#!/usr/bin/env python3 # -*- coding: utf-8 -*- ''' gnpy.core.elements ================== This module contains standard network elements. A network element is a Python callable. It takes a .info.SpectralInformation object and returns a copy with appropriate fields affected. This structure represents spectral information that is "propogated" by this network element. Network elements must have only a local "view" of the network and propogate SpectralInformation using only this information. They should be independent and self-contained. Network elements MUST implement two attributes .uid and .name representing a unique identifier and a printable name. ''' import numpy as np from scipy.constants import c, h from gnpy.core.node import Node from gnpy.core.units import UNITS from gnpy.core.utils import lin2db, db2lin, itufs class Transceiver(Node): def __init__(self, config): super().__init__(config) self.osnr_ase_01nm = None self.osnr_ase = None self.osnr_nli = None self.snr = None def _calc_snr(self, spectral_info): ase = [c.power.ase for c in spectral_info.carriers] nli = [c.power.nli for c in spectral_info.carriers] if min(ase)>1e-20: self.osnr_ase = [lin2db(c.power.signal/c.power.ase) for c in spectral_info.carriers] ratio_01nm = [lin2db(12.5e9/c.baud_rate) for c in spectral_info.carriers] self.osnr_ase_01nm = [ase - ratio for ase, ratio in zip(self.osnr_ase, ratio_01nm)] if min(nli)>1e-20: self.osnr_nli = [lin2db(c.power.signal/c.power.nli) for c in spectral_info.carriers] self.snr = [lin2db(c.power.signal/(c.power.nli+c.power.ase)) for c in spectral_info.carriers] def __repr__(self): return (f'{type(self).__name__}(' 'uid={self.uid!r}, ' 'config={self.config!r}, ' 'osnr_ase_01nm={osnr_ase_01nm!r}, ' 'osnr_ase={osnr_ase!r}, ' 'osnr_ase_nli={osnr_ase_nli!r}, ' 'snr={snr!r})') def __str__(self): if self.snr is None or self.osnr_ase is None: return f'{type(self).__name__} {self.uid}' snr = round(np.mean(self.snr),2) osnr_ase = round(np.mean(self.osnr_ase),2) osnr_ase_01nm = round(np.mean(self.osnr_ase_01nm), 2) return '\n'.join([f'{type(self).__name__} {self.uid}', f' OSNR ASE (1nm): {np.mean(self.osnr_ase_01nm):.2f}', f' OSNR ASE (signal bw): {np.mean(self.osnr_ase):.2f}', f' SNR total (signal bw): {np.mean(snr):.2f}']) def __call__(self, spectral_info): self._calc_snr(spectral_info) return spectral_info class Roadm(Node): def __init__(self, config): super().__init__(config) self.loss = 20 #dB 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: {self.loss:.2f}']) def propagate(self, *carriers): attenuation = db2lin(self.loss) for carrier in carriers: pwr = carrier.power pwr = pwr._replace(signal=pwr.signal/attenuation, nonlinear_interference=pwr.nli/attenuation, amplified_spontaneous_emission=pwr.ase/attenuation) yield carrier._replace(power=pwr) def __call__(self, spectral_info): carriers = tuple(self.propagate(*spectral_info.carriers)) return spectral_info.update(carriers=carriers) class Fiber(Node): def __init__(self, config): super().__init__(config) self.length = self.params.length * \ UNITS[self.params.length_units] #length in m self.loss_coef = self.params.loss_coef*1e-3 #lineic loss dB/m self.lin_loss_coef = self.params.loss_coef / (20*np.log10(np.exp(1))) self.dispersion = self.params.dispersion #s/m/m self.gamma = self.params.gamma #1/W/m self.loss = self.loss_coef * self.length #dB loss: useful for polymorphism (roadm, fiber, att) #TODO discuss factor 2 in the linear lineic attenuation def __repr__(self): return f'{type(self).__name__}(uid={self.uid!r}, length={self.length!r}, loss={self.loss!r})' def __str__(self): return '\n'.join([f'{type(self).__name__} {self.uid}', f' length: {self.length:.2f}', f' loss: {self.loss:.2f}']) def lin_attenuation(self): attenuation = self.length * self.loss_coef return db2lin(attenuation) @property def effective_length(self): alpha_dict = self.dbkm_2_lin() alpha = alpha_dict['alpha_acoef'] leff = (1 - np.exp(-2 * alpha * self.length)) / (2*alpha) return leff @property def asymptotic_length(self): alpha_dict = self.dbkm_2_lin() alpha = alpha_dict['alpha_acoef'] aleff = 1 / (2 * alpha) return aleff def beta2(self, ref_wavelength=None): """ Returns beta2 from dispersion parameter. Dispersion is entered in ps/nm/km. Disperion can be a numpy array or a single value. If a value ref_wavelength is not entered 1550e-9m will be assumed. ref_wavelength can be a numpy array. """ #TODO: discuss beta2 as method or attribute wl = 1550e-9 if ref_wavelength is None else ref_wavelength D = np.abs(self.dispersion) b2 = (wl**2) * D / (2 * np.pi * c) # 10^21 scales [ps^2/km] return b2 # s/Hz/m def dbkm_2_lin(self): """ calculates the linear loss coefficient """ alpha_pcoef = self.loss_coef alpha_acoef = alpha_pcoef / (2 * 10*np.log10(np.exp(1))) s = 'alpha_pcoef is linear loss coefficient in [dB/km^-1] units' s = ''.join([s, "alpha_acoef is linear loss field amplitude \ coefficient in [m^-1] units"]) d = {'alpha_pcoef': alpha_pcoef, 'alpha_acoef': alpha_acoef, 'description:': s} return d def _psi(self, carrier, interfering_carrier): """ Calculates eq. 123 from arXiv:1209.0394. """ if carrier.num_chan == interfering_carrier.num_chan: # SCI psi = np.arcsinh(0.5 * np.pi**2 * self.asymptotic_length * abs(self.beta2()) * carrier.baud_rate**2) else: # XCI delta_f = carrier.freq - interfering_carrier.freq psi = np.arcsinh(np.pi**2 * self.asymptotic_length * abs(self.beta2()) * carrier.baud_rate * (delta_f + 0.5 * interfering_carrier.baud_rate)) psi -= np.arcsinh(np.pi**2 * self.asymptotic_length * abs(self.beta2()) * carrier.baud_rate * (delta_f - 0.5 * interfering_carrier.baud_rate)) return psi def _gn_analytic(self, carrier, *carriers): """ Computes the nonlinear interference power on a single carrier. The method uses eq. 120 from arXiv:1209.0394. :param carrier: the signal under analysis :param carriers: the full WDM comb :return: carrier_nli: the amount of nonlinear interference in W on the under analysis """ g_nli = 0 for interfering_carrier in carriers: psi = self._psi(carrier, interfering_carrier) g_nli += (interfering_carrier.power.signal/interfering_carrier.baud_rate)**2 *\ (carrier.power.signal/carrier.baud_rate) * psi g_nli *= (16 / 27) * (self.gamma * self.effective_length)**2 /\ (2 * np.pi * abs(self.beta2()) * self.asymptotic_length) carrier_nli = carrier.baud_rate*g_nli return carrier_nli def propagate(self, *carriers): i=0 for carrier in carriers: pwr = carrier.power carrier_nli = self._gn_analytic(carrier, *carriers) pwr = pwr._replace(signal=pwr.signal/self.lin_attenuation(), nonlinear_interference=(pwr.nli+carrier_nli)/self.lin_attenuation(), amplified_spontaneous_emission=pwr.ase/self.lin_attenuation()) i+=1 yield carrier._replace(power=pwr) def __call__(self, spectral_info): carriers = tuple(self.propagate(*spectral_info.carriers)) return spectral_info.update(carriers=carriers) class Edfa(Node): def __init__(self, config): super().__init__(config) self.interpol_dgt = None #inerpolated dynamic gain tilt: N numpy array self.interpol_gain_ripple = None #gain ripple: N numpy array self.interpol_nf_ripple = None #nf_ripple: N numpy array self.channel_freq = None #SI channel frequencies: N numpy array """nf, gprofile, pin and pout attributs are set by interpol_params""" self.nf = None #dB edfa nf at operational.gain_target: N numpy array self.gprofile = None self.pin_db = None self.pout_db = None def __repr__(self): return (f'{type(self).__name__}(uid={self.uid!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}' return '\n'.join([f'{type(self).__name__} {self.uid}', f' gain (dB): {self.operational.gain_target:.2f}', f' noise figure (dB): {np.mean(self.nf):.2f}', f' Power In (dBm): {self.pin_db:.2f}', f' Power Out (dBm): {self.pout_db:.2f}']) def interpol_params(self, frequencies, pin, baud_rates): """interpolate SI channel frequencies with the edfa dgt and gain_ripple frquencies from json set the edfa class __init__ None parameters : self.channel_freq, self.nf, self.interpol_dgt and self.interpol_gain_ripple """ #TODO read amplifier actual frequencies from additional params in json amplifier_freq = itufs(0.05)*1e12 # Hz self.channel_freq = frequencies self.interpol_dgt = np.interp(self.channel_freq, amplifier_freq, self.params.dgt) self.interpol_gain_ripple = np.interp(self.channel_freq, amplifier_freq, self.params.gain_ripple) self.interpol_nf_ripple = np.interp(self.channel_freq, amplifier_freq, self.params.nf_ripple) self.pin_db = lin2db(np.sum(pin*1e3)) """check power saturation and correct target_gain accordingly:""" gain_target = min(self.operational.gain_target, self.params.p_max-self.pin_db) self.operational.gain_target = gain_target self.nf = self._calc_nf() self.gprofile = self._gain_profile(pin) pout = (pin + self.noise_profile(baud_rates))*db2lin(self.gprofile) self.pout_db = lin2db(np.sum(pout*1e3)) # ! ase & nli are only calculated in signal bandwidth # => pout_db is not the absolute full ouput power (negligible if sufficient channels) def _calc_nf(self): """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""" #TODO : tbd alarm rising or input VOA padding in case #gain_min > gain_target TBD: pad = max(self.params.gain_min - self.operational.gain_target, 0) gain_target = self.operational.gain_target + pad dg = gain_target - self.params.gain_flatmax # ! <0 if self.params.nf_model.enabled: g1a = gain_target - self.params.nf_model.delta_p + dg nf_avg = lin2db(db2lin(self.params.nf_model.nf1) + db2lin(self.params.nf_model.nf2)/db2lin(g1a)) else: nf_avg = np.polyval(self.params.nf_fit_coeff, dg) nf_array = self.interpol_nf_ripple + nf_avg + pad #input VOA = 1 for 1 NF degradation return nf_array def noise_profile(self, df): """ noise_profile(bw) computes amplifier ase (W) in signal bw (Hz) noise is calculated at amplifier input :bw: signal bandwidth = baud rate in Hz :type bw: float :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 * df * self.channel_freq * db2lin(self.nf) #W return ase #in W, @amplifier input def _gain_profile(self, pin): """ 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 :param gtp: gain tilt setting :type gain_ripple: numpy.ndarray :type dgt: numpy.ndarray :type Pin: numpy.ndarray :type gp: float :type gtp: float :return: gain profile in dBm :rtype: numpy.ndarray AMPLIFICATION USING INPUT PROFILE INPUTS: gain_ripple - vector of length number of channels or spectral slices DGT - vector of length number of channels or spectral slices Pin - input powers vector of length number of channels or spectral slices Gp - provisioned gain length 1 GTp - provisioned tilt length 1 OUTPUT: amp gain per channel or spectral slice NOTE: there is no checking done for violations of the total output power capability of the amp. EDIT OF PREVIOUS NOTE: power violation now added in interpol_params Ported from Matlab version written by David Boerges at Ciena. 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. """ err_tolerance = 1.0e-11 simple_opt = True # TODO check what param should be used (currently length(dgt)) nchan = np.arange(len(self.interpol_dgt)) # TODO 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 = lin2db(np.sum(pin*1e3)) # ! Pin expressed in W # Linear fit to get the p = np.polyfit(nchan, self.interpol_dgt, 1) dgt_slope = p[0] # Calculate the target slope- Currently assumes equal spaced channels # TODO make it so that supports arbitrary channel spacing. targ_slope = self.operational.tilt_target / (len(nchan) - 1) # 1st estimate of DGT scaling if abs(dgt_slope) > 0.001: # add check for div 0 due to flat dgt dgts1 = targ_slope / dgt_slope else: dgts1 = 0 # when simple_opt is true code makes 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 replace with loop if simple_opt: # 1st estimate of Er gain & voa loss g1st = np.array(self.interpol_gain_ripple) + self.params.gain_flatmax + \ np.array(self.interpol_dgt) * dgts1 voa = lin2db(np.mean(db2lin(g1st))) - self.operational.gain_target # 2nd estimate of Amp ch gain using the channel input profile g2nd = g1st - voa pout_db = lin2db(np.sum(pin*1e3*db2lin(g2nd))) dgts2 = self.operational.gain_target - (pout_db - tot_in_power_db) # Center estimate of amp ch gain xcent = dgts2 gcent = g1st - voa + np.array(self.interpol_dgt) * xcent pout_db = lin2db(np.sum(pin*1e3*db2lin(gcent))) gavg_cent = pout_db - tot_in_power_db # Lower estimate of Amp ch gain deltax = np.max(g1st) - np.min(g1st) # ! if no ripple deltax = 0 => xlow = xcent: div 0 # add check for flat gain response : if abs(deltax) > 0.05: #enough ripple to consider calculation and avoid div 0 xlow = dgts2 - deltax glow = g1st - voa + np.array(self.interpol_dgt) * xlow pout_db = lin2db(np.sum(pin*1e3*db2lin(glow))) gavg_low = pout_db - tot_in_power_db # Upper gain estimate xhigh = dgts2 + deltax ghigh = g1st - voa + np.array(self.interpol_dgt) * xhigh pout_db = lin2db(np.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 np.abs(self.operational.gain_target - gavg_cent) <= err_tolerance: dgts3 = xcent elif self.operational.gain_target < gavg_cent: dgts3 = xcent - (gavg_cent - self.operational.gain_target) / slope1 else: dgts3 = xcent + (-gavg_cent + self.operational.gain_target) / slope2 gprofile = g1st - voa + np.array(self.interpol_dgt) * dgts3 else: #not enough ripple gprofile = g1st - voa else: #simple_opt gprofile = None return gprofile def propagate(self, *carriers): """add ase noise to the propagating carriers of SpectralInformation""" i = 0 pin = np.array([c.power.signal+c.power.nli+c.power.ase for c in carriers]) #pin in W freq = np.array([c.frequency for c in carriers]) brate = np.array([c.baud_rate for c in carriers]) #interpolate the amplifier vectors with the carriers freq, calculate nf & gain profile self.interpol_params(freq, pin, brate) gain = db2lin(self.gprofile) carrier_ase = self.noise_profile(brate) for carrier in carriers: pwr = carrier.power bw = carrier.baud_rate pwr = pwr._replace(signal=pwr.signal*gain[i], nonlinear_interference=pwr.nli*gain[i], amplified_spontaneous_emission=(pwr.ase+carrier_ase[i])*gain[i]) i += 1 yield carrier._replace(power=pwr) def __call__(self, spectral_info): carriers = tuple(self.propagate(*spectral_info.carriers)) return spectral_info.update(carriers=carriers)