in' in kwargs else 0
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# if not defined in the network json connector loss in/out
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# the None value will be updated in network.py[build_network]
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# with default values from eqpt_config.json[Spans]
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self._con_in = kwargs['con_in'] if 'con_in' in kwargs else None
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self._con_out = kwargs['con_out'] if 'con_out' in kwargs else None
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self._gamma = kwargs['gamma'] # 1/W/m
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self._dispersion = kwargs['dispersion'] # s/m/m
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if 'ref_wavelength' in kwargs:
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self._ref_wavelength = kwargs['ref_wavelength']
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self._ref_frequency = c / self._ref_wavelength
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elif 'ref_frequency' in kwargs:
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self._ref_frequency = kwargs['ref_frequency']
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self._ref_wavelength = c / self._ref_frequency
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else:
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self._ref_wavelength = 1550e-9
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self._ref_frequency = c / self._ref_wavelength
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self._beta2 = (self._ref_wavelength ** 2) * abs(self._dispersion) / (2 * pi * c) # 1/(m * Hz^2)
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self._beta3 = kwargs['beta3'] if 'beta3' in kwargs else 0
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if type(kwargs['loss_coef']) == dict:
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self._loss_coef = squeeze(kwargs['loss_coef']['loss_coef_power']) * 1e-3 # lineic loss dB/m
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self._f_loss_ref = squeeze(kwargs['loss_coef']['frequency']) # Hz
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else:
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self._loss_coef = kwargs['loss_coef'] * 1e-3 # lineic loss dB/m
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self._f_loss_ref = 193.5e12 # Hz
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self._lin_attenuation = db2lin(self._length * self._loss_coef)
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self._lin_loss_exp = self._loss_coef / (10 * log10(exp(1))) # linear power exponent loss Neper/m
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self._effective_length = (1 - exp(- self._lin_loss_exp * self._length)) / self._lin_loss_exp
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self._asymptotic_length = 1 / self._lin_loss_exp
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# raman parameters (not compulsory)
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self._raman_efficiency = kwargs['raman_efficiency'] if 'raman_efficiency' in kwargs else None
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self._pumps_loss_coef = kwargs['pumps_loss_coef'] if 'pumps_loss_coef' in kwargs else None
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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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@property
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def length(self):
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return self._length
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@length.setter
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def length(self, length):
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"""length must be in m"""
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self._length = length
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@property
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def att_in(self):
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return self._att_in
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@att_in.setter
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def att_in(self, att_in):
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self._att_in = att_in
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@property
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def con_in(self):
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return self._con_in
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@con_in.setter
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def con_in(self, con_in):
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self._con_in = con_in
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@property
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def con_out(self):
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return self._con_out
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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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@property
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def dispersion(self):
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return self._dispersion
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@property
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def gamma(self):
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return self._gamma
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@property
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def ref_wavelength(self):
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return self._ref_wavelength
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@property
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def ref_frequency(self):
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return self._ref_frequency
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@property
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def beta2(self):
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return self._beta2
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@property
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def beta3(self):
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return self._beta3
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@property
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def loss_coef(self):
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return self._loss_coef
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@property
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def f_loss_ref(self):
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return self._f_loss_ref
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@property
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def lin_loss_exp(self):
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return self._lin_loss_exp
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@property
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def lin_attenuation(self):
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return self._lin_attenuation
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@property
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def effective_length(self):
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return self._effective_length
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@property
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def asymptotic_length(self):
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return self._asymptotic_length
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@property
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def raman_efficiency(self):
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return self._raman_efficiency
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@property
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def pumps_loss_coef(self):
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return self._pumps_loss_coef
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def asdict(self):
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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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return dictionary
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