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Effective Area and Raman Gain Coefficient Scaling

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1. Effective area scaling along frequency is implemented by means of a technological model.
2. Raman gain coefficient is extended coherently, including the scaling due to the pump frequency.

Change-Id: I4e8b79697500ef0f73ba2f969713d9bdb3e9949c
Co-authored-by: Giacomo Borraccini <giacomo.borraccini@polito.it>
This commit is contained in:
AndreaDAmico
2023-05-29 17:32:26 +02:00
committed by EstherLerouzic
parent 3bea4b3c9f
commit c20e6fb320
15 changed files with 1011 additions and 429 deletions
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12
docs/biblio.bib
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@@ -1848,3 +1848,15 @@ month={Sept},}
title = {Telecom Infra Project}, title = {Telecom Infra Project},
url = {https://www.telecominfraproject.com}, url = {https://www.telecominfraproject.com},
} }
@ARTICLE{DAmicoJLT2022,
author={D’Amico, Andrea and Correia, Bruno and London, Elliot and Virgillito,
Emanuele and Borraccini, Giacomo and Napoli, Antonio and Curri, Vittorio},
journal={Journal of Lightwave Technology},
title={Scalable and Disaggregated GGN Approximation Applied to a C+L+S Optical Network},
year={2022},
volume={40},
number={11},
pages={3499-3511},
doi={10.1109/JLT.2022.3162134}
}

18
docs/json.rst
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@@ -95,6 +95,24 @@ The fiber library currently describes SSMF and NZDF but additional fiber types c
| | | each relevant position (in km): | | | | each relevant position (in km): |
| | | ``{"position": 10, "loss": 1.5}``) | | | | ``{"position": 10, "loss": 1.5}``) |
+----------------------+-----------+------------------------------------------+ +----------------------+-----------+------------------------------------------+
| ``raman_coefficient``| (dict) | The fundamental parameter that describes |
| | | the regulation of the power transfer |
| | | between channels during fiber propagation|
| | | is the Raman gain coefficient (see |
| | | :cite:`DAmicoJLT2022` for further |
| | | details); :math:`f_{ref}` represents the |
| | | pump reference frequency used for the |
| | | Raman gain coefficient profile |
| | | measurement ("reference_frequency"), |
| | | :math:`\Delta f` is the frequency shift |
| | | between the pump and the specific Stokes |
| | | wave, the Raman gain coefficient |
| | | in terms of optical power |
| | | :math:`g_0`, expressed in |
| | | :math:`1/(m\;W)`. |
| | | Default values measured for a SSMF are |
| | | considered when not specified. |
+----------------------+-----------+------------------------------------------+
.. _Corning whitepaper on MFD/EA: https://www.corning.com/microsites/coc/oem/documents/specialty-fiber/WP7071-Mode-Field-Diam-and-Eff-Area.pdf .. _Corning whitepaper on MFD/EA: https://www.corning.com/microsites/coc/oem/documents/specialty-fiber/WP7071-Mode-Field-Diam-and-Eff-Area.pdf

21
gnpy/core/elements.py
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@@ -473,17 +473,6 @@ class Fiber(_Node):
self.pch_out_db = None self.pch_out_db = None
self.passive = True self.passive = True
self.propagated_labels = [""] 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 # Lumped losses
z_lumped_losses = array([lumped['position'] for lumped in self.params.lumped_losses]) # km z_lumped_losses = array([lumped['position'] for lumped in self.params.lumped_losses]) # km
@@ -566,15 +555,17 @@ class Fiber(_Node):
return self.loss_coef_func(frequency) / (10 * log10(exp(1))) return self.loss_coef_func(frequency) / (10 * log10(exp(1)))
def cr(self, frequency): def cr(self, frequency):
"""Returns the raman efficiency matrix including the vibrational loss """Returns the raman gain coefficient matrix including the vibrational loss
:param frequency: the frequency at which cr is computed [Hz] :param frequency: the frequency at which cr is computed [Hz]
:return: cr: raman efficiency matrix [1 / (W m)] :return: cr: raman gain coefficient matrix [1 / (W m)]
""" """
df = outer(ones(frequency.shape), frequency) - outer(frequency, ones(frequency.shape)) df = outer(ones(frequency.shape), frequency) - outer(frequency, ones(frequency.shape))
cr = self._cr_function(df) effective_area_overlap = self.params.effective_area_overlap(frequency, frequency)
cr = interp(df, self.params.raman_coefficient.frequency_offset,
self.params.raman_coefficient.normalized_gamma_raman) * frequency / effective_area_overlap
vibrational_loss = outer(frequency, ones(frequency.shape)) / outer(ones(frequency.shape), frequency) 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)] return cr * (cr >= 0) + cr * (cr < 0) * vibrational_loss # [1/(W m)]
def chromatic_dispersion(self, freq=None): def chromatic_dispersion(self, freq=None):
"""Returns accumulated chromatic dispersion (CD). """Returns accumulated chromatic dispersion (CD).

128
gnpy/core/parameters.py
View File

@@ -7,9 +7,10 @@ gnpy.core.parameters
This module contains all parameters to configure standard network elements. This module contains all parameters to configure standard network elements.
""" """
from collections import namedtuple
from scipy.constants import c, pi from scipy.constants import c, pi
from numpy import asarray, array from numpy import asarray, array, exp, sqrt, log, outer, ones, squeeze, append, flip
from gnpy.core.utils import convert_length from gnpy.core.utils import convert_length
from gnpy.core.exceptions import ParametersError from gnpy.core.exceptions import ParametersError
@@ -106,26 +107,50 @@ class FusedParams(Parameters):
self.loss = kwargs['loss'] if 'loss' in kwargs else 1 self.loss = kwargs['loss'] if 'loss' in kwargs else 1
# SSMF Raman coefficient profile normalized with respect to the effective area (Cr * A_eff) DEFAULT_RAMAN_COEFFICIENT = {
CR_NORM = array([ # SSMF Raman coefficient profile normalized with respect to the effective area overlap (g0 * A_eff(f_probe, f_pump))
0., 7.802e-16, 2.4236e-15, 4.0504e-15, 5.6606e-15, 6.8973e-15, 7.802e-15, 8.4162e-15, 8.8727e-15, 9.2877e-15, 'g0': array(
1.01011e-14, 1.05244e-14, 1.13295e-14, 1.2367e-14, 1.3695e-14, 1.5023e-14, 1.64091e-14, 1.81936e-14, 2.04927e-14, [0.00000000e+00, 1.12351610e-05, 3.47838074e-05, 5.79356636e-05, 8.06921680e-05, 9.79845709e-05, 1.10454361e-04,
2.28167e-14, 2.48917e-14, 2.66098e-14, 2.82615e-14, 2.98136e-14, 3.1042e-14, 3.17558e-14, 3.18803e-14, 3.17558e-14, 1.18735302e-04, 1.24736889e-04, 1.30110053e-04, 1.41001273e-04, 1.46383247e-04, 1.57011792e-04, 1.70765865e-04,
3.15566e-14, 3.11748e-14, 2.94567e-14, 3.14985e-14, 2.8552e-14, 2.43439e-14, 1.67992e-14, 9.6114e-15, 7.02180e-15, 1.88408911e-04, 2.05914127e-04, 2.24074028e-04, 2.47508283e-04, 2.77729174e-04, 3.08044243e-04, 3.34764439e-04,
5.9262e-15, 5.6938e-15, 7.055e-15, 7.4119e-15, 7.4783e-15, 6.7645e-15, 5.5361e-15, 3.6271e-15, 2.7224e-15, 3.56481704e-04, 3.77127256e-04, 3.96269124e-04, 4.10955175e-04, 4.18718761e-04, 4.19511263e-04, 4.17025384e-04,
2.4568e-15, 2.1995e-15, 2.1331e-15, 2.3323e-15, 2.5564e-15, 3.0461e-15, 4.8555e-15, 5.5029e-15, 5.2788e-15, 4.13565369e-04, 4.07726048e-04, 3.83671291e-04, 4.08564283e-04, 3.69571936e-04, 3.14442090e-04, 2.16074535e-04,
4.565e-15, 3.3698e-15, 2.2991e-15, 2.0086e-15, 1.5521e-15, 1.328e-15, 1.162e-15, 9.379e-16, 8.715e-16, 8.134e-16, 1.23097823e-04, 8.95457457e-05, 7.52470400e-05, 7.19806145e-05, 8.87961158e-05, 9.30812065e-05, 9.37058268e-05,
8.134e-16, 9.379e-16, 1.3612e-15, 1.6185e-15, 1.9754e-15, 1.8758e-15, 1.6849e-15, 1.2284e-15, 9.047e-16, 8.134e-16, 8.45719619e-05, 6.90585286e-05, 4.50407159e-05, 3.36521245e-05, 3.02292475e-05, 2.69376939e-05, 2.60020897e-05,
8.715e-16, 9.711e-16, 1.0375e-15, 1.0043e-15, 9.047e-16, 8.134e-16, 6.806e-16, 5.478e-16, 3.901e-16, 2.241e-16, 2.82958958e-05, 3.08667558e-05, 3.66024657e-05, 5.80610307e-05, 6.54797937e-05, 6.25022715e-05, 5.37806442e-05,
1.577e-16, 9.96e-17, 3.32e-17, 1.66e-17, 8.3e-18]) 3.94996621e-05, 2.68120644e-05, 2.33038554e-05, 1.79140757e-05, 1.52472424e-05, 1.32707565e-05, 1.06541760e-05,
9.84649374e-06, 9.13999627e-06, 9.08971012e-06, 1.04227525e-05, 1.50419271e-05, 1.77838232e-05, 2.15810815e-05,
2.03744008e-05, 1.81939341e-05, 1.31862121e-05, 9.65352116e-06, 8.62698322e-06, 9.18688016e-06, 1.01737784e-05,
1.08017817e-05, 1.03903588e-05, 9.30040333e-06, 8.30809173e-06, 6.90650401e-06, 5.52238029e-06, 3.90648708e-06,
2.22908227e-06, 1.55796177e-06, 9.77218716e-07, 3.23477236e-07, 1.60602454e-07, 7.97306386e-08]
), # [m/W]
# Note the non-uniform spacing of this range; this is required for properly capturing the Raman peak shape. # Note the non-uniform spacing of this range; this is required for properly capturing the Raman peak shape.
FREQ_OFFSET = array([ 'frequency_offset': array([
0., 0.5, 1., 1.5, 2., 2.5, 3., 3.5, 4., 4.5, 5., 5.5, 6., 6.5, 7., 7.5, 8., 8.5, 9., 9.5, 10., 10.5, 11., 11.5, 12., 0., 0.5, 1., 1.5, 2., 2.5, 3., 3.5, 4., 4.5, 5., 5.5, 6., 6.5, 7., 7.5, 8., 8.5, 9., 9.5, 10., 10.5, 11., 11.5,
12.5, 12.75, 13., 13.25, 13.5, 14., 14.5, 14.75, 15., 15.5, 16., 16.5, 17., 17.5, 18., 18.25, 18.5, 18.75, 19., 12.,
19.5, 20., 20.5, 21., 21.5, 22., 22.5, 23., 23.5, 24., 24.5, 25., 25.5, 26., 26.5, 27., 27.5, 28., 28.5, 29., 29.5, 12.5, 12.75, 13., 13.25, 13.5, 14., 14.5, 14.75, 15., 15.5, 16., 16.5, 17., 17.5, 18., 18.25, 18.5, 18.75, 19.,
30., 30.5, 31., 31.5, 32., 32.5, 33., 33.5, 34., 34.5, 35., 35.5, 36., 36.5, 37., 37.5, 38., 38.5, 39., 39.5, 40., 19.5, 20., 20.5, 21., 21.5, 22., 22.5, 23., 23.5, 24., 24.5, 25., 25.5, 26., 26.5, 27., 27.5, 28., 28.5, 29.,
40.5, 41., 41.5, 42.]) * 1e12 29.5,
30., 30.5, 31., 31.5, 32., 32.5, 33., 33.5, 34., 34.5, 35., 35.5, 36., 36.5, 37., 37.5, 38., 38.5, 39., 39.5,
40.,
40.5, 41., 41.5, 42.]
) * 1e12, # [Hz]
# Raman profile reference frequency
'reference_frequency': 206184634112792 # [Hz] (1454 nm)}
}
class RamanGainCoefficient(namedtuple('RamanGainCoefficient', 'normalized_gamma_raman frequency_offset')):
""" Raman Gain Coefficient Parameters
Based on:
Andrea D’Amico, Bruno Correia, Elliot London, Emanuele Virgillito, Giacomo Borraccini, Antonio Napoli,
and Vittorio Curri, "Scalable and Disaggregated GGN Approximation Applied to a C+L+S Optical Network,"
J. Lightwave Technol. 40, 3499-3511 (2022)
Section III.D
"""
class FiberParams(Parameters): class FiberParams(Parameters):
@@ -139,6 +164,8 @@ class FiberParams(Parameters):
# with default values from eqpt_config.json[Spans] # with default values from eqpt_config.json[Spans]
self._con_in = kwargs.get('con_in') self._con_in = kwargs.get('con_in')
self._con_out = kwargs.get('con_out') self._con_out = kwargs.get('con_out')
# Reference frequency (unique for all parameters: beta2, beta3, gamma, effective_area)
if 'ref_wavelength' in kwargs: if 'ref_wavelength' in kwargs:
self._ref_wavelength = kwargs['ref_wavelength'] self._ref_wavelength = kwargs['ref_wavelength']
self._ref_frequency = c / self._ref_wavelength self._ref_frequency = c / self._ref_wavelength
@@ -148,6 +175,8 @@ class FiberParams(Parameters):
else: else:
self._ref_wavelength = 1550e-9 # conventional central C band wavelength [m] self._ref_wavelength = 1550e-9 # conventional central C band wavelength [m]
self._ref_frequency = c / self._ref_wavelength self._ref_frequency = c / self._ref_wavelength
# Chromatic Dispersion
self._dispersion = kwargs['dispersion'] # s/m/m self._dispersion = kwargs['dispersion'] # s/m/m
self._dispersion_slope = \ self._dispersion_slope = \
kwargs.get('dispersion_slope', -2 * self._dispersion / self.ref_wavelength) # s/m/m/m kwargs.get('dispersion_slope', -2 * self._dispersion / self.ref_wavelength) # s/m/m/m
@@ -157,10 +186,13 @@ class FiberParams(Parameters):
# (accessed on 25 March 2018) (2005). # (accessed on 25 March 2018) (2005).
self._beta3 = ((self.dispersion_slope - (4*pi*c/self.ref_wavelength**3) * self.beta2) / self._beta3 = ((self.dispersion_slope - (4*pi*c/self.ref_wavelength**3) * self.beta2) /
(2*pi*c/self.ref_wavelength**2)**2) (2*pi*c/self.ref_wavelength**2)**2)
# Effective Area and Nonlinear Coefficient
self._effective_area = kwargs.get('effective_area') # m^2 self._effective_area = kwargs.get('effective_area') # m^2
self._n1 = 1.468 self._n1 = 1.468
self._core_radius = 4.2e-6 # m
n2 = 2.6e-20 # m^2/W n2 = 2.6e-20 # m^2/W
if self._effective_area: if self._effective_area is not None:
self._gamma = kwargs.get('gamma', 2 * pi * n2 / (self.ref_wavelength * self._effective_area)) # 1/W/m self._gamma = kwargs.get('gamma', 2 * pi * n2 / (self.ref_wavelength * self._effective_area)) # 1/W/m
elif 'gamma' in kwargs: elif 'gamma' in kwargs:
self._gamma = kwargs['gamma'] # 1/W/m self._gamma = kwargs['gamma'] # 1/W/m
@@ -168,16 +200,37 @@ class FiberParams(Parameters):
else: else:
self._gamma = 0 # 1/W/m self._gamma = 0 # 1/W/m
self._effective_area = 83e-12 # m^2 self._effective_area = 83e-12 # m^2
default_raman_efficiency = {'cr': CR_NORM / self._effective_area, 'frequency_offset': FREQ_OFFSET} self._contrast = 0.5 * (c / (2 * pi * self.ref_frequency * self._core_radius * self._n1) * exp(
self._raman_efficiency = kwargs.get('raman_efficiency', default_raman_efficiency) pi * self._core_radius ** 2 / self._effective_area)) ** 2
# Raman Gain Coefficient
raman_coefficient = kwargs.get('raman_coefficient', DEFAULT_RAMAN_COEFFICIENT)
self._g0 = asarray(raman_coefficient['g0'])
raman_reference_frequency = raman_coefficient['reference_frequency']
frequency_offset = asarray(raman_coefficient['frequency_offset'])
stokes_wave = raman_reference_frequency - frequency_offset
gamma_raman = self._g0 * self.effective_area_overlap(stokes_wave, raman_reference_frequency)
normalized_gamma_raman = gamma_raman / raman_reference_frequency # 1 / m / W / Hz
self._raman_reference_frequency = raman_reference_frequency
# Raman gain coefficient array of the frequency offset constructed such that positive frequency values
# represent a positive power transfer from higher frequency and vice versa
frequency_offset = append(-flip(frequency_offset[1:]), frequency_offset)
normalized_gamma_raman = append(- flip(normalized_gamma_raman[1:]), normalized_gamma_raman)
self._raman_coefficient = RamanGainCoefficient(normalized_gamma_raman, frequency_offset)
# Polarization Mode Dispersion
self._pmd_coef = kwargs['pmd_coef'] # s/sqrt(m) self._pmd_coef = kwargs['pmd_coef'] # s/sqrt(m)
# Loss Coefficient
if isinstance(kwargs['loss_coef'], dict): if isinstance(kwargs['loss_coef'], dict):
self._loss_coef = asarray(kwargs['loss_coef']['value']) * 1e-3 # lineic loss dB/m self._loss_coef = asarray(kwargs['loss_coef']['value']) * 1e-3 # lineic loss dB/m
self._f_loss_ref = asarray(kwargs['loss_coef']['frequency']) # Hz self._f_loss_ref = asarray(kwargs['loss_coef']['frequency']) # Hz
else: else:
self._loss_coef = asarray(kwargs['loss_coef']) * 1e-3 # lineic loss dB/m self._loss_coef = asarray(kwargs['loss_coef']) * 1e-3 # lineic loss dB/m
self._f_loss_ref = asarray(self._ref_frequency) # Hz self._f_loss_ref = asarray(self._ref_frequency) # Hz
self._lumped_losses = kwargs['lumped_losses'] if 'lumped_losses' in kwargs else [] # Lumped Losses
self._lumped_losses = kwargs['lumped_losses'] if 'lumped_losses' in kwargs else array([])
self._latency = self._length / (c / self._n1) # s self._latency = self._length / (c / self._n1) # s
except KeyError as e: except KeyError as e:
raise ParametersError(f'Fiber configurations json must include {e}. Configuration: {kwargs}') raise ParametersError(f'Fiber configurations json must include {e}. Configuration: {kwargs}')
@@ -231,6 +284,17 @@ class FiberParams(Parameters):
def gamma(self): def gamma(self):
return self._gamma return self._gamma
def effective_area_scaling(self, frequency):
V = 2 * pi * frequency / c * self._core_radius * self._n1 * sqrt(2 * self._contrast)
w = self._core_radius / sqrt(log(V))
return asarray(pi * w ** 2)
def effective_area_overlap(self, frequency_stokes_wave, frequency_pump):
effective_area_stokes_wave = self.effective_area_scaling(frequency_stokes_wave)
effective_area_pump = self.effective_area_scaling(frequency_pump)
return squeeze(outer(effective_area_stokes_wave, ones(effective_area_pump.size)) + outer(
ones(effective_area_stokes_wave.size), effective_area_pump)) / 2
@property @property
def pmd_coef(self): def pmd_coef(self):
return self._pmd_coef return self._pmd_coef
@@ -260,8 +324,8 @@ class FiberParams(Parameters):
return self._f_loss_ref return self._f_loss_ref
@property @property
def raman_efficiency(self): def raman_coefficient(self):
return self._raman_efficiency return self._raman_coefficient
@property @property
def latency(self): def latency(self):
@@ -271,10 +335,16 @@ class FiberParams(Parameters):
dictionary = super().asdict() dictionary = super().asdict()
dictionary['loss_coef'] = self.loss_coef * 1e3 dictionary['loss_coef'] = self.loss_coef * 1e3
dictionary['length_units'] = 'm' dictionary['length_units'] = 'm'
if not self.lumped_losses: if len(self.lumped_losses) == 0:
dictionary.pop('lumped_losses') dictionary.pop('lumped_losses')
if not self.raman_efficiency: if not self.raman_coefficient:
dictionary.pop('raman_efficiency') dictionary.pop('raman_coefficient')
else:
raman_frequency_offset = \
self.raman_coefficient.frequency_offset[self.raman_coefficient.frequency_offset >= 0]
dictionary['raman_coefficient'] = {'g0': self._g0.tolist(),
'frequency_offset': raman_frequency_offset.tolist(),
'reference_frequency': self._raman_reference_frequency}
return dictionary return dictionary

5
gnpy/core/science_utils.py
View File

@@ -26,6 +26,7 @@ from gnpy.core.info import SpectralInformation
logger = getLogger(__name__) logger = getLogger(__name__)
sim_params = SimParams() sim_params = SimParams()
def raised_cosine_comb(f, *carriers): def raised_cosine_comb(f, *carriers):
"""Returns an array storing the PSD of a WDM comb of raised cosine shaped """Returns an array storing the PSD of a WDM comb of raised cosine shaped
channels at the input frequencies defined in array f channels at the input frequencies defined in array f
@@ -191,13 +192,13 @@ class RamanSolver:
# calculate ase power # calculate ase power
ase = zeros(spectral_info.number_of_channels) ase = zeros(spectral_info.number_of_channels)
cr = fiber.cr(srs.frequency)[:spectral_info.number_of_channels, spectral_info.number_of_channels:]
for i, pump in enumerate(fiber.raman_pumps): for i, pump in enumerate(fiber.raman_pumps):
pump_power = srs.power_profile[spectral_info.number_of_channels + i, :] pump_power = srs.power_profile[spectral_info.number_of_channels + i, :]
df = pump.frequency - frequency df = pump.frequency - frequency
eta = - 1 / (1 - exp(h * df / (k * fiber.temperature))) eta = - 1 / (1 - exp(h * df / (k * fiber.temperature)))
cr = fiber._cr_function(df)
integral = trapz(pump_power / channels_loss, z, axis=1) integral = trapz(pump_power / channels_loss, z, axis=1)
ase += 2 * h * baud_rate * frequency * (1 + eta) * cr * (df > 0) * integral # 2 factor for double pol ase += 2 * h * baud_rate * frequency * (1 + eta) * cr[:, i] * (df > 0) * integral # 2 factor for double pol
return ase return ase
@staticmethod @staticmethod

12
gnpy/tools/json_io.py
View File

@@ -21,6 +21,7 @@ from gnpy.core.exceptions import ConfigurationError, EquipmentConfigError, Netwo
from gnpy.core.science_utils import estimate_nf_model from gnpy.core.science_utils import estimate_nf_model
from gnpy.core.info import Carrier from gnpy.core.info import Carrier
from gnpy.core.utils import automatic_nch, automatic_fmax, merge_amplifier_restrictions from gnpy.core.utils import automatic_nch, automatic_fmax, merge_amplifier_restrictions
from gnpy.core.parameters import DEFAULT_RAMAN_COEFFICIENT
from gnpy.topology.request import PathRequest, Disjunction, compute_spectrum_slot_vs_bandwidth from gnpy.topology.request import PathRequest, Disjunction, compute_spectrum_slot_vs_bandwidth
from gnpy.tools.convert import xls_to_json_data from gnpy.tools.convert import xls_to_json_data
from gnpy.tools.service_sheet import read_service_sheet from gnpy.tools.service_sheet import read_service_sheet
@@ -163,9 +164,14 @@ class Fiber(_JsonThing):
def __init__(self, **kwargs): def __init__(self, **kwargs):
self.update_attr(self.default_values, kwargs, self.__class__.__name__) self.update_attr(self.default_values, kwargs, self.__class__.__name__)
for optional in ['gamma', 'raman_efficiency']: if 'gamma' in kwargs:
if optional in kwargs: setattr(self, 'gamma', kwargs['gamma'])
setattr(self, optional, kwargs[optional]) if 'raman_efficiency' in kwargs:
raman_coefficient = kwargs['raman_efficiency']
cr = raman_coefficient.pop('cr')
raman_coefficient['g0'] = cr
raman_coefficient['reference_frequency'] = DEFAULT_RAMAN_COEFFICIENT['reference_frequency']
setattr(self, 'raman_coefficient', raman_coefficient)
class RamanFiber(Fiber): class RamanFiber(Fiber):

192
tests/data/test_lumped_losses_fiber_no_pumps.csv
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@@ -1,96 +1,96 @@
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0.001,0.0007076761917572053,0.0005453602635114508,0.00033782470837237906,0.000210615455710121,0.00013185447915895794,8.27668684152456e-05,5.2042123556757356e-05,3.275827635495e-05,2.0633982733786933e-05 0.001,0.0007074456886869565,0.0005451259321489777,0.0003375886977799707,0.0002104309633113142,0.00013172382748945162,8.26787581753205e-05,5.198427834496719e-05,3.2720889139431744e-05,2.061004372929991e-05
0.001,0.0007070821383469261,0.0005447641661727003,0.00033724305715188347,0.00021016953749834074,0.00013154248636368206,8.255804467261117e-05,5.190567535780167e-05,3.267034700418964e-05,2.057778671334095e-05 0.001,0.0007068321308093747,0.0005445103448031184,0.0003369882331820464,0.00020997072442018424,0.00013140186125447996,8.246327876643199e-05,5.18434894660219e-05,3.2630165945308436e-05,2.055206350992823e-05
0.001,0.0007064944394093131,0.0005441746082424759,0.0003366681818805846,0.00020972900481740832,0.0001312343457694376,8.23518347963793e-05,5.177094971942625e-05,3.258353364113958e-05,2.052230632028257e-05 0.001,0.0007062249619162967,0.0005439013413874698,0.00033639461163356655,0.00020951593375373114,0.00013108379599680725,8.225044792981545e-05,5.170444666951808e-05,3.254057423900392e-05,2.0494808961820763e-05
0.001,0.0007059130767664236,0.0005435915660676415,0.0003361000459642789,0.0002092938228581225,0.00013093002997966176,8.2148219149105e-05,5.163793327020207e-05,3.249782742847868e-05,2.0467535817470585e-05 0.001,0.00070562416964951,0.0005432989038865089,0.00033580780152024723,0.000209066560051587,0.00013076960667953474,8.204024755412909e-05,5.156713755893784e-05,3.2452105802317056e-05,2.0438274739819433e-05
0.001,0.0007053380324034396,0.000543015016222211,0.00033553861321181615,0.00020886395722383082,0.0001306295119366351,8.194717834254395e-05,5.150661281108238e-05,3.2413219643935075e-05,2.0413469542910655e-05 0.001,0.0007050297417863318,0.0005427030144775926,0.0003352277715833316,0.00020862257242375786,0.00013045926857287288,8.183265975513502e-05,5.143154989537623e-05,3.236475252244954e-05,2.038245556769746e-05
0.001,0.0007047693713997632,0.0005424450181544463,0.0003349839271155604,0.00020843943405997803,0.00013033280670757434,8.174872108664716e-05,5.1376993463287795e-05,3.232971335519107e-05,2.036010936323693e-05 0.001,0.0007044417829621171,0.000542113771820027,0.0003346546023905152,0.00020818402485557293,0.00013015281596227648,8.162770599537212e-05,5.129769708700084e-05,3.227852278867728e-05,2.032735670561579e-05
0.001,0.0007042070758264602,0.0005418815485793469,0.0003344359518126676,0.0002080202193468777,0.00013003988756313126,8.155282824274543e-05,5.12490622037789e-05,3.224729995882104e-05,2.0307449694639518e-05 0.001,0.0007038602809995153,0.0005415311581470623,0.00033408826282687573,0.00020775088664703998,0.00012985022429639665,8.142536853201701e-05,5.116556699709043e-05,3.219340855821242e-05,2.0272972923816322e-05
0.001,0.0007036511279085835,0.0005413245844265016,0.00033389465182139466,0.00020760627945491382,0.00012975072809427607,8.135948090770287e-05,5.1122806172421973e-05,3.216597096011364e-05,2.0255485024312964e-05 0.001,0.0007032852238493367,0.0005409551558747416,0.0003335287221154158,0.00020732312745044945,0.00012955146931603386,8.122562983828357e-05,5.103514763884953e-05,3.2109401888525124e-05,2.0219299058105656e-05
0.001,0.0007031015100243787,0.0005407741028387587,0.00033335999203785527,0.00020719758114069915,0.00012946530220888763,8.116866041129419e-05,5.0998212670124914e-05,3.208571797180734e-05,2.020420990962221e-05 0.001,0.0007027165995902542,0.0005403857476012292,0.00033297594981485264,0.00020690071726752732,0.0001292565270515082,8.102847260135871e-05,5.090642717392409e-05,3.202649493632917e-05,2.0166330009182866e-05
0.001,0.0007025584243776222,0.0005402303025747015,0.0003328321561526746,0.0002067942601549092,0.00012918370261533042,8.098042783792958e-05,5.087532120941395e-05,3.200656629292941e-05,2.015364045325786e-05 0.001,0.0007021546252265139,0.0005398231466905069,0.00033243014325207994,0.00020648380199548492,0.0001289654971719027,8.083396250547584e-05,5.077944809587575e-05,3.194471491165552e-05,2.0114083097018264e-05
0.001,0.0007020218531757066,0.0005396931605409548,0.00033231110871733115,0.00020639628295911018,0.00012890590300828107,8.079476437365657e-05,5.0754118997337993e-05,3.192850747587868e-05,2.010377117411101e-05 0.001,0.0007015992888126556,0.0005392673356287205,0.0003318912717105927,0.00020607235137144977,0.0001286783555062261,8.06420820982489e-05,5.065419847368218e-05,3.186405391103787e-05,2.006255318369691e-05
0.001,0.0007014917787802405,0.0005391626538584482,0.00033179681466138206,0.0002060036164003213,0.00012863187739818246,8.061165143636152e-05,5.063459340114707e-05,3.1851533179901e-05,2.0054596660832207e-05 0.001,0.0007010505785332368,0.0005387182970867077,0.00033135930481238495,0.00020566633548374424,0.0001283950781739038,8.04528141416734e-05,5.053066652493675e-05,3.1784504130324826e-05,2.001173519622426e-05
0.001,0.0007009681837062737,0.0005386387598611086,0.00033128923928927895,0.00020561622770725436,0.00012836160010791048,8.043107067321032e-05,5.051673194647494e-05,3.177563516985718e-05,2.0006111571018593e-05 0.001,0.000700508482702546,0.0005381760139193414,0.00033083421251577956,0.00020526572476908743,0.000128115641582197,8.026614161010359e-05,5.040884061439589e-05,3.170605786368809e-05,1.9961624125869314e-05
0.001,0.0007004510506215145,0.0005381214560945621,0.0003307883482772016,0.00020523408448658455,0.00012809504576947474,8.02530039581159e-05,5.040052231554307e-05,3.1700805315005306e-05,1.9958310630411395e-05 0.001,0.0006999729897643184,0.0005376404691648787,0.00033031596511327947,0.00020487049000982136,0.00012784002242364822,8.008204768824872e-05,5.028870925253973e-05,3.1628707502639986e-05,1.9912215027513866e-05
0.001,0.0006999403623455618,0.0005376107203148404,0.00033029410766993267,0.0002048571547192701,0.00012783218932076133,8.007743338923888e-05,5.028595234538582e-05,3.162703558779805e-05,1.9911188632102376e-05 0.001,0.0006994440882914515,0.0005371116460443127,0.0003298045332294241,0.0002044806023311588,0.0001275681976735486,7.990051576919067e-05,5.0170261094149856e-05,3.155244553506268e-05,1.9863503019009735e-05
0.001,0.0006994318218007185,0.0005371022748594453,0.00032980242468128913,0.00020448233902526256,0.00012757087888919618,7.990292563076895e-05,5.0172088326043745e-05,3.155372544493714e-05,1.9864362230287972e-05 0.001,0.0006989172535560769,0.0005365850419669894,0.000329295612913881,0.00020409280438264766,0.00012729790663346764,7.972004038787022e-05,5.0052515589855926e-05,3.147664135129994e-05,1.9815085586326718e-05
0.001,0.0006989254227893593,0.0005365961117498912,0.0003293132869081722,0.00020410962547078523,0.00012731110500315576,7.97294738559706e-05,5.005892559260895e-05,3.148087179640125e-05,1.9817829416090883e-05 0.001,0.0006983924815322933,0.0005360606511477293,0.0003287891938968759,0.00020370708584067882,0.00012702914094133737,7.954061545150935e-05,4.993546855036179e-05,3.140129216611291e-05,1.9766960914662127e-05
0.001,0.0006984211591086833,0.0005360922230056121,0.000328826681963312,0.00020373900214896472,0.00012705285821815147,7.955707126027153e-05,4.994645949643471e-05,3.140847156340457e-05,1.977158818812823e-05 0.001,0.0006978697681747856,0.000535538467781285,0.000328285265898271,0.00020332343638453147,0.00012676189224377013,7.936223487646896e-05,4.981911579385119e-05,3.1326395199731696e-05,1.971912719297887e-05
0.001,0.0006979190245507289,0.0005355906006439992,0.00032834259747533784,0.00020337045717987526,0.00012679612911684654,7.938571106126266e-05,4.9834685405128945e-05,3.133652167839011e-05,1.9725636552505712e-05 0.001,0.0006973491094188342,0.0005350184860423863,0.0003277838186277228,0.0002029418456965422,0.0001264961521961901,7.918489258833681e-05,4.970345314604528e-05,3.125194767789152e-05,1.967158261402843e-05
0.001,0.0006974169140024356,0.0005350891259362026,0.00032785895021137046,0.00020300238558847177,0.00012653979122008349,7.921463777328526e-05,4.9723109049032396e-05,3.126470337114675e-05,1.9679770678755685e-05 0.001,0.0006968284099289676,0.000534498597351666,0.00032728277956097635,0.00020256071739357045,0.0001262308004979493,7.900783730702533e-05,4.958798911408524e-05,3.1177632631488905e-05,1.9624124503146986e-05
0.001,0.0006969148254151754,0.0005345877966909636,0.0003273757374134148,0.00020263478480404742,0.000126283842486217,7.904384991646645e-05,4.9611729411717634e-05,3.1193015965876866e-05,1.9633990126415905e-05 0.001,0.0006963076677967887,0.0005339787996978908,0.00032678214616920946,0.00020218004908417888,0.0001259658352285323,7.883106763041168e-05,4.947272273059253e-05,3.110344941552445e-05,1.957675243920473e-05
0.001,0.0006964127567206359,0.0005340866106918127,0.000326892956287765,0.00020226765222458769,0.000126028280850426,7.887334599501782e-05,4.950054546622813e-05,3.112145877995457e-05,1.9588294450645086e-05 0.001,0.0006957868810888677,0.0005334590910383602,0.00032628191587964447,0.00020179983833834103,0.00012570125443876325,7.865458213663353e-05,4.935765301510161e-05,3.102939737649859e-05,1.9529465995615212e-05
0.001,0.0006959107058309331,0.0005335855656972701,0.00032641060400549705,0.0002019009852173301,0.00012577310422519486,7.870312449759832e-05,4.938955617533216e-05,3.105003112409614e-05,1.9542683202334784e-05 0.001,0.0006952660478468047,0.0005329394692990367,0.0003257820860759608,0.00020142008268794192,0.0001254370561512512,7.847837938443003e-05,4.924277897429975e-05,3.0955475852573814e-05,1.94822647404421e-05
0.001,0.0006954086706387427,0.0005330846594410641,0.0003259286777029715,0.0002015347811193284,0.00012551831050079858,7.853318389767884e-05,4.927876049177822e-05,3.0978732302531976e-05,1.9497155928222272e-05 0.001,0.00069474516608728,0.0005324199323746772,0.00032528265409871067,0.0002010407796272818,0.0001251732383608348,7.830245791348127e-05,4.91280996022673e-05,3.088168417373684e-05,1.943514823650581e-05
1 0.001 0.0007532367899292016 0.0007537739940510926 0.0005915557166626927 0.0005921033539224395 0.00038408772212450497 0.000384643453726324 0.0002466727384040634 0.00024710943492700773 0.00015734746299754382 0.00015765779381207088 9.994300566924636e-05 0.00010015276356884663 6.33121782843872e-05 6.345008665672524e-05 4.004003600460594e-05 4.012925332336102e-05 2.5295530132384264e-05 2.535268958174273e-05
2 0.001 0.0007527027569114317 0.0007532443480762404 0.000591008714088151 0.0005915606741358828 0.00038352599237371365 0.00038408573214392957 0.00024622798333442106 0.00024666764094038643 0.0001570298910751132 0.00015734224135307426 9.972770370845924e-05 9.993879038586218e-05 6.317035443958771e-05 6.330912140259024e-05 3.994817502675088e-05 4.003794165686942e-05 2.5236632189663175e-05 2.529414108865862e-05
3 0.001 0.0007521693300508858 0.0007527148738848327 0.0005904624672772725 0.0005910183036445593 0.00038296538462781346 0.0003835286706438232 0.00024578429034045 0.0002462265418760313 0.0001567131560243821 0.00015702726304657165 9.951300275831495e-05 9.972523988324127e-05 6.302894048828063e-05 6.316844848796591e-05 3.9856585263472816e-05 3.994682503096186e-05 2.5177910454727035e-05 2.5235719950422228e-05
4 0.001 0.0007516365068997756 0.0007521855703345118 0.0005899169734839468 0.0005904762413907948 0.0003824058950962419 0.00038297226807207136 0.00024534165572340287 0.0002457861365766504 0.00015639725482879884 0.00015671285789580896 9.929890059644242e-05 9.9512111295817e-05 6.288793488850427e-05 6.302806736731544e-05 3.976526568300959e-05 3.985590307420444e-05 2.5119364252406752e-05 2.5177425921282973e-05
5 0.001 0.0007511042849865948 0.0007516564362539944 0.0005893722299298781 0.0005899344862777934 0.00038184751993553635 0.00038241652320864807 0.0002449000757332959 0.0002453464238190796 0.00015608218443128777 0.00015639902485083639 9.90853949714062e-05 9.929940381972637e-05 6.274733607851798e-05 6.288797746852316e-05 3.967421524053716e-05 3.976517539750482e-05 2.5060992899059294e-05 2.511925874398998e-05
6 0.001 0.0007505726618162419 0.0007511274704431355 0.0005888282338048205 0.0005893930371697809 0.00038129025524998993 0.0003818614347679428 0.0002444595465697164 0.0002449074023149522 0.000155767941734979 0.00015608576280913723 9.887248360327204e-05 9.908711661290977e-05 6.260714247720704e-05 6.27481781932559e-05 3.958343287844485e-05 3.967464159435973e-05 2.500279570274923e-05 2.5061218149955632e-05
7 0.001 0.0007500416348701675 0.0007505986716729718 0.0005882849822668305 0.0005888518928921378 0.0003807340970923119 0.00038130700139925517 0.0002440200643826254 0.0002444690707113658 0.00015545452360392976 0.00015577307061625246 9.866016418416756e-05 9.887524879560432e-05 6.246735248448249e-05 6.260866891731864e-05 3.949291752660743e-05 3.958430124109855e-05 2.4944771963428786e-05 2.5003303859417958e-05
8 0.001 0.0007495090858251885 0.0007500678710880661 0.0005877403241312353 0.0005883088511431218 0.00038017688268912993 0.000380751009486411 0.00024357993927957565 0.0002440296996901373 0.0001551407333476893 0.00015545972380501664 9.844762961169771e-05 9.866297459432463e-05 6.232743618144161e-05 6.246890748950651e-05 3.940232665622857e-05 3.949380391318895e-05 2.488670234861411e-05 2.4945291488462056e-05
9 0.001 0.0007489750173390955 0.0007495350705096461 0.0005871942634186001 0.0005877639154939723 0.00037961861900225594 0.000380193466563788 0.0002431391779090945 0.00024358929686534952 0.00015482657614637553 0.00015514572847430736 9.823488356157069e-05 9.8450298405315e-05 6.218739605181586e-05 6.232889690863924e-05 3.931166190013138e-05 3.9403151593579556e-05 2.4828587914583644e-05 2.4887182324516918e-05
10 0.001 0.0007484394320489054 0.0007490002717484986 0.000586646804117398 0.0005872170894971442 0.0003790593129257516 0.00037963438011605534 0.00024269778684510537 0.00024314786979147204 0.00015451205711655025 0.00015483109067004745 9.80219296618084e-05 9.823722458416644e-05 6.204723454596647e-05 6.218864014470764e-05 3.922092486871355e-05 3.931234624568946e-05 2.4770429702906434e-05 2.4828977642138712e-05
11 0.001 0.0007479023325710157 0.0007484634766050366 0.0005860979501842716 0.000586668376686434 0.0003784989712866058 0.0003790737575785661 0.00024225577258776658 0.00024270542596389042 0.00015419718131198063 0.0001545158163857142 9.780877149334108e-05 9.802375744622405e-05 6.190695408131256e-05 6.204814013916893e-05 3.913011715023943e-05 3.9221389813611975e-05 2.471222874063607e-05 2.4770678703146694e-05
12 0.001 0.0007473589501028839 0.0007479199274773811 0.0005855428054911819 0.0005861128912299699 0.00037793253622143226 0.0003785065486556895 0.00024180911527200605 0.00024225795000622747 0.0001538790714407034 0.0001541970308568484 9.759345536126727e-05 9.780794472023814e-05 6.176526646718061e-05 6.190610951865765e-05 3.903840385562049e-05 3.9129447888412384e-05 2.4653449519288003e-05 2.471175028700643e-05
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0.001,0.000713506795259252,0.0005528220286453138,0.00035169279399565494,0.00022975624467522356,0.00015582695501446453,0.00011196956402493538,8.836753049012399e-05,8.174493812638424e-05,0.00010037461898917915 0.001,0.0007136243377311475,0.0005530309085687206,0.00035228649684153725,0.0002307529751854999,0.00015726269010384348,0.00011397837465469773,9.134692457668887e-05,8.694108196461404e-05,0.00011300335424584403
0.001,0.0007128565960345945,0.000552151430895953,0.0003509603274307335,0.00022909133782670745,0.00015522923567548894,0.00011139498872248412,8.774020391605217e-05,8.090698006040516e-05,9.88183119171757e-05 0.001,0.0007129540935643617,0.0005523396647985742,0.000351531107363628,0.0002300661353432777,0.0001566429968410848,0.00011337824146720777,9.06820803490277e-05,8.602718880727694e-05,0.00011120223452206596
0.001,0.000712216864792176,0.0005514925705077819,0.0003502439423799476,0.00022844416108975426,0.0001546502536108915,0.00011084054514768278,8.713560862370903e-05,8.009669495978735e-05,9.729798207441842e-05 0.001,0.0007122949300063586,0.0005516609446370161,0.0003507932546232374,0.0002293990178082647,0.00015604454205901748,0.00011280134646728481,9.004396195380538e-05,8.514641523915274e-05,0.00010944396621311202
0.001,0.0007115836310284798,0.0005508404954145605,0.0003495351144497066,0.00022780378080733367,0.0001540772565128698,0.00011029184289346583,8.653767235071846e-05,7.929694151393572e-05,9.580416231533655e-05 0.001,0.0007116422850379047,0.0005509890404263221,0.00035006301886912226,0.00022873877079420107,0.00015545216193435095,0.00011223032450910806,8.941277122627028e-05,8.427699754143763e-05,0.00010771649233683978
0.001,0.0007109568750206494,0.0005501951791779016,0.00034883379721143597,0.00022717014341128397,0.0001535101877933934,0.00010974881856197515,8.594630990981442e-05,7.850756016455259e-05,9.433633928045583e-05 0.001,0.0007109961448775265,0.0005503239315436604,0.00034934035900275196,0.00022808534468017605,0.00015486580256638324,0.00011166511373672321,8.878842266662507e-05,8.341876815036907e-05,0.00010601922394974779
0.001,0.0007103366610426475,0.0005495566796872158,0.000348140027937795,0.00022654326241267482,0.00015294904184934578,0.00010921144812810428,8.53614689629981e-05,7.772842391839216e-05,9.289404738934854e-05 0.001,0.0007103566138672342,0.0005496657159311855,0.0003486253516297722,0.00022743878433373037,0.00015428548213839886,0.00011110570832676001,8.81708780194749e-05,8.25716070442755e-05,0.00010435164299643007
0.001,0.0007097229694529674,0.0005489249706454162,0.0003474537605956092,0.00022592308482555123,0.00015239376283586453,0.00010867966921494878,8.47830660818878e-05,7.695937762456762e-05,9.147679298288125e-05 0.001,0.0007097236782517396,0.0005490143729973408,0.0003479179557857281,0.0002267990403962617,0.0001537111471536449,0.00011055204708289399,8.756005313455197e-05,8.173535064610718e-05,0.0001027131817052566
0.001,0.0007091157807724816,0.0005483000259883205,0.0003467749496226811,0.00022530955825471799,0.0001518442955755718,0.00010815342025333767,8.421101904463253e-05,7.620026875215025e-05,9.008409282780762e-05 0.001,0.0007090973244087274,0.0005483698823458906,0.00034721813091834777,0.000226166064037726,0.00015314274472297157,0.00011000406955950422,8.695586501935841e-05,8.090983803329783e-05,0.00010110328401062253
0.001,0.0007085150756775001,0.0005476818198739591,0.00034610354990018024,0.00022470263085467898,0.0001513005855077949,0.00010763264042079258,8.364524675486821e-05,7.545094724756281e-05,8.871547365198851e-05 0.001,0.0007084775388425413,0.0005477322237660661,0.0003465258368613831,0.00022553980691704316,0.00015258022251510344,0.00010946171600087524,8.635823175652435e-05,8.00949107862898e-05,9.952140501322636e-05
0.001,0.0007079210577411203,0.0005470705529810189,0.00034543974857437215,0.00022410244135876906,0.00015076272549988576,0.00010711738419088767,8.308576484396013e-05,7.471135773791005e-05,8.737059031219478e-05 0.001,0.0007078645400735427,0.0005471016129101978,0.0003458412757255009,0.00022492042013778988,0.00015202368335334558,0.00010892504915290583,8.57671759148139e-05,7.929051589048381e-05,9.796715152670613e-05
0.001,0.0007073337074276685,0.0005464661991670638,0.0003447835000304132,0.00022350893742958665,0.00015023066059546475,0.00010660759053652677,8.253249249775969e-05,7.398135232542972e-05,8.604898363826276e-05 0.001,0.0007072583145347741,0.0005464780293804963,0.00034516440688163317,0.00022430785483116316,0.0001514730744232032,0.00010839400891826328,8.518261565996433e-05,7.84964968569563e-05,9.643999410136803e-05
0.001,0.0007067530053640319,0.0005458687325221291,0.0003441347591201103,0.00022292206731207902,0.00014970433649328627,0.00010610319921945063,8.198535007121053e-05,7.326078561528489e-05,8.475020417489323e-05 0.001,0.0007066588487940405,0.000545861452976259,0.0003444951901125997,0.00022370206265319418,0.00015092834351042303,0.00010786853593723236,8.460447028623623e-05,7.771269974698105e-05,9.493941424410264e-05
0.001,0.0007061884918267415,0.0005452901895090845,0.0003435150570304173,0.0002223706511768525,0.0001492190839480495,0.00010564681060307159,8.14973775620633e-05,7.262157722718122e-05,8.35887757760078e-05 0.001,0.0007060768113835355,0.0005452653567881146,0.00034385780916102886,0.0002231355522451839,0.0001504295746545273,0.00010739737531972382,8.409424224945842e-05,7.702434963545377e-05,9.360618912201228e-05
0.001,0.0007056305731246326,0.0005447184570616724,0.0003429026954804163,0.00022182562190480802,0.00014873922974729557,0.00010519532466723484,8.101471604563165e-05,7.199020130947719e-05,8.244600742971829e-05 0.001,0.0007055014939207988,0.0005446762036784223,0.00034322791998477503,0.0002225755642266546,0.00014993632274793293,0.00010693123855410087,8.358951092264173e-05,7.634435339541097e-05,9.22944227993744e-05
0.001,0.0007050792306062142,0.0005441535103689846,0.00034229763198919826,0.0002212869318272295,0.0001482647252612499,0.00010474868943508509,8.053730000430466e-05,7.136654438032707e-05,8.132155926679031e-05 0.001,0.0007049328836329239,0.0005440939744776738,0.0003426054850001757,0.00022202205441602206,0.000149448541485554,0.00010647007505716636,8.309021096715739e-05,7.567259318109089e-05,9.100372969350909e-05
0.001,0.0007045301221900643,0.0005435909997143166,0.0003416955774702988,0.0002207511658034612,0.00014779299519236796,0.00010430493948662974,8.006352324306286e-05,7.074908147590526e-05,8.02134407838912e-05 0.001,0.0007043664108869901,0.0005435140913972429,0.00034198598587466614,0.00022147141590047654,0.00014896349766232102,0.00010601178096740148,8.259459730216135e-05,7.500736724232048e-05,8.973176442333615e-05
0.001,0.0007039832411301605,0.0005430309161342644,0.00034109651599274326,0.00022021830506410305,0.00014732401920344648,0.00010386405111494137,7.959335168171117e-05,7.013774197495506e-05,7.912139691327875e-05 0.001,0.0007038020712072354,0.0005429365478183962,0.00034136940918446675,0.0002209236320980648,0.00014848117283038098,0.00010555633431743894,8.210263744821714e-05,7.434860458126827e-05,8.847824135377829e-05
0.001,0.0007034385806771556,0.0005424732506674768,0.0003405004316631486,0.0002196883309178196,0.0001468577770769967,0.00010342600079620511,7.912675157408064e-05,6.953245613429904e-05,7.804517686317993e-05 0.001,0.0007032398600991381,0.0005423613371043419,0.00034075574151016483,0.0002203786864681782,0.00014800154862415601,0.00010510371328265478,8.161429921011451e-05,7.369623501807121e-05,8.724287943554497e-05
0.001,0.0007028961340756175,0.0005419179943504858,0.0003399073086151601,0.00021916122473559604,0.00014639424869575276,0.00010299076516650824,7.866368947795672e-05,6.89331550377599e-05,7.698453395110453e-05 0.001,0.0007026797730466592,0.0005417884525961108,0.00034014496942632764,0.0002198365624959981,0.00014752460674093983,0.00010465389615779026,8.112955064604149e-05,7.305018913687007e-05,8.602540201539488e-05
0.001,0.0007023537675633205,0.0005413629828746739,0.0003393149394667067,0.0002186351838182768,0.0001459320465988429,0.00010255726580290627,7.820326658601671e-05,6.833895931108437e-05,7.593823716905743e-05 0.001,0.0007021196874055633,0.0005412157406692665,0.0003395348927353949,0.00021929545761274023,0.00014704895219343257,0.00010420578859113516,8.064746506639534e-05,7.240953347459202e-05,8.482441661675139e-05
0.001,0.0007018114790026023,0.000540808213867173,0.0003387233205925344,0.00021811020358285168,0.0001454711649598321,0.00010212549409752022,7.774546638666711e-05,6.774982397495531e-05,7.490609035628451e-05 0.001,0.0007015596012323657,0.0005406431992153213,0.0003389255082691921,0.00021875536780717296,0.00014657457987728414,0.00010375938295149973,8.016802726693694e-05,7.17742234562459e-05,8.363970356449741e-05
0.001,0.0007012692662365054,0.0005402536849306273,0.00033813244833512493,0.00021758627942391746,0.00014501159794785406,0.00010169544146693108,7.729027246482876e-05,6.716570445567967e-05,7.388790009412214e-05 0.001,0.0007009995125587165,0.0005400708260946051,0.0003383168128172846,0.00021821628903476192,0.00014610148467187763,0.0001033146716169485,7.969122211456026e-05,7.114421486558773e-05,8.247104609297546e-05
0.001,0.000700715318007891,0.0005396844473492076,0.0003375153269484326,0.00021702692557975313,0.0001445081577421818,0.00010121198066811028,7.676796353167371e-05,6.648955946153636e-05,7.272015355307609e-05 0.001,0.000700426120570031,0.0005394817717340144,0.00033767827777106695,0.00021763678242090514,0.00014557802543177205,0.00010280805245363877,7.913535785684264e-05,7.040274681183516e-05,8.111172191387726e-05
0.001,0.0007001614595490124,0.0005391154759339646,0.0003368990409960972,0.00021646879983255348,0.00014400631993404095,0.00010073071571699363,7.624910690660519e-05,6.582015362379603e-05,7.157078178685282e-05 0.001,0.0006998527383823768,0.0005388929110101418,0.0003370405259007354,0.00021705847496470142,0.00014505616500334602,0.00010230367256069614,7.858312049704058e-05,6.966868896041247e-05,7.977423771714316e-05
0.0011743255603950788,0.002246592354465809,0.0027552987748036746,0.005194387759090383,0.009354521953613974,0.016511716182525175,0.029169668796905544,0.052621633705719505,0.09910396847768352,0.2 0.0010496228983614124,0.002020602352332682,0.0024834354692445607,0.004712247621156641,0.00854174709233096,0.015197203876184536,0.027142477151470907,0.049749449744480465,0.09589624880110185,0.2
0.00416057113135989,0.007185835867421053,0.008527170824088577,0.014669694613938154,0.02443642414582059,0.03975038656808106,0.06331729118004528,0.09842489957479945,0.14740458682357144,0.206 0.004235602571438949,0.007336805952289255,0.008714058005603317,0.015024716476258112,0.025061770718850837,0.04079161627542227,0.0649508755581543,0.10074574737193842,0.14993445055894863,0.206
1 0.001 0.0007642422168481868 0.0007651928200015371 0.0006059596816131985 0.0006070713937965498 0.0004124540545641951 0.00041423535558437785 0.00028743289525394683 0.0002897888610631573 0.00021033989197728514 0.00021340907299884933 0.0001677242405549143 0.00017198131811953097 0.00015448630019828015 0.00016127923791982795 0.00018105210067450352 0.00019503601408127412 0.00032006435169343127 0.00036549739730595273
2 0.001 0.0007636453684814074 0.0007645995806173374 0.0006053276391419513 0.0006064422887178694 0.0004117105797931639 0.00041349144049413134 0.0002867132476380171 0.00028906380160320487 0.0002096494018677202 0.00021270693331134475 0.0001670078900317705 0.0001712438796411692 0.00015362357483190292 0.0001603765556252246 0.0001797412011853683 0.00019363496213474675 0.0003172302656811063 0.00036239423700181427
3 0.001 0.0007630492230771192 0.0007640065772643761 0.0006046965215788323 0.0006058136176614386 0.0004109687952835337 0.00041274866023030733 0.000285995748681695 0.0002883403899406645 0.00020896149740334239 0.0002120069527025505 0.0001662949011112697 0.00017050944162049183 0.0001527659914515381 0.000159478716966613 0.0001784402430016445 0.00019224364342300643 0.00031442230320442356 0.0003593168093314386
4 0.001 0.0007624537779591674 0.0007634138087852742 0.0006040663257237133 0.000605185379478 0.00041022869520294497 0.0004120070127662441 0.00028528039002687875 0.0002876186224176594 0.00020827616693730144 0.00021130912485491145 0.0001655852558250782 0.00016977799240843955 0.00015191351650049715 0.0001585856969223786 0.0001771491455864909 0.00019086199210672448 0.0003116402083285432 0.0003562649023666937
5 0.001 0.0007618411150470453 0.0007628028346998721 0.0006034134049131552 0.0006045332187986192 0.0004094420572177278 0.0004112166712442958 0.00028449406848889933 0.0002868226029691562 0.00020749035347019792 0.00021050564591298308 0.00016472993112613538 0.0001688917074786142 0.00015082479786945245 0.00015743651373617922 0.00017537576485722448 0.00018893812235512526 0.0003073546770239319 0.0003514029291704815
6 0.001 0.0007612291751301334 0.0007621921143131398 0.0006027614491955141 0.0006038815297458003 0.0004086572704905903 0.00041042762231867933 0.0002837102537033676 0.00028602858816935265 0.000206707815868992 0.00020970502628600424 0.00016387931725086143 0.00016800982241073787 0.00014974419220665431 0.00015629534163924753 0.00017362055931489465 0.00018703308452519017 0.0003031290170068882 0.00034660591682276115
7 0.001 0.0007606179553856057 0.0007615816463554626 0.0006021104551256277 0.0006032303109890501 0.00040787432832054433 0.0004096398633263614 0.0002829289352705668 0.0002852365728026657 0.000205928537967639 0.00020890725596775628 0.00016303338540292863 0.00016713231600712825 0.00014867163523376724 0.00015516212643578583 0.00017188333586129508 0.00018514669874176095 0.00029896237140081527 0.00034187301148734186
8 0.001 0.0007600053015158586 0.0007609692265548555 0.0006014582116574244 0.000602577299493926 0.00040709088973388784 0.0004088509941253184 0.00028214811685008036 0.00028444450359523985 0.00020515088523411382 0.00020811064471691694 0.0001621907338278938 0.00016625772561605027 0.00014760575203092395 0.00015403541161623416 0.00017016233535919076 0.00018327705284413884 0.00029485108544654464 0.00033720006213702974
9 0.001 0.000759391216638964 0.0007603548574442921 0.0006008047235362061 0.0006019224999904981 0.00040630696329078226 0.0004080610253035772 0.0002813678065291968 0.00028365239238410656 0.00020437486190949568 0.0002073152021201705 0.00016135135725352056 0.00016538605339556675 0.00014654650960916002 0.00015291517504370476 0.00016845741882617118 0.00018142402424673905 0.0002907944492354505 0.0003325863766610152
10 0.001 0.0007587720586145543 0.0007597350402439383 0.0006001451360530016 0.0006012612395706585 0.0004055123728737174 0.0004072600935672035 0.0002805722092000019 0.0002828447987841907 0.0002035777116783122 0.00020649842223713097 0.00016048161721743102 0.00016448344741808307 0.00014543804327941403 0.00015174362495246272 0.00016665097495405112 0.00017946021794659197 0.0002864106919229302 0.00032758319657592714
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37,37,0.0001611915388800597,2.418575294396746e-08,1.5725354810318075e-07 0.0001837526367070107,2.754134148447891e-08,1.8217016915383534e-07
38,38,0.0001585906555197023,2.394366896238391e-08,1.5422386234766858e-07 0.00018076040324711669,2.727083294692081e-08,1.7859628342481742e-07
39,39,0.00015602870309712287,2.3703901303924173e-08,1.5124806354481474e-07 0.00017781203651926026,2.700283660776023e-08,1.7508570183600935e-07
40,40,0.0001535051551031788,2.3466423066629146e-08,1.4832528036973196e-07 0.0001749069829579267,2.673732582084065e-08,1.7163743894482027e-07
41,41,0.0001510212597387019,2.3231240549132765e-08,1.4545635735033158e-07 0.00017204677711063964,2.6474316260529432e-08,1.6825255881241437e-07
42,42,0.00014857641307701306,2.2998325757462813e-08,1.4264034538487909e-07 0.00016923077161388358,2.621377975863396e-08,1.6492997841294993e-07
43,43,0.00014617002011358969,2.276765094680208e-08,1.3987631140917146e-07 0.00016645832773934518,2.5955688359138642e-08,1.616686318088319e-07
44,44,0.00014368188097051016,2.252080862505795e-08,1.3704924606778587e-07 0.0001635796256227145,2.5677295228672648e-08,1.58323074374741e-07
45,45,0.00014123500136325632,2.227648394665253e-08,1.342768563640109e-07 0.00016074819635989725,2.5401698108435958e-08,1.5504245883909504e-07
46,46,0.00013882871459443323,2.2034644371118186e-08,1.31558126246417e-07 0.00015796330731148461,2.5128863163250677e-08,1.5182560446701375e-07
47,47,0.00013646341840215168,2.1795267893415555e-08,1.2889305332810146e-07 0.00015522551088837497,2.485877013840214e-08,1.486725711724886e-07
48,48,0.00013413841082142,2.155832207437193e-08,1.2628059500046815e-07 0.00015253402768104383,2.4591385101783994e-08,1.4558214032289282e-07
49,49,0.00013185300210097483,2.132377480769977e-08,1.2371972883222934e-07 0.00014988809102457626,2.4326674435323678e-08,1.4255311588294157e-07
50,50,0.00012960651423053282,2.1091594291962705e-08,1.2120945193769904e-07 0.00014728694646614827,2.4064604800471338e-08,1.395843236958893e-07
51,51,0.00012739961074346843,2.0861774576065466e-08,1.1875002031242091e-07 0.00014473141087959588,2.3805175801260087e-08,1.3667608361715406e-07
52,52,0.00012523156633255863,2.0634283235397628e-08,1.1634039147633555e-07 0.00014222067140781916,2.3548353146553164e-08,1.3382717097378918e-07
53,53,0.00012310166960708747,2.0409088189365173e-08,1.1397954484148417e-07 0.00013975393005081647,2.3294102880186107e-08,1.3103638594918425e-07
54,54,0.00012096529298347188,2.0178855168888422e-08,1.1162594309222065e-07 0.00013727002869537867,2.303248543234042e-08,1.282461473035948e-07
55,55,0.00011886718830718708,1.9950983760858723e-08,1.093208043434055e-07 0.0001348306664569764,2.2773547798807564e-08,1.255140638251511e-07
56,56,0.00011680664424128133,1.9725440474060072e-08,1.0695583879566178e-07 0.0001324350333325102,2.2517253805739807e-08,1.2272513552717813e-07
57,57,0.00011478396058700633,1.950220202088885e-08,1.0464196732373097e-07 0.00013008354253631936,2.2263580536243144e-08,1.1999714433894858e-07
58,58,0.00011279839933843229,1.9281235055452333e-08,1.0237806316789167e-07 0.00012777534792361698,2.2012491805819457e-08,1.1732875449839032e-07
59,59,0.00011084923776207272,1.906250658388995e-08,1.0016302524437989e-07 0.00012550962027927434,2.176395178881235e-08,1.1471865999927603e-07
60,60,0.00010893576763351642,1.884598391874928e-08,9.79957771514819e-08 0.00012328554642694118,2.1517924960982367e-08,1.1216558342351194e-07
61,61,0.00010705827573621923,1.8631654017664683e-08,9.587614496435341e-08 0.00012110347188084883,2.127440081340284e-08,1.0966931036940826e-07
62,62,0.00010521603014069265,1.8419483867548078e-08,9.380304193213288e-08 0.00011896255377421999,2.1033343271785093e-08,1.0722854943975324e-07
63,63,0.00010340831493293095,1.8209440802910146e-08,9.1775407295826e-08 0.00011686196716868922,2.079471662361659e-08,1.0484203954812887e-07
64,64,0.00010178667717264663,1.8029552937387797e-08,8.992671343771878e-08 0.0001149842862687527,2.0591381044652174e-08,1.0267229513392428e-07
65,65,0.00010019319833745167,1.7851381317094802e-08,8.811583172055121e-08 0.00011313924241427488,2.038998454906055e-08,1.0054740174771041e-07
66,66,9.86273426809622e-05,1.767490377589294e-08,8.634195328719705e-08 0.00011132622529329536,2.0190503422606342e-08,9.846640642649332e-08
67,67,9.70886230248071e-05,1.7500098774413897e-08,8.460432022445385e-08 0.00010954469655161574,1.9992914861970927e-08,9.642842985044247e-08
68,68,9.557652367577673e-05,1.7326944621204857e-08,8.290215772518696e-08 0.00010779406709933357,1.9797195630879305e-08,9.443255733959094e-08
69,69,9.409053958216297e-05,1.7155419863785074e-08,8.123470846820667e-08 0.0001060737596438843,1.9603322741072024e-08,9.247789444222215e-08
70,70,9.263017577528553e-05,1.698550325493719e-08,7.960123192429599e-08 0.00010438320803736288,1.9411273409584944e-08,9.05635661196983e-08
71,71,9.119540203213482e-05,1.681718296074974e-08,7.800139303615196e-08 0.00010272238658580892,1.9221036881521682e-08,8.868917351008482e-08
72,72,8.978572151582878e-05,1.6650437769918198e-08,7.643446000507212e-08 0.00010109072573662929,1.9032590330084786e-08,8.685385091954538e-08
73,73,8.840064792008674e-05,1.648524670194174e-08,7.489971794966337e-08 9.948766769187398e-05,1.884591117233303e-08,8.505675231710683e-08
74,74,8.704752218385389e-05,1.6323921332107853e-08,7.340306015991841e-08 9.792267601599323e-05,1.8663442661346744e-08,8.330556654067568e-08
75,75,8.571782151670807e-05,1.616417166590195e-08,7.228178645083984e-08 9.638489299064335e-05,1.8482768205063295e-08,8.199733139683135e-08
76,76,8.441109599127084e-05,1.6005979385519616e-08,7.117988659258102e-08 9.48738026587305e-05,1.830386814585904e-08,8.071180447583933e-08
77,77,8.312693723019766e-05,1.584932678267078e-08,7.009701621983393e-08 9.338895072626004e-05,1.8126723701740666e-08,7.944859929705862e-08
78,78,8.186491243372442e-05,1.569419608150936e-08,6.90328103730187e-08 9.192983962632809e-05,1.7951315790883015e-08,7.820729256637077e-08
79,79,8.062459786565112e-05,1.554056978222129e-08,6.798691173543069e-08 9.049598195913348e-05,1.777762563144978e-08,7.69874696390967e-08
80,80,7.94055784447779e-05,1.538843064058219e-08,6.695897028871753e-08 8.908690001727453e-05,1.7605634715742614e-08,7.578872411615343e-08
81,81,7.820755061857071e-05,1.523776381588298e-08,6.594873006137818e-08 8.77022482135352e-05,1.743532764682425e-08,7.461076199680497e-08
82,82,7.703011159024537e-05,1.5088552513719166e-08,6.495585139392737e-08 8.634156228069215e-05,1.7266686393258476e-08,7.345318831590489e-08
83,83,7.587286701386211e-05,1.494078021244127e-08,6.398000175307732e-08 8.50043874741986e-05,1.7099693224424935e-08,7.231561620951485e-08
84,84,7.483848702669918e-05,1.481852757489104e-08,6.310775801171533e-08 8.381694660770028e-05,1.6962933489525264e-08,7.130542696488026e-08
85,85,7.382059659354297e-05,1.4697575551136038e-08,6.224941913167962e-08 8.26484651987276e-05,1.682764479561346e-08,7.03113670624444e-08
86,86,7.281889798107282e-05,1.4577917663325962e-08,6.140473404853674e-08 8.149860566615124e-05,1.6693821025332528e-08,6.933314931247375e-08
87,87,7.183165666349203e-05,1.4459532716368791e-08,6.057224011868445e-08 8.036532369463753e-05,1.6561436760581683e-08,6.836903455858505e-08
88,88,7.08586484091438e-05,1.4342415672865158e-08,5.975174825816752e-08 7.924836834130927e-05,1.6430487594212236e-08,6.741880931663542e-08
89,89,6.989965271824705e-05,1.422656189652791e-08,5.8943072529944956e-08 7.814749266035204e-05,1.6300969571299207e-08,6.648226350289547e-08
90,90,6.895445267562359e-05,1.411196715686287e-08,5.814603001970201e-08 7.706245353522453e-05,1.6172879193985398e-08,6.55591902913057e-08
91,91,6.802197391413278e-05,1.3998609279853771e-08,5.735971476442124e-08 7.599203524832233e-05,1.6046190191322708e-08,6.46485554368076e-08
92,92,6.710204336793658e-05,1.3886485591019257e-08,5.6583980825863337e-08 7.493604414299442e-05,1.5920900877281144e-08,6.375019419025744e-08
93,93,6.619449037899918e-05,1.3775593851234834e-08,5.581868429617485e-08 7.389428911314724e-05,1.57970100612018e-08,6.286394397234219e-08
94,94,6.515268324911003e-05,1.3628882565600955e-08,5.494017759648076e-08 7.268136514077581e-05,1.5628904744452727e-08,6.183207553491646e-08
95,95,6.412720169535454e-05,1.3483606920728348e-08,5.407543748332493e-08 7.148784598320314e-05,1.546248763455113e-08,6.08167153176109e-08
1 signal Unnamed: 0 ase nli
2 0 0.00028461768874888705 0.00032501941662405775 0 3.436639817118301e-08 3.882736061781233e-08 2.1584395398371026e-07 2.530302086597507e-07
3 1 0.00028211666293476406 0.0003222815415358929 1 3.423996759499589e-08 3.8702821151814216e-08 2.1809245861954257e-07 2.5566893635970267e-07
4 2 0.0002796385058453953 0.0003195661748689714 2 3.411427963331182e-08 3.8578942925493807e-08 2.2028548201656645e-07 2.5824480514000414e-07
5 3 0.0002771829951915728 0.00031687313287901786 3 3.398932824433299e-08 3.845572130530932e-08 2.224238538437057e-07 2.607586632568943e-07
6 4 0.00027338904730863836 0.00031256877089880404 4 3.371109366484941e-08 3.8151588187974165e-08 2.2339638015421542e-07 2.6184297635783194e-07
7 5 0.0002696478418176822 0.00030832163273414217 5 3.3435235312069656e-08 3.7849896473142365e-08 2.2430128879557504e-07 2.628486532965104e-07
8 6 0.00026595862771951147 0.0003041309702107501 6 3.316172589770091e-08 3.755062053762566e-08 2.2514026889123164e-07 2.63777586129903e-07
9 7 0.0002623182345801782 0.0002999931829076906 7 3.289051654795761e-08 3.725370771318225e-08 2.2591288176837496e-07 2.646291066037263e-07
10 8 0.00025872603823298804 0.00029590766218524266 8 3.262158136113203e-08 3.695913382975532e-08 2.2662073272007013e-07 2.654050166835946e-07
11 9 0.0002548422077504841 0.00029147496378879135 9 3.231480491053109e-08 3.6620413511505286e-08 2.269632917207947e-07 2.6574345602771776e-07
12 10 0.0002510150800727548 0.0002871045786391862 10 3.201089350512762e-08 3.62847038107824e-08 2.2724305184561305e-07 2.660084105170287e-07
13 11 0.0002472372108596339 0.00028278798910714016 11 3.1709697987730424e-08 3.595182886920939e-08 2.274556638142854e-07 2.661946292683705e-07
14 12 0.0002435081184388883 0.0002785247470154763 12 3.1411188710954923e-08 3.562176113110911e-08 2.2760284870806473e-07 2.6630407250396607e-07
15 13 0.0002398273228957255 0.000274314403904135 13 3.1115336193393125e-08 3.5294473123494e-08 2.2768630096746835e-07 2.663386736369634e-07
16 14 0.00023610099948769217 0.00027003267516602375 14 3.081056270085416e-08 3.4954880805048256e-08 2.27617695956038e-07 2.6617827100010497e-07
17 15 0.00023242489102440902 0.00026580672005548984 15 3.050859262250336e-08 3.461827239104754e-08 2.274887362838919e-07 2.6594692111860163e-07
18 16 0.00022879847092097078 0.0002616360251570734 16 3.020939430576677e-08 3.428461757057609e-08 2.273011122655665e-07 2.65646579273305e-07
19 17 0.00022522143236587835 0.00025752034599005465 17 2.991293825364657e-08 3.395388872366444e-08 2.27056704831033e-07 2.6527944738596275e-07
20 18 0.00022169324298613107 0.00025345915709608835 18 2.9619193056612953e-08 3.362605558722993e-08 2.2315585168570266e-07 2.6062390528411136e-07
21 19 0.0002178178461312317 0.0002489837820730161 19 2.9278097616561043e-08 3.3242702224575744e-08 2.189169855906453e-07 2.555583656102669e-07
22 20 0.0002140033759047069 0.00024457734932656035 20 2.8940600865395135e-08 3.286328176612301e-08 2.1475128288390263e-07 2.5058012830302895e-07
23 21 0.00021024912752446947 0.00024023917734591996 21 2.8606658136588645e-08 3.2487750441670215e-08 2.1065775386659407e-07 2.45688111242981e-07
24 22 0.000206554289175574 0.00023596841237232834 22 2.827622313698371e-08 3.211606141812775e-08 2.0663530613832196e-07 2.408810615531071e-07
25 23 0.00020291805701626135 0.00023176420708051465 23 2.7949249962519028e-08 3.174816817069829e-08 2.0268286030011824e-07 2.361577392712943e-07
26 24 0.00019961589106178864 0.00022796955509264946 24 2.765975998385304e-08 3.142492853954814e-08 1.9907485701921993e-07 2.318666295572354e-07
27 25 0.00019636137150032674 0.00022422813584365578 25 2.7373095433009936e-08 3.1104729317697146e-08 1.9552454239910457e-07 2.2764369618498395e-07
28 26 0.0001931539326017224 0.00022053937832227754 26 2.7089223372231097e-08 3.078753835343166e-08 1.9203113225618846e-07 2.2348806312008297e-07
29 27 0.00018999370973089413 0.00021690355455251963 27 2.68081175642488e-08 3.047333197100547e-08 1.885945418493717e-07 2.1939971131843328e-07
30 28 0.00018688010986276862 0.00021332005611225162 28 2.6529745205109403e-08 3.01620779209974e-08 1.8521396370161997e-07 2.153777307708182e-07
31 29 0.00018381254538966195 0.00020978827864436746 29 2.6254073731438793e-08 2.9853744125792176e-08 1.818885993764317e-07 2.1142122009667965e-07
32 30 0.00018079043504395085 0.0002063076230434286 30 2.5981070886269e-08 2.9548298767727426e-08 1.78617660347401e-07 2.0752928769993865e-07
33 31 0.00017781329518445636 0.00020287763749134795 31 2.5710706310905614e-08 2.9245712991289203e-08 1.7540045799671345e-07 2.0370119433235788e-07
34 32 0.00017488055045064023 0.00019949772369461235 32 2.544294811944895e-08 2.894595516814791e-08 1.7223621637516225e-07 1.9993605731392634e-07
35 33 0.00017199163096488036 0.00019616728770591518 33 2.5177764647772334e-08 2.8648993828553545e-08 1.691241684952796e-07 1.9623300276884303e-07
36 34 0.00016923198600435235 0.00019299677389637712 34 2.4926164244345613e-08 2.836819529200861e-08 1.6614800249846462e-07 1.9270202998662707e-07
37 35 0.00016651223518287261 0.0001898709450753471 35 2.4676978561810828e-08 2.8090002575266257e-08 1.632195038378843e-07 1.8922739894311938e-07
38 36 0.00016383188581753214 0.00018678929584822534 36 2.443018036493577e-08 2.781438903245536e-08 1.6033800467387247e-07 1.8580835726188156e-07
39 37 0.0001611915388800597 0.0001837526367070107 37 2.418575294396746e-08 2.754134148447891e-08 1.5725354810318075e-07 1.8217016915383534e-07
40 38 0.0001585906555197023 0.00018076040324711669 38 2.394366896238391e-08 2.727083294692081e-08 1.5422386234766858e-07 1.7859628342481742e-07
41 39 0.00015602870309712287 0.00017781203651926026 39 2.3703901303924173e-08 2.700283660776023e-08 1.5124806354481474e-07 1.7508570183600935e-07
42 40 0.0001535051551031788 0.0001749069829579267 40 2.3466423066629146e-08 2.673732582084065e-08 1.4832528036973196e-07 1.7163743894482027e-07
43 41 0.0001510212597387019 0.00017204677711063964 41 2.3231240549132765e-08 2.6474316260529432e-08 1.4545635735033158e-07 1.6825255881241437e-07
44 42 0.00014857641307701306 0.00016923077161388358 42 2.2998325757462813e-08 2.621377975863396e-08 1.4264034538487909e-07 1.6492997841294993e-07
45 43 0.00014617002011358969 0.00016645832773934518 43 2.276765094680208e-08 2.5955688359138642e-08 1.3987631140917146e-07 1.616686318088319e-07
46 44 0.00014368188097051016 0.0001635796256227145 44 2.252080862505795e-08 2.5677295228672648e-08 1.3704924606778587e-07 1.58323074374741e-07
47 45 0.00014123500136325632 0.00016074819635989725 45 2.227648394665253e-08 2.5401698108435958e-08 1.342768563640109e-07 1.5504245883909504e-07
48 46 0.00013882871459443323 0.00015796330731148461 46 2.2034644371118186e-08 2.5128863163250677e-08 1.31558126246417e-07 1.5182560446701375e-07
49 47 0.00013646341840215168 0.00015522551088837497 47 2.1795267893415555e-08 2.485877013840214e-08 1.2889305332810146e-07 1.486725711724886e-07
50 48 0.00013413841082142 0.00015253402768104383 48 2.155832207437193e-08 2.4591385101783994e-08 1.2628059500046815e-07 1.4558214032289282e-07
51 49 0.00013185300210097483 0.00014988809102457626 49 2.132377480769977e-08 2.4326674435323678e-08 1.2371972883222934e-07 1.4255311588294157e-07
52 50 0.00012960651423053282 0.00014728694646614827 50 2.1091594291962705e-08 2.4064604800471338e-08 1.2120945193769904e-07 1.395843236958893e-07
53 51 0.00012739961074346843 0.00014473141087959588 51 2.0861774576065466e-08 2.3805175801260087e-08 1.1875002031242091e-07 1.3667608361715406e-07
54 52 0.00012523156633255863 0.00014222067140781916 52 2.0634283235397628e-08 2.3548353146553164e-08 1.1634039147633555e-07 1.3382717097378918e-07
55 53 0.00012310166960708747 0.00013975393005081647 53 2.0409088189365173e-08 2.3294102880186107e-08 1.1397954484148417e-07 1.3103638594918425e-07
56 54 0.00012096529298347188 0.00013727002869537867 54 2.0178855168888422e-08 2.303248543234042e-08 1.1162594309222065e-07 1.282461473035948e-07
57 55 0.00011886718830718708 0.0001348306664569764 55 1.9950983760858723e-08 2.2773547798807564e-08 1.093208043434055e-07 1.255140638251511e-07
58 56 0.00011680664424128133 0.0001324350333325102 56 1.9725440474060072e-08 2.2517253805739807e-08 1.0695583879566178e-07 1.2272513552717813e-07
59 57 0.00011478396058700633 0.00013008354253631936 57 1.950220202088885e-08 2.2263580536243144e-08 1.0464196732373097e-07 1.1999714433894858e-07
60 58 0.00011279839933843229 0.00012777534792361698 58 1.9281235055452333e-08 2.2012491805819457e-08 1.0237806316789167e-07 1.1732875449839032e-07
61 59 0.00011084923776207272 0.00012550962027927434 59 1.906250658388995e-08 2.176395178881235e-08 1.0016302524437989e-07 1.1471865999927603e-07
62 60 0.00010893576763351642 0.00012328554642694118 60 1.884598391874928e-08 2.1517924960982367e-08 9.79957771514819e-08 1.1216558342351194e-07
63 61 0.00010705827573621923 0.00012110347188084883 61 1.8631654017664683e-08 2.127440081340284e-08 9.587614496435341e-08 1.0966931036940826e-07
64 62 0.00010521603014069265 0.00011896255377421999 62 1.8419483867548078e-08 2.1033343271785093e-08 9.380304193213288e-08 1.0722854943975324e-07
65 63 0.00010340831493293095 0.00011686196716868922 63 1.8209440802910146e-08 2.079471662361659e-08 9.1775407295826e-08 1.0484203954812887e-07
66 64 0.00010178667717264663 0.0001149842862687527 64 1.8029552937387797e-08 2.0591381044652174e-08 8.992671343771878e-08 1.0267229513392428e-07
67 65 0.00010019319833745167 0.00011313924241427488 65 1.7851381317094802e-08 2.038998454906055e-08 8.811583172055121e-08 1.0054740174771041e-07
68 66 9.86273426809622e-05 0.00011132622529329536 66 1.767490377589294e-08 2.0190503422606342e-08 8.634195328719705e-08 9.846640642649332e-08
69 67 9.70886230248071e-05 0.00010954469655161574 67 1.7500098774413897e-08 1.9992914861970927e-08 8.460432022445385e-08 9.642842985044247e-08
70 68 9.557652367577673e-05 0.00010779406709933357 68 1.7326944621204857e-08 1.9797195630879305e-08 8.290215772518696e-08 9.443255733959094e-08
71 69 9.409053958216297e-05 0.0001060737596438843 69 1.7155419863785074e-08 1.9603322741072024e-08 8.123470846820667e-08 9.247789444222215e-08
72 70 9.263017577528553e-05 0.00010438320803736288 70 1.698550325493719e-08 1.9411273409584944e-08 7.960123192429599e-08 9.05635661196983e-08
73 71 9.119540203213482e-05 0.00010272238658580892 71 1.681718296074974e-08 1.9221036881521682e-08 7.800139303615196e-08 8.868917351008482e-08
74 72 8.978572151582878e-05 0.00010109072573662929 72 1.6650437769918198e-08 1.9032590330084786e-08 7.643446000507212e-08 8.685385091954538e-08
75 73 8.840064792008674e-05 9.948766769187398e-05 73 1.648524670194174e-08 1.884591117233303e-08 7.489971794966337e-08 8.505675231710683e-08
76 74 8.704752218385389e-05 9.792267601599323e-05 74 1.6323921332107853e-08 1.8663442661346744e-08 7.340306015991841e-08 8.330556654067568e-08
77 75 8.571782151670807e-05 9.638489299064335e-05 75 1.616417166590195e-08 1.8482768205063295e-08 7.228178645083984e-08 8.199733139683135e-08
78 76 8.441109599127084e-05 9.48738026587305e-05 76 1.6005979385519616e-08 1.830386814585904e-08 7.117988659258102e-08 8.071180447583933e-08
79 77 8.312693723019766e-05 9.338895072626004e-05 77 1.584932678267078e-08 1.8126723701740666e-08 7.009701621983393e-08 7.944859929705862e-08
80 78 8.186491243372442e-05 9.192983962632809e-05 78 1.569419608150936e-08 1.7951315790883015e-08 6.90328103730187e-08 7.820729256637077e-08
81 79 8.062459786565112e-05 9.049598195913348e-05 79 1.554056978222129e-08 1.777762563144978e-08 6.798691173543069e-08 7.69874696390967e-08
82 80 7.94055784447779e-05 8.908690001727453e-05 80 1.538843064058219e-08 1.7605634715742614e-08 6.695897028871753e-08 7.578872411615343e-08
83 81 7.820755061857071e-05 8.77022482135352e-05 81 1.523776381588298e-08 1.743532764682425e-08 6.594873006137818e-08 7.461076199680497e-08
84 82 7.703011159024537e-05 8.634156228069215e-05 82 1.5088552513719166e-08 1.7266686393258476e-08 6.495585139392737e-08 7.345318831590489e-08
85 83 7.587286701386211e-05 8.50043874741986e-05 83 1.494078021244127e-08 1.7099693224424935e-08 6.398000175307732e-08 7.231561620951485e-08
86 84 7.483848702669918e-05 8.381694660770028e-05 84 1.481852757489104e-08 1.6962933489525264e-08 6.310775801171533e-08 7.130542696488026e-08
87 85 7.382059659354297e-05 8.26484651987276e-05 85 1.4697575551136038e-08 1.682764479561346e-08 6.224941913167962e-08 7.03113670624444e-08
88 86 7.281889798107282e-05 8.149860566615124e-05 86 1.4577917663325962e-08 1.6693821025332528e-08 6.140473404853674e-08 6.933314931247375e-08
89 87 7.183165666349203e-05 8.036532369463753e-05 87 1.4459532716368791e-08 1.6561436760581683e-08 6.057224011868445e-08 6.836903455858505e-08
90 88 7.08586484091438e-05 7.924836834130927e-05 88 1.4342415672865158e-08 1.6430487594212236e-08 5.975174825816752e-08 6.741880931663542e-08
91 89 6.989965271824705e-05 7.814749266035204e-05 89 1.422656189652791e-08 1.6300969571299207e-08 5.8943072529944956e-08 6.648226350289547e-08
92 90 6.895445267562359e-05 7.706245353522453e-05 90 1.411196715686287e-08 1.6172879193985398e-08 5.814603001970201e-08 6.55591902913057e-08
93 91 6.802197391413278e-05 7.599203524832233e-05 91 1.3998609279853771e-08 1.6046190191322708e-08 5.735971476442124e-08 6.46485554368076e-08
94 92 6.710204336793658e-05 7.493604414299442e-05 92 1.3886485591019257e-08 1.5920900877281144e-08 5.6583980825863337e-08 6.375019419025744e-08
95 93 6.619449037899918e-05 7.389428911314724e-05 93 1.3775593851234834e-08 1.57970100612018e-08 5.581868429617485e-08 6.286394397234219e-08
96 94 6.515268324911003e-05 7.268136514077581e-05 94 1.3628882565600955e-08 1.5628904744452727e-08 5.494017759648076e-08 6.183207553491646e-08
97 95 6.412720169535454e-05 7.148784598320314e-05 95 1.3483606920728348e-08 1.546248763455113e-08 5.407543748332493e-08 6.08167153176109e-08

2
tests/invocation/logs_path_request
View File

@@ -1,5 +1,5 @@
INFO gnpy.tools.cli_examples:cli_examples.py:326 Computing path requests meshTopologyExampleV2.xls into JSON format INFO gnpy.tools.cli_examples:cli_examples.py:326 Computing path requests meshTopologyExampleV2.xls into JSON format
INFO gnpy.tools.json_io:json_io.py:548 Automatically converting requests from XLS to JSON INFO gnpy.tools.json_io:json_io.py:554 Automatically converting requests from XLS to JSON
INFO gnpy.topology.request:request.py:1104 INFO gnpy.topology.request:request.py:1104
request 0 request 0
Computing path from trx Lorient_KMA to trx Vannes_KBE Computing path from trx Lorient_KMA to trx Vannes_KBE

170
tests/invocation/transmission_main_example__raman
View File

@@ -23,18 +23,18 @@ RamanFiber Span1
total loss (dB): 17.00 total loss (dB): 17.00
(includes conn loss (dB) in: 0.50 out: 0.50) (includes conn loss (dB) in: 0.50 out: 0.50)
(conn loss out includes EOL margin defined in eqpt_config.json) (conn loss out includes EOL margin defined in eqpt_config.json)
reference pch out (dBm): -7.77 reference pch out (dBm): -7.20
actual pch out (dBm): -8.03 actual pch out (dBm): -7.47
Fused Fused1 Fused Fused1
loss (dB): 0.00 loss (dB): 0.00
Edfa Edfa1 Edfa Edfa1
type_variety: std_low_gain type_variety: std_low_gain
effective gain(dB): 5.77 effective gain(dB): 5.20
(before att_in and before output VOA) (before att_in and before output VOA)
noise figure (dB): 13.23 noise figure (dB): 13.80
(including att_in) (including att_in)
pad att_in (dB): 2.23 pad att_in (dB): 2.80
Power In (dBm): 11.04 Power In (dBm): 11.61
Power Out (dBm): 16.81 Power Out (dBm): 16.81
Delta_P (dB): -2.00 Delta_P (dB): -2.00
target pch (dBm): -2.00 target pch (dBm): -2.00
@@ -42,8 +42,8 @@ Edfa Edfa1
actual pch out (dBm): -2.26 actual pch out (dBm): -2.26
output VOA (dB): 0.00 output VOA (dB): 0.00
Transceiver Site_B Transceiver Site_B
GSNR (0.1nm, dB): 31.44 GSNR (0.1nm, dB): 31.40
GSNR (signal bw, dB): 27.36 GSNR (signal bw, dB): 27.32
OSNR ASE (0.1nm, dB): 34.21 OSNR ASE (0.1nm, dB): 34.21
OSNR ASE (signal bw, dB): 30.13 OSNR ASE (signal bw, dB): 30.13
CD (ps/nm): 1336.00 CD (ps/nm): 1336.00
@@ -52,86 +52,86 @@ Transceiver Site_B
Latency (ms): 0.39 Latency (ms): 0.39
Transmission result for input power = 0.00 dBm: Transmission result for input power = 0.00 dBm:
Final GSNR (0.1 nm): 31.44 dB Final GSNR (0.1 nm): 31.40 dB
The GSNR per channel at the end of the line is: The GSNR per channel at the end of the line is:
Ch. # Channel frequency (THz) Channel power (dBm) OSNR ASE (signal bw, dB) SNR NLI (signal bw, dB) GSNR (signal bw, dB) Ch. # Channel frequency (THz) Channel power (dBm) OSNR ASE (signal bw, dB) SNR NLI (signal bw, dB) GSNR (signal bw, dB)
1 191.35000 0.21 31.62 31.43 28.52 1 191.35000 0.22 31.64 31.32 28.46
2 191.40000 0.17 31.60 31.35 28.46 2 191.40000 0.18 31.62 31.23 28.41
3 191.45000 0.13 31.58 31.26 28.41 3 191.45000 0.15 31.60 31.15 28.36
4 191.50000 0.09 31.56 31.18 28.36 4 191.50000 0.11 31.58 31.07 28.31
5 191.55000 0.03 31.53 31.10 28.30 5 191.55000 0.05 31.54 30.99 28.25
6 191.60000 -0.02 31.50 31.02 28.24 6 191.60000 -0.01 31.51 30.92 28.19
7 191.65000 -0.08 31.46 30.94 28.19 7 191.65000 -0.07 31.48 30.84 28.14
8 191.70000 -0.14 31.43 30.87 28.13 8 191.70000 -0.12 31.45 30.76 28.08
9 191.75000 -0.20 31.40 30.79 28.08 9 191.75000 -0.18 31.42 30.69 28.03
10 191.80000 -0.27 31.37 30.72 28.02 10 191.80000 -0.25 31.38 30.62 27.97
11 191.85000 -0.33 31.33 30.65 27.97 11 191.85000 -0.31 31.35 30.55 27.92
12 191.90000 -0.40 31.29 30.58 27.91 12 191.90000 -0.38 31.31 30.48 27.86
13 191.95000 -0.46 31.26 30.51 27.86 13 191.95000 -0.44 31.27 30.41 27.81
14 192.00000 -0.53 31.22 30.44 27.80 14 192.00000 -0.51 31.24 30.34 27.76
15 192.05000 -0.59 31.18 30.37 27.75 15 192.05000 -0.58 31.20 30.27 27.70
16 192.10000 -0.66 31.15 30.30 27.69 16 192.10000 -0.64 31.16 30.21 27.65
17 192.15000 -0.73 31.11 30.24 27.64 17 192.15000 -0.71 31.12 30.14 27.60
18 192.20000 -0.80 31.07 30.17 27.59 18 192.20000 -0.78 31.08 30.08 27.54
19 192.25000 -0.86 31.03 30.18 27.57 19 192.25000 -0.85 31.04 30.09 27.53
20 192.30000 -0.94 30.99 30.19 27.56 20 192.30000 -0.93 31.00 30.10 27.52
21 192.35000 -1.02 30.94 30.20 27.54 21 192.35000 -1.00 30.96 30.11 27.50
22 192.40000 -1.09 30.90 30.20 27.53 22 192.40000 -1.08 30.91 30.12 27.49
23 192.45000 -1.17 30.86 30.21 27.51 23 192.45000 -1.16 30.87 30.12 27.47
24 192.50000 -1.24 30.81 30.22 27.50 24 192.50000 -1.23 30.82 30.13 27.45
25 192.55000 -1.31 30.77 30.23 27.48 25 192.55000 -1.30 30.78 30.14 27.44
26 192.60000 -1.38 30.73 30.23 27.46 26 192.60000 -1.37 30.74 30.15 27.42
27 192.65000 -1.45 30.69 30.24 27.45 27 192.65000 -1.44 30.70 30.16 27.41
28 192.70000 -1.52 30.65 30.25 27.43 28 192.70000 -1.52 30.65 30.17 27.39
29 192.75000 -1.59 30.60 30.26 27.42 29 192.75000 -1.59 30.61 30.18 27.38
30 192.80000 -1.67 30.56 30.27 27.40 30 192.80000 -1.66 30.57 30.19 27.36
31 192.85000 -1.74 30.52 30.27 27.38 31 192.85000 -1.73 30.52 30.20 27.35
32 192.90000 -1.81 30.47 30.28 27.37 32 192.90000 -1.80 30.48 30.20 27.33
33 192.95000 -1.88 30.43 30.29 27.35 33 192.95000 -1.87 30.43 30.21 27.31
34 193.00000 -1.95 30.39 30.30 27.33 34 193.00000 -1.94 30.39 30.22 27.30
35 193.05000 -2.02 30.34 30.30 27.31 35 193.05000 -2.01 30.35 30.23 27.28
36 193.10000 -2.08 30.30 30.31 27.30 36 193.10000 -2.08 30.30 30.24 27.26
37 193.15000 -2.15 30.26 30.32 27.28 37 193.15000 -2.15 30.26 30.25 27.24
38 193.20000 -2.22 30.21 30.34 27.26 38 193.20000 -2.22 30.22 30.27 27.23
39 193.25000 -2.29 30.17 30.36 27.25 39 193.25000 -2.29 30.17 30.29 27.22
40 193.30000 -2.36 30.13 30.37 27.24 40 193.30000 -2.36 30.13 30.31 27.20
41 193.35000 -2.43 30.08 30.39 27.22 41 193.35000 -2.43 30.08 30.32 27.19
42 193.40000 -2.50 30.04 30.41 27.21 42 193.40000 -2.50 30.04 30.34 27.18
43 193.45000 -2.56 29.99 30.43 27.19 43 193.45000 -2.56 29.99 30.36 27.16
44 193.50000 -2.63 29.95 30.44 27.18 44 193.50000 -2.63 29.95 30.38 27.15
45 193.55000 -2.70 29.90 30.46 27.16 45 193.55000 -2.71 29.90 30.40 27.13
46 193.60000 -2.78 29.85 30.48 27.15 46 193.60000 -2.78 29.85 30.42 27.11
47 193.65000 -2.85 29.80 30.50 27.13 47 193.65000 -2.85 29.80 30.44 27.10
48 193.70000 -2.92 29.76 30.52 27.11 48 193.70000 -2.93 29.75 30.46 27.08
49 193.75000 -2.99 29.71 30.54 27.09 49 193.75000 -3.00 29.70 30.48 27.06
50 193.80000 -3.06 29.66 30.55 27.07 50 193.80000 -3.07 29.65 30.50 27.04
51 193.85000 -3.14 29.61 30.57 27.06 51 193.85000 -3.15 29.60 30.51 27.02
52 193.90000 -3.21 29.56 30.59 27.04 52 193.90000 -3.22 29.55 30.53 27.01
53 193.95000 -3.28 29.52 30.61 27.02 53 193.95000 -3.29 29.50 30.55 26.99
54 194.00000 -3.35 29.47 30.63 27.00 54 194.00000 -3.37 29.45 30.57 26.97
55 194.05000 -3.42 29.42 30.65 26.98 55 194.05000 -3.44 29.40 30.59 26.95
56 194.10000 -3.50 29.37 30.67 26.96 56 194.10000 -3.52 29.35 30.61 26.93
57 194.15000 -3.57 29.32 30.72 26.95 57 194.15000 -3.59 29.30 30.67 26.92
58 194.20000 -3.64 29.26 30.78 26.95 58 194.20000 -3.66 29.25 30.73 26.91
59 194.25000 -3.72 29.21 30.84 26.94 59 194.25000 -3.74 29.19 30.79 26.91
60 194.30000 -3.79 29.16 30.90 26.94 60 194.30000 -3.81 29.14 30.85 26.90
61 194.35000 -3.86 29.11 30.96 26.93 61 194.35000 -3.89 29.09 30.91 26.89
62 194.40000 -3.93 29.06 31.02 26.92 62 194.40000 -3.96 29.04 30.97 26.89
63 194.45000 -4.01 29.01 31.09 26.91 63 194.45000 -4.04 28.98 31.03 26.88
64 194.50000 -4.08 28.96 31.15 26.91 64 194.50000 -4.11 28.93 31.10 26.87
65 194.55000 -4.14 28.91 31.21 26.90 65 194.55000 -4.18 28.88 31.16 26.86
66 194.60000 -4.21 28.86 31.28 26.90 66 194.60000 -4.25 28.83 31.23 26.86
67 194.65000 -4.27 28.82 31.34 26.89 67 194.65000 -4.31 28.78 31.30 26.85
68 194.70000 -4.34 28.77 31.41 26.88 68 194.70000 -4.38 28.74 31.36 26.84
69 194.75000 -4.41 28.72 31.48 26.88 69 194.75000 -4.45 28.69 31.43 26.84
70 194.80000 -4.47 28.67 31.55 26.87 70 194.80000 -4.51 28.64 31.50 26.83
71 194.85000 -4.54 28.63 31.62 26.86 71 194.85000 -4.58 28.59 31.57 26.82
72 194.90000 -4.60 28.58 31.69 26.85 72 194.90000 -4.65 28.54 31.64 26.81
73 194.95000 -4.67 28.53 31.76 26.84 73 194.95000 -4.71 28.49 31.72 26.80
74 195.00000 -4.73 28.48 31.84 26.83 74 195.00000 -4.78 28.44 31.79 26.79
75 195.05000 -4.80 28.43 31.91 26.82 75 195.05000 -4.85 28.39 31.87 26.78
76 195.10000 -4.86 28.38 31.91 26.79 76 195.10000 -4.91 28.34 31.87 26.74
(No source node specified: picked Site_A) (No source node specified: picked Site_A)

35
tests/test_parameters.py
View File

@@ -6,7 +6,13 @@ Checks that the class SimParams behaves as a mutable Singleton.
""" """
import pytest import pytest
from gnpy.core.parameters import SimParams from pathlib import Path
from numpy.testing import assert_allclose
from gnpy.core.parameters import SimParams, FiberParams
from gnpy.tools.json_io import load_json, Fiber
TEST_DIR = Path(__file__).parent
@pytest.mark.usefixtures('set_sim_params') @pytest.mark.usefixtures('set_sim_params')
@@ -21,3 +27,30 @@ def test_sim_parameters():
SimParams.set_params(sim_params) SimParams.set_params(sim_params)
assert s2.raman_params.flag assert s2.raman_params.flag
assert s1.raman_params.flag assert s1.raman_params.flag
def test_fiber_parameters():
fiber_dict_explicit_g0 = load_json(TEST_DIR/'data'/'test_parameters_fiber_config.json')['params']
fiber_params_explicit_g0 = FiberParams(**fiber_dict_explicit_g0)
fiber_dict_default_g0 = load_json(TEST_DIR/'data'/'test_science_utils_fiber_config.json')['params']
fiber_params_default_g0 = FiberParams(**fiber_dict_default_g0)
fiber_dict_cr = load_json(TEST_DIR/'data'/'test_old_parameters_fiber_config.json')['params']
fiber_dict_cr.update(Fiber(**fiber_dict_cr).__dict__)
fiber_params_cr = FiberParams(**fiber_dict_cr)
raman_coefficient_explicit_g0 = fiber_params_explicit_g0.raman_coefficient
raman_coefficient_explicit_g0 =\
raman_coefficient_explicit_g0.normalized_gamma_raman * fiber_params_explicit_g0._raman_reference_frequency
raman_coefficient_default_g0 = fiber_params_default_g0.raman_coefficient
raman_coefficient_default_g0 = \
raman_coefficient_default_g0.normalized_gamma_raman * fiber_params_default_g0._raman_reference_frequency
raman_coefficient_cr = fiber_params_cr.raman_coefficient
raman_coefficient_cr = \
raman_coefficient_cr.normalized_gamma_raman * fiber_params_cr._raman_reference_frequency
assert_allclose(raman_coefficient_explicit_g0, raman_coefficient_default_g0, rtol=1e-10)
assert_allclose(raman_coefficient_explicit_g0, raman_coefficient_cr, rtol=1e-10)

6
tests/test_science_utils.py
View File

@@ -30,7 +30,8 @@ def test_fiber():
# fix grid spectral information generation # fix grid spectral information generation
spectral_info_input = create_input_spectral_information(f_min=191.3e12, f_max=196.1e12, roll_off=0.15, spectral_info_input = create_input_spectral_information(f_min=191.3e12, f_max=196.1e12, roll_off=0.15,
baud_rate=32e9, power=1e-3, spacing=50e9, tx_osnr=40.0, baud_rate=32e9, power=1e-3, spacing=50e9, tx_osnr=40.0,
ref_carrier=ReferenceCarrier(baud_rate=32e9, slot_width=50e9)) ref_carrier=
ReferenceCarrier(baud_rate=32e9, slot_width=50e9))
# propagation # propagation
spectral_info_out = fiber(spectral_info_input) spectral_info_out = fiber(spectral_info_input)
@@ -108,7 +109,8 @@ def test_fiber_lumped_losses_srs(set_sim_params):
# spectral information generation # spectral information generation
spectral_info_input = create_input_spectral_information(f_min=191.3e12, f_max=196.1e12, roll_off=0.15, spectral_info_input = create_input_spectral_information(f_min=191.3e12, f_max=196.1e12, roll_off=0.15,
baud_rate=32e9, power=1e-3, spacing=50e9, tx_osnr=40.0, baud_rate=32e9, power=1e-3, spacing=50e9, tx_osnr=40.0,
ref_carrier=ReferenceCarrier(baud_rate=32e9, slot_width=50e9)) ref_carrier=
ReferenceCarrier(baud_rate=32e9, slot_width=50e9))
SimParams.set_params(load_json(TEST_DIR / 'data' / 'sim_params.json')) SimParams.set_params(load_json(TEST_DIR / 'data' / 'sim_params.json'))
fiber = Fiber(**load_json(TEST_DIR / 'data' / 'test_lumped_losses_raman_fiber_config.json')) fiber = Fiber(**load_json(TEST_DIR / 'data' / 'test_lumped_losses_raman_fiber_config.json'))