19 Commits
v2.5 ... v2.6

Author SHA1 Message Date
Jan Kundrát
a7ec7e2ed6 Merge changes from topic "mixed-rate"
* changes:
  Change saturation verification to total input power
  Prepare for Pref definition
  Add utilities
2022-09-19 09:30:59 +00:00
Jan Kundrát
00ee102b3a docs: fix RST formatting
...of a bullet list. This ain't markdown, apparently.

Change-Id: I4f7a55a4084eda6463636c1dd5c41ef43ef78921
2022-09-18 12:46:19 +02:00
EstherLerouzic
ce11524ad9 Correct dgt vector: listed in the reversed order
This was corrected for example-data but not for tests data
(in commit 3a72ce84d0, related
to issue #390)

Signed-off-by: EstherLerouzic <esther.lerouzic@orange.com>
Change-Id: I929beeb034166d30aa994439a1d6a26350f5c3e9
2022-09-14 05:35:15 +00:00
EstherLerouzic
33c6038921 Change saturation verification to total input power
Previous check was made on reference channel computation.
Now we use the actual total input power to compute the actual gain.

Signed-off-by: EstherLerouzic <esther.lerouzic@orange.com>
Change-Id: I3e0db72fdb030a49e2b06cdcfb442b5e642c1777
2022-08-17 14:13:38 +02:00
EstherLerouzic
119c9eda90 Prepare for Pref definition
Mainly changes self.pch_out_db to self.ref_pch_out_dbm in order
to reflect real unit for the value and to remind that this value
is defined for a reference noiseless channel (whose power is recorded
in p_spani in Pref).

Signed-off-by: EstherLerouzic <esther.lerouzic@orange.com>
Change-Id: If0e008c3efc36ce73c9df01c76cf46985543d9fa
2022-08-17 14:13:38 +02:00
EstherLerouzic
b63e146bf4 Add utilities
to convert from/to watt, mW, dBm, power spectral density ...

Signed-off-by: EstherLerouzic <esther.lerouzic@orange.com>
Change-Id: I9b9684c1ad096aa54d01ef3f0242ecd2dcae79aa
2022-08-17 14:13:38 +02:00
gborrach
09dba8a166 Fix: Raman pumps SRS solver
In the previous version, when the values of the counter-propagating Raman pump profiles were flipped, the pumps resulted flipped also in frequency.

Change-Id: I66f7c2aff35c72f5dcb4fb11f7a82fe1df2ee3f2
Co-authored-by: Andrea D'Amico <andrea.damico@polito.it>
2022-07-27 00:20:47 +02:00
Jan Kundrát
7f5043622b CI: GitHub: show all build failures
Change-Id: I4a5aeb123aa89a30371a36788188964ca924ca12
2022-07-07 16:32:13 +02:00
Jan Kundrát
6ad4593f41 CI: GitHub: test on Mac OS as well
Change-Id: Ifb8ba470765fc02e3367beeaf8f048da6fdad6d7
2022-07-05 13:18:37 +02:00
Jan Kundrát
706661d801 CI: GitHub: use new test-requirements
Follow-up: b86fe960 I8d2e610c91da728d72c7d19590b25bbd8713f0de tests: Easier installation of test requirements via PIP
Change-Id: I870ed27af7301829ced572214f517ca76a94a0dc
2022-07-05 13:07:18 +02:00
Jan Kundrát
a408d28911 Merge "Remove Travis-CI leftovers" 2022-07-05 11:07:08 +00:00
Jan Kundrát
b86fe96032 tests: Easier installation of test requirements via PIP
Burying these behind the tox.ini works fine on CI, but it means that
someone has to ask the users to run an extra `pip install pytest` for
the test suite. Not nice.

This will need a follow-up commit to adjust the GitHub action to use
this new simplified way. That cannot land via Gerrit due to GitHub's
permission model.

Change-Id: I8d2e610c91da728d72c7d19590b25bbd8713f0de
2022-07-05 12:45:19 +02:00
Jan Kundrát
43926518ad Remove Travis-CI leftovers
These builds have not been running for about an year. Now that we have
Zuul for day-to-day CI and GitHub actions for some auxiliary package
building, remove Travis.

Change-Id: I1dd7f70045fd24d2f73f0d2086cb49edde2093c7
2022-07-05 10:14:18 +02:00
Jan Kundrát
128a6e816b docs: better anchor for legacy JSON
Change-Id: I410a4c0cdd34dd9aa5d8e34bb859746236fffdd2
2022-07-03 01:03:26 +02:00
Jan Kundrát
44db951261 docs: show gnpy.app
Change-Id: I7ec5eec72fb0f07e277ac849da09003d376eec17
2022-04-12 11:42:06 +02:00
Jonas Mårtensson
3c3d919b77 Merge "Fix: penalties are not correctly initialized" 2022-04-11 18:55:42 +00:00
EstherLerouzic
2079d2bc5b Fix: penalties are not correctly initialized
When mode is not given and propagation is performed bidir, penalties
corresponding to automatically  selected mode are not correctly
initialized in request, and gnpy-path-request fails.

Signed-off-by: EstherLerouzic <esther.lerouzic@orange.com>
Change-Id: If045624ff5dad7f0dfdec93eaa05bb5eae86e643
2022-04-06 17:17:21 +02:00
Jonas Mårtensson
062e2076ed Properly initialize power profiles for Raman calculation
numpy.empty should not be used for initializing arrays without manually
setting values since it does not initialize entries. Use numpy.zeros
instead.

Signed-off-by: Jonas Mårtensson <jonas.martensson@ri.se>
Change-Id: I4e85eb39bdce00663c0cab9582ea7ae25eb90986
2022-03-29 21:41:57 +02:00
Jonas Mårtensson
1dd1bad273 Fix bug in Raman calculation without counterpropagating pumps
The counterpropagating power profile was initialized based on number of
copropagating frequencies, which caused simulation to crash when no
counterpropagating pumps were present.

Signed-off-by: Jonas Mårtensson <jonas.martensson@ri.se>
Change-Id: I685e5438fda06058f0757ff51fdd67bc68aa1352
2022-03-29 21:27:18 +02:00
24 changed files with 498 additions and 492 deletions

View File

@@ -1,47 +0,0 @@
#!/bin/bash
set -e
IMAGE_NAME=telecominfraproject/oopt-gnpy
IMAGE_TAG=$(git describe --tags)
ALREADY_FOUND=0
docker pull ${IMAGE_NAME}:${IMAGE_TAG} && ALREADY_FOUND=1
if [[ $ALREADY_FOUND == 0 ]]; then
docker build . -t ${IMAGE_NAME}
docker tag ${IMAGE_NAME} ${IMAGE_NAME}:${IMAGE_TAG}
# shared directory setup: do not clobber the real data
mkdir trash
cd trash
docker run -it --rm --volume $(pwd):/shared ${IMAGE_NAME} gnpy-transmission-example
else
echo "Image ${IMAGE_NAME}:${IMAGE_TAG} already available, will just update the other tags"
fi
docker images
do_docker_login() {
echo "${DOCKER_PASSWORD}" | docker login -u "${DOCKER_USERNAME}" --password-stdin
}
if [[ "${TRAVIS_PULL_REQUEST}" == "false" ]]; then
if [[ "${TRAVIS_BRANCH}" == "develop" || "${TRAVIS_BRANCH}" == "docker" ]]; then
echo "Publishing latest"
docker tag ${IMAGE_NAME}:${IMAGE_TAG} ${IMAGE_NAME}:latest
do_docker_login
if [[ $ALREADY_FOUND == 0 ]]; then
docker push ${IMAGE_NAME}:${IMAGE_TAG}
fi
docker push ${IMAGE_NAME}:latest
elif [[ "${TRAVIS_BRANCH}" == "master" ]]; then
echo "Publishing stable"
docker tag ${IMAGE_NAME}:${IMAGE_TAG} ${IMAGE_NAME}:stable
do_docker_login
if [[ $ALREADY_FOUND == 0 ]]; then
docker push ${IMAGE_NAME}:${IMAGE_TAG}
fi
docker push ${IMAGE_NAME}:stable
fi
fi

View File

@@ -23,6 +23,7 @@ jobs:
with:
files: ${{ github.workspace }}/cover/coverage.xml
strategy:
fail-fast: false
matrix:
tox_env:
- py38
@@ -92,9 +93,9 @@ jobs:
telecominfraproject/oopt-gnpy:${{ steps.extract_tag_name.outputs.GIT_DESC }}
telecominfraproject/oopt-gnpy:latest
windows:
name: Tests on Windows
runs-on: windows-2019
other-platforms:
name: Tests on other platforms
runs-on: ${{ matrix.os }}
steps:
- uses: actions/checkout@v2
with:
@@ -103,10 +104,14 @@ jobs:
with:
python-version: ${{ matrix.python_version }}
- run: |
pip install -r tests/requirements.txt
pip install --editable .
pip install 'pytest>=6.2.5,<7'
pytest -vv
strategy:
fail-fast: false
matrix:
python_version:
- "3.10"
include:
- os: windows-2019
python_version: "3.10"
- os: macos-12
python_version: "3.10"

View File

@@ -1,27 +0,0 @@
dist: focal
os: linux
language: python
services: docker
python:
- "3.8"
- "3.9"
before_install:
- sudo apt-get -y install graphviz
install: skip
script:
- pip install --editable .
- pip install pytest-cov rstcheck
- pytest --cov-report=xml --cov=gnpy -v
- pip install -r docs/requirements.txt
- rstcheck --ignore-roles cite *.rst
- sphinx-build -W --keep-going docs/ x-throwaway-location
after_success:
- bash <(curl -s https://codecov.io/bash)
jobs:
include:
- stage: test
name: Docker image
script:
- git fetch --unshallow
- ./.docker-travis.sh
- docker images

View File

@@ -26,4 +26,6 @@ This example demonstrates how GNPy can be used to check the expected SNR at the
![Running a simple simulation example](https://telecominfraproject.github.io/oopt-gnpy/docs/images/transmission_main_example.svg)
GNPy can do much more, including acting as a Path Computation Engine, tracking bandwidth requests, or advising the SDN controller about a best possible path through a large DWDM network.
Learn more about this [in the documentation](https://gnpy.readthedocs.io/).
Learn more about this [in the documentation](https://gnpy.readthedocs.io/), or give it a [try online at `gnpy.app`](https://gnpy.app/):
[![Path propagation at gnpy.app](docs/images/2022-04-12-gnpy-app.png)](https://gnpy.app/)

View File

@@ -29,7 +29,7 @@ This path is directional, and all "GNPy elements" along the path match the unidi
The network topology contains not just the physical topology of the network, but also references to the :ref:`equipment library<concepts-equipment>` and a set of *operating parameters* for each entity.
These parameters include the **fiber length** of each fiber, the connector **attenutation losses**, or an amplifier's specific **gain setting**.
The topology is specified via :ref:`XLS files<excel>` or via :ref:`JSON<json>`.
The topology is specified via :ref:`XLS files<excel>` or via :ref:`JSON<legacy-json>`.
.. _complete-vs-incomplete:

View File

@@ -100,10 +100,10 @@ Raman Amplifiers
An accurate simulation of Raman amplification requires knowledge of:
- the *power* and *wavelength* of all Raman pumping lasers,
- the *direction*, whether it is co-propagating or counter-propagating,
- the Raman efficiency of the fiber,
- the fiber temperature.
* the *power* and *wavelength* of all Raman pumping lasers,
* the *direction*, whether it is co-propagating or counter-propagating,
* the Raman efficiency of the fiber,
* the fiber temperature.
Under certain scenarios it is useful to be able to run a simulation without an accurate Raman description.
For these purposes, it is possible to approximate a Raman amplifier via a fixed-gain EDFA with the :ref:`polynomial NF<ext-nf-model-polynomial-NF>` model using :math:`\text{a} = \text{b} = \text{c} = 0`, and a desired effective :math:`\text{d} = NF`.

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@@ -1,4 +1,4 @@
.. _json:
.. _legacy-json:
JSON Input Files
================

View File

@@ -26,7 +26,7 @@ from scipy.constants import h, c
from scipy.interpolate import interp1d
from collections import namedtuple
from gnpy.core.utils import lin2db, db2lin, arrange_frequencies, snr_sum
from gnpy.core.utils import lin2db, db2lin, arrange_frequencies, snr_sum, watt2dbm
from gnpy.core.parameters import RoadmParams, FusedParams, FiberParams, PumpParams, EdfaParams, EdfaOperational
from gnpy.core.science_utils import NliSolver, RamanSolver
from gnpy.core.info import SpectralInformation
@@ -216,21 +216,21 @@ class Roadm(_Node):
if not params:
params = {}
super().__init__(*args, params=RoadmParams(**params), **kwargs)
self.pch_out_db = self.params.target_pch_out_db
self.ref_pch_out_dbm = self.params.target_pch_out_db
self.loss = 0 # auto-design interest
self.effective_loss = None
self.passive = True
self.restrictions = self.params.restrictions
self.per_degree_pch_out_db = self.params.per_degree_pch_out_db
self.per_degree_pch_out_dbm = self.params.per_degree_pch_out_db
@property
def to_json(self):
return {'uid': self.uid,
'type': type(self).__name__,
'params': {
'target_pch_out_db': self.pch_out_db,
'target_pch_out_db': self.ref_pch_out_dbm,
'restrictions': self.restrictions,
'per_degree_pch_out_db': self.per_degree_pch_out_db
'per_degree_pch_out_db': self.per_degree_pch_out_dbm
},
'metadata': {
'location': self.metadata['location']._asdict()
@@ -246,7 +246,7 @@ class Roadm(_Node):
return '\n'.join([f'{type(self).__name__} {self.uid}',
f' effective loss (dB): {self.effective_loss:.2f}',
f' pch out (dBm): {self.pch_out_db:.2f}'])
f' pch out (dBm): {self.ref_pch_out_dbm:.2f}'])
def propagate(self, spectral_info, degree):
# pin_target and loss are read from eqpt_config.json['Roadm']
@@ -258,10 +258,17 @@ class Roadm(_Node):
# if the input power is lower than the target one, use the input power instead because
# a ROADM doesn't amplify, it can only attenuate
# TODO maybe add a minimum loss for the ROADM
per_degree_pch = self.per_degree_pch_out_db[degree] \
if degree in self.per_degree_pch_out_db else self.pch_out_db
self.pch_out_db = min(spectral_info.pref.p_spani, per_degree_pch)
self.effective_loss = spectral_info.pref.p_spani - self.pch_out_db
per_degree_pch = self.per_degree_pch_out_dbm.get(degree, self.ref_pch_out_dbm)
# Definition of ref_pch_out_dbm for the reference channel:
# Depending on propagation upstream from this ROADM, the input power (p_spani) might be smaller than
# the target power out configured for this ROADM degree's egress. Since ROADM does not amplify,
# the power out of the ROADM for the ref channel is the min value between target power and input power.
# (TODO add a minimum loss for the ROADM crossing)
self.ref_pch_out_dbm = min(spectral_info.pref.p_spani, per_degree_pch)
# Definition of effective_loss:
# Optical power of carriers are equalized by the ROADM, so that the experienced loss is not the same for
# different carriers. effective_loss records the loss for a reference carrier.
self.effective_loss = spectral_info.pref.p_spani - self.ref_pch_out_dbm
input_power = spectral_info.signal + spectral_info.nli + spectral_info.ase
min_power = min(lin2db(input_power * 1e3))
per_degree_pch = per_degree_pch if per_degree_pch < min_power else min_power
@@ -271,7 +278,8 @@ class Roadm(_Node):
spectral_info.pdl = sqrt(spectral_info.pdl ** 2 + self.params.pdl ** 2)
def update_pref(self, spectral_info):
spectral_info.pref = spectral_info.pref._replace(p_span0=spectral_info.pref.p_span0, p_spani=self.pch_out_db)
spectral_info.pref = spectral_info.pref._replace(p_span0=spectral_info.pref.p_span0,
p_spani=self.ref_pch_out_dbm)
def __call__(self, spectral_info, degree):
self.propagate(spectral_info, degree=degree)
@@ -639,7 +647,7 @@ class Edfa(_Node):
self.nch = spectral_info.number_of_channels
pin = spectral_info.signal + spectral_info.ase + spectral_info.nli
self.pin_db = lin2db(sum(pin * 1e3))
self.pin_db = watt2dbm(sum(pin))
# The following should be changed when we have the new spectral information including slot widths.
# For now, with homogeneous spectrum, we can calculate it as the difference between neighbouring channels.
self.slot_width = self.channel_freq[1] - self.channel_freq[0]
@@ -652,15 +660,14 @@ class Edfa(_Node):
self.effective_gain = self.target_pch_out_db - pref.p_spani
"""check power saturation and correct effective gain & power accordingly:"""
# Compute the saturation accounting for actual power at the input of the amp
self.effective_gain = min(
self.effective_gain,
self.params.p_max - (pref.p_spani + pref.neq_ch)
self.params.p_max - self.pin_db
)
#print(self.uid, self.effective_gain, self.operational.gain_target)
self.effective_pch_out_db = round(pref.p_spani + self.effective_gain, 2)
"""check power saturation and correct target_gain accordingly:"""
#print(self.uid, self.effective_gain, self.pin_db, pref.p_spani)
self.nf = self._calc_nf()
self.gprofile = self._gain_profile(pin)

View File

@@ -238,7 +238,7 @@ def set_egress_amplifier(network, this_node, equipment, pref_ch_db, pref_total_d
"""
power_mode = equipment['Span']['default'].power_mode
next_oms = (n for n in network.successors(this_node) if not isinstance(n, elements.Transceiver))
this_node_degree = {k: v for k, v in this_node.per_degree_pch_out_db.items()} if hasattr(this_node, 'per_degree_pch_out_db') else {}
this_node_degree = getattr(this_node, 'per_degree_pch_out_dbm', {})
for oms in next_oms:
# go through all the OMS departing from the ROADM
prev_node = this_node
@@ -334,7 +334,7 @@ def set_egress_amplifier(network, this_node, equipment, pref_ch_db, pref_total_d
# print(f'{node.uid}')
if isinstance(this_node, elements.Roadm):
this_node.per_degree_pch_out_db = {k: v for k, v in this_node_degree.items()}
this_node.per_degree_pch_out_dbm = {k: v for k, v in this_node_degree.items()}
def add_roadm_booster(network, roadm):

View File

@@ -10,7 +10,7 @@ Solver definitions to calculate the Raman effect and the nonlinear interference
The solvers take as input instances of the spectral information, the fiber and the simulation parameters
"""
from numpy import interp, pi, zeros, shape, where, cos, array, append, ones, exp, arange, sqrt, empty, trapz, arcsinh, \
from numpy import interp, pi, zeros, shape, where, cos, array, append, ones, exp, arange, sqrt, trapz, arcsinh, \
clip, abs, sum, concatenate, flip, outer, inner, transpose, max, format_float_scientific, diag, prod, argwhere, \
unique, argsort, cumprod
from logging import getLogger
@@ -131,20 +131,20 @@ class RamanSolver:
cnt_frequency = array([pump.frequency for pump in fiber.raman_pumps
if pump.propagation_direction == 'counterprop'])
# Co-propagating profile initialization
co_power_profile = empty([co_frequency.size, z.size])
co_power_profile = zeros([co_frequency.size, z.size])
if co_frequency.size:
co_cr = fiber.cr(co_frequency)
co_alpha = fiber.alpha(co_frequency)
co_power_profile = \
RamanSolver.first_order_derivative_solution(co_power, co_alpha, co_cr, z, lumped_losses)
# Counter-propagating profile initialization
cnt_power_profile = empty([co_frequency.size, z.size])
cnt_power_profile = zeros([cnt_frequency.size, z.size])
if cnt_frequency.size:
cnt_cr = fiber.cr(cnt_frequency)
cnt_alpha = fiber.alpha(cnt_frequency)
cnt_power_profile = \
flip(RamanSolver.first_order_derivative_solution(cnt_power, cnt_alpha, cnt_cr,
z[-1] - flip(z), flip(lumped_losses)))
z[-1] - flip(z), flip(lumped_losses)), axis=1)
# Co-propagating and Counter-propagating Profile Computation
if co_frequency.size and cnt_frequency.size:
co_power_profile, cnt_power_profile = \

View File

@@ -106,6 +106,69 @@ def db2lin(value):
return 10**(value / 10)
def watt2dbm(value):
"""Convert Watt units to dBm
>>> round(watt2dbm(0.001), 1)
0.0
>>> round(watt2dbm(0.02), 1)
13.0
"""
return lin2db(value * 1e3)
def dbm2watt(value):
"""Convert dBm units to Watt
>>> round(dbm2watt(0), 4)
0.001
>>> round(dbm2watt(-3), 4)
0.0005
>>> round(dbm2watt(13), 4)
0.02
"""
return db2lin(value) * 1e-3
def psd2powerdbm(psd_mwperghz, baudrate_baud):
"""computes power in dBm based on baudrate in bauds and psd in mW/GHz
>>> round(psd2powerdbm(0.031176, 64e9),3)
3.0
>>> round(psd2powerdbm(0.062352, 32e9),3)
3.0
>>> round(psd2powerdbm(0.015625, 64e9),3)
0.0
"""
return lin2db(baudrate_baud * psd_mwperghz * 1e-9)
def power_dbm_to_psd_mw_ghz(power_dbm, baudrate_baud):
"""computes power spectral density in mW/GHz based on baudrate in bauds and power in dBm
>>> power_dbm_to_psd_mw_ghz(0, 64e9)
0.015625
>>> round(power_dbm_to_psd_mw_ghz(3, 64e9), 6)
0.031176
>>> round(power_dbm_to_psd_mw_ghz(3, 32e9), 6)
0.062352
"""
return db2lin(power_dbm) / (baudrate_baud * 1e-9)
def psd_mw_per_ghz(power_watt, baudrate_baud):
"""computes power spectral density in mW/GHz based on baudrate in bauds and power in W
>>> psd_mw_per_ghz(2e-3, 32e9)
0.0625
>>> psd_mw_per_ghz(1e-3, 64e9)
0.015625
>>> psd_mw_per_ghz(0.5e-3, 32e9)
0.015625
"""
return power_watt * 1e3 / (baudrate_baud * 1e-9)
def round2float(number, step):
"""Round a floating point number so that its "resolution" is not bigger than 'step'

View File

@@ -1142,6 +1142,7 @@ def compute_path_with_disjunction(network, equipment, pathreqlist, pathlist):
pathreq.OSNR = mode['OSNR']
pathreq.tx_osnr = mode['tx_osnr']
pathreq.bit_rate = mode['bit_rate']
pathreq.penalties = mode['penalties']
# other blocking reason should not appear at this point
except AttributeError:
pathreq.baud_rate = mode['baud_rate']
@@ -1150,6 +1151,7 @@ def compute_path_with_disjunction(network, equipment, pathreqlist, pathlist):
pathreq.OSNR = mode['OSNR']
pathreq.tx_osnr = mode['tx_osnr']
pathreq.bit_rate = mode['bit_rate']
pathreq.penalties = mode['penalties']
# reversed path is needed for correct spectrum assignment
reversed_path = find_reversed_path(pathlist[i])

View File

@@ -196,101 +196,101 @@
0.0
],
"dgt": [
2.714526681131686,
2.705443819238505,
2.6947834587664494,
2.6841217449620203,
2.6681935771243177,
2.6521732021128046,
2.630396440815385,
2.602860350286428,
2.5696460593920065,
2.5364027376452056,
2.499446286796604,
2.4587748041127506,
2.414398437185221,
2.3699990328716107,
2.322373696229342,
2.271520771371253,
2.2174389328192197,
2.16337565384239,
2.1183028432496016,
2.082225099873648,
2.055100772005235,
2.0279625371819305,
2.0008103857988204,
1.9736443063300082,
1.9482128147680253,
1.9245345552113182,
1.9026104247588487,
1.8806927939516411,
1.862235672444246,
1.847275503201129,
1.835814081380705,
1.824381436842932,
1.8139629377087627,
1.8045606557581335,
1.7961751115773796,
1.7877868031023945,
1.7793941781790852,
1.7709972329654864,
1.7625959636196327,
1.7541903672600494,
1.7459181197626403,
1.737780757913635,
1.7297783508684146,
1.7217732861435076,
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View File

@@ -1258,7 +1258,7 @@
},
{
"metric-type": "SNR-0.1nm",
"accumulative-value": 28.77
"accumulative-value": 28.78
},
{
"metric-type": "OSNR-bandwidth",

View File

@@ -3,5 +3,5 @@ response-id,source,destination,path_bandwidth,Pass?,nb of tsp pairs,total cost,t
1,trx Brest_KLA,trx Vannes_KBE,10.0,True,1,1,Voyager,mode 1,22.65,22.11,18.03,32.0,1.0,trx Brest_KLA | roadm Brest_KLA | east edfa in Brest_KLA to Morlaix | fiber (Brest_KLA → Morlaix)-F060 | east fused spans in Morlaix | fiber (Morlaix → Lannion_CAS)-F059 | west edfa in Lannion_CAS to Morlaix | roadm Lannion_CAS | east edfa in Lannion_CAS to Corlay | fiber (Lannion_CAS → Corlay)-F061 | west fused spans in Corlay | fiber (Corlay → Loudeac)-F010 | west fused spans in Loudeac | fiber (Loudeac → Lorient_KMA)-F054 | west edfa in Lorient_KMA to Loudeac | roadm Lorient_KMA | east edfa in Lorient_KMA to Vannes_KBE | fiber (Lorient_KMA → Vannes_KBE)-F055 | west edfa in Vannes_KBE to Lorient_KMA | roadm Vannes_KBE | trx Vannes_KBE,"-276, 4",,,
3,trx Lannion_CAS,trx Rennes_STA,60.0,True,1,1,vendorA_trx-type1,mode 1,28.29,25.85,21.77,32.0,1.0,trx Lannion_CAS | roadm Lannion_CAS | east edfa in Lannion_CAS to Stbrieuc | fiber (Lannion_CAS → Stbrieuc)-F056 | east edfa in Stbrieuc to Rennes_STA | fiber (Stbrieuc → Rennes_STA)-F057 | west edfa in Rennes_STA to Stbrieuc | roadm Rennes_STA | trx Rennes_STA,"-284, 4",,,
4,trx Rennes_STA,trx Lannion_CAS,150.0,True,1,1,vendorA_trx-type1,mode 2,22.27,22.15,15.05,64.0,0.0,trx Rennes_STA | roadm Rennes_STA | east edfa in Rennes_STA to Ploermel | fiber (Rennes_STA → Ploermel)- | east edfa in Ploermel to Vannes_KBE | fiber (Ploermel → Vannes_KBE)- | west edfa in Vannes_KBE to Ploermel | roadm Vannes_KBE | east edfa in Vannes_KBE to Lorient_KMA | fiber (Vannes_KBE → Lorient_KMA)-F055 | west edfa in Lorient_KMA to Vannes_KBE | roadm Lorient_KMA | east edfa in Lorient_KMA to Loudeac | fiber (Lorient_KMA → Loudeac)-F054 | east fused spans in Loudeac | fiber (Loudeac → Corlay)-F010 | east fused spans in Corlay | fiber (Corlay → Lannion_CAS)-F061 | west edfa in Lannion_CAS to Corlay | roadm Lannion_CAS | trx Lannion_CAS,"-266, 6",,,
5,trx Rennes_STA,trx Lannion_CAS,20.0,True,1,1,vendorA_trx-type1,mode 2,30.79,28.77,21.68,64.0,3.0,trx Rennes_STA | roadm Rennes_STA | east edfa in Rennes_STA to Stbrieuc | fiber (Rennes_STA → Stbrieuc)-F057 | west edfa in Stbrieuc to Rennes_STA | fiber (Stbrieuc → Lannion_CAS)-F056 | west edfa in Lannion_CAS to Stbrieuc | roadm Lannion_CAS | trx Lannion_CAS,"-274, 6",,,
5,trx Rennes_STA,trx Lannion_CAS,20.0,True,1,1,vendorA_trx-type1,mode 2,30.79,28.78,21.68,64.0,3.0,trx Rennes_STA | roadm Rennes_STA | east edfa in Rennes_STA to Stbrieuc | fiber (Rennes_STA → Stbrieuc)-F057 | west edfa in Stbrieuc to Rennes_STA | fiber (Stbrieuc → Lannion_CAS)-F056 | west edfa in Lannion_CAS to Stbrieuc | roadm Lannion_CAS | trx Lannion_CAS,"-274, 6",,,
6,,,,NO_PATH,,,,,,,,,,,,,,
1 response-id source destination path_bandwidth Pass? nb of tsp pairs total cost transponder-type transponder-mode OSNR-0.1nm SNR-0.1nm SNR-bandwidth baud rate (Gbaud) input power (dBm) path spectrum (N,M) reversed path OSNR-0.1nm reversed path SNR-0.1nm reversed path SNR-bandwidth
3 1 trx Brest_KLA trx Vannes_KBE 10.0 True 1 1 Voyager mode 1 22.65 22.11 18.03 32.0 1.0 trx Brest_KLA | roadm Brest_KLA | east edfa in Brest_KLA to Morlaix | fiber (Brest_KLA → Morlaix)-F060 | east fused spans in Morlaix | fiber (Morlaix → Lannion_CAS)-F059 | west edfa in Lannion_CAS to Morlaix | roadm Lannion_CAS | east edfa in Lannion_CAS to Corlay | fiber (Lannion_CAS → Corlay)-F061 | west fused spans in Corlay | fiber (Corlay → Loudeac)-F010 | west fused spans in Loudeac | fiber (Loudeac → Lorient_KMA)-F054 | west edfa in Lorient_KMA to Loudeac | roadm Lorient_KMA | east edfa in Lorient_KMA to Vannes_KBE | fiber (Lorient_KMA → Vannes_KBE)-F055 | west edfa in Vannes_KBE to Lorient_KMA | roadm Vannes_KBE | trx Vannes_KBE -276, 4
4 3 trx Lannion_CAS trx Rennes_STA 60.0 True 1 1 vendorA_trx-type1 mode 1 28.29 25.85 21.77 32.0 1.0 trx Lannion_CAS | roadm Lannion_CAS | east edfa in Lannion_CAS to Stbrieuc | fiber (Lannion_CAS → Stbrieuc)-F056 | east edfa in Stbrieuc to Rennes_STA | fiber (Stbrieuc → Rennes_STA)-F057 | west edfa in Rennes_STA to Stbrieuc | roadm Rennes_STA | trx Rennes_STA -284, 4
5 4 trx Rennes_STA trx Lannion_CAS 150.0 True 1 1 vendorA_trx-type1 mode 2 22.27 22.15 15.05 64.0 0.0 trx Rennes_STA | roadm Rennes_STA | east edfa in Rennes_STA to Ploermel | fiber (Rennes_STA → Ploermel)- | east edfa in Ploermel to Vannes_KBE | fiber (Ploermel → Vannes_KBE)- | west edfa in Vannes_KBE to Ploermel | roadm Vannes_KBE | east edfa in Vannes_KBE to Lorient_KMA | fiber (Vannes_KBE → Lorient_KMA)-F055 | west edfa in Lorient_KMA to Vannes_KBE | roadm Lorient_KMA | east edfa in Lorient_KMA to Loudeac | fiber (Lorient_KMA → Loudeac)-F054 | east fused spans in Loudeac | fiber (Loudeac → Corlay)-F010 | east fused spans in Corlay | fiber (Corlay → Lannion_CAS)-F061 | west edfa in Lannion_CAS to Corlay | roadm Lannion_CAS | trx Lannion_CAS -266, 6
6 5 trx Rennes_STA trx Lannion_CAS 20.0 True 1 1 vendorA_trx-type1 mode 2 30.79 28.77 28.78 21.68 64.0 3.0 trx Rennes_STA | roadm Rennes_STA | east edfa in Rennes_STA to Stbrieuc | fiber (Rennes_STA → Stbrieuc)-F057 | west edfa in Stbrieuc to Rennes_STA | fiber (Stbrieuc → Lannion_CAS)-F056 | west edfa in Lannion_CAS to Stbrieuc | roadm Lannion_CAS | trx Lannion_CAS -274, 6
7 6 NO_PATH

View File

@@ -1,98 +1,98 @@
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69,69,9.409053958216297e-05,1.7155419863785074e-08,8.123470846820667e-08
70,70,9.263017577528553e-05,1.698550325493719e-08,7.960123192429599e-08
71,71,9.119540203213482e-05,1.681718296074974e-08,7.800139303615196e-08
72,72,8.978572151582878e-05,1.6650437769918198e-08,7.643446000507212e-08
73,73,8.840064792008674e-05,1.648524670194174e-08,7.489971794966337e-08
74,74,8.704752218385389e-05,1.6323921332107853e-08,7.340306015991841e-08
75,75,8.571782151670807e-05,1.616417166590195e-08,7.228178645083984e-08
76,76,8.441109599127084e-05,1.6005979385519616e-08,7.117988659258102e-08
77,77,8.312693723019766e-05,1.584932678267078e-08,7.009701621983393e-08
78,78,8.186491243372442e-05,1.569419608150936e-08,6.90328103730187e-08
79,79,8.062459786565112e-05,1.554056978222129e-08,6.798691173543069e-08
80,80,7.94055784447779e-05,1.538843064058219e-08,6.695897028871753e-08
81,81,7.820755061857071e-05,1.523776381588298e-08,6.594873006137818e-08
82,82,7.703011159024537e-05,1.5088552513719166e-08,6.495585139392737e-08
83,83,7.587286701386211e-05,1.494078021244127e-08,6.398000175307732e-08
84,84,7.483848702669918e-05,1.481852757489104e-08,6.310775801171533e-08
85,85,7.382059659354297e-05,1.4697575551136038e-08,6.224941913167962e-08
86,86,7.281889798107282e-05,1.4577917663325962e-08,6.140473404853674e-08
87,87,7.183165666349203e-05,1.4459532716368791e-08,6.057224011868445e-08
88,88,7.08586484091438e-05,1.4342415672865158e-08,5.975174825816752e-08
89,89,6.989965271824705e-05,1.422656189652791e-08,5.8943072529944956e-08
90,90,6.895445267562359e-05,1.411196715686287e-08,5.814603001970201e-08
91,91,6.802197391413278e-05,1.3998609279853771e-08,5.735971476442124e-08
92,92,6.710204336793658e-05,1.3886485591019257e-08,5.6583980825863337e-08
93,93,6.619449037899918e-05,1.3775593851234834e-08,5.581868429617485e-08
94,94,6.515268324911003e-05,1.3628882565600955e-08,5.494017759648076e-08
95,95,6.412720169535454e-05,1.3483606920728348e-08,5.407543748332493e-08
1 Unnamed: 0 signal ase nli
2 0 0 0.0002866683470642085 0.00028461768874888705 3.455694800734997e-08 3.436639817118301e-08 2.1767706055953313e-07 2.1584395398371026e-07
3 1 1 0.0002842930902246378 0.00028211666293476406 3.4445260342151434e-08 3.423996759499589e-08 2.20064716108892e-07 2.1809245861954257e-07
4 2 2 0.00028193841273409963 0.0002796385058453953 3.4334217950641774e-08 3.411427963331182e-08 2.2239856929822977e-07 2.2028548201656645e-07
5 3 3 0.0002796041237984927 0.0002771829951915728 3.422381587730477e-08 3.398932824433299e-08 2.2467937700272344e-07 2.224238538437057e-07
6 4 4 0.00027589218358262 0.00027338904730863836 3.3956266402003705e-08 3.371109366484941e-08 2.2576401766047814e-07 2.2339638015421542e-07
7 5 5 0.0002722303444814487 0.0002696478418176822 3.3690926476196685e-08 3.3435235312069656e-08 2.2678094635121413e-07 2.2430128879557504e-07
8 6 6 0.00026861791294303266 0.00026595862771951147 3.342777134334201e-08 3.316172589770091e-08 2.2773179097640802e-07 2.2514026889123164e-07
9 7 7 0.00026505174756069215 0.0002623182345801782 3.316675429137828e-08 3.289051654795761e-08 2.2861602718115889e-07 2.2591288176837496e-07
10 8 8 0.0002615312775878486 0.00025872603823298804 3.290785186664305e-08 3.262158136113203e-08 2.294352005376256e-07 2.2662073272007013e-07
11 9 9 0.0002577007690018081 0.0002548422077504841 3.26092154155734e-08 3.231480491053109e-08 2.2987401053105823e-07 2.269632917207947e-07
12 10 10 0.00025392474812815994 0.0002510150800727548 3.231329178154223e-08 3.201089350512762e-08 2.3024928325076607e-07 2.2724305184561305e-07
13 11 11 0.0002501957390130402 0.0002472372108596339 3.201993265117851e-08 3.1709697987730424e-08 2.3055653947072044e-07 2.274556638142854e-07
14 12 12 0.0002465133077961936 0.0002435081184388883 3.172911082648897e-08 3.1411188710954923e-08 2.3079745224174315e-07 2.2760284870806473e-07
15 13 13 0.00024287702172285261 0.0002398273228957255 3.144079924487205e-08 3.1115336193393125e-08 2.309736700807475e-07 2.2768630096746835e-07
16 14 14 0.00023918644496802598 0.00023610099948769217 3.1142660565561954e-08 3.081056270085416e-08 2.3099023972100006e-07 2.27617695956038e-07
17 15 15 0.00023554415781363666 0.00023242489102440902 3.084719002003063e-08 3.050859262250336e-08 2.3094533745656626e-07 2.274887362838919e-07
18 16 16 0.0002319496781605366 0.00022879847092097078 3.0554358283826426e-08 3.020939430576677e-08 2.3084061926161906e-07 2.273011122655665e-07
19 17 17 0.000228402746264896 0.00022522143236587835 3.026413819712743e-08 2.991293825364657e-08 2.306779372506828e-07 2.27056704831033e-07
20 18 18 0.00022490287297566154 0.00022169324298613107 2.997650061885732e-08 2.9619193056612953e-08 2.2679314039447925e-07 2.2315585168570266e-07
21 19 19 0.0002210339853226993 0.0002178178461312317 2.9639081336421986e-08 2.9278097616561043e-08 2.225476971173533e-07 2.189169855906453e-07
22 20 20 0.00021722472675673681 0.0002140033759047069 2.9305156366940595e-08 2.8940600865395135e-08 2.1837424228396343e-07 2.1475128288390263e-07
23 21 21 0.00021347443350916938 0.00021024912752446947 2.8974683300060073e-08 2.8606658136588645e-08 2.1427183120915025e-07 2.1065775386659407e-07
24 22 22 0.00020978233224910872 0.000206554289175574 2.864761802749998e-08 2.827622313698371e-08 2.1023941353936252e-07 2.0663530613832196e-07
25 23 23 0.0002061476568412488 0.00020291805701626135 2.832391679540816e-08 2.7949249962519028e-08 2.0627595093585302e-07 2.0268286030011824e-07
26 24 24 0.0002028237056285935 0.00019961589106178864 2.8034565895618217e-08 2.765975998385304e-08 2.0263423697267893e-07 1.9907485701921993e-07
27 25 25 0.00019954715529254185 0.00019636137150032674 2.7748013284124615e-08 2.7373095433009936e-08 1.9905015325521452e-07 1.9552454239910457e-07
28 26 26 0.0001963174528000437 0.0001931539326017224 2.7464226779716075e-08 2.7089223372231097e-08 1.9552292765090665e-07 1.9203113225618846e-07
29 27 27 0.00019313475803109547 0.00018999370973089413 2.718318103861899e-08 2.68081175642488e-08 1.920525003885138e-07 1.885945418493717e-07
30 28 28 0.00018999848980183525 0.00018688010986276862 2.6904843987797665e-08 2.6529745205109403e-08 1.8863807494364378e-07 1.8521396370161997e-07
31 29 29 0.00018690807208013476 0.00018381254538966195 2.6629183784477213e-08 2.6254073731438793e-08 1.852788635171717e-07 1.818885993764317e-07
32 30 30 0.00018386293497138034 0.00018079043504395085 2.635616888672288e-08 2.5981070886269e-08 1.8197408797080072e-07 1.78617660347401e-07
33 31 31 0.0001808626075954048 0.00017781329518445636 2.608576967648626e-08 2.5710706310905614e-08 1.7872307155753016e-07 1.7540045799671345e-07
34 32 32 0.00017790652540681915 0.00017488055045064023 2.5817954962418864e-08 2.544294811944895e-08 1.7552504805064169e-07 1.7223621637516225e-07
35 33 33 0.00017499412908771533 0.00017199163096488036 2.5552693765056307e-08 2.5177764647772334e-08 1.723792598876346e-07 1.691241684952796e-07
36 34 34 0.0001721914205512116 0.00016923198600435235 2.529821177538242e-08 2.4926164244345613e-08 1.69350416018518e-07 1.6614800249846462e-07
37 35 35 0.00016942913344260413 0.00016651223518287261 2.5046172734840803e-08 2.4676978561810828e-08 1.663699885487624e-07 1.632195038378843e-07
38 36 36 0.0001667067703020692 0.00016383188581753214 2.4796549229968703e-08 2.443018036493577e-08 1.6343730102750106e-07 1.6033800467387247e-07
39 37 37 0.00016402494737034808 0.0001611915388800597 2.4549324625938814e-08 2.418575294396746e-08 1.602954566731582e-07 1.5725354810318075e-07
40 38 38 0.0001613831201060569 0.0001585906555197023 2.430447139995257e-08 2.394366896238391e-08 1.5720933628441338e-07 1.5422386234766858e-07
41 39 39 0.00015878075021906192 0.00015602870309712287 2.406196225171638e-08 2.3703901303924173e-08 1.541780421866172e-07 1.5124806354481474e-07
42 40 40 0.00015621730558753943 0.0001535051551031788 2.3821770097360405e-08 2.3466423066629146e-08 1.5120068940449393e-07 1.4832528036973196e-07
43 41 41 0.000153694061439545 0.0001510212597387019 2.3583901792213434e-08 2.3231240549132765e-08 1.4827814327673852e-07 1.4545635735033158e-07
44 42 42 0.00015121040694331307 0.00014857641307701306 2.3348329147104765e-08 2.2998325757462813e-08 1.4540943949658482e-07 1.4264034538487909e-07
45 43 43 0.00014876574026315321 0.00014617002011358969 2.3115024224465226e-08 2.276765094680208e-08 1.425936300160931e-07 1.3987631140917146e-07
46 44 44 0.00014623043935025647 0.00014368188097051016 2.2864250993313975e-08 2.252080862505795e-08 1.397065102093322e-07 1.3704924606778587e-07
47 45 45 0.00014373723010448477 0.00014123500136325632 2.2616046400344574e-08 2.227648394665253e-08 1.3687531950007013e-07 1.342768563640109e-07
48 46 46 0.0001412854316913198 0.00013882871459443323 2.2370377299191064e-08 2.2034644371118186e-08 1.3409901704421416e-07 1.31558126246417e-07
49 47 47 0.00013887544742801196 0.00013646341840215168 2.2127221340618422e-08 2.1795267893415555e-08 1.313775961274423e-07 1.2889305332810146e-07
50 48 48 0.0001365065605420479 0.00013413841082142 2.1886545482453933e-08 2.155832207437193e-08 1.2870998887773554e-07 1.2628059500046815e-07
51 49 49 0.00013417806673897108 0.00013185300210097483 2.164831702445933e-08 2.132377480769977e-08 1.2609514810979993e-07 1.2371972883222934e-07
52 50 50 0.00013188927370907155 0.00012960651423053282 2.1412503578881085e-08 2.1091594291962705e-08 1.2353204666803017e-07 1.2120945193769904e-07
53 51 51 0.00012964085531237725 0.00012739961074346843 2.117909919147538e-08 2.0861774576065466e-08 1.2102094138348867e-07 1.1875002031242091e-07
54 52 52 0.00012743207116500861 0.00012523156633255863 2.094807084934547e-08 2.0634283235397628e-08 1.1856076487337955e-07 1.1634039147633555e-07
55 53 53 0.0001252621950917354 0.00012310166960708747 2.0719385892834214e-08 2.0409088189365173e-08 1.161504721855161e-07 1.1397954484148417e-07
56 54 54 0.00012308423338164536 0.00012096529298347188 2.0485363383535514e-08 2.0178855168888422e-08 1.1374627006340893e-07 1.1162594309222065e-07
57 55 55 0.00012094535834842106 0.00011886718830718708 2.0253755300794944e-08 1.9950983760858723e-08 1.1139168040879032e-07 1.093208043434055e-07
58 56 56 0.00011884484242431182 0.00011680664424128133 2.0024527468430072e-08 1.9725440474060072e-08 1.0897675288127846e-07 1.0695583879566178e-07
59 57 57 0.00011678298769107047 0.00011478396058700633 1.9797656169961167e-08 1.950220202088885e-08 1.0661409941810817e-07 1.0464196732373097e-07
60 58 58 0.0001147590394591346 0.00011279839933843229 1.9573107382367898e-08 1.9281235055452333e-08 1.0430256532408603e-07 1.0237806316789167e-07
61 59 59 0.00011277225867179729 0.00011084923776207272 1.935084744632288e-08 1.906250658388995e-08 1.0204102225018314e-07 1.0016302524437989e-07
62 60 60 0.00011082192110664448 0.00010893576763351642 1.913084301808743e-08 1.884598391874928e-08 9.982836715827351e-08 9.79957771514819e-08
63 61 61 0.00010890831555726861 0.00010705827573621923 1.8913080844667903e-08 1.8631654017664683e-08 9.76644171489201e-08 9.587614496435341e-08
64 62 62 0.00010703069380321927 0.00010521603014069265 1.8697527263494167e-08 1.8419483867548078e-08 9.554805889434612e-08 9.380304193213288e-08
65 63 63 0.00010518832400867466 0.00010340831493293095 1.8484148971365717e-08 1.8209440802910146e-08 9.347820572370307e-08 9.1775407295826e-08
66 64 64 0.00010353027948847247 0.00010178667717264663 1.8300360477604286e-08 1.8029552937387797e-08 9.158630538270034e-08 8.992671343771878e-08
67 65 65 0.00010190114820620951 0.00010019319833745167 1.8118339508838893e-08 1.7851381317094802e-08 8.97332684173061e-08 8.811583172055121e-08
68 66 66 0.00010030037817345079 9.86273426809622e-05 1.7938063167097722e-08 1.767490377589294e-08 8.791826040657302e-08 8.634195328719705e-08
69 67 67 9.872746699919663e-05 9.70886230248071e-05 1.775950920143011e-08 1.7500098774413897e-08 8.614049912759125e-08 8.460432022445385e-08
70 68 68 9.718188342878233e-05 9.557652367577673e-05 1.7582655209782296e-08 1.7326944621204857e-08 8.439918541238176e-08 8.290215772518696e-08
71 69 69 9.566310719955732e-05 9.409053958216297e-05 1.740747903977101e-08 1.7155419863785074e-08 8.269353818341506e-08 8.123470846820667e-08
72 70 70 9.417062845393912e-05 9.263017577528553e-05 1.7233958752991638e-08 1.698550325493719e-08 8.102279372648069e-08 7.960123192429599e-08
73 71 71 9.27044102709892e-05 9.119540203213482e-05 1.7062082036004708e-08 1.681718296074974e-08 7.938660156523957e-08 7.800139303615196e-08
74 72 72 9.12639411274833e-05 8.978572151582878e-05 1.6891826998956218e-08 1.6650437769918198e-08 7.778420731697601e-08 7.643446000507212e-08
75 73 73 8.984872036936478e-05 8.840064792008674e-05 1.6723171993235663e-08 1.648524670194174e-08 7.621487408851861e-08 7.489971794966337e-08
76 74 74 8.845926525396718e-05 8.704752218385389e-05 1.6557077218868872e-08 1.6323921332107853e-08 7.467873241780861e-08 7.340306015991841e-08
77 75 75 8.709405706837696e-05 8.571782151670807e-05 1.6392620086127443e-08 1.616417166590195e-08 7.352620174175576e-08 7.228178645083984e-08
78 76 76 8.575262707251024e-05 8.441109599127084e-05 1.6229781230847938e-08 1.6005979385519616e-08 7.239374499535451e-08 7.117988659258102e-08
79 77 77 8.44345490553445e-05 8.312693723019766e-05 1.6068541919248203e-08 1.584932678267078e-08 7.128100236441453e-08 7.009701621983393e-08
80 78 78 8.313937224726923e-05 8.186491243372442e-05 1.5908883354530017e-08 1.569419608150936e-08 7.018759330199112e-08 6.90328103730187e-08
81 79 79 8.18666553697718e-05 8.062459786565112e-05 1.5750787028460176e-08 1.554056978222129e-08 6.91131452736768e-08 6.798691173543069e-08
82 80 80 8.061596620368467e-05 7.94055784447779e-05 1.5594234699428168e-08 1.538843064058219e-08 6.80572933931087e-08 6.695897028871753e-08
83 81 81 7.93869860927298e-05 7.820755061857071e-05 1.54392105845737e-08 1.523776381588298e-08 6.701976864553917e-08 6.594873006137818e-08
84 82 82 7.81792957880057e-05 7.703011159024537e-05 1.528569691328632e-08 1.5088552513719166e-08 6.600021709431229e-08 6.495585139392737e-08
85 83 83 7.69924848842345e-05 7.587286701386211e-05 1.5133676199095668e-08 1.494078021244127e-08 6.499829226870119e-08 6.398000175307732e-08
86 84 84 7.592423495462984e-05 7.483848702669918e-05 1.5006007729453082e-08 1.481852757489104e-08 6.40964585216171e-08 6.310775801171533e-08
87 85 85 7.487323130273564e-05 7.382059659354297e-05 1.4879695488874347e-08 1.4697575551136038e-08 6.320918435915818e-08 6.224941913167962e-08
88 86 86 7.383915942693816e-05 7.281889798107282e-05 1.475473179887786e-08 1.4577917663325962e-08 6.233620427401105e-08 6.140473404853674e-08
89 87 87 7.282024738915393e-05 7.183165666349203e-05 1.4631093950979863e-08 1.4459532716368791e-08 6.147602236758762e-08 6.057224011868445e-08
90 88 88 7.181625694043332e-05 7.08586484091438e-05 1.4508775744557438e-08 1.4342415672865158e-08 6.062843750629826e-08 5.975174825816752e-08
91 89 89 7.082695384072487e-05 6.989965271824705e-05 1.4387771381868581e-08 1.422656189652791e-08 5.979325194092954e-08 5.8943072529944956e-08
92 90 90 6.985210770121701e-05 6.895445267562359e-05 1.4268075471622408e-08 1.411196715686287e-08 5.8970271173546604e-08 5.814603001970201e-08
93 91 91 6.889061932028676e-05 6.802197391413278e-05 1.414966436041678e-08 1.3998609279853771e-08 5.8158567240485706e-08 5.735971476442124e-08
94 92 92 6.79423037538828e-05 6.710204336793658e-05 1.4032534237451406e-08 1.3886485591019257e-08 5.7357984009008465e-08 5.6583980825863337e-08
95 93 93 6.700697867481953e-05 6.619449037899918e-05 1.3916681725635683e-08 1.3775593851234834e-08 5.656836755557654e-08 5.581868429617485e-08
96 94 94 6.594003301527265e-05 6.515268324911003e-05 1.3765768410137306e-08 1.3628882565600955e-08 5.5667634894222326e-08 5.494017759648076e-08
97 95 95 6.489000516837228e-05 6.412720169535454e-05 1.3616343300622314e-08 1.3483606920728348e-08 5.4781184522010985e-08 5.407543748332493e-08

View File

@@ -146,7 +146,7 @@ req id demand GSNR@bandwidth A-Z (Z-A) GSNR@0
0 trx Lorient_KMA to trx Vannes_KBE : 24.83 28.92 14 mode 1 100.0 1 (-284,4)
1 trx Brest_KLA to trx Vannes_KBE : 17.75 21.83 14 mode 1 200.0 2 (-272,8)
3 trx Lannion_CAS to trx Rennes_STA : 22.21 26.29 13 mode 1 60.0 1 (-284,4)
4 trx Rennes_STA to trx Lannion_CAS : 16.07 23.29 17 mode 2 150.0 1 (-258,6)
4 trx Rennes_STA to trx Lannion_CAS : 16.06 23.29 17 mode 2 150.0 1 (-258,6)
5 trx Rennes_STA to trx Lannion_CAS : 20.31 27.54 17 mode 2 20.0 1 (-274,6)
7 | 6 trx Lannion_CAS to trx Lorient_KMA : 19.52 23.61 14 mode 1 700.0 7 (-224,28)
7b trx Lannion_CAS to trx Lorient_KMA : 19.61 23.69 14 mode 1 400.0 4 (-172,24)

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@@ -22,18 +22,18 @@ RamanFiber Span1
total loss (dB): 17.00
(includes conn loss (dB) in: 0.50 out: 0.50)
(conn loss out includes EOL margin defined in eqpt_config.json)
pch out (dBm): -7.71
pch out (dBm): -7.77
Fused Fused1
loss (dB): 0.00
Edfa Edfa1
type_variety: std_low_gain
effective gain(dB): 5.71
effective gain(dB): 5.77
(before att_in and before output VOA)
noise figure (dB): 13.29
noise figure (dB): 13.23
(including att_in)
pad att_in (dB): 2.29
Power In (dBm): 11.11
Power Out (dBm): 16.82
pad att_in (dB): 2.23
Power In (dBm): 11.04
Power Out (dBm): 16.81
Delta_P (dB): -2.00
target pch (dBm): -2.00
effective pch (dBm): -2.00
@@ -41,8 +41,8 @@ Edfa Edfa1
Transceiver Site_B
GSNR (0.1nm, dB): 31.44
GSNR (signal bw, dB): 27.36
OSNR ASE (0.1nm, dB): 34.22
OSNR ASE (signal bw, dB): 30.14
OSNR ASE (0.1nm, dB): 34.21
OSNR ASE (signal bw, dB): 30.13
CD (ps/nm): 1336.00
PMD (ps): 0.36
PDL (dB): 0.00
@@ -52,82 +52,82 @@ Transmission result for input power = 0.00 dBm:
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)
1 191.35 0.18 31.61 31.43 28.51
2 191.40 0.14 31.59 31.34 28.45
3 191.45 0.11 31.57 31.26 28.40
4 191.50 0.07 31.55 31.17 28.35
5 191.55 0.02 31.52 31.09 28.29
6 191.60 -0.04 31.49 31.02 28.24
7 191.65 -0.10 31.46 30.94 28.18
8 191.70 -0.16 31.43 30.86 28.13
9 191.75 -0.21 31.40 30.79 28.07
10 191.80 -0.28 31.36 30.71 28.02
11 191.85 -0.34 31.33 30.64 27.96
12 191.90 -0.40 31.29 30.57 27.91
13 191.95 -0.47 31.26 30.50 27.85
14 192.00 -0.53 31.22 30.43 27.80
15 192.05 -0.60 31.18 30.36 27.74
1 191.35 0.21 31.62 31.43 28.52
2 191.40 0.17 31.60 31.35 28.46
3 191.45 0.13 31.58 31.26 28.41
4 191.50 0.09 31.56 31.18 28.36
5 191.55 0.03 31.53 31.10 28.30
6 191.60 -0.02 31.50 31.02 28.24
7 191.65 -0.08 31.46 30.94 28.19
8 191.70 -0.14 31.43 30.87 28.13
9 191.75 -0.20 31.40 30.79 28.08
10 191.80 -0.27 31.37 30.72 28.02
11 191.85 -0.33 31.33 30.65 27.97
12 191.90 -0.40 31.29 30.58 27.91
13 191.95 -0.46 31.26 30.51 27.86
14 192.00 -0.53 31.22 30.44 27.80
15 192.05 -0.59 31.18 30.37 27.75
16 192.10 -0.66 31.15 30.30 27.69
17 192.15 -0.73 31.11 30.23 27.64
18 192.20 -0.79 31.07 30.16 27.58
19 192.25 -0.86 31.04 30.17 27.57
20 192.30 -0.94 30.99 30.18 27.56
21 192.35 -1.01 30.95 30.19 27.54
22 192.40 -1.08 30.91 30.20 27.53
23 192.45 -1.16 30.86 30.20 27.51
24 192.50 -1.23 30.82 30.21 27.49
25 192.55 -1.30 30.78 30.22 27.48
26 192.60 -1.37 30.74 30.23 27.46
27 192.65 -1.44 30.70 30.23 27.45
28 192.70 -1.51 30.65 30.24 27.43
29 192.75 -1.58 30.61 30.25 27.42
30 192.80 -1.65 30.57 30.26 27.40
31 192.85 -1.72 30.53 30.27 27.38
32 192.90 -1.79 30.48 30.27 27.37
33 192.95 -1.86 30.44 30.28 27.35
34 193.00 -1.93 30.40 30.29 27.33
35 193.05 -2.00 30.35 30.30 27.32
36 193.10 -2.07 30.31 30.30 27.30
37 193.15 -2.14 30.27 30.31 27.28
38 193.20 -2.20 30.22 30.33 27.27
39 193.25 -2.27 30.18 30.35 27.25
40 193.30 -2.34 30.14 30.37 27.24
41 193.35 -2.41 30.09 30.38 27.23
42 193.40 -2.48 30.05 30.40 27.21
43 193.45 -2.55 30.00 30.42 27.20
44 193.50 -2.61 29.96 30.44 27.18
45 193.55 -2.69 29.91 30.46 27.16
46 193.60 -2.76 29.86 30.47 27.15
47 193.65 -2.83 29.82 30.49 27.13
48 193.70 -2.90 29.77 30.51 27.11
49 193.75 -2.98 29.72 30.53 27.10
50 193.80 -3.05 29.67 30.55 27.08
51 193.85 -3.12 29.62 30.57 27.06
52 193.90 -3.19 29.58 30.58 27.04
53 193.95 -3.26 29.53 30.60 27.02
54 194.00 -3.33 29.48 30.62 27.00
55 194.05 -3.41 29.43 30.64 26.98
56 194.10 -3.48 29.38 30.66 26.96
57 194.15 -3.55 29.33 30.72 26.96
58 194.20 -3.63 29.28 30.78 26.95
59 194.25 -3.70 29.23 30.83 26.95
60 194.30 -3.77 29.18 30.89 26.94
61 194.35 -3.85 29.12 30.96 26.93
62 194.40 -3.92 29.07 31.02 26.93
63 194.45 -3.99 29.02 31.08 26.92
64 194.50 -4.06 28.97 31.14 26.91
65 194.55 -4.13 28.92 31.21 26.91
66 194.60 -4.19 28.88 31.27 26.90
67 194.65 -4.26 28.83 31.34 26.89
68 194.70 -4.33 28.78 31.41 26.89
69 194.75 -4.39 28.73 31.47 26.88
70 194.80 -4.46 28.68 31.54 26.87
71 194.85 -4.52 28.64 31.61 26.86
72 194.90 -4.59 28.59 31.68 26.86
73 194.95 -4.65 28.54 31.76 26.85
74 195.00 -4.72 28.49 31.83 26.84
75 195.05 -4.78 28.44 31.91 26.83
76 195.10 -4.85 28.39 31.91 26.79
17 192.15 -0.73 31.11 30.24 27.64
18 192.20 -0.80 31.07 30.17 27.59
19 192.25 -0.86 31.03 30.18 27.57
20 192.30 -0.94 30.99 30.19 27.56
21 192.35 -1.02 30.94 30.20 27.54
22 192.40 -1.09 30.90 30.20 27.53
23 192.45 -1.17 30.86 30.21 27.51
24 192.50 -1.24 30.81 30.22 27.50
25 192.55 -1.31 30.77 30.23 27.48
26 192.60 -1.38 30.73 30.23 27.46
27 192.65 -1.45 30.69 30.24 27.45
28 192.70 -1.52 30.65 30.25 27.43
29 192.75 -1.59 30.60 30.26 27.42
30 192.80 -1.67 30.56 30.27 27.40
31 192.85 -1.74 30.52 30.27 27.38
32 192.90 -1.81 30.47 30.28 27.37
33 192.95 -1.88 30.43 30.29 27.35
34 193.00 -1.95 30.39 30.30 27.33
35 193.05 -2.02 30.34 30.30 27.31
36 193.10 -2.08 30.30 30.31 27.30
37 193.15 -2.15 30.26 30.32 27.28
38 193.20 -2.22 30.21 30.34 27.26
39 193.25 -2.29 30.17 30.36 27.25
40 193.30 -2.36 30.13 30.37 27.24
41 193.35 -2.43 30.08 30.39 27.22
42 193.40 -2.50 30.04 30.41 27.21
43 193.45 -2.56 29.99 30.43 27.19
44 193.50 -2.63 29.95 30.44 27.18
45 193.55 -2.70 29.90 30.46 27.16
46 193.60 -2.78 29.85 30.48 27.15
47 193.65 -2.85 29.80 30.50 27.13
48 193.70 -2.92 29.76 30.52 27.11
49 193.75 -2.99 29.71 30.54 27.09
50 193.80 -3.06 29.66 30.55 27.07
51 193.85 -3.14 29.61 30.57 27.06
52 193.90 -3.21 29.56 30.59 27.04
53 193.95 -3.28 29.52 30.61 27.02
54 194.00 -3.35 29.47 30.63 27.00
55 194.05 -3.42 29.42 30.65 26.98
56 194.10 -3.50 29.37 30.67 26.96
57 194.15 -3.57 29.32 30.72 26.95
58 194.20 -3.64 29.26 30.78 26.95
59 194.25 -3.72 29.21 30.84 26.94
60 194.30 -3.79 29.16 30.90 26.94
61 194.35 -3.86 29.11 30.96 26.93
62 194.40 -3.93 29.06 31.02 26.92
63 194.45 -4.01 29.01 31.09 26.91
64 194.50 -4.08 28.96 31.15 26.91
65 194.55 -4.14 28.91 31.21 26.90
66 194.60 -4.21 28.86 31.28 26.90
67 194.65 -4.27 28.82 31.34 26.89
68 194.70 -4.34 28.77 31.41 26.88
69 194.75 -4.41 28.72 31.48 26.88
70 194.80 -4.47 28.67 31.55 26.87
71 194.85 -4.54 28.63 31.62 26.86
72 194.90 -4.60 28.58 31.69 26.85
73 194.95 -4.67 28.53 31.76 26.84
74 195.00 -4.73 28.48 31.84 26.83
75 195.05 -4.80 28.43 31.91 26.82
76 195.10 -4.86 28.38 31.91 26.79
(No source node specified: picked Site_A)

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@@ -297,19 +297,19 @@ Fiber fiber (Loudeac → Lorient_KMA)-F054
pch out (dBm): -26.82
Edfa west edfa in Lorient_KMA to Loudeac
type_variety: test
effective gain(dB): 28.00
effective gain(dB): 27.99
(before att_in and before output VOA)
noise figure (dB): 5.76
(including att_in)
pad att_in (dB): 0.00
Power In (dBm): -6.99
Power Out (dBm): 21.04
Power Out (dBm): 21.03
Delta_P (dB): -1.82
target pch (dBm): 1.18
effective pch (dBm): 1.18
effective pch (dBm): 1.17
output VOA (dB): 0.00
Roadm roadm Lorient_KMA
effective loss (dB): 21.18
effective loss (dB): 21.17
pch out (dBm): -20.00
Transceiver trx Lorient_KMA
GSNR (0.1nm, dB): 23.94

1
tests/requirements.txt Normal file
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@@ -0,0 +1 @@
pytest>=6.2.5,<7

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@@ -271,14 +271,14 @@ def test_roadm_target_power(prev_node_type, effective_pch_out_db, power_dbm):
if prev_node_type == 'edfa':
# edfa prev_node sets input power to roadm to a high enough value:
# check that target power is correctly set in the ROADM
assert_allclose(el.pch_out_db, effective_pch_out_db, rtol=1e-3)
assert_allclose(el.ref_pch_out_dbm, effective_pch_out_db, rtol=1e-3)
# Check that egress power of roadm is equal to target power
assert_allclose(power_out_roadm, db2lin(effective_pch_out_db - 30), rtol=1e-3)
elif prev_node_type == 'fused':
# fused prev_node does reamplfy power after fiber propagation, so input power
# to roadm is low.
# check that target power correctly reports power_dbm from previous propagation
assert_allclose(el.pch_out_db, effective_pch_out_db + power_dbm, rtol=1e-3)
assert_allclose(el.ref_pch_out_dbm, effective_pch_out_db + power_dbm, rtol=1e-3)
# Check that egress power of roadm is equalized to the min carrier input power.
assert_allclose(power_out_roadm, min_power_in_roadm, rtol=1e-3)
else:

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@@ -4,7 +4,7 @@ skipsdist = True
[testenv]
deps =
-r{toxinidir}/requirements.txt
pytest>=6.2.5,<7
-r{toxinidir}/tests/requirements.txt
cover: pytest-cov
linters: flake8
linters: pep8-naming