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19
AUTHORS.rst
Normal file
19
AUTHORS.rst
Normal file
@@ -0,0 +1,19 @@
|
||||
gnpy is written and maintained by the Telecom Infra Project with work
|
||||
contributed by the following TIP members.
|
||||
|
||||
To learn how to contribute, please see CONTRIBUTING.md
|
||||
|
||||
(*in alphabetical order*)
|
||||
|
||||
- Alessio Ferrari (Politecnico di Torino) <alessio.ferrari@polito.it>
|
||||
- Brian Taylor (Facebook) <briantaylor@fb.com>
|
||||
- David Boertjes (Ciena) <dboertje@ciena.com>
|
||||
- Esther Le Rouzic (Orange) <esther.lerouzic@orange.com>
|
||||
- Gabriele Galimberti (Cisco) <ggalimbe@cisco.com>
|
||||
- Gert Grammel (Juniper Networks) <ggrammel@juniper.net>
|
||||
- Gilad Goldfarb (Facebook) <giladg@fb.com>
|
||||
- James Powell (Telecom Infra Project) <james.powell@telecominfraproject.com>
|
||||
- Jeanluc Auge (Orange) <jeanluc.auge@orange.com>
|
||||
- Mattia Cantono (Politecnico di Torino) <mattia.cantono@polito.it>
|
||||
- Vittorio Curri (Politecnico di Torino) <vittorio.curri@polito.it>
|
||||
- Xufeng Liu (Jabil) <xufeng_liu@jabil.com>
|
||||
@@ -1,17 +0,0 @@
|
||||
Contributors in alphabetical order
|
||||
==================================
|
||||
|
||||
Name | Surname | Affiliation | Contact
|
||||
-----|---------|-------------|--------
|
||||
Alessio | Ferrari | Politecnico di Torino | alessio.ferrari@polito.it
|
||||
Brian | Taylor | Facebook | briantaylor@fb.com
|
||||
David | Boertjes | Ciena | dboertje@ciena.com
|
||||
Esther | Le Rouzic | Orange | esther.lerouzic@orange.com
|
||||
Gabriele | Galimberti | Cisco | ggalimbe@cisco.com
|
||||
Gert | Grammel | Juniper Networks | ggrammel@juniper.net
|
||||
Gilad | Goldfarb | Facebook | giladg@fb.com
|
||||
James | Powell | Consultant | james@dontusethiscode.com
|
||||
Jeanluc | Auge | Orange | jeanluc.auge@orange.com
|
||||
Liu | Xufeng | Jabil | Xufeng_Liu@jabil.com
|
||||
Mattia | Cantono | Politecnico di Torino | mattia.cantono@polito.it
|
||||
Vittorio | Curri | Politecnico di Torino | vittorio.curri@polito.it
|
||||
12
README.rst
12
README.rst
@@ -1,13 +1,13 @@
|
||||
====
|
||||
====================================================================
|
||||
`gnpy`: mesh optical network route planning and optimization library
|
||||
====
|
||||
====================================================================
|
||||
|
||||
|docs| |build|
|
||||
|
||||
**gnpy is an open-source, community-developed library for building route planning
|
||||
and optimization tools in real-world mesh optical networks.**
|
||||
|
||||
`gnpy <http://github.com/telecominfraproject/gnpy>`_ is:
|
||||
`gnpy <http://github.com/telecominfraproject/gnpy>`__ is:
|
||||
|
||||
- a sponsored project of the `OOPT/PSE <http://telecominfraproject.com/project-groups-2/backhaul-projects/open-optical-packet-transport/>`_ working group of the `Telecom Infra Project <http://telecominfraproject.com>`_.
|
||||
- fully community-driven, fully open source library
|
||||
@@ -21,7 +21,7 @@ Documentation: https://gnpy.readthedocs.io
|
||||
Installation
|
||||
------------
|
||||
|
||||
``gnpy`` is hosted in the `Python Package Index <http://pypi.org/>`_ (`gnpy <https://pypi.org/project/gnpy/>`_). It can be installed via:
|
||||
``gnpy`` is hosted in the `Python Package Index <http://pypi.org/>`_ (`gnpy <https://pypi.org/project/gnpy/>`__). It can be installed via:
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
@@ -114,7 +114,7 @@ See the `Onboarding Guide
|
||||
<https://github.com/Telecominfraproject/gnpy/wiki/Onboarding-Guide>`_ for
|
||||
specific details on code contribtions.
|
||||
|
||||
See `AUTHORS.Md <AUTHORS.Md>`_ for past and present contributors.
|
||||
See `AUTHORS.rst <AUTHORS.rst>`_ for past and present contributors.
|
||||
|
||||
Project Background
|
||||
------------------
|
||||
@@ -187,5 +187,5 @@ License
|
||||
|
||||
``gnpy`` is distributed under a standard BSD 3-Clause License.
|
||||
|
||||
See `LICENSE <LICENSE>`_ for more details.
|
||||
See `LICENSE <LICENSE>`__ for more details.
|
||||
|
||||
|
||||
@@ -1,17 +0,0 @@
|
||||
Contributors in alphabetical order
|
||||
==================================
|
||||
|
||||
Name | Surname | Affiliation | Contact
|
||||
-----|---------|-------------|--------
|
||||
Alessio | Ferrari | Politecnico di Torino | alessio.ferrari@polito.it
|
||||
Brian | Taylor | Facebook | briantaylor@fb.com
|
||||
David | Boertjes | Ciena | dboertje@ciena.com
|
||||
Esther | Le Rouzic | Orange | esther.lerouzic@orange.com
|
||||
Gabriele | Galimberti | Cisco | ggalimbe@cisco.com
|
||||
Gert | Grammel | Juniper Networks | ggrammel@juniper.net
|
||||
Gilad | Goldfarb | Facebook | giladg@fb.com
|
||||
James | Powell | Consultant | james@dontusethiscode.com
|
||||
Jeanluc | Auge | Orange | jeanluc.auge@orange.com
|
||||
Liu | Xufeng | Jabil | Xufeng_Liu@jabil.com
|
||||
Mattia | Cantono | Politecnico di Torino | mattia.cantono@polito.it
|
||||
Vittorio | Curri | Politecnico di Torino | vittorio.curri@polito.it
|
||||
16
docs/conf.py
16
docs/conf.py
@@ -1,7 +1,7 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
#
|
||||
# GNpy documentation build configuration file, created by
|
||||
# gnpy documentation build configuration file, created by
|
||||
# sphinx-quickstart on Mon Dec 18 14:41:01 2017.
|
||||
#
|
||||
# This file is execfile()d with the current directory set to its
|
||||
@@ -47,8 +47,8 @@ source_suffix = ['.rst', '.md']
|
||||
master_doc = 'index'
|
||||
|
||||
# General information about the project.
|
||||
project = 'GNpy'
|
||||
copyright = '2017, Telecom InfraProject - OOPT PSE Group'
|
||||
project = 'gnpy'
|
||||
copyright = '2018, Telecom InfraProject - OOPT PSE Group'
|
||||
author = 'Telecom InfraProject - OOPT PSE Group'
|
||||
|
||||
# The version info for the project you're documenting, acts as replacement for
|
||||
@@ -120,7 +120,7 @@ html_sidebars = {
|
||||
# -- Options for HTMLHelp output ------------------------------------------
|
||||
|
||||
# Output file base name for HTML help builder.
|
||||
htmlhelp_basename = 'GNpydoc'
|
||||
htmlhelp_basename = 'gnpydoc'
|
||||
|
||||
|
||||
# -- Options for LaTeX output ---------------------------------------------
|
||||
@@ -147,7 +147,7 @@ latex_elements = {
|
||||
# (source start file, target name, title,
|
||||
# author, documentclass [howto, manual, or own class]).
|
||||
latex_documents = [
|
||||
(master_doc, 'GNpy.tex', 'GNpy Documentation',
|
||||
(master_doc, 'gnpy.tex', 'gnpy Documentation',
|
||||
'Telecom InfraProject - OOPT PSE Group', 'manual'),
|
||||
]
|
||||
|
||||
@@ -157,7 +157,7 @@ latex_documents = [
|
||||
# One entry per manual page. List of tuples
|
||||
# (source start file, name, description, authors, manual section).
|
||||
man_pages = [
|
||||
(master_doc, 'gnpy', 'GNpy Documentation',
|
||||
(master_doc, 'gnpy', 'gnpy Documentation',
|
||||
[author], 1)
|
||||
]
|
||||
|
||||
@@ -168,8 +168,8 @@ man_pages = [
|
||||
# (source start file, target name, title, author,
|
||||
# dir menu entry, description, category)
|
||||
texinfo_documents = [
|
||||
(master_doc, 'GNpy', 'GNpy Documentation',
|
||||
author, 'GNpy', 'One line description of project.',
|
||||
(master_doc, 'gnpy', 'gnpy Documentation',
|
||||
author, 'gnpy', 'One line description of project.',
|
||||
'Miscellaneous'),
|
||||
]
|
||||
|
||||
|
||||
@@ -1,37 +1,33 @@
|
||||
.. GNpy documentation master file, created by
|
||||
.. gnpy documentation master file, created by
|
||||
sphinx-quickstart on Mon Dec 18 14:41:01 2017.
|
||||
You can adapt this file completely to your liking, but it should at least
|
||||
contain the root `toctree` directive.
|
||||
|
||||
Welcome to GNpy's documentation!
|
||||
Welcome to gnpy's documentation!
|
||||
================================
|
||||
|
||||
Gaussian Noise (GN) based modeling library for physical layer impairment evaluation in optical networks.
|
||||
**gnpy is an open-source, community-developed library for building route planning
|
||||
and optimization tools in real-world mesh optical networks.**
|
||||
|
||||
Summary
|
||||
--------
|
||||
|
||||
We believe that openly sharing ideas, specifications, and other intellectual property is the key to maximizing innovation and reducing complexity
|
||||
|
||||
PSE WG Charter
|
||||
--------------
|
||||
|
||||
- Goal is to build an end-to-end simulation environment which defines the network models of the optical device transfer functions and their parameters. This environment will provide validation of the optical performance requirements for the TIP OLS building blocks.
|
||||
- The model may be approximate or complete depending on the network complexity. Each model shall be validated against the proposed network scenario.
|
||||
- The environment must be able to process network models from multiple vendors, and also allow users to pick any implementation in an open source framework.
|
||||
- The PSE will influence and benefit from the innovation of the DTC, API, and OLS working groups.
|
||||
- The PSE represents a step along the journey towards multi-layer optimization.
|
||||
`gnpy <http://github.com/telecominfraproject/gnpy>`_ is:
|
||||
|
||||
- a sponsored project of the `OOPT/PSE <http://telecominfraproject.com/project-groups-2/backhaul-projects/open-optical-packet-transport/>`_ working group of the `Telecom Infra Project <http://telecominfraproject.com>`_.
|
||||
- fully community-driven, fully open source library
|
||||
- driven by a consortium of operators, vendors, and academic researchers
|
||||
- intended for rapid development of production-grade route planning tools
|
||||
- easily extensible to include custom network elements
|
||||
- performant to the scale of real-world mesh optical networks
|
||||
|
||||
Documentation
|
||||
=============
|
||||
|
||||
The following pages are meant to describe specific implementation details and modeling assumptions behind GNpy.
|
||||
The following pages are meant to describe specific implementation details and
|
||||
modeling assumptions behind gnpy.
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
|
||||
gn_model
|
||||
model
|
||||
|
||||
Indices and tables
|
||||
==================
|
||||
@@ -42,29 +38,44 @@ Indices and tables
|
||||
|
||||
Contributors in alphabetical order
|
||||
==================================
|
||||
+----------+------------+-----------------------+----------------------------+
|
||||
| Name | Surname | Affiliation | Contact |
|
||||
+==========+============+=======================+============================+
|
||||
| Alessio | Ferrari | Politecnico di Torino | alessio.ferrari@polito.it |
|
||||
+----------+------------+-----------------------+----------------------------+
|
||||
| Brian | Taylor | Facebook | briantaylor@fb.com |
|
||||
+----------+------------+-----------------------+----------------------------+
|
||||
| David | Boertjes | Ciena | dboertje@ciena.com |
|
||||
+----------+------------+-----------------------+----------------------------+
|
||||
| Esther | Le Rouzic | Orange | esther.lerouzic@orange.com |
|
||||
+----------+------------+-----------------------+----------------------------+
|
||||
| Gabriele | Galimberti | Cisco | ggalimbe@cisco.com |
|
||||
+----------+------------+-----------------------+----------------------------+
|
||||
| Gert | Grammel | Juniper Networks | ggrammel@juniper.net |
|
||||
+----------+------------+-----------------------+----------------------------+
|
||||
| Gilad | Goldfarb | Facebook | giladg@fb.com |
|
||||
+----------+------------+-----------------------+----------------------------+
|
||||
| James | Powell | Consultant | james@dontusethiscode.com |
|
||||
+----------+------------+-----------------------+----------------------------+
|
||||
| Jeanluc | Auge | Orange | jeanluc.auge@orange.com |
|
||||
+----------+------------+-----------------------+----------------------------+
|
||||
| Mattia | Cantono | Politecnico di Torino | mattia.cantono@polito.it |
|
||||
+----------+------------+-----------------------+----------------------------+
|
||||
| Vittorio | Curri | Politecnico di Torino | vittorio.curri@polito.it |
|
||||
+----------+------------+-----------------------+----------------------------+
|
||||
+----------+------------+-----------------------+--------------------------------------+
|
||||
| Name | Surname | Affiliation | Contact |
|
||||
+==========+============+=======================+======================================+
|
||||
| Alessio | Ferrari | Politecnico di Torino | alessio.ferrari@polito.it |
|
||||
+----------+------------+-----------------------+--------------------------------------+
|
||||
| Brian | Taylor | Facebook | briantaylor@fb.com |
|
||||
+----------+------------+-----------------------+--------------------------------------+
|
||||
| David | Boertjes | Ciena | dboertje@ciena.com |
|
||||
+----------+------------+-----------------------+--------------------------------------+
|
||||
| Esther | Le Rouzic | Orange | esther.lerouzic@orange.com |
|
||||
+----------+------------+-----------------------+--------------------------------------+
|
||||
| Gabriele | Galimberti | Cisco | ggalimbe@cisco.com |
|
||||
+----------+------------+-----------------------+--------------------------------------+
|
||||
| Gert | Grammel | Juniper Networks | ggrammel@juniper.net |
|
||||
+----------+------------+-----------------------+--------------------------------------+
|
||||
| Gilad | Goldfarb | Facebook | giladg@fb.com |
|
||||
+----------+------------+-----------------------+--------------------------------------+
|
||||
| James | Powell | Telecom Infra Project | james.powell@telecominfraproject.com |
|
||||
+----------+------------+-----------------------+--------------------------------------+
|
||||
| Jeanluc | Auge | Orange | jeanluc.auge@orange.com |
|
||||
+----------+------------+-----------------------+--------------------------------------+
|
||||
| Mattia | Cantono | Politecnico di Torino | mattia.cantono@polito.it |
|
||||
+----------+------------+-----------------------+--------------------------------------+
|
||||
| Vittorio | Curri | Politecnico di Torino | vittorio.curri@polito.it |
|
||||
+----------+------------+-----------------------+--------------------------------------+
|
||||
|
||||
PSE WG Charter
|
||||
--------------
|
||||
|
||||
- Goal is to build an end-to-end simulation environment which defines the
|
||||
network models of the optical device transfer functions and their parameters.
|
||||
This environment will provide validation of the optical performance
|
||||
requirements for the TIP OLS building blocks.
|
||||
- The model may be approximate or complete depending on the network complexity.
|
||||
Each model shall be validated against the proposed network scenario.
|
||||
- The environment must be able to process network models from multiple vendors,
|
||||
and also allow users to pick any implementation in an open source framework.
|
||||
- The PSE will influence and benefit from the innovation of the DTC, API, and
|
||||
OLS working groups.
|
||||
- The PSE represents a step along the journey towards multi-layer optimization.
|
||||
|
||||
|
||||
@@ -4,15 +4,15 @@ The QoT estimation in the PSE framework of TIP-OOPT
|
||||
QoT-E including ASE noise and NLI accumulation
|
||||
----------------------------------------------
|
||||
|
||||
The operations of PSE simulative framework are based on the capability to estimate the QoT of one
|
||||
or more channels operating lightpaths over a given network route. For
|
||||
backbone transport networks, we can suppose that transceivers are
|
||||
operating polarization-division-multiplexed multilevel modulation
|
||||
formats with DSP-based coherent receivers, including equalization. For
|
||||
the optical links, we focus on state-of-the-art amplified and
|
||||
uncompensated fiber links, connecting network nodes including ROADMs,
|
||||
where add and drop operations on data traffic are performed. In such a
|
||||
transmission scenario, it is well accepted
|
||||
The operations of PSE simulative framework are based on the capability to
|
||||
estimate the QoT of one or more channels operating lightpaths over a given
|
||||
network route. For backbone transport networks, we can suppose that
|
||||
transceivers are operating polarization-division-multiplexed multilevel
|
||||
modulation formats with DSP-based coherent receivers, including equalization.
|
||||
For the optical links, we focus on state-of-the-art amplified and uncompensated
|
||||
fiber links, connecting network nodes including ROADMs, where add and drop
|
||||
operations on data traffic are performed. In such a transmission scenario, it
|
||||
is well accepted
|
||||
:cite:`vacondio_nonlinear_2012,bononi_modeling_2012,carena_modeling_2012,mecozzi_nonlinear_2012,secondini_analytical_2012,johannisson_perturbation_2013,dar_properties_2013,serena_alternative_2013,secondini_achievable_2013,poggiolini_gn-model_2014,dar_accumulation_2014,poggiolini_analytical_2011,savory_approximations_2013,bononi_single-_2013,johannisson_modeling_2014`
|
||||
to assume that transmission performances are limited by the amplified
|
||||
spontaneous emission (ASE) noise generated by optical amplifiers and and
|
||||
@@ -49,7 +49,6 @@ filtering effects. Note that for state-of-the art equipment, filtering
|
||||
effects can be typically neglected over routes with few hops
|
||||
:cite:`rahman_mitigation_2014,foggi_overcoming_2015`.
|
||||
|
||||
|
||||
To properly estimate :math:`P_{\text{ch}}` and :math:`P_{\text{ASE}}`
|
||||
the transmitted power at the beginning of the considered route must be
|
||||
known, and losses and amplifiers gain and noise figure, including their
|
||||
@@ -62,8 +61,10 @@ models have been proposed and validated in the technical literature
|
||||
The decision about which model to test within the PSE activities was
|
||||
driven by requirements of the entire PSE framework:
|
||||
|
||||
i. the model must be *local*, i.e., related individually to each network element (i.e. fiber span) generating NLI, independently of preceding and subsequent elements; and
|
||||
ii. the related computational time must be compatible with interactive operations.
|
||||
i. the model must be *local*, i.e., related individually to each network
|
||||
element (i.e. fiber span) generating NLI, independently of preceding and
|
||||
subsequent elements; and ii. the related computational time must be compatible
|
||||
with interactive operations.
|
||||
|
||||
So, the choice fell on the Gaussian Noise
|
||||
(GN) model with incoherent accumulation of NLI over fiber spans
|
||||
@@ -79,46 +80,67 @@ for fiber types with chromatic dispersion roughly larger than 4
|
||||
ps/nm/km, the analytical approximation ensures an excellent accuracy
|
||||
with a computational time compatible with real-time operations.
|
||||
|
||||
|
||||
|
||||
The Gaussian Noise Model to evaluate the NLI
|
||||
--------------------------------------------
|
||||
As previously stated, fiber propagation of multilevel modulation formats relying on the polarization-division-multiplexing
|
||||
generates impairments that can be summarized as a disturbance called nonlinear interference (NLI),
|
||||
when exploiting a DSP-based coherent receiver, as in all state-of-the-art equipment.
|
||||
From a practical point of view, the NLI can be modeled as an additive
|
||||
Gaussian random process added by each fiber span, and whose strength depends on the cube of the input power spectral density and
|
||||
on the fiber-span parameters.
|
||||
|
||||
Since the introduction in the market in 2007 of the first transponder based on such a transmission technique, the scientific
|
||||
community has intensively worked to define the propagation behavior of such a trasnmission technique.
|
||||
First, the role of in-line chromatic dispersion compensation has been investigated, deducing that besides being
|
||||
not essential, it is indeed detrimental for performances :cite:`curri_dispersion_2008`.
|
||||
Then, it has been observed that the fiber propagation impairments are practically summarized by the sole NLI, being all the other
|
||||
phenomena compensated for by the blind equalizer implemented in the receiver DSP :cite:`carena_statistical_2010`.
|
||||
Once these assessments have been accepted by the community, several prestigious research groups have started to work
|
||||
on deriving analytical models able to estimating the NLI accumulation, and consequentially the generalized SNR that sets the BER,
|
||||
according to the transponder BER vs. SNR performance.
|
||||
Many models delivering different levels of accuracy have been developed and validated. As previously clarified, for the purposes
|
||||
of the PSE framework, the GN-model with incoherent accumulation of NLI over fiber spans has been selected as adequate.
|
||||
The reason for such a choice is first such a model being a "local" model, so related to each fiber spans, independently of
|
||||
the preceding and succeeding network elements. The other model characteristic driving the choice is
|
||||
the availability of a closed form for the model, so permitting a real-time evaluation, as required by the PSE framework.
|
||||
For a detailed derivation of the model, please refer to :cite:`poggiolini_analytical_2011`, while a qualitative description
|
||||
can be summarized as in the following.
|
||||
The GN-model assumes that the channel comb propagating in the fiber is well approximated by unpolarized spectrally shaped
|
||||
Gaussian noise. In such a scenario, supposing to rely - as in state-of-the-art equipment - on a receiver entirely compensating for linear propagation effects, propagation in the fiber only excites the four-wave mixing (FWM) process among the continuity of
|
||||
the tones occupying the bandwidth. Such a FWM generates an unpolarized complex Gaussian disturbance in each spectral slot
|
||||
that can be easily evaluated extending the FWM theory from a set of discrete tones - the standard FWM theory introduced back in the 90s by Inoue :cite:`Innoue-FWM`- to a continuity of tones, possibly spectrally shaped.
|
||||
Signals propagating in the fiber are not equivalent to Gaussian noise, but thanks to the absence of in-line compensation for choromatic dispersion,
|
||||
the become so, over short distances.
|
||||
So, the Gaussian noise model with incoherent accumulation of NLI has estensively proved to be a quick yet accurate and conservative tool
|
||||
to estimate propagation impairments of fiber propagation.
|
||||
Note that the GN-model has not been derived with the aim of an *exact* performance estimation, but to pursue a conservative performance prediction. So, considering these characteristics, and the fact that the NLI is always a secondary effect with respect to the ASE noise accumulation, and - most importantly - that typically linear propagation parameters (losses, gains and noise figures) are known within
|
||||
a variation range, a QoT estimator based on the GN model is adequate to deliver performance predictions in terms of a reasonable SNR range, rather than an exact value.
|
||||
As final remark, it must be clarified that the GN-model is adequate to be used when relying on a relatively narrow bandwidth up to few THz. When exceeding such a bandwidth occupation, the GN-model must be generalized introducing the interaction with the Stimulated
|
||||
Raman Scattering in order to give a proper estimation for all channels :cite:`cantono2018modeling`.
|
||||
This will be the main upgrade required within the PSE framework.
|
||||
As previously stated, fiber propagation of multilevel modulation formats
|
||||
relying on the polarization-division-multiplexing generates impairments that
|
||||
can be summarized as a disturbance called nonlinear interference (NLI), when
|
||||
exploiting a DSP-based coherent receiver, as in all state-of-the-art equipment.
|
||||
From a practical point of view, the NLI can be modeled as an additive Gaussian
|
||||
random process added by each fiber span, and whose strength depends on the cube
|
||||
of the input power spectral density and on the fiber-span parameters.
|
||||
|
||||
Since the introduction in the market in 2007 of the first transponder based on
|
||||
such a transmission technique, the scientific community has intensively worked
|
||||
to define the propagation behavior of such a trasnmission technique. First,
|
||||
the role of in-line chromatic dispersion compensation has been investigated,
|
||||
deducing that besides being not essential, it is indeed detrimental for
|
||||
performances :cite:`curri_dispersion_2008`. Then, it has been observed that
|
||||
the fiber propagation impairments are practically summarized by the sole NLI,
|
||||
being all the other phenomena compensated for by the blind equalizer
|
||||
implemented in the receiver DSP :cite:`carena_statistical_2010`. Once these
|
||||
assessments have been accepted by the community, several prestigious research
|
||||
groups have started to work on deriving analytical models able to estimating
|
||||
the NLI accumulation, and consequentially the generalized SNR that sets the
|
||||
BER, according to the transponder BER vs. SNR performance. Many models
|
||||
delivering different levels of accuracy have been developed and validated. As
|
||||
previously clarified, for the purposes of the PSE framework, the GN-model with
|
||||
incoherent accumulation of NLI over fiber spans has been selected as adequate.
|
||||
The reason for such a choice is first such a model being a "local" model, so
|
||||
related to each fiber spans, independently of the preceding and succeeding
|
||||
network elements. The other model characteristic driving the choice is the
|
||||
availability of a closed form for the model, so permitting a real-time
|
||||
evaluation, as required by the PSE framework. For a detailed derivation of the
|
||||
model, please refer to :cite:`poggiolini_analytical_2011`, while a qualitative
|
||||
description can be summarized as in the following. The GN-model assumes that
|
||||
the channel comb propagating in the fiber is well approximated by unpolarized
|
||||
spectrally shaped Gaussian noise. In such a scenario, supposing to rely - as in
|
||||
state-of-the-art equipment - on a receiver entirely compensating for linear
|
||||
propagation effects, propagation in the fiber only excites the four-wave mixing
|
||||
(FWM) process among the continuity of the tones occupying the bandwidth. Such a
|
||||
FWM generates an unpolarized complex Gaussian disturbance in each spectral slot
|
||||
that can be easily evaluated extending the FWM theory from a set of discrete
|
||||
tones - the standard FWM theory introduced back in the 90s by Inoue
|
||||
:cite:`Innoue-FWM`- to a continuity of tones, possibly spectrally shaped.
|
||||
Signals propagating in the fiber are not equivalent to Gaussian noise, but
|
||||
thanks to the absence of in-line compensation for choromatic dispersion, the
|
||||
become so, over short distances. So, the Gaussian noise model with incoherent
|
||||
accumulation of NLI has estensively proved to be a quick yet accurate and
|
||||
conservative tool to estimate propagation impairments of fiber propagation.
|
||||
Note that the GN-model has not been derived with the aim of an *exact*
|
||||
performance estimation, but to pursue a conservative performance prediction.
|
||||
So, considering these characteristics, and the fact that the NLI is always a
|
||||
secondary effect with respect to the ASE noise accumulation, and - most
|
||||
importantly - that typically linear propagation parameters (losses, gains and
|
||||
noise figures) are known within a variation range, a QoT estimator based on the
|
||||
GN model is adequate to deliver performance predictions in terms of a
|
||||
reasonable SNR range, rather than an exact value. As final remark, it must be
|
||||
clarified that the GN-model is adequate to be used when relying on a relatively
|
||||
narrow bandwidth up to few THz. When exceeding such a bandwidth occupation, the
|
||||
GN-model must be generalized introducing the interaction with the Stimulated
|
||||
Raman Scattering in order to give a proper estimation for all channels
|
||||
:cite:`cantono2018modeling`. This will be the main upgrade required within the
|
||||
PSE framework.
|
||||
|
||||
.. bibliography:: biblio.bib
|
||||
.. bibliography:: biblio.bib
|
||||
Binary file not shown.
@@ -1,11 +0,0 @@
|
||||
REGIONS = europe asia conus
|
||||
TARGETS = $(foreach region,$(REGIONS),coronet.$(region).json)
|
||||
|
||||
all: $(TARGETS)
|
||||
|
||||
$(TARGETS): convert.py CORONET_Global_Topology.xls
|
||||
python $< -f $(subst .json,,$(subst coronet.,,$@)) > $@
|
||||
|
||||
.PHONY: clean
|
||||
clean:
|
||||
-rm $(TARGETS) -f
|
||||
@@ -1,143 +0,0 @@
|
||||
{
|
||||
"networks": {
|
||||
"network": [
|
||||
{
|
||||
"network-types": {
|
||||
"tip-oopt-pse": {}
|
||||
},
|
||||
"network-id": "pt-to-pt",
|
||||
"node": [
|
||||
{
|
||||
"node-id": "M_KMA",
|
||||
"type":"roadm",
|
||||
"termination-point": [
|
||||
{
|
||||
"tp-id": "1-2-1"
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"node-id": "T_CAS",
|
||||
"type":"roadm",
|
||||
"termination-point": [
|
||||
{
|
||||
"tp-id": "2-1-1"
|
||||
},
|
||||
{
|
||||
"tp-id": "2-3-1"
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"node-id": "LA",
|
||||
"type":"ila",
|
||||
"termination-point": [
|
||||
{
|
||||
"tp-id": "3-2-1"
|
||||
},
|
||||
{
|
||||
"tp-id": "3-4-1"
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"node-id": "SR",
|
||||
"type":"fused",
|
||||
"termination-point": [
|
||||
{
|
||||
"tp-id": "4-3-1"
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"node-id": "C",
|
||||
"type":"ila",
|
||||
"termination-point": [
|
||||
{
|
||||
"tp-id": "5-6-1"
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"node-id": "N_KBE",
|
||||
"type":"roadm",
|
||||
"termination-point": [
|
||||
{
|
||||
"tp-id": "6-5-1"
|
||||
},
|
||||
{
|
||||
"tp-id": "6-7-1"
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"node-id": "N_KBA",
|
||||
"type":"roadm",
|
||||
"termination-point": [
|
||||
{
|
||||
"tp-id": "7-6-1"
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"link": [
|
||||
{
|
||||
"link-id": "M_KMA,1-2-1,T_CAS,2-1-1",
|
||||
"source": {
|
||||
"source-node": "M_KMA",
|
||||
"source-tp": "1-2-1"
|
||||
}
|
||||
"destination": {
|
||||
"dest-node": "T_CAS",
|
||||
"dest-tp": "2-1-1"
|
||||
}
|
||||
},
|
||||
{
|
||||
"link-id": "T_CAS,2-3-1,LA,3-2-1",
|
||||
"source": {
|
||||
"source-node": "T_CAS",
|
||||
"source-tp": "2-3-1"
|
||||
}
|
||||
"destination": {
|
||||
"dest-node": "LA",
|
||||
"dest-tp": "3-2-1"
|
||||
}
|
||||
},
|
||||
{
|
||||
"link-id": "LA,3-4-1,SR,4-3-1",
|
||||
"source": {
|
||||
"source-node": "LA",
|
||||
"source-tp": "3-4-1"
|
||||
}
|
||||
"destination": {
|
||||
"dest-node": "SR",
|
||||
"dest-tp": "4-3-1"
|
||||
}
|
||||
},
|
||||
{
|
||||
"link-id": "C,5-6-1,N_KBE,6-5-1",
|
||||
"source": {
|
||||
"source-node": "C",
|
||||
"source-tp": "5-6-1"
|
||||
}
|
||||
"destination": {
|
||||
"dest-node": "N_KBE",
|
||||
"dest-tp": "6-5-1"
|
||||
}
|
||||
},
|
||||
{
|
||||
"link-id": "N_KBE,6-7-1,N_KBA,7-6-1",
|
||||
"source": {
|
||||
"source-node": "N_KBE",
|
||||
"source-tp": "6-7-1"
|
||||
}
|
||||
"destination": {
|
||||
"dest-node": "N_KBA",
|
||||
"dest-tp": "7-6-1"
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
@@ -1,157 +0,0 @@
|
||||
{
|
||||
"network_name": "pt to pt",
|
||||
"nodes_elements":
|
||||
[
|
||||
{
|
||||
"id":"M_KMA",
|
||||
"type":"ROADM",
|
||||
"metadata": {
|
||||
"city":"M",
|
||||
"region":"RLD",
|
||||
"latitude":0,
|
||||
"longitude":0
|
||||
}
|
||||
},
|
||||
{
|
||||
"id":"T_CAS",
|
||||
"type":"ROADM",
|
||||
"metadata": {
|
||||
"city":"T",
|
||||
"region":"RLD",
|
||||
"latitude":0,
|
||||
"longitude":0
|
||||
}
|
||||
},
|
||||
{
|
||||
"id":"LA",
|
||||
"type":"ILA",
|
||||
"metadata": {
|
||||
"city":"LA",
|
||||
"region":"RLD",
|
||||
"latitude":0,
|
||||
"longitude":0
|
||||
}
|
||||
},
|
||||
{
|
||||
"id":"SR",
|
||||
"type":"fused",
|
||||
"metadata": {
|
||||
"city":"SR",
|
||||
"region":"RLD",
|
||||
"latitude":0,
|
||||
"longitude":0
|
||||
}
|
||||
},
|
||||
{
|
||||
"id":"C",
|
||||
"type":"ILA",
|
||||
"metadata": {
|
||||
"city":"C",
|
||||
"region":"RLD",
|
||||
"latitude":0,
|
||||
"longitude":0
|
||||
}
|
||||
},
|
||||
{
|
||||
"id":"N_KBE",
|
||||
"type":"ROADM",
|
||||
"metadata": {
|
||||
"city":"N",
|
||||
"region":"RLD",
|
||||
"latitude":0,
|
||||
"longitude":0
|
||||
}
|
||||
},
|
||||
{
|
||||
"id":"N_KBA",
|
||||
"type":"ROADM",
|
||||
"metadata": {
|
||||
"city":"N",
|
||||
"region":"RLD",
|
||||
"latitude":0,
|
||||
"longitude":0
|
||||
}
|
||||
},
|
||||
],
|
||||
"OTS_elements":[
|
||||
{
|
||||
"id":1,
|
||||
"source_id":"M_KMA",
|
||||
"dest_id":"T_CAS",
|
||||
"parameters_cable":{
|
||||
"units":"km",
|
||||
"length":60,
|
||||
"id":"F060",
|
||||
"type":"G652"
|
||||
},
|
||||
"parameters_east":{
|
||||
"con_in":0.5,
|
||||
"con_out":0.5,
|
||||
"loss":16,
|
||||
"pmd":2,
|
||||
"fo_id":5
|
||||
},
|
||||
"parameters_west":{
|
||||
"con_in":0.5,
|
||||
"con_out":0.5,
|
||||
"loss":15,
|
||||
"pmd":2,
|
||||
"fo_id":6
|
||||
}
|
||||
},
|
||||
{
|
||||
"id":2,
|
||||
"source_id":"T_CAS",
|
||||
"dest_id":"LA",
|
||||
"parameters_cable":{
|
||||
},
|
||||
"parameters_east":{
|
||||
},
|
||||
"parameters_west":{
|
||||
}
|
||||
},
|
||||
{
|
||||
"id":3,
|
||||
"source_id":"LA",
|
||||
"dest_id":"SR",
|
||||
"parameters_cable":{
|
||||
},
|
||||
"parameters_east":{
|
||||
},
|
||||
"parameters_west":{
|
||||
}
|
||||
},
|
||||
{
|
||||
"id":3,
|
||||
"source_id":"SR",
|
||||
"dest_id":"C",
|
||||
"parameters_cable":{
|
||||
},
|
||||
"parameters_east":{
|
||||
},
|
||||
"parameters_west":{
|
||||
}
|
||||
},
|
||||
{
|
||||
"id":5,
|
||||
"source_id":"C",
|
||||
"dest_id":"N_KBE",
|
||||
"parameters_cable":{
|
||||
},
|
||||
"parameters_east":{
|
||||
},
|
||||
"parameters_west":{
|
||||
}
|
||||
},
|
||||
{
|
||||
"id":6,
|
||||
"source_id":"N_KBE",
|
||||
"dest_id":"N_KBA",
|
||||
"parameters_cable":{
|
||||
},
|
||||
"parameters_east":{
|
||||
},
|
||||
"parameters_west":{
|
||||
}
|
||||
},
|
||||
]}
|
||||
@@ -1,162 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
from sys import exit
|
||||
try:
|
||||
from xlrd import open_workbook
|
||||
except ModuleNotFoundError:
|
||||
exit('Required: `pip install xlrd`')
|
||||
from argparse import ArgumentParser
|
||||
from collections import namedtuple, Counter
|
||||
from itertools import chain
|
||||
from json import dumps
|
||||
from uuid import uuid4
|
||||
import math
|
||||
import numpy as np
|
||||
|
||||
output_json_file_name = 'coronet_conus_example.json'
|
||||
Node = namedtuple('Node', 'city state country region latitude longitude')
|
||||
class Link(namedtuple('Link', 'from_city to_city distance distance_units')):
|
||||
def __new__(cls, from_city, to_city, distance, distance_units='km'):
|
||||
return super().__new__(cls, from_city, to_city, distance, distance_units)
|
||||
|
||||
def define_span_range(min_span, max_span, nspans):
|
||||
srange = (max_span - min_span) + min_span*np.random.rand(nspans)
|
||||
return srange
|
||||
|
||||
def amp_spacings(min_span,max_span,length):
|
||||
nspans = math.ceil(length/100)
|
||||
spans = define_span_range(min_span, max_span, nspans)
|
||||
tot = spans.sum()
|
||||
delta = length -tot
|
||||
if delta > 0 and delta < 25:
|
||||
ind = np.where(np.min(spans))
|
||||
spans[ind] = spans[ind] + delta
|
||||
elif delta >= 25 and delta < 40:
|
||||
spans = spans + delta/float(nspans)
|
||||
elif delta > 40 and delta < 100:
|
||||
spans = np.append(spans,delta)
|
||||
elif delta > 100:
|
||||
spans = np.append(spans, [delta/2, delta/2])
|
||||
elif delta < 0:
|
||||
spans = spans + delta/float(nspans)
|
||||
return list(spans)
|
||||
|
||||
def parse_excel(args):
|
||||
with open_workbook(args.workbook) as wb:
|
||||
nodes_sheet = wb.sheet_by_name('Nodes')
|
||||
links_sheet = wb.sheet_by_name('Links')
|
||||
|
||||
# sanity check
|
||||
header = [x.value.strip() for x in nodes_sheet.row(4)]
|
||||
expected = ['City', 'State', 'Country', 'Region', 'Latitude', 'Longitude']
|
||||
if header != expected:
|
||||
raise ValueError(f'Malformed header on Nodes sheet: {header} != {expected}')
|
||||
|
||||
nodes = []
|
||||
for row in all_rows(nodes_sheet, start=5):
|
||||
nodes.append(Node(*(x.value for x in row)))
|
||||
|
||||
# sanity check
|
||||
header = [x.value.strip() for x in links_sheet.row(4)]
|
||||
expected = ['Node A', 'Node Z', 'Distance (km)']
|
||||
if header != expected:
|
||||
raise ValueError(f'Malformed header on Nodes sheet: {header} != {expected}')
|
||||
|
||||
links = []
|
||||
for row in all_rows(links_sheet, start=5):
|
||||
links.append(Link(*(x.value for x in row)))
|
||||
|
||||
# sanity check
|
||||
all_cities = Counter(n.city for n in nodes)
|
||||
if len(all_cities) != len(nodes):
|
||||
ValueError(f'Duplicate city: {all_cities}')
|
||||
if any(ln.from_city not in all_cities or
|
||||
ln.to_city not in all_cities for ln in links):
|
||||
ValueError(f'Bad link.')
|
||||
|
||||
return nodes, links
|
||||
|
||||
parser = ArgumentParser()
|
||||
parser.add_argument('workbook', nargs='?', default='CORONET_Global_Topology.xls')
|
||||
parser.add_argument('-f', '--filter-region', action='append', default=[])
|
||||
|
||||
all_rows = lambda sh, start=0: (sh.row(x) for x in range(start, sh.nrows))
|
||||
|
||||
def midpoint(city_a, city_b):
|
||||
lats = city_a.latitude, city_b.latitude
|
||||
longs = city_a.longitude, city_b.longitude
|
||||
return {
|
||||
'latitude': sum(lats) / 2,
|
||||
'longitude': sum(longs) / 2,
|
||||
}
|
||||
|
||||
if __name__ == '__main__':
|
||||
args = parser.parse_args()
|
||||
nodes, links = parse_excel(args)
|
||||
|
||||
if args.filter_region:
|
||||
nodes = [n for n in nodes if n.region.lower() in args.filter_region]
|
||||
cities = {n.city for n in nodes}
|
||||
links = [lnk for lnk in links if lnk.from_city in cities and
|
||||
lnk.to_city in cities]
|
||||
cities = {lnk.from_city for lnk in links} | {lnk.to_city for lnk in links}
|
||||
nodes = [n for n in nodes if n.city in cities]
|
||||
|
||||
nodes_by_city = {n.city: n for n in nodes}
|
||||
|
||||
data = {
|
||||
'elements':
|
||||
[{'uid': f'trx {x.city}',
|
||||
'metadata': {'location': {'city': x.city,
|
||||
'region': x.region,
|
||||
'latitude': x.latitude,
|
||||
'longitude': x.longitude}},
|
||||
'type': 'Transceiver'}
|
||||
for x in nodes] +
|
||||
[{'uid': f'roadm {x.city}',
|
||||
'metadata': {'location': {'city': x.city,
|
||||
'region': x.region,
|
||||
'latitude': x.latitude,
|
||||
'longitude': x.longitude}},
|
||||
'type': 'Roadm'}
|
||||
for x in nodes] +
|
||||
[{'uid': f'fiber ({x.from_city} → {x.to_city})',
|
||||
'metadata': {'location': midpoint(nodes_by_city[x.from_city],
|
||||
nodes_by_city[x.to_city])},
|
||||
'type': 'Fiber',
|
||||
'params': {'length': round(x.distance, 3),
|
||||
'length_units': x.distance_units,
|
||||
'loss_coef': 0.2,
|
||||
'dispersion': 16.7E-6,
|
||||
'gamma': 1.27E-3}
|
||||
}
|
||||
for x in links],
|
||||
'connections':
|
||||
list(chain.from_iterable(zip( # put bidi next to each other
|
||||
[{'from_node': f'roadm {x.from_city}',
|
||||
'to_node': f'fiber ({x.from_city} → {x.to_city})'}
|
||||
for x in links],
|
||||
[{'from_node': f'fiber ({x.from_city} → {x.to_city})',
|
||||
'to_node': f'roadm {x.from_city}'}
|
||||
for x in links])))
|
||||
+
|
||||
list(chain.from_iterable(zip(
|
||||
[{'from_node': f'fiber ({x.from_city} → {x.to_city})',
|
||||
'to_node': f'roadm {x.to_city}'}
|
||||
for x in links],
|
||||
[{'from_node': f'roadm {x.to_city}',
|
||||
'to_node': f'fiber ({x.from_city} → {x.to_city})'}
|
||||
for x in links])))
|
||||
+
|
||||
list(chain.from_iterable(zip(
|
||||
[{'from_node': f'trx {x.city}',
|
||||
'to_node': f'roadm {x.city}'}
|
||||
for x in nodes],
|
||||
[{'from_node': f'roadm {x.city}',
|
||||
'to_node': f'trx {x.city}'}
|
||||
for x in nodes])))
|
||||
}
|
||||
|
||||
print(dumps(data, indent=2))
|
||||
with open(output_json_file_name,'w') as edfa_json_file:
|
||||
edfa_json_file.write(dumps(data, indent=2))
|
||||
@@ -1,542 +0,0 @@
|
||||
{
|
||||
"elements": [
|
||||
{
|
||||
"uid": "Bangkok",
|
||||
"metadata": {
|
||||
"location": {
|
||||
"city": "Bangkok",
|
||||
"region": "Asia",
|
||||
"latitude": 13.73,
|
||||
"longitude": 100.5
|
||||
}
|
||||
},
|
||||
"type": "Transceiver"
|
||||
},
|
||||
{
|
||||
"uid": "Beijing",
|
||||
"metadata": {
|
||||
"location": {
|
||||
"city": "Beijing",
|
||||
"region": "Asia",
|
||||
"latitude": 39.92999979,
|
||||
"longitude": 116.4000013
|
||||
}
|
||||
},
|
||||
"type": "Transceiver"
|
||||
},
|
||||
{
|
||||
"uid": "Delhi",
|
||||
"metadata": {
|
||||
"location": {
|
||||
"city": "Delhi",
|
||||
"region": "Asia",
|
||||
"latitude": 28.6700003,
|
||||
"longitude": 77.2099989
|
||||
}
|
||||
},
|
||||
"type": "Transceiver"
|
||||
},
|
||||
{
|
||||
"uid": "Hong_Kong",
|
||||
"metadata": {
|
||||
"location": {
|
||||
"city": "Hong_Kong",
|
||||
"region": "Asia",
|
||||
"latitude": 22.267,
|
||||
"longitude": 114.14
|
||||
}
|
||||
},
|
||||
"type": "Transceiver"
|
||||
},
|
||||
{
|
||||
"uid": "Honolulu",
|
||||
"metadata": {
|
||||
"location": {
|
||||
"city": "Honolulu",
|
||||
"region": "Asia",
|
||||
"latitude": 21.3199996,
|
||||
"longitude": -157.800003
|
||||
}
|
||||
},
|
||||
"type": "Transceiver"
|
||||
},
|
||||
{
|
||||
"uid": "Mumbai",
|
||||
"metadata": {
|
||||
"location": {
|
||||
"city": "Mumbai",
|
||||
"region": "Asia",
|
||||
"latitude": 18.9599987,
|
||||
"longitude": 72.8199999
|
||||
}
|
||||
},
|
||||
"type": "Transceiver"
|
||||
},
|
||||
{
|
||||
"uid": "Seoul",
|
||||
"metadata": {
|
||||
"location": {
|
||||
"city": "Seoul",
|
||||
"region": "Asia",
|
||||
"latitude": 37.56000108,
|
||||
"longitude": 126.9899988
|
||||
}
|
||||
},
|
||||
"type": "Transceiver"
|
||||
},
|
||||
{
|
||||
"uid": "Shanghai",
|
||||
"metadata": {
|
||||
"location": {
|
||||
"city": "Shanghai",
|
||||
"region": "Asia",
|
||||
"latitude": 31.23,
|
||||
"longitude": 121.47
|
||||
}
|
||||
},
|
||||
"type": "Transceiver"
|
||||
},
|
||||
{
|
||||
"uid": "Singapore",
|
||||
"metadata": {
|
||||
"location": {
|
||||
"city": "Singapore",
|
||||
"region": "Asia",
|
||||
"latitude": 1.299999907,
|
||||
"longitude": 103.8499992
|
||||
}
|
||||
},
|
||||
"type": "Transceiver"
|
||||
},
|
||||
{
|
||||
"uid": "Sydney",
|
||||
"metadata": {
|
||||
"location": {
|
||||
"city": "Sydney",
|
||||
"region": "Asia",
|
||||
"latitude": -33.86999896,
|
||||
"longitude": 151.2100066
|
||||
}
|
||||
},
|
||||
"type": "Transceiver"
|
||||
},
|
||||
{
|
||||
"uid": "Taipei",
|
||||
"metadata": {
|
||||
"location": {
|
||||
"city": "Taipei",
|
||||
"region": "Asia",
|
||||
"latitude": 25.0200005,
|
||||
"longitude": 121.449997
|
||||
}
|
||||
},
|
||||
"type": "Transceiver"
|
||||
},
|
||||
{
|
||||
"uid": "Tokyo",
|
||||
"metadata": {
|
||||
"location": {
|
||||
"city": "Tokyo",
|
||||
"region": "Asia",
|
||||
"latitude": 35.6699986,
|
||||
"longitude": 139.770004
|
||||
}
|
||||
},
|
||||
"type": "Transceiver"
|
||||
},
|
||||
{
|
||||
"uid": "fiber (Bangkok \u2192 Delhi)",
|
||||
"metadata": {
|
||||
"length": 3505.95,
|
||||
"units": "km",
|
||||
"location": {
|
||||
"latitude": 21.20000015,
|
||||
"longitude": 88.85499945000001
|
||||
}
|
||||
},
|
||||
"type": "Fiber"
|
||||
},
|
||||
{
|
||||
"uid": "fiber (Bangkok \u2192 Hong_Kong)",
|
||||
"metadata": {
|
||||
"length": 2070.724,
|
||||
"units": "km",
|
||||
"location": {
|
||||
"latitude": 17.9985,
|
||||
"longitude": 107.32
|
||||
}
|
||||
},
|
||||
"type": "Fiber"
|
||||
},
|
||||
{
|
||||
"uid": "fiber (Beijing \u2192 Seoul)",
|
||||
"metadata": {
|
||||
"length": 1146.124,
|
||||
"units": "km",
|
||||
"location": {
|
||||
"latitude": 38.745000434999994,
|
||||
"longitude": 121.69500005
|
||||
}
|
||||
},
|
||||
"type": "Fiber"
|
||||
},
|
||||
{
|
||||
"uid": "fiber (Beijing \u2192 Shanghai)",
|
||||
"metadata": {
|
||||
"length": 1284.465,
|
||||
"units": "km",
|
||||
"location": {
|
||||
"latitude": 35.579999895,
|
||||
"longitude": 118.93500065
|
||||
}
|
||||
},
|
||||
"type": "Fiber"
|
||||
},
|
||||
{
|
||||
"uid": "fiber (Delhi \u2192 Mumbai)",
|
||||
"metadata": {
|
||||
"length": 1402.141,
|
||||
"units": "km",
|
||||
"location": {
|
||||
"latitude": 23.8149995,
|
||||
"longitude": 75.0149994
|
||||
}
|
||||
},
|
||||
"type": "Fiber"
|
||||
},
|
||||
{
|
||||
"uid": "fiber (Hong_Kong \u2192 Shanghai)",
|
||||
"metadata": {
|
||||
"length": 1480.406,
|
||||
"units": "km",
|
||||
"location": {
|
||||
"latitude": 26.7485,
|
||||
"longitude": 117.805
|
||||
}
|
||||
},
|
||||
"type": "Fiber"
|
||||
},
|
||||
{
|
||||
"uid": "fiber (Hong_Kong \u2192 Sydney)",
|
||||
"metadata": {
|
||||
"length": 8856.6,
|
||||
"units": "km",
|
||||
"location": {
|
||||
"latitude": -5.801499479999999,
|
||||
"longitude": 132.67500330000001
|
||||
}
|
||||
},
|
||||
"type": "Fiber"
|
||||
},
|
||||
{
|
||||
"uid": "fiber (Hong_Kong \u2192 Taipei)",
|
||||
"metadata": {
|
||||
"length": 966.177,
|
||||
"units": "km",
|
||||
"location": {
|
||||
"latitude": 23.64350025,
|
||||
"longitude": 117.79499849999999
|
||||
}
|
||||
},
|
||||
"type": "Fiber"
|
||||
},
|
||||
{
|
||||
"uid": "fiber (Honolulu \u2192 Sydney)",
|
||||
"metadata": {
|
||||
"length": 9808.616,
|
||||
"units": "km",
|
||||
"location": {
|
||||
"latitude": -6.274999679999999,
|
||||
"longitude": -3.294998199999995
|
||||
}
|
||||
},
|
||||
"type": "Fiber"
|
||||
},
|
||||
{
|
||||
"uid": "fiber (Honolulu \u2192 Taipei)",
|
||||
"metadata": {
|
||||
"length": 9767.013,
|
||||
"units": "km",
|
||||
"location": {
|
||||
"latitude": 23.17000005,
|
||||
"longitude": -18.175003000000004
|
||||
}
|
||||
},
|
||||
"type": "Fiber"
|
||||
},
|
||||
{
|
||||
"uid": "fiber (Mumbai \u2192 Singapore)",
|
||||
"metadata": {
|
||||
"length": 4692.708,
|
||||
"units": "km",
|
||||
"location": {
|
||||
"latitude": 10.1299993035,
|
||||
"longitude": 88.33499954999999
|
||||
}
|
||||
},
|
||||
"type": "Fiber"
|
||||
},
|
||||
{
|
||||
"uid": "fiber (Seoul \u2192 Tokyo)",
|
||||
"metadata": {
|
||||
"length": 1391.085,
|
||||
"units": "km",
|
||||
"location": {
|
||||
"latitude": 36.614999839999996,
|
||||
"longitude": 133.3800014
|
||||
}
|
||||
},
|
||||
"type": "Fiber"
|
||||
},
|
||||
{
|
||||
"uid": "fiber (Singapore \u2192 Sydney)",
|
||||
"metadata": {
|
||||
"length": 7562.331,
|
||||
"units": "km",
|
||||
"location": {
|
||||
"latitude": -16.2849995265,
|
||||
"longitude": 127.5300029
|
||||
}
|
||||
},
|
||||
"type": "Fiber"
|
||||
},
|
||||
{
|
||||
"uid": "fiber (Taipei \u2192 Tokyo)",
|
||||
"metadata": {
|
||||
"length": 2537.345,
|
||||
"units": "km",
|
||||
"location": {
|
||||
"latitude": 30.344999549999997,
|
||||
"longitude": 130.6100005
|
||||
}
|
||||
},
|
||||
"type": "Fiber"
|
||||
}
|
||||
],
|
||||
"connections": [
|
||||
{
|
||||
"from_node": "Bangkok",
|
||||
"to_node": "fiber (Bangkok \u2192 Delhi)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Bangkok \u2192 Delhi)",
|
||||
"to_node": "Bangkok"
|
||||
},
|
||||
{
|
||||
"from_node": "Bangkok",
|
||||
"to_node": "fiber (Bangkok \u2192 Hong_Kong)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Bangkok \u2192 Hong_Kong)",
|
||||
"to_node": "Bangkok"
|
||||
},
|
||||
{
|
||||
"from_node": "Beijing",
|
||||
"to_node": "fiber (Beijing \u2192 Seoul)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Beijing \u2192 Seoul)",
|
||||
"to_node": "Beijing"
|
||||
},
|
||||
{
|
||||
"from_node": "Beijing",
|
||||
"to_node": "fiber (Beijing \u2192 Shanghai)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Beijing \u2192 Shanghai)",
|
||||
"to_node": "Beijing"
|
||||
},
|
||||
{
|
||||
"from_node": "Delhi",
|
||||
"to_node": "fiber (Delhi \u2192 Mumbai)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Delhi \u2192 Mumbai)",
|
||||
"to_node": "Delhi"
|
||||
},
|
||||
{
|
||||
"from_node": "Hong_Kong",
|
||||
"to_node": "fiber (Hong_Kong \u2192 Shanghai)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Hong_Kong \u2192 Shanghai)",
|
||||
"to_node": "Hong_Kong"
|
||||
},
|
||||
{
|
||||
"from_node": "Hong_Kong",
|
||||
"to_node": "fiber (Hong_Kong \u2192 Sydney)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Hong_Kong \u2192 Sydney)",
|
||||
"to_node": "Hong_Kong"
|
||||
},
|
||||
{
|
||||
"from_node": "Hong_Kong",
|
||||
"to_node": "fiber (Hong_Kong \u2192 Taipei)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Hong_Kong \u2192 Taipei)",
|
||||
"to_node": "Hong_Kong"
|
||||
},
|
||||
{
|
||||
"from_node": "Honolulu",
|
||||
"to_node": "fiber (Honolulu \u2192 Sydney)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Honolulu \u2192 Sydney)",
|
||||
"to_node": "Honolulu"
|
||||
},
|
||||
{
|
||||
"from_node": "Honolulu",
|
||||
"to_node": "fiber (Honolulu \u2192 Taipei)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Honolulu \u2192 Taipei)",
|
||||
"to_node": "Honolulu"
|
||||
},
|
||||
{
|
||||
"from_node": "Mumbai",
|
||||
"to_node": "fiber (Mumbai \u2192 Singapore)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Mumbai \u2192 Singapore)",
|
||||
"to_node": "Mumbai"
|
||||
},
|
||||
{
|
||||
"from_node": "Seoul",
|
||||
"to_node": "fiber (Seoul \u2192 Tokyo)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Seoul \u2192 Tokyo)",
|
||||
"to_node": "Seoul"
|
||||
},
|
||||
{
|
||||
"from_node": "Singapore",
|
||||
"to_node": "fiber (Singapore \u2192 Sydney)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Singapore \u2192 Sydney)",
|
||||
"to_node": "Singapore"
|
||||
},
|
||||
{
|
||||
"from_node": "Taipei",
|
||||
"to_node": "fiber (Taipei \u2192 Tokyo)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Taipei \u2192 Tokyo)",
|
||||
"to_node": "Taipei"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Bangkok \u2192 Delhi)",
|
||||
"to_node": "Delhi"
|
||||
},
|
||||
{
|
||||
"from_node": "Delhi",
|
||||
"to_node": "fiber (Bangkok \u2192 Delhi)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Bangkok \u2192 Hong_Kong)",
|
||||
"to_node": "Hong_Kong"
|
||||
},
|
||||
{
|
||||
"from_node": "Hong_Kong",
|
||||
"to_node": "fiber (Bangkok \u2192 Hong_Kong)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Beijing \u2192 Seoul)",
|
||||
"to_node": "Seoul"
|
||||
},
|
||||
{
|
||||
"from_node": "Seoul",
|
||||
"to_node": "fiber (Beijing \u2192 Seoul)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Beijing \u2192 Shanghai)",
|
||||
"to_node": "Shanghai"
|
||||
},
|
||||
{
|
||||
"from_node": "Shanghai",
|
||||
"to_node": "fiber (Beijing \u2192 Shanghai)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Delhi \u2192 Mumbai)",
|
||||
"to_node": "Mumbai"
|
||||
},
|
||||
{
|
||||
"from_node": "Mumbai",
|
||||
"to_node": "fiber (Delhi \u2192 Mumbai)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Hong_Kong \u2192 Shanghai)",
|
||||
"to_node": "Shanghai"
|
||||
},
|
||||
{
|
||||
"from_node": "Shanghai",
|
||||
"to_node": "fiber (Hong_Kong \u2192 Shanghai)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Hong_Kong \u2192 Sydney)",
|
||||
"to_node": "Sydney"
|
||||
},
|
||||
{
|
||||
"from_node": "Sydney",
|
||||
"to_node": "fiber (Hong_Kong \u2192 Sydney)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Hong_Kong \u2192 Taipei)",
|
||||
"to_node": "Taipei"
|
||||
},
|
||||
{
|
||||
"from_node": "Taipei",
|
||||
"to_node": "fiber (Hong_Kong \u2192 Taipei)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Honolulu \u2192 Sydney)",
|
||||
"to_node": "Sydney"
|
||||
},
|
||||
{
|
||||
"from_node": "Sydney",
|
||||
"to_node": "fiber (Honolulu \u2192 Sydney)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Honolulu \u2192 Taipei)",
|
||||
"to_node": "Taipei"
|
||||
},
|
||||
{
|
||||
"from_node": "Taipei",
|
||||
"to_node": "fiber (Honolulu \u2192 Taipei)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Mumbai \u2192 Singapore)",
|
||||
"to_node": "Singapore"
|
||||
},
|
||||
{
|
||||
"from_node": "Singapore",
|
||||
"to_node": "fiber (Mumbai \u2192 Singapore)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Seoul \u2192 Tokyo)",
|
||||
"to_node": "Tokyo"
|
||||
},
|
||||
{
|
||||
"from_node": "Tokyo",
|
||||
"to_node": "fiber (Seoul \u2192 Tokyo)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Singapore \u2192 Sydney)",
|
||||
"to_node": "Sydney"
|
||||
},
|
||||
{
|
||||
"from_node": "Sydney",
|
||||
"to_node": "fiber (Singapore \u2192 Sydney)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Taipei \u2192 Tokyo)",
|
||||
"to_node": "Tokyo"
|
||||
},
|
||||
{
|
||||
"from_node": "Tokyo",
|
||||
"to_node": "fiber (Taipei \u2192 Tokyo)"
|
||||
}
|
||||
]
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,582 +0,0 @@
|
||||
{
|
||||
"elements": [
|
||||
{
|
||||
"uid": "Amsterdam",
|
||||
"metadata": {
|
||||
"location": {
|
||||
"city": "Amsterdam",
|
||||
"region": "Europe",
|
||||
"latitude": 52.3699996,
|
||||
"longitude": 4.88999915
|
||||
}
|
||||
},
|
||||
"type": "Transceiver"
|
||||
},
|
||||
{
|
||||
"uid": "Berlin",
|
||||
"metadata": {
|
||||
"location": {
|
||||
"city": "Berlin",
|
||||
"region": "Europe",
|
||||
"latitude": 52.520002,
|
||||
"longitude": 13.379995
|
||||
}
|
||||
},
|
||||
"type": "Transceiver"
|
||||
},
|
||||
{
|
||||
"uid": "Brussels",
|
||||
"metadata": {
|
||||
"location": {
|
||||
"city": "Brussels",
|
||||
"region": "Europe",
|
||||
"latitude": 50.830002,
|
||||
"longitude": 4.330002
|
||||
}
|
||||
},
|
||||
"type": "Transceiver"
|
||||
},
|
||||
{
|
||||
"uid": "Bucharest",
|
||||
"metadata": {
|
||||
"location": {
|
||||
"city": "Bucharest",
|
||||
"region": "Europe",
|
||||
"latitude": 44.44,
|
||||
"longitude": 26.1
|
||||
}
|
||||
},
|
||||
"type": "Transceiver"
|
||||
},
|
||||
{
|
||||
"uid": "Frankfurt",
|
||||
"metadata": {
|
||||
"location": {
|
||||
"city": "Frankfurt",
|
||||
"region": "Europe",
|
||||
"latitude": 50.1199992,
|
||||
"longitude": 8.68000104
|
||||
}
|
||||
},
|
||||
"type": "Transceiver"
|
||||
},
|
||||
{
|
||||
"uid": "Istanbul",
|
||||
"metadata": {
|
||||
"location": {
|
||||
"city": "Istanbul",
|
||||
"region": "Europe",
|
||||
"latitude": 41.1,
|
||||
"longitude": 29.0
|
||||
}
|
||||
},
|
||||
"type": "Transceiver"
|
||||
},
|
||||
{
|
||||
"uid": "London",
|
||||
"metadata": {
|
||||
"location": {
|
||||
"city": "London",
|
||||
"region": "Europe",
|
||||
"latitude": 51.5200005,
|
||||
"longitude": -0.100000296
|
||||
}
|
||||
},
|
||||
"type": "Transceiver"
|
||||
},
|
||||
{
|
||||
"uid": "Madrid",
|
||||
"metadata": {
|
||||
"location": {
|
||||
"city": "Madrid",
|
||||
"region": "Europe",
|
||||
"latitude": 40.419998,
|
||||
"longitude": -3.7100002
|
||||
}
|
||||
},
|
||||
"type": "Transceiver"
|
||||
},
|
||||
{
|
||||
"uid": "Paris",
|
||||
"metadata": {
|
||||
"location": {
|
||||
"city": "Paris",
|
||||
"region": "Europe",
|
||||
"latitude": 48.86,
|
||||
"longitude": 2.3399995
|
||||
}
|
||||
},
|
||||
"type": "Transceiver"
|
||||
},
|
||||
{
|
||||
"uid": "Rome",
|
||||
"metadata": {
|
||||
"location": {
|
||||
"city": "Rome",
|
||||
"region": "Europe",
|
||||
"latitude": 41.8899996,
|
||||
"longitude": 12.5000004
|
||||
}
|
||||
},
|
||||
"type": "Transceiver"
|
||||
},
|
||||
{
|
||||
"uid": "Vienna",
|
||||
"metadata": {
|
||||
"location": {
|
||||
"city": "Vienna",
|
||||
"region": "Europe",
|
||||
"latitude": 48.2200024,
|
||||
"longitude": 16.3700005
|
||||
}
|
||||
},
|
||||
"type": "Transceiver"
|
||||
},
|
||||
{
|
||||
"uid": "Warsaw",
|
||||
"metadata": {
|
||||
"location": {
|
||||
"city": "Warsaw",
|
||||
"region": "Europe",
|
||||
"latitude": 52.2599987,
|
||||
"longitude": 21.0200005
|
||||
}
|
||||
},
|
||||
"type": "Transceiver"
|
||||
},
|
||||
{
|
||||
"uid": "Zurich",
|
||||
"metadata": {
|
||||
"location": {
|
||||
"city": "Zurich",
|
||||
"region": "Europe",
|
||||
"latitude": 47.3800015,
|
||||
"longitude": 8.5399996
|
||||
}
|
||||
},
|
||||
"type": "Transceiver"
|
||||
},
|
||||
{
|
||||
"uid": "fiber (Amsterdam \u2192 Berlin)",
|
||||
"metadata": {
|
||||
"length": 690.608,
|
||||
"units": "km",
|
||||
"location": {
|
||||
"latitude": 52.4450008,
|
||||
"longitude": 9.134997075
|
||||
}
|
||||
},
|
||||
"type": "Fiber"
|
||||
},
|
||||
{
|
||||
"uid": "fiber (Amsterdam \u2192 Brussels)",
|
||||
"metadata": {
|
||||
"length": 210.729,
|
||||
"units": "km",
|
||||
"location": {
|
||||
"latitude": 51.600000800000004,
|
||||
"longitude": 4.610000575000001
|
||||
}
|
||||
},
|
||||
"type": "Fiber"
|
||||
},
|
||||
{
|
||||
"uid": "fiber (Amsterdam \u2192 Frankfurt)",
|
||||
"metadata": {
|
||||
"length": 436.324,
|
||||
"units": "km",
|
||||
"location": {
|
||||
"latitude": 51.2449994,
|
||||
"longitude": 6.785000095000001
|
||||
}
|
||||
},
|
||||
"type": "Fiber"
|
||||
},
|
||||
{
|
||||
"uid": "fiber (Berlin \u2192 Warsaw)",
|
||||
"metadata": {
|
||||
"length": 623.015,
|
||||
"units": "km",
|
||||
"location": {
|
||||
"latitude": 52.390000349999994,
|
||||
"longitude": 17.199997749999998
|
||||
}
|
||||
},
|
||||
"type": "Fiber"
|
||||
},
|
||||
{
|
||||
"uid": "fiber (Brussels \u2192 London)",
|
||||
"metadata": {
|
||||
"length": 381.913,
|
||||
"units": "km",
|
||||
"location": {
|
||||
"latitude": 51.17500125,
|
||||
"longitude": 2.115000852
|
||||
}
|
||||
},
|
||||
"type": "Fiber"
|
||||
},
|
||||
{
|
||||
"uid": "fiber (Bucharest \u2192 Istanbul)",
|
||||
"metadata": {
|
||||
"length": 528.58,
|
||||
"units": "km",
|
||||
"location": {
|
||||
"latitude": 42.769999999999996,
|
||||
"longitude": 27.55
|
||||
}
|
||||
},
|
||||
"type": "Fiber"
|
||||
},
|
||||
{
|
||||
"uid": "fiber (Bucharest \u2192 Warsaw)",
|
||||
"metadata": {
|
||||
"length": 1136.2,
|
||||
"units": "km",
|
||||
"location": {
|
||||
"latitude": 48.34999935,
|
||||
"longitude": 23.56000025
|
||||
}
|
||||
},
|
||||
"type": "Fiber"
|
||||
},
|
||||
{
|
||||
"uid": "fiber (Frankfurt \u2192 Vienna)",
|
||||
"metadata": {
|
||||
"length": 717.001,
|
||||
"units": "km",
|
||||
"location": {
|
||||
"latitude": 49.1700008,
|
||||
"longitude": 12.52500077
|
||||
}
|
||||
},
|
||||
"type": "Fiber"
|
||||
},
|
||||
{
|
||||
"uid": "fiber (Istanbul \u2192 Rome)",
|
||||
"metadata": {
|
||||
"length": 1650.406,
|
||||
"units": "km",
|
||||
"location": {
|
||||
"latitude": 41.4949998,
|
||||
"longitude": 20.7500002
|
||||
}
|
||||
},
|
||||
"type": "Fiber"
|
||||
},
|
||||
{
|
||||
"uid": "fiber (London \u2192 Paris)",
|
||||
"metadata": {
|
||||
"length": 411.692,
|
||||
"units": "km",
|
||||
"location": {
|
||||
"latitude": 50.19000025,
|
||||
"longitude": 1.1199996019999998
|
||||
}
|
||||
},
|
||||
"type": "Fiber"
|
||||
},
|
||||
{
|
||||
"uid": "fiber (Madrid \u2192 Paris)",
|
||||
"metadata": {
|
||||
"length": 1263.619,
|
||||
"units": "km",
|
||||
"location": {
|
||||
"latitude": 44.639999,
|
||||
"longitude": -0.6850003500000001
|
||||
}
|
||||
},
|
||||
"type": "Fiber"
|
||||
},
|
||||
{
|
||||
"uid": "fiber (Madrid \u2192 Zurich)",
|
||||
"metadata": {
|
||||
"length": 1497.358,
|
||||
"units": "km",
|
||||
"location": {
|
||||
"latitude": 43.89999975,
|
||||
"longitude": 2.4149997
|
||||
}
|
||||
},
|
||||
"type": "Fiber"
|
||||
},
|
||||
{
|
||||
"uid": "fiber (Rome \u2192 Vienna)",
|
||||
"metadata": {
|
||||
"length": 920.026,
|
||||
"units": "km",
|
||||
"location": {
|
||||
"latitude": 45.055001000000004,
|
||||
"longitude": 14.43500045
|
||||
}
|
||||
},
|
||||
"type": "Fiber"
|
||||
},
|
||||
{
|
||||
"uid": "fiber (Rome \u2192 Zurich)",
|
||||
"metadata": {
|
||||
"length": 823.4,
|
||||
"units": "km",
|
||||
"location": {
|
||||
"latitude": 44.63500055,
|
||||
"longitude": 10.52
|
||||
}
|
||||
},
|
||||
"type": "Fiber"
|
||||
},
|
||||
{
|
||||
"uid": "fiber (Vienna \u2192 Warsaw)",
|
||||
"metadata": {
|
||||
"length": 669.297,
|
||||
"units": "km",
|
||||
"location": {
|
||||
"latitude": 50.24000055,
|
||||
"longitude": 18.6950005
|
||||
}
|
||||
},
|
||||
"type": "Fiber"
|
||||
}
|
||||
],
|
||||
"connections": [
|
||||
{
|
||||
"from_node": "Amsterdam",
|
||||
"to_node": "fiber (Amsterdam \u2192 Berlin)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Amsterdam \u2192 Berlin)",
|
||||
"to_node": "Amsterdam"
|
||||
},
|
||||
{
|
||||
"from_node": "Amsterdam",
|
||||
"to_node": "fiber (Amsterdam \u2192 Brussels)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Amsterdam \u2192 Brussels)",
|
||||
"to_node": "Amsterdam"
|
||||
},
|
||||
{
|
||||
"from_node": "Amsterdam",
|
||||
"to_node": "fiber (Amsterdam \u2192 Frankfurt)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Amsterdam \u2192 Frankfurt)",
|
||||
"to_node": "Amsterdam"
|
||||
},
|
||||
{
|
||||
"from_node": "Berlin",
|
||||
"to_node": "fiber (Berlin \u2192 Warsaw)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Berlin \u2192 Warsaw)",
|
||||
"to_node": "Berlin"
|
||||
},
|
||||
{
|
||||
"from_node": "Brussels",
|
||||
"to_node": "fiber (Brussels \u2192 London)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Brussels \u2192 London)",
|
||||
"to_node": "Brussels"
|
||||
},
|
||||
{
|
||||
"from_node": "Bucharest",
|
||||
"to_node": "fiber (Bucharest \u2192 Istanbul)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Bucharest \u2192 Istanbul)",
|
||||
"to_node": "Bucharest"
|
||||
},
|
||||
{
|
||||
"from_node": "Bucharest",
|
||||
"to_node": "fiber (Bucharest \u2192 Warsaw)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Bucharest \u2192 Warsaw)",
|
||||
"to_node": "Bucharest"
|
||||
},
|
||||
{
|
||||
"from_node": "Frankfurt",
|
||||
"to_node": "fiber (Frankfurt \u2192 Vienna)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Frankfurt \u2192 Vienna)",
|
||||
"to_node": "Frankfurt"
|
||||
},
|
||||
{
|
||||
"from_node": "Istanbul",
|
||||
"to_node": "fiber (Istanbul \u2192 Rome)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Istanbul \u2192 Rome)",
|
||||
"to_node": "Istanbul"
|
||||
},
|
||||
{
|
||||
"from_node": "London",
|
||||
"to_node": "fiber (London \u2192 Paris)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (London \u2192 Paris)",
|
||||
"to_node": "London"
|
||||
},
|
||||
{
|
||||
"from_node": "Madrid",
|
||||
"to_node": "fiber (Madrid \u2192 Paris)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Madrid \u2192 Paris)",
|
||||
"to_node": "Madrid"
|
||||
},
|
||||
{
|
||||
"from_node": "Madrid",
|
||||
"to_node": "fiber (Madrid \u2192 Zurich)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Madrid \u2192 Zurich)",
|
||||
"to_node": "Madrid"
|
||||
},
|
||||
{
|
||||
"from_node": "Rome",
|
||||
"to_node": "fiber (Rome \u2192 Vienna)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Rome \u2192 Vienna)",
|
||||
"to_node": "Rome"
|
||||
},
|
||||
{
|
||||
"from_node": "Rome",
|
||||
"to_node": "fiber (Rome \u2192 Zurich)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Rome \u2192 Zurich)",
|
||||
"to_node": "Rome"
|
||||
},
|
||||
{
|
||||
"from_node": "Vienna",
|
||||
"to_node": "fiber (Vienna \u2192 Warsaw)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Vienna \u2192 Warsaw)",
|
||||
"to_node": "Vienna"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Amsterdam \u2192 Berlin)",
|
||||
"to_node": "Berlin"
|
||||
},
|
||||
{
|
||||
"from_node": "Berlin",
|
||||
"to_node": "fiber (Amsterdam \u2192 Berlin)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Amsterdam \u2192 Brussels)",
|
||||
"to_node": "Brussels"
|
||||
},
|
||||
{
|
||||
"from_node": "Brussels",
|
||||
"to_node": "fiber (Amsterdam \u2192 Brussels)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Amsterdam \u2192 Frankfurt)",
|
||||
"to_node": "Frankfurt"
|
||||
},
|
||||
{
|
||||
"from_node": "Frankfurt",
|
||||
"to_node": "fiber (Amsterdam \u2192 Frankfurt)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Berlin \u2192 Warsaw)",
|
||||
"to_node": "Warsaw"
|
||||
},
|
||||
{
|
||||
"from_node": "Warsaw",
|
||||
"to_node": "fiber (Berlin \u2192 Warsaw)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Brussels \u2192 London)",
|
||||
"to_node": "London"
|
||||
},
|
||||
{
|
||||
"from_node": "London",
|
||||
"to_node": "fiber (Brussels \u2192 London)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Bucharest \u2192 Istanbul)",
|
||||
"to_node": "Istanbul"
|
||||
},
|
||||
{
|
||||
"from_node": "Istanbul",
|
||||
"to_node": "fiber (Bucharest \u2192 Istanbul)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Bucharest \u2192 Warsaw)",
|
||||
"to_node": "Warsaw"
|
||||
},
|
||||
{
|
||||
"from_node": "Warsaw",
|
||||
"to_node": "fiber (Bucharest \u2192 Warsaw)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Frankfurt \u2192 Vienna)",
|
||||
"to_node": "Vienna"
|
||||
},
|
||||
{
|
||||
"from_node": "Vienna",
|
||||
"to_node": "fiber (Frankfurt \u2192 Vienna)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Istanbul \u2192 Rome)",
|
||||
"to_node": "Rome"
|
||||
},
|
||||
{
|
||||
"from_node": "Rome",
|
||||
"to_node": "fiber (Istanbul \u2192 Rome)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (London \u2192 Paris)",
|
||||
"to_node": "Paris"
|
||||
},
|
||||
{
|
||||
"from_node": "Paris",
|
||||
"to_node": "fiber (London \u2192 Paris)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Madrid \u2192 Paris)",
|
||||
"to_node": "Paris"
|
||||
},
|
||||
{
|
||||
"from_node": "Paris",
|
||||
"to_node": "fiber (Madrid \u2192 Paris)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Madrid \u2192 Zurich)",
|
||||
"to_node": "Zurich"
|
||||
},
|
||||
{
|
||||
"from_node": "Zurich",
|
||||
"to_node": "fiber (Madrid \u2192 Zurich)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Rome \u2192 Vienna)",
|
||||
"to_node": "Vienna"
|
||||
},
|
||||
{
|
||||
"from_node": "Vienna",
|
||||
"to_node": "fiber (Rome \u2192 Vienna)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Rome \u2192 Zurich)",
|
||||
"to_node": "Zurich"
|
||||
},
|
||||
{
|
||||
"from_node": "Zurich",
|
||||
"to_node": "fiber (Rome \u2192 Zurich)"
|
||||
},
|
||||
{
|
||||
"from_node": "fiber (Vienna \u2192 Warsaw)",
|
||||
"to_node": "Warsaw"
|
||||
},
|
||||
{
|
||||
"from_node": "Warsaw",
|
||||
"to_node": "fiber (Vienna \u2192 Warsaw)"
|
||||
}
|
||||
]
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,4 +1,28 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
########################################################################
|
||||
# _____ ___ ____ ____ ____ _____ #
|
||||
# |_ _|_ _| _ \ | _ \/ ___|| ____| #
|
||||
# | | | || |_) | | |_) \___ \| _| #
|
||||
# | | | || __/ | __/ ___) | |___ #
|
||||
# |_| |___|_| |_| |____/|_____| #
|
||||
# #
|
||||
# == Physical Simulation Environment == #
|
||||
# #
|
||||
########################################################################
|
||||
|
||||
|
||||
'''
|
||||
gnpy route planning and optimization library
|
||||
============================================
|
||||
|
||||
gnpy is a route planning and optimization library, written in Python, for
|
||||
operators of large-scale mesh optical networks.
|
||||
|
||||
:copyright: © 2018, Telecom Infra Project
|
||||
:license: BSD 3-Clause, see LICENSE for more details.
|
||||
'''
|
||||
|
||||
|
||||
from . import elements
|
||||
from .execute import *
|
||||
|
||||
@@ -1,17 +1,24 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
"""
|
||||
Network elements class with SpectralInformation propagation using
|
||||
__call__ and propagate methods
|
||||
@author: Alessio Ferrari
|
||||
@author: Mattia Cantono
|
||||
@author: Vittorio Curri
|
||||
@author: giladgoldfarb
|
||||
@author: briantaylor
|
||||
@author: jeanluc-auge
|
||||
@acknowledgement : Dave Boertjes
|
||||
"""
|
||||
'''
|
||||
gnpy.core.elements
|
||||
==================
|
||||
|
||||
This module contains standard network elements.
|
||||
|
||||
A network element is a Python callable. It takes a .info.SpectralInformation
|
||||
object and returns a copy with appropriate fields affected. This structure
|
||||
represents spectral information that is "propogated" by this network element.
|
||||
Network elements must have only a local "view" of the network and propogate
|
||||
SpectralInformation using only this information. They should be independent and
|
||||
self-contained.
|
||||
|
||||
Network elements MUST implement two attributes .uid and .name representing a
|
||||
unique identifier and a printable name.
|
||||
'''
|
||||
|
||||
|
||||
import numpy as np
|
||||
from scipy.constants import c, h
|
||||
|
||||
@@ -31,28 +38,38 @@ class Transceiver(Node):
|
||||
def _calc_snr(self, spectral_info):
|
||||
ase = [c.power.ase for c in spectral_info.carriers]
|
||||
nli = [c.power.nli for c in spectral_info.carriers]
|
||||
if min(ase)>1e-20:
|
||||
if min(ase)>1e-20:
|
||||
self.osnr_ase = [lin2db(c.power.signal/c.power.ase)
|
||||
for c in spectral_info.carriers]
|
||||
ratio_01nm = [lin2db(12.5e9/c.baud_rate) for c in spectral_info.carriers]
|
||||
self.osnr_ase_01nm = [ase - ratio for ase, ratio
|
||||
self.osnr_ase_01nm = [ase - ratio for ase, ratio
|
||||
in zip(self.osnr_ase, ratio_01nm)]
|
||||
if min(nli)>1e-20:
|
||||
self.osnr_nli = [lin2db(c.power.signal/c.power.nli)
|
||||
self.osnr_nli = [lin2db(c.power.signal/c.power.nli)
|
||||
for c in spectral_info.carriers]
|
||||
self.snr = [lin2db(c.power.signal/(c.power.nli+c.power.ase))
|
||||
self.snr = [lin2db(c.power.signal/(c.power.nli+c.power.ase))
|
||||
for c in spectral_info.carriers]
|
||||
|
||||
def __repr__(self):
|
||||
if self.snr != None and self.osnr_ase != None:
|
||||
snr = round(np.mean(self.snr),2)
|
||||
osnr_ase = round(np.mean(self.osnr_ase),2)
|
||||
osnr_ase_01nm = round(np.mean(self.osnr_ase_01nm), 2)
|
||||
return f'{type(self).__name__}(uid={self.uid}, \
|
||||
osnr_ase(in 0.1nm)={osnr_ase_01nm}dB, osnr_ase(in signal bw)={osnr_ase}dB, \
|
||||
snr total(in signal bw)={snr})'
|
||||
else:
|
||||
return f'{type(self).__name__}(uid={self.uid})'
|
||||
return (f'{type(self).__name__}('
|
||||
'uid={self.uid!r}, '
|
||||
'config={self.config!r}, '
|
||||
'osnr_ase_01nm={osnr_ase_01nm!r}, '
|
||||
'osnr_ase={osnr_ase!r}, '
|
||||
'osnr_ase_nli={osnr_ase_nli!r}, '
|
||||
'snr={snr!r})')
|
||||
|
||||
def __str__(self):
|
||||
if self.snr is None or self.osnr_ase is None:
|
||||
return f'{type(self).__name__} {self.uid}'
|
||||
snr = round(np.mean(self.snr),2)
|
||||
osnr_ase = round(np.mean(self.osnr_ase),2)
|
||||
osnr_ase_01nm = round(np.mean(self.osnr_ase_01nm), 2)
|
||||
|
||||
return '\n'.join([f'{type(self).__name__} {self.uid}',
|
||||
f' OSNR ASE (1nm): {np.mean(self.osnr_ase_01nm):.2f}',
|
||||
f' OSNR ASE (signal bw): {np.mean(self.osnr_ase):.2f}',
|
||||
f' SNR total (signal bw): {np.mean(snr):.2f}'])
|
||||
|
||||
def __call__(self, spectral_info):
|
||||
self._calc_snr(spectral_info)
|
||||
@@ -64,14 +81,17 @@ class Roadm(Node):
|
||||
self.loss = 20 #dB
|
||||
|
||||
def __repr__(self):
|
||||
return f'{type(self).__name__}(uid={self.uid}, \
|
||||
loss={round(self.loss,1)}dB)'
|
||||
return f'{type(self).__name__}(uid={self.uid!r}, loss={self.loss!r})'
|
||||
|
||||
def __str__(self):
|
||||
return '\n'.join([f'{type(self).__name__} {self.uid}',
|
||||
f' loss (dB): {self.loss:.2f}'])
|
||||
|
||||
def propagate(self, *carriers):
|
||||
attenuation = db2lin(self.loss)
|
||||
|
||||
for carrier in carriers:
|
||||
pwr = carrier.power
|
||||
pwr = carrier.power
|
||||
pwr = pwr._replace(signal=pwr.signal/attenuation,
|
||||
nonlinear_interference=pwr.nli/attenuation,
|
||||
amplified_spontaneous_emission=pwr.ase/attenuation)
|
||||
@@ -94,9 +114,12 @@ class Fiber(Node):
|
||||
#TODO discuss factor 2 in the linear lineic attenuation
|
||||
|
||||
def __repr__(self):
|
||||
k = UNITS['km']
|
||||
return f'{type(self).__name__}(uid={self.uid}, \
|
||||
length={round(self.length/k, 1)}km, loss={round(self.loss,1)}dB)'
|
||||
return f'{type(self).__name__}(uid={self.uid!r}, length={self.length!r}, loss={self.loss!r})'
|
||||
|
||||
def __str__(self):
|
||||
return '\n'.join([f'{type(self).__name__} {self.uid}',
|
||||
f' length (m): {self.length:.2f}',
|
||||
f' loss (dB): {self.loss:.2f}'])
|
||||
|
||||
def lin_attenuation(self):
|
||||
attenuation = self.length * self.loss_coef
|
||||
@@ -141,7 +164,7 @@ length={round(self.length/k, 1)}km, loss={round(self.loss,1)}dB)'
|
||||
'alpha_acoef': alpha_acoef,
|
||||
'description:': s}
|
||||
return d
|
||||
|
||||
|
||||
def _psi(self, carrier, interfering_carrier):
|
||||
""" Calculates eq. 123 from arXiv:1209.0394.
|
||||
"""
|
||||
@@ -156,7 +179,7 @@ length={round(self.length/k, 1)}km, loss={round(self.loss,1)}dB)'
|
||||
carrier.baud_rate * (delta_f - 0.5 * interfering_carrier.baud_rate))
|
||||
|
||||
return psi
|
||||
|
||||
|
||||
def _gn_analytic(self, carrier, *carriers):
|
||||
""" Computes the nonlinear interference power on a single carrier.
|
||||
The method uses eq. 120 from arXiv:1209.0394.
|
||||
@@ -202,20 +225,31 @@ class Edfa(Node):
|
||||
self.interpol_nf_ripple = None #nf_ripple: N numpy array
|
||||
self.channel_freq = None #SI channel frequencies: N numpy array
|
||||
"""nf, gprofile, pin and pout attributs are set by interpol_params"""
|
||||
self.nf = None #dB edfa nf at operational.gain_target: N numpy array
|
||||
self.nf = None #dB edfa nf at operational.gain_target: N numpy array
|
||||
self.gprofile = None
|
||||
self.pin_db = None
|
||||
self.pout_db = None
|
||||
|
||||
def __repr__(self):
|
||||
if self.pin_db != None and self.pout_db != None:
|
||||
nf_avg = round(np.mean(self.nf),1)
|
||||
return f'{type(self).__name__}(uid={self.uid}, \
|
||||
gain={round(self.operational.gain_target,1)}dB, NF={nf_avg}dB, \
|
||||
Pin={round(self.pin_db, 1)}dBm, Pout={round(self.pout_db,1)}dBm)'
|
||||
else:
|
||||
return f'{type(self).__name__}(uid={self.uid}, \
|
||||
gain={self.operational.gain_target})'
|
||||
return (f'{type(self).__name__}(uid={self.uid!r}, '
|
||||
f'interpol_dgt={self.interpol_dgt!r}, '
|
||||
f'interpol_gain_ripple={self.interpol_gain_ripple!r}, '
|
||||
f'interpol_nf_ripple={self.interpol_nf_ripple!r}, '
|
||||
f'channel_freq={self.channel_freq!r}, '
|
||||
f'nf={self.nf!r}, '
|
||||
f'gprofile={self.gprofile!r}, '
|
||||
f'pin_db={self.pin_db!r}, '
|
||||
f'pout_db={self.pout_db!r})')
|
||||
|
||||
def __str__(self):
|
||||
if self.pin_db is None or self.pout_db is None:
|
||||
return f'{type(self).__name__} {self.uid}'
|
||||
|
||||
return '\n'.join([f'{type(self).__name__} {self.uid}',
|
||||
f' gain (dB): {self.operational.gain_target:.2f}',
|
||||
f' noise figure (dB): {np.mean(self.nf):.2f}',
|
||||
f' Power In (dBm): {self.pin_db:.2f}',
|
||||
f' Power Out (dBm): {self.pout_db:.2f}'])
|
||||
|
||||
def interpol_params(self, frequencies, pin, baud_rates):
|
||||
"""interpolate SI channel frequencies with the edfa dgt and gain_ripple frquencies from json
|
||||
@@ -261,7 +295,7 @@ Pin={round(self.pin_db, 1)}dBm, Pout={round(self.pout_db,1)}dBm)'
|
||||
return nf_array
|
||||
|
||||
def noise_profile(self, df):
|
||||
""" noise_profile(bw) computes amplifier ase (W) in signal bw (Hz)
|
||||
""" noise_profile(bw) computes amplifier ase (W) in signal bw (Hz)
|
||||
noise is calculated at amplifier input
|
||||
|
||||
:bw: signal bandwidth = baud rate in Hz
|
||||
@@ -297,7 +331,7 @@ Pin={round(self.pin_db, 1)}dBm, Pout={round(self.pout_db,1)}dBm)'
|
||||
e.g. 12.5GHz."""
|
||||
|
||||
ase = h * df * self.channel_freq * db2lin(self.nf) #W
|
||||
return ase #in W, @amplifier input
|
||||
return ase #in W, @amplifier input
|
||||
|
||||
def _gain_profile(self, pin):
|
||||
"""
|
||||
@@ -397,7 +431,7 @@ Pin={round(self.pin_db, 1)}dBm, Pout={round(self.pout_db,1)}dBm)'
|
||||
# Upper gain estimate
|
||||
xhigh = dgts2 + deltax
|
||||
ghigh = g1st - voa + np.array(self.interpol_dgt) * xhigh
|
||||
pout_db = lin2db(np.sum(pin*1e3*db2lin(ghigh)))
|
||||
pout_db = lin2db(np.sum(pin*1e3*db2lin(ghigh)))
|
||||
gavg_high = pout_db - tot_in_power_db
|
||||
|
||||
# compute slope
|
||||
@@ -426,14 +460,14 @@ Pin={round(self.pin_db, 1)}dBm, Pout={round(self.pout_db,1)}dBm)'
|
||||
freq = np.array([c.frequency for c in carriers])
|
||||
brate = np.array([c.baud_rate for c in carriers])
|
||||
#interpolate the amplifier vectors with the carriers freq, calculate nf & gain profile
|
||||
self.interpol_params(freq, pin, brate)
|
||||
self.interpol_params(freq, pin, brate)
|
||||
gain = db2lin(self.gprofile)
|
||||
carrier_ase = self.noise_profile(brate)
|
||||
|
||||
for carrier in carriers:
|
||||
|
||||
pwr = carrier.power
|
||||
bw = carrier.baud_rate
|
||||
bw = carrier.baud_rate
|
||||
pwr = pwr._replace(signal=pwr.signal*gain[i],
|
||||
nonlinear_interference=pwr.nli*gain[i],
|
||||
amplified_spontaneous_emission=(pwr.ase+carrier_ase[i])*gain[i])
|
||||
|
||||
@@ -1,2 +1,9 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
'''
|
||||
gnpy.core.execute
|
||||
=================
|
||||
|
||||
This module contains functions for executing the propogation of
|
||||
spectral information on a `gnpy` network.
|
||||
'''
|
||||
|
||||
@@ -1,8 +1,18 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
'''
|
||||
gnpy.core.info
|
||||
==============
|
||||
|
||||
This module contains classes for modelling SpectralInformation.
|
||||
'''
|
||||
|
||||
|
||||
from collections import namedtuple
|
||||
|
||||
|
||||
class ConvenienceAccess:
|
||||
|
||||
def __init_subclass__(cls):
|
||||
for abbrev, field in getattr(cls, '_ABBREVS', {}).items():
|
||||
setattr(cls, abbrev, property(lambda self, f=field: getattr(self, f)))
|
||||
@@ -13,20 +23,27 @@ class ConvenienceAccess:
|
||||
kwargs[field] = kwargs.pop(abbrev)
|
||||
return self._replace(**kwargs)
|
||||
|
||||
|
||||
class Power(namedtuple('Power', 'signal nonlinear_interference amplified_spontaneous_emission'), ConvenienceAccess):
|
||||
|
||||
_ABBREVS = {'nli': 'nonlinear_interference',
|
||||
'ase': 'amplified_spontaneous_emission',}
|
||||
|
||||
|
||||
class Channel(namedtuple('Channel', 'channel_number frequency baud_rate roll_off power'), ConvenienceAccess):
|
||||
_ABBREVS = {'channel': 'channel_number',
|
||||
|
||||
_ABBREVS = {'channel': 'channel_number',
|
||||
'num_chan': 'channel_number',
|
||||
'ffs': 'frequency',
|
||||
'freq': 'frequency',}
|
||||
'ffs': 'frequency',
|
||||
'freq': 'frequency',}
|
||||
|
||||
|
||||
class SpectralInformation(namedtuple('SpectralInformation', 'carriers'), ConvenienceAccess):
|
||||
|
||||
def __new__(cls, *carriers):
|
||||
return super().__new__(cls, carriers)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
si = SpectralInformation(
|
||||
Channel(1, 193.95e12, 32e9, 0.15, # 193.95 THz, 32 Gbaud
|
||||
@@ -39,12 +56,10 @@ if __name__ == '__main__':
|
||||
spacing = 0.05 #THz
|
||||
|
||||
si = si.update(carriers=tuple(Channel(f+1, 191.3+spacing*(f+1), 32e9, 0.15, Power(1e-3, f, 1)) for f in range(96)))
|
||||
|
||||
|
||||
print(f'si = {si}')
|
||||
print(f'si = {si.carriers[0].power.nli}')
|
||||
print(f'si = {si.carriers[20].power.nli}')
|
||||
"""
|
||||
si2 = si.update(carriers=tuple(c.update(power = c.power.update(nli = c.power.nli * 1e5))
|
||||
for c in si.carriers))
|
||||
print(f'si2 = {si2}')
|
||||
"""
|
||||
@@ -1,15 +1,25 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
'''
|
||||
gnpy.core.network
|
||||
=================
|
||||
|
||||
This module contains functions for constructing networks of network elements.
|
||||
'''
|
||||
|
||||
|
||||
from networkx import DiGraph
|
||||
|
||||
from gnpy.core import elements
|
||||
from gnpy.core.elements import Fiber, Edfa, Transceiver, Roadm
|
||||
from gnpy.core.units import UNITS
|
||||
|
||||
|
||||
MAX_SPAN_LENGTH = 125000
|
||||
TARGET_SPAN_LENGTH = 100000
|
||||
MIN_SPAN_LENGTH = 75000
|
||||
|
||||
|
||||
def network_from_json(json_data):
|
||||
# NOTE|dutc: we could use the following, but it would tie our data format
|
||||
# too closely to the graph library
|
||||
@@ -30,21 +40,21 @@ def calculate_new_length(fiber_length):
|
||||
result = (fiber_length, 1)
|
||||
if fiber_length > MAX_SPAN_LENGTH:
|
||||
n_spans = int(fiber_length // TARGET_SPAN_LENGTH)
|
||||
|
||||
|
||||
length1 = fiber_length / (n_spans+1)
|
||||
result1 = (length1, n_spans+1)
|
||||
delta1 = TARGET_SPAN_LENGTH-length1
|
||||
|
||||
|
||||
length2 = fiber_length / n_spans
|
||||
delta2 = length2-TARGET_SPAN_LENGTH
|
||||
result2 = (length2, n_spans)
|
||||
|
||||
|
||||
if length1<MIN_SPAN_LENGTH and length2<MAX_SPAN_LENGTH:
|
||||
result = result2
|
||||
elif length2>MAX_SPAN_LENGTH and length1>MIN_SPAN_LENGTH:
|
||||
result = result1
|
||||
else:
|
||||
if delta1 < delta2:
|
||||
if delta1 < delta2:
|
||||
result = result1
|
||||
else:
|
||||
result = result2
|
||||
@@ -78,17 +88,17 @@ def split_fiber(network, fiber):
|
||||
|
||||
for next_node in next_nodes:
|
||||
network.add_edge(prev_node, next_node)
|
||||
|
||||
|
||||
network = add_egress_amplifier(network, prev_node)
|
||||
return network
|
||||
|
||||
def add_egress_amplifier(network, node):
|
||||
next_nodes = [n for n in network.successors(node)
|
||||
next_nodes = [n for n in network.successors(node)
|
||||
if not (isinstance(n, Edfa) or isinstance(n, Transceiver))]
|
||||
i = 1
|
||||
for next_node in next_nodes:
|
||||
network.remove_edge(node, next_node)
|
||||
|
||||
|
||||
uid = 'Edfa' + str(i)+ '_' + str(node.uid)
|
||||
metadata = next_node.metadata
|
||||
operational = {'gain_target': node.loss, 'tilt_target': 0}
|
||||
|
||||
@@ -1,5 +1,23 @@
|
||||
#! /bin/usr/python3
|
||||
|
||||
'''
|
||||
gnpy.core.node
|
||||
==============
|
||||
|
||||
This module contains the base class for a network element.
|
||||
|
||||
Strictly, a network element is any callable which accepts an immutable
|
||||
.info.SpectralInformation object and returns a .info.SpectralInformation object
|
||||
(a copy.)
|
||||
|
||||
Network elements MUST implement two attributes .uid and .name representing a
|
||||
unique identifier and a printable name.
|
||||
|
||||
This base class provides a mode convenient way to define a network element
|
||||
via subclassing.
|
||||
'''
|
||||
|
||||
|
||||
from uuid import uuid4
|
||||
from gnpy.core.utils import load_json
|
||||
|
||||
@@ -33,11 +51,11 @@ class Node:
|
||||
else:
|
||||
self.uid = self.config.uid
|
||||
if hasattr(self.config, 'params'):
|
||||
self.params = self.config.params
|
||||
self.params = self.config.params
|
||||
if hasattr(self.config, 'metadata'):
|
||||
self.metadata = self.config.metadata
|
||||
if hasattr(self.config, 'operational'):
|
||||
self.operational = self.config.operational
|
||||
self.operational = self.config.operational
|
||||
|
||||
@property
|
||||
def coords(self):
|
||||
|
||||
@@ -1,10 +1,19 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
'''
|
||||
gnpy.core.utils
|
||||
===============
|
||||
|
||||
This module contains utility functions that are used with gnpy.
|
||||
'''
|
||||
|
||||
|
||||
import json
|
||||
|
||||
import numpy as np
|
||||
from numpy import pi, cos, sqrt, log10
|
||||
from scipy import constants
|
||||
|
||||
|
||||
def load_json(filename):
|
||||
@@ -22,7 +31,7 @@ def c():
|
||||
"""
|
||||
Returns the speed of light in meters per second
|
||||
"""
|
||||
return 299792458.0
|
||||
return constants.c
|
||||
|
||||
|
||||
def itufs(spacing, startf=191.35, stopf=196.10):
|
||||
@@ -45,7 +54,7 @@ def h():
|
||||
"""
|
||||
Returns plank's constant in J*s
|
||||
"""
|
||||
return 6.62607004e-34
|
||||
return constants.h
|
||||
|
||||
|
||||
def lin2db(value):
|
||||
@@ -56,11 +65,8 @@ def db2lin(value):
|
||||
return 10**(value / 10)
|
||||
|
||||
|
||||
def wavelength2freq(value):
|
||||
""" Converts wavelength units to frequeuncy units.
|
||||
"""
|
||||
return c() / value
|
||||
|
||||
wavelength2freq = constants.lambda2nu
|
||||
freq2wavelength = constants.nu2lambda
|
||||
|
||||
def freq2wavelength(value):
|
||||
""" Converts frequency units to wavelength units.
|
||||
|
||||
31
setup.py
31
setup.py
@@ -11,29 +11,28 @@ with open(path.join(here, 'README.rst'), encoding='utf-8') as f:
|
||||
|
||||
setup(
|
||||
name='gnpy',
|
||||
version='0.0.1',
|
||||
description='TIP optical network modeling library',
|
||||
version='0.1.3',
|
||||
description='route planning and optimization tool for mesh optical networks',
|
||||
long_description=long_description,
|
||||
long_description_content_type='text/x-rst; charset=UTF-8',
|
||||
url='https://github.com/Telecominfraproject/gnpy',
|
||||
author='Telecom Infra Project',
|
||||
author_email='james.powell@telecominfraproject.com',
|
||||
classifiers=[
|
||||
'Development Status :: 3 - Alpha',
|
||||
'Intended Audience :: Developers',
|
||||
'Intended Audience :: Science/Research',
|
||||
'Intended Audience :: Telecommunications Industry',
|
||||
'License :: OSI Approved :: BSD License',
|
||||
'Natural Language :: English',
|
||||
'Programming Language :: Python',
|
||||
'Programming Language :: Python :: 3 :: Only',
|
||||
'Programming Language :: Python :: 3.6',
|
||||
'Programming Language :: Python :: Implementation :: CPython',
|
||||
'Topic :: Scientific/Engineering',
|
||||
'Topic :: Scientific/Engineering :: Physics',
|
||||
'License :: OSI Approved :: BSD License',
|
||||
'Programming Language :: Python :: 3.6',
|
||||
],
|
||||
keywords='optics network fiber communication',
|
||||
keywords='optics network fiber communication route planning optimization',
|
||||
packages=find_packages(exclude=['examples', 'docs', 'tests']), # Required
|
||||
install_requires=['cycler',
|
||||
'decorator',
|
||||
'matplotlib',
|
||||
'networkx',
|
||||
'numpy',
|
||||
'scipy',
|
||||
'pyparsing',
|
||||
'python-dateutil',
|
||||
'pytz',
|
||||
'six',
|
||||
'xlrd']
|
||||
install_requires=list(open('requirements.txt'))
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user