fix plot bug: do not overwrite the path used for plot

The plot function failed to recognize 'path' as part
of the network due to the reuse of the 'path' variable.
This led to errors when attempting to plot.

Solution is to use a different name for the deepcopy of
path elements used to record the propagation results
'propagated_path'.

Signed-off-by: EstherLerouzic <esther.lerouzic@orange.com>
Change-Id: I0351c37de0d74391ebeb68e974b777b1f51572aa

.docker-travis.sh

@@ -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

.github/workflows/main.yml

@@ -11,23 +11,26 @@ jobs:
     name: Tox test
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
         with:
           fetch-depth: 0
-      - uses: fedora-python/tox-github-action@v0.4
+      - uses: fedora-python/tox-github-action@v37.0
         with:
           tox_env: ${{ matrix.tox_env }}
           dnf_install: ${{ matrix.dnf_install }}
-      - uses: codecov/codecov-action@29386c70ef20e286228c72b668a06fd0e8399192
+      - uses: codecov/codecov-action@v3.1.1
         if: ${{ endswith(matrix.tox_env, '-cover') }}
         with:
           files: ${{ github.workspace }}/cover/coverage.xml
     strategy:
+      fail-fast: false
       matrix:
         tox_env:
           - py38
           - py39
-          - py310-cover
+          - py310
+          - py311
+          - py312-cover
         include:
           - tox_env: docs
             dnf_install: graphviz
@@ -38,13 +41,13 @@ jobs:
     name: PyPI packaging
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
         with:
           fetch-depth: 0
-      - uses: actions/setup-python@v2
+      - uses: actions/setup-python@v4
         name: Install Python
         with:
-          python-version: '3.10'
+          python-version: '3.12'
       - uses: casperdcl/deploy-pypi@bb869aafd89f657ceaafe9561d3b5584766c0f95
         with:
           password: ${{ secrets.PYPI_API_TOKEN }}
@@ -62,7 +65,7 @@ jobs:
         with:
           username: jktjkt
           password: ${{ secrets.DOCKERHUB_TOKEN }}
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
         with:
           fetch-depth: 0
       - name: Extract tag name
@@ -92,21 +95,51 @@ jobs:
             telecominfraproject/oopt-gnpy:${{ 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
+      - uses: actions/checkout@v3
         with:
           fetch-depth: 0
-      - uses: actions/setup-python@v2
+      - uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python_version }}
       - run: |
-          pip install --editable .
-          pip install 'pytest>=6.2.5,<7'
+          pip install --editable .[tests]
           pytest -vv
     strategy:
+      fail-fast: false
       matrix:
-        python_version:
-          - "3.10"
+        include:
+          - os: windows-2019
+            python_version: "3.10"
+          - os: windows-2022
+            python_version: "3.11"
+          - os: windows-2022
+            python_version: "3.12"
+          - os: macos-13
+            python_version: "3.12"
+          - os: macos-14
+            python_version: "3.12"
+
+  paywalled-platforms:
+    name: Tests on paywalled platforms
+    if: github.repository_owner == 'Telecominfraproject'
+    runs-on: ${{ matrix.os }}
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+      - uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python_version }}
+      - run: |
+          pip install --editable .[tests]
+          pytest -vv
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - os: macos-13-xlarge # Apple M1 CPU
+            python_version: "3.12"

.readthedocs.yml

@@ -1,5 +1,17 @@
+version: 2
 build:
-    image: latest
+  os: ubuntu-22.04
+  tools:
+    python: "3.12"
+  apt_packages:
+    - graphviz
+
 python:
-    version: 3.8
-requirements_file: docs/requirements.txt
+  install:
+    - method: pip
+      path: .
+      extra_requirements:
+        - docs
+
+sphinx:
+  configuration: docs/conf.py

.travis.yml

@@ -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

.zuul.yaml

@@ -2,10 +2,18 @@
 - project:
     check:
       jobs:
-        - tox-py38
-        - tox-py39
-        - tox-py310-cover
-        - tox-docs-f35
+        - tox-py38:
+            vars:
+              ensure_tox_version: '<4'
+        - tox-py39:
+            vars:
+              ensure_tox_version: '<4'
+        - tox-py310-cover:
+            vars:
+              ensure_tox_version: '<4'
+        - tox-docs-f36:
+            vars:
+              ensure_tox_version: '<4'
         - coverage-diff:
             voting: false
             dependencies:
@@ -16,7 +24,11 @@
               coverage_job_name_current: tox-py310-cover
         - tox-linters-diff-n-report:
             voting: false
-        - tox-py310-cover-previous
+            vars:
+              ensure_tox_version: '<4'
+        - tox-py310-cover-previous:
+            vars:
+              ensure_tox_version: '<4'
     tag:
       jobs:
         - oopt-release-python:

AUTHORS.rst

@@ -11,18 +11,21 @@ To learn how to contribute, please see CONTRIBUTING.md
 - Brian Taylor (Facebook) <briantaylor@fb.com>
 - David Boertjes (Ciena) <dboertje@ciena.com>
 - Diego Landa (Facebook) <dlanda@fb.com>
+- Emmanuelle Delfour (Orange) <WEDE7391@orange.com>
 - Esther Le Rouzic (Orange) <esther.lerouzic@orange.com>
 - Gabriele Galimberti (Cisco) <ggalimbe@cisco.com>
 - Gert Grammel (Juniper Networks) <ggrammel@juniper.net>
+- Giacomo Borraccini (Politecnico di Torino) <giacomo.borraccini@polito.it>
 - Gilad Goldfarb (Facebook) <giladg@fb.com>
 - James Powell (Telecom Infra Project) <james.powell@telecominfraproject.com>
-- Jan Kundrát (Telecom Infra Project) <jan.kundrat@telecominfraproject.com>
+- Jan Kundrát (Telecom Infra Project) <jkt@jankundrat.com>
 - Jeanluc Augé (Orange) <jeanluc.auge@orange.com>
 - Jonas Mårtensson (RISE) <jonas.martensson@ri.se>
 - Mattia Cantono (Politecnico di Torino) <mattia.cantono@polito.it>
 - Miguel Garrich (University Catalunya) <miquel.garrich@upct.es>
 - Raj Nagarajan (Lumentum) <raj.nagarajan@lumentum.com>
 - Roberts Miculens (Lattelecom) <roberts.miculens@lattelecom.lv>
+- Sami Alavi (NUST) <sami.mansooralavi1999@gmail.com>
 - Shengxiang Zhu (University of Arizona) <szhu@email.arizona.edu>
 - Stefan Melin (Telia Company) <Stefan.Melin@teliacompany.com>
 - Vittorio Curri (Politecnico di Torino) <vittorio.curri@polito.it>

README.md

@@ -3,12 +3,12 @@
 [![Install via pip](https://img.shields.io/pypi/v/gnpy)](https://pypi.org/project/gnpy/)
 [![Python versions](https://img.shields.io/pypi/pyversions/gnpy)](https://pypi.org/project/gnpy/)
 [![Documentation status](https://readthedocs.org/projects/gnpy/badge/?version=master)](http://gnpy.readthedocs.io/en/master/?badge=master)
-[![GitHub Workflow Status](https://img.shields.io/github/workflow/status/Telecominfraproject/oopt-gnpy/build)](https://github.com/Telecominfraproject/oopt-gnpy/actions/workflows/main.yml)
+[![GitHub Workflow Status](https://img.shields.io/github/actions/workflow/status/Telecominfraproject/oopt-gnpy/main.yml)](https://github.com/Telecominfraproject/oopt-gnpy/actions/workflows/main.yml)
 [![Gerrit](https://img.shields.io/badge/patches-via%20Gerrit-blue)](https://review.gerrithub.io/q/project:Telecominfraproject/oopt-gnpy+is:open)
 [![Contributors](https://img.shields.io/github/contributors-anon/Telecominfraproject/oopt-gnpy)](https://github.com/Telecominfraproject/oopt-gnpy/graphs/contributors)
-[![Code Quality via LGTM.com](https://img.shields.io/lgtm/grade/python/github/Telecominfraproject/oopt-gnpy)](https://lgtm.com/projects/g/Telecominfraproject/oopt-gnpy/)
 [![Code Coverage via codecov](https://img.shields.io/codecov/c/github/Telecominfraproject/oopt-gnpy)](https://codecov.io/gh/Telecominfraproject/oopt-gnpy)
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3458319.svg)](https://doi.org/10.5281/zenodo.3458319)
+[![Matrix chat](https://img.shields.io/matrix/oopt-gnpy:matrix.org)](https://matrix.to/#/%23oopt-gnpy%3Amatrix.org?via=matrix.org)
 
 GNPy is an open-source, community-developed library for building route planning and optimization tools in real-world mesh optical networks.
 We are a consortium of operators, vendors, and academic researchers sponsored via the [Telecom Infra Project](http://telecominfraproject.com)'s [OOPT/PSE](https://telecominfraproject.com/open-optical-packet-transport) working group.
@@ -23,7 +23,9 @@ Read our [documentation](https://gnpy.readthedocs.io/), learn from the demos, an
 
 This example demonstrates how GNPy can be used to check the expected SNR at the end of the line by varying the channel input power:
 
-![Running a simple simulation example](https://telecominfraproject.github.io/oopt-gnpy/docs/images/transmission_main_example.svg)
+![Running a simple simulation example](docs/images/gnpy-transmission-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/)

docs/about-project.md

@@ -12,7 +12,7 @@ We encourage all interested people outside the TIP to [join the project](https:/
 
 `gnpy` is looking for additional contributors, especially those with experience planning and maintaining large-scale, real-world mesh optical networks.
 
-To get involved, please contact [Jan Kundrát](mailto:jan.kundrat@telecominfraproject.com) or [Gert Grammel](mailto:ggrammel@juniper.net).
+To get involved, please contact [Jan Kundrát](mailto:jkt@jankundrat.com) or [Gert Grammel](mailto:ggrammel@juniper.net).
 
 `gnpy` contributions are currently limited to members of [TIP](http://telecominfraproject.com).
 Membership is free and open to all.

docs/biblio.bib

@@ -1848,3 +1848,15 @@ month={Sept},}
   title = {Telecom Infra Project},
   url = {https://www.telecominfraproject.com},
 }
+
+@ARTICLE{DAmicoJLT2022,
+  author={D’Amico, Andrea and Correia, Bruno and London, Elliot and Virgillito,
+  Emanuele and Borraccini, Giacomo and Napoli, Antonio and Curri, Vittorio},
+  journal={Journal of Lightwave Technology},
+  title={Scalable and Disaggregated GGN Approximation Applied to a C+L+S Optical Network},
+  year={2022},
+  volume={40},
+  number={11},
+  pages={3499-3511},
+  doi={10.1109/JLT.2022.3162134}
+}

docs/concepts.rst

@@ -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:
 

docs/conf.py

@@ -65,7 +65,7 @@ author = 'Telecom Infra Project - OOPT PSE Group'
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.
-language = None
+language = 'en'
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
@@ -84,18 +84,11 @@ todo_include_todos = False
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-on_rtd = os.environ.get('READTHEDOCS') == 'True'
-if on_rtd:
-    html_theme = 'default'
-    html_theme_options = {
-        'logo_only': True,
-    }
-else:
-    html_theme = 'alabaster'
-    html_theme_options = {
-        'logo': 'images/GNPy-logo.png',
-        'logo_name': False,
-    }
+html_theme = 'alabaster'
+html_theme_options = {
+    'logo': 'images/GNPy-logo.png',
+    'logo_name': False,
+}
 
 html_logo = 'images/GNPy-logo.png'
 

docs/excel.rst

@@ -114,10 +114,6 @@ and a fiber span from node3 to node6::
   If filled they must contain strings with the same constraint as "City" names. Its value is used to differenate links having the same end points. In this case different Id should be used. Cable Ids are not meant to be unique in general.
 
 
-
-
-(in progress)
-
 .. _excel-equipment-sheet:
 
 Eqpt sheet 
@@ -192,7 +188,42 @@ This generates a text file meshTopologyExampleV2_eqt_sheet.txt  whose content ca
 
 - **delta_p**, in dBm,  is not mandatory. If filled it is used to set the output target power per channel at the output of the amplifier, if power_mode is True. The output power is then set to power_dbm + delta_power.
 
-# to be completed #
+
+.. _excel-roadms-sheet:
+
+Roadms sheet 
+------------
+
+The ROADM sheet (named "Roadms") is optional.
+If provided, it can be used to specify:
+
+  - per channel power target on a specific ROADM degree (*per_degree_pch_out_db*),
+  - ROADM type variety,
+  - impairment ID (identifier) on a particular ROADM path (from degree - to degree).
+
+This sheet contains six columns:
+
+  Node A ; Node Z ; per degree target power (dBm) ; type_variety ; from degrees ; from degree to degree impairment id
+
+- **Node A** is mandatory. Name of the ROADM node (as listed in Nodes sheet).
+  Must be a 'ROADM' (Type attribute in Node sheet), its number of occurence may be equal to its degree.
+
+- **Node Z** is mandatory. Egress direction from the *Node A* ROADM site. Multiple Links between the same Node A
+  and NodeZ is not supported. 
+
+- **per degree target power (dBm)** (optional). 
+  If filled it must contain a value in dBm corresponding to :ref:`per_degree_pch_out_db<roadm_json_instance>` on the **Node Z** degree.
+  Defaults to equipment library value if not filled.
+
+- **type_variety** (optional). Must be the same for all ROADM entries if filled,
+  and defined in the :ref:`equipment library<roadm>`. Defaults to 'default' if not filled.
+
+- **from degrees** (optional): List of Node names separated by ' | '. Names must be present in Node sheet.
+  Together with Node Z, they define a list of internal path in ROADM for which the impairment ID applies
+
+- **from degree to degree impairment id** (optional):List of impairment IDs separated by ' | '. Must be filled
+  if **from degrees** is defined.
+  The impairment ID must be defined in the equipment library and be of "express" type.
 
 (in progress)
 

docs/extending.rst

@@ -91,7 +91,8 @@ Advanced Specification
 **********************
 
 The amplifier performance can be further described in terms of gain ripple, NF ripple, and the dynamic gain tilt.
-When provided, the amplifier characteristic is fine-tuned as a function of carrier frequency.
+When provided, the amplifier characteristic is fine-tuned as a function of carrier frequency. Note that in this advanced
+specification tilt is defined vs frequency while tilt_target specified in EDFA instances is defined vs wavelength.
 
 .. _extending-raman:
 
@@ -100,10 +101,10 @@ 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`.
@@ -119,38 +120,32 @@ A *mode* usually refers to a particular performance point that is defined by a c
 
 The following data are required for each mode:
 
-``bit-rate``
-  Data bit rate, in :math:`\text{Gbits}\times s^{-1}`.
-``baud-rate``
-  Symbol modulation rate, in :math:`\text{Gbaud}`.
-``required-osnr``
-  Minimal allowed OSNR for the receiver.
+``bit_rate``
+  Data bit rate, in :math:`\text{bits}\times s^{-1}`.
+``baud_rate``
+  Symbol modulation rate, in :math:`\text{baud}`.
+``OSNR``
+  Minimal required OSNR for the receiver. In :math:`\text{dB}`
 ``tx-osnr``
-  Initial OSNR at the transmitter's output.
-``grid-spacing``
+  Initial OSNR at the transmitter's output. In :math:`\text{dB}`
+``min-spacing``
   Minimal grid spacing, i.e., an effective channel spectral bandwidth.
   In :math:`\text{Hz}`.
-``tx-roll-off``
+``roll-off``
   Roll-off parameter (:math:`\beta`) of the TX pulse shaping filter.
   This assumes a raised-cosine filter.
 ``rx-power-min`` and ``rx-power-max``
-  The allowed range of power at the receiver.
+  (work in progress) The allowed range of power at the receiver.
   In :math:`\text{dBm}`.
-``cd-max``
-  Maximal allowed Chromatic Dispersion (CD).
-  In :math:`\text{ps}/\text{nm}`.
-``pmd-max``
-  Maximal allowed Polarization Mode Dispersion (PMD).
-  In :math:`\text{ps}`.
-``cd-penalty``
-  *Work-in-progress.*
-  Describes the increase of the requires GSNR as the :abbr:`CD (Chromatic Dispersion)` deteriorates.
-``dgd-penalty``
-  *Work-in-progress.*
-  Describes the increase of the requires GSNR as the :abbr:`DGD (Differential Group Delay)` deteriorates.
-``pmd-penalty``
-  *Work-in-progress.*
-  Describes the increase of the requires GSNR as the :abbr:`PMD (Polarization Mode Dispersion)` deteriorates.
+``penalties``
+  Impairments such as Chromatic Dispersion (CD), Polarization Mode Dispersion (PMD), and Polarization Dispersion Loss (PDL)
+  result in penalties at the receiver. The receiver's ability to handle these impairments can be defined for each mode as
+  a list of {impairment: in defined units, 'penalty_value' in dB} (see `transceiver section here <json.rst#_transceiver>`).
+  Maximum allowed CD, maximum allowed PMD, and maximum allowed PDL should be listed there with corresponding penalties.
+  Impairments experienced during propagation are linearly interpolated between given points to obtain the corresponding penalty.
+  The accumulated penalties are subtracted from the path GSNR before comparing with the minimum required OSNR.
+  Impairments: PMD in :math:`\text{ps}`, CD in :math:`\text{ps/nm}`, PDL in :math:`\text{dB}`, penalty_value in :math:`\text{dB}`
+
 
 GNPy does not directly track the FEC performance, so the type of chosen FEC is likely indicated in the *name* of the selected transponder mode alone.
 
@@ -168,6 +163,7 @@ The set of parameters for each ROADM model therefore includes:
   Per-channel target TX power towards the egress amplifier.
   Within GNPy, a ROADM is expected to attenuate any signal that enters the ROADM node to this level.
   This can be overridden on a per-link in the network topology.
+  Targets can be set using power or power spectral density (see `roadm section here <json.rst#__roadm>`)
 ``pmd``
   Polarization mode dispersion (PMD) penalty of the express path.
   In :math:`\text{ps}`.

docs/index.rst

@@ -17,6 +17,7 @@ in real-world mesh optical networks. It is based on the Gaussian Noise Model.
    about-project
    model
    gnpy-api
+   release-notes
 
 Indices and tables
 ==================

docs/intro.rst

@@ -10,32 +10,33 @@ fully-functional programs.
 
     **Note**: *If you are a network operator or involved in route planning and
     optimization for your organization, please contact project maintainer Jan
-    Kundrát <jan.kundrat@telecominfraproject.com>. gnpy is looking for users with
+    Kundrát <jkt@jankundrat.com>. gnpy is looking for users with
     specific, delineated use cases to drive requirements for future
     development.*
 
-This example demonstrates how GNPy can be used to check the expected SNR at the end of the line by varying the channel input power:
+This example demonstrates how GNPy can be used to check the expected SNR at the end of the line by varying the channel input power,
+or to run a planning script to check SNR of several services:
 
-.. image:: https://telecominfraproject.github.io/oopt-gnpy/docs/images/transmission_main_example.svg
+.. image:: images/gnpy-transmission-example.svg
    :width: 100%
    :align: left
    :alt: Running a simple simulation example
 
-By default, this script operates on a single span network defined in
-`gnpy/example-data/edfa_example_network.json <gnpy/example-data/edfa_example_network.json>`_
+By default, the gnpy-transmission-example script operates on a single span network defined in
+`gnpy/example-data/edfa_example_network.json <../gnpy/example-data/edfa_example_network.json>`_
 
 You can specify a different network at the command line as follows. For
 example, to use the CORONET Global network defined in
-`gnpy/example-data/CORONET_Global_Topology.json <gnpy/example-data/CORONET_Global_Topology.json>`_:
+`gnpy/example-data/CORONET_Global_Topology.json <../gnpy/example-data/CORONET_Global_Topology.json>`_:
 
 .. code-block:: shell-session
 
     $ gnpy-transmission-example $(gnpy-example-data)/CORONET_Global_Topology.json
 
 It is also possible to use an Excel file input (for example
-`gnpy/example-data/CORONET_Global_Topology.xls <gnpy/example-data/CORONET_Global_Topology.xls>`_).
+`gnpy/example-data/CORONET_Global_Topology.xls <../gnpy/example-data/CORONET_Global_Topology.xls>`_).
 The Excel file will be processed into a JSON file with the same prefix.
-Further details about the Excel data structure are available `in the documentation <docs/excel.rst>`__.
+Further details about the Excel data structure are available `in the documentation <excel.rst>`__.
 
 The main transmission example will calculate the average signal OSNR and SNR
 across network elements (transceiver, ROADMs, fibers, and amplifiers)
@@ -56,10 +57,10 @@ interference noise.
 Further Instructions for Use
 ----------------------------
 
-Simulations are driven by a set of `JSON <docs/json.rst>`__ or `XLS <docs/excel.rst>`__ files.
+Simulations are driven by a set of `JSON <json.rst>`__ or `XLS <excel.rst>`__ files.
 
 The ``gnpy-transmission-example`` script propagates a spectrum of channels at 32 Gbaud, 50 GHz spacing and 0 dBm/channel. 
-Launch power can be overridden by using the ``--power`` argument.
+Launch power in fiber spans can be overridden by using the ``--power`` argument.
 Spectrum information is not yet parametrized but can be modified directly in the ``eqpt_config.json`` (via the ``SpectralInformation`` -SI- structure) to accommodate any baud rate or spacing.
 The number of channel is computed based on ``spacing`` and ``f_min``, ``f_max`` values.
 
@@ -71,8 +72,8 @@ An experimental support for Raman amplification is available:
        $(gnpy-example-data)/raman_edfa_example_network.json \
        --sim $(gnpy-example-data)/sim_params.json --show-channels
 
-Configuration of Raman pumps (their frequencies, power and pumping direction) is done via the `RamanFiber element in the network topology <gnpy/example-data/raman_edfa_example_network.json>`_.
-General numeric parameters for simulation control are provided in the `gnpy/example-data/sim_params.json <gnpy/example-data/sim_params.json>`_.
+Configuration of Raman pumps (their frequencies, power and pumping direction) is done via the `RamanFiber element in the network topology <../gnpy/example-data/raman_edfa_example_network.json>`_.
+General numeric parameters for simulation control are provided in the `gnpy/example-data/sim_params.json <../gnpy/example-data/sim_params.json>`_.
 
 Use ``gnpy-path-request`` to request several paths at once:
 
@@ -82,7 +83,7 @@ Use ``gnpy-path-request`` to request several paths at once:
      $ gnpy-path-request -o output_file.json \
        meshTopologyExampleV2.xls meshTopologyExampleV2_services.json
 
-This program operates on a network topology (`JSON <docs/json.rst>`__ or `Excel <docs/excel.rst>`__ format), processing the list of service requests (JSON or XLS again).
+This program operates on a network topology (`JSON <json.rst>`__ or `Excel <excel.rst>`__ format), processing the list of service requests (JSON or XLS again).
 The service requests and reply formats are based on the `draft-ietf-teas-yang-path-computation-01 <https://tools.ietf.org/html/draft-ietf-teas-yang-path-computation-01>`__ with custom extensions (e.g., for transponder modes).
 An example of the JSON input is provided in file `service-template.json`, while results are shown in `path_result_template.json`.
 

docs/model.rst

@@ -126,9 +126,9 @@ 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
+thanks to the absence of in-line compensation for chromatic 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
+accumulation of NLI has extensively 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.

docs/release-notes.rst

@@ -0,0 +1,463 @@
+.. _release-notes:
+
+Release change log
+==================
+
+Each release introduces some changes and new features.
+
+(prepare text for next release)
+
+v2.11
+-----
+
+**New feature**
+
+A new type_def for amplifiers has been introduced: multi_band. This allows the definition of a
+multiband amplifier site composed of several amplifiers per band (a typical application is C+L transmission). The
+release also includes autodesign for links (Optical Multiplex Section, OMS) composed of multi_band amplifiers.
+Multi_band autodesign includes basic tilt and tilt_target calculation when the Raman flag is enabled with the
+--sim-params option. The spectrum is demultiplexed before propagation in the amplifier and multiplexed in the output
+fiber at the amplifier output.
+
+
+In the library:
+
+    .. code-block:: json
+
+        {
+            "type_variety": "std_medium_gain_C",
+            "f_min": 191.225e12,
+            "f_max": 196.125e12,
+            "type_def": "variable_gain",
+            "gain_flatmax": 26,
+            "gain_min": 15,
+            "p_max": 21,
+            "nf_min": 6,
+            "nf_max": 10,
+            "out_voa_auto": false,
+            "allowed_for_design": false
+        },
+        {
+            "type_variety": "std_medium_gain_L",
+            "f_min": 186.5e12,
+            "f_max": 190.1e12,
+            "type_def": "variable_gain",
+            "gain_flatmax": 26,
+            "gain_min": 15,
+            "p_max": 21,
+            "nf_min": 6,
+            "nf_max": 10,
+            "out_voa_auto": false,
+            "allowed_for_design": true
+        },
+        {
+            "type_variety": "std_medium_gain_multiband",
+            "type_def": "multi_band",
+            "amplifiers": [
+                "std_medium_gain_C",
+                "std_medium_gain_L"
+            ],
+            "allowed_for_design": false
+        },
+
+In the network topology:
+
+    .. code-block:: json
+
+      {
+          "uid": "east edfa in Site_A to Site_B",
+          "type": "Multiband_amplifier",
+          "type_variety": "std_medium_gain_multiband",
+          "amplifiers": [{
+                  "type_variety": "std_medium_gain_C",
+                  "operational": {
+                      "gain_target": 22.55,
+                      "delta_p": 0.9,
+                      "out_voa": 3.0,
+                      "tilt_target": 0.0
+                  }
+              }, {
+                  "type_variety": "std_medium_gain_L",
+                  "operational": {
+                      "gain_target": 21,
+                      "delta_p": 3.0,
+                      "out_voa": 3.0,
+                      "tilt_target": 0.0
+                  }
+              }
+          ]
+      }
+
+**Network design**
+
+Optionally, users can define a design target per OMS (single or multi-band), with specific frequency ranges.
+Default design bands are defined in the SI.
+
+    .. code-block:: json
+
+      {
+          "uid": "roadm Site_A",
+          "type": "Roadm",
+          "params": {
+              "target_pch_out_db": -20,
+              "design_bands": [{"f_min": 191.3e12, "f_max": 195.1e12}]
+          }
+      }
+
+It is possible to define a set of bands in the SI block instead of a single Spectrum Information.
+In this case type_variety must be used.
+Each set defines a reference channel used for design functions and autodesign.
+
+The default design settings for the path-request-run script have been modified.
+Now, design is performed once for the reference channel defined in the SI block of the eqpt_config,
+and requests are propagated based on this design.
+The --redesign-per-request option can be used to restore previous behaviour
+(design using request channel types).
+
+The autodesign function has been updated to insert multiband booster, preamp or inline amplifiers based on the OMS
+nature. If nothing is stated (no amplifier defined in the OMS, no design_bands attribute in the ROADM), then
+it uses single band Edfas.
+
+**Propagation**
+
+Only carriers within the amplifier bandwidth are propagated, improving system coherence. This more rigorous checking
+of the spectrum to be propagated and the amplifier bandwidth may lead to changes in the total number of channels
+compared to previous releases. The range can be adjusted by changing the values ​​of ``f_min`` and ``f_max``
+in the amplifier library.
+
+
+``f_min`` and ``f_max`` represent the boundary frequencies of the amplification bandwidth (the entire channel must fit
+within this range).
+In the example below, a signal center frequency of 190.05THz with a 50GHz width cannot fit within the amplifier band.
+Note that this has a different meaning in the SI or Transceiver blocks, where ``f_min`` and ``f_max`` refers to the
+minimum / maximum values of the carrier center frequency.
+
+    .. code-block:: json
+
+      {
+          "type_variety": "std_booster_L",
+          "f_min": 186.55e12,
+          "f_max": 190.05e12,
+          "type_def": "fixed_gain",
+          "gain_flatmax": 21,
+          "gain_min": 20,
+          "p_max": 21,
+          "nf0": 5,
+          "allowed_for_design": false
+      }
+
+
+**Display**
+
+The CLI output for the transmission_main_example now displays the channels used for design and simulation,
+as well as the tilt target of amplifiers.
+
+  .. code-block:: text
+
+    Reference used for design: (Input optical power reference in span = 0.00dBm,
+                                spacing = 50.00GHz
+                                nb_channels = 76)
+
+    Channels propagating: (Input optical power deviation in span = 0.00dB,
+                          spacing = 50.00GHz,
+                          transceiver output power = 0.00dBm,
+                          nb_channels = 76)
+
+The CLI output displays the settings of each amplifier:
+
+  .. code-block:: text
+
+    Multiband_amplifier east edfa in Site_A to Site_B
+      type_variety:           std_medium_gain_multiband
+      type_variety:           std_medium_gain_C    type_variety:           std_medium_gain_L
+      effective gain(dB):     20.90                effective gain(dB):     22.19
+      (before att_in and before output VOA)        (before att_in and before output VOA)
+      tilt-target(dB)         0.00                 tilt-target(dB)         0.00
+      noise figure (dB):      6.38                 noise figure (dB):      6.19
+      (including att_in)                           (including att_in)
+      pad att_in (dB):        0.00                 pad att_in (dB):        0.00
+      Power In (dBm):         -1.08                Power In (dBm):         -1.49
+      Power Out (dBm):        19.83                Power Out (dBm):        20.71
+      Delta_P (dB):           0.90                 Delta_P (dB):           2.19
+      target pch (dBm):       0.90                 target pch (dBm):       3.00
+      actual pch out (dBm):   -2.09                actual pch out (dBm):   -0.80
+      output VOA (dB):        3.00                 output VOA (dB):        3.00
+
+
+**New feature**
+
+The preturbative Raman and the approximated GGN models are introduced for a faster evaluation of the Raman and
+Kerr effects, respectively.
+These implementation are intended to reduce the computational effort required by multiband transmission scenarios.
+
+Both the novel models have been validated with exstensive simulations
+(see `arXiv:2304.11756 <https://arxiv.org/abs/2304.11756>`_ for the new Raman model and
+`jlt:9741324 <https://eeexplore.ieee.org/document/9741324>`_ for the new NLI model).
+Additionally, they have been experimentally validated in a laboratory setup composed of commertial equipment
+(see `icton:10648172 <https://eeexplore.ieee.org/document/10648172>`_).
+
+
+v2.10
+-----
+
+ROADM impairments can be defined per degree and roadm-path type (add, drop or express).
+Minimum loss when crossing a ROADM is no more 0 dB. It can be set per ROADM degree with roadm-path-impairments.
+
+The transceiver output power, which was previously set using the same parameter as the input span power (power_dbm),
+can now be set using a different parameter. It can be set as:
+
+  - for all channels, with tx_power_dbm using SI similarly to tx_osnr (gnpy-transmission-example script)
+
+    .. code-block:: json
+
+      "SI": [{
+              "f_min": 191.35e12,
+              "baud_rate": 32e9,
+              "f_max": 196.1e12,
+              "spacing": 50e9,
+              "power_dbm": 3,
+              "power_range_db": [0, 0, 1],
+              "roll_off": 0.15,
+              "tx_osnr": 40,
+              "tx_power_dbm": -10,
+              "sys_margins": 2
+          }
+      ]
+
+  - for certain channels, using -spectrum option and tx_channel_power_dbm option (gnpy-transmission-example script).
+
+    .. code-block:: json
+
+      {
+        "spectrum": [
+          {
+            "f_min": 191.35e12,
+            "f_max":193.1e12,
+            "baud_rate": 32e9,
+            "slot_width": 50e9,
+            "power_dbm": 0,
+            "roll_off": 0.15,
+            "tx_osnr": 40
+          },
+          {
+            "f_min": 193.15e12,
+            "f_max":193.15e12,
+            "baud_rate": 32e9,
+            "slot_width": 50e9,
+            "power_dbm": 0,
+            "roll_off": 0.15,
+            "tx_osnr": 40,
+            "tx_power_dbm": -10
+          },
+          {
+            "f_min": 193.2e12,
+            "f_max":195.1e12,
+            "baud_rate": 32e9,
+            "slot_width": 50e9,
+            "power_dbm": 0,
+            "roll_off": 0.15,
+            "tx_osnr": 40
+          }
+        ]
+      }
+
+  - per service using the additional parameter ``tx_power`` which similarly to ``power`` should be defined in Watt (gnpy-path-request script)
+
+    .. code-block:: json
+
+      {
+        "path-request": [
+          {
+            "request-id": "0",
+            "source": "trx SITE1",
+            "destination": "trx SITE2",
+            "src-tp-id": "trx SITE1",
+            "dst-tp-id": "trx SITE2",
+            "bidirectional": false,
+            "path-constraints": {
+              "te-bandwidth": {
+                "technology": "flexi-grid",
+                "trx_type": "Voyager",
+                "trx_mode": "mode 1",
+                "spacing": 50000000000.0,
+                "path_bandwidth": 100000000000.0
+              }
+            }
+          },
+          {
+            "request-id": "0 with tx_power",
+            "source": "trx SITE1",
+            "destination": "trx SITE2",
+            "src-tp-id": "trx SITE1",
+            "dst-tp-id": "trx SITE2",
+            "bidirectional": false,
+            "path-constraints": {
+              "te-bandwidth": {
+                "technology": "flexi-grid",
+                "trx_type": "Voyager",
+                "trx_mode": "mode 1",
+                "tx_power": 0.0001,
+                "spacing": 50000000000.0,
+                "path_bandwidth": 100000000000.0
+              }
+            }
+          }
+        ]
+      }
+
+v2.9
+----
+
+The revision introduces a major refactor that separates design and propagation. Most of these changes have no impact
+on the user experience, except the following ones:
+
+**Network design - amplifiers**: amplifier saturation is checked during design in all cases, even if type_variety is
+set; amplifier gain is no more computed on the fly but only at design phase.
+
+Before, the design did not consider amplifier power saturation during design if amplifier type_variety was stated.
+With this revision, the saturation is always applied:
+If design is made for a per channel power that leads to saturation, the target are properly reduced and the design
+is freezed. So that when a new simulation is performed on the same network for lower levels of power per channel
+the same gain target is applied. Before these were recomputed, changing the gain targets, so the simulation was
+not considering the exact same working points for amplifiers in case of saturation.
+
+Note that this case (working with saturation settings) is not recommended.
+
+The gain of amplifiers was estimated on the fly also in case of RamanFiber preceding elements. The refactor now
+requires that an estimation of Raman gain of the RamanFiber is done during design to properly compute a gain target.
+The Raman gain is estimated at design for every RamanFiber span and also during propagation instead of being only
+estimated at propagation stage for those Raman Fiber spans concerned with the transmission. The auto-design is more
+accurate for unpropagated spans, but this results in an increase overall computation time.
+This will be improved in the future.
+
+**Network design - ROADMs**: ROADM target power settings are verified during design.
+
+Design checks that expected power coming from every directions ingress from a ROADM are consistent with output power
+targets. The checks only considers the adjacent previous hop. If the expected power at the input of this ROADM is
+lower than the target power on the out-degree of the ROADM, a warning is displayed, and user is asked to review the
+input network to avoid this situation. This does not change the design or propagation behaviour.
+
+**Propagation**: amplifier gain target is no more recomputed during propagation. It is now possible to freeze
+the design and propagate without automatic changes.
+
+In previous release, gain was recomputed during propagation based on an hypothetical reference noiseless channel
+propagation. It was not possible to «freeze» the autodesign, and propagate without recomputing the gain target
+of amplifiers.
+With this new release, the design is freezed, so that it is possible to compare performances on same basis.
+
+**Display**: "effective pch (dbm)" is removed. Display contains the target pch which is the target power per channel
+in dBm, computed based on reference channel used for design and the amplifier delta_p in dB (and before out VOA
+contribution). Note that "actual pch out (dBm)" is the actual propagated total power per channel averaged per spectrum
+band definition at the output of the amplifier element, including noises and out VOA contribution.
+
+v2.8
+----
+
+**Spectrum assignment**: requests can now support multiple slots.
+The definition in service file supports multiple assignments (unchanged syntax):
+
+  .. code-block:: json
+
+          "effective-freq-slot": [
+            {
+              "N": 0,
+              "M": 4
+            }, {
+              "N": 50,
+              "M": 4
+            }
+          ],
+
+But in results, label-hop is now a list of slots and center frequency index:
+
+  .. code-block:: json
+
+          {
+            "path-route-object": {
+              "index": 4,
+              "label-hop": [
+                {
+                  "N": 0,
+                  "M": 4
+                }, {
+                  "N": 50,
+                  "M": 4
+                }
+              ]
+            }
+          },
+
+instead of 
+
+  .. code-block:: json
+
+          {
+            "path-route-object": {
+              "index": 4,
+              "label-hop": {
+                "N": 0,
+                "M": 4
+              }
+            }
+          },
+
+
+
+**change in display**: only warnings are displayed ; information are disabled and needs the -v (verbose)
+option to be displayed on standard output.
+
+**frequency scaling**: A more accurate description of fiber parameters is implemented, including frequency scaling of
+chromatic dispersion, effective area, Raman gain coefficient, and nonlinear coefficient.
+
+In particular:
+
+1. Chromatic dispersion can be defined with ``'dispersion'`` and ``'dispersion_slope'``, as in previous versions, or
+with ``'dispersion_per_frequency'``; the latter must be defined as a dictionary with two keys, ``'value'`` and
+``'frequency'`` and it has higher priority than the entries ``'dispersion'`` and ``'dispersion_slope'``.
+Essential change: In previous versions, when it was not provided the ``'dispersion_slope'`` was calculated in an
+involute manner to get a vanishing beta3 , and this was a mere artifact for NLI evaluation purposes (namely to evaluate
+beta2 and beta3, not for total dispersion accumulation). Now, the evaluation of beta2 and beta3 is performed explicitly
+in the element.py module.
+
+2. The effective area is provided as a scalar value evaluated at the Fiber reference frequency and properly scaled
+considering the Fiber refractive indices n1 and n2, and the core radius. These quantities are assumed to be fixed and
+are hard coded in the parameters.py module. Essential change: The effective area is always scaled along the frequency.
+
+3. The Raman gain coefficient is properly scaled considering the overlapping of fiber effective area values scaled at
+the interacting frequencies. Essential change: In previous version the Raman gain coefficient depends only on
+the frequency offset.
+
+4. The nonlinear coefficient ``'gamma'`` is properly scaled considering the refractive index n2 and the scaling
+effective area.  Essential change: As the effective area, the nonlinear coefficient is always scaled along the
+frequency.
+
+**power offset**: Power equalization now enables defining a power offset in transceiver library to represent
+the deviation from the general equalisation strategy defined in ROADMs.
+
+  .. code-block:: json
+
+            "mode": [{
+                    "format": "100G",
+                    "baud_rate": 32.0e9,
+                    "tx_osnr": 35.0,
+                    "min_spacing": 50.0e9,
+                    "cost": 1,
+                    "OSNR": 10.0,
+                    "bit_rate": 100.0e9,
+                    "roll_off": 0.2,
+                    "equalization_offset_db": 0.0
+                }, {
+                    "format": "200G",
+                    "baud_rate": 64.0e9,
+                    "tx_osnr": 35.0,
+                    "min_spacing": 75.0e9,
+                    "cost": 1,
+                    "OSNR": 13.0,
+                    "bit_rate": 200.0e9,
+                    "roll_off": 0.2,
+                    "equalization_offset_db": 1.76
+                }
+            ]
+
+v2.7
+----

docs/requirements.txt

@@ -1,7 +0,0 @@
-alabaster>=0.7.12,<1
-docutils>=0.17.1,<1
-myst-parser>=0.16.1,<1
-Pygments>=2.11.2,<3
-rstcheck
-Sphinx>=4.4.0,<5
-sphinxcontrib-bibtex>=2.4.1,<3

gnpy/__init__.py

@@ -1,8 +1,8 @@
-'''
+"""
 GNPy is an open-source, community-developed library for building route planning and optimization tools in real-world mesh optical networks. It is based on the Gaussian Noise Model.
 
 Signal propagation is implemented in :py:mod:`.core`.
 Path finding and spectrum assignment is in :py:mod:`.topology`.
 Various tools and auxiliary code, including the JSON I/O handling, is in
 :py:mod:`.tools`.
-'''
+"""

gnpy/core/__init__.py

@@ -1,4 +1,4 @@
-'''
+"""
 Simulation of signal propagation in the DWDM network
 
 Optical signals, as defined via :class:`.info.SpectralInformation`, enter
@@ -6,4 +6,4 @@ Optical signals, as defined via :class:`.info.SpectralInformation`, enter
 through the :py:mod:`.network`.
 The simulation is controlled via :py:mod:`.parameters` and implemented mainly
 via :py:mod:`.science_utils`.
-'''
+"""

gnpy/core/ansi_escapes.py

@@ -1,12 +1,12 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 
-'''
+"""
 gnpy.core.ansi_escapes
 ======================
 
 A random subset of ANSI terminal escape codes for colored messages
-'''
+"""
 
 red = '\x1b[1;31;40m'
 blue = '\x1b[1;34;40m'

gnpy/core/equipment.py

@@ -1,75 +1,132 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 
-'''
+"""
 gnpy.core.equipment
 ===================
 
 This module contains functionality for specifying equipment.
-'''
+"""
+from collections import defaultdict
+from functools import reduce
+from typing import List
 
-from gnpy.core.utils import automatic_nch, db2lin
-from gnpy.core.exceptions import EquipmentConfigError
+from gnpy.core.exceptions import EquipmentConfigError, ConfigurationError
 
 
 def trx_mode_params(equipment, trx_type_variety='', trx_mode='', error_message=False):
-    """return the trx and SI parameters from eqpt_config for a given type_variety and mode (ie format)"""
+    """return the trx and SI parameters from eqpt_config for a given type_variety and mode (ie format)
+
+    if the type or mode do no match an existing transceiver in the library, then the function
+    raises an error if error_message is True else returns a default mode based on equipment['SI']['default']
+    If trx_mode is None (but type is valid), it returns an undetermined mode whatever the error message:
+    this is a special case for automatic mode selection.
+    """
     trx_params = {}
     default_si_data = equipment['SI']['default']
+    # default transponder characteristics
+    # mainly used with transmission_main_example.py
+    default_trx_params = {
+        'f_min': default_si_data.f_min,
+        'f_max': default_si_data.f_max,
+        'baud_rate': default_si_data.baud_rate,
+        'spacing': default_si_data.spacing,
+        'OSNR': None,
+        'penalties': {},
+        'bit_rate': None,
+        'cost': None,
+        'roll_off': default_si_data.roll_off,
+        'tx_osnr': default_si_data.tx_osnr,
+        'min_spacing': None,
+        'equalization_offset_db': 0
+    }
+    # Undetermined transponder characteristics
+    # mainly used with path_request_run.py for the automatic mode computation case
+    undetermined_trx_params = {
+        "format": "undetermined",
+        "baud_rate": None,
+        "OSNR": None,
+        "penalties": None,
+        "bit_rate": None,
+        "roll_off": None,
+        "tx_osnr": None,
+        "min_spacing": None,
+        "cost": None,
+        "equalization_offset_db": 0
+    }
 
-    try:
-        trxs = equipment['Transceiver']
-        # if called from path_requests_run.py, trx_mode is filled with None when not specified by user
-        # if called from transmission_main.py, trx_mode is ''
-        if trx_mode is not None:
-            mode_params = next(mode for trx in trxs
-                               if trx == trx_type_variety
-                               for mode in trxs[trx].mode
-                               if mode['format'] == trx_mode)
-            trx_params = {**mode_params}
-            # sanity check: spacing baudrate must be smaller than min spacing
+    trxs = equipment['Transceiver']
+    if trx_type_variety in trxs:
+        modes = {mode['format']: mode for mode in trxs[trx_type_variety].mode}
+        trx_frequencies = {'f_min': trxs[trx_type_variety].frequency['min'],
+                           'f_max': trxs[trx_type_variety].frequency['max']}
+        if trx_mode in modes:
+            # if called from transmission_main.py, trx_mode is ''
+            trx_params = {**modes[trx_mode], **trx_frequencies}
             if trx_params['baud_rate'] > trx_params['min_spacing']:
-                raise EquipmentConfigError(f'Inconsistency in equipment library:\n Transpoder "{trx_type_variety}" mode "{trx_params["format"]}" ' +
-                                           f'has baud rate {trx_params["baud_rate"]*1e-9} GHz greater than min_spacing {trx_params["min_spacing"]*1e-9}.')
-        else:
-            mode_params = {"format": "undetermined",
-                           "baud_rate": None,
-                           "OSNR": None,
-                           "penalties": None,
-                           "bit_rate": None,
-                           "roll_off": None,
-                           "tx_osnr": None,
-                           "min_spacing": None,
-                           "cost": None}
-            trx_params = {**mode_params}
-        trx_params['f_min'] = equipment['Transceiver'][trx_type_variety].frequency['min']
-        trx_params['f_max'] = equipment['Transceiver'][trx_type_variety].frequency['max']
-
-        # TODO: novel automatic feature maybe unwanted if spacing is specified
-        # trx_params['spacing'] = _automatic_spacing(trx_params['baud_rate'])
-        # temp = trx_params['spacing']
-        # print(f'spacing {temp}')
-    except StopIteration:
-        if error_message:
-            raise EquipmentConfigError(f'Could not find transponder "{trx_type_variety}" with mode "{trx_mode}" in equipment library')
-        else:
-            # default transponder charcteristics
-            # mainly used with transmission_main_example.py
-            trx_params['f_min'] = default_si_data.f_min
-            trx_params['f_max'] = default_si_data.f_max
-            trx_params['baud_rate'] = default_si_data.baud_rate
-            trx_params['spacing'] = default_si_data.spacing
-            trx_params['OSNR'] = None
-            trx_params['penalties'] = {}
-            trx_params['bit_rate'] = None
-            trx_params['cost'] = None
-            trx_params['roll_off'] = default_si_data.roll_off
-            trx_params['tx_osnr'] = default_si_data.tx_osnr
-            trx_params['min_spacing'] = None
-            nch = automatic_nch(trx_params['f_min'], trx_params['f_max'], trx_params['spacing'])
-            trx_params['nb_channel'] = nch
-            print(f'There are {nch} channels propagating')
-
-    trx_params['power'] = db2lin(default_si_data.power_dbm) * 1e-3
+                # sanity check: baudrate must be smaller than min spacing
+                raise EquipmentConfigError(f'Inconsistency in equipment library:\n Transponder "{trx_type_variety}" '
+                                           + f'mode "{trx_params["format"]}" has baud rate '
+                                           + f'{trx_params["baud_rate"] * 1e-9:.2f} GHz greater than min_spacing '
+                                           + f'{trx_params["min_spacing"] * 1e-9:.2f}.')
+            trx_params['equalization_offset_db'] = trx_params.get('equalization_offset_db', 0)
+            return trx_params
+        if trx_mode is None:
+            # if called from path_requests_run.py, trx_mode is filled with None when not specified by user
+            trx_params = {**undetermined_trx_params, **trx_frequencies}
+            return trx_params
+    if trx_type_variety in trxs and error_message:
+        raise EquipmentConfigError(f'Could not find transponder "{trx_type_variety}" with mode "{trx_mode}" '
+                                   + 'in equipment library')
+    if error_message:
+        raise EquipmentConfigError(f'Could not find transponder "{trx_type_variety}" in equipment library')
 
+    trx_params = {**default_trx_params}
     return trx_params
+
+
+def find_type_variety(amps: List[str], equipment: dict) -> List[str]:
+    """Returns the multiband type_variety associated with a list of single band type_varieties
+    Args:
+    amps (List[str]): A list of single band type_varieties.
+    equipment (dict): A dictionary containing equipment information.
+
+    Returns:
+    str: an amplifier type variety
+    """
+    listes = find_type_varieties(amps, equipment)
+
+    _found_type = list(reduce(lambda x, y: set(x) & set(y), listes))
+    # Given a list of single band amplifiers, find the multiband amplifier whose multi_band group
+    # matches. For example, if amps list contains ["a1_LBAND", "a2_CBAND"], with a1.multi_band = [a1_LBAND, a1_CBAND]
+    # and a2.multi_band = [a1_LBAND, a2_CBAND], then:
+    # possible_type_varieties = {"a1_LBAND": ["a1", "a2"], "a2_CBAND": ["a2"]}
+    # listes = [["a1", "a2"],  ["a2"]]
+    # and _found_type = [a2]
+    if not _found_type:
+        msg = f'{amps} amps do not belong to the same amp type {listes}'
+        raise ConfigurationError(msg)
+    return _found_type
+
+
+def find_type_varieties(amps: List[str], equipment: dict) -> List[List[str]]:
+    """Returns the multiband list of type_varieties associated with a list of single band type_varieties
+    Args:
+    amps (List[str]): A list of single band type_varieties.
+    equipment (dict): A dictionary containing equipment information.
+
+    Returns:
+    List[List[str]]: A list of lists containing the multiband type_varieties
+    associated with each single band type_variety.
+    """
+    possible_type_varieties = defaultdict(list)
+    for amp_name, amp in equipment['Edfa'].items():
+        if amp.multi_band is not None:
+            for elem in amp.multi_band:
+                # possible_type_varieties stores the list of multiband amp names that list this elem as
+                # a possible amplifier of the multiband group. For example, if "std_medium_gain_multiband"
+                # and "std_medium_gain_multiband_new" contain "std_medium_gain_C" in their "multi_band" list, then:
+                # possible_type_varieties["std_medium_gain_C"] =
+                # ["std_medium_gain_multiband", "std_medium_gain_multiband_new"]
+                possible_type_varieties[elem].append(amp_name)
+    return [possible_type_varieties[a] for a in amps]

gnpy/core/info.py

@@ -11,10 +11,11 @@ This module contains classes for modelling :class:`SpectralInformation`.
 from __future__ import annotations
 from collections import namedtuple
 from collections.abc import Iterable
-from typing import Union
+from typing import Union, List, Optional
+from dataclasses import dataclass
 from numpy import argsort, mean, array, append, ones, ceil, any, zeros, outer, full, ndarray, asarray
 
-from gnpy.core.utils import automatic_nch, lin2db, db2lin
+from gnpy.core.utils import automatic_nch, db2lin, watt2dbm
 from gnpy.core.exceptions import SpectrumError
 
 DEFAULT_SLOT_WIDTH_STEP = 12.5e9  # Hz
@@ -26,33 +27,32 @@ class Power(namedtuple('Power', 'signal nli ase')):
     """carriers power in W"""
 
 
-class Channel(namedtuple('Channel',
-                         'channel_number frequency baud_rate slot_width roll_off power chromatic_dispersion pmd pdl')):
-    """ Class containing the parameters of a WDM signal.
-        :param channel_number: channel number in the WDM grid
-        :param frequency: central frequency of the signal (Hz)
-        :param baud_rate: the symbol rate of the signal (Baud)
-        :param slot_width: the slot width (Hz)
-        :param roll_off: the roll off of the signal. It is a pure number between 0 and 1
-        :param power (gnpy.core.info.Power): power of signal, ASE noise and NLI (W)
-        :param chromatic_dispersion: chromatic dispersion (s/m)
-        :param pmd: polarization mode dispersion (s)
-        :param pdl: polarization dependent loss (dB)
+class Channel(
+    namedtuple('Channel',
+               'channel_number frequency baud_rate slot_width roll_off power chromatic_dispersion pmd pdl latency')):
+    """Class containing the parameters of a WDM signal.
+
+    :param channel_number: channel number in the WDM grid
+    :param frequency: central frequency of the signal (Hz)
+    :param baud_rate: the symbol rate of the signal (Baud)
+    :param slot_width: the slot width (Hz)
+    :param roll_off: the roll off of the signal. It is a pure number between 0 and 1
+    :param power (gnpy.core.info.Power): power of signal, ASE noise and NLI (W)
+    :param chromatic_dispersion: chromatic dispersion (s/m)
+    :param pmd: polarization mode dispersion (s)
+    :param pdl: polarization dependent loss (dB)
+    :param latency: propagation latency (s)
     """
 
 
-class Pref(namedtuple('Pref', 'p_span0, p_spani, neq_ch ')):
-    """noiseless reference power in dBm:
-    p_span0: inital target carrier power
-    p_spani: carrier power after element i
-    neq_ch: equivalent channel count in dB"""
-
-
 class SpectralInformation(object):
-    """ Class containing the parameters of the entire WDM comb."""
+    """Class containing the parameters of the entire WDM comb.
+
+    delta_pdb_per_channel: (per frequency) per channel delta power in dbm for the actual mix of channels"""
 
     def __init__(self, frequency: array, baud_rate: array, slot_width: array, signal: array, nli: array, ase: array,
-                 roll_off: array, chromatic_dispersion: array, pmd: array, pdl: array):
+                 roll_off: array, chromatic_dispersion: array, pmd: array, pdl: array, latency: array,
+                 delta_pdb_per_channel: array, tx_osnr: array, tx_power: array, label: array):
         indices = argsort(frequency)
         self._frequency = frequency[indices]
         self._df = outer(ones(frequency.shape), frequency) - outer(frequency, ones(frequency.shape))
@@ -77,17 +77,11 @@ class SpectralInformation(object):
         self._chromatic_dispersion = chromatic_dispersion[indices]
         self._pmd = pmd[indices]
         self._pdl = pdl[indices]
-        pref = lin2db(mean(signal) * 1e3)
-        self._pref = Pref(pref, pref, lin2db(self._number_of_channels))
-
-    @property
-    def pref(self):
-        """Instance of gnpy.info.Pref"""
-        return self._pref
-
-    @pref.setter
-    def pref(self, pref: Pref):
-        self._pref = pref
+        self._latency = latency[indices]
+        self._delta_pdb_per_channel = delta_pdb_per_channel[indices]
+        self._tx_osnr = tx_osnr[indices]
+        self._tx_power = tx_power[indices]
+        self._label = label[indices]
 
     @property
     def frequency(self):
@@ -155,6 +149,10 @@ class SpectralInformation(object):
     def pmd(self):
         return self._pmd
 
+    @property
+    def label(self):
+        return self._label
+
     @pmd.setter
     def pmd(self, pmd):
         self._pmd = pmd
@@ -167,6 +165,38 @@ class SpectralInformation(object):
     def pdl(self, pdl):
         self._pdl = pdl
 
+    @property
+    def latency(self):
+        return self._latency
+
+    @latency.setter
+    def latency(self, latency):
+        self._latency = latency
+
+    @property
+    def delta_pdb_per_channel(self):
+        return self._delta_pdb_per_channel
+
+    @delta_pdb_per_channel.setter
+    def delta_pdb_per_channel(self, delta_pdb_per_channel):
+        self._delta_pdb_per_channel = delta_pdb_per_channel
+
+    @property
+    def tx_osnr(self):
+        return self._tx_osnr
+
+    @tx_osnr.setter
+    def tx_osnr(self, tx_osnr):
+        self._tx_osnr = tx_osnr
+
+    @property
+    def tx_power(self):
+        return self._tx_power
+
+    @tx_power.setter
+    def tx_power(self, tx_power):
+        self._tx_power = tx_power
+
     @property
     def channel_number(self):
         return self._channel_number
@@ -174,7 +204,7 @@ class SpectralInformation(object):
     @property
     def carriers(self):
         entries = zip(self.channel_number, self.frequency, self.baud_rate, self.slot_width,
-                      self.roll_off, self.powers, self.chromatic_dispersion, self.pmd, self.pdl)
+                      self.roll_off, self.powers, self.chromatic_dispersion, self.pmd, self.pdl, self.latency)
         return [Channel(*entry) for entry in entries]
 
     def apply_attenuation_lin(self, attenuation_lin):
@@ -206,29 +236,40 @@ class SpectralInformation(object):
                                        chromatic_dispersion=append(self.chromatic_dispersion,
                                                                    other.chromatic_dispersion),
                                        pmd=append(self.pmd, other.pmd),
-                                       pdl=append(self.pdl, other.pdl))
+                                       pdl=append(self.pdl, other.pdl),
+                                       latency=append(self.latency, other.latency),
+                                       delta_pdb_per_channel=append(self.delta_pdb_per_channel,
+                                                                    other.delta_pdb_per_channel),
+                                       tx_osnr=append(self.tx_osnr, other.tx_osnr),
+                                       tx_power=append(self.tx_power, other.tx_power),
+                                       label=append(self.label, other.label))
         except SpectrumError:
             raise SpectrumError('Spectra cannot be summed: channels overlapping.')
 
-    def _replace(self, carriers, pref):
+    def _replace(self, carriers):
         self.chromatic_dispersion = array([c.chromatic_dispersion for c in carriers])
         self.pmd = array([c.pmd for c in carriers])
         self.pdl = array([c.pdl for c in carriers])
+        self.latency = array([c.latency for c in carriers])
         self.signal = array([c.power.signal for c in carriers])
         self.nli = array([c.power.nli for c in carriers])
         self.ase = array([c.power.ase for c in carriers])
-        self.pref = pref
         return self
 
 
-def create_arbitrary_spectral_information(frequency: Union[ndarray, Iterable, int, float],
-                                          signal: Union[int, float, ndarray, Iterable],
-                                          baud_rate: Union[int, float, ndarray, Iterable],
-                                          slot_width: Union[int, float, ndarray, Iterable] = None,
-                                          roll_off: Union[int, float, ndarray, Iterable] = 0.,
-                                          chromatic_dispersion: Union[int, float, ndarray, Iterable] = 0.,
-                                          pmd: Union[int, float, ndarray, Iterable] = 0.,
-                                          pdl: Union[int, float, ndarray, Iterable] = 0.):
+def create_arbitrary_spectral_information(frequency: Union[ndarray, Iterable, float],
+                                          signal: Union[float, ndarray, Iterable],
+                                          baud_rate: Union[float, ndarray, Iterable],
+                                          tx_osnr: Union[float, ndarray, Iterable],
+                                          tx_power: Union[float, ndarray, Iterable] = None,
+                                          delta_pdb_per_channel: Union[float, ndarray, Iterable] = 0.,
+                                          slot_width: Union[float, ndarray, Iterable] = None,
+                                          roll_off: Union[float, ndarray, Iterable] = 0.,
+                                          chromatic_dispersion: Union[float, ndarray, Iterable] = 0.,
+                                          pmd: Union[float, ndarray, Iterable] = 0.,
+                                          pdl: Union[float, ndarray, Iterable] = 0.,
+                                          latency: Union[float, ndarray, Iterable] = 0.,
+                                          label: Union[str, ndarray, Iterable] = None):
     """This is just a wrapper around the SpectralInformation.__init__() that simplifies the creation of
     a non-uniform spectral information with NLI and ASE powers set to zero."""
     frequency = asarray(frequency)
@@ -242,13 +283,20 @@ def create_arbitrary_spectral_information(frequency: Union[ndarray, Iterable, in
         chromatic_dispersion = full(number_of_channels, chromatic_dispersion)
         pmd = full(number_of_channels, pmd)
         pdl = full(number_of_channels, pdl)
+        latency = full(number_of_channels, latency)
         nli = zeros(number_of_channels)
         ase = zeros(number_of_channels)
+        delta_pdb_per_channel = full(number_of_channels, delta_pdb_per_channel)
+        tx_osnr = full(number_of_channels, tx_osnr)
+        tx_power = full(number_of_channels, tx_power)
+        label = full(number_of_channels, label)
         return SpectralInformation(frequency=frequency, slot_width=slot_width,
                                    signal=signal, nli=nli, ase=ase,
                                    baud_rate=baud_rate, roll_off=roll_off,
                                    chromatic_dispersion=chromatic_dispersion,
-                                   pmd=pmd, pdl=pdl)
+                                   pmd=pmd, pdl=pdl, latency=latency,
+                                   delta_pdb_per_channel=delta_pdb_per_channel,
+                                   tx_osnr=tx_osnr, tx_power=tx_power, label=label)
     except ValueError as e:
         if 'could not broadcast' in str(e):
             raise SpectrumError('Dimension mismatch in input fields.')
@@ -256,9 +304,125 @@ def create_arbitrary_spectral_information(frequency: Union[ndarray, Iterable, in
             raise
 
 
-def create_input_spectral_information(f_min, f_max, roll_off, baud_rate, power, spacing):
-    """ Creates a fixed slot width spectral information with flat power """
-    nb_channel = automatic_nch(f_min, f_max, spacing)
-    frequency = [(f_min + spacing * i) for i in range(1, nb_channel + 1)]
-    return create_arbitrary_spectral_information(frequency, slot_width=spacing, signal=power, baud_rate=baud_rate,
-                                                 roll_off=roll_off)
+def create_input_spectral_information(f_min, f_max, roll_off, baud_rate, spacing, tx_osnr, tx_power,
+                                      delta_pdb=0):
+    """Creates a fixed slot width spectral information with flat power.
+    all arguments are scalar values"""
+    number_of_channels = automatic_nch(f_min, f_max, spacing)
+    frequency = [(f_min + spacing * i) for i in range(1, number_of_channels + 1)]
+    delta_pdb_per_channel = delta_pdb * ones(number_of_channels)
+    label = [f'{baud_rate * 1e-9 :.2f}G' for i in range(number_of_channels)]
+    return create_arbitrary_spectral_information(frequency, slot_width=spacing, signal=tx_power, baud_rate=baud_rate,
+                                                 roll_off=roll_off, delta_pdb_per_channel=delta_pdb_per_channel,
+                                                 tx_osnr=tx_osnr, tx_power=tx_power, label=label)
+
+
+def is_in_band(frequency: float, band: dict) -> bool:
+    """band has {"f_min": value, "f_max": value} format
+    """
+    if frequency >= band['f_min'] and frequency <= band['f_max']:
+        return True
+    return False
+
+
+def demuxed_spectral_information(input_si: SpectralInformation, band: dict) -> Optional[SpectralInformation]:
+    """extract a si based on band
+    """
+    filtered_indices = [i for i, f in enumerate(input_si.frequency)
+                        if is_in_band(f - input_si.slot_width[i] / 2, band)
+                        and is_in_band(f + input_si.slot_width[i] / 2, band)]
+    if filtered_indices:
+        frequency = input_si.frequency[filtered_indices]
+        baud_rate = input_si.baud_rate[filtered_indices]
+        slot_width = input_si.slot_width[filtered_indices]
+        signal = input_si.signal[filtered_indices]
+        nli = input_si.nli[filtered_indices]
+        ase = input_si.ase[filtered_indices]
+        roll_off = input_si.roll_off[filtered_indices]
+        chromatic_dispersion = input_si.chromatic_dispersion[filtered_indices]
+        pmd = input_si.pmd[filtered_indices]
+        pdl = input_si.pdl[filtered_indices]
+        latency = input_si.latency[filtered_indices]
+        delta_pdb_per_channel = input_si.delta_pdb_per_channel[filtered_indices]
+        tx_osnr = input_si.tx_osnr[filtered_indices]
+        tx_power = input_si.tx_power[filtered_indices]
+        label = input_si.label[filtered_indices]
+
+        return SpectralInformation(frequency=frequency, baud_rate=baud_rate, slot_width=slot_width, signal=signal,
+                                   nli=nli, ase=ase, roll_off=roll_off, chromatic_dispersion=chromatic_dispersion,
+                                   pmd=pmd, pdl=pdl, latency=latency, delta_pdb_per_channel=delta_pdb_per_channel,
+                                   tx_osnr=tx_osnr, tx_power=tx_power, label=label)
+    return None
+
+
+def muxed_spectral_information(input_si_list: List[SpectralInformation]) -> SpectralInformation:
+    """return the assembled spectrum
+    """
+    if input_si_list and len(input_si_list) > 1:
+        si = input_si_list[0] + muxed_spectral_information(input_si_list[1:])
+        return si
+    elif input_si_list and len(input_si_list) == 1:
+        return input_si_list[0]
+    else:
+        raise ValueError('liste vide')
+
+
+def carriers_to_spectral_information(initial_spectrum: dict[float, Carrier],
+                                     power: float) -> SpectralInformation:
+    """Initial spectrum is a dict with key = carrier frequency, and value a Carrier object.
+    :param initial_spectrum: indexed by frequency in Hz, with power offset (delta_pdb), baudrate, slot width,
+    tx_osnr, tx_power and roll off.
+    :param power: power of the request
+    """
+    frequency = list(initial_spectrum.keys())
+    signal = [c.tx_power for c in initial_spectrum.values()]
+    roll_off = [c.roll_off for c in initial_spectrum.values()]
+    baud_rate = [c.baud_rate for c in initial_spectrum.values()]
+    delta_pdb_per_channel = [c.delta_pdb for c in initial_spectrum.values()]
+    slot_width = [c.slot_width for c in initial_spectrum.values()]
+    tx_osnr = [c.tx_osnr for c in initial_spectrum.values()]
+    tx_power = [c.tx_power for c in initial_spectrum.values()]
+    label = [c.label for c in initial_spectrum.values()]
+    return create_arbitrary_spectral_information(frequency=frequency, signal=signal, baud_rate=baud_rate,
+                                                 slot_width=slot_width, roll_off=roll_off,
+                                                 delta_pdb_per_channel=delta_pdb_per_channel, tx_osnr=tx_osnr,
+                                                 tx_power=tx_power, label=label)
+
+
+@dataclass
+class Carrier:
+    """One channel in the initial mixed-type spectrum definition, each type being defined by
+    its delta_pdb (power offset with respect to reference power), baud rate, slot_width, roll_off
+    tx_power, and tx_osnr. delta_pdb offset is applied to target power out of Roadm.
+    Label is used to group carriers which belong to the same partition when printing results.
+    """
+    delta_pdb: float
+    baud_rate: float
+    slot_width: float
+    roll_off: float
+    tx_osnr: float
+    tx_power: float
+    label: str
+
+
+@dataclass
+class ReferenceCarrier:
+    """Reference channel type is used to determine target power out of ROADM for the reference channel when
+    constant power spectral density (PSD) equalization is set. Reference channel is the type that has been defined
+    in SI block and used for the initial design of the network.
+    Computing the power out of ROADM for the reference channel is required to correctly compute the loss
+    experienced by reference channel in Roadm element.
+
+    Baud rate is required to find the target power in constant PSD: power = PSD_target * baud_rate.
+    For example, if target PSD is 3.125e4mW/GHz and reference carrier type a 32 GBaud channel then
+    output power should be -20 dBm and for a 64 GBaud channel power target would need 3 dB more: -17 dBm.
+
+    Slot width is required to find the target power in constant PSW (constant power per slot width equalization):
+    power = PSW_target * slot_width.
+    For example, if target PSW is 2e4mW/GHz and reference carrier type a 32 GBaud channel in a 50GHz slot width then
+    output power should be -20 dBm and for a 64 GBaud channel  in a 75 GHz slot width, power target would be -18.24 dBm.
+
+    Other attributes (like roll-off) may be added there for future equalization purpose.
+    """
+    baud_rate: float
+    slot_width: float

gnpy/core/parameters.py

@@ -7,9 +7,11 @@ gnpy.core.parameters
 
 This module contains all parameters to configure standard network elements.
 """
-
+from collections import namedtuple
+from copy import deepcopy
+from dataclasses import dataclass
 from scipy.constants import c, pi
-from numpy import asarray, array
+from numpy import asarray, array, exp, sqrt, log, outer, ones, squeeze, append, flip, linspace, full
 
 from gnpy.core.utils import convert_length
 from gnpy.core.exceptions import ParametersError
@@ -34,49 +36,64 @@ class PumpParams(Parameters):
 
 
 class RamanParams(Parameters):
-    def __init__(self, flag=False, result_spatial_resolution=10e3, solver_spatial_resolution=50):
-        """ Simulation parameters used within the Raman Solver
+    def __init__(self, flag=False, method='perturbative', order=2, result_spatial_resolution=10e3,
+                 solver_spatial_resolution=10e3):
+        """Simulation parameters used within the Raman Solver
+
         :params flag: boolean for enabling/disable the evaluation of the Raman power profile in frequency and position
+        :params method: Raman solver method
+        :params order: solution order for perturbative method
         :params result_spatial_resolution: spatial resolution of the evaluated Raman power profile
         :params solver_spatial_resolution: spatial step for the iterative solution of the first order ode
         """
         self.flag = flag
+        self.method = method
+        self.order = order
         self.result_spatial_resolution = result_spatial_resolution  # [m]
         self.solver_spatial_resolution = solver_spatial_resolution  # [m]
 
+    def to_json(self):
+        return {"flag": self.flag,
+                "method": self.method,
+                "order": self.order,
+                "result_spatial_resolution": self.result_spatial_resolution,
+                "solver_spatial_resolution": self.solver_spatial_resolution}
+
 
 class NLIParams(Parameters):
-    def __init__(self, method='gn_model_analytic', dispersion_tolerance=1, phase_shift_tolerance=0.1,
-                 computed_channels=None):
-        """ Simulation parameters used within the Nli Solver
+    def __init__(self, method='gn_model_analytic', dispersion_tolerance=4, phase_shift_tolerance=0.1,
+                 computed_channels=None, computed_number_of_channels=None):
+        """Simulation parameters used within the Nli Solver
+
         :params method: formula for NLI calculation
         :params dispersion_tolerance: tuning parameter for ggn model solution
         :params phase_shift_tolerance: tuning parameter for ggn model solution
         :params computed_channels: the NLI is evaluated for these channels and extrapolated for the others
+        :params computed_number_of_channels: the NLI is evaluated for this number of channels equally distributed
+        in the spectrum and extrapolated for the others
         """
         self.method = method.lower()
         self.dispersion_tolerance = dispersion_tolerance
         self.phase_shift_tolerance = phase_shift_tolerance
         self.computed_channels = computed_channels
+        self.computed_number_of_channels = computed_number_of_channels
+
+    def to_json(self):
+        return {"method": self.method,
+                "dispersion_tolerance": self.dispersion_tolerance,
+                "phase_shift_tolerance": self.phase_shift_tolerance,
+                "computed_channels": self.computed_channels,
+                "computed_number_of_channels": self.computed_number_of_channels}
 
 
 class SimParams(Parameters):
     _shared_dict = {'nli_params': NLIParams(), 'raman_params': RamanParams()}
 
-    def __init__(self):
-        if type(self) == SimParams:
-            raise NotImplementedError('Instances of SimParams cannot be generated')
-
     @classmethod
     def set_params(cls, sim_params):
         cls._shared_dict['nli_params'] = NLIParams(**sim_params.get('nli_params', {}))
         cls._shared_dict['raman_params'] = RamanParams(**sim_params.get('raman_params', {}))
 
-    @classmethod
-    def get(cls):
-        self = cls.__new__(cls)
-        return self
-
     @property
     def nli_params(self):
         return self._shared_dict['nli_params']
@@ -88,15 +105,84 @@ class SimParams(Parameters):
 
 class RoadmParams(Parameters):
     def __init__(self, **kwargs):
+        self.target_pch_out_db = kwargs.get('target_pch_out_db')
+        self.target_psd_out_mWperGHz = kwargs.get('target_psd_out_mWperGHz')
+        self.target_out_mWperSlotWidth = kwargs.get('target_out_mWperSlotWidth')
+        equalisation_type = ['target_pch_out_db', 'target_psd_out_mWperGHz', 'target_out_mWperSlotWidth']
+        temp = [kwargs.get(k) is not None for k in equalisation_type]
+        if sum(temp) > 1:
+            raise ParametersError('ROADM config contains more than one equalisation type.'
+                                  + 'Please choose only one', kwargs)
+        self.per_degree_pch_out_db = kwargs.get('per_degree_pch_out_db', {})
+        self.per_degree_pch_psd = kwargs.get('per_degree_psd_out_mWperGHz', {})
+        self.per_degree_pch_psw = kwargs.get('per_degree_psd_out_mWperSlotWidth', {})
         try:
-            self.target_pch_out_db = kwargs['target_pch_out_db']
             self.add_drop_osnr = kwargs['add_drop_osnr']
             self.pmd = kwargs['pmd']
             self.pdl = kwargs['pdl']
             self.restrictions = kwargs['restrictions']
-            self.per_degree_pch_out_db = kwargs['per_degree_pch_out_db'] if 'per_degree_pch_out_db' in kwargs else {}
+            self.roadm_path_impairments = self.get_roadm_path_impairments(kwargs['roadm-path-impairments'])
         except KeyError as e:
             raise ParametersError(f'ROADM configurations must include {e}. Configuration: {kwargs}')
+        self.per_degree_impairments = kwargs.get('per_degree_impairments', [])
+        self.design_bands = kwargs.get('design_bands', [])
+        self.per_degree_design_bands = kwargs.get('per_degree_design_bands', {})
+
+    def get_roadm_path_impairments(self, path_impairments_list):
+        """Get the ROADM list of profiles for impairments definition
+
+        transform the ietf model into gnpy internal model: add a path-type in the attributes
+        """
+        if not path_impairments_list:
+            return {}
+        authorized_path_types = {
+            'roadm-express-path': 'express',
+            'roadm-add-path': 'add',
+            'roadm-drop-path': 'drop',
+        }
+        roadm_path_impairments = {}
+        for path_impairment in path_impairments_list:
+            index = path_impairment['roadm-path-impairments-id']
+            path_type = next(key for key in path_impairment if key in authorized_path_types.keys())
+            impairment_dict = {'path-type': authorized_path_types[path_type], 'impairment': path_impairment[path_type]}
+            roadm_path_impairments[index] = RoadmImpairment(impairment_dict)
+        return roadm_path_impairments
+
+
+class RoadmPath:
+    def __init__(self, from_degree, to_degree, path_type, impairment_id=None, impairment=None):
+        """Records roadm internal paths, types and impairment
+
+        path_type must be in "express", "add", "drop"
+        impairment_id must be one of the id detailed in equipement
+        """
+        self.from_degree = from_degree
+        self.to_degree = to_degree
+        self.path_type = path_type
+        self.impairment_id = impairment_id
+        self.impairment = impairment
+
+
+class RoadmImpairment:
+    """Generic definition of impairments for express, add and drop"""
+    default_values = {
+        'roadm-pmd': None,
+        'roadm-cd': None,
+        'roadm-pdl': None,
+        'roadm-inband-crosstalk': None,
+        'roadm-maxloss': 0,
+        'roadm-osnr': None,
+        'roadm-pmax': None,
+        'roadm-noise-figure': None,
+        'minloss': None,
+        'typloss': None,
+        'pmin': None,
+        'ptyp': None
+    }
+
+    def __init__(self, params):
+        self.path_type = params.get('path-type')
+        self.impairments = params['impairment']
 
 
 class FusedParams(Parameters):
@@ -104,26 +190,75 @@ class FusedParams(Parameters):
         self.loss = kwargs['loss'] if 'loss' in kwargs else 1
 
 
-# SSMF Raman coefficient profile normalized with respect to the effective area (Cr * A_eff)
-CR_NORM = array([
-    0., 7.802e-16, 2.4236e-15, 4.0504e-15, 5.6606e-15, 6.8973e-15, 7.802e-15, 8.4162e-15, 8.8727e-15, 9.2877e-15,
-    1.01011e-14, 1.05244e-14, 1.13295e-14, 1.2367e-14, 1.3695e-14, 1.5023e-14, 1.64091e-14, 1.81936e-14, 2.04927e-14,
-    2.28167e-14, 2.48917e-14, 2.66098e-14, 2.82615e-14, 2.98136e-14, 3.1042e-14, 3.17558e-14, 3.18803e-14, 3.17558e-14,
-    3.15566e-14, 3.11748e-14, 2.94567e-14, 3.14985e-14, 2.8552e-14, 2.43439e-14, 1.67992e-14, 9.6114e-15, 7.02180e-15,
-    5.9262e-15, 5.6938e-15, 7.055e-15, 7.4119e-15, 7.4783e-15, 6.7645e-15, 5.5361e-15, 3.6271e-15, 2.7224e-15,
-    2.4568e-15, 2.1995e-15, 2.1331e-15, 2.3323e-15, 2.5564e-15, 3.0461e-15, 4.8555e-15, 5.5029e-15, 5.2788e-15,
-    4.565e-15, 3.3698e-15, 2.2991e-15, 2.0086e-15, 1.5521e-15, 1.328e-15, 1.162e-15, 9.379e-16, 8.715e-16, 8.134e-16,
-    8.134e-16, 9.379e-16, 1.3612e-15, 1.6185e-15, 1.9754e-15, 1.8758e-15, 1.6849e-15, 1.2284e-15, 9.047e-16, 8.134e-16,
-    8.715e-16, 9.711e-16, 1.0375e-15, 1.0043e-15, 9.047e-16, 8.134e-16, 6.806e-16, 5.478e-16, 3.901e-16, 2.241e-16,
-    1.577e-16, 9.96e-17, 3.32e-17, 1.66e-17, 8.3e-18])
+DEFAULT_RAMAN_COEFFICIENT = {
+    # SSMF Raman coefficient profile in terms of mode intensity (g0 * A_ff_overlap)
+    'gamma_raman': array(
+        [0.0, 8.524419934705497e-16, 2.643567866245371e-15, 4.410548410941305e-15, 6.153422961291078e-15,
+         7.484924703044943e-15, 8.452060808349209e-15, 9.101549322698156e-15, 9.57837595158966e-15,
+         1.0008642675474562e-14, 1.0865773569905647e-14, 1.1300776305865833e-14, 1.2143238647099625e-14,
+         1.3231065750676068e-14, 1.4624900971525384e-14, 1.6013330554840492e-14, 1.7458119359310242e-14,
+         1.9320241330434762e-14, 2.1720395392873534e-14, 2.4137337406734775e-14, 2.628163218460466e-14,
+         2.8041019963285974e-14, 2.9723155447089933e-14, 3.129353531005888e-14, 3.251796163324624e-14,
+         3.3198839487612773e-14, 3.329527690685666e-14, 3.313155691238456e-14, 3.289013852154548e-14,
+         3.2458917188506916e-14, 3.060684277937575e-14, 3.2660349473783173e-14, 2.957419109657689e-14,
+         2.518894321396672e-14, 1.734560485857344e-14, 9.902860761605233e-15, 7.219176385099358e-15,
+         6.079565990401311e-15, 5.828373065963427e-15, 7.20580801091692e-15, 7.561924351387493e-15,
+         7.621152352332206e-15, 6.8859886780643254e-15, 5.629181047471162e-15, 3.679727598966185e-15,
+         2.7555869742500355e-15, 2.4810133942597675e-15, 2.2160080532403624e-15, 2.1440626024765557e-15,
+         2.33873070799544e-15, 2.557317929858713e-15, 3.039839048226572e-15, 4.8337165515610065e-15,
+         5.4647431818257436e-15, 5.229187813711269e-15, 4.510768525811313e-15, 3.3213473130607794e-15,
+         2.2602577027996455e-15, 1.969576495866441e-15, 1.5179853954188527e-15, 1.2953988551200156e-15,
+         1.1304672156251838e-15, 9.10004390675213e-16, 8.432919922183503e-16, 7.849224069008326e-16,
+         7.827568196032024e-16, 9.000514440646232e-16, 1.3025926460013665e-15, 1.5444108938497558e-15,
+         1.8795594063060786e-15, 1.7796130169921014e-15, 1.5938159865046653e-15, 1.1585522355108287e-15,
+         8.507044444633358e-16, 7.625404663756823e-16, 8.14510750925789e-16, 9.047944693473188e-16,
+         9.636431901702084e-16, 9.298633899602105e-16, 8.349739503637023e-16, 7.482901278066085e-16,
+         6.240794767134268e-16, 5.00652535687506e-16, 3.553373263685851e-16, 2.0344217706119682e-16,
+         1.4267522642294203e-16, 8.980016576743517e-17, 2.9829068181832594e-17, 1.4861959129014824e-17,
+         7.404482113326137e-18]
+    ),  # m/W
+    # SSMF Raman coefficient profile
+    'g0': array(
+        [0.00000000e+00, 1.12351610e-05, 3.47838074e-05, 5.79356636e-05, 8.06921680e-05, 9.79845709e-05, 1.10454361e-04,
+         1.18735302e-04, 1.24736889e-04, 1.30110053e-04, 1.41001273e-04, 1.46383247e-04, 1.57011792e-04, 1.70765865e-04,
+         1.88408911e-04, 2.05914127e-04, 2.24074028e-04, 2.47508283e-04, 2.77729174e-04, 3.08044243e-04, 3.34764439e-04,
+         3.56481704e-04, 3.77127256e-04, 3.96269124e-04, 4.10955175e-04, 4.18718761e-04, 4.19511263e-04, 4.17025384e-04,
+         4.13565369e-04, 4.07726048e-04, 3.83671291e-04, 4.08564283e-04, 3.69571936e-04, 3.14442090e-04, 2.16074535e-04,
+         1.23097823e-04, 8.95457457e-05, 7.52470400e-05, 7.19806145e-05, 8.87961158e-05, 9.30812065e-05, 9.37058268e-05,
+         8.45719619e-05, 6.90585286e-05, 4.50407159e-05, 3.36521245e-05, 3.02292475e-05, 2.69376939e-05, 2.60020897e-05,
+         2.82958958e-05, 3.08667558e-05, 3.66024657e-05, 5.80610307e-05, 6.54797937e-05, 6.25022715e-05, 5.37806442e-05,
+         3.94996621e-05, 2.68120644e-05, 2.33038554e-05, 1.79140757e-05, 1.52472424e-05, 1.32707565e-05, 1.06541760e-05,
+         9.84649374e-06, 9.13999627e-06, 9.08971012e-06, 1.04227525e-05, 1.50419271e-05, 1.77838232e-05, 2.15810815e-05,
+         2.03744008e-05, 1.81939341e-05, 1.31862121e-05, 9.65352116e-06, 8.62698322e-06, 9.18688016e-06, 1.01737784e-05,
+         1.08017817e-05, 1.03903588e-05, 9.30040333e-06, 8.30809173e-06, 6.90650401e-06, 5.52238029e-06, 3.90648708e-06,
+         2.22908227e-06, 1.55796177e-06, 9.77218716e-07, 3.23477236e-07, 1.60602454e-07, 7.97306386e-08]
+    ),  # [1 / (W m)]
 
-# Note the non-uniform spacing of this range; this is required for properly capturing the Raman peak shape.
-FREQ_OFFSET = array([
-    0., 0.5, 1., 1.5, 2., 2.5, 3., 3.5, 4., 4.5, 5., 5.5, 6., 6.5, 7., 7.5, 8., 8.5, 9., 9.5, 10., 10.5, 11., 11.5, 12.,
-    12.5, 12.75, 13., 13.25, 13.5, 14., 14.5, 14.75, 15., 15.5, 16., 16.5, 17., 17.5, 18., 18.25, 18.5, 18.75, 19.,
-    19.5, 20., 20.5, 21., 21.5, 22., 22.5, 23., 23.5, 24., 24.5, 25., 25.5, 26., 26.5, 27., 27.5, 28., 28.5, 29., 29.5,
-    30., 30.5, 31., 31.5, 32., 32.5, 33., 33.5, 34., 34.5, 35., 35.5, 36., 36.5, 37., 37.5, 38., 38.5, 39., 39.5, 40.,
-    40.5, 41., 41.5, 42.]) * 1e12
+    # Note the non-uniform spacing of this range; this is required for properly capturing the Raman peak shape.
+    'frequency_offset': array([
+        0., 0.5, 1., 1.5, 2., 2.5, 3., 3.5, 4., 4.5, 5., 5.5, 6., 6.5, 7., 7.5, 8., 8.5, 9., 9.5, 10., 10.5, 11., 11.5,
+        12., 12.5, 12.75, 13., 13.25, 13.5, 14., 14.5, 14.75, 15., 15.5, 16., 16.5, 17., 17.5, 18., 18.25, 18.5, 18.75,
+        19., 19.5, 20., 20.5, 21., 21.5, 22., 22.5, 23., 23.5, 24., 24.5, 25., 25.5, 26., 26.5, 27., 27.5, 28., 28.5,
+        29., 29.5, 30., 30.5, 31., 31.5, 32., 32.5, 33., 33.5, 34., 34.5, 35., 35.5, 36., 36.5, 37., 37.5, 38., 38.5,
+        39., 39.5, 40., 40.5, 41., 41.5, 42.]) * 1e12,  # [Hz]
+
+    # Raman profile reference frequency
+    'reference_frequency': 206.184634112792e12,  # [Hz] (1454 nm)
+
+    # Raman profile reference effective area
+    'reference_effective_area': 75.74659443542413e-12  # [m^2] (@1454 nm)
+}
+
+
+class RamanGainCoefficient(namedtuple('RamanGainCoefficient', 'normalized_gamma_raman frequency_offset')):
+    """ Raman Gain Coefficient Parameters
+
+        Based on:
+            Andrea D’Amico, Bruno Correia, Elliot London, Emanuele Virgillito, Giacomo Borraccini, Antonio Napoli,
+            and Vittorio Curri, "Scalable and Disaggregated GGN Approximation Applied to a C+L+S Optical Network,"
+            J. Lightwave Technol. 40, 3499-3511 (2022)
+            Section III.D
+    """
 
 
 class FiberParams(Parameters):
@@ -137,6 +272,8 @@ class FiberParams(Parameters):
             # with default values from eqpt_config.json[Spans]
             self._con_in = kwargs.get('con_in')
             self._con_out = kwargs.get('con_out')
+
+            # Reference frequency (unique for all parameters: beta2, beta3, gamma, effective_area)
             if 'ref_wavelength' in kwargs:
                 self._ref_wavelength = kwargs['ref_wavelength']
                 self._ref_frequency = c / self._ref_wavelength
@@ -146,35 +283,77 @@ class FiberParams(Parameters):
             else:
                 self._ref_wavelength = 1550e-9  # conventional central C band wavelength [m]
                 self._ref_frequency = c / self._ref_wavelength
-            self._dispersion = kwargs['dispersion']  # s/m/m
-            self._dispersion_slope = \
-                kwargs.get('dispersion_slope', -2 * self._dispersion / self.ref_wavelength)  # s/m/m/m
-            self._beta2 = -(self.ref_wavelength ** 2) * self.dispersion / (2 * pi * c)  # 1/(m * Hz^2)
-            # Eq. (3.23) in  Abramczyk, Halina. "Dispersion phenomena in optical fibers." Virtual European University
-            # on Lasers. Available online: http://mitr.p.lodz.pl/evu/lectures/Abramczyk3.pdf
-            # (accessed on 25 March 2018) (2005).
-            self._beta3 = ((self.dispersion_slope - (4*pi*c/self.ref_wavelength**3) * self.beta2) /
-                           (2*pi*c/self.ref_wavelength**2)**2)
+
+            # Chromatic Dispersion
+            if 'dispersion_per_frequency' in kwargs:
+                # Frequency-dependent dispersion
+                self._dispersion = asarray(kwargs['dispersion_per_frequency']['value'])  # s/m/m
+                self._f_dispersion_ref = asarray(kwargs['dispersion_per_frequency']['frequency'])  # Hz
+                self._dispersion_slope = None
+            elif 'dispersion' in kwargs:
+                # Single value dispersion
+                self._dispersion = asarray(kwargs['dispersion'])  # s/m/m
+                self._dispersion_slope = kwargs.get('dispersion_slope')  # s/m/m/m
+                self._f_dispersion_ref = asarray(self._ref_frequency)  # Hz
+            else:
+                # Default single value dispersion
+                self._dispersion = asarray(1.67e-05)  # s/m/m
+                self._dispersion_slope = None
+                self._f_dispersion_ref = asarray(self.ref_frequency)  # Hz
+
+            # Effective Area and Nonlinear Coefficient
             self._effective_area = kwargs.get('effective_area')  # m^2
-            n2 = 2.6e-20  # m^2/W
-            if self._effective_area:
-                self._gamma = kwargs.get('gamma', 2 * pi * n2 / (self.ref_wavelength * self._effective_area))  # 1/W/m
+            self._n1 = 1.468
+            self._core_radius = 4.2e-6  # m
+            self._n2 = 2.6e-20  # m^2/W
+            if self._effective_area is not None:
+                default_gamma = 2 * pi * self._n2 / (self._ref_wavelength * self._effective_area)
+                self._gamma = kwargs.get('gamma', default_gamma)  # 1/W/m
             elif 'gamma' in kwargs:
                 self._gamma = kwargs['gamma']  # 1/W/m
-                self._effective_area = 2 * pi * n2 / (self.ref_wavelength * self._gamma)  # m^2
+                self._effective_area = 2 * pi * self._n2 / (self._ref_wavelength * self._gamma)  # m^2
             else:
-                self._gamma = 0  # 1/W/m
                 self._effective_area = 83e-12  # m^2
-            default_raman_efficiency = {'cr': CR_NORM / self._effective_area, 'frequency_offset': FREQ_OFFSET}
-            self._raman_efficiency = kwargs.get('raman_efficiency', default_raman_efficiency)
+                self._gamma = 2 * pi * self._n2 / (self._ref_wavelength * self._effective_area)  # 1/W/m
+            self._contrast = 0.5 * (c / (2 * pi * self._ref_frequency * self._core_radius * self._n1) * exp(
+                pi * self._core_radius ** 2 / self._effective_area)) ** 2
+
+            # Raman Gain Coefficient
+            raman_coefficient = kwargs.get('raman_coefficient')
+            if raman_coefficient is None:
+                self._raman_reference_frequency = DEFAULT_RAMAN_COEFFICIENT['reference_frequency']
+                frequency_offset = asarray(DEFAULT_RAMAN_COEFFICIENT['frequency_offset'])
+                gamma_raman = asarray(DEFAULT_RAMAN_COEFFICIENT['gamma_raman'])
+                stokes_wave = self._raman_reference_frequency - frequency_offset
+                normalized_gamma_raman = gamma_raman / self._raman_reference_frequency  # 1 / m / W / Hz
+                self._g0 = gamma_raman / self.effective_area_overlap(stokes_wave, self._raman_reference_frequency)
+            else:
+                self._raman_reference_frequency = raman_coefficient['reference_frequency']
+                frequency_offset = asarray(raman_coefficient['frequency_offset'])
+                stokes_wave = self._raman_reference_frequency - frequency_offset
+                self._g0 = asarray(raman_coefficient['g0'])
+                gamma_raman = self._g0 * self.effective_area_overlap(stokes_wave, self._raman_reference_frequency)
+                normalized_gamma_raman = gamma_raman / self._raman_reference_frequency  # 1 / m / W / Hz
+
+            # Raman gain coefficient array of the frequency offset constructed such that positive frequency values
+            # represent a positive power transfer from higher frequency and vice versa
+            frequency_offset = append(-flip(frequency_offset[1:]), frequency_offset)
+            normalized_gamma_raman = append(- flip(normalized_gamma_raman[1:]), normalized_gamma_raman)
+            self._raman_coefficient = RamanGainCoefficient(normalized_gamma_raman, frequency_offset)
+
+            # Polarization Mode Dispersion
             self._pmd_coef = kwargs['pmd_coef']  # s/sqrt(m)
-            if type(kwargs['loss_coef']) == dict:
+
+            # Loss Coefficient
+            if isinstance(kwargs['loss_coef'], dict):
                 self._loss_coef = asarray(kwargs['loss_coef']['value']) * 1e-3  # lineic loss dB/m
                 self._f_loss_ref = asarray(kwargs['loss_coef']['frequency'])  # Hz
             else:
                 self._loss_coef = asarray(kwargs['loss_coef']) * 1e-3  # lineic loss dB/m
                 self._f_loss_ref = asarray(self._ref_frequency)  # Hz
-            self._lumped_losses = kwargs['lumped_losses'] if 'lumped_losses' in kwargs else []
+            # Lumped Losses
+            self._lumped_losses = kwargs['lumped_losses'] if 'lumped_losses' in kwargs else array([])
+            self._latency = self._length / (c / self._n1)  # s
         except KeyError as e:
             raise ParametersError(f'Fiber configurations json must include {e}. Configuration: {kwargs}')
 
@@ -219,6 +398,10 @@ class FiberParams(Parameters):
     def dispersion(self):
         return self._dispersion
 
+    @property
+    def f_dispersion_ref(self):
+        return self._f_dispersion_ref
+
     @property
     def dispersion_slope(self):
         return self._dispersion_slope
@@ -227,6 +410,20 @@ class FiberParams(Parameters):
     def gamma(self):
         return self._gamma
 
+    def effective_area_scaling(self, frequency):
+        V = 2 * pi * frequency / c * self._core_radius * self._n1 * sqrt(2 * self._contrast)
+        w = self._core_radius / sqrt(log(V))
+        return asarray(pi * w ** 2)
+
+    def effective_area_overlap(self, frequency_stokes_wave, frequency_pump):
+        effective_area_stokes_wave = self.effective_area_scaling(frequency_stokes_wave)
+        effective_area_pump = self.effective_area_scaling(frequency_pump)
+        return squeeze(outer(effective_area_stokes_wave, ones(effective_area_pump.size)) + outer(
+            ones(effective_area_stokes_wave.size), effective_area_pump)) / 2
+
+    def gamma_scaling(self, frequency):
+        return asarray(2 * pi * self._n2 * frequency / (c * self.effective_area_scaling(frequency)))
+
     @property
     def pmd_coef(self):
         return self._pmd_coef
@@ -239,14 +436,6 @@ class FiberParams(Parameters):
     def ref_frequency(self):
         return self._ref_frequency
 
-    @property
-    def beta2(self):
-        return self._beta2
-
-    @property
-    def beta3(self):
-        return self._beta3
-
     @property
     def loss_coef(self):
         return self._loss_coef
@@ -256,40 +445,163 @@ class FiberParams(Parameters):
         return self._f_loss_ref
 
     @property
-    def raman_efficiency(self):
-        return self._raman_efficiency
+    def raman_coefficient(self):
+        return self._raman_coefficient
+
+    @property
+    def latency(self):
+        return self._latency
 
     def asdict(self):
         dictionary = super().asdict()
         dictionary['loss_coef'] = self.loss_coef * 1e3
         dictionary['length_units'] = 'm'
-        if not self.lumped_losses:
+        if len(self.lumped_losses) == 0:
             dictionary.pop('lumped_losses')
-        if not self.raman_efficiency:
-            dictionary.pop('raman_efficiency')
+        if not self.raman_coefficient:
+            dictionary.pop('raman_coefficient')
+        else:
+            raman_frequency_offset = \
+                self.raman_coefficient.frequency_offset[self.raman_coefficient.frequency_offset >= 0]
+            dictionary['raman_coefficient'] = {'g0': self._g0.tolist(),
+                                               'frequency_offset': raman_frequency_offset.tolist(),
+                                               'reference_frequency': self._raman_reference_frequency}
         return dictionary
 
 
 class EdfaParams:
+    default_values = {
+        'f_min': None,
+        'f_max': None,
+        'multi_band': None,
+        'bands': None,
+        'type_variety': '',
+        'type_def': '',
+        'gain_flatmax': None,
+        'gain_min': None,
+        'p_max': None,
+        'nf_model': None,
+        'dual_stage_model': None,
+        'preamp_variety': None,
+        'booster_variety': None,
+        'nf_min': None,
+        'nf_max': None,
+        'nf_coef': None,
+        'nf0': None,
+        'nf_fit_coeff': None,
+        'nf_ripple': 0,
+        'dgt': None,
+        'gain_ripple': 0,
+        'tilt_ripple': 0,
+        'f_ripple_ref': None,
+        'out_voa_auto': False,
+        'allowed_for_design': False,
+        'raman': False,
+        'pmd': 0,
+        'pdl': 0,
+        'advance_configurations_from_json': None
+    }
+
     def __init__(self, **params):
-        self.update_params(params)
-        if params == {}:
-            self.type_variety = ''
-            self.type_def = ''
-            # self.gain_flatmax = 0
-            # self.gain_min = 0
-            # self.p_max = 0
-            # self.nf_model = None
-            # self.nf_fit_coeff = None
-            # self.nf_ripple = None
-            # self.dgt = None
-            # self.gain_ripple = None
-            # self.out_voa_auto = False
-            # self.allowed_for_design = None
+        try:
+            self.type_variety = params['type_variety']
+            self.type_def = params['type_def']
+
+            # Bandwidth
+            self.f_min = params['f_min']
+            self.f_max = params['f_max']
+            self.bandwidth = self.f_max - self.f_min if self.f_max and self.f_min else None
+            self.f_cent = (self.f_max + self.f_min) / 2 if self.f_max and self.f_min else None
+            self.f_ripple_ref = params['f_ripple_ref']
+            self.bands = [{'f_min': params['f_min'],
+                           'f_max': params['f_max']}]
+
+            # Gain
+            self.gain_flatmax = params['gain_flatmax']
+            self.gain_min = params['gain_min']
+
+            gain_ripple = params['gain_ripple']
+            if gain_ripple == 0:
+                self.gain_ripple = asarray([0, 0])
+                self.f_ripple_ref = asarray([self.f_min, self.f_max])
+            else:
+                self.gain_ripple = asarray(gain_ripple)
+                if self.f_ripple_ref is not None:
+                    if (self.f_ripple_ref[0] != self.f_min) or (self.f_ripple_ref[-1] != self.f_max):
+                        raise ParametersError("The reference ripple frequency maximum and minimum have to coincide "
+                                              "with the EDFA frequency maximum and minimum.")
+                    elif self.gain_ripple.size != self.f_ripple_ref.size:
+                        raise ParametersError("The reference ripple frequency and the gain ripple must have the same "
+                                              "size.")
+                else:
+                    self.f_ripple_ref = linspace(self.f_min, self.f_max, self.gain_ripple.size)
+
+            tilt_ripple = params['tilt_ripple']
+
+            if tilt_ripple == 0:
+                self.tilt_ripple = full(self.gain_ripple.size, 0)
+            else:
+                self.tilt_ripple = asarray(tilt_ripple)
+                if self.tilt_ripple.size != self.gain_ripple.size:
+                    raise ParametersError("The tilt ripple and the gain ripple must have the same size.")
+
+            # Power
+            self.p_max = params['p_max']
+
+            # Noise Figure
+            self.nf_model = params['nf_model']
+            self.nf_min = params['nf_min']
+            self.nf_max = params['nf_max']
+            self.nf_coef = params['nf_coef']
+            self.nf0 = params['nf0']
+            self.nf_fit_coeff = params['nf_fit_coeff']
+
+            nf_ripple = params['nf_ripple']
+            if nf_ripple == 0:
+                self.nf_ripple = full(self.gain_ripple.size, 0)
+            else:
+                self.nf_ripple = asarray(nf_ripple)
+                if self.nf_ripple.size != self.gain_ripple.size:
+                    raise ParametersError("The noise figure ripple and the gain ripple must have the same size.")
+
+            # VOA
+            self.out_voa_auto = params['out_voa_auto']
+
+            # Dual Stage
+            self.dual_stage_model = params['dual_stage_model']
+            if self.dual_stage_model is not None:
+                # Preamp
+                self.preamp_variety = params['preamp_variety']
+                self.preamp_type_def = params['preamp_type_def']
+                self.preamp_nf_model = params['preamp_nf_model']
+                self.preamp_nf_fit_coeff = params['preamp_nf_fit_coeff']
+                self.preamp_gain_min = params['preamp_gain_min']
+                self.preamp_gain_flatmax = params['preamp_gain_flatmax']
+
+                # Booster
+                self.booster_variety = params['booster_variety']
+                self.booster_type_def = params['booster_type_def']
+                self.booster_nf_model = params['booster_nf_model']
+                self.booster_nf_fit_coeff = params['booster_nf_fit_coeff']
+                self.booster_gain_min = params['booster_gain_min']
+                self.booster_gain_flatmax = params['booster_gain_flatmax']
+
+            # Others
+            self.pmd = params['pmd']
+            self.pdl = params['pdl']
+            self.raman = params['raman']
+            self.dgt = params['dgt']
+            self.advance_configurations_from_json = params['advance_configurations_from_json']
+
+            # Design
+            self.allowed_for_design = params['allowed_for_design']
+
+        except KeyError as e:
+            raise ParametersError(f'Edfa configurations json must include {e}. Configuration: {params}')
 
     def update_params(self, kwargs):
         for k, v in kwargs.items():
-            setattr(self, k, self.update_params(**v) if isinstance(v, dict) else v)
+            setattr(self, k, v)
 
 
 class EdfaOperational:
@@ -297,7 +609,7 @@ class EdfaOperational:
         'gain_target': None,
         'delta_p': None,
         'out_voa': None,
-        'tilt_target': 0
+        'tilt_target': None
     }
 
     def __init__(self, **operational):
@@ -312,3 +624,62 @@ class EdfaOperational:
         return (f'{type(self).__name__}('
                 f'gain_target={self.gain_target!r}, '
                 f'tilt_target={self.tilt_target!r})')
+
+
+class MultiBandParams:
+    default_values = {
+        'bands': [],
+        'type_variety': '',
+        'type_def': None,
+        'allowed_for_design': False
+    }
+
+    def __init__(self, **params):
+        try:
+            self.update_attr(params)
+        except KeyError as e:
+            raise ParametersError(f'Multiband configurations json must include {e}. Configuration: {params}')
+
+    def update_attr(self, kwargs):
+        clean_kwargs = {k: v for k, v in kwargs.items() if v != ''}
+        for k, v in self.default_values.items():
+            # use deepcopy to avoid sharing same object amongst all instance when v is a list or a dict!
+            if isinstance(v, (list, dict)):
+                setattr(self, k, clean_kwargs.get(k, deepcopy(v)))
+            else:
+                setattr(self, k, clean_kwargs.get(k, v))
+
+
+class TransceiverParams:
+    def __init__(self, **params):
+        self.design_bands = params.get('design_bands', [])
+        self.per_degree_design_bands = params.get('per_degree_design_bands', {})
+
+
+@dataclass
+class FrequencyBand:
+    """Frequency band
+    """
+    f_min: float
+    f_max: float
+
+
+DEFAULT_BANDS_DEFINITION = {
+    "LBAND": FrequencyBand(f_min=187e12, f_max=189e12),
+    "CBAND": FrequencyBand(f_min=191.3e12, f_max=196.0e12)
+}
+# use this definition to index amplifiers'element of a multiband amplifier.
+# this is not the design band
+
+
+def find_band_name(band: FrequencyBand) -> str:
+    """return the default band name (CBAND, LBAND, ...) that corresponds to the band frequency range
+    Use the band center frequency: if center frequency is inside the band then returns CBAND.
+    This is to flexibly encompass all kind of bands definitions.
+    returns the first matching band name.
+    """
+    for band_name, frequency_range in DEFAULT_BANDS_DEFINITION.items():
+        center_frequency = (band.f_min + band.f_max) / 2
+        if center_frequency >= frequency_range.f_min and center_frequency <= frequency_range.f_max:
+            return band_name
+    return 'unknown_band'

gnpy/core/science_utils.py

@@ -10,45 +10,44 @@ 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, \
-    clip, abs, sum, concatenate, flip, outer, inner, transpose, max, format_float_scientific, diag, prod, argwhere, \
-    unique, argsort, cumprod
+from numpy import interp, pi, zeros, cos, array, append, ones, exp, arange, sqrt, trapz, arcsinh, clip, abs, sum, \
+    concatenate, flip, outer, inner, transpose, max, format_float_scientific, diag, sort, unique, argsort, cumprod, \
+    polyfit, log, reshape, swapaxes, full, nan, cumsum
 from logging import getLogger
 from scipy.constants import k, h
 from scipy.interpolate import interp1d
-from math import isclose
+from math import isclose, factorial
 
 from gnpy.core.utils import db2lin, lin2db
-from gnpy.core.exceptions import EquipmentConfigError
+from gnpy.core.exceptions import EquipmentConfigError, ParametersError
 from gnpy.core.parameters import SimParams
 from gnpy.core.info import SpectralInformation
 
 logger = getLogger(__name__)
-sim_params = SimParams.get()
+sim_params = SimParams()
 
-def raised_cosine_comb(f, *carriers):
-    """ Returns an array storing the PSD of a WDM comb of raised cosine shaped
-    channels at the input frequencies defined in array f
 
-    :param f: numpy array of frequencies in Hz
-    :param carriers: namedtuple describing the WDM comb
-    :return: PSD of the WDM comb evaluated over f
+def raised_cosine(frequency, channel_frequency, channel_baud_rate, channel_roll_off):
+    """Returns a unitary raised cosine profile for the given parame
+
+    :param frequency: numpy array of frequencies in Hz for the resulting raised cosine
+    :param channel_frequency: channel frequencies in Hz
+    :param channel_baud_rate: channel baud rate in Hz
+    :param channel_roll_off: channel roll off
     """
-    psd = zeros(shape(f))
-    for carrier in carriers:
-        f_nch = carrier.frequency
-        g_ch = carrier.power.signal / carrier.baud_rate
-        ts = 1 / carrier.baud_rate
-        pass_band = (1 - carrier.roll_off) / (2 / carrier.baud_rate)
-        stop_band = (1 + carrier.roll_off) / (2 / carrier.baud_rate)
-        ff = abs(f - f_nch)
-        tf = ff - pass_band
-        if carrier.roll_off == 0:
-            psd = where(tf <= 0, g_ch, 0.) + psd
-        else:
-            psd = g_ch * (where(tf <= 0, 1., 0.) + 1 / 2 * (1 + cos(pi * ts / carrier.roll_off * tf)) *
-                          where(tf > 0, 1., 0.) * where(abs(ff) <= stop_band, 1., 0.)) + psd
-    return psd
+    raised_cosine_mask = zeros(frequency.size)
+    base_frequency = frequency - channel_frequency
+    ts = 1 / channel_baud_rate
+    pass_band = (1 - channel_roll_off) * channel_baud_rate / 2
+    stop_band = (1 + channel_roll_off) * channel_baud_rate / 2
+
+    flat_condition = (abs(base_frequency) <= pass_band) == 1
+    cosine_condition = (pass_band < abs(base_frequency)) * (abs(base_frequency) < stop_band) == 1
+
+    raised_cosine_mask[flat_condition] = 1
+    raised_cosine_mask[cosine_condition] = \
+        0.5 * (1 + cos(pi * ts / channel_roll_off * (abs(base_frequency[cosine_condition]) - pass_band)))
+    return raised_cosine_mask
 
 
 class StimulatedRamanScattering:
@@ -110,6 +109,7 @@ class RamanSolver:
             z_step = sim_params.raman_params.solver_spatial_resolution
             z = append(arange(0, fiber.params.length, z_step), fiber.params.length)
             z_final = append(arange(0, fiber.params.length, z_resolution), fiber.params.length)
+            z_final = sort(unique(concatenate((fiber.z_lumped_losses, z_final))))
 
             # Lumped losses array definition
             z, lumped_losses = RamanSolver._create_lumped_losses(z, fiber.lumped_losses, fiber.z_lumped_losses)
@@ -131,20 +131,22 @@ 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)
+                        RamanSolver.calculate_unidirectional_stimulated_raman_scattering(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)))
+                    cnt_power_profile = flip(
+                        RamanSolver.calculate_unidirectional_stimulated_raman_scattering(cnt_power, cnt_alpha, cnt_cr,
+                                                                                         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 = \
@@ -163,8 +165,9 @@ class RamanSolver:
                 alpha = fiber.alpha(spectral_info.frequency)
                 cr = fiber.cr(spectral_info.frequency)
                 # Power profile
-                power_profile = \
-                    RamanSolver.first_order_derivative_solution(spectral_info.signal, alpha, cr, z, lumped_losses)
+                power_profile = (
+                    RamanSolver.calculate_unidirectional_stimulated_raman_scattering(spectral_info.signal, alpha, cr, z,
+                                                                                     lumped_losses))
                 # Loss profile
                 loss_profile = power_profile / outer(spectral_info.signal, ones(z.size))
                 frequency = spectral_info.frequency
@@ -190,18 +193,18 @@ class RamanSolver:
 
         # calculate ase power
         ase = zeros(spectral_info.number_of_channels)
+        cr = fiber.cr(srs.frequency)[:spectral_info.number_of_channels, spectral_info.number_of_channels:]
         for i, pump in enumerate(fiber.raman_pumps):
             pump_power = srs.power_profile[spectral_info.number_of_channels + i, :]
             df = pump.frequency - frequency
             eta = - 1 / (1 - exp(h * df / (k * fiber.temperature)))
-            cr = fiber._cr_function(df)
             integral = trapz(pump_power / channels_loss, z, axis=1)
-            ase += 2 * h * baud_rate * frequency * (1 + eta) * cr * (df > 0) * integral  # 2 factor for double pol
+            ase += 2 * h * baud_rate * frequency * (1 + eta) * cr[:, i] * (df > 0) * integral  # 2 factor for double pol
         return ase
 
     @staticmethod
-    def first_order_derivative_solution(power_in, alpha, cr, z, lumped_losses):
-        """Solves the Raman first order derivative equation
+    def calculate_unidirectional_stimulated_raman_scattering(power_in, alpha, cr, z, lumped_losses):
+        """Solves the Raman equation
 
         :param power_in: launch power array
         :param alpha: loss coefficient array
@@ -210,18 +213,66 @@ class RamanSolver:
         :param lumped_losses: concentrated losses array along the fiber span
         :return: power profile matrix
         """
-        dz = z[1:] - z[:-1]
-        power = outer(power_in, ones(z.size))
-        for i in range(1, z.size):
-            power[:, i] = \
-                power[:, i - 1] * (1 + (- alpha + sum(cr * power[:, i - 1], 1)) * dz[i - 1]) * lumped_losses[i - 1]
+        if sim_params.raman_params.method == 'perturbative':
+            if sim_params.raman_params.order > 4:
+                raise ValueError(f'Order {sim_params.raman_params.order} not implemented in Raman Solver.')
+            z_lumped_losses = append(z[lumped_losses != 1], z[-1])
+            llumped_losses = append(1, lumped_losses[lumped_losses != 1])
+            power = outer(power_in, ones(z.size))
+            last_position = 0
+            z_indices = arange(0, z.size)
+
+            for z_lumped_loss, lumped_loss in zip(z_lumped_losses, llumped_losses):
+                if last_position < z[-1]:
+                    interval = z_indices[(z >= last_position) * (z <= z_lumped_loss) == 1]
+                    z_interval = z[interval] - last_position
+                    dz = z_interval[1:] - z_interval[:-1]
+                    last_position = z[interval][-1]
+                    p0 = power_in * lumped_loss
+                    power_interval = outer(p0, ones(z_interval.size))
+                    alphaz = outer(alpha, z_interval)
+                    expz = exp(- alphaz)
+                    eff_length = 1 / outer(alpha, ones(z_interval.size)) * (1 - expz)
+                    crpz = transpose(ones([z_interval.size, cr.shape[0], cr.shape[1]]) * cr * p0, (1, 2, 0))
+                    exponent = - alphaz
+                    if sim_params.raman_params.order >= 1:
+                        gamma1 = sum(crpz * eff_length, 1)
+                        exponent += gamma1
+                    if sim_params.raman_params.order >= 2:
+                        z_integrand = expz * gamma1
+                        z_integral = cumsum((z_integrand[:, :-1] + z_integrand[:, 1:]) / 2 * dz, 1)
+                        gamma2 = zeros(gamma1.shape)
+                        gamma2[:, 1:] = sum(crpz[:, :, 1:] * z_integral, 1)
+                        exponent += gamma2
+                    if sim_params.raman_params.order >= 3:
+                        z_integrand = expz * (gamma2 + 1/2 * gamma1**2)
+                        z_integral = cumsum((z_integrand[:, :-1] + z_integrand[:, 1:]) / 2 * dz, 1)
+                        gamma3 = zeros(gamma1.shape)
+                        gamma3[:, 1:] = sum(crpz[:, :, 1:] * z_integral, 1)
+                        exponent += gamma3
+                    if sim_params.raman_params.order >= 4:
+                        z_integrand = expz * (gamma3 + gamma1 * gamma2 + 1/factorial(3) * gamma1**3)
+                        z_integral = cumsum((z_integrand[:, :-1] + z_integrand[:, 1:]) / 2 * dz, 1)
+                        gamma4 = zeros(gamma1.shape)
+                        gamma4[:, 1:] = sum(crpz[:, :, 1:] * z_integral, 1)
+                        exponent += gamma4
+                    power_interval *= exp(exponent)
+                    power[:, interval[1:]] = power_interval[:, 1:]
+                    power_in = power_interval[:, -1]
+        elif sim_params.raman_params.method == 'numerical':
+            dz = z[1:] - z[:-1]
+            power = outer(power_in, ones(z.size))
+            for i in range(1, z.size):
+                power[:, i] = (power[:, i - 1] * (1 + (- alpha + sum(cr * power[:, i - 1], 1)) * dz[i - 1]) *
+                               lumped_losses[i - 1])
+        else:
+            raise ValueError(f'Method {sim_params.raman_params.method} not implemented in Raman Solver.')
         return power
 
     @staticmethod
     def iterative_algorithm(co_initial_guess_power, cnt_initial_guess_power, co_frequency, cnt_frequency, z, fiber,
                             lumped_losses):
-        """Solves the Raman first order derivative equation in case of both co- and counter-propagating
-        frequencies
+        """Solves the Raman equation in case of both co- and counter-propagating frequencies
 
         :param co_initial_guess_power: co-propagationg Raman first order derivative equation solution
         :param cnt_initial_guess_power: counter-propagationg Raman first order derivative equation solution
@@ -271,13 +322,17 @@ class RamanSolver:
 
 
 class NliSolver:
-    """ This class implements the NLI models.
-        Model and method can be specified in `sim_params.nli_params.method`.
-        List of implemented methods:
-        'gn_model_analytic': eq. 120 from arXiv:1209.0394
-        'ggn_spectrally_separated': eq. 21 from arXiv: 1710.02225 spectrally separated
+    """This class implements the NLI models.
+    Model and method can be specified in `sim_params.nli_params.method`.
+    List of implemented methods:
+    'gn_model_analytic': eq. 120 from arXiv:1209.0394
+    'ggn_spectrally_separated': eq. 21 from arXiv: 1710.02225
+    'ggn_approx': eq. 24-25 jlt:9741324
     """
 
+    SPM_WEIGHT = (16.0 / 27.0)
+    XPM_WEIGHT = 2 * (16.0 / 27.0)
+
     @staticmethod
     def effective_length(alpha, length):
         """The effective length identify the region in which the NLI has a significant contribution to
@@ -287,58 +342,107 @@ class NliSolver:
 
     @staticmethod
     def compute_nli(spectral_info: SpectralInformation, srs: StimulatedRamanScattering, fiber):
-        """ Compute NLI power generated by the WDM comb `*carriers` on the channel under test `carrier`
+        """Compute NLI power generated by the WDM comb `*carriers` on the channel under test `carrier`
         at the end of the fiber span.
         """
         logger.debug('Start computing fiber NLI noise')
-        # Physical fiber parameters
-        alpha = fiber.alpha(spectral_info.frequency)
-        beta2 = fiber.params.beta2
-        beta3 = fiber.params.beta3
-        f_ref_beta = fiber.params.ref_frequency
-        gamma = fiber.params.gamma
-        length = fiber.params.length
 
         if 'gn_model_analytic' == sim_params.nli_params.method:
-            nli = NliSolver._gn_analytic(spectral_info, alpha, beta2, gamma, length)
+            eta = NliSolver._gn_analytic(spectral_info, fiber)
+
+            cut_power = outer(spectral_info.signal, ones(spectral_info.number_of_channels))
+            pump_power = outer(ones(spectral_info.number_of_channels), spectral_info.signal)
+            nli_matrix = cut_power * pump_power ** 2 * eta
+            nli = sum(nli_matrix, 1)
         elif 'ggn_spectrally_separated' in sim_params.nli_params.method:
-            nli = NliSolver._ggn_spectrally_separated(spectral_info, srs, alpha, beta2, beta3, f_ref_beta, gamma)
+            if sim_params.nli_params.computed_channels is not None:
+                cut_indices = array(sim_params.nli_params.computed_channels) - 1
+            elif sim_params.nli_params.computed_number_of_channels is not None:
+                nb_ch_computed = sim_params.nli_params.computed_number_of_channels
+                nb_ch = len(spectral_info.channel_number)
+                cut_indices = array([round(i * (nb_ch - 1) / (nb_ch_computed - 1)) for i in range(0, nb_ch_computed)])
+            else:
+                cut_indices = array(spectral_info.channel_number) - 1
+
+            eta = NliSolver._ggn_spectrally_separated(cut_indices, spectral_info, fiber, srs)
+
+            # Interpolation over the channels not indicated as compted channels in simulation parameters
+            cut_power = outer(spectral_info.signal[cut_indices], ones(spectral_info.number_of_channels))
+            cut_frequency = spectral_info.frequency[cut_indices]
+            pump_power = outer(ones(cut_indices.size), spectral_info.signal)
+            cut_baud_rate = outer(spectral_info.baud_rate[cut_indices], ones(spectral_info.number_of_channels))
+
+            g_nli = eta * cut_power * pump_power**2 / cut_baud_rate
+            g_nli = sum(g_nli, 1)
+            g_nli = interp(spectral_info.frequency, cut_frequency, g_nli)
+            nli = spectral_info.baud_rate * g_nli  # Local white noise
+        elif 'ggn_approx' in sim_params.nli_params.method:
+            if sim_params.nli_params.computed_channels is not None:
+                cut_indices = array(sim_params.nli_params.computed_channels) - 1
+            elif sim_params.nli_params.computed_number_of_channels is not None:
+                nb_ch_computed = sim_params.nli_params.computed_number_of_channels
+                nb_ch = len(spectral_info.channel_number)
+                cut_indices = array([round(i * (nb_ch - 1) / (nb_ch_computed - 1)) for i in range(0, nb_ch_computed)])
+            else:
+                cut_indices = array(spectral_info.channel_number) - 1
+
+            eta = NliSolver._ggn_approx(cut_indices, spectral_info, fiber, srs)
+
+            # Interpolation over the channels not indicated as computed channels in simulation parameters
+            cut_power = outer(spectral_info.signal[cut_indices], ones(spectral_info.number_of_channels))
+            cut_frequency = spectral_info.frequency[cut_indices]
+            pump_power = outer(ones(cut_indices.size), spectral_info.signal)
+            cut_baud_rate = outer(spectral_info.baud_rate[cut_indices], ones(spectral_info.number_of_channels))
+
+            g_nli = eta * cut_power * pump_power ** 2 / cut_baud_rate
+            g_nli = sum(g_nli, 1)
+            g_nli = interp(spectral_info.frequency, cut_frequency, g_nli)
+            nli = spectral_info.baud_rate * g_nli  # Local white noise
         else:
             raise ValueError(f'Method {sim_params.nli_params.method} not implemented.')
+
         return nli
 
-    # Methods for computing GN-model
+    # Methods for computing GN-model eta matrix
     @staticmethod
-    def _gn_analytic(spectral_info: SpectralInformation, alpha, beta2, gamma, length):
-        """ Computes the nonlinear interference power evaluated at the fiber input.
+    def _gn_analytic(spectral_info, fiber, spm_weight=SPM_WEIGHT, xpm_weight=XPM_WEIGHT):
+        """Computes the nonlinear interference power evaluated at the fiber input.
         The method uses eq. 120 from arXiv:1209.0394
         """
-        spm_weight = (16.0 / 27.0) * gamma ** 2
-        xpm_weight = 2 * (16.0 / 27.0) * gamma ** 2
-
+        # Spectral Features
         nch = spectral_info.number_of_channels
+        frequency = spectral_info.frequency
+        baud_rate = spectral_info.baud_rate
+        delta_frequency = spectral_info.df
+
+        # Physical fiber parameters
+        alpha = fiber.alpha(frequency)
+        beta2 = fiber.beta2(frequency)
+        gamma = outer(fiber.gamma(frequency), ones(nch))
+        length = fiber.params.length
+
         identity = diag(ones(nch))
         weight = spm_weight * identity + xpm_weight * (ones([nch, nch]) - identity)
 
         effective_length = NliSolver.effective_length(alpha, length)
         asymptotic_length = 1 / alpha
 
-        df = spectral_info.df
-        baud_rate = spectral_info.baud_rate
+        cut_baud_rate = outer(baud_rate, ones(nch))
+        pump_baud_rate = outer(ones(nch), baud_rate)
 
-        psd = spectral_info.signal / baud_rate
-        ggg = outer(psd, psd**2)
-
-        psi = NliSolver._psi(df, baud_rate, beta2, effective_length, asymptotic_length)
-        g_nli = sum(weight * ggg * psi, 1)
-        nli = spectral_info.baud_rate * g_nli  # Local white noise
-        return nli
+        psi = NliSolver._psi(delta_frequency, baud_rate, beta2, effective_length, asymptotic_length)
+        eta_cut_central_frequency = gamma ** 2 * weight * psi / (cut_baud_rate * pump_baud_rate ** 2)
+        eta = cut_baud_rate * eta_cut_central_frequency  # Local white noise
+        return eta
 
     @staticmethod
     def _psi(df, baud_rate, beta2, effective_length, asymptotic_length):
         """Calculates eq. 123 from `arXiv:1209.0394 <https://arxiv.org/abs/1209.0394>`__"""
         cut_baud_rate = outer(baud_rate, ones(baud_rate.size))
+        cut_beta = outer(beta2, ones(baud_rate.size))
         pump_baud_rate = baud_rate
+        pump_beta = outer(ones(baud_rate.size), beta2)
+        beta2 = (cut_beta + pump_beta) / 2
         right_extreme = df + pump_baud_rate / 2
         left_extreme = df - pump_baud_rate / 2
         psi = (arcsinh(pi ** 2 * asymptotic_length * abs(beta2) * cut_baud_rate * right_extreme) -
@@ -346,112 +450,133 @@ class NliSolver:
         psi *= effective_length ** 2 / (2 * pi * abs(beta2) * asymptotic_length)
         return psi
 
-    # Methods for computing the GGN-model
+    # Methods for computing the GGN-model eta matrix
     @staticmethod
-    def _ggn_spectrally_separated(spectral_info: SpectralInformation, srs: StimulatedRamanScattering,
-                                  alpha, beta2, beta3, f_ref_beta, gamma):
-        """ Computes the nonlinear interference power evaluated at the fiber input.
+    def _ggn_spectrally_separated(cut_indices, spectral_info, fiber, srs, spm_weight=SPM_WEIGHT, xpm_weight=XPM_WEIGHT):
+        """Computes the nonlinear interference power evaluated at the fiber input.
         The method uses eq. 21 from arXiv: 1710.02225
         """
+        # Spectral Features
+        nch = spectral_info.number_of_channels
+        frequency = spectral_info.frequency
+        baud_rate = spectral_info.baud_rate
+        slot_width = spectral_info.slot_width
+        roll_off = spectral_info.roll_off
+
+        # Physical fiber parameters
+        alpha = fiber.alpha(frequency)
+        beta2 = fiber.beta2(frequency)
+        gamma = outer(fiber.gamma(frequency[cut_indices]), ones(nch))
+
+        identity = diag(ones(nch))
+        weight = spm_weight * identity + xpm_weight * (ones([nch, nch]) - identity)
+        weight = weight[cut_indices, :]
+
         dispersion_tolerance = sim_params.nli_params.dispersion_tolerance
         phase_shift_tolerance = sim_params.nli_params.phase_shift_tolerance
-        slot_width = max(spectral_info.slot_width)
+        max_slot_width = max(slot_width)
         delta_z = sim_params.raman_params.result_spatial_resolution
-        spm_weight = (16.0 / 27.0) * gamma ** 2
-        xpm_weight = 2 * (16.0 / 27.0) * gamma ** 2
-        cuts = [carrier for carrier in spectral_info.carriers if carrier.channel_number
-                in sim_params.nli_params.computed_channels] if sim_params.nli_params.computed_channels \
-            else spectral_info.carriers
 
-        g_nli = array([])
-        f_nli = array([])
-        for cut_carrier in cuts:
-            logger.debug(f'Start computing fiber NLI noise of cut: {cut_carrier}')
-            f_eval = cut_carrier.frequency
-            g_nli_computed = 0
-            g_cut = (cut_carrier.power.signal / cut_carrier.baud_rate)
-            for j, pump_carrier in enumerate(spectral_info.carriers):
-                dn = abs(pump_carrier.channel_number - cut_carrier.channel_number)
-                delta_f = abs(cut_carrier.frequency - pump_carrier.frequency)
-                k_tol = dispersion_tolerance * abs(alpha[j])
+        psi_cut_central_frequency = zeros([cut_indices.size, nch])
+        for i, cut_index in enumerate(cut_indices):
+            logger.debug(f'Start computing fiber NLI noise of cut: {cut_index + 1}')
+            cut_frequency = frequency[cut_index]
+            cut_baud_rate = baud_rate[cut_index]
+            cut_roll_off = roll_off[cut_index]
+            cut_number = cut_index + 1
+            cut_beta2 = beta2[cut_index]
+            cut_base_frequency = frequency - cut_frequency
+            cut_beta_coefficients = polyfit(cut_base_frequency, beta2, 2)
+            cut_beta3 = cut_beta_coefficients[1] / (2 * pi)
+
+            for pump_index in range(nch):
+                pump_frequency = frequency[pump_index]
+                pump_baud_rate = baud_rate[pump_index]
+                pump_roll_off = roll_off[pump_index]
+                pump_number = pump_index + 1
+                pump_alpha = alpha[pump_index]
+                dn = abs(pump_number - cut_number)
+                delta_f = abs(cut_frequency - pump_frequency)
+                k_tol = dispersion_tolerance * abs(alpha[pump_index])
                 phi_tol = phase_shift_tolerance / delta_z
-                f_cut_resolution = min(k_tol, phi_tol) / abs(beta2) / (4 * pi ** 2 * (1 + dn) * slot_width)
-                f_pump_resolution = min(k_tol, phi_tol) / abs(beta2) / (4 * pi ** 2 * slot_width)
-                if dn == 0:  # SPM
-                    ggg = g_cut ** 3
-                    g_nli_computed += \
-                        spm_weight * ggg * NliSolver._generalized_psi(f_eval, cut_carrier, pump_carrier,
-                                                                      f_cut_resolution, f_pump_resolution,
-                                                                      srs, alpha[j], beta2, beta3, f_ref_beta)
+                f_cut_resolution = min(k_tol, phi_tol) / abs(cut_beta2) / (4 * pi ** 2 * (1 + dn) * max_slot_width)
+                f_pump_resolution = min(k_tol, phi_tol) / abs(cut_beta2) / (4 * pi ** 2 * max_slot_width)
+                if cut_index == pump_index:  # SPM
+                    psi_cut_central_frequency[i, pump_index] = \
+                        NliSolver._generalized_psi(cut_frequency, cut_frequency, cut_baud_rate, cut_roll_off,
+                                                   pump_frequency, pump_baud_rate, pump_roll_off, f_cut_resolution,
+                                                   f_pump_resolution, srs, pump_alpha, cut_beta2, cut_beta3,
+                                                   cut_frequency)
                 else:  # XPM
-                    g_pump = (pump_carrier.power.signal / pump_carrier.baud_rate)
-                    ggg = g_cut * g_pump ** 2
-                    frequency_offset_threshold = NliSolver._frequency_offset_threshold(beta2, pump_carrier.baud_rate)
+                    frequency_offset_threshold = NliSolver._frequency_offset_threshold(cut_beta2, pump_baud_rate)
                     if abs(delta_f) <= frequency_offset_threshold:
-                        g_nli_computed += \
-                            xpm_weight * ggg * NliSolver._generalized_psi(f_eval, cut_carrier, pump_carrier,
-                                                                          f_cut_resolution, f_pump_resolution,
-                                                                          srs, alpha[j], beta2, beta3, f_ref_beta)
+                        psi_cut_central_frequency[i, pump_index] = \
+                            NliSolver._generalized_psi(cut_frequency, cut_frequency, cut_baud_rate, cut_roll_off,
+                                                       pump_frequency, pump_baud_rate, pump_roll_off, f_cut_resolution,
+                                                       f_pump_resolution, srs, pump_alpha, cut_beta2, cut_beta3,
+                                                       cut_frequency)
                     else:
-                        g_nli_computed += \
-                            xpm_weight * ggg * NliSolver._fast_generalized_psi(f_eval, cut_carrier, pump_carrier,
-                                                                               f_cut_resolution, srs, alpha[j], beta2,
-                                                                               beta3, f_ref_beta)
-            f_nli = append(f_nli, cut_carrier.frequency)
-            g_nli = append(g_nli, g_nli_computed)
-        g_nli = interp(spectral_info.frequency, f_nli, g_nli)
-        nli = spectral_info.baud_rate * g_nli  # Local white noise
-        return nli
+                        psi_cut_central_frequency[i, pump_index] = \
+                            NliSolver._fast_generalized_psi(cut_frequency, cut_frequency, cut_baud_rate, cut_roll_off,
+                                                            pump_frequency, pump_baud_rate, pump_roll_off,
+                                                            f_cut_resolution, srs, pump_alpha, cut_beta2, cut_beta3,
+                                                            cut_frequency)
+
+        cut_baud_rate = outer(baud_rate[cut_indices], ones(nch))
+        pump_baud_rate = outer(ones(cut_indices.size), baud_rate)
+
+        eta_cut_central_frequency = \
+            gamma ** 2 * weight * psi_cut_central_frequency / (cut_baud_rate * pump_baud_rate ** 2)
+        eta = cut_baud_rate * eta_cut_central_frequency  # Local white noise
+        return eta
 
     @staticmethod
-    def _fast_generalized_psi(f_eval, cut_carrier, pump_carrier, f_cut_resolution, srs, alpha, beta2, beta3,
-                              f_ref_beta):
-        """Computes the generalized psi function similarly to the one used in the GN model."""
-        z = srs.z
-        rho_norm = srs.rho * exp(outer(alpha/2, z))
-        rho_pump = interp1d(srs.frequency, rho_norm, axis=0)(pump_carrier.frequency)
-
-        f1_array = array([pump_carrier.frequency - (pump_carrier.baud_rate * (1 + pump_carrier.roll_off) / 2),
-                          pump_carrier.frequency + (pump_carrier.baud_rate * (1 + pump_carrier.roll_off) / 2)])
-        f2_array = arange(cut_carrier.frequency,
-                          cut_carrier.frequency + (cut_carrier.baud_rate * (1 + cut_carrier.roll_off) / 2),
-                          f_cut_resolution)  # Only positive f2 is used since integrand_f2 is symmetric
-
-        integrand_f1 = zeros(len(f1_array))
-        for f1_index, f1 in enumerate(f1_array):
-            delta_beta = 4 * pi ** 2 * (f1 - f_eval) * (f2_array - f_eval) * \
-                (beta2 + pi * beta3 * (f1 + f2_array - 2 * f_ref_beta))
-            integrand_f2 = NliSolver._generalized_rho_nli(delta_beta, rho_pump, z, alpha)
-            integrand_f1[f1_index] = 2 * trapz(integrand_f2, f2_array)  # 2x since integrand_f2 is symmetric in f2
-        generalized_psi = 0.5 * sum(integrand_f1) * pump_carrier.baud_rate
-        return generalized_psi
-
-    @staticmethod
-    def _generalized_psi(f_eval, cut_carrier, pump_carrier, f_cut_resolution, f_pump_resolution, srs, alpha, beta2,
-                         beta3, f_ref_beta):
+    def _fast_generalized_psi(f_eval, cut_frequency, cut_baud_rate, cut_roll_off, pump_frequency, pump_baud_rate,
+                              pump_roll_off, f_cut_resolution, srs, alpha, beta2, beta3, f_ref_beta):
         """Computes the generalized psi function similarly to the one used in the GN model."""
         z = srs.z
         rho_norm = srs.rho * exp(outer(alpha / 2, z))
-        rho_pump = interp1d(srs.frequency, rho_norm, axis=0)(pump_carrier.frequency)
+        rho_pump = interp1d(srs.frequency, rho_norm, axis=0)(pump_frequency)
 
-        f1_array = arange(pump_carrier.frequency - (pump_carrier.baud_rate * (1 + pump_carrier.roll_off) / 2),
-                          pump_carrier.frequency + (pump_carrier.baud_rate * (1 + pump_carrier.roll_off) / 2),
+        f1_array = array([pump_frequency - (pump_baud_rate * (1 + pump_roll_off) / 2),
+                          pump_frequency + (pump_baud_rate * (1 + pump_roll_off) / 2)])
+        f2_array = arange(cut_frequency, cut_frequency + (cut_baud_rate * (1 + cut_roll_off) / 2),
+                          f_cut_resolution)  # Only positive f2 is used since integrand_f2 is symmetric
+
+        integrand_f1 = zeros(f1_array.size)
+        for f1_index, f1 in enumerate(f1_array):
+            delta_beta = 4 * pi ** 2 * (f1 - f_eval) * (f2_array - f_eval) * (
+                        beta2 + pi * beta3 * (f1 + f2_array - 2 * f_ref_beta))
+            integrand_f2 = NliSolver._generalized_rho_nli(delta_beta, rho_pump, z, alpha)
+            integrand_f1[f1_index] = 2 * trapz(integrand_f2, f2_array)  # 2x since integrand_f2 is symmetric in f2
+        generalized_psi = 0.5 * sum(integrand_f1) * pump_baud_rate
+        return generalized_psi
+
+    @staticmethod
+    def _generalized_psi(f_eval, cut_frequency, cut_baud_rate, cut_roll_off, pump_frequency, pump_baud_rate,
+                         pump_roll_off, f_cut_resolution, f_pump_resolution, srs, alpha, beta2, beta3, f_ref_beta):
+        """Computes the generalized psi function similarly to the one used in the GN model."""
+        z = srs.z
+        rho_norm = srs.rho * exp(outer(alpha / 2, z))
+        rho_pump = interp1d(srs.frequency, rho_norm, axis=0)(pump_frequency)
+
+        f1_array = arange(pump_frequency - (pump_baud_rate * (1 + pump_roll_off) / 2),
+                          pump_frequency + (pump_baud_rate * (1 + pump_roll_off) / 2),
                           f_pump_resolution)
-        f2_array = arange(cut_carrier.frequency - (cut_carrier.baud_rate * (1 + cut_carrier.roll_off) / 2),
-                          cut_carrier.frequency + (cut_carrier.baud_rate * (1 + cut_carrier.roll_off) / 2),
+        f2_array = arange(cut_frequency - (cut_baud_rate * (1 + cut_roll_off) / 2),
+                          cut_frequency + (cut_baud_rate * (1 + cut_roll_off) / 2),
                           f_cut_resolution)
-        psd1 = raised_cosine_comb(f1_array, pump_carrier) * (pump_carrier.baud_rate / pump_carrier.power.signal)
+        rc1 = raised_cosine(f1_array, pump_frequency, pump_baud_rate, pump_roll_off)
 
-        integrand_f1 = zeros(len(f1_array))
-        for f1_index, (f1, psd1_sample) in enumerate(zip(f1_array, psd1)):
-            f3_array = f1 + f2_array - f_eval
-            psd2 = raised_cosine_comb(f2_array, cut_carrier) * (cut_carrier.baud_rate / cut_carrier.power.signal)
-            psd3 = raised_cosine_comb(f3_array, pump_carrier) * (pump_carrier.baud_rate / pump_carrier.power.signal)
-            ggg = psd1_sample * psd2 * psd3
-            delta_beta = 4 * pi**2 * (f1 - f_eval) * (f2_array - f_eval) * \
-                (beta2 + pi * beta3 * (f1 + f2_array - 2 * f_ref_beta))
-            integrand_f2 = ggg * NliSolver._generalized_rho_nli(delta_beta, rho_pump, z, alpha)
-            integrand_f1[f1_index] = trapz(integrand_f2, f2_array)
+        integrand_f1 = zeros(f1_array.size)
+        for i in range(f1_array.size):
+            f3_array = f1_array[i] + f2_array - f_eval
+            rc2 = raised_cosine(f2_array, cut_frequency, cut_baud_rate, cut_roll_off)
+            rc3 = raised_cosine(f3_array, pump_frequency, pump_baud_rate, pump_roll_off)
+            delta_beta = 4 * pi ** 2 * (f1_array[i] - f_eval) * (f2_array - f_eval) * (
+                        beta2 + pi * beta3 * (f1_array[i] + f2_array - 2 * f_ref_beta))
+            integrand_f2 = rc1[i] * rc2 * rc3 * NliSolver._generalized_rho_nli(delta_beta, rho_pump, z, alpha)
+            integrand_f1[i] = trapz(integrand_f2, f2_array)
         generalized_psi = trapz(integrand_f1, f1_array)
         return generalized_psi
 
@@ -475,6 +600,89 @@ class NliSolver:
         freq_offset_th = ((k_ref * delta_f_ref) * rs_ref * beta2_ref) / (beta2 * symbol_rate)
         return freq_offset_th
 
+    @staticmethod
+    def _ggn_approx(cut_indices, spectral_info: SpectralInformation, fiber, srs, spm_weight=SPM_WEIGHT,
+                    xpm_weight=XPM_WEIGHT):
+        """Computes the nonlinear interference power evaluated at the fiber input.
+        The method uses eq. 24-25 of https://ieeexplore.ieee.org/document/9741324
+        """
+        # Spectral Features
+        nch = spectral_info.number_of_channels
+        frequency = spectral_info.frequency
+        baud_rate = spectral_info.baud_rate
+        slot_width = spectral_info.slot_width
+        roll_off = spectral_info.roll_off
+        df = spectral_info.df + diag(full(nch, nan))
+
+        # Physical fiber parameters
+        alpha = fiber.alpha(frequency)
+        beta2 = fiber.beta2(frequency)
+        gamma = outer(fiber.gamma(frequency[cut_indices]), ones(nch))
+
+        identity = diag(ones(nch))
+        weight = spm_weight * identity + xpm_weight * (ones([nch, nch]) - identity)
+        weight = weight[cut_indices, :]
+
+        dispersion_tolerance = sim_params.nli_params.dispersion_tolerance
+        phase_shift_tolerance = sim_params.nli_params.phase_shift_tolerance
+        max_slot_width = max(slot_width)
+        max_beta2 = max(abs(beta2))
+        delta_z = sim_params.raman_params.result_spatial_resolution
+
+        # Approximation psi
+        loss_profile = srs.loss_profile[:nch]
+        z = srs.z
+        psi = NliSolver._approx_psi(df=df, frequency=frequency, beta2=beta2, baud_rate=baud_rate,
+                                    loss_profile=loss_profile, z=z)
+
+        # GGN for SPM
+        for cut_index in cut_indices:
+            dn = 0
+            cut_frequency = frequency[cut_index]
+            cut_baud_rate = baud_rate[cut_index]
+            cut_roll_off = roll_off[cut_index]
+            cut_beta2 = beta2[cut_index]
+            cut_alpha = alpha[cut_index]
+            k_tol = dispersion_tolerance * abs(cut_alpha)
+            phi_tol = phase_shift_tolerance / delta_z
+            f_cut_resolution = min(k_tol, phi_tol) / abs(max_beta2) / (4 * pi ** 2 * (1 + dn) * max_slot_width)
+            f_pump_resolution = min(k_tol, phi_tol) / abs(max_beta2) / (4 * pi ** 2 * max_slot_width)
+            psi[cut_index, cut_index] = NliSolver._generalized_psi(cut_frequency, cut_frequency, cut_baud_rate,
+                                                                   cut_roll_off, cut_frequency, cut_baud_rate,
+                                                                   cut_roll_off, f_cut_resolution, f_pump_resolution,
+                                                                   srs, cut_alpha, cut_beta2, 0, cut_frequency)
+        psi = psi[cut_indices, :]
+        cut_baud_rate = outer(baud_rate[cut_indices], ones(nch))
+        pump_baud_rate = outer(ones(cut_indices.size), baud_rate)
+
+        eta_cut_central_frequency = \
+            gamma ** 2 * weight * psi / (cut_baud_rate * pump_baud_rate ** 2)
+        eta = cut_baud_rate * eta_cut_central_frequency  # Local white noise
+
+        return eta
+
+    @staticmethod
+    def _approx_psi(df, frequency, baud_rate, beta2, loss_profile, z):
+        """Computes the approximated psi function similarly to the one used in the GN model.
+        The method uses eq. 25 of https://ieeexplore.ieee.org/document/9741324"""
+        pump_baud_rate = outer(ones(frequency.size), baud_rate)
+        cut_beta = outer(beta2, ones(frequency.size))
+        pump_beta = outer(ones(frequency.size), beta2)
+        delta_z = abs(z[:-1] - z[1:])
+
+        loss_lin = log(loss_profile)
+        pump_alpha = (loss_lin[:, 1:] - loss_lin[:, :-1]) / delta_z
+        leff = abs((loss_profile[:, 1:] - loss_profile[:, :-1]) / sqrt(abs(pump_alpha))) * pump_alpha / abs(pump_alpha)
+        leff = reshape(outer(leff, ones(z.size - 1)), newshape=[leff.shape[0], leff.shape[1], leff.shape[1]])
+        leff2 = leff * swapaxes(leff, 2, 1)
+        leff2 = sum(leff2, axis=(1, 2))
+        z_int = outer(ones(frequency.size), leff2)
+
+        delta_beta = (cut_beta + pump_beta) / 2
+        psi = z_int * pump_baud_rate / (4 * pi * abs(delta_beta * df))
+        return psi
+
+
 
 def estimate_nf_model(type_variety, gain_min, gain_max, nf_min, nf_max):
     if nf_min < -10:

gnpy/core/utils.py

@@ -9,8 +9,10 @@ This module contains utility functions that are used with gnpy.
 """
 
 from csv import writer
-from numpy import pi, cos, sqrt, log10, linspace, zeros, shape, where, logical_and
+from numpy import pi, cos, sqrt, log10, linspace, zeros, shape, where, logical_and, mean, array
 from scipy import constants
+from copy import deepcopy
+from typing import List, Union
 
 from gnpy.core.exceptions import ConfigurationError
 
@@ -106,6 +108,69 @@ 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'
 
@@ -149,25 +214,39 @@ wavelength2freq = constants.lambda2nu
 freq2wavelength = constants.nu2lambda
 
 
-def freq2wavelength(value):
-    """ Converts frequency units to wavelength units.
-
-    >>> round(freq2wavelength(191.35e12) * 1e9, 3)
-    1566.723
-    >>> round(freq2wavelength(196.1e12) * 1e9, 3)
-    1528.773
-    """
-    return constants.c / value
-
-
 def snr_sum(snr, bw, snr_added, bw_added=12.5e9):
     snr_added = snr_added - lin2db(bw / bw_added)
     snr = -lin2db(db2lin(-snr) + db2lin(-snr_added))
     return snr
 
 
+def per_label_average(values, labels):
+    """computes the average per defined spectrum band, using labels
+
+    >>> labels = ['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'C', 'D', 'D', 'D', 'D']
+    >>> values = [28.51, 28.23, 28.15, 28.17, 28.36, 28.53, 28.64, 28.68, 28.7, 28.71, 28.72, 28.73, 28.74, 28.91, 27.96, 27.85, 27.87, 28.02]
+    >>> per_label_average(values, labels)
+    {'A': 28.28, 'B': 28.68, 'C': 28.91, 'D': 27.92}
+    """
+
+    label_set = sorted(set(labels))
+    summary = {}
+    for label in label_set:
+        vals = [val for val, lab in zip(values, labels) if lab == label]
+        summary[label] = round(mean(vals), 2)
+    return summary
+
+
+def pretty_summary_print(summary):
+    """Build a prettty string that shows the summary dict values per label with 2 digits"""
+    if len(summary) == 1:
+        return f'{list(summary.values())[0]:.2f}'
+    text = ', '.join([f'{label}: {value:.2f}' for label, value in summary.items()])
+    return text
+
+
 def deltawl2deltaf(delta_wl, wavelength):
-    """ deltawl2deltaf(delta_wl, wavelength):
+    """deltawl2deltaf(delta_wl, wavelength):
     delta_wl is BW in wavelength units
     wavelength is the center wl
     units for delta_wl and wavelength must be same
@@ -185,9 +264,9 @@ def deltawl2deltaf(delta_wl, wavelength):
 
 
 def deltaf2deltawl(delta_f, frequency):
-    """ deltawl2deltaf(delta_f, frequency):
-        converts delta frequency to delta wavelength
-        units for delta_wl and wavelength must be same
+    """convert delta frequency to delta wavelength
+
+    Units for delta_wl and wavelength must be same.
 
     :param delta_f: delta frequency in same units as frequency
     :param frequency: frequency BW is relevant for
@@ -202,8 +281,7 @@ def deltaf2deltawl(delta_f, frequency):
 
 
 def rrc(ffs, baud_rate, alpha):
-    """ rrc(ffs, baud_rate, alpha): computes the root-raised cosine filter
-    function.
+    """compute the root-raised cosine filter function
 
     :param ffs: A numpy array of frequencies
     :param baud_rate: The Baud Rate of the System
@@ -229,7 +307,7 @@ def rrc(ffs, baud_rate, alpha):
 
 
 def merge_amplifier_restrictions(dict1, dict2):
-    """Updates contents of dicts recursively
+    """Update contents of dicts recursively
 
     >>> d1 = {'params': {'restrictions': {'preamp_variety_list': [], 'booster_variety_list': []}}}
     >>> d2 = {'params': {'target_pch_out_db': -20}}
@@ -324,3 +402,159 @@ def convert_length(value, units):
         return value * 1e3
     else:
         raise ConfigurationError(f'Cannot convert length in "{units}" into meters')
+
+
+def replace_none(dictionary):
+    """ Replaces None with inf values in a frequency slots dict
+
+    >>> replace_none({'N': 3, 'M': None})
+    {'N': 3, 'M': inf}
+
+    """
+    for key, val in dictionary.items():
+        if val is None:
+            dictionary[key] = float('inf')
+        if val == float('inf'):
+            dictionary[key] = None
+    return dictionary
+
+
+def order_slots(slots):
+    """ Order frequency slots from larger slots to smaller ones up to None
+
+    >>> l = [{'N': 3, 'M': None}, {'N': 2, 'M': 1}, {'N': None, 'M': None},{'N': 7, 'M': 2},{'N': None, 'M': 1} , {'N': None, 'M': 0}]
+    >>> order_slots(l)
+    ([7, 2, None, None, 3, None], [2, 1, 1, 0, None, None], [3, 1, 4, 5, 0, 2])
+    """
+    slots_list = deepcopy(slots)
+    slots_list = [replace_none(e) for e in slots_list]
+    for i, e in enumerate(slots_list):
+        e['i'] = i
+    slots_list = sorted(slots_list, key=lambda x: (-x['M'], x['N']) if x['M'] != float('inf') else (x['M'], x['N']))
+    slots_list = [replace_none(e) for e in slots_list]
+    return [e['N'] for e in slots_list], [e['M'] for e in slots_list], [e['i'] for e in slots_list]
+
+
+def restore_order(elements, order):
+    """ Use order to re-order the element of the list, and ignore None values
+
+    >>> restore_order([7, 2, None, None, 3, None], [3, 1, 4, 5, 0, 2])
+    [3, 2, 7]
+    """
+    return [elements[i[0]] for i in sorted(enumerate(order), key=lambda x:x[1]) if elements[i[0]] is not None]
+
+
+def unique_ordered(elements):
+    """
+    """
+    unique_elements = []
+    for element in elements:
+        if element not in unique_elements:
+            unique_elements.append(element)
+    return unique_elements
+
+
+def calculate_absolute_min_or_zero(x: array) -> array:
+    """Calculates the element-wise absolute minimum between the x and zero.
+
+    Parameters:
+    x (array): The first input array.
+
+    Returns:
+    array: The element-wise absolute minimum between x and zero.
+
+    Example:
+    >>> x = array([-1, 2, -3])
+    >>> calculate_absolute_min_or_zero(x)
+    array([1., 0., 3.])
+    """
+    return (abs(x) - x) / 2
+
+
+def nice_column_str(data: List[List[str]], max_length: int = 30, padding: int = 1) -> str:
+    """data is a list of rows, creates strings with nice alignment per colum and padding with spaces
+    letf justified
+
+    >>> table_data = [['aaa', 'b', 'c'], ['aaaaaaaa', 'bbb', 'c'], ['a', 'bbbbbbbbbb', 'c']]
+    >>> print(nice_column_str(table_data))
+    aaa      b          c 
+    aaaaaaaa bbb        c 
+    a        bbbbbbbbbb c 
+    """
+    # transpose data to determine size of columns
+    transposed_data = list(map(list, zip(*data)))
+    column_width = [max(len(word) for word in column) + padding for column in transposed_data]
+    nice_str = []
+    for row in data:
+        column = ''.join(word[0:max_length].ljust(min(width, max_length)) for width, word in zip(column_width, row))
+        nice_str.append(f'{column}')
+    return '\n'.join(nice_str)
+
+
+def find_common_range(amp_bands: List[List[dict]], default_band_f_min: float, default_band_f_max: float) \
+        -> List[dict]:
+    """Find the common frequency range of bands
+    If there are no amplifiers in the path, then use default band
+
+    >>> amp_bands = [[{'f_min': 191e12, 'f_max' : 195e12}, {'f_min': 186e12, 'f_max' : 190e12} ], \
+        [{'f_min': 185e12, 'f_max' : 189e12}, {'f_min': 192e12, 'f_max' : 196e12}], \
+        [{'f_min': 186e12, 'f_max': 193e12}]]
+    >>> find_common_range(amp_bands, 190e12, 195e12)
+    [{'f_min': 186000000000000.0, 'f_max': 189000000000000.0}, {'f_min': 192000000000000.0, 'f_max': 193000000000000.0}]
+    >>> amp_bands = [[{'f_min': 191e12, 'f_max' : 195e12}, {'f_min': 186e12, 'f_max' : 190e12} ], \
+        [{'f_min': 185e12, 'f_max' : 189e12}, {'f_min': 192e12, 'f_max' : 196e12}], \
+        [{'f_min': 186e12, 'f_max': 192e12}]]
+    >>> find_common_range(amp_bands, 190e12, 195e12)
+    [{'f_min': 186000000000000.0, 'f_max': 189000000000000.0}]
+
+    """
+    _amp_bands = [sorted(amp, key=lambda x: x['f_min']) for amp in amp_bands]
+    _temp = []
+    # remove None bands
+    for amp in _amp_bands:
+        is_band = True
+        for band in amp:
+            if not (is_band and band['f_min'] and band['f_max']):
+                is_band = False
+        if is_band:
+            _temp.append(amp)
+
+    # remove duplicate
+    unique_amp_bands = []
+    for amp in _temp:
+        if amp not in unique_amp_bands:
+            unique_amp_bands.append(amp)
+    if unique_amp_bands:
+        common_range = unique_amp_bands[0]
+    else:
+        if default_band_f_min is None or default_band_f_max is None:
+            return []
+        common_range = [{'f_min': default_band_f_min, 'f_max': default_band_f_max}]
+    for bands in unique_amp_bands:
+        common_range = [{'f_min': max(first['f_min'], second['f_min']), 'f_max': min(first['f_max'], second['f_max'])}
+                        for first in common_range for second in bands
+                        if max(first['f_min'], second['f_min']) < min(first['f_max'], second['f_max'])]
+    return sorted(common_range, key=lambda x: x['f_min'])
+
+
+def transform_data(data: str) -> Union[List[int], None]:
+    """Transforms a float into an list of one integer or a string separated by "|" into a list of integers.
+
+    Args:
+        data (float or str): The data to transform.
+
+    Returns:
+        list of int: The transformed data as a list of integers.
+
+    Examples:
+        >>> transform_data(5.0)
+        [5]
+
+        >>> transform_data('1 | 2 | 3')
+        [1, 2, 3]
+    """
+    if isinstance(data, float):
+        return [int(data)]
+    if isinstance(data, str):
+        return [int(x) for x in data.split(' | ')]
+    return None

gnpy/example-data/Juniper-BoosterHG.json

@@ -1,160 +1,160 @@
 {
-    "nf_fit_coeff": [
-        0.0008,
-        0.0272,
-        -0.2249,
-        6.4902
-    ],
-    "f_min": 191.4e12,
-    "f_max": 196.1e12,
-    "nf_ripple": [
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0
-    ],
-    "gain_ripple": [
-        -0.15656302345061,
-        -0.22244242043552,
-        -0.25188965661642,
-        -0.23575900335007,
-        -0.20897508375209,
-        -0.19440221943049,
-        -0.18324644053602,
-        -0.18053287269681,
-        -0.17113588777219,
-        -0.15460322445561,
-        -0.13550774706866,
-        -0.10606051088777,
-        -0.0765630234506,
-        -0.04962835008375,
-        -0.01319618927973,
-        0.01027114740367,
-        0.03378873534338,
-        0.04961788107202,
-        0.04494451423784,
-        0.0399193886097,
-        0.01584903685091,
-        -0.00420121440538,
-        -0.01847257118928,
-        -0.02475397822447,
-        -0.01053287269681,
-        0.01509526800668,
-        0.05921587102177,
-        0.1191656197655,
-        0.18147717755444,
-        0.23579878559464,
-        0.26941687604691,
-        0.27836159966498,
-        0.26956762981574,
-        0.23826109715241,
-        0.18936662479061,
-        0.1204721524288,
-        0.0453465242881,
-        -0.00877407872698,
-        -0.02199015912898,
-        0.00107516750419,
-        0.02795958961474,
-        0.02740682579566,
-        -0.01028161641541,
-        -0.05982935510889,
-        -0.06701528475711,
-        0.00223094639866,
-        0.14157768006701,
-        0.15017064489112
-    ],
-    "dgt": [
-        1.0,
-        1.03941448941778,
-        1.07773189112355,
-        1.11575888725852,
-        1.15209185089701,
-        1.18632744096844,
-        1.21911100318577,
-        1.24931318255134,
-        1.27657903892303,
-        1.30069883494415,
-        1.32210817897091,
-        1.3405812000038,
-        1.35690844654118,
-        1.3710092503689,
-        1.38430337205545,
-        1.3966294751726,
-        1.40864903907609,
-        1.42089447397912,
-        1.43476940680732,
-        1.44977369463316,
-        1.46637521309853,
-        1.48420288841848,
-        1.50335352244996,
-        1.5242627235492,
-        1.54578500307573,
-        1.56750088631614,
-        1.58973304612691,
-        1.61073904908309,
-        1.63068023161292,
-        1.64799163036252,
-        1.66286684904577,
-        1.6761448370895,
-        1.68845480656382,
-        1.70379790088896,
-        1.72461030013125,
-        1.75428006928365,
-        1.79748596476494,
-        1.85543800978691,
-        1.92915262384742,
-        2.01414465424155,
-        2.10336369905543,
-        2.19013043016015,
-        2.26678136721453,
-        2.33147727493671,
-        2.38192717604575,
-        2.41879254989742,
-        2.44342862248888,
-        2.4553191172498
-    ]
+  "nf_fit_coeff": [
+    0.0008,
+    0.0272,
+    -0.2249,
+    6.4902
+  ],
+  "f_min": 191.4e12,
+  "f_max": 196.1e12,
+  "nf_ripple": [
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0,
+    0.0
+  ],
+  "gain_ripple": [
+    -0.15656302345061,
+    -0.22244242043552,
+    -0.25188965661642,
+    -0.23575900335007,
+    -0.20897508375209,
+    -0.19440221943049,
+    -0.18324644053602,
+    -0.18053287269681,
+    -0.17113588777219,
+    -0.15460322445561,
+    -0.13550774706866,
+    -0.10606051088777,
+    -0.0765630234506,
+    -0.04962835008375,
+    -0.01319618927973,
+    0.01027114740367,
+    0.03378873534338,
+    0.04961788107202,
+    0.04494451423784,
+    0.0399193886097,
+    0.01584903685091,
+    -0.00420121440538,
+    -0.01847257118928,
+    -0.02475397822447,
+    -0.01053287269681,
+    0.01509526800668,
+    0.05921587102177,
+    0.1191656197655,
+    0.18147717755444,
+    0.23579878559464,
+    0.26941687604691,
+    0.27836159966498,
+    0.26956762981574,
+    0.23826109715241,
+    0.18936662479061,
+    0.1204721524288,
+    0.0453465242881,
+    -0.00877407872698,
+    -0.02199015912898,
+    0.00107516750419,
+    0.02795958961474,
+    0.02740682579566,
+    -0.01028161641541,
+    -0.05982935510889,
+    -0.06701528475711,
+    0.00223094639866,
+    0.14157768006701,
+    0.15017064489112
+  ],
+  "dgt": [
+    1.0,
+    1.03941448941778,
+    1.07773189112355,
+    1.11575888725852,
+    1.15209185089701,
+    1.18632744096844,
+    1.21911100318577,
+    1.24931318255134,
+    1.27657903892303,
+    1.30069883494415,
+    1.32210817897091,
+    1.3405812000038,
+    1.35690844654118,
+    1.3710092503689,
+    1.38430337205545,
+    1.3966294751726,
+    1.40864903907609,
+    1.42089447397912,
+    1.43476940680732,
+    1.44977369463316,
+    1.46637521309853,
+    1.48420288841848,
+    1.50335352244996,
+    1.5242627235492,
+    1.54578500307573,
+    1.56750088631614,
+    1.58973304612691,
+    1.61073904908309,
+    1.63068023161292,
+    1.64799163036252,
+    1.66286684904577,
+    1.6761448370895,
+    1.68845480656382,
+    1.70379790088896,
+    1.72461030013125,
+    1.75428006928365,
+    1.79748596476494,
+    1.85543800978691,
+    1.92915262384742,
+    2.01414465424155,
+    2.10336369905543,
+    2.19013043016015,
+    2.26678136721453,
+    2.33147727493671,
+    2.38192717604575,
+    2.41879254989742,
+    2.44342862248888,
+    2.4553191172498
+  ]
 }

gnpy/example-data/default_edfa_config.json

@@ -1,106 +1,108 @@
 {
-    "nf_ripple": [
-        0.0
-    ],
-    "gain_ripple": [
-        0.0
-    ],
-    "dgt": [
-        1.0,
-        1.017807767853702,
-        1.0356155337864215,
-        1.0534217504465226,
-        1.0712204022764056,
-        1.0895983485572227,
-        1.108555289615659,
-        1.1280891949729075,
-        1.1476135933863398,
-        1.1672278304018044,
-        1.1869318618366975,
-        1.2067249615595257,
-        1.2264996957264114,
-        1.2428104897182262,
-        1.2556591482982988,
-        1.2650555289898042,
-        1.2744470198196236,
-        1.2838336236692311,
-        1.2932153453410835,
-        1.3040618749785347,
-        1.316383926863083,
-        1.3301807335621048,
-        1.3439818461440451,
-        1.3598972673004606,
-        1.3779439775587023,
-        1.3981208704326855,
-        1.418273806730323,
-        1.4340878115214444,
-        1.445565137158368,
-        1.45273959485914,
-        1.4599103316162523,
-        1.4670307626366115,
-        1.474100442252211,
-        1.48111939735681,
-        1.488134243479226,
-        1.495145456062699,
-        1.502153039909686,
-        1.5097346239790443,
-        1.5178910621476225,
-        1.5266220576235803,
-        1.5353620432989845,
-        1.545374152761467,
-        1.5566577309558969,
-        1.569199764184379,
-        1.5817353179379183,
-        1.5986915141218316,
-        1.6201194134191075,
-        1.6460167077689267,
-        1.6719047669939942,
-        1.6918150918099673,
-        1.7057507692361864,
-        1.7137640932265894,
-        1.7217732861435076,
-        1.7297783508684146,
-        1.737780757913635,
-        1.7459181197626403,
-        1.7541903672600494,
-        1.7625959636196327,
-        1.7709972329654864,
-        1.7793941781790852,
-        1.7877868031023945,
-        1.7961751115773796,
-        1.8045606557581335,
-        1.8139629377087627,
-        1.824381436842932,
-        1.835814081380705,
-        1.847275503201129,
-        1.862235672444246,
-        1.8806927939516411,
-        1.9026104247588487,
-        1.9245345552113182,
-        1.9482128147680253,
-        1.9736443063300082,
-        2.0008103857988204,
-        2.0279625371819305,
-        2.055100772005235,
-        2.082225099873648,
-        2.1183028432496016,
-        2.16337565384239,
-        2.2174389328192197,
-        2.271520771371253,
-        2.322373696229342,
-        2.3699990328716107,
-        2.414398437185221,
-        2.4587748041127506,
-        2.499446286796604,
-        2.5364027376452056,
-        2.5696460593920065,
-        2.602860350286428,
-        2.630396440815385,
-        2.6521732021128046,
-        2.6681935771243177,
-        2.6841217449620203,
-        2.6947834587664494,
-        2.705443819238505,
-        2.714526681131686
-    ]
+  "nf_ripple": [
+    0.0
+  ],
+  "gain_ripple": [
+    0.0
+  ],
+  "f_min": 191.275e12,
+  "f_max": 196.125e12,
+  "dgt": [
+    1.0,
+    1.017807767853702,
+    1.0356155337864215,
+    1.0534217504465226,
+    1.0712204022764056,
+    1.0895983485572227,
+    1.108555289615659,
+    1.1280891949729075,
+    1.1476135933863398,
+    1.1672278304018044,
+    1.1869318618366975,
+    1.2067249615595257,
+    1.2264996957264114,
+    1.2428104897182262,
+    1.2556591482982988,
+    1.2650555289898042,
+    1.2744470198196236,
+    1.2838336236692311,
+    1.2932153453410835,
+    1.3040618749785347,
+    1.316383926863083,
+    1.3301807335621048,
+    1.3439818461440451,
+    1.3598972673004606,
+    1.3779439775587023,
+    1.3981208704326855,
+    1.418273806730323,
+    1.4340878115214444,
+    1.445565137158368,
+    1.45273959485914,
+    1.4599103316162523,
+    1.4670307626366115,
+    1.474100442252211,
+    1.48111939735681,
+    1.488134243479226,
+    1.495145456062699,
+    1.502153039909686,
+    1.5097346239790443,
+    1.5178910621476225,
+    1.5266220576235803,
+    1.5353620432989845,
+    1.545374152761467,
+    1.5566577309558969,
+    1.569199764184379,
+    1.5817353179379183,
+    1.5986915141218316,
+    1.6201194134191075,
+    1.6460167077689267,
+    1.6719047669939942,
+    1.6918150918099673,
+    1.7057507692361864,
+    1.7137640932265894,
+    1.7217732861435076,
+    1.7297783508684146,
+    1.737780757913635,
+    1.7459181197626403,
+    1.7541903672600494,
+    1.7625959636196327,
+    1.7709972329654864,
+    1.7793941781790852,
+    1.7877868031023945,
+    1.7961751115773796,
+    1.8045606557581335,
+    1.8139629377087627,
+    1.824381436842932,
+    1.835814081380705,
+    1.847275503201129,
+    1.862235672444246,
+    1.8806927939516411,
+    1.9026104247588487,
+    1.9245345552113182,
+    1.9482128147680253,
+    1.9736443063300082,
+    2.0008103857988204,
+    2.0279625371819305,
+    2.055100772005235,
+    2.082225099873648,
+    2.1183028432496016,
+    2.16337565384239,
+    2.2174389328192197,
+    2.271520771371253,
+    2.322373696229342,
+    2.3699990328716107,
+    2.414398437185221,
+    2.4587748041127506,
+    2.499446286796604,
+    2.5364027376452056,
+    2.5696460593920065,
+    2.602860350286428,
+    2.630396440815385,
+    2.6521732021128046,
+    2.6681935771243177,
+    2.6841217449620203,
+    2.6947834587664494,
+    2.705443819238505,
+    2.714526681131686
+  ]
 }

gnpy/example-data/edfa_example_network.json

@@ -1,80 +1,80 @@
 {
-    "network_name": "EDFA Example Network - P2P",
-    "elements": [{
-            "uid": "Site_A",
-            "type": "Transceiver",
-            "metadata": {
-                "location": {
-                    "city": "Site A",
-                    "region": "",
-                    "latitude": 0,
-                    "longitude": 0
-                }
-            }
-        },
-        {
-            "uid": "Span1",
-            "type": "Fiber",
-            "type_variety": "SSMF",
-            "params": {
-                "length": 80,
-                "loss_coef": 0.2,
-                "length_units": "km",
-                "att_in": 0,
-                "con_in": 0.5,
-                "con_out": 0.5                
-                },
-            "metadata": {
-                "location": {
-                    "region": "",
-                    "latitude": 1,
-                    "longitude": 0
-                }
-            }
-        },
-        {
-            "uid": "Edfa1",
-            "type": "Edfa",
-            "type_variety": "std_low_gain",            
-            "operational": {
-                "gain_target": 17,
-                "tilt_target": 0,
-                "out_voa": 0
-            },
-            "metadata": {
-                "location": {
-                    "region": "",
-                    "latitude": 2,
-                    "longitude": 0
-                }
-            }
-        },
-        {
-            "uid": "Site_B",
-            "type": "Transceiver",
-            "metadata": {
-                "location": {
-                    "city": "Site B",
-                    "region": "",
-                    "latitude": 2,
-                    "longitude": 0
-                }
-            }
+  "network_name": "EDFA Example Network - P2P",
+  "elements": [
+    {
+      "uid": "Site_A",
+      "type": "Transceiver",
+      "metadata": {
+        "location": {
+          "city": "Site A",
+          "region": "",
+          "latitude": 0,
+          "longitude": 0
         }
-
-    ],
-    "connections": [{
-            "from_node": "Site_A",
-            "to_node": "Span1"
-        },
-        {
-            "from_node": "Span1",
-            "to_node": "Edfa1"
-        },
-        {
-            "from_node": "Edfa1",
-            "to_node": "Site_B"
+      }
+    },
+    {
+      "uid": "Span1",
+      "type": "Fiber",
+      "type_variety": "SSMF",
+      "params": {
+        "length": 80,
+        "loss_coef": 0.2,
+        "length_units": "km",
+        "att_in": 0,
+        "con_in": 0.5,
+        "con_out": 0.5
+      },
+      "metadata": {
+        "location": {
+          "region": "",
+          "latitude": 1,
+          "longitude": 0
         }
-
-    ]
+      }
+    },
+    {
+      "uid": "Edfa1",
+      "type": "Edfa",
+      "type_variety": "std_low_gain",
+      "operational": {
+        "gain_target": 17,
+        "tilt_target": 0,
+        "out_voa": 0
+      },
+      "metadata": {
+        "location": {
+          "region": "",
+          "latitude": 2,
+          "longitude": 0
+        }
+      }
+    },
+    {
+      "uid": "Site_B",
+      "type": "Transceiver",
+      "metadata": {
+        "location": {
+          "city": "Site B",
+          "region": "",
+          "latitude": 2,
+          "longitude": 0
+        }
+      }
+    }
+  ],
+  "connections": [
+    {
+      "from_node": "Site_A",
+      "to_node": "Span1"
+    },
+    {
+      "from_node": "Span1",
+      "to_node": "Edfa1"
+    },
+    {
+      "from_node": "Edfa1",
+      "to_node": "Site_B"
+    }
+  ]
 }

gnpy/example-data/eqpt_config.json

@@ -1,323 +1,443 @@
-{     "Edfa":[{
-            "type_variety": "high_detail_model_example",
-            "type_def": "advanced_model",
-            "gain_flatmax": 25,
-            "gain_min": 15,
-            "p_max": 21,
-            "advanced_config_from_json": "std_medium_gain_advanced_config.json",
-            "out_voa_auto": false,
-            "allowed_for_design": false
-            },                  {
-            "type_variety": "Juniper_BoosterHG",
-            "type_def": "advanced_model",
-            "gain_flatmax": 25,
-            "gain_min": 10,
-            "p_max": 21,
-            "advanced_config_from_json": "Juniper-BoosterHG.json",
-            "out_voa_auto": false,
-            "allowed_for_design": false
-            }, 
-            {
-            "type_variety": "operator_model_example",
-            "type_def": "variable_gain",
-            "gain_flatmax": 26,
-            "gain_min": 15,
-            "p_max": 23,
-            "nf_min": 6,
-            "nf_max": 10,
-            "out_voa_auto": false,
-            "allowed_for_design": false           
-            },
-            {
-            "type_variety": "openroadm_ila_low_noise",
-            "type_def": "openroadm",
-            "gain_flatmax": 27,
-            "gain_min": 0,
-            "p_max": 22,
-            "nf_coef": [-8.104e-4,-6.221e-2,-5.889e-1,37.62],
-            "allowed_for_design": false
-            },          
-            {
-            "type_variety": "openroadm_ila_standard",
-            "type_def": "openroadm",
-            "gain_flatmax": 27,
-            "gain_min": 0,
-            "p_max": 22,
-            "nf_coef": [-5.952e-4,-6.250e-2,-1.071,28.99],
-            "allowed_for_design": false
-            },
-            {
-            "type_variety": "openroadm_mw_mw_preamp",
-            "type_def": "openroadm_preamp",
-            "gain_flatmax": 27,
-            "gain_min": 0,
-            "p_max": 22,
-            "allowed_for_design": false
-            },
-            {
-            "type_variety": "openroadm_mw_mw_preamp_typical_ver5",
-            "type_def": "openroadm",
-            "gain_flatmax": 27,
-            "gain_min": 0,
-            "p_max": 22,
-            "nf_coef": [-5.952e-4,-6.250e-2,-1.071,28.99],
-            "allowed_for_design": false
-            },
-            {
-            "type_variety": "openroadm_mw_mw_preamp_worstcase_ver5",
-            "type_def": "openroadm",
-            "gain_flatmax": 27,
-            "gain_min": 0,
-            "p_max": 22,
-            "nf_coef": [-5.952e-4,-6.250e-2,-1.071,27.99],
-            "allowed_for_design": false
-            },
-            {
-            "type_variety": "openroadm_mw_mw_booster",
-            "type_def": "openroadm_booster",
-            "gain_flatmax": 32,
-            "gain_min": 0,
-            "p_max": 22,
-            "allowed_for_design": false
-            },
-            {
-            "type_variety": "std_high_gain",
-            "type_def": "variable_gain",
-            "gain_flatmax": 35,
-            "gain_min": 25,
-            "p_max": 21,
-            "nf_min": 5.5,
-            "nf_max": 7,
-            "out_voa_auto": false,
-            "allowed_for_design": true
-            },            
-            {
-            "type_variety": "std_medium_gain",
-            "type_def": "variable_gain",
-            "gain_flatmax": 26,
-            "gain_min": 15,
-            "p_max": 23,
-            "nf_min": 6,
-            "nf_max": 10,
-            "out_voa_auto": false,
-            "allowed_for_design": true
-            },
-            {
-            "type_variety": "std_low_gain",
-            "type_def": "variable_gain",
-            "gain_flatmax": 16,
-            "gain_min": 8,
-            "p_max": 23,
-            "nf_min": 6.5,
-            "nf_max": 11,
-            "out_voa_auto": false,
-            "allowed_for_design": true
-            },
-            {
-            "type_variety": "high_power",
-            "type_def": "variable_gain",
-            "gain_flatmax": 16,
-            "gain_min": 8,
-            "p_max": 25,
-            "nf_min": 9,
-            "nf_max": 15,
-            "out_voa_auto": false,
-            "allowed_for_design": false
-            },            
-            {
-            "type_variety": "std_fixed_gain",
-            "type_def": "fixed_gain",
-            "gain_flatmax": 21,
-            "gain_min": 20,
-            "p_max": 21,
-            "nf0": 5.5,
-            "allowed_for_design": false
-            },
-            {
-            "type_variety": "4pumps_raman",
-            "type_def": "fixed_gain",
-            "gain_flatmax": 12,
-            "gain_min": 12,
-            "p_max": 21,
-            "nf0": -1,
-            "allowed_for_design": false
-            },   
-            {
-            "type_variety": "hybrid_4pumps_lowgain",
-            "type_def": "dual_stage",
-            "raman": true,
-            "gain_min": 25,
-            "preamp_variety": "4pumps_raman",
-            "booster_variety": "std_low_gain",
-            "allowed_for_design": true
-            },
-            {
-            "type_variety": "hybrid_4pumps_mediumgain",
-            "type_def": "dual_stage",
-            "raman": true,
-            "gain_min": 25,
-            "preamp_variety": "4pumps_raman",
-            "booster_variety": "std_medium_gain",
-            "allowed_for_design": true
-            },            
-            {
-            "type_variety": "medium+low_gain",
-            "type_def": "dual_stage",
-            "gain_min": 25,
-            "preamp_variety": "std_medium_gain",
-            "booster_variety": "std_low_gain",
-            "allowed_for_design": true
-            },
-            {
-            "type_variety": "medium+high_power",
-            "type_def": "dual_stage",
-            "gain_min": 25,
-            "preamp_variety": "std_medium_gain",
-            "booster_variety": "high_power",
-            "allowed_for_design": false
-            }                                  
+{
+  "Edfa": [
+    {
+      "type_variety": "high_detail_model_example",
+      "type_def": "advanced_model",
+      "gain_flatmax": 25,
+      "gain_min": 15,
+      "p_max": 21,
+      "advanced_config_from_json": "std_medium_gain_advanced_config.json",
+      "out_voa_auto": false,
+      "allowed_for_design": false
+    },
+    {
+      "type_variety": "Juniper_BoosterHG",
+      "type_def": "advanced_model",
+      "gain_flatmax": 25,
+      "gain_min": 10,
+      "p_max": 21,
+      "advanced_config_from_json": "Juniper-BoosterHG.json",
+      "out_voa_auto": false,
+      "allowed_for_design": false
+    },
+    {
+      "type_variety": "operator_model_example",
+      "type_def": "variable_gain",
+      "gain_flatmax": 26,
+      "gain_min": 15,
+      "p_max": 23,
+      "nf_min": 6,
+      "nf_max": 10,
+      "out_voa_auto": false,
+      "allowed_for_design": false
+    },
+    {
+      "type_variety": "openroadm_ila_low_noise",
+      "type_def": "openroadm",
+      "gain_flatmax": 27,
+      "gain_min": 0,
+      "p_max": 22,
+      "nf_coef": [
+        -8.104e-4,
+        -6.221e-2,
+        -5.889e-1,
+        37.62
       ],
-      "Fiber":[{
-            "type_variety": "SSMF",
-            "dispersion": 1.67e-05,
-            "effective_area": 83e-12,
-            "pmd_coef": 1.265e-15
-            },
-            {
-            "type_variety": "NZDF",
-            "dispersion": 0.5e-05,
-            "effective_area": 72e-12,
-            "pmd_coef": 1.265e-15
-            },
-            {
-            "type_variety": "LOF",
-            "dispersion": 2.2e-05,
-            "effective_area": 125e-12,
-            "pmd_coef": 1.265e-15
-            }
+      "allowed_for_design": false
+    },
+    {
+      "type_variety": "openroadm_ila_standard",
+      "type_def": "openroadm",
+      "gain_flatmax": 27,
+      "gain_min": 0,
+      "p_max": 22,
+      "nf_coef": [
+        -5.952e-4,
+        -6.250e-2,
+        -1.071,
+        28.99
       ],
-      "RamanFiber":[{
-            "type_variety": "SSMF",
-            "dispersion": 1.67e-05,
-            "effective_area": 83e-12,
-            "pmd_coef": 1.265e-15
-            }
+      "allowed_for_design": false
+    },
+    {
+      "type_variety": "openroadm_mw_mw_preamp",
+      "type_def": "openroadm_preamp",
+      "gain_flatmax": 27,
+      "gain_min": 0,
+      "p_max": 22,
+      "allowed_for_design": false
+    },
+    {
+      "type_variety": "openroadm_mw_mw_preamp_typical_ver5",
+      "type_def": "openroadm",
+      "gain_flatmax": 27,
+      "gain_min": 0,
+      "p_max": 22,
+      "nf_coef": [
+        -5.952e-4,
+        -6.250e-2,
+        -1.071,
+        28.99
       ],
-      "Span":[{
-            "power_mode":true,
-            "delta_power_range_db": [-2,3,0.5],
-            "max_fiber_lineic_loss_for_raman": 0.25,
-            "target_extended_gain": 2.5,
-            "max_length": 150,
-            "length_units": "km",
-            "max_loss": 28,
-            "padding": 10,
-            "EOL": 0,
-            "con_in": 0,
-            "con_out": 0
-            }
+      "allowed_for_design": false
+    },
+    {
+      "type_variety": "openroadm_mw_mw_preamp_worstcase_ver5",
+      "type_def": "openroadm",
+      "gain_flatmax": 27,
+      "gain_min": 0,
+      "p_max": 22,
+      "nf_coef": [
+        -5.952e-4,
+        -6.250e-2,
+        -1.071,
+        27.99
       ],
-      "Roadm":[{
-            "target_pch_out_db": -20,
-            "add_drop_osnr": 38,
-            "pmd": 0,
-            "pdl": 0,
-            "restrictions": {
-                            "preamp_variety_list":[],
-                            "booster_variety_list":[]
-                            }            
-            }],
-      "SI":[{
-            "f_min": 191.3e12,
-            "baud_rate": 32e9,
-            "f_max":195.1e12,
-            "spacing": 50e9,
-            "power_dbm": 0,
-            "power_range_db": [0,0,1],
-            "roll_off": 0.15,
-            "tx_osnr": 40,
-            "sys_margins": 2
-            }],
-      "Transceiver":[
+      "allowed_for_design": false
+    },
+    {
+      "type_variety": "openroadm_mw_mw_booster",
+      "type_def": "openroadm_booster",
+      "gain_flatmax": 32,
+      "gain_min": 0,
+      "p_max": 22,
+      "allowed_for_design": false
+    },
+    {
+      "type_variety": "std_high_gain",
+      "type_def": "variable_gain",
+      "gain_flatmax": 35,
+      "gain_min": 25,
+      "p_max": 21,
+      "nf_min": 5.5,
+      "nf_max": 7,
+      "out_voa_auto": false,
+      "allowed_for_design": true
+    },
+    {
+      "type_variety": "std_medium_gain",
+      "type_def": "variable_gain",
+      "gain_flatmax": 26,
+      "gain_min": 15,
+      "p_max": 23,
+      "nf_min": 6,
+      "nf_max": 10,
+      "out_voa_auto": false,
+      "allowed_for_design": true
+    },
+    {
+      "type_variety": "std_low_gain",
+      "type_def": "variable_gain",
+      "gain_flatmax": 16,
+      "gain_min": 8,
+      "p_max": 23,
+      "nf_min": 6.5,
+      "nf_max": 11,
+      "out_voa_auto": false,
+      "allowed_for_design": true
+    },
+    {
+      "type_variety": "high_power",
+      "type_def": "variable_gain",
+      "gain_flatmax": 16,
+      "gain_min": 8,
+      "p_max": 25,
+      "nf_min": 9,
+      "nf_max": 15,
+      "out_voa_auto": false,
+      "allowed_for_design": false
+    },
+    {
+      "type_variety": "std_fixed_gain",
+      "type_def": "fixed_gain",
+      "gain_flatmax": 21,
+      "gain_min": 20,
+      "p_max": 21,
+      "nf0": 5.5,
+      "allowed_for_design": false
+    },
+    {
+      "type_variety": "4pumps_raman",
+      "type_def": "fixed_gain",
+      "gain_flatmax": 12,
+      "gain_min": 12,
+      "p_max": 21,
+      "nf0": -1,
+      "allowed_for_design": false
+    },
+    {
+      "type_variety": "hybrid_4pumps_lowgain",
+      "type_def": "dual_stage",
+      "raman": true,
+      "gain_min": 25,
+      "preamp_variety": "4pumps_raman",
+      "booster_variety": "std_low_gain",
+      "allowed_for_design": true
+    },
+    {
+      "type_variety": "hybrid_4pumps_mediumgain",
+      "type_def": "dual_stage",
+      "raman": true,
+      "gain_min": 25,
+      "preamp_variety": "4pumps_raman",
+      "booster_variety": "std_medium_gain",
+      "allowed_for_design": true
+    },
+    {
+      "type_variety": "medium+low_gain",
+      "type_def": "dual_stage",
+      "gain_min": 25,
+      "preamp_variety": "std_medium_gain",
+      "booster_variety": "std_low_gain",
+      "allowed_for_design": true
+    },
+    {
+      "type_variety": "medium+high_power",
+      "type_def": "dual_stage",
+      "gain_min": 25,
+      "preamp_variety": "std_medium_gain",
+      "booster_variety": "high_power",
+      "allowed_for_design": false
+    }
+  ],
+  "Fiber": [
+    {
+      "type_variety": "SSMF",
+      "dispersion": 1.67e-05,
+      "effective_area": 83e-12,
+      "pmd_coef": 1.265e-15
+    },
+    {
+      "type_variety": "NZDF",
+      "dispersion": 0.5e-05,
+      "effective_area": 72e-12,
+      "pmd_coef": 1.265e-15
+    },
+    {
+      "type_variety": "LOF",
+      "dispersion": 2.2e-05,
+      "effective_area": 125e-12,
+      "pmd_coef": 1.265e-15
+    }
+  ],
+  "RamanFiber": [
+    {
+      "type_variety": "SSMF",
+      "dispersion": 1.67e-05,
+      "effective_area": 83e-12,
+      "pmd_coef": 1.265e-15
+    }
+  ],
+  "Span": [
+    {
+      "power_mode": true,
+      "delta_power_range_db": [
+        -2,
+        3,
+        0.5
+      ],
+      "max_fiber_lineic_loss_for_raman": 0.25,
+      "target_extended_gain": 2.5,
+      "max_length": 150,
+      "length_units": "km",
+      "max_loss": 28,
+      "padding": 10,
+      "EOL": 0,
+      "con_in": 0,
+      "con_out": 0
+    }
+  ],
+  "Roadm": [
+    {
+      "target_pch_out_db": -20,
+      "add_drop_osnr": 38,
+      "pmd": 0,
+      "pdl": 0,
+      "restrictions": {
+        "preamp_variety_list": [],
+        "booster_variety_list": []
+      }
+    },
+    {
+      "type_variety": "roadm_type_1",
+      "target_pch_out_db": -18,
+      "add_drop_osnr": 35,
+      "pmd": 0,
+      "pdl": 0,
+      "restrictions": {
+        "preamp_variety_list": [],
+        "booster_variety_list": []
+      },
+      "roadm-path-impairments": []
+    },
+    {
+      "type_variety": "detailed_impairments",
+      "target_pch_out_db": -20,
+      "add_drop_osnr": 38,
+      "pmd": 0,
+      "pdl": 0,
+      "restrictions": {
+        "preamp_variety_list": [],
+        "booster_variety_list": []
+      },
+      "roadm-path-impairments": [
+        {
+          "roadm-path-impairments-id": 0,
+          "roadm-express-path": [
             {
-            "type_variety": "vendorA_trx-type1",
-            "frequency":{
-                        "min": 191.35e12,
-                        "max": 196.1e12
-                        },
-            "mode":[
-                       {
-
-                       "format": "mode 1",
-                       "baud_rate": 32e9,
-                       "OSNR": 11,
-                       "bit_rate": 100e9,
-                       "roll_off": 0.15,
-                       "tx_osnr": 40,
-                       "min_spacing": 37.5e9,
-                       "cost":1
-                       },
-                       {
-                       "format": "mode 2",
-                       "baud_rate": 66e9,
-                       "OSNR": 15,
-                       "bit_rate": 200e9,
-                       "roll_off": 0.15,
-                       "tx_osnr": 40,
-                       "min_spacing": 75e9,
-                       "cost":1
-                       }
-                   ]
-            },
-            {
-            "type_variety": "Voyager",
-            "frequency":{
-                        "min": 191.35e12,
-                        "max": 196.1e12
-                        },
-            "mode":[
-                       {
-                       "format": "mode 1",
-                       "baud_rate": 32e9,
-                       "OSNR": 12,
-                       "bit_rate": 100e9,
-                       "roll_off": 0.15,
-                       "tx_osnr": 40,
-                       "min_spacing": 37.5e9,
-                       "cost":1
-                       },
-                       {
-                       "format": "mode 3",
-                       "baud_rate": 44e9,
-                       "OSNR": 18,
-                       "bit_rate": 300e9,
-                       "roll_off": 0.15,
-                       "tx_osnr": 40,
-                       "min_spacing": 62.5e9,
-                       "cost":1
-                       },
-                       {
-                       "format": "mode 2",
-                       "baud_rate": 66e9,
-                       "OSNR": 21,
-                       "bit_rate": 400e9,
-                       "roll_off": 0.15,
-                       "tx_osnr": 40,
-                       "min_spacing": 75e9,
-                       "cost":1
-                       },
-                       {
-                       "format": "mode 4",
-                       "baud_rate": 66e9,
-                       "OSNR": 16,
-                       "bit_rate": 200e9,
-                       "roll_off": 0.15,
-                       "tx_osnr": 40,
-                       "min_spacing": 75e9,
-                       "cost":1
-                       }
-                   ]
+              "frequency-range": {
+                "lower-frequency": 191.3e12,
+                "upper-frequency": 196.1e12
+              },
+              "roadm-pmd": 0,
+              "roadm-cd": 0,
+              "roadm-pdl": 0,
+              "roadm-inband-crosstalk": 0,
+              "roadm-maxloss": 16.5
             }
+          ]
+        },
+        {
+          "roadm-path-impairments-id": 1,
+          "roadm-add-path": [
+            {
+              "frequency-range": {
+                "lower-frequency": 191.3e12,
+                "upper-frequency": 196.1e12
+              },
+              "roadm-pmd": 0,
+              "roadm-cd": 0,
+              "roadm-pdl": 0,
+              "roadm-inband-crosstalk": 0,
+              "roadm-maxloss": 11.5,
+              "roadm-pmax": 2.5,
+              "roadm-osnr": 41,
+              "roadm-noise-figure": 23
+            }
+          ]
+        },
+        {
+          "roadm-path-impairments-id": 2,
+          "roadm-drop-path": [
+            {
+              "frequency-range": {
+                "lower-frequency": 191.3e12,
+                "upper-frequency": 196.1e12
+              },
+              "roadm-pmd": 0,
+              "roadm-cd": 0,
+              "roadm-pdl": 0,
+              "roadm-inband-crosstalk": 0,
+              "roadm-maxloss": 11.5,
+              "roadm-minloss": 7.5,
+              "roadm-typloss": 10,
+              "roadm-pmin": -13.5,
+              "roadm-pmax": -9.5,
+              "roadm-ptyp": -12,
+              "roadm-osnr": 41,
+              "roadm-noise-figure": 15
+            }
+          ]
+        }
       ]
-
+    }
+  ],
+  "SI": [
+    {
+      "f_min": 191.3e12,
+      "baud_rate": 32e9,
+      "f_max": 195.1e12,
+      "spacing": 50e9,
+      "power_dbm": 0,
+      "power_range_db": [
+        0,
+        0,
+        1
+      ],
+      "tx_power_dbm": 0,
+      "roll_off": 0.15,
+      "tx_osnr": 40,
+      "sys_margins": 2
+    }
+  ],
+  "Transceiver": [
+    {
+      "type_variety": "vendorA_trx-type1",
+      "frequency": {
+        "min": 191.35e12,
+        "max": 196.1e12
+      },
+      "mode": [
+        {
+          "format": "mode 1",
+          "baud_rate": 32e9,
+          "OSNR": 11,
+          "bit_rate": 100e9,
+          "roll_off": 0.15,
+          "tx_osnr": 40,
+          "min_spacing": 37.5e9,
+          "cost": 1
+        },
+        {
+          "format": "mode 2",
+          "baud_rate": 66e9,
+          "OSNR": 15,
+          "bit_rate": 200e9,
+          "roll_off": 0.15,
+          "tx_osnr": 40,
+          "min_spacing": 75e9,
+          "cost": 1
+        }
+      ]
+    },
+    {
+      "type_variety": "Voyager",
+      "frequency": {
+        "min": 191.35e12,
+        "max": 196.1e12
+      },
+      "mode": [
+        {
+          "format": "mode 1",
+          "baud_rate": 32e9,
+          "OSNR": 12,
+          "bit_rate": 100e9,
+          "roll_off": 0.15,
+          "tx_osnr": 40,
+          "min_spacing": 37.5e9,
+          "cost": 1
+        },
+        {
+          "format": "mode 3",
+          "baud_rate": 44e9,
+          "OSNR": 18,
+          "bit_rate": 300e9,
+          "roll_off": 0.15,
+          "tx_osnr": 40,
+          "min_spacing": 62.5e9,
+          "cost": 1
+        },
+        {
+          "format": "mode 2",
+          "baud_rate": 66e9,
+          "OSNR": 21,
+          "bit_rate": 400e9,
+          "roll_off": 0.15,
+          "tx_osnr": 40,
+          "min_spacing": 75e9,
+          "cost": 1
+        },
+        {
+          "format": "mode 4",
+          "baud_rate": 66e9,
+          "OSNR": 16,
+          "bit_rate": 200e9,
+          "roll_off": 0.15,
+          "tx_osnr": 40,
+          "min_spacing": 75e9,
+          "cost": 1
+        }
+      ]
+    }
+  ]
 }

gnpy/example-data/eqpt_config_multiband.json

@@ -0,0 +1,479 @@
+{
+    "Edfa": [
+        {
+            "type_variety": "std_high_gain",
+            "type_def": "variable_gain",
+            "gain_flatmax": 35,
+            "gain_min": 25,
+            "p_max": 21,
+            "nf_min": 5.5,
+            "nf_max": 7,
+            "out_voa_auto": false,
+            "allowed_for_design": true
+        },
+        {
+            "type_variety": "std_medium_gain",
+            "type_def": "variable_gain",
+            "gain_flatmax": 26,
+            "gain_min": 15,
+            "p_max": 23,
+            "nf_min": 6,
+            "nf_max": 10,
+            "out_voa_auto": false,
+            "allowed_for_design": true
+        },
+        {
+            "type_variety": "std_low_gain",
+            "type_def": "variable_gain",
+            "gain_flatmax": 16,
+            "gain_min": 8,
+            "p_max": 23,
+            "nf_min": 6.5,
+            "nf_max": 11,
+            "out_voa_auto": false,
+            "allowed_for_design": true
+        },
+        {
+            "type_variety": "high_power",
+            "type_def": "variable_gain",
+            "gain_flatmax": 16,
+            "gain_min": 8,
+            "p_max": 25,
+            "nf_min": 9,
+            "nf_max": 15,
+            "out_voa_auto": false,
+            "allowed_for_design": false
+        },
+        {
+            "type_variety": "std_fixed_gain",
+            "type_def": "fixed_gain",
+            "gain_flatmax": 21,
+            "gain_min": 20,
+            "p_max": 21,
+            "nf0": 5.5,
+            "allowed_for_design": false
+        },
+        {
+            "type_variety": "4pumps_raman",
+            "type_def": "fixed_gain",
+            "gain_flatmax": 12,
+            "gain_min": 12,
+            "p_max": 21,
+            "nf0": -1,
+            "allowed_for_design": false
+        },
+        {
+            "type_variety": "hybrid_4pumps_lowgain",
+            "type_def": "dual_stage",
+            "raman": true,
+            "gain_min": 25,
+            "preamp_variety": "4pumps_raman",
+            "booster_variety": "std_low_gain",
+            "allowed_for_design": true
+        },
+        {
+            "type_variety": "hybrid_4pumps_mediumgain",
+            "type_def": "dual_stage",
+            "raman": true,
+            "gain_min": 25,
+            "preamp_variety": "4pumps_raman",
+            "booster_variety": "std_medium_gain",
+            "allowed_for_design": true
+        },
+        {
+            "type_variety": "medium+low_gain",
+            "type_def": "dual_stage",
+            "gain_min": 25,
+            "preamp_variety": "std_medium_gain",
+            "booster_variety": "std_low_gain",
+            "allowed_for_design": true
+        },
+        {
+            "type_variety": "medium+high_power",
+            "type_def": "dual_stage",
+            "gain_min": 25,
+            "preamp_variety": "std_medium_gain",
+            "booster_variety": "high_power",
+            "allowed_for_design": false
+        },
+        {
+            "type_variety": "std_medium_gain_C",
+            "f_min": 191.225e12,
+            "f_max": 196.125e12,
+            "type_def": "variable_gain",
+            "gain_flatmax": 26,
+            "gain_min": 15,
+            "p_max": 21,
+            "nf_min": 6,
+            "nf_max": 10,
+            "out_voa_auto": false,
+            "allowed_for_design": false
+        },
+        {
+            "type_variety": "std_medium_gain_L",
+            "f_min": 186.5e12,
+            "f_max": 190.1e12,
+            "type_def": "variable_gain",
+            "gain_flatmax": 26,
+            "gain_min": 15,
+            "p_max": 21,
+            "nf_min": 6,
+            "nf_max": 10,
+            "out_voa_auto": false,
+            "allowed_for_design": true
+        },
+        {
+            "type_variety": "std_low_gain",
+            "f_min": 191.25e12,
+            "f_max": 196.15e12,
+            "type_def": "variable_gain",
+            "gain_flatmax": 16,
+            "gain_min": 8,
+            "p_max": 21,
+            "nf_min": 7,
+            "nf_max": 11,
+            "out_voa_auto": false,
+            "allowed_for_design": true
+        },
+        {
+            "type_variety": "std_low_gain_reduced_band",
+            "f_min": 192.25e12,
+            "f_max": 196.15e12,
+            "type_def": "variable_gain",
+            "gain_flatmax": 16,
+            "gain_min": 8,
+            "p_max": 21,
+            "nf_min": 7,
+            "nf_max": 11,
+            "out_voa_auto": false,
+            "allowed_for_design": true
+        },
+        {
+            "type_variety": "std_low_gain_bis",
+            "f_min": 191.25e12,
+            "f_max": 196.15e12,
+            "type_def": "variable_gain",
+            "gain_flatmax": 16,
+            "gain_min": 8,
+            "p_max": 21,
+            "nf_min": 6,
+            "nf_max": 10,
+            "out_voa_auto": false,
+            "allowed_for_design": true
+        },
+        {
+            "type_variety": "std_low_gain_L_ter",
+            "f_min": 186.55e12,
+            "f_max": 190.05e12,
+            "type_def": "variable_gain",
+            "gain_flatmax": 16,
+            "gain_min": 8,
+            "p_max": 16,
+            "nf_min": 7,
+            "nf_max": 11,
+            "out_voa_auto": false,
+            "allowed_for_design": true
+        },
+        {
+            "type_variety": "std_low_gain_L",
+            "f_min": 186.55e12,
+            "f_max": 190.05e12,
+            "type_def": "variable_gain",
+            "gain_flatmax": 16,
+            "gain_min": 8,
+            "p_max": 21,
+            "nf_min": 7,
+            "nf_max": 11,
+            "out_voa_auto": false,
+            "allowed_for_design": true
+        },
+        {
+            "type_variety": "std_low_gain_L_reduced_band",
+            "f_min": 187.3e12,
+            "f_max": 190.05e12,
+            "type_def": "variable_gain",
+            "gain_flatmax": 16,
+            "gain_min": 8,
+            "p_max": 21,
+            "nf_min": 7,
+            "nf_max": 11,
+            "out_voa_auto": false,
+            "allowed_for_design": true
+        },
+        {
+            "type_variety": "test",
+            "type_def": "variable_gain",
+            "gain_flatmax": 25,
+            "gain_min": 15,
+            "p_max": 21,
+            "nf_min": 5.8,
+            "nf_max": 10,
+            "out_voa_auto": false,
+            "allowed_for_design": true
+        },
+        {
+            "type_variety": "test_fixed_gain",
+            "type_def": "fixed_gain",
+            "gain_flatmax": 21,
+            "gain_min": 20,
+            "p_max": 21,
+            "nf0": 5,
+            "allowed_for_design": true
+        },
+        {
+            "type_variety": "std_booster",
+            "type_def": "fixed_gain",
+            "gain_flatmax": 21,
+            "gain_min": 20,
+            "p_max": 21,
+            "nf0": 5,
+            "allowed_for_design": false
+        },
+        {
+            "type_variety": "std_booster_L",
+            "f_min": 186.55e12,
+            "f_max": 190.05e12,
+            "type_def": "fixed_gain",
+            "gain_flatmax": 21,
+            "gain_min": 20,
+            "p_max": 21,
+            "nf0": 5,
+            "allowed_for_design": false
+        },
+        {
+            "type_variety": "std_booster_multiband",
+            "type_def": "multi_band",
+            "amplifiers": [
+                "std_booster",
+                "std_booster_L"
+            ],
+            "allowed_for_design": false
+        },
+        {
+            "type_variety": "std_medium_gain_multiband",
+            "type_def": "multi_band",
+            "amplifiers": [
+                "std_medium_gain_C",
+                "std_medium_gain_L"
+            ],
+            "allowed_for_design": false
+        },
+        {
+            "type_variety": "std_low_gain_multiband",
+            "type_def": "multi_band",
+            "amplifiers": [
+                "std_low_gain",
+                "std_low_gain_L"
+            ],
+            "allowed_for_design": false
+        },
+        {
+            "type_variety": "std_low_gain_multiband_ter",
+            "type_def": "multi_band",
+            "amplifiers": [
+                "std_low_gain",
+                "std_low_gain_L_ter"
+            ],
+            "allowed_for_design": false
+        },
+        {
+            "type_variety": "std_low_gain_multiband_bis",
+            "type_def": "multi_band",
+            "amplifiers": [
+                "std_low_gain_bis",
+                "std_low_gain_L"
+            ],
+            "allowed_for_design": true
+        },
+        {
+            "type_variety": "std_low_gain_multiband_reduced",
+            "type_def": "multi_band",
+            "amplifiers": [
+                "std_low_gain_reduced",
+                "std_low_gain_L"
+            ],
+            "allowed_for_design": true
+        },
+        {
+            "type_variety": "std_low_gain_multiband_reduced",
+            "type_def": "multi_band",
+            "amplifiers": [
+                "std_low_gain_bis",
+                "std_low_gain_L_reduced_band"
+            ],
+            "allowed_for_design": true
+        }
+    ],
+    "Fiber": [
+        {
+            "type_variety": "SSMF",
+            "dispersion": 1.67e-05,
+            "effective_area": 83e-12,
+            "pmd_coef": 1.265e-15
+        },
+        {
+            "type_variety": "NZDF",
+            "dispersion": 0.5e-05,
+            "effective_area": 72e-12,
+            "pmd_coef": 1.265e-15
+        },
+        {
+            "type_variety": "LOF",
+            "dispersion": 2.2e-05,
+            "effective_area": 125e-12,
+            "pmd_coef": 1.265e-15
+        }
+    ],
+    "RamanFiber": [
+        {
+            "type_variety": "SSMF",
+            "dispersion": 1.67e-05,
+            "effective_area": 83e-12,
+            "pmd_coef": 1.265e-15
+        }
+    ],
+    "Span": [
+        {
+            "power_mode": true,
+            "delta_power_range_db": [
+                -2,
+                3,
+                0.5
+            ],
+            "max_fiber_lineic_loss_for_raman": 0.25,
+            "target_extended_gain": 2.5,
+            "max_length": 150,
+            "length_units": "km",
+            "max_loss": 28,
+            "padding": 10,
+            "EOL": 0,
+            "con_in": 0,
+            "con_out": 0
+        }
+    ],
+    "Roadm": [
+        {
+            "target_pch_out_db": -20,
+            "add_drop_osnr": 38,
+            "pmd": 0,
+            "pdl": 0,
+            "restrictions": {
+                "preamp_variety_list": [],
+                "booster_variety_list": []
+            }
+        }
+    ],
+    "SI": [
+        {
+            "f_min": 191.3e12,
+            "baud_rate": 32e9,
+            "f_max": 195.1e12,
+            "spacing": 50e9,
+            "power_dbm": 0,
+            "power_range_db": [
+                0,
+                0,
+                1
+            ],
+            "roll_off": 0.15,
+            "tx_osnr": 40,
+            "sys_margins": 2
+        },
+        {
+            "type_variety": "lband",
+            "f_min": 186.3e12,
+            "baud_rate": 32e9,
+            "f_max": 190.1e12,
+            "spacing": 50e9,
+            "power_dbm": 0,
+            "power_range_db": [
+                0,
+                0,
+                1
+            ],
+            "roll_off": 0.15,
+            "tx_osnr": 40,
+            "sys_margins": 2
+        }
+    ],
+    "Transceiver": [
+        {
+            "type_variety": "vendorA_trx-type1",
+            "frequency": {
+                "min": 191.35e12,
+                "max": 196.1e12
+            },
+            "mode": [
+                {
+                    "format": "mode 1",
+                    "baud_rate": 32e9,
+                    "OSNR": 11,
+                    "bit_rate": 100e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 40,
+                    "min_spacing": 37.5e9,
+                    "cost": 1
+                },
+                {
+                    "format": "mode 2",
+                    "baud_rate": 66e9,
+                    "OSNR": 15,
+                    "bit_rate": 200e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 40,
+                    "min_spacing": 75e9,
+                    "cost": 1
+                }
+            ]
+        },
+        {
+            "type_variety": "Voyager",
+            "frequency": {
+                "min": 191.35e12,
+                "max": 196.1e12
+            },
+            "mode": [
+                {
+                    "format": "mode 1",
+                    "baud_rate": 32e9,
+                    "OSNR": 12,
+                    "bit_rate": 100e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 40,
+                    "min_spacing": 37.5e9,
+                    "cost": 1
+                },
+                {
+                    "format": "mode 3",
+                    "baud_rate": 44e9,
+                    "OSNR": 18,
+                    "bit_rate": 300e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 40,
+                    "min_spacing": 62.5e9,
+                    "cost": 1
+                },
+                {
+                    "format": "mode 2",
+                    "baud_rate": 66e9,
+                    "OSNR": 21,
+                    "bit_rate": 400e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 40,
+                    "min_spacing": 75e9,
+                    "cost": 1
+                },
+                {
+                    "format": "mode 4",
+                    "baud_rate": 66e9,
+                    "OSNR": 16,
+                    "bit_rate": 200e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 40,
+                    "min_spacing": 75e9,
+                    "cost": 1
+                }
+            ]
+        }
+    ]
+}

gnpy/example-data/eqpt_config_openroadm_ver4.json

@@ -1,349 +1,371 @@
 {
-    "Edfa": [
+  "Edfa": [
+    {
+      "type_variety": "openroadm_ila_low_noise",
+      "type_def": "openroadm",
+      "gain_flatmax": 27,
+      "gain_min": 0,
+      "p_max": 22,
+      "nf_coef": [
+        -8.104e-4,
+        -6.221e-2,
+        -5.889e-1,
+        37.62
+      ],
+      "pmd": 3e-12,
+      "pdl": 0.7,
+      "allowed_for_design": true
+    },
+    {
+      "type_variety": "openroadm_ila_standard",
+      "type_def": "openroadm",
+      "gain_flatmax": 27,
+      "gain_min": 0,
+      "p_max": 22,
+      "nf_coef": [
+        -5.952e-4,
+        -6.250e-2,
+        -1.071,
+        28.99
+      ],
+      "pmd": 3e-12,
+      "pdl": 0.7,
+      "allowed_for_design": true
+    },
+    {
+      "type_variety": "openroadm_mw_mw_preamp",
+      "type_def": "openroadm_preamp",
+      "gain_flatmax": 27,
+      "gain_min": 0,
+      "p_max": 22,
+      "pmd": 0,
+      "pdl": 0,
+      "allowed_for_design": false
+    },
+    {
+      "type_variety": "openroadm_mw_mw_booster",
+      "type_def": "openroadm_booster",
+      "gain_flatmax": 32,
+      "gain_min": 0,
+      "p_max": 22,
+      "pmd": 0,
+      "pdl": 0,
+      "allowed_for_design": false
+    }
+  ],
+  "Fiber": [
+    {
+      "type_variety": "SSMF",
+      "dispersion": 1.67e-05,
+      "effective_area": 83e-12,
+      "pmd_coef": 1.265e-15
+    },
+    {
+      "type_variety": "NZDF",
+      "dispersion": 0.5e-05,
+      "effective_area": 72e-12,
+      "pmd_coef": 1.265e-15
+    },
+    {
+      "type_variety": "LOF",
+      "dispersion": 2.2e-05,
+      "effective_area": 125e-12,
+      "pmd_coef": 1.265e-15
+    }
+  ],
+  "RamanFiber": [
+    {
+      "type_variety": "SSMF",
+      "dispersion": 1.67e-05,
+      "effective_area": 83e-12,
+      "pmd_coef": 1.265e-15
+    }
+  ],
+  "Span": [
+    {
+      "power_mode": true,
+      "delta_power_range_db": [
+        0,
+        0,
+        0
+      ],
+      "max_fiber_lineic_loss_for_raman": 0.25,
+      "target_extended_gain": 0,
+      "max_length": 135,
+      "length_units": "km",
+      "max_loss": 28,
+      "padding": 11,
+      "EOL": 0,
+      "con_in": 0,
+      "con_out": 0
+    }
+  ],
+  "Roadm": [
+    {
+      "target_pch_out_db": -20,
+      "add_drop_osnr": 30,
+      "pmd": 3e-12,
+      "pdl": 1.5,
+      "restrictions": {
+        "preamp_variety_list": [
+          "openroadm_mw_mw_preamp"
+        ],
+        "booster_variety_list": [
+          "openroadm_mw_mw_booster"
+        ]
+      }
+    }
+  ],
+  "SI": [
+    {
+      "f_min": 191.3e12,
+      "baud_rate": 31.57e9,
+      "f_max": 196.1e12,
+      "spacing": 50e9,
+      "power_dbm": 2,
+      "power_range_db": [
+        0,
+        0,
+        1
+      ],
+      "roll_off": 0.15,
+      "tx_osnr": 35,
+      "sys_margins": 2
+    }
+  ],
+  "Transceiver": [
+    {
+      "type_variety": "OpenROADM MSA ver. 4.0",
+      "frequency": {
+        "min": 191.35e12,
+        "max": 196.1e12
+      },
+      "mode": [
         {
-            "type_variety": "openroadm_ila_low_noise",
-            "type_def": "openroadm",
-            "gain_flatmax": 27,
-            "gain_min": 0,
-            "p_max": 22,
-            "nf_coef": [-8.104e-4, -6.221e-2, -5.889e-1, 37.62],
-            "pmd": 3e-12,
-            "pdl": 0.7,
-            "allowed_for_design": true
-        },
-        {
-            "type_variety": "openroadm_ila_standard",
-            "type_def": "openroadm",
-            "gain_flatmax": 27,
-            "gain_min": 0,
-            "p_max": 22,
-            "nf_coef": [-5.952e-4, -6.250e-2, -1.071, 28.99],
-            "pmd": 3e-12,
-            "pdl": 0.7,
-            "allowed_for_design": true
-        },
-        {
-            "type_variety": "openroadm_mw_mw_preamp",
-            "type_def": "openroadm_preamp",
-            "gain_flatmax": 27,
-            "gain_min": 0,
-            "p_max": 22,
-            "pmd": 0,
-            "pdl": 0,
-            "allowed_for_design": false
-        },
-        {
-            "type_variety": "openroadm_mw_mw_booster",
-            "type_def": "openroadm_booster",
-            "gain_flatmax": 32,
-            "gain_min": 0,
-            "p_max": 22,
-            "pmd": 0,
-            "pdl": 0,
-            "allowed_for_design": false
-        }
-    ],
-    "Fiber": [
-        {
-            "type_variety": "SSMF",
-            "dispersion": 1.67e-05,
-            "effective_area": 83e-12,
-            "pmd_coef": 1.265e-15
-        },
-        {
-            "type_variety": "NZDF",
-            "dispersion": 0.5e-05,
-            "effective_area": 72e-12,
-            "pmd_coef": 1.265e-15
-        },
-        {
-            "type_variety": "LOF",
-            "dispersion": 2.2e-05,
-            "effective_area": 125e-12,
-            "pmd_coef": 1.265e-15
-        }
-    ],
-    "RamanFiber": [
-        {
-            "type_variety": "SSMF",
-            "dispersion": 1.67e-05,
-            "effective_area": 83e-12,
-            "pmd_coef": 1.265e-15
-        }
-    ],
-    "Span": [
-        {
-            "power_mode": true,
-            "delta_power_range_db": [0, 0, 0],
-            "max_fiber_lineic_loss_for_raman": 0.25,
-            "target_extended_gain": 0,
-            "max_length": 135,
-            "length_units": "km",
-            "max_loss": 28,
-            "padding": 11,
-            "EOL": 0,
-            "con_in": 0,
-            "con_out": 0
-        }
-    ],
-    "Roadm": [
-        {
-            "target_pch_out_db": -20,
-            "add_drop_osnr": 30,
-            "pmd": 3e-12,
-            "pdl": 1.5,
-            "restrictions": {
-                "preamp_variety_list": ["openroadm_mw_mw_preamp"],
-                "booster_variety_list": ["openroadm_mw_mw_booster"]
-            }
-        }
-    ],
-    "SI": [
-        {
-            "f_min": 191.3e12,
-            "baud_rate": 31.57e9,
-            "f_max": 196.1e12,
-            "spacing": 50e9,
-            "power_dbm": 2,
-            "power_range_db": [0, 0, 1],
-            "roll_off": 0.15,
-            "tx_osnr": 35,
-            "sys_margins": 2
-        }
-    ],
-    "Transceiver": [
-        {
-            "type_variety": "OpenROADM MSA ver. 4.0",
-            "frequency": {
-                "min": 191.35e12,
-                "max": 196.1e12
+          "format": "100 Gbit/s, 27.95 Gbaud, DP-QPSK",
+          "baud_rate": 27.95e9,
+          "OSNR": 17,
+          "bit_rate": 100e9,
+          "roll_off": null,
+          "tx_osnr": 33,
+          "penalties": [
+            {
+              "chromatic_dispersion": 4e3,
+              "penalty_value": 0
             },
-            "mode": [
-                {
-                    "format": "100 Gbit/s, 27.95 Gbaud, DP-QPSK",
-                    "baud_rate": 27.95e9,
-                    "OSNR": 17,
-                    "bit_rate": 100e9,
-                    "roll_off": null,
-                    "tx_osnr": 33,
-                    "penalties": [
-                        {
-                            "chromatic_dispersion": 4e3,
-                            "penalty_value": 0
-                        },
-                        {
-                            "chromatic_dispersion": 18e3,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pmd": 10,
-                            "penalty_value": 0
-                        },
-                        {
-                            "pmd": 30,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pdl": 1,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pdl": 2,
-                            "penalty_value": 1
-                        },
-                        {
-                            "pdl": 4,
-                            "penalty_value": 2.5
-                        },
-                        {
-                            "pdl": 6,
-                            "penalty_value": 4
-                        }
-                    ],
-                    "min_spacing": 50e9,
-                    "cost": 1
-                },
-                {
-                    "format": "100 Gbit/s, 31.57 Gbaud, DP-QPSK",
-                    "baud_rate": 31.57e9,
-                    "OSNR": 12,
-                    "bit_rate": 100e9,
-                    "roll_off": 0.15,
-                    "tx_osnr": 35,
-                    "penalties": [
-                        {
-                            "chromatic_dispersion": -1e3,
-                            "penalty_value": 0
-                        },
-                        {
-                            "chromatic_dispersion": 4e3,
-                            "penalty_value": 0
-                        },
-                        {
-                            "chromatic_dispersion": 40e3,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pmd": 10,
-                            "penalty_value": 0
-                        },
-                        {
-                            "pmd": 30,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pdl": 1,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pdl": 2,
-                            "penalty_value": 1
-                        },
-                        {
-                            "pdl": 4,
-                            "penalty_value": 2.5
-                        },
-                        {
-                            "pdl": 6,
-                            "penalty_value": 4
-                        }
-                    ],
-                    "min_spacing": 50e9,
-                    "cost": 1
-                },
-                {
-                    "format": "200 Gbit/s, DP-QPSK",
-                    "baud_rate": 63.1e9,
-                    "OSNR": 17,
-                    "bit_rate": 200e9,
-                    "roll_off": 0.15,
-                    "tx_osnr": 36,
-                    "penalties": [
-                        {
-                            "chromatic_dispersion": -1e3,
-                            "penalty_value": 0
-                        },
-                        {
-                            "chromatic_dispersion": 4e3,
-                            "penalty_value": 0
-                        },
-                        {
-                            "chromatic_dispersion": 24e3,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pmd": 10,
-                            "penalty_value": 0
-                        },
-                        {
-                            "pmd": 25,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pdl": 1,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pdl": 2,
-                            "penalty_value": 1
-                        },
-                        {
-                            "pdl": 4,
-                            "penalty_value": 2.5
-                        }
-                    ],
-                    "min_spacing": 87.5e9,
-                    "cost": 1
-                },
-                {
-                    "format": "300 Gbit/s, DP-8QAM",
-                    "baud_rate": 63.1e9,
-                    "OSNR": 21,
-                    "bit_rate": 300e9,
-                    "roll_off": 0.15,
-                    "tx_osnr": 36,
-                    "penalties": [
-                        {
-                            "chromatic_dispersion": -1e3,
-                            "penalty_value": 0
-                        },
-                        {
-                            "chromatic_dispersion": 4e3,
-                            "penalty_value": 0
-                        },
-                        {
-                            "chromatic_dispersion": 18e3,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pmd": 10,
-                            "penalty_value": 0
-                        },
-                        {
-                            "pmd": 25,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pdl": 1,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pdl": 2,
-                            "penalty_value": 1
-                        },
-                        {
-                            "pdl": 4,
-                            "penalty_value": 2.5
-                        }
-                    ],
-                    "min_spacing": 87.5e9,
-                    "cost": 1
-                },
-                {
-                    "format": "400 Gbit/s, DP-16QAM",
-                    "baud_rate": 63.1e9,
-                    "OSNR": 24,
-                    "bit_rate": 400e9,
-                    "roll_off": 0.15,
-                    "tx_osnr": 36,
-                    "penalties": [
-                        {
-                            "chromatic_dispersion": -1e3,
-                            "penalty_value": 0
-                        },
-                        {
-                            "chromatic_dispersion": 4e3,
-                            "penalty_value": 0
-                        },
-                        {
-                            "chromatic_dispersion": 12e3,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pmd": 10,
-                            "penalty_value": 0
-                        },
-                        {
-                            "pmd": 20,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pdl": 1,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pdl": 2,
-                            "penalty_value": 1
-                        },
-                        {
-                            "pdl": 4,
-                            "penalty_value": 2.5
-                        }
-                    ],
-                    "min_spacing": 87.5e9,
-                    "cost": 1
-                }
-            ]
+            {
+              "chromatic_dispersion": 18e3,
+              "penalty_value": 0.5
+            },
+            {
+              "pmd": 10,
+              "penalty_value": 0
+            },
+            {
+              "pmd": 30,
+              "penalty_value": 0.5
+            },
+            {
+              "pdl": 1,
+              "penalty_value": 0.5
+            },
+            {
+              "pdl": 2,
+              "penalty_value": 1
+            },
+            {
+              "pdl": 4,
+              "penalty_value": 2.5
+            },
+            {
+              "pdl": 6,
+              "penalty_value": 4
+            }
+          ],
+          "min_spacing": 50e9,
+          "cost": 1
+        },
+        {
+          "format": "100 Gbit/s, 31.57 Gbaud, DP-QPSK",
+          "baud_rate": 31.57e9,
+          "OSNR": 12,
+          "bit_rate": 100e9,
+          "roll_off": 0.15,
+          "tx_osnr": 35,
+          "penalties": [
+            {
+              "chromatic_dispersion": -1e3,
+              "penalty_value": 0
+            },
+            {
+              "chromatic_dispersion": 4e3,
+              "penalty_value": 0
+            },
+            {
+              "chromatic_dispersion": 40e3,
+              "penalty_value": 0.5
+            },
+            {
+              "pmd": 10,
+              "penalty_value": 0
+            },
+            {
+              "pmd": 30,
+              "penalty_value": 0.5
+            },
+            {
+              "pdl": 1,
+              "penalty_value": 0.5
+            },
+            {
+              "pdl": 2,
+              "penalty_value": 1
+            },
+            {
+              "pdl": 4,
+              "penalty_value": 2.5
+            },
+            {
+              "pdl": 6,
+              "penalty_value": 4
+            }
+          ],
+          "min_spacing": 50e9,
+          "cost": 1
+        },
+        {
+          "format": "200 Gbit/s, DP-QPSK",
+          "baud_rate": 63.1e9,
+          "OSNR": 17,
+          "bit_rate": 200e9,
+          "roll_off": 0.15,
+          "tx_osnr": 36,
+          "penalties": [
+            {
+              "chromatic_dispersion": -1e3,
+              "penalty_value": 0
+            },
+            {
+              "chromatic_dispersion": 4e3,
+              "penalty_value": 0
+            },
+            {
+              "chromatic_dispersion": 24e3,
+              "penalty_value": 0.5
+            },
+            {
+              "pmd": 10,
+              "penalty_value": 0
+            },
+            {
+              "pmd": 25,
+              "penalty_value": 0.5
+            },
+            {
+              "pdl": 1,
+              "penalty_value": 0.5
+            },
+            {
+              "pdl": 2,
+              "penalty_value": 1
+            },
+            {
+              "pdl": 4,
+              "penalty_value": 2.5
+            }
+          ],
+          "min_spacing": 87.5e9,
+          "cost": 1
+        },
+        {
+          "format": "300 Gbit/s, DP-8QAM",
+          "baud_rate": 63.1e9,
+          "OSNR": 21,
+          "bit_rate": 300e9,
+          "roll_off": 0.15,
+          "tx_osnr": 36,
+          "penalties": [
+            {
+              "chromatic_dispersion": -1e3,
+              "penalty_value": 0
+            },
+            {
+              "chromatic_dispersion": 4e3,
+              "penalty_value": 0
+            },
+            {
+              "chromatic_dispersion": 18e3,
+              "penalty_value": 0.5
+            },
+            {
+              "pmd": 10,
+              "penalty_value": 0
+            },
+            {
+              "pmd": 25,
+              "penalty_value": 0.5
+            },
+            {
+              "pdl": 1,
+              "penalty_value": 0.5
+            },
+            {
+              "pdl": 2,
+              "penalty_value": 1
+            },
+            {
+              "pdl": 4,
+              "penalty_value": 2.5
+            }
+          ],
+          "min_spacing": 87.5e9,
+          "cost": 1
+        },
+        {
+          "format": "400 Gbit/s, DP-16QAM",
+          "baud_rate": 63.1e9,
+          "OSNR": 24,
+          "bit_rate": 400e9,
+          "roll_off": 0.15,
+          "tx_osnr": 36,
+          "penalties": [
+            {
+              "chromatic_dispersion": -1e3,
+              "penalty_value": 0
+            },
+            {
+              "chromatic_dispersion": 4e3,
+              "penalty_value": 0
+            },
+            {
+              "chromatic_dispersion": 12e3,
+              "penalty_value": 0.5
+            },
+            {
+              "pmd": 10,
+              "penalty_value": 0
+            },
+            {
+              "pmd": 20,
+              "penalty_value": 0.5
+            },
+            {
+              "pdl": 1,
+              "penalty_value": 0.5
+            },
+            {
+              "pdl": 2,
+              "penalty_value": 1
+            },
+            {
+              "pdl": 4,
+              "penalty_value": 2.5
+            }
+          ],
+          "min_spacing": 87.5e9,
+          "cost": 1
         }
-    ]
+      ]
+    }
+  ]
 }

gnpy/example-data/eqpt_config_openroadm_ver5.json

@@ -1,409 +1,441 @@
 {
-    "Edfa": [
+  "Edfa": [
+    {
+      "type_variety": "openroadm_ila_low_noise",
+      "type_def": "openroadm",
+      "gain_flatmax": 27,
+      "gain_min": 0,
+      "p_max": 22,
+      "nf_coef": [
+        -8.104e-4,
+        -6.221e-2,
+        -5.889e-1,
+        37.62
+      ],
+      "pmd": 3e-12,
+      "pdl": 0.7,
+      "allowed_for_design": true
+    },
+    {
+      "type_variety": "openroadm_ila_standard",
+      "type_def": "openroadm",
+      "gain_flatmax": 27,
+      "gain_min": 0,
+      "p_max": 22,
+      "nf_coef": [
+        -5.952e-4,
+        -6.250e-2,
+        -1.071,
+        28.99
+      ],
+      "pmd": 3e-12,
+      "pdl": 0.7,
+      "allowed_for_design": true
+    },
+    {
+      "type_variety": "openroadm_mw_mw_preamp_typical_ver5",
+      "type_def": "openroadm",
+      "gain_flatmax": 27,
+      "gain_min": 0,
+      "p_max": 22,
+      "nf_coef": [
+        -5.952e-4,
+        -6.250e-2,
+        -1.071,
+        28.99
+      ],
+      "pmd": 0,
+      "pdl": 0,
+      "allowed_for_design": false
+    },
+    {
+      "type_variety": "openroadm_mw_mw_preamp_worstcase_ver5",
+      "type_def": "openroadm",
+      "gain_flatmax": 27,
+      "gain_min": 0,
+      "p_max": 22,
+      "nf_coef": [
+        -5.952e-4,
+        -6.250e-2,
+        -1.071,
+        27.99
+      ],
+      "pmd": 0,
+      "pdl": 0,
+      "allowed_for_design": false
+    },
+    {
+      "type_variety": "openroadm_mw_mw_booster",
+      "type_def": "openroadm_booster",
+      "gain_flatmax": 32,
+      "gain_min": 0,
+      "p_max": 22,
+      "pmd": 0,
+      "pdl": 0,
+      "allowed_for_design": false
+    }
+  ],
+  "Fiber": [
+    {
+      "type_variety": "SSMF",
+      "dispersion": 1.67e-05,
+      "effective_area": 83e-12,
+      "pmd_coef": 1.265e-15
+    },
+    {
+      "type_variety": "NZDF",
+      "dispersion": 0.5e-05,
+      "effective_area": 72e-12,
+      "pmd_coef": 1.265e-15
+    },
+    {
+      "type_variety": "LOF",
+      "dispersion": 2.2e-05,
+      "effective_area": 125e-12,
+      "pmd_coef": 1.265e-15
+    }
+  ],
+  "RamanFiber": [
+    {
+      "type_variety": "SSMF",
+      "dispersion": 1.67e-05,
+      "effective_area": 83e-12,
+      "pmd_coef": 1.265e-15
+    }
+  ],
+  "Span": [
+    {
+      "power_mode": true,
+      "delta_power_range_db": [
+        0,
+        0,
+        0
+      ],
+      "max_fiber_lineic_loss_for_raman": 0.25,
+      "target_extended_gain": 0,
+      "max_length": 135,
+      "length_units": "km",
+      "max_loss": 28,
+      "padding": 11,
+      "EOL": 0,
+      "con_in": 0,
+      "con_out": 0
+    }
+  ],
+  "Roadm": [
+    {
+      "target_pch_out_db": -20,
+      "add_drop_osnr": 33,
+      "pmd": 3e-12,
+      "pdl": 1.5,
+      "restrictions": {
+        "preamp_variety_list": [
+          "openroadm_mw_mw_preamp_worstcase_ver5"
+        ],
+        "booster_variety_list": [
+          "openroadm_mw_mw_booster"
+        ]
+      }
+    }
+  ],
+  "SI": [
+    {
+      "f_min": 191.3e12,
+      "baud_rate": 31.57e9,
+      "f_max": 196.1e12,
+      "spacing": 50e9,
+      "power_dbm": 2,
+      "power_range_db": [
+        0,
+        0,
+        1
+      ],
+      "roll_off": 0.15,
+      "tx_osnr": 35,
+      "sys_margins": 2
+    }
+  ],
+  "Transceiver": [
+    {
+      "type_variety": "OpenROADM MSA ver. 5.0",
+      "frequency": {
+        "min": 191.35e12,
+        "max": 196.1e12
+      },
+      "mode": [
         {
-            "type_variety": "openroadm_ila_low_noise",
-            "type_def": "openroadm",
-            "gain_flatmax": 27,
-            "gain_min": 0,
-            "p_max": 22,
-            "nf_coef": [-8.104e-4, -6.221e-2, -5.889e-1, 37.62],
-            "pmd": 3e-12,
-            "pdl": 0.7,
-            "allowed_for_design": true
-        },
-        {
-            "type_variety": "openroadm_ila_standard",
-            "type_def": "openroadm",
-            "gain_flatmax": 27,
-            "gain_min": 0,
-            "p_max": 22,
-            "nf_coef": [-5.952e-4, -6.250e-2, -1.071, 28.99],
-            "pmd": 3e-12,
-            "pdl": 0.7,
-            "allowed_for_design": true
-        },
-        {
-            "type_variety": "openroadm_mw_mw_preamp_typical_ver5",
-            "type_def": "openroadm",
-            "gain_flatmax": 27,
-            "gain_min": 0,
-            "p_max": 22,
-            "nf_coef": [-5.952e-4, -6.250e-2, -1.071, 28.99],
-            "pmd": 0,
-            "pdl": 0,
-            "allowed_for_design": false
-        },
-        {
-            "type_variety": "openroadm_mw_mw_preamp_worstcase_ver5",
-            "type_def": "openroadm",
-            "gain_flatmax": 27,
-            "gain_min": 0,
-            "p_max": 22,
-            "nf_coef": [-5.952e-4, -6.250e-2, -1.071, 27.99],
-            "pmd": 0,
-            "pdl": 0,
-            "allowed_for_design": false
-        },
-        {
-            "type_variety": "openroadm_mw_mw_booster",
-            "type_def": "openroadm_booster",
-            "gain_flatmax": 32,
-            "gain_min": 0,
-            "p_max": 22,
-            "pmd": 0,
-            "pdl": 0,
-            "allowed_for_design": false
-        }
-    ],
-    "Fiber": [
-        {
-            "type_variety": "SSMF",
-            "dispersion": 1.67e-05,
-            "effective_area": 83e-12,
-            "pmd_coef": 1.265e-15
-        },
-        {
-            "type_variety": "NZDF",
-            "dispersion": 0.5e-05,
-            "effective_area": 72e-12,
-            "pmd_coef": 1.265e-15
-        },
-        {
-            "type_variety": "LOF",
-            "dispersion": 2.2e-05,
-            "effective_area": 125e-12,
-            "pmd_coef": 1.265e-15
-        }
-    ],
-    "RamanFiber": [
-        {
-            "type_variety": "SSMF",
-            "dispersion": 1.67e-05,
-            "effective_area": 83e-12,
-            "pmd_coef": 1.265e-15
-        }
-    ],
-    "Span": [
-        {
-            "power_mode": true,
-            "delta_power_range_db": [0, 0, 0],
-            "max_fiber_lineic_loss_for_raman": 0.25,
-            "target_extended_gain": 0,
-            "max_length": 135,
-            "length_units": "km",
-            "max_loss": 28,
-            "padding": 11,
-            "EOL": 0,
-            "con_in": 0,
-            "con_out": 0
-        }
-    ],
-    "Roadm": [
-        {
-            "target_pch_out_db": -20,
-            "add_drop_osnr": 33,
-            "pmd": 3e-12,
-            "pdl": 1.5,
-            "restrictions": {
-                "preamp_variety_list": ["openroadm_mw_mw_preamp_worstcase_ver5"],
-                "booster_variety_list": ["openroadm_mw_mw_booster"]
-            }
-        }
-    ],
-    "SI": [
-        {
-            "f_min": 191.3e12,
-            "baud_rate": 31.57e9,
-            "f_max": 196.1e12,
-            "spacing": 50e9,
-            "power_dbm": 2,
-            "power_range_db": [0, 0, 1],
-            "roll_off": 0.15,
-            "tx_osnr": 35,
-            "sys_margins": 2
-        }
-    ],
-    "Transceiver": [
-        {
-            "type_variety": "OpenROADM MSA ver. 5.0",
-            "frequency": {
-                "min": 191.35e12,
-                "max": 196.1e12
+          "format": "100 Gbit/s, 27.95 Gbaud, DP-QPSK",
+          "baud_rate": 27.95e9,
+          "OSNR": 17,
+          "bit_rate": 100e9,
+          "roll_off": null,
+          "tx_osnr": 33,
+          "penalties": [
+            {
+              "chromatic_dispersion": 4e3,
+              "penalty_value": 0
             },
-            "mode": [
-                {
-                    "format": "100 Gbit/s, 27.95 Gbaud, DP-QPSK",
-                    "baud_rate": 27.95e9,
-                    "OSNR": 17,
-                    "bit_rate": 100e9,
-                    "roll_off": null,
-                    "tx_osnr": 33,
-                    "penalties": [
-                        {
-                            "chromatic_dispersion": 4e3,
-                            "penalty_value": 0
-                        },
-                        {
-                            "chromatic_dispersion": 18e3,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pmd": 10,
-                            "penalty_value": 0
-                        },
-                        {
-                            "pmd": 30,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pdl": 1,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pdl": 2,
-                            "penalty_value": 1
-                        },
-                        {
-                            "pdl": 4,
-                            "penalty_value": 2.5
-                        },
-                        {
-                            "pdl": 6,
-                            "penalty_value": 4
-                        }
-                    ],
-                    "min_spacing": 50e9,
-                    "cost": 1
-                },
-                {
-                    "format": "100 Gbit/s, 31.57 Gbaud, DP-QPSK",
-                    "baud_rate": 31.57e9,
-                    "OSNR": 12,
-                    "bit_rate": 100e9,
-                    "roll_off": 0.15,
-                    "tx_osnr": 36,
-                    "penalties": [
-                        {
-                            "chromatic_dispersion": -1e3,
-                            "penalty_value": 0
-                        },
-                        {
-                            "chromatic_dispersion": 4e3,
-                            "penalty_value": 0
-                        },
-                        {
-                            "chromatic_dispersion": 48e3,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pmd": 10,
-                            "penalty_value": 0
-                        },
-                        {
-                            "pmd": 30,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pdl": 1,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pdl": 2,
-                            "penalty_value": 1
-                        },
-                        {
-                            "pdl": 4,
-                            "penalty_value": 2.5
-                        },
-                        {
-                            "pdl": 6,
-                            "penalty_value": 4
-                        }
-                    ],
-                    "min_spacing": 50e9,
-                    "cost": 1
-                },
-                {
-                    "format": "200 Gbit/s, 31.57 Gbaud, DP-16QAM",
-                    "baud_rate": 31.57e9,
-                    "OSNR": 20.5,
-                    "bit_rate": 100e9,
-                    "roll_off": 0.15,
-                    "tx_osnr": 36,
-                    "penalties": [
-                        {
-                            "chromatic_dispersion": -1e3,
-                            "penalty_value": 0
-                        },
-                        {
-                            "chromatic_dispersion": 4e3,
-                            "penalty_value": 0
-                        },
-                        {
-                            "chromatic_dispersion": 24e3,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pmd": 10,
-                            "penalty_value": 0
-                        },
-                        {
-                            "pmd": 30,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pdl": 1,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pdl": 2,
-                            "penalty_value": 1
-                        },
-                        {
-                            "pdl": 4,
-                            "penalty_value": 2.5
-                        },
-                        {
-                            "pdl": 6,
-                            "penalty_value": 4
-                        }
-                    ],
-                    "min_spacing": 50e9,
-                    "cost": 1
-                },
-                {
-                    "format": "200 Gbit/s, DP-QPSK",
-                    "baud_rate": 63.1e9,
-                    "OSNR": 17,
-                    "bit_rate": 200e9,
-                    "roll_off": 0.15,
-                    "tx_osnr": 36,
-                    "penalties": [
-                        {
-                            "chromatic_dispersion": -1e3,
-                            "penalty_value": 0
-                        },
-                        {
-                            "chromatic_dispersion": 4e3,
-                            "penalty_value": 0
-                        },
-                        {
-                            "chromatic_dispersion": 24e3,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pmd": 10,
-                            "penalty_value": 0
-                        },
-                        {
-                            "pmd": 25,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pdl": 1,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pdl": 2,
-                            "penalty_value": 1
-                        },
-                        {
-                            "pdl": 4,
-                            "penalty_value": 2.5
-                        }
-                    ],
-                    "min_spacing": 87.5e9,
-                    "cost": 1
-                },
-                {
-                    "format": "300 Gbit/s, DP-8QAM",
-                    "baud_rate": 63.1e9,
-                    "OSNR": 21,
-                    "bit_rate": 300e9,
-                    "roll_off": 0.15,
-                    "tx_osnr": 36,
-                    "penalties": [
-                        {
-                            "chromatic_dispersion": -1e3,
-                            "penalty_value": 0
-                        },
-                        {
-                            "chromatic_dispersion": 4e3,
-                            "penalty_value": 0
-                        },
-                        {
-                            "chromatic_dispersion": 18e3,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pmd": 10,
-                            "penalty_value": 0
-                        },
-                        {
-                            "pmd": 25,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pdl": 1,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pdl": 2,
-                            "penalty_value": 1
-                        },
-                        {
-                            "pdl": 4,
-                            "penalty_value": 2.5
-                        }
-                    ],
-                    "min_spacing": 87.5e9,
-                    "cost": 1
-                },
-                {
-                    "format": "400 Gbit/s, DP-16QAM",
-                    "baud_rate": 63.1e9,
-                    "OSNR": 24,
-                    "bit_rate": 400e9,
-                    "roll_off": 0.15,
-                    "tx_osnr": 36,
-                    "penalties": [
-                        {
-                            "chromatic_dispersion": -1e3,
-                            "penalty_value": 0
-                        },
-                        {
-                            "chromatic_dispersion": 4e3,
-                            "penalty_value": 0
-                        },
-                        {
-                            "chromatic_dispersion": 12e3,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pmd": 10,
-                            "penalty_value": 0
-                        },
-                        {
-                            "pmd": 20,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pdl": 1,
-                            "penalty_value": 0.5
-                        },
-                        {
-                            "pdl": 2,
-                            "penalty_value": 1
-                        },
-                        {
-                            "pdl": 4,
-                            "penalty_value": 2.5
-                        }
-                    ],
-                    "min_spacing": 87.5e9,
-                    "cost": 1
-                }
-            ]
+            {
+              "chromatic_dispersion": 18e3,
+              "penalty_value": 0.5
+            },
+            {
+              "pmd": 10,
+              "penalty_value": 0
+            },
+            {
+              "pmd": 30,
+              "penalty_value": 0.5
+            },
+            {
+              "pdl": 1,
+              "penalty_value": 0.5
+            },
+            {
+              "pdl": 2,
+              "penalty_value": 1
+            },
+            {
+              "pdl": 4,
+              "penalty_value": 2.5
+            },
+            {
+              "pdl": 6,
+              "penalty_value": 4
+            }
+          ],
+          "min_spacing": 50e9,
+          "cost": 1
+        },
+        {
+          "format": "100 Gbit/s, 31.57 Gbaud, DP-QPSK",
+          "baud_rate": 31.57e9,
+          "OSNR": 12,
+          "bit_rate": 100e9,
+          "roll_off": 0.15,
+          "tx_osnr": 36,
+          "penalties": [
+            {
+              "chromatic_dispersion": -1e3,
+              "penalty_value": 0
+            },
+            {
+              "chromatic_dispersion": 4e3,
+              "penalty_value": 0
+            },
+            {
+              "chromatic_dispersion": 48e3,
+              "penalty_value": 0.5
+            },
+            {
+              "pmd": 10,
+              "penalty_value": 0
+            },
+            {
+              "pmd": 30,
+              "penalty_value": 0.5
+            },
+            {
+              "pdl": 1,
+              "penalty_value": 0.5
+            },
+            {
+              "pdl": 2,
+              "penalty_value": 1
+            },
+            {
+              "pdl": 4,
+              "penalty_value": 2.5
+            },
+            {
+              "pdl": 6,
+              "penalty_value": 4
+            }
+          ],
+          "min_spacing": 50e9,
+          "cost": 1
+        },
+        {
+          "format": "200 Gbit/s, 31.57 Gbaud, DP-16QAM",
+          "baud_rate": 31.57e9,
+          "OSNR": 20.5,
+          "bit_rate": 100e9,
+          "roll_off": 0.15,
+          "tx_osnr": 36,
+          "penalties": [
+            {
+              "chromatic_dispersion": -1e3,
+              "penalty_value": 0
+            },
+            {
+              "chromatic_dispersion": 4e3,
+              "penalty_value": 0
+            },
+            {
+              "chromatic_dispersion": 24e3,
+              "penalty_value": 0.5
+            },
+            {
+              "pmd": 10,
+              "penalty_value": 0
+            },
+            {
+              "pmd": 30,
+              "penalty_value": 0.5
+            },
+            {
+              "pdl": 1,
+              "penalty_value": 0.5
+            },
+            {
+              "pdl": 2,
+              "penalty_value": 1
+            },
+            {
+              "pdl": 4,
+              "penalty_value": 2.5
+            },
+            {
+              "pdl": 6,
+              "penalty_value": 4
+            }
+          ],
+          "min_spacing": 50e9,
+          "cost": 1
+        },
+        {
+          "format": "200 Gbit/s, DP-QPSK",
+          "baud_rate": 63.1e9,
+          "OSNR": 17,
+          "bit_rate": 200e9,
+          "roll_off": 0.15,
+          "tx_osnr": 36,
+          "penalties": [
+            {
+              "chromatic_dispersion": -1e3,
+              "penalty_value": 0
+            },
+            {
+              "chromatic_dispersion": 4e3,
+              "penalty_value": 0
+            },
+            {
+              "chromatic_dispersion": 24e3,
+              "penalty_value": 0.5
+            },
+            {
+              "pmd": 10,
+              "penalty_value": 0
+            },
+            {
+              "pmd": 25,
+              "penalty_value": 0.5
+            },
+            {
+              "pdl": 1,
+              "penalty_value": 0.5
+            },
+            {
+              "pdl": 2,
+              "penalty_value": 1
+            },
+            {
+              "pdl": 4,
+              "penalty_value": 2.5
+            }
+          ],
+          "min_spacing": 87.5e9,
+          "cost": 1
+        },
+        {
+          "format": "300 Gbit/s, DP-8QAM",
+          "baud_rate": 63.1e9,
+          "OSNR": 21,
+          "bit_rate": 300e9,
+          "roll_off": 0.15,
+          "tx_osnr": 36,
+          "penalties": [
+            {
+              "chromatic_dispersion": -1e3,
+              "penalty_value": 0
+            },
+            {
+              "chromatic_dispersion": 4e3,
+              "penalty_value": 0
+            },
+            {
+              "chromatic_dispersion": 18e3,
+              "penalty_value": 0.5
+            },
+            {
+              "pmd": 10,
+              "penalty_value": 0
+            },
+            {
+              "pmd": 25,
+              "penalty_value": 0.5
+            },
+            {
+              "pdl": 1,
+              "penalty_value": 0.5
+            },
+            {
+              "pdl": 2,
+              "penalty_value": 1
+            },
+            {
+              "pdl": 4,
+              "penalty_value": 2.5
+            }
+          ],
+          "min_spacing": 87.5e9,
+          "cost": 1
+        },
+        {
+          "format": "400 Gbit/s, DP-16QAM",
+          "baud_rate": 63.1e9,
+          "OSNR": 24,
+          "bit_rate": 400e9,
+          "roll_off": 0.15,
+          "tx_osnr": 36,
+          "penalties": [
+            {
+              "chromatic_dispersion": -1e3,
+              "penalty_value": 0
+            },
+            {
+              "chromatic_dispersion": 4e3,
+              "penalty_value": 0
+            },
+            {
+              "chromatic_dispersion": 12e3,
+              "penalty_value": 0.5
+            },
+            {
+              "pmd": 10,
+              "penalty_value": 0
+            },
+            {
+              "pmd": 20,
+              "penalty_value": 0.5
+            },
+            {
+              "pdl": 1,
+              "penalty_value": 0.5
+            },
+            {
+              "pdl": 2,
+              "penalty_value": 1
+            },
+            {
+              "pdl": 4,
+              "penalty_value": 2.5
+            }
+          ],
+          "min_spacing": 87.5e9,
+          "cost": 1
         }
-    ]
+      ]
+    }
+  ]
 }

gnpy/example-data/initial_spectrum1.json

@@ -0,0 +1,12 @@
+{
+  "spectrum": [
+    {
+      "f_min": 191.35e12,
+      "f_max": 195.1e12,
+      "baud_rate": 32e9,
+      "slot_width": 50e9,
+      "roll_off": 0.15,
+      "tx_osnr": 40
+    }
+  ]
+}

gnpy/example-data/initial_spectrum2.json

@@ -0,0 +1,23 @@
+{
+  "spectrum": [
+    {
+      "f_min": 191.4e12,
+      "f_max": 193.1e12,
+      "baud_rate": 32e9,
+      "slot_width": 50e9,
+      "delta_pdb": 0,
+      "roll_off": 0.15,
+      "tx_osnr": 40,
+      "label": "mode_1"
+    },
+    {
+      "f_min": 193.1625e12,
+      "f_max": 195e12,
+      "baud_rate": 64e9,
+      "slot_width": 75e9,
+      "roll_off": 0.15,
+      "tx_osnr": 40,
+      "label": "mode_2"
+    }
+  ]
+}

gnpy/example-data/multiband_spectrum.json

@@ -0,0 +1,24 @@
+{
+    "spectrum": [
+        {
+            "f_min": 191.25e12,
+            "baud_rate": 32e9,
+            "f_max": 195.1e12,
+            "slot_width": 50e9,
+            "delta_pdb": 0,
+            "roll_off": 0.15,
+            "tx_osnr": 40,
+            "label": "cband"
+        },
+        {
+            "f_min": 186.3e12,
+            "baud_rate": 32e9,
+            "f_max": 190.1e12,
+            "slot_width": 50e9,
+            "delta_pdb": 0,
+            "roll_off": 0.15,
+            "tx_osnr": 40,
+            "label": "lband"
+        }
+    ]
+}

gnpy/example-data/sim_params.json

@@ -8,6 +8,12 @@
     "method": "ggn_spectrally_separated",
     "dispersion_tolerance": 1,
     "phase_shift_tolerance": 0.1,
-    "computed_channels": [1, 18, 37, 56, 75]
+    "computed_channels": [
+      1,
+      18,
+      37,
+      56,
+      75
+    ]
   }
 }

gnpy/example-data/std_medium_gain_advanced_config.json

@@ -1,304 +1,304 @@
 {
-    "nf_fit_coeff": [
-        0.000168241,
-        0.0469961,
-        0.0359549,
-        5.82851
-    ],
-    "f_min": 191.35e12,
-    "f_max": 196.1e12,
-    "nf_ripple": [
-        0.4372876328262819,
-        0.4372876328262819,
-        0.41270842850729195,
-        0.38814205928193013,
-        0.36358851509924695,
-        0.3390191214858807,
-        0.30474360397422756,
-        0.27048596623174515,
-        0.23624619427167134,
-        0.202035284929368,
-        0.1694483010211072,
-        0.13687829834471027,
-        0.1043252636301016,
-        0.07184040799914815,
-        0.061288823415841555,
-        0.050742731588695494,
-        0.04020212822983975,
-        0.029667009055877668,
-        0.01913736978785662,
-        0.00861320615127981,
-        -0.010157321677553965,
-        -0.028982516728038848,
-        -0.04779792991567815,
-        -0.06660356886269536,
-        -0.06256260169582961,
-        -0.05832916277634124,
-        -0.05409792133358102,
-        -0.04990610405914272,
-        -0.05078533294804249,
-        -0.05166410580536087,
-        -0.05254242298580185,
-        -0.05342028484370278,
-        -0.051742390657545205,
-        -0.050039429413028365,
-        -0.048337350303318156,
-        -0.04663615264317309,
-        -0.04493583574805963,
-        -0.043236398934156144,
-        -0.035622012697103154,
-        -0.027999803010447587,
-        -0.02038153550619876,
-        -0.012779471908040341,
-        -0.006436207679519103,
-        -9.622162373026585e-05,
-        0.006240488799898697,
-        0.012573926129294415,
-        0.021418708618354456,
-        0.030289222542492025,
-        0.03915515813685565,
-        0.047899419704645264,
-        0.04256372893215024,
-        0.03723078993416436,
-        0.03190060058247842,
-        0.02657315875107553,
-        0.021248462316134083,
-        0.01605877647020772,
-        0.02326948274513522,
-        0.03047647598902483,
-        0.037679759069084225,
-        0.044883315610536455,
-        0.052470799141237305,
-        0.06005437964543287,
-        0.0676340601339394,
-        0.07521193198077789,
-        0.08415906712621996,
-        0.09310160456603413,
-        0.1020395478432815,
-        0.11079585523492333,
-        0.1018180306253394,
-        0.09284481475528361,
-        0.0838762040768461,
-        0.07482015390297145,
-        0.05670549786742816,
-        0.03860013139908377,
-        0.020504047353947653,
-        0.0024172385953583004,
-        -0.015660302006048,
-        -0.03372858157230583,
-        -0.07037375788020579,
-        -0.10709599992470213,
-        -0.14379944379052215,
-        -0.18048410390821285,
-        -0.20911178784023846,
-        -0.23772399031437283,
-        -0.26632156113294336,
-        -0.2949045115165272,
-        -0.30206775396360075,
-        -0.30915729645781326,
-        -0.31624321721895354,
-        -0.3233255190215882,
-        -0.32037911876162584,
-        -0.3172854168606314,
-        -0.31419329378173544,
-        -0.31110274831665313,
-        -0.3110761646066259,
-        -0.3110761646066259
-    ],
-    "dgt": [
-        1.0,
-        1.017807767853702,
-        1.0356155337864215,
-        1.0534217504465226,
-        1.0712204022764056,
-        1.0895983485572227,
-        1.108555289615659,
-        1.1280891949729075,
-        1.1476135933863398,
-        1.1672278304018044,
-        1.1869318618366975,
-        1.2067249615595257,
-        1.2264996957264114,
-        1.2428104897182262,
-        1.2556591482982988,
-        1.2650555289898042,
-        1.2744470198196236,
-        1.2838336236692311,
-        1.2932153453410835,
-        1.3040618749785347,
-        1.316383926863083,
-        1.3301807335621048,
-        1.3439818461440451,
-        1.3598972673004606,
-        1.3779439775587023,
-        1.3981208704326855,
-        1.418273806730323,
-        1.4340878115214444,
-        1.445565137158368,
-        1.45273959485914,
-        1.4599103316162523,
-        1.4670307626366115,
-        1.474100442252211,
-        1.48111939735681,
-        1.488134243479226,
-        1.495145456062699,
-        1.502153039909686,
-        1.5097346239790443,
-        1.5178910621476225,
-        1.5266220576235803,
-        1.5353620432989845,
-        1.545374152761467,
-        1.5566577309558969,
-        1.569199764184379,
-        1.5817353179379183,
-        1.5986915141218316,
-        1.6201194134191075,
-        1.6460167077689267,
-        1.6719047669939942,
-        1.6918150918099673,
-        1.7057507692361864,
-        1.7137640932265894,
-        1.7217732861435076,
-        1.7297783508684146,
-        1.737780757913635,
-        1.7459181197626403,
-        1.7541903672600494,
-        1.7625959636196327,
-        1.7709972329654864,
-        1.7793941781790852,
-        1.7877868031023945,
-        1.7961751115773796,
-        1.8045606557581335,
-        1.8139629377087627,
-        1.824381436842932,
-        1.835814081380705,
-        1.847275503201129,
-        1.862235672444246,
-        1.8806927939516411,
-        1.9026104247588487,
-        1.9245345552113182,
-        1.9482128147680253,
-        1.9736443063300082,
-        2.0008103857988204,
-        2.0279625371819305,
-        2.055100772005235,
-        2.082225099873648,
-        2.1183028432496016,
-        2.16337565384239,
-        2.2174389328192197,
-        2.271520771371253,
-        2.322373696229342,
-        2.3699990328716107,
-        2.414398437185221,
-        2.4587748041127506,
-        2.499446286796604,
-        2.5364027376452056,
-        2.5696460593920065,
-        2.602860350286428,
-        2.630396440815385,
-        2.6521732021128046,
-        2.6681935771243177,
-        2.6841217449620203,
-        2.6947834587664494,
-        2.705443819238505,
-        2.714526681131686
-    ],
-    "gain_ripple": [
-        0.07704745697916238,
-        0.06479749697916048,
-        0.05257029697916238,
-        0.040326236979161934,
-        0.028098946979159933,
-        0.01393231697916164,
-        -0.0021726530208390216,
-        -0.01819858302084043,
-        -0.03218106302083967,
-        -0.042428283020839785,
-        -0.05095282302083959,
-        -0.05947139302083926,
-        -0.06968090302083851,
-        -0.07844600302084004,
-        -0.08407607302083875,
-        -0.0865687230208394,
-        -0.08906007302083907,
-        -0.0913487130208388,
-        -0.09343261302083761,
-        -0.09717347302083823,
-        -0.1027863830208382,
-        -0.11089282302084058,
-        -0.11963431302083904,
-        -0.1279646530208396,
-        -0.13525493302083902,
-        -0.1409032730208395,
-        -0.14591937302083835,
-        -0.14823350302084037,
-        -0.1484450830208388,
-        -0.1455411330208385,
-        -0.14160178302083892,
-        -0.1353792530208402,
-        -0.12789859302083784,
-        -0.11916081302083725,
-        -0.11041488302083735,
-        -0.10103437302083762,
-        -0.09101254302083817,
-        -0.07868024302083754,
-        -0.06468462302083822,
-        -0.051112303020840244,
-        -0.039618433020837784,
-        -0.028748483020837767,
-        -0.016475303020840215,
-        -0.006936193020838033,
-        -0.0015763130208377163,
-        0.0007104669791608842,
-        0.0040435869791615175,
-        0.006965146979162284,
-        0.00842583697916055,
-        0.00874012697916271,
-        0.00936596697916059,
-        0.01030063697916006,
-        0.011234826979162449,
-        0.013321846979160057,
-        0.01659282697915998,
-        0.023488786979161347,
-        0.03285456697916089,
-        0.04072968697916224,
-        0.04467697697916151,
-        0.04551704697916037,
-        0.04717897697916129,
-        0.04946107697915991,
-        0.05154489697916276,
-        0.05447361697916264,
-        0.05848224697916038,
-        0.06916723697916183,
-        0.08548825697916129,
-        0.10802383697916085,
-        0.13114358697916018,
-        0.15216302697916007,
-        0.17037189697916233,
-        0.1767381569791624,
-        0.1739275269791598,
-        0.15945681697916214,
-        0.14239527697916188,
-        0.12276252697916235,
-        0.10313984697916112,
-        0.08731066697916035,
-        0.07533675697916209,
-        0.07114372697916238,
-        0.07094413697916124,
-        0.07091459697916136,
-        0.0670723869791594,
-        0.054956336979159914,
-        0.038328296979159404,
-        0.017572956979162058,
-        -0.0028138630208403015,
-        -0.016792253020838643,
-        -0.0246928330208398,
-        -0.018326963020840026,
-        -0.0036199830208403228,
-        0.02602813697916062,
-        0.06245819697916133,
-        0.09542181697916163,
-        0.11822862697916037,
-        0.1359703369791596
-    ]
+  "nf_fit_coeff": [
+    0.000168241,
+    0.0469961,
+    0.0359549,
+    5.82851
+  ],
+  "f_min": 191.275e12,
+  "f_max": 196.125e12,
+  "nf_ripple": [
+    0.4372876328262819,
+    0.4372876328262819,
+    0.41270842850729195,
+    0.38814205928193013,
+    0.36358851509924695,
+    0.3390191214858807,
+    0.30474360397422756,
+    0.27048596623174515,
+    0.23624619427167134,
+    0.202035284929368,
+    0.1694483010211072,
+    0.13687829834471027,
+    0.1043252636301016,
+    0.07184040799914815,
+    0.061288823415841555,
+    0.050742731588695494,
+    0.04020212822983975,
+    0.029667009055877668,
+    0.01913736978785662,
+    0.00861320615127981,
+    -0.010157321677553965,
+    -0.028982516728038848,
+    -0.04779792991567815,
+    -0.06660356886269536,
+    -0.06256260169582961,
+    -0.05832916277634124,
+    -0.05409792133358102,
+    -0.04990610405914272,
+    -0.05078533294804249,
+    -0.05166410580536087,
+    -0.05254242298580185,
+    -0.05342028484370278,
+    -0.051742390657545205,
+    -0.050039429413028365,
+    -0.048337350303318156,
+    -0.04663615264317309,
+    -0.04493583574805963,
+    -0.043236398934156144,
+    -0.035622012697103154,
+    -0.027999803010447587,
+    -0.02038153550619876,
+    -0.012779471908040341,
+    -0.006436207679519103,
+    -9.622162373026585e-05,
+    0.006240488799898697,
+    0.012573926129294415,
+    0.021418708618354456,
+    0.030289222542492025,
+    0.03915515813685565,
+    0.047899419704645264,
+    0.04256372893215024,
+    0.03723078993416436,
+    0.03190060058247842,
+    0.02657315875107553,
+    0.021248462316134083,
+    0.01605877647020772,
+    0.02326948274513522,
+    0.03047647598902483,
+    0.037679759069084225,
+    0.044883315610536455,
+    0.052470799141237305,
+    0.06005437964543287,
+    0.0676340601339394,
+    0.07521193198077789,
+    0.08415906712621996,
+    0.09310160456603413,
+    0.1020395478432815,
+    0.11079585523492333,
+    0.1018180306253394,
+    0.09284481475528361,
+    0.0838762040768461,
+    0.07482015390297145,
+    0.05670549786742816,
+    0.03860013139908377,
+    0.020504047353947653,
+    0.0024172385953583004,
+    -0.015660302006048,
+    -0.03372858157230583,
+    -0.07037375788020579,
+    -0.10709599992470213,
+    -0.14379944379052215,
+    -0.18048410390821285,
+    -0.20911178784023846,
+    -0.23772399031437283,
+    -0.26632156113294336,
+    -0.2949045115165272,
+    -0.30206775396360075,
+    -0.30915729645781326,
+    -0.31624321721895354,
+    -0.3233255190215882,
+    -0.32037911876162584,
+    -0.3172854168606314,
+    -0.31419329378173544,
+    -0.31110274831665313,
+    -0.3110761646066259,
+    -0.3110761646066259
+  ],
+  "dgt": [
+    1.0,
+    1.017807767853702,
+    1.0356155337864215,
+    1.0534217504465226,
+    1.0712204022764056,
+    1.0895983485572227,
+    1.108555289615659,
+    1.1280891949729075,
+    1.1476135933863398,
+    1.1672278304018044,
+    1.1869318618366975,
+    1.2067249615595257,
+    1.2264996957264114,
+    1.2428104897182262,
+    1.2556591482982988,
+    1.2650555289898042,
+    1.2744470198196236,
+    1.2838336236692311,
+    1.2932153453410835,
+    1.3040618749785347,
+    1.316383926863083,
+    1.3301807335621048,
+    1.3439818461440451,
+    1.3598972673004606,
+    1.3779439775587023,
+    1.3981208704326855,
+    1.418273806730323,
+    1.4340878115214444,
+    1.445565137158368,
+    1.45273959485914,
+    1.4599103316162523,
+    1.4670307626366115,
+    1.474100442252211,
+    1.48111939735681,
+    1.488134243479226,
+    1.495145456062699,
+    1.502153039909686,
+    1.5097346239790443,
+    1.5178910621476225,
+    1.5266220576235803,
+    1.5353620432989845,
+    1.545374152761467,
+    1.5566577309558969,
+    1.569199764184379,
+    1.5817353179379183,
+    1.5986915141218316,
+    1.6201194134191075,
+    1.6460167077689267,
+    1.6719047669939942,
+    1.6918150918099673,
+    1.7057507692361864,
+    1.7137640932265894,
+    1.7217732861435076,
+    1.7297783508684146,
+    1.737780757913635,
+    1.7459181197626403,
+    1.7541903672600494,
+    1.7625959636196327,
+    1.7709972329654864,
+    1.7793941781790852,
+    1.7877868031023945,
+    1.7961751115773796,
+    1.8045606557581335,
+    1.8139629377087627,
+    1.824381436842932,
+    1.835814081380705,
+    1.847275503201129,
+    1.862235672444246,
+    1.8806927939516411,
+    1.9026104247588487,
+    1.9245345552113182,
+    1.9482128147680253,
+    1.9736443063300082,
+    2.0008103857988204,
+    2.0279625371819305,
+    2.055100772005235,
+    2.082225099873648,
+    2.1183028432496016,
+    2.16337565384239,
+    2.2174389328192197,
+    2.271520771371253,
+    2.322373696229342,
+    2.3699990328716107,
+    2.414398437185221,
+    2.4587748041127506,
+    2.499446286796604,
+    2.5364027376452056,
+    2.5696460593920065,
+    2.602860350286428,
+    2.630396440815385,
+    2.6521732021128046,
+    2.6681935771243177,
+    2.6841217449620203,
+    2.6947834587664494,
+    2.705443819238505,
+    2.714526681131686
+  ],
+  "gain_ripple": [
+    0.07704745697916238,
+    0.06479749697916048,
+    0.05257029697916238,
+    0.040326236979161934,
+    0.028098946979159933,
+    0.01393231697916164,
+    -0.0021726530208390216,
+    -0.01819858302084043,
+    -0.03218106302083967,
+    -0.042428283020839785,
+    -0.05095282302083959,
+    -0.05947139302083926,
+    -0.06968090302083851,
+    -0.07844600302084004,
+    -0.08407607302083875,
+    -0.0865687230208394,
+    -0.08906007302083907,
+    -0.0913487130208388,
+    -0.09343261302083761,
+    -0.09717347302083823,
+    -0.1027863830208382,
+    -0.11089282302084058,
+    -0.11963431302083904,
+    -0.1279646530208396,
+    -0.13525493302083902,
+    -0.1409032730208395,
+    -0.14591937302083835,
+    -0.14823350302084037,
+    -0.1484450830208388,
+    -0.1455411330208385,
+    -0.14160178302083892,
+    -0.1353792530208402,
+    -0.12789859302083784,
+    -0.11916081302083725,
+    -0.11041488302083735,
+    -0.10103437302083762,
+    -0.09101254302083817,
+    -0.07868024302083754,
+    -0.06468462302083822,
+    -0.051112303020840244,
+    -0.039618433020837784,
+    -0.028748483020837767,
+    -0.016475303020840215,
+    -0.006936193020838033,
+    -0.0015763130208377163,
+    0.0007104669791608842,
+    0.0040435869791615175,
+    0.006965146979162284,
+    0.00842583697916055,
+    0.00874012697916271,
+    0.00936596697916059,
+    0.01030063697916006,
+    0.011234826979162449,
+    0.013321846979160057,
+    0.01659282697915998,
+    0.023488786979161347,
+    0.03285456697916089,
+    0.04072968697916224,
+    0.04467697697916151,
+    0.04551704697916037,
+    0.04717897697916129,
+    0.04946107697915991,
+    0.05154489697916276,
+    0.05447361697916264,
+    0.05848224697916038,
+    0.06916723697916183,
+    0.08548825697916129,
+    0.10802383697916085,
+    0.13114358697916018,
+    0.15216302697916007,
+    0.17037189697916233,
+    0.1767381569791624,
+    0.1739275269791598,
+    0.15945681697916214,
+    0.14239527697916188,
+    0.12276252697916235,
+    0.10313984697916112,
+    0.08731066697916035,
+    0.07533675697916209,
+    0.07114372697916238,
+    0.07094413697916124,
+    0.07091459697916136,
+    0.0670723869791594,
+    0.054956336979159914,
+    0.038328296979159404,
+    0.017572956979162058,
+    -0.0028138630208403015,
+    -0.016792253020838643,
+    -0.0246928330208398,
+    -0.018326963020840026,
+    -0.0036199830208403228,
+    0.02602813697916062,
+    0.06245819697916133,
+    0.09542181697916163,
+    0.11822862697916037,
+    0.1359703369791596
+  ]
 }

gnpy/tools/__init__.py

@@ -1,5 +1,5 @@
-'''
+"""
 Processing of data via :py:mod:`.json_io`.
 Utilities for Excel conversion in :py:mod:`.convert` and :py:mod:`.service_sheet`.
 Example code in :py:mod:`.cli_examples` and :py:mod:`.plots`.
-'''
+"""

gnpy/tools/cli_examples.py

@@ -1,34 +1,31 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 
-'''
+"""
 gnpy.tools.cli_examples
 =======================
 
 Common code for CLI examples
-'''
+"""
 
 import argparse
 import logging
 import sys
-from math import ceil
-from numpy import linspace, mean
 from pathlib import Path
-import gnpy.core.ansi_escapes as ansi_escapes
+from math import ceil
+from numpy import mean
+
+from gnpy.core import ansi_escapes
 from gnpy.core.elements import Transceiver, Fiber, RamanFiber
-from gnpy.core.equipment import trx_mode_params
-import gnpy.core.exceptions as exceptions
-from gnpy.core.network import build_network
+from gnpy.core import exceptions
 from gnpy.core.parameters import SimParams
-from gnpy.core.utils import db2lin, lin2db, automatic_nch
-from gnpy.topology.request import (ResultElement, jsontocsv, compute_path_dsjctn, requests_aggregation,
-                                   BLOCKING_NOPATH, correct_json_route_list,
-                                   deduplicate_disjunctions, compute_path_with_disjunction,
-                                   PathRequest, compute_constrained_path, propagate)
-from gnpy.topology.spectrum_assignment import build_oms_list, pth_assign_spectrum
-from gnpy.tools.json_io import load_equipment, load_network, load_json, load_requests, save_network, \
-                               requests_from_json, disjunctions_from_json, save_json
+from gnpy.core.utils import lin2db, pretty_summary_print, per_label_average, watt2dbm
+from gnpy.topology.request import (ResultElement, jsontocsv, BLOCKING_NOPATH)
+from gnpy.tools.json_io import (load_equipment, load_network, load_json, load_requests, save_network,
+                                requests_from_json, save_json, load_initial_spectrum)
 from gnpy.tools.plots import plot_baseline, plot_results
+from gnpy.tools.worker_utils import designed_network, transmission_simulation, planning
+
 
 _logger = logging.getLogger(__name__)
 _examples_dir = Path(__file__).parent.parent / 'example-data'
@@ -47,7 +44,7 @@ def show_example_data_dir():
 
 
 def load_common_data(equipment_filename, topology_filename, simulation_filename, save_raw_network_filename):
-    '''Load common configuration from JSON files'''
+    """Load common configuration from JSON files"""
 
     try:
         equipment = load_equipment(equipment_filename)
@@ -84,7 +81,7 @@ def load_common_data(equipment_filename, topology_filename, simulation_filename,
 
 
 def _setup_logging(args):
-    logging.basicConfig(level={2: logging.DEBUG, 1: logging.INFO, 0: logging.CRITICAL}.get(args.verbose, logging.DEBUG))
+    logging.basicConfig(level={2: logging.DEBUG, 1: logging.INFO, 0: logging.WARNING}.get(args.verbose, logging.DEBUG))
 
 
 def _add_common_options(parser: argparse.ArgumentParser, network_default: Path):
@@ -108,16 +105,20 @@ def _add_common_options(parser: argparse.ArgumentParser, network_default: Path):
 
 
 def transmission_main_example(args=None):
+    """Main script running a single simulation. It returns the detailed power across crossed elements and
+    average performance accross all channels.
+    """
     parser = argparse.ArgumentParser(
         description='Send a full spectrum load through the network from point A to point B',
         epilog=_help_footer,
         formatter_class=argparse.ArgumentDefaultsHelpFormatter,
-        )
+    )
     _add_common_options(parser, network_default=_examples_dir / 'edfa_example_network.json')
     parser.add_argument('--show-channels', action='store_true', help='Show final per-channel OSNR and GSNR summary')
     parser.add_argument('-pl', '--plot', action='store_true')
     parser.add_argument('-l', '--list-nodes', action='store_true', help='list all transceiver nodes')
     parser.add_argument('-po', '--power', default=0, help='channel ref power in dBm')
+    parser.add_argument('--spectrum', type=Path, help='user defined mixed rate spectrum JSON file')
     parser.add_argument('source', nargs='?', help='source node')
     parser.add_argument('destination', nargs='?', help='destination node')
 
@@ -142,19 +143,17 @@ def transmission_main_example(args=None):
         sys.exit()
 
     # First try to find exact match if source/destination provided
+    source = None
     if args.source:
         source = transceivers.pop(args.source, None)
-        valid_source = True if source else False
-    else:
-        source = None
-        _logger.info('No source node specified: picking random transceiver')
+        valid_source = bool(source)
 
+    destination = None
+    nodes_list = []
+    loose_list = []
     if args.destination:
         destination = transceivers.pop(args.destination, None)
-        valid_destination = True if destination else False
-    else:
-        destination = None
-        _logger.info('No destination node specified: picking random transceiver')
+        valid_destination = bool(destination)
 
     # If no exact match try to find partial match
     if args.source and not source:
@@ -171,79 +170,77 @@ def transmission_main_example(args=None):
     if not source:
         source = list(transceivers.values())[0]
         del transceivers[source.uid]
+        _logger.info('No source node specified: picking random transceiver')
 
     if not destination:
         destination = list(transceivers.values())[0]
+        nodes_list = [destination.uid]
+        loose_list = ['STRICT']
+        _logger.info('No destination node specified: picking random transceiver')
 
-    _logger.info(f'source = {args.source!r}')
-    _logger.info(f'destination = {args.destination!r}')
-
-    params = {}
-    params['request_id'] = 0
-    params['trx_type'] = ''
-    params['trx_mode'] = ''
-    params['source'] = source.uid
-    params['destination'] = destination.uid
-    params['bidir'] = False
-    params['nodes_list'] = [destination.uid]
-    params['loose_list'] = ['strict']
-    params['format'] = ''
-    params['path_bandwidth'] = 0
-    params['effective_freq_slot'] = None
-    trx_params = trx_mode_params(equipment)
-    if args.power:
-        trx_params['power'] = db2lin(float(args.power)) * 1e-3
-    params.update(trx_params)
-    req = PathRequest(**params)
+    _logger.info(f'source = {source.uid!r}')
+    _logger.info(f'destination = {destination.uid!r}')
 
+    initial_spectrum = None
+    if args.spectrum:
+        # use the spectrum defined by user for the propagation.
+        # the nb of channel for design remains the one of the reference channel
+        initial_spectrum = load_initial_spectrum(args.spectrum)
+        print('User input for spectrum used for propagation instead of SI')
     power_mode = equipment['Span']['default'].power_mode
     print('\n'.join([f'Power mode is set to {power_mode}',
-                     f'=> it can be modified in eqpt_config.json - Span']))
+                     '=> it can be modified in eqpt_config.json - Span']))
 
-    pref_ch_db = lin2db(req.power * 1e3)  # reference channel power / span (SL=20dB)
-    pref_total_db = pref_ch_db + lin2db(req.nb_channel)  # reference total power / span (SL=20dB)
+    # Simulate !
     try:
-        build_network(network, equipment, pref_ch_db, pref_total_db, args.no_insert_edfas)
+        network, req, ref_req = designed_network(equipment, network, source.uid, destination.uid,
+                                                 nodes_list=nodes_list, loose_list=loose_list,
+                                                 args_power=args.power,
+                                                 initial_spectrum=initial_spectrum,
+                                                 no_insert_edfas=args.no_insert_edfas)
+        path, propagations_for_path, powers_dbm, infos = transmission_simulation(equipment, network, req, ref_req)
     except exceptions.NetworkTopologyError as e:
         print(f'{ansi_escapes.red}Invalid network definition:{ansi_escapes.reset} {e}')
         sys.exit(1)
     except exceptions.ConfigurationError as e:
         print(f'{ansi_escapes.red}Configuration error:{ansi_escapes.reset} {e}')
         sys.exit(1)
-    path = compute_constrained_path(network, req)
-
-    spans = [s.params.length for s in path if isinstance(s, RamanFiber) or isinstance(s, Fiber)]
-    print(f'\nThere are {len(spans)} fiber spans over {sum(spans)/1000:.0f} km between {source.uid} '
+    except exceptions.ServiceError as e:
+        print(f'Service error: {e}')
+        sys.exit(1)
+    except ValueError:
+        sys.exit(1)
+    # print or export results
+    spans = [s.params.length for s in path if isinstance(s, (Fiber, RamanFiber))]
+    print(f'\nThere are {len(spans)} fiber spans over {sum(spans) / 1000:.0f} km between {source.uid} '
           f'and {destination.uid}')
     print(f'\nNow propagating between {source.uid} and {destination.uid}:')
-
-    power_range = [0]
-    if power_mode:
-        # power cannot be changed in gain mode
-        try:
-            p_start, p_stop, p_step = equipment['SI']['default'].power_range_db
-            p_num = abs(int(round((p_stop - p_start) / p_step))) + 1 if p_step != 0 else 1
-            power_range = list(linspace(p_start, p_stop, p_num))
-        except TypeError:
-            print('invalid power range definition in eqpt_config, should be power_range_db: [lower, upper, step]')
-
-    for dp_db in power_range:
-        req.power = db2lin(pref_ch_db + dp_db) * 1e-3
+    print(f'Reference used for design: (Input optical power reference in span = {watt2dbm(ref_req.power):.2f}dBm,\n'
+          + f'                            spacing = {ref_req.spacing * 1e-9:.2f}GHz\n'
+          + f'                            nb_channels = {ref_req.nb_channel})')
+    print('\nChannels propagating: (Input optical power deviation in span = '
+          + f'{pretty_summary_print(per_label_average(infos.delta_pdb_per_channel, infos.label))}dB,\n'
+          + '                       spacing = '
+          + f'{pretty_summary_print(per_label_average(infos.slot_width * 1e-9, infos.label))}GHz,\n'
+          + '                       transceiver output power = '
+          + f'{pretty_summary_print(per_label_average(watt2dbm(infos.tx_power), infos.label))}dBm,\n'
+          + f'                       nb_channels = {infos.number_of_channels})')
+    for mypath, power_dbm in zip(propagations_for_path, powers_dbm):
         if power_mode:
-            print(f'\nPropagating with input power = {ansi_escapes.cyan}{lin2db(req.power*1e3):.2f} dBm{ansi_escapes.reset}:')
+            print(f'Input optical power reference in span = {ansi_escapes.cyan}{power_dbm:.2f} '
+                  + f'dBm{ansi_escapes.reset}:')
         else:
-            print(f'\nPropagating in {ansi_escapes.cyan}gain mode{ansi_escapes.reset}: power cannot be set manually')
-        infos = propagate(path, req, equipment)
-        if len(power_range) == 1:
-            for elem in path:
+            print('\nPropagating in {ansi_escapes.cyan}gain mode{ansi_escapes.reset}: power cannot be set manually')
+        if len(powers_dbm) == 1:
+            for elem in mypath:
                 print(elem)
             if power_mode:
-                print(f'\nTransmission result for input power = {lin2db(req.power*1e3):.2f} dBm:')
+                print(f'\nTransmission result for input optical power reference in span = {power_dbm:.2f} dBm:')
             else:
-                print(f'\nTransmission results:')
+                print('\nTransmission results:')
             print(f'  Final GSNR (0.1 nm): {ansi_escapes.cyan}{mean(destination.snr_01nm):.02f} dB{ansi_escapes.reset}')
         else:
-            print(path[-1])
+            print(mypath[-1])
 
     if args.save_network is not None:
         save_network(network, args.save_network)
@@ -264,9 +261,9 @@ def transmission_main_example(args=None):
             ch_freq = final_carrier.frequency * 1e-12
             ch_power = lin2db(final_carrier.power.signal * 1e3)
             print(
-                '{:5}{:26.2f}{:26.2f}{:28.2f}{:28.2f}{:28.2f}' .format(
+                '{:5}{:26.5f}{:26.2f}{:28.2f}{:28.2f}{:28.2f}' .format(
                     final_carrier.channel_number, round(
-                        ch_freq, 2), round(
+                        ch_freq, 5), round(
                         ch_power, 2), round(
                         ch_osnr, 2), round(
                         ch_snr_nl, 2), round(
@@ -291,11 +288,14 @@ def _path_result_json(pathresult):
 
 
 def path_requests_run(args=None):
+    """Main script running several services simulations. It returns a summary of the average performance
+    for each service.
+    """
     parser = argparse.ArgumentParser(
         description='Compute performance for a list of services provided in a json file or an excel sheet',
         epilog=_help_footer,
         formatter_class=argparse.ArgumentDefaultsHelpFormatter,
-        )
+    )
     _add_common_options(parser, network_default=_examples_dir / 'meshTopologyExampleV2.xls')
     parser.add_argument('service_filename', nargs='?', type=Path, metavar='SERVICES-REQUESTS.(json|xls|xlsx)',
                         default=_examples_dir / 'meshTopologyExampleV2.xls',
@@ -304,84 +304,48 @@ def path_requests_run(args=None):
                         help='considers that all demands are bidir')
     parser.add_argument('-o', '--output', type=Path, metavar=_help_fname_json_csv,
                         help='Store satisifed requests into a JSON or CSV file')
+    parser.add_argument('--redesign-per-request', action='store_true', help='Redesign the network at each request'
+                        + ' computation using the request as the reference channel')
 
     args = parser.parse_args(args if args is not None else sys.argv[1:])
     _setup_logging(args)
 
-    _logger.info(f'Computing path requests {args.service_filename} into JSON format')
+    _logger.info(f'Computing path requests {args.service_filename.name} into JSON format')
 
-    (equipment, network) = load_common_data(args.equipment, args.topology, args.sim_params, args.save_network_before_autodesign)
+    (equipment, network) = \
+        load_common_data(args.equipment, args.topology, args.sim_params, args.save_network_before_autodesign)
 
     # Build the network once using the default power defined in SI in eqpt config
-    # TODO power density: db2linp(ower_dbm": 0)/power_dbm": 0 * nb channels as defined by
-    # spacing, f_min and f_max
-    p_db = equipment['SI']['default'].power_dbm
 
-    p_total_db = p_db + lin2db(automatic_nch(equipment['SI']['default'].f_min,
-                                             equipment['SI']['default'].f_max, equipment['SI']['default'].spacing))
     try:
-        build_network(network, equipment, p_db, p_total_db, args.no_insert_edfas)
+        network, _, _ = designed_network(equipment, network, no_insert_edfas=args.no_insert_edfas)
+        data = load_requests(args.service_filename, equipment, bidir=args.bidir,
+                             network=network, network_filename=args.topology)
+        _data = requests_from_json(data, equipment)
+        _, propagatedpths, reversed_propagatedpths, rqs, dsjn, result = \
+            planning(network, equipment, data, redesign=args.redesign_per_request)
     except exceptions.NetworkTopologyError as e:
         print(f'{ansi_escapes.red}Invalid network definition:{ansi_escapes.reset} {e}')
         sys.exit(1)
     except exceptions.ConfigurationError as e:
         print(f'{ansi_escapes.red}Configuration error:{ansi_escapes.reset} {e}')
         sys.exit(1)
-    if args.save_network is not None:
-        save_network(network, args.save_network)
-        print(f'{ansi_escapes.blue}Network (after autodesign) saved to {args.save_network}{ansi_escapes.reset}')
-    oms_list = build_oms_list(network, equipment)
-
-    try:
-        data = load_requests(args.service_filename, equipment, bidir=args.bidir,
-                             network=network, network_filename=args.topology)
-        rqs = requests_from_json(data, equipment)
-    except exceptions.ServiceError as e:
-        print(f'{ansi_escapes.red}Service error:{ansi_escapes.reset} {e}')
-        sys.exit(1)
-    # check that request ids are unique. Non unique ids, may
-    # mess the computation: better to stop the computation
-    all_ids = [r.request_id for r in rqs]
-    if len(all_ids) != len(set(all_ids)):
-        for item in list(set(all_ids)):
-            all_ids.remove(item)
-        msg = f'Requests id {all_ids} are not unique'
-        _logger.critical(msg)
-        sys.exit()
-    rqs = correct_json_route_list(network, rqs)
-
-    # pths = compute_path(network, equipment, rqs)
-    dsjn = disjunctions_from_json(data)
-
-    print(f'{ansi_escapes.blue}List of disjunctions{ansi_escapes.reset}')
-    print(dsjn)
-    # need to warn or correct in case of wrong disjunction form
-    # disjunction must not be repeated with same or different ids
-    dsjn = deduplicate_disjunctions(dsjn)
-
-    # Aggregate demands with same exact constraints
-    print(f'{ansi_escapes.blue}Aggregating similar requests{ansi_escapes.reset}')
-
-    rqs, dsjn = requests_aggregation(rqs, dsjn)
-    # TODO export novel set of aggregated demands in a json file
-
-    print(f'{ansi_escapes.blue}The following services have been requested:{ansi_escapes.reset}')
-    print(rqs)
-
-    print(f'{ansi_escapes.blue}Computing all paths with constraints{ansi_escapes.reset}')
-    try:
-        pths = compute_path_dsjctn(network, equipment, rqs, dsjn)
     except exceptions.DisjunctionError as this_e:
         print(f'{ansi_escapes.red}Disjunction error:{ansi_escapes.reset} {this_e}')
         sys.exit(1)
+    except exceptions.ServiceError as e:
+        print(f'Service error: {e}')
+        sys.exit(1)
+    except ValueError:
+        sys.exit(1)
+    print(f'{ansi_escapes.blue}List of disjunctions{ansi_escapes.reset}')
+    print(dsjn)
+    print(f'{ansi_escapes.blue}The following services have been requested:{ansi_escapes.reset}')
+    print(_data)
 
-    print(f'{ansi_escapes.blue}Propagating on selected path{ansi_escapes.reset}')
-    propagatedpths, reversed_pths, reversed_propagatedpths = compute_path_with_disjunction(network, equipment, rqs, pths)
-    # Note that deepcopy used in compute_path_with_disjunction returns
-    # a list of nodes which are not belonging to network (they are copies of the node objects).
-    # so there can not be propagation on these nodes.
-
-    pth_assign_spectrum(pths, rqs, oms_list, reversed_pths)
+    if args.save_network is not None:
+        save_network(network, args.save_network)
+        print(f'Network (after autodesign) saved to {args.save_network}')
 
     print(f'{ansi_escapes.blue}Result summary{ansi_escapes.reset}')
     header = ['req id', '  demand', ' GSNR@bandwidth A-Z (Z-A)', ' GSNR@0.1nm A-Z (Z-A)',
@@ -392,27 +356,27 @@ def path_requests_run(args=None):
     for i, this_p in enumerate(propagatedpths):
         rev_pth = reversed_propagatedpths[i]
         if rev_pth and this_p:
-            psnrb = f'{round(mean(this_p[-1].snr),2)} ({round(mean(rev_pth[-1].snr),2)})'
+            psnrb = f'{round(mean(this_p[-1].snr), 2)} ({round(mean(rev_pth[-1].snr), 2)})'
             psnr = f'{round(mean(this_p[-1].snr_01nm), 2)}' +\
-                f' ({round(mean(rev_pth[-1].snr_01nm),2)})'
+                f' ({round(mean(rev_pth[-1].snr_01nm), 2)})'
         elif this_p:
-            psnrb = f'{round(mean(this_p[-1].snr),2)}'
-            psnr = f'{round(mean(this_p[-1].snr_01nm),2)}'
+            psnrb = f'{round(mean(this_p[-1].snr), 2)}'
+            psnr = f'{round(mean(this_p[-1].snr_01nm), 2)}'
 
         try:
             if rqs[i].blocking_reason in BLOCKING_NOPATH:
                 line = [f'{rqs[i].request_id}', f' {rqs[i].source} to {rqs[i].destination} :',
-                        f'-', f'-', f'-', f'{rqs[i].tsp_mode}', f'{round(rqs[i].path_bandwidth * 1e-9,2)}',
-                        f'-', f'{rqs[i].blocking_reason}']
+                        '-', '-', '-', f'{rqs[i].tsp_mode}', f'{round(rqs[i].path_bandwidth * 1e-9, 2)}',
+                        '-', '{rqs[i].blocking_reason}']
             else:
                 line = [f'{rqs[i].request_id}', f' {rqs[i].source} to {rqs[i].destination} : ', psnrb,
-                        psnr, f'-', f'{rqs[i].tsp_mode}', f'{round(rqs[i].path_bandwidth * 1e-9, 2)}',
-                        f'-', f'{rqs[i].blocking_reason}']
+                        psnr, '-', f'{rqs[i].tsp_mode}', f'{round(rqs[i].path_bandwidth * 1e-9, 2)}',
+                        '-', f'{rqs[i].blocking_reason}']
         except AttributeError:
             line = [f'{rqs[i].request_id}', f' {rqs[i].source} to {rqs[i].destination} : ', psnrb,
                     psnr, f'{rqs[i].OSNR + equipment["SI"]["default"].sys_margins}',
-                    f'{rqs[i].tsp_mode}', f'{round(rqs[i].path_bandwidth * 1e-9,2)}',
-                    f'{ceil(rqs[i].path_bandwidth / rqs[i].bit_rate) }', f'({rqs[i].N},{rqs[i].M})']
+                    f'{rqs[i].tsp_mode}', f'{round(rqs[i].path_bandwidth * 1e-9, 2)}',
+                    f'{ceil(rqs[i].path_bandwidth / rqs[i].bit_rate)}', f'({rqs[i].N},{rqs[i].M})']
         data.append(line)
 
     col_width = max(len(word) for row in data for word in row[2:])   # padding

gnpy/tools/convert.py

@@ -20,26 +20,37 @@ In the "Links" sheet, only the first three columns ("Node A", "Node Z" and
 the "east" information so that it is possible to input undirected data.
 """
 
-from xlrd import open_workbook
+from logging import getLogger
 from argparse import ArgumentParser
 from collections import namedtuple, Counter, defaultdict
 from itertools import chain
 from json import dumps
 from pathlib import Path
 from copy import copy
-from gnpy.core import ansi_escapes
-from gnpy.core.utils import silent_remove
+from typing import Dict, List, Tuple, DefaultDict
+from xlrd import open_workbook
+from xlrd.biffh import XLRDError
+from networkx import DiGraph
+
+from gnpy.core.utils import silent_remove, transform_data
 from gnpy.core.exceptions import NetworkTopologyError
 from gnpy.core.elements import Edfa, Fused, Fiber
 
 
+_logger = getLogger(__name__)
+
+
 def all_rows(sh, start=0):
+    """Returns all rows of the xls(x) sheet starting from start row
+    """
     return (sh.row(x) for x in range(start, sh.nrows))
 
 
-class Node(object):
+class Node:
+    """Node data class
+    """
     def __init__(self, **kwargs):
-        super(Node, self).__init__()
+        super().__init__()
         self.update_attr(kwargs)
 
     def update_attr(self, kwargs):
@@ -61,13 +72,13 @@ class Node(object):
     }
 
 
-class Link(object):
+class Link:
     """attribtes from west parse_ept_headers dict
     +node_a, node_z, west_fiber_con_in, east_fiber_con_in
     """
 
     def __init__(self, **kwargs):
-        super(Link, self).__init__()
+        super().__init__()
         self.update_attr(kwargs)
         self.distance_units = 'km'
 
@@ -76,7 +87,7 @@ class Link(object):
         for k, v in self.default_values.items():
             v = clean_kwargs.get(k, v)
             setattr(self, k, v)
-            k = 'west' + k.split('east')[-1]
+            k = 'west' + k.rsplit('east', maxsplit=1)[-1]
             v = clean_kwargs.get(k, v)
             setattr(self, k, v)
 
@@ -97,9 +108,11 @@ class Link(object):
     }
 
 
-class Eqpt(object):
+class Eqpt:
+    """
+    """
     def __init__(self, **kwargs):
-        super(Eqpt, self).__init__()
+        super().__init__()
         self.update_attr(kwargs)
 
     def update_attr(self, kwargs):
@@ -107,7 +120,7 @@ class Eqpt(object):
         for k, v in self.default_values.items():
             v_east = clean_kwargs.get(k, v)
             setattr(self, k, v_east)
-            k = 'west' + k.split('east')[-1]
+            k = 'west' + k.rsplit('east', maxsplit=1)[-1]
             v_west = clean_kwargs.get(k, v)
             setattr(self, k, v_west)
 
@@ -115,17 +128,18 @@ class Eqpt(object):
         'from_city': '',
         'to_city': '',
         'east_amp_type': '',
-        'east_att_in': 0,
         'east_amp_gain': None,
         'east_amp_dp': None,
-        'east_tilt': 0,
+        'east_tilt_vs_wavelength': None,
         'east_att_out': None
     }
 
 
-class Roadm(object):
+class Roadm:
+    """
+    """
     def __init__(self, **kwargs):
-        super(Roadm, self).__init__()
+        super().__init__()
         self.update_attr(kwargs)
 
     def update_attr(self, kwargs):
@@ -136,7 +150,10 @@ class Roadm(object):
 
     default_values = {'from_node': '',
                       'to_node': '',
-                      'target_pch_out_db': None
+                      'target_pch_out_db': None,
+                      'type_variety': None,
+                      'from_degrees': None,
+                      'impairment_ids': None
                       }
 
 
@@ -149,7 +166,7 @@ def read_header(my_sheet, line, slice_):
     try:
         header = [x.value.strip() for x in my_sheet.row_slice(line, slice_[0], slice_[1])]
         header_i = [Param_header(header, i + slice_[0]) for i, header in enumerate(header) if header != '']
-    except Exception:
+    except (AttributeError, IndexError):
         header_i = []
     if header_i != [] and header_i[-1].colindex != slice_[1]:
         header_i.append(Param_header('', slice_[1]))
@@ -165,7 +182,7 @@ def read_slice(my_sheet, line, slice_, header):
         try:
             slice_range = next((h.colindex, header_i[i + 1].colindex)
                                for i, h in enumerate(header_i) if header in h.header)
-        except Exception:
+        except StopIteration:
             pass
     return slice_range
 
@@ -183,76 +200,123 @@ def parse_headers(my_sheet, input_headers_dict, headers, start_line, slice_in):
             slice_out = read_slice(my_sheet, start_line + iteration, slice_in, h0)
             iteration += 1
         if slice_out == (-1, -1):
+            msg = f'missing header {h0}'
             if h0 in ('east', 'Node A', 'Node Z', 'City'):
-                print(f'{ansi_escapes.red}CRITICAL{ansi_escapes.reset}: missing _{h0}_ header: EXECUTION ENDS')
-                raise NetworkTopologyError(f'Missing _{h0}_ header')
-            else:
-                print(f'missing header {h0}')
+                raise NetworkTopologyError(msg)
+            _logger.warning(msg)
         elif not isinstance(input_headers_dict[h0], dict):
             headers[slice_out[0]] = input_headers_dict[h0]
         else:
             headers = parse_headers(my_sheet, input_headers_dict[h0], headers, start_line + 1, slice_out)
     if headers == {}:
-        print(f'{ansi_escapes.red}CRITICAL ERROR{ansi_escapes.reset}: could not find any header to read _ ABORT')
-        raise NetworkTopologyError('Could not find any header to read')
+        msg = 'CRITICAL ERROR: could not find any header to read _ ABORT'
+        raise NetworkTopologyError(msg)
     return headers
 
 
 def parse_row(row, headers):
+    """
+    """
     return {f: r.value for f, r in
-            zip([label for label in headers.values()], [row[i] for i in headers])}
+            zip(list(headers.values()), [row[i] for i in headers])}
 
 
 def parse_sheet(my_sheet, input_headers_dict, header_line, start_line, column):
+    """
+    """
     headers = parse_headers(my_sheet, input_headers_dict, {}, header_line, (0, column))
     for row in all_rows(my_sheet, start=start_line):
         yield parse_row(row[0: column], headers)
 
 
-def _format_items(items):
+def _format_items(items: List[str]):
+    """formating utils
+    """
     return '\n'.join(f' - {item}' for item in items)
 
 
-def sanity_check(nodes, links, nodes_by_city, links_by_city, eqpts_by_city):
-
+def sanity_check(nodes: List[Node], links: List[Link],
+                 nodes_by_city: Dict[str, Node], links_by_city: DefaultDict[str, List[Link]],
+                 eqpts_by_city: DefaultDict[str, List[Eqpt]]) -> Tuple[List[Node], List[Link]]:
+    """Raise correct issues if xls(x) is not correct, Correct type to ROADM if more tha 2-degrees
+    checks duplicate links, unreferenced nodes in links, in eqpts, unreferenced link in eqpts,
+    duplicate items
+    """
     duplicate_links = []
     for l1 in links:
         for l2 in links:
             if l1 is not l2 and l1 == l2 and l2 not in duplicate_links:
-                print(f'\nWARNING\n \
+                _logger.warning(f'\nWARNING\n \
                     link {l1.from_city}-{l1.to_city} is duplicate \
                     \nthe 1st duplicate link will be removed but you should check Links sheet input')
                 duplicate_links.append(l1)
-    for l in duplicate_links:
-        links.remove(l)
-        links_by_city[l.from_city].remove(l)
-        links_by_city[l.to_city].remove(l)
 
+    if duplicate_links:
+        msg = 'XLS error: ' \
+              + f'links {_format_items([(d.from_city, d.to_city) for d in duplicate_links])} are duplicate'
+        raise NetworkTopologyError(msg)
     unreferenced_nodes = [n for n in nodes_by_city if n not in links_by_city]
     if unreferenced_nodes:
-        raise NetworkTopologyError(f'{ansi_escapes.red}XLS error:{ansi_escapes.reset} The following nodes are not '
-                                   f'referenced from the {ansi_escapes.cyan}Links{ansi_escapes.reset} sheet. '
-                                   f'If unused, remove them from the {ansi_escapes.cyan}Nodes{ansi_escapes.reset} '
-                                   f'sheet:\n'
-                                   + _format_items(unreferenced_nodes))
+        msg = 'XLS error: The following nodes are not ' \
+              + 'referenced from the Links sheet. ' \
+              + 'If unused, remove them from the Nodes sheet:\n' \
+              + _format_items(unreferenced_nodes)
+        raise NetworkTopologyError(msg)
     # no need to check "Links" for invalid nodes because that's already in parse_excel()
     wrong_eqpt_from = [n for n in eqpts_by_city if n not in nodes_by_city]
     wrong_eqpt_to = [n.to_city for destinations in eqpts_by_city.values()
                      for n in destinations if n.to_city not in nodes_by_city]
     wrong_eqpt = wrong_eqpt_from + wrong_eqpt_to
     if wrong_eqpt:
-        raise NetworkTopologyError(f'{ansi_escapes.red}XLS error:{ansi_escapes.reset} '
-                                   f'The {ansi_escapes.cyan}Eqpt{ansi_escapes.reset} sheet refers to nodes that '
-                                   f'are not defined in the {ansi_escapes.cyan}Nodes{ansi_escapes.reset} sheet:\n'
-                                   + _format_items(wrong_eqpt))
+        msg = 'XLS error: ' \
+              + 'The Eqpt sheet refers to nodes that ' \
+              + 'are not defined in the Nodes sheet:\n'\
+              + _format_items(wrong_eqpt)
+        raise NetworkTopologyError(msg)
+    # Now check links that are not listed in Links sheet, and duplicates
+    bad_eqpt = []
+    possible_links = [f'{e.from_city}|{e.to_city}' for e in links] + [f'{e.to_city}|{e.from_city}' for e in links]
+    possible_eqpt = []
+    duplicate_eqpt = []
+    duplicate_ila = []
+    for city, eqpts in eqpts_by_city.items():
+        for eqpt in eqpts:
+            # Check that each node_A-node_Z exists in links
+            nodea_nodez = f'{eqpt.from_city}|{eqpt.to_city}'
+            nodez_nodea = f'{eqpt.to_city}|{eqpt.from_city}'
+            if nodea_nodez not in possible_links \
+                    or nodez_nodea not in possible_links:
+                bad_eqpt.append([eqpt.from_city, eqpt.to_city])
+            else:
+                # Check that there are no duplicate lines in the Eqpt sheet
+                if nodea_nodez in possible_eqpt:
+                    duplicate_eqpt.append([eqpt.from_city, eqpt.to_city])
+                else:
+                    possible_eqpt.append(nodea_nodez)
+            # check that there are no two lines defining an ILA with different directions
+        if nodes_by_city[city].node_type == 'ILA' and len(eqpts) > 1:
+            duplicate_ila.append(city)
+    if bad_eqpt:
+        msg = 'XLS error: ' \
+              + 'The Eqpt sheet references links that ' \
+              + 'are not defined in the Links sheet:\n' \
+              + _format_items(f'{item[0]} -> {item[1]}' for item in bad_eqpt)
+        raise NetworkTopologyError(msg)
+    if duplicate_eqpt:
+        msg = 'XLS error: Duplicate lines in Eqpt sheet:' \
+              + _format_items(f'{item[0]} -> {item[1]}' for item in duplicate_eqpt)
+        raise NetworkTopologyError(msg)
+    if duplicate_ila:
+        msg = 'XLS error: Duplicate ILA eqpt definition in Eqpt sheet:' \
+              + _format_items(duplicate_ila)
+        raise NetworkTopologyError(msg)
 
     for city, link in links_by_city.items():
         if nodes_by_city[city].node_type.lower() == 'ila' and len(link) != 2:
             # wrong input: ILA sites can only be Degree 2
             # => correct to make it a ROADM and remove entry in links_by_city
-            # TODO: put in log rather than print
-            print(f'invalid node type ({nodes_by_city[city].node_type})\
-                  specified in {city}, replaced by ROADM')
+            _logger.warning(f'invalid node type ({nodes_by_city[city].node_type}) '
+                            + f'specified in {city}, replaced by ROADM')
             nodes_by_city[city].node_type = 'ROADM'
             for n in nodes:
                 if n.city == city:
@@ -275,13 +339,29 @@ def create_roadm_element(node, roadms_by_city):
                'booster_variety_list': silent_remove(node.booster_restriction.split(' | '), '')}
                           }
     if node.city in roadms_by_city.keys():
-        if 'params' not in roadm.keys():
+        if 'params' not in roadm:
             roadm['params'] = {}
         roadm['params']['per_degree_pch_out_db'] = {}
         for elem in roadms_by_city[node.city]:
             to_node = f'east edfa in {node.city} to {elem.to_node}'
             if elem.target_pch_out_db is not None:
                 roadm['params']['per_degree_pch_out_db'][to_node] = elem.target_pch_out_db
+            if elem.from_degrees is not None and elem.impairment_ids is not None:
+                # only set per degree impairment if there is an entry (reduce verbose)
+                if roadm['params'].get('per_degree_impairments') is None:
+                    roadm['params']['per_degree_impairments'] = []
+                fromdegrees = elem.from_degrees.split(' | ')
+                impairment_ids = transform_data(elem.impairment_ids)
+                if len(fromdegrees) != len(impairment_ids):
+                    msg = f'Roadm {node.city} per degree impairment id do not match with from degree definition'
+                    raise NetworkTopologyError(msg)
+                for from_degree, impairment_id in zip(fromdegrees, impairment_ids):
+                    from_node = f'west edfa in {node.city} to {from_degree}'
+                    roadm['params']['per_degree_impairments'].append({'from_degree': from_node,
+                                                                      'to_degree': to_node,
+                                                                      'impairment_id': impairment_id})
+            if elem.type_variety is not None:
+                roadm['type_variety'] = elem.type_variety
     roadm['metadata'] = {'location': {'city':      node.city,
                                       'region':    node.region,
                                       'latitude':  node.latitude,
@@ -290,7 +370,7 @@ def create_roadm_element(node, roadms_by_city):
     return roadm
 
 
-def create_east_eqpt_element(node):
+def create_east_eqpt_element(node: Node, nodes_by_city: Dict[str, Node]) -> dict:
     """ create amplifiers json elements for the east direction.
     this includes the case where the case of a fused element defined instead of an
     ILA in eqpt sheet
@@ -305,13 +385,13 @@ def create_east_eqpt_element(node):
         eqpt['type_variety'] = f'{node.east_amp_type}'
         eqpt['operational'] = {'gain_target': node.east_amp_gain,
                                'delta_p':     node.east_amp_dp,
-                               'tilt_target': node.east_tilt,
+                               'tilt_target': node.east_tilt_vs_wavelength,
                                'out_voa':     node.east_att_out}
     elif node.east_amp_type.lower() == '':
         eqpt['type'] = 'Edfa'
         eqpt['operational'] = {'gain_target': node.east_amp_gain,
                                'delta_p':     node.east_amp_dp,
-                               'tilt_target': node.east_tilt,
+                               'tilt_target': node.east_tilt_vs_wavelength,
                                'out_voa':     node.east_att_out}
     elif node.east_amp_type.lower() == 'fused':
         # fused edfa variety is a hack to indicate that there should not be
@@ -323,7 +403,7 @@ def create_east_eqpt_element(node):
     return eqpt
 
 
-def create_west_eqpt_element(node):
+def create_west_eqpt_element(node: Node, nodes_by_city: Dict[str, Node]) -> dict:
     """ create amplifiers json elements for the west direction.
     this includes the case where the case of a fused element defined instead of an
     ILA in eqpt sheet
@@ -338,19 +418,25 @@ def create_west_eqpt_element(node):
         eqpt['type_variety'] = f'{node.west_amp_type}'
         eqpt['operational'] = {'gain_target': node.west_amp_gain,
                                'delta_p':     node.west_amp_dp,
-                               'tilt_target': node.west_tilt,
+                               'tilt_target': node.west_tilt_vs_wavelength,
                                'out_voa':     node.west_att_out}
     elif node.west_amp_type.lower() == '':
         eqpt['operational'] = {'gain_target': node.west_amp_gain,
                                'delta_p':     node.west_amp_dp,
-                               'tilt_target': node.west_tilt,
+                               'tilt_target': node.west_tilt_vs_wavelength,
                                'out_voa':     node.west_att_out}
     elif node.west_amp_type.lower() == 'fused':
         eqpt['type'] = 'Fused'
         eqpt['params'] = {'loss': 0}
     return eqpt
 
-def xls_to_json_data(input_filename, filter_region=[]):
+
+def xls_to_json_data(input_filename: Path, filter_region: List[str] = None) -> Dict:
+    """Read the excel sheets and produces the json dict in GNPy format (legacy)
+    returns json dict
+    """
+    if filter_region is None:
+        filter_region = []
     nodes, links, eqpts, roadms = parse_excel(input_filename)
     if filter_region:
         nodes = [n for n in nodes if n.region.lower() in filter_region]
@@ -360,16 +446,13 @@ def xls_to_json_data(input_filename, filter_region=[]):
         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]
 
-    global nodes_by_city
     nodes_by_city = {n.city: n for n in nodes}
 
-    global links_by_city
     links_by_city = defaultdict(list)
     for link in links:
         links_by_city[link.from_city].append(link)
         links_by_city[link.to_city].append(link)
 
-    global eqpts_by_city
     eqpts_by_city = defaultdict(list)
     for eqpt in eqpts:
         eqpts_by_city[eqpt.from_city].append(eqpt)
@@ -435,7 +518,7 @@ def xls_to_json_data(input_filename, filter_region=[]):
                                         'longitude': x.longitude}},
               'type': 'Edfa',
               'operational': {'gain_target': None,
-                              'tilt_target': 0}
+                              'tilt_target': None}
               } for x in nodes_by_city.values() if x.node_type.lower() == 'ila' and x.city not in eqpts_by_city] +
             [{'uid': f'east edfa in {x.city}',
               'metadata': {'location': {'city': x.city,
@@ -444,25 +527,26 @@ def xls_to_json_data(input_filename, filter_region=[]):
                                         'longitude': x.longitude}},
               'type': 'Edfa',
               'operational': {'gain_target': None,
-                              'tilt_target': 0}
-              } for x in nodes_by_city.values() if x.node_type.lower() == 'ila' and x.city not in eqpts_by_city] +
-            [create_east_eqpt_element(e) for e in eqpts] +
-            [create_west_eqpt_element(e) for e in eqpts],
+                              'tilt_target': None}
+              } for x in nodes_by_city.values() if x.node_type.lower() == 'ila' and x.city not in eqpts_by_city]
+            + [create_east_eqpt_element(e, nodes_by_city) for e in eqpts]
+            + [create_west_eqpt_element(e, nodes_by_city) for e in eqpts],
         'connections':
-            list(chain.from_iterable([eqpt_connection_by_city(n.city)
+            list(chain.from_iterable([eqpt_connection_by_city(n.city, eqpts_by_city, links_by_city, nodes_by_city)
                                       for n in nodes]))
-            +
-            list(chain.from_iterable(zip(
-                [{'from_node': f'trx {x.city}',
-                  'to_node': f'roadm {x.city}'}
+            + list(chain.from_iterable(zip(
+                [{'from_node': f'trx {x.city}', 'to_node': f'roadm {x.city}'}
                  for x in nodes_by_city.values() if x.node_type.lower() == 'roadm'],
-                [{'from_node': f'roadm {x.city}',
-                  'to_node': f'trx {x.city}'}
+                [{'from_node': f'roadm {x.city}', 'to_node': f'trx {x.city}'}
                  for x in nodes_by_city.values() if x.node_type.lower() == 'roadm'])))
     }
 
 
-def convert_file(input_filename, filter_region=[], output_json_file_name=None):
+def convert_file(input_filename: Path, filter_region: List[str] = None, output_json_file_name: Path = None):
+    """Save the conversion into
+    """
+    if filter_region is None:
+        filter_region = []
     data = xls_to_json_data(input_filename, filter_region)
     if output_json_file_name is None:
         output_json_file_name = input_filename.with_suffix('.json')
@@ -472,79 +556,77 @@ def convert_file(input_filename, filter_region=[], output_json_file_name=None):
     return output_json_file_name
 
 
-def corresp_names(input_filename, network):
+def corresp_names(input_filename: Path, network: DiGraph):
     """ a function that builds the correspondance between names given in the excel,
         and names used in the json, and created by the autodesign.
         All names are listed
     """
-    nodes, links, eqpts, roadms = parse_excel(input_filename)
+    nodes, links, eqpts, _ = parse_excel(input_filename)
     fused = [n.uid for n in network.nodes() if isinstance(n, Fused)]
     ila = [n.uid for n in network.nodes() if isinstance(n, Edfa)]
 
     corresp_roadm = {x.city: [f'roadm {x.city}'] for x in nodes
                      if x.node_type.lower() == 'roadm'}
     corresp_fused = {x.city: [f'west fused spans in {x.city}', f'east fused spans in {x.city}']
-                     for x in nodes if x.node_type.lower() == 'fused' and
-                     f'west fused spans in {x.city}' in fused and
-                     f'east fused spans in {x.city}' in fused}
-
+                     for x in nodes if x.node_type.lower() == 'fused'
+                     and f'west fused spans in {x.city}' in fused
+                     and f'east fused spans in {x.city}' in fused}
+    corresp_ila = defaultdict(list)
     # add the special cases when an ila is changed into a fused
     for my_e in eqpts:
         name = f'east edfa in {my_e.from_city} to {my_e.to_city}'
         if my_e.east_amp_type.lower() == 'fused' and name in fused:
-            if my_e.from_city in corresp_fused.keys():
-                corresp_fused[my_e.from_city].append(name)
-            else:
-                corresp_fused[my_e.from_city] = [name]
+            corresp_fused.get(my_e.from_city, []).append(name)
         name = f'west edfa in {my_e.from_city} to {my_e.to_city}'
         if my_e.west_amp_type.lower() == 'fused' and name in fused:
-            if my_e.from_city in corresp_fused.keys():
-                corresp_fused[my_e.from_city].append(name)
-            else:
-                corresp_fused[my_e.from_city] = [name]
+            corresp_fused.get(my_e.from_city, []).append(name)
     # build corresp ila based on eqpt sheet
     # start with east direction
-    corresp_ila = {e.from_city: [f'east edfa in {e.from_city} to {e.to_city}']
-                   for e in eqpts if f'east edfa in {e.from_city} to {e.to_city}' in ila}
-    # west direction, append name or create a new item in dict
     for my_e in eqpts:
-        name = f'west edfa in {my_e.from_city} to {my_e.to_city}'
-        if name in ila:
-            if my_e.from_city in corresp_ila.keys():
+        for name in [f'east edfa in {my_e.from_city} to {my_e.to_city}',
+                     f'west edfa in {my_e.from_city} to {my_e.to_city}']:
+            if name in ila:
                 corresp_ila[my_e.from_city].append(name)
-            else:
-                corresp_ila[my_e.from_city] = [name]
     # complete with potential autodesign names: amplifiers
     for my_l in links:
-        name = f'Edfa0_fiber ({my_l.to_city} \u2192 {my_l.from_city})-{my_l.west_cable}'
-        if name in ila:
-            if my_l.from_city in corresp_ila.keys():
+        # create names whatever the type and filter them out
+        # from-to direction
+        names = [f'Edfa_preamp_roadm {my_l.from_city}_from_fiber ({my_l.to_city} \u2192 {my_l.from_city})-{my_l.west_cable}',
+                 f'Edfa_booster_roadm {my_l.from_city}_to_fiber ({my_l.from_city} \u2192 {my_l.to_city})-{my_l.east_cable}']
+        for name in names:
+            if name in ila:
                 # "east edfa in Stbrieuc to Rennes_STA"  is equivalent name as
-                # "Edfa0_fiber (Lannion_CAS → Stbrieuc)-F056"
+                # "Edfa_booster_roadm Stbrieuc_to_fiber (Lannion_CAS → Stbrieuc)-F056"
                 # "west edfa in Stbrieuc to Rennes_STA"  is equivalent name as
-                # "Edfa0_fiber (Rennes_STA → Stbrieuc)-F057"
-                # does not filter names: all types (except boosters) are created.
-                # in case fibers are splitted the name here is a prefix
+                # "Edfa_preamp_roadm Stbrieuc_to_fiber (Rennes_STA → Stbrieuc)-F057"
+                # in case fibers are splitted the name here is a
                 corresp_ila[my_l.from_city].append(name)
-            else:
-                corresp_ila[my_l.from_city] = [name]
-        name = f'Edfa0_fiber ({my_l.from_city} \u2192 {my_l.to_city})-{my_l.east_cable}'
-        if name in ila:
-            if my_l.to_city in corresp_ila.keys():
+        # to-from direction
+        names = [f'Edfa_preamp_roadm {my_l.to_city}_from_fiber ({my_l.from_city} \u2192 {my_l.to_city})-{my_l.east_cable}',
+                 f'Edfa_booster_roadm {my_l.to_city}_to_fiber ({my_l.to_city} \u2192 {my_l.from_city})-{my_l.west_cable}']
+        for name in names:
+            if name in ila:
                 corresp_ila[my_l.to_city].append(name)
-            else:
-                corresp_ila[my_l.to_city] = [name]
+    for node in nodes:
+        names = [f'east edfa in {node.city}', f'west edfa in {node.city}']
+        for name in names:
+            if name in ila:
+                # "east edfa in Stbrieuc to Rennes_STA" (created with Eqpt) is equivalent name as
+                # "east edfa in Stbrieuc" or "west edfa in Stbrieuc" (created with Links sheet)
+                # depending on link node order
+                corresp_ila[node.city].append(name)
+
     # merge fused with ila:
     for key, val in corresp_fused.items():
-        if key in corresp_ila.keys():
-            corresp_ila[key].extend(val)
-        else:
-            corresp_ila[key] = val
+        corresp_ila[key].extend(val)
         # no need of roadm booster
     return corresp_roadm, corresp_fused, corresp_ila
 
 
-def parse_excel(input_filename):
+def parse_excel(input_filename: Path) -> Tuple[List[Node], List[Link], List[Eqpt], List[Roadm]]:
+    """reads xls(x) sheets among Nodes, Eqpts, Links, Roadms and parse the data in the sheets
+    into internal data structure Node, Link, Eqpt, Roadm, classes
+    """
     link_headers = {
         'Node A': 'from_city',
         'Node Z': 'to_city',
@@ -583,7 +665,6 @@ def parse_excel(input_filename):
         'Node Z': 'to_city',
         'east': {
             'amp type': 'east_amp_type',
-            'att_in': 'east_att_in',
             'amp gain': 'east_amp_gain',
             'delta p': 'east_amp_dp',
             'tilt': 'east_tilt',
@@ -591,7 +672,6 @@ def parse_excel(input_filename):
         },
         'west': {
             'amp type': 'west_amp_type',
-            'att_in': 'west_att_in',
             'amp gain': 'west_amp_gain',
             'delta p': 'west_amp_dp',
             'tilt': 'west_tilt',
@@ -600,7 +680,10 @@ def parse_excel(input_filename):
     }
     roadm_headers = {'Node A': 'from_node',
                      'Node Z': 'to_node',
-                     'per degree target power (dBm)': 'target_pch_out_db'
+                     'per degree target power (dBm)': 'target_pch_out_db',
+                     'type_variety': 'type_variety',
+                     'from degrees': 'from_degrees',
+                     'from degree to degree impairment id': 'impairment_ids'
                      }
 
     with open_workbook(input_filename) as wb:
@@ -608,81 +691,84 @@ def parse_excel(input_filename):
         links_sheet = wb.sheet_by_name('Links')
         try:
             eqpt_sheet = wb.sheet_by_name('Eqpt')
-        except Exception:
+        except XLRDError:
             # eqpt_sheet is optional
             eqpt_sheet = None
         try:
             roadm_sheet = wb.sheet_by_name('Roadms')
-        except Exception:
+        except XLRDError:
             # roadm_sheet is optional
             roadm_sheet = None
 
-        nodes = []
-        for node in parse_sheet(nodes_sheet, node_headers, NODES_LINE, NODES_LINE + 1, NODES_COLUMN):
-            nodes.append(Node(**node))
+        nodes = [Node(**node) for node in parse_sheet(nodes_sheet, node_headers,
+                                                      NODES_LINE, NODES_LINE + 1, NODES_COLUMN)]
         expected_node_types = {'ROADM', 'ILA', 'FUSED'}
         for n in nodes:
             if n.node_type not in expected_node_types:
                 n.node_type = 'ILA'
 
-        links = []
-        for link in parse_sheet(links_sheet, link_headers, LINKS_LINE, LINKS_LINE + 2, LINKS_COLUMN):
-            links.append(Link(**link))
-
+        links = [Link(**link) for link in parse_sheet(links_sheet, link_headers,
+                                                      LINKS_LINE, LINKS_LINE + 2, LINKS_COLUMN)]
         eqpts = []
         if eqpt_sheet is not None:
-            for eqpt in parse_sheet(eqpt_sheet, eqpt_headers, EQPTS_LINE, EQPTS_LINE + 2, EQPTS_COLUMN):
-                eqpts.append(Eqpt(**eqpt))
-
+            eqpts = [Eqpt(**eqpt) for eqpt in parse_sheet(eqpt_sheet, eqpt_headers,
+                                                          EQPTS_LINE, EQPTS_LINE + 2, EQPTS_COLUMN)]
         roadms = []
         if roadm_sheet is not None:
-            for roadm in parse_sheet(roadm_sheet, roadm_headers, ROADMS_LINE, ROADMS_LINE+2, ROADMS_COLUMN):
-                roadms.append(Roadm(**roadm))
+            roadms = [Roadm(**roadm) for roadm in parse_sheet(roadm_sheet, roadm_headers,
+                                                              ROADMS_LINE, ROADMS_LINE + 2, ROADMS_COLUMN)]
 
     # sanity check
     all_cities = Counter(n.city for n in nodes)
     if len(all_cities) != len(nodes):
-        raise ValueError(f'Duplicate city: {all_cities}')
+        msg = f'Duplicate city: {all_cities}'
+        raise NetworkTopologyError(msg)
     bad_links = []
     for lnk in links:
         if lnk.from_city not in all_cities or lnk.to_city not in all_cities:
             bad_links.append([lnk.from_city, lnk.to_city])
 
     if bad_links:
-        raise NetworkTopologyError(f'{ansi_escapes.red}XLS error:{ansi_escapes.reset} '
-                                   f'The {ansi_escapes.cyan}Links{ansi_escapes.reset} sheet references nodes that '
-                                   f'are not defined in the {ansi_escapes.cyan}Nodes{ansi_escapes.reset} sheet:\n'
-                                   + _format_items(f'{item[0]} -> {item[1]}' for item in bad_links))
+        msg = 'XLS error: ' \
+              + 'The Links sheet references nodes that ' \
+              + 'are not defined in the Nodes sheet:\n' \
+              + _format_items(f'{item[0]} -> {item[1]}' for item in bad_links)
+        raise NetworkTopologyError(msg)
 
     return nodes, links, eqpts, roadms
 
 
-def eqpt_connection_by_city(city_name):
-    other_cities = fiber_dest_from_source(city_name)
+def eqpt_connection_by_city(city_name: str, eqpts_by_city: DefaultDict[str, List[Eqpt]],
+                            links_by_city: DefaultDict[str, List[Link]], nodes_by_city: Dict[str, Node]) -> list:
+    """
+    """
+    other_cities = fiber_dest_from_source(city_name, links_by_city)
     subdata = []
     if nodes_by_city[city_name].node_type.lower() in {'ila', 'fused'}:
         # Then len(other_cities) == 2
         direction = ['west', 'east']
         for i in range(2):
-            from_ = fiber_link(other_cities[i], city_name)
-            in_ = eqpt_in_city_to_city(city_name, other_cities[0], direction[i])
-            to_ = fiber_link(city_name, other_cities[1 - i])
+            from_ = fiber_link(other_cities[i], city_name, links_by_city)
+            in_ = eqpt_in_city_to_city(city_name, other_cities[0], eqpts_by_city, nodes_by_city, direction[i])
+            to_ = fiber_link(city_name, other_cities[1 - i], links_by_city)
             subdata += connect_eqpt(from_, in_, to_)
     elif nodes_by_city[city_name].node_type.lower() == 'roadm':
         for other_city in other_cities:
             from_ = f'roadm {city_name}'
-            in_ = eqpt_in_city_to_city(city_name, other_city)
-            to_ = fiber_link(city_name, other_city)
+            in_ = eqpt_in_city_to_city(city_name, other_city, eqpts_by_city, nodes_by_city)
+            to_ = fiber_link(city_name, other_city, links_by_city)
             subdata += connect_eqpt(from_, in_, to_)
 
-            from_ = fiber_link(other_city, city_name)
-            in_ = eqpt_in_city_to_city(city_name, other_city, "west")
+            from_ = fiber_link(other_city, city_name, links_by_city)
+            in_ = eqpt_in_city_to_city(city_name, other_city, eqpts_by_city, nodes_by_city, "west")
             to_ = f'roadm {city_name}'
             subdata += connect_eqpt(from_, in_, to_)
     return subdata
 
 
-def connect_eqpt(from_, in_, to_):
+def connect_eqpt(from_: str, in_: str, to_: str) -> List[dict]:
+    """Utils: create the topology connection json dict between in and to
+    """
     connections = []
     if in_ != '':
         connections = [{'from_node': from_, 'to_node': in_},
@@ -692,7 +778,11 @@ def connect_eqpt(from_, in_, to_):
     return connections
 
 
-def eqpt_in_city_to_city(in_city, to_city, direction='east'):
+def eqpt_in_city_to_city(in_city: str, to_city: str,
+                         eqpts_by_city: DefaultDict[str, List[Eqpt]], nodes_by_city: Dict[str, Node],
+                         direction: str = 'east') -> str:
+    """Utils: returns the formatted dtring corresponding to in_city types and direction
+    """
     rev_direction = 'west' if direction == 'east' else 'east'
     return_eqpt = ''
     if in_city in eqpts_by_city:
@@ -711,22 +801,25 @@ def eqpt_in_city_to_city(in_city, to_city, direction='east'):
     return return_eqpt
 
 
-def corresp_next_node(network, corresp_ila, corresp_roadm):
+def corresp_next_node(network: DiGraph, corresp_ila: dict, corresp_roadm: dict) -> Tuple[dict, dict]:
     """ for each name in corresp dictionnaries find the next node in network and its name
         given by user in excel. for meshTopology_exampleV2.xls:
         user ILA name Stbrieuc covers the two direction. convert.py creates 2 different ILA
         with possible names (depending on the direction and if the eqpt was defined in eqpt
         sheet)
+        for an ILA and if it is defined in eqpt:
         - east edfa in Stbrieuc to Rennes_STA
         - west edfa in Stbrieuc to Rennes_STA
-        - Edfa0_fiber (Lannion_CAS → Stbrieuc)-F056
-        - Edfa0_fiber (Rennes_STA → Stbrieuc)-F057
+        for an ILA and if it is not defined in eqpt:
+        - east edfa in Stbrieuc
+        - west edfa in Stbrieuc
+        for a roadm
+        "Edfa_preamp_roadm node1_from_fiber (siteE → node1)-CABLES#19"
+        "Edfa_booster_roadm node1_to_fiber (node1 → siteE)-CABLES#19"
         next_nodes finds the user defined name of next node to be able to map the path constraints
         - east edfa in Stbrieuc to Rennes_STA      next node = Rennes_STA
         - west edfa in Stbrieuc to Rennes_STA      next node Lannion_CAS
 
-        Edfa0_fiber (Lannion_CAS → Stbrieuc)-F056 and Edfa0_fiber (Rennes_STA → Stbrieuc)-F057
-        do not exist
         the function supports fiber splitting, fused nodes and shall only be called if
         excel format is used for both network and service
     """
@@ -737,8 +830,8 @@ def corresp_next_node(network, corresp_ila, corresp_roadm):
         for ila_elem in ila_list:
             # find the node with ila_elem string _in_ the node uid. 'in' is used instead of
             # '==' to find composed nodes due to fiber splitting in autodesign.
-            # eg if elem_ila is 'Edfa0_fiber (Lannion_CAS → Stbrieuc)-F056',
-            # node uid 'Edfa0_fiber (Lannion_CAS → Stbrieuc)-F056_(1/2)' is possible
+            # eg if elem_ila is 'east edfa in Stbrieuc to Rennes_STA',
+            # node uid 'east edfa in Stbrieuc to Rennes_STA-_(1/2)' is possible
             correct_ila_name = next(n.uid for n in network.nodes() if ila_elem in n.uid)
             temp.remove(ila_elem)
             temp.append(correct_ila_name)
@@ -755,7 +848,7 @@ def corresp_next_node(network, corresp_ila, corresp_roadm):
                     break
             # if next_nd was not already added in the dict with the previous loop,
             # add the first found correspondance in ila names
-            if correct_ila_name not in next_node.keys():
+            if correct_ila_name not in next_node:
                 for key, val in corresp_ila.items():
                     # in case of splitted fibers the ila name might not be exact match
                     if [e for e in val if e in next_nd.uid]:
@@ -766,7 +859,9 @@ def corresp_next_node(network, corresp_ila, corresp_roadm):
     return corresp_ila, next_node
 
 
-def fiber_dest_from_source(city_name):
+def fiber_dest_from_source(city_name: str, links_by_city: DefaultDict[str, List[Link]]) -> List[str]:
+    """Returns the list of cities city_name is connected to
+    """
     destinations = []
     links_from_city = links_by_city[city_name]
     for l in links_from_city:
@@ -777,7 +872,9 @@ def fiber_dest_from_source(city_name):
     return destinations
 
 
-def fiber_link(from_city, to_city):
+def fiber_link(from_city: str, to_city: str, links_by_city: DefaultDict[str, List[Link]]) -> str:
+    """utils: returns formatted uid for fibers between from_city and to_city
+    """
     source_dest = (from_city, to_city)
     links = links_by_city[from_city]
     link = next(l for l in links if l.from_city in source_dest and l.to_city in source_dest)
@@ -788,7 +885,9 @@ def fiber_link(from_city, to_city):
     return fiber
 
 
-def midpoint(city_a, city_b):
+def midpoint(city_a: Node, city_b:Node) -> dict:
+    """Computes mipoint coordinates
+    """
     lats = city_a.latitude, city_b.latitude
     longs = city_a.longitude, city_b.longitude
     try:
@@ -813,10 +912,12 @@ LINKS_LINE = 3
 EQPTS_LINE = 3
 EQPTS_COLUMN = 14
 ROADMS_LINE = 3
-ROADMS_COLUMN = 3
+ROADMS_COLUMN = 6
 
 
 def _do_convert():
+    """Main function for xls(x) topology conversion to JSON format
+    """
     parser = ArgumentParser()
     parser.add_argument('workbook', type=Path)
     parser.add_argument('-f', '--filter-region', action='append', default=[])

gnpy/tools/json_io.py

@@ -1,24 +1,32 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 
-'''
+"""
 gnpy.tools.json_io
 ==================
 
 Loading and saving data from JSON files in GNPy's internal data format
-'''
+"""
 
-from networkx import DiGraph
 from logging import getLogger
 from pathlib import Path
 import json
 from collections import namedtuple
-from gnpy.core import ansi_escapes, elements
-from gnpy.core.equipment import trx_mode_params
+from copy import deepcopy
+from typing import Union, Dict, List
+from networkx import DiGraph
+from numpy import arange
+
+
+from gnpy.core import elements
+from gnpy.core.equipment import trx_mode_params, find_type_variety
 from gnpy.core.exceptions import ConfigurationError, EquipmentConfigError, NetworkTopologyError, ServiceError
 from gnpy.core.science_utils import estimate_nf_model
-from gnpy.core.utils import automatic_nch, automatic_fmax, merge_amplifier_restrictions
+from gnpy.core.info import Carrier
+from gnpy.core.utils import automatic_nch, automatic_fmax, merge_amplifier_restrictions, dbm2watt
+from gnpy.core.parameters import DEFAULT_RAMAN_COEFFICIENT, EdfaParams, MultiBandParams
 from gnpy.topology.request import PathRequest, Disjunction, compute_spectrum_slot_vs_bandwidth
+from gnpy.topology.spectrum_assignment import mvalue_to_slots
 from gnpy.tools.convert import xls_to_json_data
 from gnpy.tools.service_sheet import read_service_sheet
 
@@ -34,26 +42,34 @@ Model_dual_stage = namedtuple('Model_dual_stage', 'preamp_variety booster_variet
 
 
 class Model_openroadm_preamp:
-    pass
+    """class to hold nf model specific to OpenROADM preamp
+    """
 
 
 class Model_openroadm_booster:
-    pass
+    """class to hold nf model specific to OpenROADM booster
+    """
 
 
 class _JsonThing:
+    """Base class for json equipment
+    """
     def update_attr(self, default_values, kwargs, name):
+        """Build the attributes based on kwargs dict
+        """
         clean_kwargs = {k: v for k, v in kwargs.items() if v != ''}
         for k, v in default_values.items():
             setattr(self, k, clean_kwargs.get(k, v))
-            if k not in clean_kwargs and name != 'Amp':
-                print(ansi_escapes.red +
-                      f'\n WARNING missing {k} attribute in eqpt_config.json[{name}]' +
-                      f'\n default value is {k} = {v}' +
-                      ansi_escapes.reset)
+            if k not in clean_kwargs and name != 'Amp' and v is not None and v != []:
+                # do not show this warning if the default value is None
+                msg = f'\n\tWARNING missing {k} attribute in eqpt_config.json[{name}]' \
+                    + f'\n\tdefault value is {k} = {v}\n'
+                _logger.warning(msg)
 
 
 class SI(_JsonThing):
+    """Spectrum Information
+    """
     default_values = {
         "f_min": 191.35e12,
         "f_max": 196.1e12,
@@ -63,7 +79,8 @@ class SI(_JsonThing):
         "power_range_db": [0, 0, 0.5],
         "roll_off": 0.15,
         "tx_osnr": 45,
-        "sys_margins": 0
+        "sys_margins": 0,
+        "tx_power_dbm": None  # optional value in SI
     }
 
     def __init__(self, **kwargs):
@@ -71,6 +88,8 @@ class SI(_JsonThing):
 
 
 class Span(_JsonThing):
+    """Span simulations definition
+    """
     default_values = {
         'power_mode': True,
         'delta_power_range_db': None,
@@ -90,22 +109,42 @@ class Span(_JsonThing):
 
 
 class Roadm(_JsonThing):
+    """List of ROADM and their specs
+    """
     default_values = {
-        'target_pch_out_db': -17,
+        'type_variety': 'default',
         'add_drop_osnr': 100,
         'pmd': 0,
         'pdl': 0,
         'restrictions': {
             'preamp_variety_list': [],
             'booster_variety_list': []
-        }
+        },
+        'roadm-path-impairments': []
     }
 
     def __init__(self, **kwargs):
+        # If equalization is not defined in equipment, then raise an error.
+        # Only one type of equalization must be defined.
+        allowed_equalisations = ['target_pch_out_db', 'target_psd_out_mWperGHz', 'target_out_mWperSlotWidth']
+        requested_eq_mask = [eq in kwargs for eq in allowed_equalisations]
+        if sum(requested_eq_mask) > 1:
+            msg = 'Only one equalization type should be set in ROADM, found: ' \
+                  + ', '.join(eq for eq in allowed_equalisations if eq in kwargs)
+            raise EquipmentConfigError(msg)
+        if not any(requested_eq_mask):
+            msg = 'No equalization type set in ROADM'
+            raise EquipmentConfigError(msg)
+        for key in allowed_equalisations:
+            if key in kwargs:
+                setattr(self, key, kwargs[key])
+                break
         self.update_attr(self.default_values, kwargs, 'Roadm')
 
 
 class Transceiver(_JsonThing):
+    """List of transceivers and their modes
+    """
     default_values = {
         'type_variety': None,
         'frequency': None,
@@ -117,6 +156,7 @@ class Transceiver(_JsonThing):
         for mode_params in self.mode:
             penalties = mode_params.get('penalties')
             mode_params['penalties'] = {}
+            mode_params['equalization_offset_db'] = mode_params.get('equalization_offset_db', 0)
             if not penalties:
                 continue
             for impairment in ('chromatic_dispersion', 'pmd', 'pdl'):
@@ -137,6 +177,8 @@ class Transceiver(_JsonThing):
 
 
 class Fiber(_JsonThing):
+    """Fiber default settings
+    """
     default_values = {
         'type_variety': '',
         'dispersion': None,
@@ -146,54 +188,47 @@ class Fiber(_JsonThing):
 
     def __init__(self, **kwargs):
         self.update_attr(self.default_values, kwargs, self.__class__.__name__)
-        for optional in ['gamma', 'raman_efficiency']:
-            if optional in kwargs:
-                setattr(self, optional, kwargs[optional])
+        if 'gamma' in kwargs:
+            setattr(self, 'gamma', kwargs['gamma'])
+        if 'raman_efficiency' in kwargs:
+            raman_coefficient = kwargs['raman_efficiency']
+            cr = raman_coefficient.pop('cr')
+            raman_coefficient['g0'] = cr
+            raman_coefficient['reference_frequency'] = DEFAULT_RAMAN_COEFFICIENT['reference_frequency']
+            setattr(self, 'raman_coefficient', raman_coefficient)
 
 
 class RamanFiber(Fiber):
-    pass
+    """Raman Fiber default settings
+    """
 
 
 class Amp(_JsonThing):
-    default_values = {
-        'f_min': 191.35e12,
-        'f_max': 196.1e12,
-        'type_variety': '',
-        'type_def': '',
-        'gain_flatmax': None,
-        'gain_min': None,
-        'p_max': None,
-        'nf_model': None,
-        'dual_stage_model': None,
-        'nf_fit_coeff': None,
-        'nf_ripple': None,
-        'dgt': None,
-        'gain_ripple': None,
-        'out_voa_auto': False,
-        'allowed_for_design': False,
-        'raman': False,
-        'pmd': 0,
-        'pdl': 0
-    }
+    """List of amplifiers with their specs
+    """
+    default_values = EdfaParams.default_values
 
     def __init__(self, **kwargs):
         self.update_attr(self.default_values, kwargs, 'Amp')
 
     @classmethod
     def from_json(cls, filename, **kwargs):
+        """
+        """
         config = Path(filename).parent / 'default_edfa_config.json'
-
+        # default_edfa_config.json assumes a DGT profile independantly from fmin/fmax, that's a generic profile
         type_variety = kwargs['type_variety']
         type_def = kwargs.get('type_def', 'variable_gain')  # default compatibility with older json eqpt files
         nf_def = None
         dual_stage_def = None
+        amplifiers = None
 
         if type_def == 'fixed_gain':
             try:
                 nf0 = kwargs.pop('nf0')
-            except KeyError:  # nf0 is expected for a fixed gain amp
-                raise EquipmentConfigError(f'missing nf0 value input for amplifier: {type_variety} in equipment config')
+            except KeyError as exc:  # nf0 is expected for a fixed gain amp
+                msg = f'missing nf0 value input for amplifier: {type_variety} in equipment config'
+                raise EquipmentConfigError(msg) from exc
             for k in ('nf_min', 'nf_max'):
                 try:
                     del kwargs[k]
@@ -207,8 +242,9 @@ class Amp(_JsonThing):
             try:  # nf_min and nf_max are expected for a variable gain amp
                 nf_min = kwargs.pop('nf_min')
                 nf_max = kwargs.pop('nf_max')
-            except KeyError:
-                raise EquipmentConfigError(f'missing nf_min or nf_max value input for amplifier: {type_variety} in equipment config')
+            except KeyError as exc:
+                msg = f'missing nf_min or nf_max value input for amplifier: {type_variety} in equipment config'
+                raise EquipmentConfigError(msg) from exc
             try:  # remove all remaining nf inputs
                 del kwargs['nf0']
             except KeyError:
@@ -218,8 +254,8 @@ class Amp(_JsonThing):
         elif type_def == 'openroadm':
             try:
                 nf_coef = kwargs.pop('nf_coef')
-            except KeyError:  # nf_coef is expected for openroadm amp
-                raise EquipmentConfigError(f'missing nf_coef input for amplifier: {type_variety} in equipment config')
+            except KeyError as exc:  # nf_coef is expected for openroadm amp
+                raise EquipmentConfigError(f'missing nf_coef input for amplifier: {type_variety} in equipment config') from exc
             nf_def = Model_openroadm_ila(nf_coef)
         elif type_def == 'openroadm_preamp':
             nf_def = Model_openroadm_preamp()
@@ -229,32 +265,122 @@ class Amp(_JsonThing):
             try:  # nf_ram and gain_ram are expected for a hybrid amp
                 preamp_variety = kwargs.pop('preamp_variety')
                 booster_variety = kwargs.pop('booster_variety')
-            except KeyError:
-                raise EquipmentConfigError(f'missing preamp/booster variety input for amplifier: {type_variety} in equipment config')
+            except KeyError as exc:
+                raise EquipmentConfigError(f'missing preamp/booster variety input for amplifier: {type_variety}'
+                                           + ' in equipment config') from exc
             dual_stage_def = Model_dual_stage(preamp_variety, booster_variety)
+        elif type_def == 'multi_band':
+            amplifiers = kwargs['amplifiers']
         else:
             raise EquipmentConfigError(f'Edfa type_def {type_def} does not exist')
 
         json_data = load_json(config)
-
+        # raise an error if config does not contain f_min, f_max
+        if 'f_min' not in json_data or 'f_max' not in json_data:
+            raise EquipmentConfigError('default Edfa config does not contain f_min and f_max values.'
+                                       + ' Please correct file.')
+        # use f_min, f_max from kwargs
+        if 'f_min' in kwargs:
+            json_data.pop('f_min', None)
+            json_data.pop('f_max', None)
         return cls(**{**kwargs, **json_data,
-                      'nf_model': nf_def, 'dual_stage_model': dual_stage_def})
+                      'nf_model': nf_def, 'dual_stage_model': dual_stage_def, 'multi_band': amplifiers})
 
 
-def _automatic_spacing(baud_rate):
-    """return the min possible channel spacing for a given baud rate"""
-    # TODO : this should parametrized in a cfg file
-    # list of possible tuples [(max_baud_rate, spacing_for_this_baud_rate)]
-    spacing_list = [(33e9, 37.5e9), (38e9, 50e9), (50e9, 62.5e9), (67e9, 75e9), (92e9, 100e9)]
-    return min((s[1] for s in spacing_list if s[0] > baud_rate), default=baud_rate * 1.2)
+def _spectrum_from_json(json_data: dict):
+    """JSON_data is a list of spectrum partitions each with
+    {f_min, f_max, baud_rate, roll_off, delta_pdb, slot_width, tx_osnr, label}
+    Creates the per freq Carrier's dict.
+    f_min, f_max, baud_rate, slot_width and roll_off are mandatory
+    label, tx_osnr and delta_pdb are created if not present
+    label should be different for each partition
+    >>> json_data = {'spectrum': \
+        [{'f_min': 193.2e12, 'f_max': 193.4e12, 'slot_width': 50e9, 'baud_rate': 32e9, 'roll_off': 0.15, \
+            'delta_pdb': 1, 'tx_osnr': 45, 'tx_power_dbm': -7},\
+        {'f_min': 193.4625e12, 'f_max': 193.9875e12, 'slot_width': 75e9, 'baud_rate': 64e9, 'roll_off': 0.15},\
+        {'f_min': 194.075e12, 'f_max': 194.075e12, 'slot_width': 100e9, 'baud_rate': 90e9, 'roll_off': 0.15},\
+        {'f_min': 194.2e12, 'f_max': 194.35e12, 'slot_width': 50e9, 'baud_rate': 32e9, 'roll_off': 0.15}]}
+    >>> spectrum = _spectrum_from_json(json_data['spectrum'])
+    >>> for k, v in spectrum.items():
+    ...     print(f'{k}: {v}')
+    ...
+    193200000000000.0: Carrier(delta_pdb=1, baud_rate=32000000000.0, slot_width=50000000000.0, roll_off=0.15, tx_osnr=45, tx_power=0.00019952623149688798, label='0-32.00G')
+    193250000000000.0: Carrier(delta_pdb=1, baud_rate=32000000000.0, slot_width=50000000000.0, roll_off=0.15, tx_osnr=45, tx_power=0.00019952623149688798, label='0-32.00G')
+    193300000000000.0: Carrier(delta_pdb=1, baud_rate=32000000000.0, slot_width=50000000000.0, roll_off=0.15, tx_osnr=45, tx_power=0.00019952623149688798, label='0-32.00G')
+    193350000000000.0: Carrier(delta_pdb=1, baud_rate=32000000000.0, slot_width=50000000000.0, roll_off=0.15, tx_osnr=45, tx_power=0.00019952623149688798, label='0-32.00G')
+    193400000000000.0: Carrier(delta_pdb=1, baud_rate=32000000000.0, slot_width=50000000000.0, roll_off=0.15, tx_osnr=45, tx_power=0.00019952623149688798, label='0-32.00G')
+    193462500000000.0: Carrier(delta_pdb=0, baud_rate=64000000000.0, slot_width=75000000000.0, roll_off=0.15, tx_osnr=40, tx_power=0.001, label='1-64.00G')
+    193537500000000.0: Carrier(delta_pdb=0, baud_rate=64000000000.0, slot_width=75000000000.0, roll_off=0.15, tx_osnr=40, tx_power=0.001, label='1-64.00G')
+    193612500000000.0: Carrier(delta_pdb=0, baud_rate=64000000000.0, slot_width=75000000000.0, roll_off=0.15, tx_osnr=40, tx_power=0.001, label='1-64.00G')
+    193687500000000.0: Carrier(delta_pdb=0, baud_rate=64000000000.0, slot_width=75000000000.0, roll_off=0.15, tx_osnr=40, tx_power=0.001, label='1-64.00G')
+    193762500000000.0: Carrier(delta_pdb=0, baud_rate=64000000000.0, slot_width=75000000000.0, roll_off=0.15, tx_osnr=40, tx_power=0.001, label='1-64.00G')
+    193837500000000.0: Carrier(delta_pdb=0, baud_rate=64000000000.0, slot_width=75000000000.0, roll_off=0.15, tx_osnr=40, tx_power=0.001, label='1-64.00G')
+    193912500000000.0: Carrier(delta_pdb=0, baud_rate=64000000000.0, slot_width=75000000000.0, roll_off=0.15, tx_osnr=40, tx_power=0.001, label='1-64.00G')
+    193987500000000.0: Carrier(delta_pdb=0, baud_rate=64000000000.0, slot_width=75000000000.0, roll_off=0.15, tx_osnr=40, tx_power=0.001, label='1-64.00G')
+    194075000000000.0: Carrier(delta_pdb=0, baud_rate=90000000000.0, slot_width=100000000000.0, roll_off=0.15, tx_osnr=40, tx_power=0.001, label='2-90.00G')
+    194200000000000.0: Carrier(delta_pdb=0, baud_rate=32000000000.0, slot_width=50000000000.0, roll_off=0.15, tx_osnr=40, tx_power=0.001, label='3-32.00G')
+    194250000000000.0: Carrier(delta_pdb=0, baud_rate=32000000000.0, slot_width=50000000000.0, roll_off=0.15, tx_osnr=40, tx_power=0.001, label='3-32.00G')
+    194300000000000.0: Carrier(delta_pdb=0, baud_rate=32000000000.0, slot_width=50000000000.0, roll_off=0.15, tx_osnr=40, tx_power=0.001, label='3-32.00G')
+    194350000000000.0: Carrier(delta_pdb=0, baud_rate=32000000000.0, slot_width=50000000000.0, roll_off=0.15, tx_osnr=40, tx_power=0.001, label='3-32.00G')
+    """
+    spectrum = {}
+    json_data = sorted(json_data, key=lambda x: x['f_min'])
+    # min freq of occupation is f_min - slot_width/2 (numbering starts at 0)
+    previous_part_max_freq = 0.0
+    for index, part in enumerate(json_data):
+        # default delta_pdb is 0 dB
+        part.setdefault('delta_pdb', 0)
+        # add a label to the partition for the printings
+        part.setdefault('label', f'{index}-{part["baud_rate"] * 1e-9:.2f}G')
+        # default tx_osnr is set to 40 dB
+        part.setdefault('tx_osnr', 40)
+        # default tx_power_dbm is set to 0 dBn
+        part.setdefault('tx_power_dbm', 0)
+        # starting freq is exactly f_min to be consistent with utils.automatic_nch
+        # first partition min occupation is f_min - slot_width / 2 (central_frequency is f_min)
+        # supposes that carriers are centered on frequency
+        if previous_part_max_freq > (part['f_min'] - part['slot_width'] / 2):
+            # check that previous part last channel does not overlap on next part first channel
+            # max center of the part should be below part['f_max'] and aligned on the slot_width
+            msg = 'Not a valid initial spectrum definition:\nprevious spectrum last carrier max occupation ' +\
+                f'{previous_part_max_freq * 1e-12:.5f}GHz ' +\
+                'overlaps on next spectrum first carrier occupation ' +\
+                f'{(part["f_min"] - part["slot_width"] / 2) * 1e-12:.5f}GHz'
+            raise ValueError(msg)
+
+        max_range = ((part['f_max'] - part['f_min']) // part['slot_width'] + 1) * part['slot_width']
+        previous_part_max_freq = None
+        for current_freq in arange(part['f_min'],
+                                   part['f_min'] + max_range,
+                                   part['slot_width']):
+            spectrum[current_freq] = Carrier(delta_pdb=part['delta_pdb'], baud_rate=part['baud_rate'],
+                                             slot_width=part['slot_width'], roll_off=part['roll_off'],
+                                             tx_osnr=part['tx_osnr'], tx_power=dbm2watt(part['tx_power_dbm']),
+                                             label=part['label'])
+        previous_part_max_freq = current_freq + part['slot_width'] / 2
+    return spectrum
 
 
-def load_equipment(filename):
+def load_equipment(filename: Path) -> dict:
+    """Load equipment, returns equipment dict
+    """
     json_data = load_json(filename)
     return _equipment_from_json(json_data, filename)
 
 
-def _update_dual_stage(equipment):
+def load_initial_spectrum(filename: Path) -> dict:
+    """Load spectrum to propagate, returns spectrum dict
+    """
+    json_data = load_json(filename)
+    return _spectrum_from_json(json_data['spectrum'])
+
+
+def _update_dual_stage(equipment: dict) -> dict:
+    """Update attributes of all dual stage amps with the preamp and booster attributes
+    (defined in the equipment dictionary)
+
+    Returns the updated equiment dictionary
+    """
     edfa_dict = equipment['Edfa']
     for edfa in edfa_dict.values():
         if edfa.type_def == 'dual_stage':
@@ -273,18 +399,43 @@ def _update_dual_stage(equipment):
     return equipment
 
 
-def _roadm_restrictions_sanity_check(equipment):
-    """ verifies that booster and preamp restrictions specified in roadm equipment are listed
-    in the edfa.
+def _update_band(equipment: dict) -> dict:
+    """Creates a list of bands for this amplifier, and remove other parameters which are not applicable
     """
-    restrictions = equipment['Roadm']['default'].restrictions['booster_variety_list'] + \
-        equipment['Roadm']['default'].restrictions['preamp_variety_list']
-    for amp_name in restrictions:
-        if amp_name not in equipment['Edfa']:
-            raise EquipmentConfigError(f'ROADM restriction {amp_name} does not refer to a defined EDFA name')
+    amp_dict = equipment['Edfa']
+    for amplifier in amp_dict.values():
+        if amplifier.type_def != 'multi_band':
+            amplifier.bands = [{'f_min': amplifier.f_min,
+                                'f_max': amplifier.f_max}]
+            # updates band parameter
+        else:
+            _bands = [{'f_min': amp_dict[a].f_min,
+                       'f_max': amp_dict[a].f_max} for a in amp_dict[amplifier.type_variety].multi_band]
+            # remove duplicates
+            amplifier.bands = []
+            for b in _bands:
+                if b not in amplifier.bands:
+                    amplifier.bands.append(b)
+            # remove non applicable parameters
+            for key in ['f_min', 'f_max', 'gain_flatmax', 'gain_min', 'p_max', 'nf_model', 'dual_stage_model',
+                        'nf_fit_coeff', 'nf_ripple', 'dgt', 'gain_ripple']:
+                delattr(amplifier, key)
+
+    return equipment
 
 
-def _check_fiber_vs_raman_fiber(equipment):
+def _roadm_restrictions_sanity_check(equipment: dict):
+    """verifies that booster and preamp restrictions specified in roadm equipment are listed in the edfa."""
+    for roadm_type, roadm_eqpt in equipment['Roadm'].items():
+        restrictions = roadm_eqpt.restrictions['booster_variety_list'] + \
+            roadm_eqpt.restrictions['preamp_variety_list']
+        for amp_name in restrictions:
+            if amp_name not in equipment['Edfa']:
+                raise EquipmentConfigError(f'ROADM {roadm_type} restriction {amp_name} does not refer to a '
+                                           + 'defined EDFA name')
+
+
+def _check_fiber_vs_raman_fiber(equipment: dict):
     """Ensure that Fiber and RamanFiber with the same name define common properties equally"""
     if 'RamanFiber' not in equipment:
         return
@@ -299,7 +450,7 @@ def _check_fiber_vs_raman_fiber(equipment):
                                            f'disagrees for "{attr}": {a} != {b}')
 
 
-def _equipment_from_json(json_data, filename):
+def _equipment_from_json(json_data: dict, filename: Path) -> dict:
     """build global dictionnary eqpt_library that stores all eqpt characteristics:
     edfa type type_variety, fiber type_variety
     from the eqpt_config.json (filename parameter)
@@ -322,6 +473,9 @@ def _equipment_from_json(json_data, filename):
             elif key == 'Roadm':
                 equipment[key][subkey] = Roadm(**entry)
             elif key == 'SI':
+                # use power_dbm value for tx_power_dbm if the key is not in 'SI'
+                # if 'tx_power_dbm' not in entry.keys():
+                #     entry['tx_power_dbm'] = entry['power_dbm']
                 equipment[key][subkey] = SI(**entry)
             elif key == 'Transceiver':
                 equipment[key][subkey] = Transceiver(**entry)
@@ -331,11 +485,22 @@ def _equipment_from_json(json_data, filename):
                 raise EquipmentConfigError(f'Unrecognized network element type "{key}"')
     _check_fiber_vs_raman_fiber(equipment)
     equipment = _update_dual_stage(equipment)
+    equipment = _update_band(equipment)
     _roadm_restrictions_sanity_check(equipment)
+    possible_SI = list(equipment['SI'].keys())
+    if 'default' not in possible_SI:
+        # Use "default" key in the equipment, using the first listed keys
+        equipment['SI']['default'] = equipment['SI'][possible_SI[0]]
+        del equipment['SI'][possible_SI[0]]
     return equipment
 
 
-def load_network(filename, equipment):
+def load_network(filename: Path, equipment: dict) -> DiGraph:
+    """load network json or excel
+
+    :param filename: input file to read from
+    :param equipment: equipment library
+    """
     if filename.suffix.lower() in ('.xls', '.xlsx'):
         json_data = xls_to_json_data(filename)
     elif filename.suffix.lower() == '.json':
@@ -346,11 +511,11 @@ def load_network(filename, equipment):
 
 
 def save_network(network: DiGraph, filename: str):
-    '''Dump the network into a JSON file
+    """Dump the network into a JSON file
 
     :param network: network to work on
     :param filename: file to write to
-    '''
+    """
     save_json(network_to_json(network), filename)
 
 
@@ -359,19 +524,22 @@ def _cls_for(equipment_type):
         return elements.Edfa
     if equipment_type == 'Fused':
         return elements.Fused
-    elif equipment_type == 'Roadm':
+    if equipment_type == 'Roadm':
         return elements.Roadm
-    elif equipment_type == 'Transceiver':
+    if equipment_type == 'Transceiver':
         return elements.Transceiver
-    elif equipment_type == 'Fiber':
+    if equipment_type == 'Fiber':
         return elements.Fiber
-    elif equipment_type == 'RamanFiber':
+    if equipment_type == 'RamanFiber':
         return elements.RamanFiber
-    else:
-        raise ConfigurationError(f'Unknown network equipment "{equipment_type}"')
+    if equipment_type == 'Multiband_amplifier':
+        return elements.Multiband_amplifier
+    raise ConfigurationError(f'Unknown network equipment "{equipment_type}"')
 
 
-def network_from_json(json_data, equipment):
+def network_from_json(json_data: dict, equipment: dict) -> DiGraph:
+    """create digraph based on json input dict and using equipment library to fill in the gaps
+    """
     # NOTE|dutc: we could use the following, but it would tie our data format
     #            too closely to the graph library
     # from networkx import node_link_graph
@@ -380,18 +548,80 @@ def network_from_json(json_data, equipment):
         typ = el_config.pop('type')
         variety = el_config.pop('type_variety', 'default')
         cls = _cls_for(typ)
-        if typ == 'Fused':
+        if typ == 'Transceiver':
+            temp = el_config.setdefault('params', {})
+        if typ == 'Multiband_amplifier':
+            if variety in ['default', '']:
+                extra_params = None
+                temp = el_config.setdefault('params', {})
+                temp = merge_amplifier_restrictions(temp, deepcopy(MultiBandParams.default_values))
+                el_config['params'] = temp
+            else:
+                extra_params = equipment['Edfa'][variety]
+                temp = el_config.setdefault('params', {})
+                # use config params preferably to library params, only use library params to fill in
+                # the missing attribute
+                temp = merge_amplifier_restrictions(temp, deepcopy(extra_params.__dict__))
+                el_config['params'] = temp
+                el_config['type_variety'] = variety
+            # if config does not contain any amp list create one
+            amps = el_config.setdefault('amplifiers', [])
+            for amp in amps:
+                amp_variety = amp['type_variety']    # juste pour essayer
+                amp_extra_params = equipment['Edfa'][amp_variety]
+                temp = amp.setdefault('params', {})
+                temp = merge_amplifier_restrictions(temp, amp_extra_params.__dict__)
+                amp['params'] = temp
+                amp['type_variety'] = amp_variety
+            # check type_variety consistant with amps type_variety
+            if amps:
+                try:
+                    multiband_type_variety = find_type_variety([a['type_variety'] for a in amps], equipment)
+                except ConfigurationError as e:
+                    msg = f'Node {el_config["uid"]}: {e}'
+                    raise ConfigurationError(msg)
+                if variety is not None and variety not in multiband_type_variety:
+                    raise ConfigurationError(f'In node {el_config["uid"]}: multiband amplifier type_variety is not '
+                                             + 'consistent with its amps type varieties.')
+            if not amps and extra_params is not None:
+                # the amp config does not contain the amplifiers operational settings, but has a type_variety
+                # defined so that it is possible to create the template of amps for design for each band. This
+                # defines the default design bands.
+                # This lopp populates each amp with default values, for each band
+                for band in extra_params.bands:
+                    params = {k: v for k, v in Amp.default_values.items()}
+                    # update frequencies with band values
+                    params['f_min'] = band['f_min']
+                    params['f_max'] = band['f_max']
+                    amps.append({'params': params})
+            # without type_variety, it is not possible to set the amplifier dict at this point: need to wait
+            # for design, and use user defined design-bands
+        elif typ == 'Fused':
             # well, there's no variety for the 'Fused' node type
             pass
         elif variety in equipment[typ]:
-            extra_params = equipment[typ][variety]
+            extra_params = equipment[typ][variety].__dict__
             temp = el_config.setdefault('params', {})
-            temp = merge_amplifier_restrictions(temp, extra_params.__dict__)
+            if typ == 'Roadm':
+                # if equalization is defined, remove default equalization from the extra_params
+                # If equalisation is not defined in the element config, then use the default one from equipment
+                # if more than one equalization was defined in element config, then raise an error
+                extra_params = merge_equalization(temp, extra_params)
+                if not extra_params:
+                    msg = f'ROADM {el_config["uid"]}: invalid equalization settings'
+                    raise ConfigurationError(msg)
+            temp = merge_amplifier_restrictions(temp, extra_params)
             el_config['params'] = temp
             el_config['type_variety'] = variety
-        elif (typ in ['Fiber', 'RamanFiber']) or (typ == 'Edfa' and variety not in ['default', '']):
+        elif (typ in ['Fiber', 'RamanFiber', 'Roadm']):
             raise ConfigurationError(f'The {typ} of variety type {variety} was not recognized:'
                                      '\nplease check it is properly defined in the eqpt_config json file')
+        elif typ == 'Edfa':
+            if variety in ['default', '']:
+                el_config['params'] = Amp.default_values
+            else:
+                raise ConfigurationError(f'The Edfa of variety type {variety} was not recognized:'
+                                         '\nplease check it is properly defined in the eqpt_config json file')
         el = cls(**el_config)
         g.add_node(el)
 
@@ -405,13 +635,16 @@ def network_from_json(json_data, equipment):
             else:
                 edge_length = 0.01
             g.add_edge(nodes[from_node], nodes[to_node], weight=edge_length)
-        except KeyError:
-            raise NetworkTopologyError(f'can not find {from_node} or {to_node} defined in {cx}')
+        except KeyError as exc:
+            msg = f'can not find {from_node} or {to_node} defined in {cx}'
+            raise NetworkTopologyError(msg) from exc
 
     return g
 
 
-def network_to_json(network):
+def network_to_json(network: DiGraph) -> dict:
+    """Export network graph as a json dict
+    """
     data = {
         'elements': [n.to_json for n in network]
     }
@@ -425,65 +658,74 @@ def network_to_json(network):
     return data
 
 
-def load_json(filename):
+def load_json(filename: Path) -> dict:
+    """load json data, convert from the yang to the legacy
+    supports both legacy ang yang formatted inputs based on yang models
+    """
     with open(filename, 'r', encoding='utf-8') as f:
         data = json.load(f)
     return data
 
 
-def save_json(obj, filename):
+def save_json(obj: Dict, filename: Path):
+    """Save in json format. Use yang formatted data for Topo and Services
+    """
     with open(filename, 'w', encoding='utf-8') as f:
         json.dump(obj, f, indent=2, ensure_ascii=False)
 
 
-def load_requests(filename, eqpt, bidir, network, network_filename):
-    """ loads the requests from a json or an excel file into a data string
-    """
+def load_requests(filename: Path, eqpt: dict, bidir: bool, network: DiGraph, network_filename: str) -> dict:
+    """loads the requests from a json or an excel file into a data string"""
     if filename.suffix.lower() in ('.xls', '.xlsx'):
         _logger.info('Automatically converting requests from XLS to JSON')
         try:
             return convert_service_sheet(filename, eqpt, network, network_filename=network_filename, bidir=bidir)
         except ServiceError as this_e:
-            print(f'{ansi_escapes.red}Service error:{ansi_escapes.reset} {this_e}')
-            exit(1)
+            raise ServiceError(f'Service error: {this_e}') from this_e
     else:
         return load_json(filename)
 
 
-def requests_from_json(json_data, equipment):
+def requests_from_json(json_data: dict, equipment: dict) -> List[PathRequest]:
     """Extract list of requests from data parsed from JSON"""
     requests_list = []
 
     for req in json_data['path-request']:
         # init all params from request
-        params = {}
-        params['request_id'] = req['request-id']
-        params['source'] = req['source']
-        params['bidir'] = req['bidirectional']
-        params['destination'] = req['destination']
-        params['trx_type'] = req['path-constraints']['te-bandwidth']['trx_type']
-        params['trx_mode'] = req['path-constraints']['te-bandwidth']['trx_mode']
-        params['format'] = params['trx_mode']
-        params['spacing'] = req['path-constraints']['te-bandwidth']['spacing']
+        trx_type = req['path-constraints']['te-bandwidth']['trx_type']
+        trx_mode = req['path-constraints']['te-bandwidth'].get('trx_mode', None)
+        if trx_type is None:
+            msg = f'Request {req["request-id"]} has no transceiver type defined.'
+            raise ServiceError(msg)
         try:
-            nd_list = req['explicit-route-objects']['route-object-include-exclude']
+            nd_list = sorted(req['explicit-route-objects']['route-object-include-exclude'], key=lambda x: x['index'])
         except KeyError:
             nd_list = []
-        params['nodes_list'] = [n['num-unnum-hop']['node-id'] for n in nd_list]
-        params['loose_list'] = [n['num-unnum-hop']['hop-type'] for n in nd_list]
+        params = {
+            'request_id': f'{req["request-id"]}',
+            'source': req['source'],
+            'destination': req['destination'],
+            'bidir': req['bidirectional'],
+            'trx_type': trx_type,
+            'trx_mode': trx_mode,
+            'format': trx_mode,
+            'spacing': req['path-constraints']['te-bandwidth']['spacing'],
+            'nodes_list': [n['num-unnum-hop']['node-id'] for n in nd_list],
+            'loose_list': [n['num-unnum-hop']['hop-type'] for n in nd_list]
+        }
         # recover trx physical param (baudrate, ...) from type and mode
-        # in trx_mode_params optical power is read from equipment['SI']['default'] and
         # nb_channel is computed based on min max frequency and spacing
-        trx_params = trx_mode_params(equipment, params['trx_type'], params['trx_mode'], True)
-        params.update(trx_params)
-        # print(trx_params['min_spacing'])
-        # optical power might be set differently in the request. if it is indicated then the
-        # params['power'] is updated
         try:
-            if req['path-constraints']['te-bandwidth']['output-power']:
-                params['power'] = req['path-constraints']['te-bandwidth']['output-power']
-        except KeyError:
-            pass
+            trx_params = trx_mode_params(equipment, params['trx_type'], params['trx_mode'], True)
+        except EquipmentConfigError as e:
+            msg = f'Equipment Config error in {req["request-id"]}: {e}'
+            raise EquipmentConfigError(msg) from e
+        params.update(trx_params)
+        params['power'] = req['path-constraints']['te-bandwidth'].get('output-power')
+        # params must not be None, but user can set to None: catch this case
+        if params['power'] is None:
+            params['power'] = dbm2watt(equipment['SI']['default'].power_dbm)
+
         # same process for nb-channel
         f_min = params['f_min']
         f_max_from_si = params['f_max']
@@ -497,58 +739,89 @@ def requests_from_json(json_data, equipment):
                 params['nb_channel'] = automatic_nch(f_min, f_max_from_si, params['spacing'])
         except KeyError:
             params['nb_channel'] = automatic_nch(f_min, f_max_from_si, params['spacing'])
-        params['effective_freq_slot'] = req['path-constraints']['te-bandwidth'].get('effective-freq-slot', [None])[0]
+        params['effective_freq_slot'] = \
+            req['path-constraints']['te-bandwidth'].get('effective-freq-slot', [{'N': None, 'M': None}])
         try:
             params['path_bandwidth'] = req['path-constraints']['te-bandwidth']['path_bandwidth']
         except KeyError:
             pass
+        params['tx_power'] = req['path-constraints']['te-bandwidth'].get('tx_power')
+        default_tx_power_dbm = equipment['SI']['default'].tx_power_dbm
+        if params['tx_power'] is None:
+            # use request's input power in span instead
+            params['tx_power'] = params['power']
+            if default_tx_power_dbm is not None:
+                # use default tx power
+                params['tx_power'] = dbm2watt(default_tx_power_dbm)
         _check_one_request(params, f_max_from_si)
         requests_list.append(PathRequest(**params))
     return requests_list
 
 
-def _check_one_request(params, f_max_from_si):
+def _check_one_request(params: dict, f_max_from_si: float):
     """Checks that the requested parameters are consistant (spacing vs nb channel vs transponder mode...)"""
     f_min = params['f_min']
     f_max = params['f_max']
-    max_recommanded_nb_channels = automatic_nch(f_min, f_max, params['spacing'])
+    max_recommanded_nb_channels = automatic_nch(f_min, f_max_from_si, params['spacing'])
     if params['baud_rate'] is not None:
         # implicitly means that a mode is defined with min_spacing
         if params['min_spacing'] > params['spacing']:
             msg = f'Request {params["request_id"]} has spacing below transponder ' +\
                   f'{params["trx_type"]} {params["trx_mode"]} min spacing value ' +\
-                  f'{params["min_spacing"]*1e-9}GHz.\nComputation stopped'
-            print(msg)
-            _logger.critical(msg)
+                  f'{params["min_spacing"] * 1e-9}GHz.\nComputation stopped'
             raise ServiceError(msg)
         if f_max > f_max_from_si:
-            msg = f'''Requested channel number {params["nb_channel"]}, baud rate {params["baud_rate"]} GHz
-            and requested spacing {params["spacing"]*1e-9}GHz is not consistent with frequency range
-            {f_min*1e-12} THz, {f_max*1e-12} THz, min recommanded spacing {params["min_spacing"]*1e-9}GHz.
-            max recommanded nb of channels is {max_recommanded_nb_channels}.'''
-            _logger.critical(msg)
+            msg = f'Requested channel number {params["nb_channel"]}, baud rate {params["baud_rate"] * 1e-9} GHz' \
+                  + f' and requested spacing {params["spacing"] * 1e-9}GHz is not consistent with frequency range' \
+                  + f' {f_min * 1e-12} THz, {f_max_from_si * 1e-12} THz.' \
+                  + f' Max recommanded nb of channels is {max_recommanded_nb_channels}.'
             raise ServiceError(msg)
     # Transponder mode already selected; will it fit to the requested bandwidth?
-    if params['trx_mode'] is not None and params['effective_freq_slot'] is not None \
-            and params['effective_freq_slot']['M'] is not None:
-        _, requested_m = compute_spectrum_slot_vs_bandwidth(params['path_bandwidth'],
-                                                            params['spacing'],
-                                                            params['bit_rate'])
-        # params['effective_freq_slot']['M'] value should be bigger than the computed requested_m (simple estimate)
+    if params['trx_mode'] is not None and params['effective_freq_slot'] is not None:
+        required_nb_of_channels, _ = compute_spectrum_slot_vs_bandwidth(params['path_bandwidth'],
+                                                                        params['spacing'],
+                                                                        params['bit_rate'])
+        _, per_channel_m = compute_spectrum_slot_vs_bandwidth(params['bit_rate'],
+                                                              params['spacing'],
+                                                              params['bit_rate'])
+        # each M should fit one or more channels if it is not None
+        # spectrum slots should not overlap
+        # resulting nb of channels should be bigger than the nb computed with path_bandwidth
+        # without being splitted
         # TODO: elaborate a more accurate estimate with nb_wl * tx_osnr + possibly guardbands in case of
         # superchannel closed packing.
-
-        if requested_m > params['effective_freq_slot']['M']:
-            msg = f'requested M {params["effective_freq_slot"]["M"]} number of slots for request' +\
-                  f'{params["request_id"]} should be greater than {requested_m} to support request' +\
-                  f'{params["path_bandwidth"] * 1e-9} Gbit/s with {params["trx_type"]} {params["trx_mode"]}'
-            _logger.critical(msg)
+        nb_of_channels = 0
+        # order slots
+        slots = sorted(params['effective_freq_slot'], key=lambda x: float('inf') if x['N'] is None else x['N'])
+        for slot in slots:
+            nb_of_channels = nb_of_channels + slot['M'] // per_channel_m if slot['M'] is not None \
+                and nb_of_channels is not None else None
+            if slot['M'] is not None and slot['M'] < per_channel_m:
+                msg = f'Requested M {slot} number of slots for request' +\
+                      f' {params["request_id"]} should be greater than {per_channel_m} to support request' +\
+                      f'with {params["trx_type"]} {params["trx_mode"]}'
+                _logger.critical(msg)
+        if nb_of_channels is not None and nb_of_channels < required_nb_of_channels:
+            msg = f'Requested M {slots} number of slots for request {params["request_id"]} support {nb_of_channels}' +\
+                  f' nb of channels while {required_nb_of_channels} are required to support request' +\
+                  f' {params["path_bandwidth"] * 1e-9} Gbit/s with {params["trx_type"]} {params["trx_mode"]}'
             raise ServiceError(msg)
+        if nb_of_channels is not None:
+            _, stop0n = mvalue_to_slots(slots[0]['N'], slots[0]['M'])
+            i = 1
+            while i < len(slots):
+                slot = slots[i]
+                startn, stopn = mvalue_to_slots(slot['N'], slot['M'])
+                if startn <= stop0n:
+                    msg = f'Requested M {slots} for request {params["request_id"]} overlap'
+                    raise ServiceError(msg)
+                _, stop0n = startn, stopn
+                i += 1
 
 
-def disjunctions_from_json(json_data):
-    """ reads the disjunction requests from the json dict and create the list
-        of requested disjunctions for this set of requests
+def disjunctions_from_json(json_data: dict) -> List[Disjunction]:
+    """reads the disjunction requests from the json dict and create the list
+    of requested disjunctions for this set of requests
     """
     disjunctions_list = []
     if 'synchronization' in json_data:
@@ -565,14 +838,63 @@ def disjunctions_from_json(json_data):
 
 
 def convert_service_sheet(
-        input_filename,
-        eqpt,
-        network,
-        network_filename=None,
-        output_filename='',
-        bidir=False):
+        input_filename: Path,
+        eqpt: dict,
+        network: DiGraph,
+        network_filename: Union[Path, None] = None,
+        output_filename: str = '',
+        bidir: bool = False):
+    """Converts xls into json format services
+
+    :param input_filename: xls(x) file containing the service sheet
+    :param eqpt: equipment library
+    :param network: network for which these services apply (required for xls inputs to correct names)
+    :param network_filename: optional network file name that was used for network creation
+                             (required for xls inputs to correct names)
+    :param output_filename: name of the file where converted data are savec
+    :param bidir: set all services bidir attribute with this bool
+    """
     if output_filename == '':
-        output_filename = f'{str(input_filename)[0:len(str(input_filename))-len(str(input_filename.suffixes[0]))]}_services.json'
+        output_filename = f'{str(input_filename)[0:len(str(input_filename)) - len(str(input_filename.suffixes[0]))]}_services.json'
     data = read_service_sheet(input_filename, eqpt, network, network_filename, bidir)
     save_json(data, output_filename)
     return data
+
+
+def find_equalisation(params: Dict, equalization_types: List[str]):
+    """Find the equalization(s) defined in params. params can be a dict or a Roadm object.
+
+    >>> roadm = {'add_drop_osnr': 100, 'pmd': 1, 'pdl': 0.5,
+    ...     'restrictions': {'preamp_variety_list': ['a'], 'booster_variety_list': ['b']},
+    ...     'target_psd_out_mWperGHz': 4e-4}
+    >>> equalization_types = ['target_pch_out_db', 'target_psd_out_mWperGHz']
+    >>> find_equalisation(roadm, equalization_types)
+    {'target_pch_out_db': False, 'target_psd_out_mWperGHz': True}
+    """
+    equalization = {e: False for e in equalization_types}
+    for equ in equalization_types:
+        if equ in params:
+            equalization[equ] = True
+    return equalization
+
+
+def merge_equalization(params: dict, extra_params: dict) -> Union[dict, None]:
+    """params contains ROADM element config and extra_params default values from equipment library.
+    If equalization is not defined in ROADM element use the one defined in equipment library.
+    Only one type of equalization must be defined: power (target_pch_out_db) or PSD (target_psd_out_mWperGHz)
+    or PSW (target_out_mWperSlotWidth)
+    params and extra_params are dict
+    """
+    equalization_types = ['target_pch_out_db', 'target_psd_out_mWperGHz', 'target_out_mWperSlotWidth']
+    roadm_equalizations = find_equalisation(params, equalization_types)
+    if sum(roadm_equalizations.values()) > 1:
+        # if ROADM config contains more than one equalization type then this is an error
+        return None
+    if sum(roadm_equalizations.values()) == 1:
+        # if ROADM config contains one equalization
+        # don't use the default equalization
+        return {k: v for k, v in extra_params.items() if k not in equalization_types}
+    if sum(roadm_equalizations.values()) == 0:
+        # If ROADM config doesn't contain any equalization type, keep the default one
+        return extra_params
+    return None

gnpy/tools/plots.py

@@ -1,12 +1,12 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 
-'''
+"""
 gnpy.tools.plots
 ================
 
 Graphs and plots usable from a CLI application
-'''
+"""
 
 from matplotlib.pyplot import show, axis, figure, title, text
 from networkx import draw_networkx

gnpy/tools/service_sheet.py

@@ -11,109 +11,150 @@ Yang model for requesting path computation.
 See: draft-ietf-teas-yang-path-computation-01.txt
 """
 
-from xlrd import open_workbook, XL_CELL_EMPTY
 from collections import namedtuple
 from logging import getLogger
 from copy import deepcopy
+from pathlib import Path
+from typing import Dict, List
+from networkx import DiGraph
+from xlrd import open_workbook, XL_CELL_EMPTY
+
 from gnpy.core.utils import db2lin
 from gnpy.core.exceptions import ServiceError
 from gnpy.core.elements import Transceiver, Roadm, Edfa, Fiber
-import gnpy.core.ansi_escapes as ansi_escapes
-from gnpy.tools.convert import corresp_names, corresp_next_node
+from gnpy.tools.convert import corresp_names, corresp_next_node, all_rows
 
 SERVICES_COLUMN = 12
 
 
-def all_rows(sheet, start=0):
-    return (sheet.row(x) for x in range(start, sheet.nrows))
-
-
 logger = getLogger(__name__)
 
 
-class Request(namedtuple('Request', 'request_id source destination trx_type mode \
-    spacing power nb_channel disjoint_from nodes_list is_loose path_bandwidth')):
-    def __new__(cls, request_id, source, destination, trx_type,  mode=None, spacing=None, power=None, nb_channel=None, disjoint_from='',  nodes_list=None, is_loose='', path_bandwidth=None):
-        return super().__new__(cls, request_id, source, destination, trx_type, mode, spacing, power, nb_channel, disjoint_from,  nodes_list, is_loose, path_bandwidth)
+class Request(namedtuple('request_param', 'request_id source destination trx_type mode \
+        spacing power nb_channel disjoint_from nodes_list is_loose path_bandwidth')):
+    """DATA class for a request.
+
+    :params request_id (int): The unique identifier for the request.
+    :params source (str): The source node for the communication.
+    :params destination (str): The destination node for the communication.
+    :params trx_type (str): The type of transmission for the communication.
+    :params mode (str, optional): The mode of transmission. Defaults to None.
+    :params spacing (float, optional): The spacing between channels. Defaults to None.
+    :params power (float, optional): The power level for the communication. Defaults to None.
+    :params nb_channel (int, optional): The number of channels required for the communication. Defaults to None.
+    :params disjoint_from (str, optional): The node to be disjoint from. Defaults to ''.
+    :params nodes_list (list, optional): The list of nodes involved in the communication. Defaults to None.
+    :params is_loose (str, optional): Indicates if the communication is loose. Defaults to ''.
+    :params path_bandwidth (float, optional): The bandwidth required for the communication. Defaults to None.
+    """
+    def __new__(cls, request_id, source, destination, trx_type, mode=None, spacing=None, power=None, nb_channel=None,
+                disjoint_from='', nodes_list=None, is_loose='', path_bandwidth=None):
+        return super().__new__(cls, request_id, source, destination, trx_type, mode, spacing, power, nb_channel,
+                               disjoint_from, nodes_list, is_loose, path_bandwidth)
 
 
 class Element:
+    """
+    """
+    def __init__(self, uid):
+        self.uid = uid
+
     def __eq__(self, other):
-        return type(self) == type(other) and self.uid == other.uid
+        return isinstance(other, type(self)) and self.uid == other.ui
 
     def __hash__(self):
         return hash((type(self), self.uid))
 
 
 class Request_element(Element):
-    def __init__(self, Request, equipment, bidir):
+    """Class that generate the request in the json format
+
+    :params request_param (Request): The request object containing the information for the element.
+    :params equipment (dict): The equipment configuration for the communication.
+    :params bidir (bool): Indicates if the communication is bidirectional.
+
+    Attributes:
+        request_id (str): The unique identifier for the request.
+        source (str): The source node for the communication.
+        destination (str): The destination node for the communication.
+        srctpid (str): The source TP ID for the communication.
+        dsttpid (str): The destination TP ID for the communication.
+        bidir (bool): Indicates if the communication is bidirectional.
+        trx_type (str): The type of transmission for the communication.
+        mode (str): The mode of transmission for the communication.
+        spacing (float): The spacing between channels for the communication.
+        power (float): The power level for the communication.
+        nb_channel (int): The number of channels required for the communication.
+        disjoint_from (list): The list of nodes to be disjoint from.
+        nodes_list (list): The list of nodes involved in the communication.
+        loose (str): Indicates if the communication is loose or strict.
+        path_bandwidth (float): The bandwidth required for the communication.
+    """
+    def __init__(self, request_param: Request, equipment: Dict, bidir: bool):
+        """
+        """
+        super().__init__(uid=request_param.request_id)
         # request_id is str
         # excel has automatic number formatting that adds .0 on integer values
         # the next lines recover the pure int value, assuming this .0 is unwanted
-        self.request_id = correct_xlrd_int_to_str_reading(Request.request_id)
-        self.source = f'trx {Request.source}'
-        self.destination = f'trx {Request.destination}'
+        self.request_id = correct_xlrd_int_to_str_reading(request_param.request_id)
+        self.source = f'trx {request_param.source}'
+        self.destination = f'trx {request_param.destination}'
         # TODO: the automatic naming generated by excel parser requires that source and dest name
         # be a string starting with 'trx' : this is manually added here.
-        self.srctpid = f'trx {Request.source}'
-        self.dsttpid = f'trx {Request.destination}'
+        self.srctpid = f'trx {request_param.source}'
+        self.dsttpid = f'trx {request_param.destination}'
         self.bidir = bidir
         # test that trx_type belongs to eqpt_config.json
         # if not replace it with a default
         try:
-            if equipment['Transceiver'][Request.trx_type]:
-                self.trx_type = correct_xlrd_int_to_str_reading(Request.trx_type)
-            if Request.mode is not None:
-                Requestmode = correct_xlrd_int_to_str_reading(Request.mode)
-                if [mode for mode in equipment['Transceiver'][Request.trx_type].mode if mode['format'] == Requestmode]:
-                    self.mode = Requestmode
+            if equipment['Transceiver'][request_param.trx_type]:
+                self.trx_type = correct_xlrd_int_to_str_reading(request_param.trx_type)
+            if request_param.mode is not None:
+                request_mode = correct_xlrd_int_to_str_reading(request_param.mode)
+                if [mode for mode in equipment['Transceiver'][request_param.trx_type].mode
+                        if mode['format'] == request_mode]:
+                    self.mode = request_mode
                 else:
-                    msg = f'Request Id: {self.request_id} - could not find tsp : \'{Request.trx_type}\' with mode: \'{Requestmode}\' in eqpt library \nComputation stopped.'
-                    # print(msg)
-                    logger.critical(msg)
+                    msg = f'Request Id: {self.request_id} - could not find tsp : \'{request_param.trx_type}\' ' \
+                          + f'with mode: \'{request_mode}\' in eqpt library \nComputation stopped.'
                     raise ServiceError(msg)
             else:
-                Requestmode = None
-                self.mode = Request.mode
-        except KeyError:
-            msg = f'Request Id: {self.request_id} - could not find tsp : \'{Request.trx_type}\' with mode: \'{Request.mode}\' in eqpt library \nComputation stopped.'
-            # print(msg)
-            logger.critical(msg)
-            raise ServiceError(msg)
+                request_mode = None
+                self.mode = request_param.mode
+        except KeyError as e:
+            msg = f'Request Id: {self.request_id} - could not find tsp : \'{request_param.trx_type}\' with mode: ' \
+                  + f'\'{request_param.mode}\' in eqpt library \nComputation stopped.'
+            raise ServiceError(msg) from e
         # excel input are in GHz and dBm
-        if Request.spacing is not None:
-            self.spacing = Request.spacing * 1e9
+        if request_param.spacing is not None:
+            self.spacing = request_param.spacing * 1e9
         else:
             msg = f'Request {self.request_id} missing spacing: spacing is mandatory.\ncomputation stopped'
-            logger.critical(msg)
             raise ServiceError(msg)
-        if Request.power is not None:
-            self.power = db2lin(Request.power) * 1e-3
-        else:
-            self.power = None
-        if Request.nb_channel is not None:
-            self.nb_channel = int(Request.nb_channel)
-        else:
-            self.nb_channel = None
+        self.power = None
+        if request_param.power is not None:
+            self.power = db2lin(request_param.power) * 1e-3
+        self.nb_channel = None
+        if request_param.nb_channel is not None:
+            self.nb_channel = int(request_param.nb_channel)
 
-        value = correct_xlrd_int_to_str_reading(Request.disjoint_from)
+        value = correct_xlrd_int_to_str_reading(request_param.disjoint_from)
         self.disjoint_from = [n for n in value.split(' | ') if value]
         self.nodes_list = []
-        if Request.nodes_list:
-            self.nodes_list = Request.nodes_list.split(' | ')
+        if request_param.nodes_list:
+            self.nodes_list = request_param.nodes_list.split(' | ')
         self.loose = 'LOOSE'
-        if Request.is_loose.lower() == 'no':
+        if request_param.is_loose.lower() == 'no':
             self.loose = 'STRICT'
-        self.path_bandwidth = None
-        if Request.path_bandwidth is not None:
-            self.path_bandwidth = Request.path_bandwidth * 1e9
-        else:
-            self.path_bandwidth = 0
-
-    uid = property(lambda self: repr(self))
+        self.path_bandwidth = 0
+        if request_param.path_bandwidth is not None:
+            self.path_bandwidth = request_param.path_bandwidth * 1e9
 
     @property
     def pathrequest(self):
+        """Creates json dictionnary for the request
+        """
         # Default assumption for bidir is False
         req_dictionnary = {
             'request-id': self.request_id,
@@ -156,29 +197,32 @@ class Request_element(Element):
 
     @property
     def pathsync(self):
+        """Creates json dictionnary for disjunction list (synchronization vector)
+        """
         if self.disjoint_from:
             return {'synchronization-id': self.request_id,
                     'svec': {
                         'relaxable': 'false',
                         'disjointness': 'node link',
-                        'request-id-number': [self.request_id] + [n for n in self.disjoint_from]
+                        'request-id-number': [self.request_id] + list(self.disjoint_from)
                     }
                     }
-        else:
-            return None
+        return None
         # TO-DO: avoid multiple entries with same synchronisation vectors
 
     @property
     def json(self):
+        """Returns the json dictionnary for requests and for synchronisation vector
+        """
         return self.pathrequest, self.pathsync
 
 
 def read_service_sheet(
-        input_filename,
-        eqpt,
-        network,
-        network_filename=None,
-        bidir=False):
+        input_filename: Path,
+        eqpt: Dict,
+        network: DiGraph,
+        network_filename: Path = None,
+        bidir: bool = False) -> Dict:
     """ converts a service sheet into a json structure
     """
     if network_filename is None:
@@ -188,19 +232,16 @@ def read_service_sheet(
     req = correct_xls_route_list(network_filename, network, req)
     # if there is no sync vector , do not write any synchronization
     synchro = [n.json[1] for n in req if n.json[1] is not None]
+    data = {'path-request': [n.json[0] for n in req]}
     if synchro:
-        data = {
-            'path-request': [n.json[0] for n in req],
-            'synchronization': synchro
-        }
-    else:
-        data = {
-            'path-request': [n.json[0] for n in req]
-        }
+        data['synchronization'] = synchro
     return data
 
 
 def correct_xlrd_int_to_str_reading(v):
+    """Utils: ensure that int values in id are read as strings containing the int and
+    do not use the automatic float conversion from xlrd
+    """
     if not isinstance(v, str):
         value = str(int(v))
         if value.endswith('.0'):
@@ -210,22 +251,27 @@ def correct_xlrd_int_to_str_reading(v):
     return value
 
 
-def parse_row(row, fieldnames):
+def parse_row(row: List, fieldnames: List[str]) -> Dict:
+    """Reads each values in a row and creates a dict using field names
+    """
     return {f: r.value for f, r in zip(fieldnames, row[0:SERVICES_COLUMN])
             if r.ctype != XL_CELL_EMPTY}
 
 
-def parse_excel(input_filename):
+def parse_excel(input_filename: Path) -> List[Request]:
+    """Opens xls_file and reads 'Service' sheet
+    Returns the list of services data in Request class
+    """
     with open_workbook(input_filename) as wb:
         service_sheet = wb.sheet_by_name('Service')
         services = list(parse_service_sheet(service_sheet))
     return services
 
 
-def parse_service_sheet(service_sheet):
+def parse_service_sheet(service_sheet) -> Request:
     """ reads each column according to authorized fieldnames. order is not important.
     """
-    logger.info(f'Validating headers on {service_sheet.name!r}')
+    logger.debug('Validating headers on %r', service_sheet.name)
     # add a test on field to enable the '' field case that arises when columns on the
     # right hand side are used as comments or drawing in the excel sheet
     header = [x.value.strip() for x in service_sheet.row(4)[0:SERVICES_COLUMN]
@@ -243,15 +289,52 @@ def parse_service_sheet(service_sheet):
         'routing: is loose?': 'is_loose', 'path bandwidth': 'path_bandwidth'}
     try:
         service_fieldnames = [authorized_fieldnames[e] for e in header]
-    except KeyError:
+    except KeyError as e:
         msg = f'Malformed header on Service sheet: {header} field not in {authorized_fieldnames}'
-        logger.critical(msg)
-        raise ValueError(msg)
+        raise ValueError(msg) from e
     for row in all_rows(service_sheet, start=5):
         yield Request(**parse_row(row[0:SERVICES_COLUMN], service_fieldnames))
 
 
-def correct_xls_route_list(network_filename, network, pathreqlist):
+def check_end_points(pathreq: Request_element, network: DiGraph):
+    """Raise error if end point is not correct
+    """
+    transponders = [n.uid for n in network.nodes() if isinstance(n, Transceiver)]
+    if pathreq.source not in transponders:
+        msg = f'Request: {pathreq.request_id}: could not find' +\
+            f' transponder source : {pathreq.source}.'
+        logger.critical(msg)
+        raise ServiceError(msg)
+    if pathreq.destination not in transponders:
+        msg = f'Request: {pathreq.request_id}: could not find' +\
+            f' transponder destination: {pathreq.destination}.'
+        logger.critical(msg)
+        raise ServiceError(msg)
+
+
+def find_node_sugestion(n_id, corresp_roadm, corresp_fused, corresp_ila, network):
+    """
+    """
+    roadmtype = [n.uid for n in network.nodes() if isinstance(n, Roadm)]
+    edfatype = [n.uid for n in network.nodes() if isinstance(n, Edfa)]
+    # check that n_id is in the node list, if not find a correspondance name
+    if n_id in roadmtype + edfatype:
+        return [n_id]
+    # checks first roadm, fused, and ila in this order, because ila automatic name
+    # contains roadm names. If it is a fused node, next ila names might be correct
+    # suggestions, especially if following fibers were splitted and ila names
+    # created with the name of the fused node
+    if n_id in corresp_roadm.keys():
+        return corresp_roadm[n_id]
+    if n_id in corresp_fused.keys():
+        return corresp_fused[n_id] + corresp_ila[n_id]
+    if n_id in corresp_ila.keys():
+        return corresp_ila[n_id]
+    return []
+
+
+def correct_xls_route_list(network_filename: Path, network: DiGraph,
+                           pathreqlist: List[Request_element]) -> List[Request_element]:
     """ prepares the format of route list of nodes to be consistant with nodes names:
         remove wrong names, find correct names for ila, roadm and fused if the entry was
         xls.
@@ -265,32 +348,17 @@ def correct_xls_route_list(network_filename, network, pathreqlist):
     corresp_ila, next_node = corresp_next_node(network, corresp_ila, corresp_roadm)
     # finally correct constraints based on these dict
     trxfibertype = [n.uid for n in network.nodes() if isinstance(n, (Transceiver, Fiber))]
-    roadmtype = [n.uid for n in network.nodes() if isinstance(n, Roadm)]
-    edfatype = [n.uid for n in network.nodes() if isinstance(n, Edfa)]
     # TODO there is a problem of identification of fibers in case of parallel
     # fibers between two adjacent roadms so fiber constraint is not supported
-    transponders = [n.uid for n in network.nodes() if isinstance(n, Transceiver)]
     for pathreq in pathreqlist:
         # first check that source and dest are transceivers
-        if pathreq.source not in transponders:
-            msg = f'{ansi_escapes.red}Request: {pathreq.request_id}: could not find' +\
-                f' transponder source : {pathreq.source}.{ansi_escapes.reset}'
-            logger.critical(msg)
-            raise ServiceError(msg)
-
-        if pathreq.destination not in transponders:
-            msg = f'{ansi_escapes.red}Request: {pathreq.request_id}: could not find' +\
-                f' transponder destination: {pathreq.destination}.{ansi_escapes.reset}'
-            logger.critical(msg)
-            raise ServiceError(msg)
+        check_end_points(pathreq, network)
         # silently pop source and dest nodes from the list if they were added by the user as first
         # and last elem in the constraints respectively. Other positions must lead to an error
         # caught later on
         if pathreq.nodes_list and pathreq.source == pathreq.nodes_list[0]:
-            pathreq.loose_list.pop(0)
             pathreq.nodes_list.pop(0)
         if pathreq.nodes_list and pathreq.destination == pathreq.nodes_list[-1]:
-            pathreq.loose_list.pop(-1)
             pathreq.nodes_list.pop(-1)
         # Then process user defined constraints with respect to automatic namings
         temp = deepcopy(pathreq)
@@ -300,79 +368,57 @@ def correct_xls_route_list(network_filename, network, pathreqlist):
             # n_id must not be a transceiver and must not be a fiber (non supported, user
             # can not enter fiber names in excel)
             if n_id not in trxfibertype:
-                # check that n_id is in the node list, if not find a correspondance name
-                if n_id in roadmtype + edfatype:
-                    nodes_suggestion = [n_id]
-                else:
-                    # checks first roadm, fused, and ila in this order, because ila automatic name
-                    # contain roadm names. If it is a fused node, next ila names might be correct
-                    # suggestions, especially if following fibers were splitted and ila names
-                    # created with the name of the fused node
-                    if n_id in corresp_roadm.keys():
-                        nodes_suggestion = corresp_roadm[n_id]
-                    elif n_id in corresp_fused.keys():
-                        nodes_suggestion = corresp_fused[n_id] + corresp_ila[n_id]
-                    elif n_id in corresp_ila.keys():
-                        nodes_suggestion = corresp_ila[n_id]
+                nodes_suggestion = find_node_sugestion(n_id, corresp_roadm, corresp_fused, corresp_ila, network)
+                try:
+                    if len(nodes_suggestion) > 1:
+                        # if there is more than one suggestion, we need to choose the direction
+                        # we rely on the next node provided by the user for this purpose
+                        new_n = next(n for n in nodes_suggestion
+                                     if n in next_node
+                                     and next_node[n] in temp.nodes_list[i:] + [pathreq.destination]
+                                     and next_node[n] not in temp.nodes_list[:i])
+                    elif len(nodes_suggestion) == 1:
+                        new_n = nodes_suggestion[0]
                     else:
-                        nodes_suggestion = []
-                if nodes_suggestion:
-                    try:
-                        if len(nodes_suggestion) > 1:
-                            # if there is more than one suggestion, we need to choose the direction
-                            # we rely on the next node provided by the user for this purpose
-                            new_n = next(n for n in nodes_suggestion
-                                         if n in next_node.keys() and next_node[n]
-                                         in temp.nodes_list[i:] + [pathreq.destination] and
-                                         next_node[n] not in temp.nodes_list[:i])
-                        else:
-                            new_n = nodes_suggestion[0]
-                        if new_n != n_id:
-                            # warns the user when the correct name is used only in verbose mode,
-                            # eg 'a' is a roadm and correct name is 'roadm a' or when there was
-                            # too much ambiguity, 'b' is an ila, its name can be:
-                            # Edfa0_fiber (a → b)-xx if next node is c or
-                            # Edfa0_fiber (c → b)-xx if next node is a
-                            msg = f'{ansi_escapes.yellow}Invalid route node specified:' +\
-                                f'\n\t\'{n_id}\', replaced with \'{new_n}\'{ansi_escapes.reset}'
+                        if temp.loose == 'LOOSE':
+                            # if no matching can be found in the network just ignore this constraint
+                            # if it is a loose constraint
+                            # warns the user that this node is not part of the topology
+                            msg = f'{pathreq.request_id}: Invalid node specified:\n\t\'{n_id}\'' \
+                                + ', could not use it as constraint, skipped!'
+                            print(msg)
                             logger.info(msg)
-                            pathreq.nodes_list[pathreq.nodes_list.index(n_id)] = new_n
-                    except StopIteration:
-                        # shall not come in this case, unless requested direction does not exist
-                        msg = f'{ansi_escapes.yellow}Invalid route specified {n_id}: could' +\
-                            f' not decide on direction, skipped!.\nPlease add a valid' +\
-                            f' direction in constraints (next neighbour node){ansi_escapes.reset}'
-                        print(msg)
-                        logger.info(msg)
-                        pathreq.loose_list.pop(pathreq.nodes_list.index(n_id))
-                        pathreq.nodes_list.remove(n_id)
-                else:
-                    if temp.loose_list[i] == 'LOOSE':
-                        # if no matching can be found in the network just ignore this constraint
-                        # if it is a loose constraint
-                        # warns the user that this node is not part of the topology
-                        msg = f'{ansi_escapes.yellow}Invalid node specified:\n\t\'{n_id}\'' +\
-                            f', could not use it as constraint, skipped!{ansi_escapes.reset}'
-                        print(msg)
-                        logger.info(msg)
-                        pathreq.loose_list.pop(pathreq.nodes_list.index(n_id))
-                        pathreq.nodes_list.remove(n_id)
-                    else:
-                        msg = f'{ansi_escapes.red}Could not find node:\n\t\'{n_id}\' in network' +\
-                            f' topology. Strict constraint can not be applied.{ansi_escapes.reset}'
-                        logger.critical(msg)
+                            pathreq.nodes_list.remove(n_id)
+                            continue
+                        msg = f'{pathreq.request_id}: Could not find node:\n\t\'{n_id}\' in network' \
+                            + ' topology. Strict constraint can not be applied.'
                         raise ServiceError(msg)
+                    if new_n != n_id:
+                        # warns the user when the correct name is used only in verbose mode,
+                        # eg 'a' is a roadm and correct name is 'roadm a' or when there was
+                        # too much ambiguity, 'b' is an ila, its name can be:
+                        # "east edfa in b to c", or "west edfa in b to a" if next node is c or
+                        # "west edfa in b to c", or "east edfa in b to a" if next node is a
+                        msg = f'{pathreq.request_id}: Invalid route node specified:' \
+                            + f'\n\t\'{n_id}\', replaced with \'{new_n}\''
+                        logger.info(msg)
+                        pathreq.nodes_list[pathreq.nodes_list.index(n_id)] = new_n
+                except StopIteration:
+                    # shall not come in this case, unless requested direction does not exist
+                    msg = f'{pathreq.request_id}: Invalid route specified {n_id}: could' \
+                        + ' not decide on direction, skipped!.\nPlease add a valid' \
+                        + ' direction in constraints (next neighbour node)'
+                    logger.info(msg)
+                    pathreq.nodes_list.remove(n_id)
             else:
-                if temp.loose_list[i] == 'LOOSE':
-                    print(f'{ansi_escapes.yellow}Invalid route node specified:\n\t\'{n_id}\'' +
-                          f' type is not supported as constraint with xls network input,' +
-                          f' skipped!{ansi_escapes.reset}')
-                    pathreq.loose_list.pop(pathreq.nodes_list.index(n_id))
+                if temp.loose == 'LOOSE':
+                    msg = f'{pathreq.request_id}: Invalid route node specified:\n\t\'{n_id}\'' \
+                        + ' type is not supported as constraint with xls network input, skipped!'
+                    logger.warning(msg)
                     pathreq.nodes_list.remove(n_id)
                 else:
-                    msg = f'{ansi_escapes.red}Invalid route node specified \n\t\'{n_id}\'' +\
-                        f' type is not supported as constraint with xls network input,' +\
-                        f', Strict constraint can not be applied.{ansi_escapes.reset}'
-                    logger.critical(msg)
+                    msg = f'{pathreq.request_id}: Invalid route node specified \n\t\'{n_id}\'' \
+                        + ' type is not supported as constraint with xls network input,' \
+                        + ', Strict constraint can not be applied.'
                     raise ServiceError(msg)
     return pathreqlist

gnpy/tools/worker_utils.py

@@ -0,0 +1,248 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+'''
+gnpy.tools.worker_utils
+=======================
+
+Common code for CLI examples and API
+'''
+import logging
+from copy import deepcopy
+from typing import Union, List, Tuple
+from numpy import linspace
+from networkx import DiGraph
+
+from gnpy.core.utils import automatic_nch, watt2dbm, dbm2watt, pretty_summary_print, per_label_average
+from gnpy.core.equipment import trx_mode_params
+from gnpy.core.network import add_missing_elements_in_network, design_network
+from gnpy.core import exceptions
+from gnpy.core.info import SpectralInformation
+from gnpy.topology.spectrum_assignment import build_oms_list, pth_assign_spectrum, OMS
+from gnpy.topology.request import correct_json_route_list, deduplicate_disjunctions, requests_aggregation, \
+    compute_path_dsjctn, compute_path_with_disjunction, ResultElement, PathRequest, Disjunction, \
+    compute_constrained_path, propagate
+from gnpy.tools.json_io import requests_from_json, disjunctions_from_json
+
+
+logger = logging.getLogger(__name__)
+
+
+def designed_network(equipment: dict, network: DiGraph, source: str = None, destination: str = None,
+                     nodes_list: List[str] = None, loose_list: List[str] = None,
+                     initial_spectrum: dict = None, no_insert_edfas: bool = False,
+                     args_power: Union[str, float, int] = None,
+                     service_req: PathRequest = None) -> Tuple[DiGraph, PathRequest, PathRequest]:
+    """Build the reference channels based on inputs and design the network for this reference channel, and build the
+    channel to be propagated for the single transmission script.
+
+    Reference channel (target input power in spans, nb of channels, transceiver output power) is built using
+    equipment['SI'] information. If indicated,  with target input power in spans is updated with args_power.
+    Channel to be propagated is using the same channel reference, except if different settings are provided
+    with service_req and initial_spectrum. The service to be propagated uses specified source, destination
+    and list nodes_list of include nodes constraint except if the service_req is specified.
+
+    Args:
+    - equipment: a dictionary containing equipment information.
+    - network: a directed graph representing the initial network.
+    - no_insert_edfas: a boolean indicating whether to insert EDFAs in the network.
+    - args_power: the power to be used for the network design.
+    - service_req: the service request the user wants to propagate.
+    - source: the source node for the channel to be propagated if no service_req is specified.
+    - destination: the destination node for the channel to be propagated if no service_req is specified.
+    - nodes_list: a list of nodes to be included ifor the channel to be propagated if no service_req is specified.
+    - loose_list: a list of loose nodes to be included in the network design.
+    - initial_spectrum: a dictionary representing the initial spectrum to propagate.
+
+    Returns:
+    - The designed network.
+    - The channel to propagate.
+    - The reference channel used for the design.
+    """
+    if loose_list is None:
+        loose_list = []
+    if nodes_list is None:
+        nodes_list = []
+    if not no_insert_edfas:
+        add_missing_elements_in_network(network, equipment)
+
+    if not nodes_list:
+        if destination:
+            nodes_list = [destination]
+            loose_list = ['STRICT']
+        else:
+            nodes_list = []
+            loose_list = []
+    params = {
+        'request_id': 'reference',
+        'trx_type': '',
+        'trx_mode': '',
+        'source': source,
+        'destination': destination,
+        'bidir': False,
+        'nodes_list': nodes_list,
+        'loose_list': loose_list,
+        'format': '',
+        'path_bandwidth': 0,
+        'effective_freq_slot': None,
+        'nb_channel': automatic_nch(equipment['SI']['default'].f_min, equipment['SI']['default'].f_max,
+                                    equipment['SI']['default'].spacing),
+        'power': dbm2watt(equipment['SI']['default'].power_dbm),
+        'tx_power': None
+    }
+    params['tx_power'] = dbm2watt(equipment['SI']['default'].power_dbm)
+    if equipment['SI']['default'].tx_power_dbm is not None:
+        # use SI tx_power if present
+        params['tx_power'] = dbm2watt(equipment['SI']['default'].tx_power_dbm)
+    trx_params = trx_mode_params(equipment)
+    params.update(trx_params)
+
+    # use args_power instead of si
+    if args_power:
+        params['power'] = dbm2watt(float(args_power))
+        if equipment['SI']['default'].tx_power_dbm is None:
+            params['tx_power'] = params['power']
+
+    # use si as reference channel
+    reference_channel = PathRequest(**params)
+    # temporary till multiband design feat is available: do not design for L band
+    reference_channel.nb_channel = min(params['nb_channel'], automatic_nch(191.2e12, 196.0e12, params['spacing']))
+
+    if service_req:
+        # use service_req as reference channel with si tx_power if service_req tx_power is None
+        if service_req.tx_power is None:
+            service_req.tx_power = params['tx_power']
+        reference_channel = service_req
+
+    design_network(reference_channel, network, equipment, set_connector_losses=True, verbose=True)
+
+    if initial_spectrum:
+        params['nb_channel'] = len(initial_spectrum)
+
+    req = PathRequest(**params)
+    if service_req:
+        req = service_req
+
+    req.initial_spectrum = initial_spectrum
+    return network, req, reference_channel
+
+
+def check_request_path_ids(rqs: List[PathRequest]):
+    """check that request ids are unique. Non unique ids, may
+    mess the computation: better to stop the computation
+    """
+    all_ids = [r.request_id for r in rqs]
+    if len(all_ids) != len(set(all_ids)):
+        for item in list(set(all_ids)):
+            all_ids.remove(item)
+        msg = f'Requests id {all_ids} are not unique'
+        logger.error(msg)
+        raise ValueError(msg)
+
+
+def planning(network: DiGraph, equipment: dict, data: dict, redesign: bool = False) \
+        -> Tuple[List[OMS], list, list, List[PathRequest], List[Disjunction], List[ResultElement]]:
+    """Run planning
+    data contain the service dict from json
+    redesign True means that network is redesign using each request as reference channel
+    when False it means that the design is made once and successive propagation use the settings
+    computed with this design.
+    """
+    oms_list = build_oms_list(network, equipment)
+    rqs = requests_from_json(data, equipment)
+    # check that request ids are unique.
+    check_request_path_ids(rqs)
+    rqs = correct_json_route_list(network, rqs)
+    dsjn = disjunctions_from_json(data)
+    logger.info('List of disjunctions:\n%s', dsjn)
+    # need to warn or correct in case of wrong disjunction form
+    # disjunction must not be repeated with same or different ids
+    dsjn = deduplicate_disjunctions(dsjn)
+    logger.info('Aggregating similar requests')
+    rqs, dsjn = requests_aggregation(rqs, dsjn)
+    logger.info('The following services have been requested:\n%s', rqs)
+    # logger.info('Computing all paths with constraints for request %s', optical_path_result_id)
+
+    pths = compute_path_dsjctn(network, equipment, rqs, dsjn)
+    logger.info('Propagating on selected path')
+    propagatedpths, reversed_pths, reversed_propagatedpths = \
+        compute_path_with_disjunction(network, equipment, rqs, pths, redesign=redesign)
+    # Note that deepcopy used in compute_path_with_disjunction returns
+    # a list of nodes which are not belonging to network (they are copies of the node objects).
+    # so there can not be propagation on these nodes.
+
+    # Allowed user_policy are first_fit and 2partition
+    pth_assign_spectrum(pths, rqs, oms_list, reversed_pths)
+    for i, rq in enumerate(rqs):
+        if hasattr(rq, 'OSNR') and rq.OSNR:
+            rq.osnr_with_sys_margin = rq.OSNR + equipment["SI"]["default"].sys_margins
+
+    # assumes that list of rqs and list of propgatedpths have same order
+    result = [ResultElement(rq, pth, rpth) for rq, pth, rpth in zip(rqs, propagatedpths, reversed_propagatedpths)]
+    return oms_list, propagatedpths, reversed_propagatedpths, rqs, dsjn, result
+
+
+def transmission_simulation(equipment: dict, network: DiGraph, req: PathRequest, ref_req: PathRequest) \
+        -> Tuple[list, List[list], List[Union[float, int]], SpectralInformation]:
+    """Run simulation and returms the propagation result for each power sweep iteration.
+    Args:
+    - equipment: a dictionary containing equipment information.
+    - network: network after being designed using ref_req. Any missing information (amp gain or delta_p) must have
+    been filled using ref_req as reference channel previuos to this function.
+    - req: channel to be propagated.
+    - ref_req: the reference channel used for filling missing information in the network.
+    In case of power sweep, network is redesigned using ref_req whose target input power in span is
+    updated with the power step.
+
+    Returns a tuple containing:
+    - path: last propagated path. Power sweep is not possible with gain mode (as gain targets are used)
+    - propagations: list of propagated path for each power iteration
+    - powers_dbm: list of power used for the power sweep
+    - infos: last propagated spectral information
+    """
+    power_mode = equipment['Span']['default'].power_mode
+    logger.info('Power mode is set to %s=> it can be modified in eqpt_config.json - Span', power_mode)
+    # initial network is designed using ref_req. that is that any missing information (amp gain or delta_p) is filled
+    # using this ref_req.power, previous to any sweep requested later on.
+
+    pref_ch_db = watt2dbm(ref_req.power)
+    p_ch_db = watt2dbm(req.power)
+    path = compute_constrained_path(network, req)
+    power_range = [0]
+    if power_mode:
+        # power cannot be changed in gain mode
+        try:
+            p_start, p_stop, p_step = equipment['SI']['default'].power_range_db
+            p_num = abs(int(round((p_stop - p_start) / p_step))) + 1 if p_step != 0 else 1
+            power_range = list(linspace(p_start, p_stop, p_num))
+        except TypeError as e:
+            msg = 'invalid power range definition in eqpt_config, should be power_range_db: [lower, upper, step]'
+            logger.error(msg)
+            raise exceptions.EquipmentConfigError(msg) from e
+
+    logger.info('Now propagating between %s and %s', req.source, req.destination)
+
+    propagations = []
+    powers_dbm = []
+    for dp_db in power_range:
+        ref_req.power = dbm2watt(pref_ch_db + dp_db)
+        req.power = dbm2watt(p_ch_db + dp_db)
+
+        # Power sweep is made to evaluate different span input powers, so redesign is mandatory for each power,
+        #  but no need to redesign if there are no power sweep
+        if len(power_range) > 1:
+            design_network(ref_req, network.subgraph(path), equipment, set_connector_losses=False, verbose=False)
+
+        infos = propagate(path, req, equipment)
+        propagations.append(deepcopy(path))
+        powers_dbm.append(pref_ch_db + dp_db)
+        logger.info('\nChannels propagating: (Input optical power deviation in span = '
+                    + f'{pretty_summary_print(per_label_average(infos.delta_pdb_per_channel, infos.label))}dB,\n'
+                    + '                       spacing = '
+                    + f'{pretty_summary_print(per_label_average(infos.slot_width * 1e-9, infos.label))}GHz,\n'
+                    + '                       transceiver output power = '
+                    + f'{pretty_summary_print(per_label_average(watt2dbm(infos.tx_power), infos.label))}dBm,\n'
+                    + f'                       nb_channels = {infos.number_of_channels})')
+        if not power_mode:
+            logger.info('\n\tPropagating using gain targets: Input optical power deviation in span ignored')
+    return path, propagations, powers_dbm, infos

gnpy/topology/__init__.py

@@ -1,3 +1,3 @@
-'''
+"""
 Tracking :py:mod:`.request` for spectrum and their :py:mod:`.spectrum_assignment`.
-'''
+"""

gnpy/topology/request.py

@@ -16,16 +16,19 @@ See: draft-ietf-teas-yang-path-computation-01.txt
 """
 
 from collections import namedtuple, OrderedDict
+from typing import List
 from logging import getLogger
 from networkx import (dijkstra_path, NetworkXNoPath,
                       all_simple_paths, shortest_simple_paths)
 from networkx.utils import pairwise
 from numpy import mean, argmin
-from gnpy.core.elements import Transceiver, Roadm
-from gnpy.core.utils import lin2db
-from gnpy.core.info import create_input_spectral_information
+
+from gnpy.core.elements import Transceiver, Roadm, Edfa, Multiband_amplifier
+from gnpy.core.utils import lin2db, unique_ordered, find_common_range
+from gnpy.core.info import create_input_spectral_information, carriers_to_spectral_information, \
+    demuxed_spectral_information, muxed_spectral_information, SpectralInformation
+from gnpy.core import network as network_module
 from gnpy.core.exceptions import ServiceError, DisjunctionError
-import gnpy.core.ansi_escapes as ansi_escapes
 from copy import deepcopy
 from csv import writer
 from math import ceil
@@ -35,15 +38,14 @@ LOGGER = getLogger(__name__)
 RequestParams = namedtuple('RequestParams', 'request_id source destination bidir trx_type'
                            ' trx_mode nodes_list loose_list spacing power nb_channel f_min'
                            ' f_max format baud_rate OSNR penalties bit_rate'
-                           ' roll_off tx_osnr min_spacing cost path_bandwidth effective_freq_slot')
+                           ' roll_off tx_osnr min_spacing cost path_bandwidth effective_freq_slot'
+                           ' equalization_offset_db, tx_power')
 DisjunctionParams = namedtuple('DisjunctionParams', 'disjunction_id relaxable link_diverse'
                                ' node_diverse disjunctions_req')
 
 
 class PathRequest:
-    """ the class that contains all attributes related to a request
-    """
-
+    """the class that contains all attributes related to a request"""
     def __init__(self, *args, **params):
         params = RequestParams(**params)
         self.request_id = params.request_id
@@ -66,12 +68,15 @@ class PathRequest:
         self.bit_rate = params.bit_rate
         self.roll_off = params.roll_off
         self.tx_osnr = params.tx_osnr
+        self.tx_power = params.tx_power
         self.min_spacing = params.min_spacing
         self.cost = params.cost
         self.path_bandwidth = params.path_bandwidth
         if params.effective_freq_slot is not None:
-            self.N = params.effective_freq_slot['N']
-            self.M = params.effective_freq_slot['M']
+            self.N = [s['N'] for s in params.effective_freq_slot]
+            self.M = [s['M'] for s in params.effective_freq_slot]
+        self.initial_spectrum = None
+        self.offset_db = params.equalization_offset_db
 
     def __str__(self):
         return '\n\t'.join([f'{type(self).__name__} {self.request_id}',
@@ -94,7 +99,8 @@ class PathRequest:
                             f'baud_rate:\t{temp} Gbaud',
                             f'bit_rate:\t{temp2} Gb/s',
                             f'spacing:\t{self.spacing * 1e-9} GHz',
-                            f'power:  \t{round(lin2db(self.power)+30, 2)} dBm',
+                            f'power:  \t{round(lin2db(self.power) + 30, 2)} dBm',
+                            f'tx_power_dbm:  \t{round(lin2db(self.tx_power) + 30, 2)} dBm',
                             f'nb channels: \t{self.nb_channel}',
                             f'path_bandwidth: \t{round(self.path_bandwidth * 1e-9, 2)} Gbit/s',
                             f'nodes-list:\t{self.nodes_list}',
@@ -103,8 +109,7 @@ class PathRequest:
 
 
 class Disjunction:
-    """ the class that contains all attributes related to disjunction constraints
-    """
+    """the class that contains all attributes related to disjunction constraints"""
 
     def __init__(self, *args, **params):
         params = DisjunctionParams(**params)
@@ -149,8 +154,7 @@ class ResultElement:
 
     @property
     def detailed_path_json(self):
-        """ a function that builds path object for normal and blocking cases
-        """
+        """a function that builds path object for normal and blocking cases"""
         index = 0
         pro_list = []
         for element in self.computed_path:
@@ -174,10 +178,10 @@ class ResultElement:
                 temp = {
                     'path-route-object': {
                         'index': index,
-                        "label-hop": {
-                            "N": self.path_request.N,
-                            "M": self.path_request.M
-                        },
+                        "label-hop": [{
+                            "N": n,
+                            "M": m
+                        } for n, m in zip(self.path_request.N, self.path_request.M)],
                     }
                 }
                 pro_list.append(temp)
@@ -206,11 +210,9 @@ class ResultElement:
 
     @property
     def path_properties(self):
-        """ a function that returns the path properties (metrics, crossed elements) into a dict
-        """
+        """a function that returns the path properties (metrics, crossed elements) into a dict"""
         def path_metric(pth, req):
-            """ creates the metrics dictionary
-            """
+            """creates the metrics dictionary"""
             return [
                 {
                     'metric-type': 'SNR-bandwidth',
@@ -252,8 +254,7 @@ class ResultElement:
 
     @property
     def pathresult(self):
-        """ create the result dictionnary (response for a request)
-        """
+        """create the result dictionnary (response for a request)"""
         try:
             if self.path_request.blocking_reason in BLOCKING_NOPATH:
                 response = {
@@ -291,7 +292,6 @@ def compute_constrained_path(network, req):
         # been corrected and harmonized before
         msg = (f'Request {req.request_id} malformed list of nodes: last node should '
                'be destination trx')
-        LOGGER.critical(msg)
         raise ValueError()
 
     trx = [n for n in network if isinstance(n, Transceiver)]
@@ -301,15 +301,16 @@ def compute_constrained_path(network, req):
     nodes_list = []
     for node in req.nodes_list[:-1]:
         nodes_list.append(next(el for el in network if el.uid == node))
-
+    total_path = explicit_path(nodes_list, source, destination, network)
+    if total_path is not None:
+        return total_path
     try:
         path_generator = shortest_simple_paths(network, source, destination, weight='weight')
         total_path = next(path for path in path_generator if ispart(nodes_list, path))
     except NetworkXNoPath:
-        msg = (f'{ansi_escapes.yellow}Request {req.request_id} could not find a path from'
-               f' {source.uid} to node: {destination.uid} in network topology{ansi_escapes.reset}')
+        msg = (f'Request {req.request_id} could not find a path from'
+               f' {source.uid} to node: {destination.uid} in network topology')
         LOGGER.critical(msg)
-        print(msg)
         req.blocking_reason = 'NO_PATH'
         total_path = []
     except StopIteration:
@@ -318,82 +319,115 @@ def compute_constrained_path(network, req):
         # last node which is the transceiver)
         # if all nodes i n node_list are LOOSE constraint, skip the constraints and find
         # a path w/o constraints, else there is no possible path
-        print(f'{ansi_escapes.yellow}Request {req.request_id} could not find a path crossing '
-              f'{[el.uid for el in nodes_list[:-1]]} in network topology{ansi_escapes.reset}')
+        LOGGER.warning(f'Request {req.request_id} could not find a path crossing '
+                       f'{[el.uid for el in nodes_list[:-1]]} in network topology')
 
         if 'STRICT' not in req.loose_list[:-1]:
-            msg = (f'{ansi_escapes.yellow}Request {req.request_id} could not find a path with user_'
-                   f'include node constraints{ansi_escapes.reset}')
-            LOGGER.info(msg)
-            print(f'constraint ignored')
+            msg = (f'Request {req.request_id} could not find a path with user_'
+                   f'include node constraints. Constraint ignored')
+            LOGGER.warning(msg)
             total_path = dijkstra_path(network, source, destination, weight='weight')
         else:
             # one STRICT makes the whole list STRICT
-            msg = (f'{ansi_escapes.yellow}Request {req.request_id} could not find a path with user '
-                   f'include node constraints.\nNo path computed{ansi_escapes.reset}')
+            msg = (f'Request {req.request_id} could not find a path with user '
+                   f'include node constraints.\nNo path computed')
             LOGGER.critical(msg)
-            print(msg)
             req.blocking_reason = 'NO_PATH_WITH_CONSTRAINT'
             total_path = []
-
     return total_path
 
 
+def filter_si(path: list, equipment: dict, si: SpectralInformation) -> SpectralInformation:
+    """Filter spectral information based on the amplifiers common range"""
+    # First retrieve f_min, f_max spectrum according to amplifiers' spectrum on the path
+    common_range = find_elements_common_range(path, equipment)
+    # filter out frequencies that should not be created
+    filtered_si = []
+    for band in common_range:
+        temp = demuxed_spectral_information(si, band)
+        if temp:
+            filtered_si.append(temp)
+    if not filtered_si:
+        raise ValueError('Defined propagation band does not match amplifiers band.')
+    return muxed_spectral_information(filtered_si)
+
+
 def propagate(path, req, equipment):
-    si = create_input_spectral_information(
-        req.f_min, req.f_max, req.roll_off, req.baud_rate,
-        req.power, req.spacing)
+    """propagates signals in each element according to initial spectrum set by user
+    Spectrum is specified in request through f_min, f_max and spacing, or initial_spectrum
+    and amps frequency band on the path is used to filter out frequencies"""
+    # generates spectrum based on request
+    if req.initial_spectrum is not None:
+        si = carriers_to_spectral_information(initial_spectrum=req.initial_spectrum, power=req.power)
+    else:
+        si = create_input_spectral_information(
+            f_min=req.f_min, f_max=req.f_max, roll_off=req.roll_off, baud_rate=req.baud_rate,
+            spacing=req.spacing, tx_osnr=req.tx_osnr, tx_power=req.tx_power, delta_pdb=req.offset_db)
+    # filter out frequencies that should not be created
+    si = filter_si(path, equipment, si)
+    roadm_osnr = []
     for i, el in enumerate(path):
         if isinstance(el, Roadm):
-            si = el(si, degree=path[i+1].uid)
+            si = el(si, degree=path[i + 1].uid, from_degree=path[i - 1].uid)
+            roadm_osnr.append(el.get_impairment('roadm-osnr', si.frequency,
+                                                from_degree=path[i - 1].uid, degree=path[i + 1].uid))
         else:
             si = el(si)
-    path[0].update_snr(req.tx_osnr)
+    path[0].update_snr(si.tx_osnr)
     path[0].calc_penalties(req.penalties)
-    if any(isinstance(el, Roadm) for el in path):
-        path[-1].update_snr(req.tx_osnr, equipment['Roadm']['default'].add_drop_osnr)
-    else:
-        path[-1].update_snr(req.tx_osnr)
+    roadm_osnr.append(si.tx_osnr)
+    path[-1].update_snr(*roadm_osnr)
     path[-1].calc_penalties(req.penalties)
     return si
 
 
 def propagate_and_optimize_mode(path, req, equipment):
     # if mode is unknown : loops on the modes starting from the highest baudrate fiting in the
-    # step 1: create an ordered list of modes based on baudrate
-    baudrate_to_explore = list(set([this_mode['baud_rate']
-                                    for this_mode in equipment['Transceiver'][req.tsp].mode
-                                    if float(this_mode['min_spacing']) <= req.spacing]))
+    # step 1: create an ordered list of modes based on baudrate and power offset
+    # order higher baudrate with higher power offset first
+    baudrate_offset_to_explore = list(set([(this_mode['baud_rate'], this_mode['equalization_offset_db'])
+                                           for this_mode in equipment['Transceiver'][req.tsp].mode
+                                           if float(this_mode['min_spacing']) <= req.spacing]))
     # TODO be carefull on limits cases if spacing very close to req spacing eg 50.001 50.000
-    baudrate_to_explore = sorted(baudrate_to_explore, reverse=True)
-    if baudrate_to_explore:
+    baudrate_offset_to_explore = sorted(baudrate_offset_to_explore, reverse=True)
+    if baudrate_offset_to_explore:
         # at least 1 baudrate can be tested wrt spacing
-        for this_br in baudrate_to_explore:
+        for (this_br, this_offset) in baudrate_offset_to_explore:
             modes_to_explore = [this_mode for this_mode in equipment['Transceiver'][req.tsp].mode
-                                if this_mode['baud_rate'] == this_br and
-                                float(this_mode['min_spacing']) <= req.spacing]
+                                if this_mode['baud_rate'] == this_br
+                                and float(this_mode['min_spacing']) <= req.spacing]
             modes_to_explore = sorted(modes_to_explore,
-                                      key=lambda x: x['bit_rate'], reverse=True)
-            # print(modes_to_explore)
+                                      key=lambda x: (x['bit_rate'], x['equalization_offset_db']), reverse=True)
             # step2: computes propagation for each baudrate: stop and select the first that passes
-            # TODO: the case of roll of is not included: for now use SI one
+            # TODO: the case of roll off is not included: for now use SI one
             # TODO: if the loop in mode optimization does not have a feasible path, then bugs
-            spc_info = create_input_spectral_information(req.f_min, req.f_max,
-                                                         equipment['SI']['default'].roll_off,
-                                                         this_br, req.power, req.spacing)
+            if req.initial_spectrum is not None:
+                # this case is not yet handled: spectrum can not be defined for the path-request-run function
+                # and this function is only called in this case. so coming here should not be considered yet.
+                msg = f'Request: {req.request_id} contains a unexpected initial_spectrum.'
+                raise ServiceError(msg)
+            spc_info = create_input_spectral_information(f_min=req.f_min, f_max=req.f_max,
+                                                         roll_off=equipment['SI']['default'].roll_off,
+                                                         baud_rate=this_br, spacing=req.spacing,
+                                                         delta_pdb=this_offset, tx_osnr=req.tx_osnr,
+                                                         tx_power=req.tx_power)
+            spc_info = filter_si(path, equipment, spc_info)
+            roadm_osnr = []
             for i, el in enumerate(path):
                 if isinstance(el, Roadm):
-                    spc_info = el(spc_info, degree=path[i+1].uid)
+                    spc_info = el(spc_info, degree=path[i + 1].uid, from_degree=path[i - 1].uid)
+                    roadm_osnr.append(el.get_impairment('roadm-osnr', spc_info.frequency,
+                                                        from_degree=path[i - 1].uid, degree=path[i + 1].uid))
                 else:
                     spc_info = el(spc_info)
             for this_mode in modes_to_explore:
                 if path[-1].snr is not None:
                     path[0].update_snr(this_mode['tx_osnr'])
                     path[0].calc_penalties(this_mode['penalties'])
-                    if any(isinstance(el, Roadm) for el in path):
-                        path[-1].update_snr(this_mode['tx_osnr'], equipment['Roadm']['default'].add_drop_osnr)
-                    else:
-                        path[-1].update_snr(this_mode['tx_osnr'])
+                    roadm_osnr.append(this_mode['tx_osnr'])
+                    path[-1].update_snr(*roadm_osnr)
+                    # remove the tx_osnr from roadm_osnr list for the next iteration
+                    del roadm_osnr[-1]
                     path[-1].calc_penalties(this_mode['penalties'])
                     if round(min(path[-1].snr_01nm - path[-1].total_penalty), 2) \
                             > this_mode['OSNR'] + equipment['SI']['default'].sys_margins:
@@ -407,22 +441,19 @@ def propagate_and_optimize_mode(path, req, equipment):
 
         # returns the last propagated path and mode
         msg = f'\tWarning! Request {req.request_id}: no mode satisfies path SNR requirement.\n'
-        print(msg)
-        LOGGER.info(msg)
+        LOGGER.warning(msg)
         req.blocking_reason = 'NO_FEASIBLE_MODE'
         return path, last_explored_mode
     else:
         # no baudrate satisfying spacing
         msg = f'\tWarning! Request {req.request_id}: no baudrate satisfies spacing requirement.\n'
-        print(msg)
-        LOGGER.info(msg)
+        LOGGER.warning(msg)
         req.blocking_reason = 'NO_FEASIBLE_BAUDRATE_WITH_SPACING'
         return [], None
 
 
 def jsontopath_metric(path_metric):
-    """ a functions that reads resulting metric  from json string
-    """
+    """a functions that reads resulting metric  from json string"""
     output_snr = next(e['accumulative-value']
                       for e in path_metric if e['metric-type'] == 'SNR-0.1nm')
     output_snrbandwidth = next(e['accumulative-value']
@@ -440,9 +471,7 @@ def jsontopath_metric(path_metric):
 
 
 def jsontoparams(my_p, tsp, mode, equipment):
-    """ a function that derives optical params from transponder type and mode
-        supports the no mode case
-    """
+    """a function that derives optical params from transponder type and mode supports the no mode case"""
     temp = []
     for elem in my_p['path-properties']['path-route-objects']:
         if 'num-unnum-hop' in elem['path-route-object']:
@@ -452,8 +481,8 @@ def jsontoparams(my_p, tsp, mode, equipment):
     temp2 = []
     for elem in my_p['path-properties']['path-route-objects']:
         if 'label-hop' in elem['path-route-object'].keys():
-            temp2.append(f'{elem["path-route-object"]["label-hop"]["N"]}, ' +
-                         f'{elem["path-route-object"]["label-hop"]["M"]}')
+            temp2.append(f'{[e["N"] for e in elem["path-route-object"]["label-hop"]]}, '
+                         + f'{[e["M"] for e in elem["path-route-object"]["label-hop"]]}')
     # OrderedDict.fromkeys returns the unique set of strings.
     # TODO: if spectrum changes along the path, we should be able to give the segments
     #       eg for regeneration case
@@ -477,10 +506,10 @@ def jsontoparams(my_p, tsp, mode, equipment):
 
 
 def jsontocsv(json_data, equipment, fileout):
-    """ reads json path result file in accordance with:
-        Yang model for requesting Path Computation
-        draft-ietf-teas-yang-path-computation-01.txt.
-        and write results in an CSV file
+    """reads json path result file in accordance with:
+    Yang model for requesting Path Computation
+    draft-ietf-teas-yang-path-computation-01.txt.
+    and write results in an CSV file
     """
     mywriter = writer(fileout)
     mywriter.writerow(('response-id', 'source', 'destination', 'path_bandwidth', 'Pass?',
@@ -815,13 +844,13 @@ def compute_path_dsjctn(network, equipment, pathreqlist, disjunctions_list):
                     if not ispart(allpaths[id(pth)].req.nodes_list, pth):
                         testispartok = False
                         if 'STRICT' in allpaths[id(pth)].req.loose_list:
-                            LOGGER.info(f'removing solution from candidate paths\n{pth}')
+                            LOGGER.debug(f'removing solution from candidate paths\n{pth}')
                             testispartnokloose = False
                             break
             if testispartok:
                 temp.append(sol)
             elif testispartnokloose:
-                LOGGER.info(f'Adding solution as alternate solution not satisfying constraint\n{pth}')
+                LOGGER.debug(f'Adding solution as alternate solution not satisfying constraint\n{pth}')
                 alternatetemp.append(sol)
         if temp:
             candidates[this_d.disjunction_id] = temp
@@ -843,9 +872,7 @@ def compute_path_dsjctn(network, equipment, pathreqlist, disjunctions_list):
                     # remove duplicated candidates
                     candidates = remove_candidate(candidates, allpaths, allpaths[id(pth)].req, pth)
         else:
-            msg = f'No disjoint path found with added constraint'
-            LOGGER.critical(msg)
-            print(f'{msg}\nComputation stopped.')
+            msg = 'No disjoint path found with added constraint\nComputation stopped.'
             # TODO in this case: replay step 5  with the candidate without constraints
             raise DisjunctionError(msg)
 
@@ -866,8 +893,7 @@ def compute_path_dsjctn(network, equipment, pathreqlist, disjunctions_list):
 
 
 def isdisjoint(pth1, pth2):
-    """ returns 0 if disjoint
-    """
+    """returns 0 if disjoint"""
     edge1 = list(pairwise(pth1))
     edge2 = list(pairwise(pth2))
     for edge in edge1:
@@ -877,9 +903,9 @@ def isdisjoint(pth1, pth2):
 
 
 def find_reversed_path(pth):
-    """ select of intermediate roadms and find the path between them
-        note that this function may not give an exact result in case of multiple
-        links between two adjacent nodes.
+    """select of intermediate roadms and find the path between them
+    note that this function may not give an exact result in case of multiple
+    links between two adjacent nodes.
     """
     # TODO add some indication on elements to indicate from which other they
     # are the reversed direction. This is partly done with oms indication
@@ -902,9 +928,8 @@ def find_reversed_path(pth):
             # concatenation should be [roadma el1 el2 roadmb el3 el4 roadmc]
             reversed_path = list(OrderedDict.fromkeys(reversed_path))
         else:
-            msg = f'Error while handling reversed path {pth[-1].uid} to {pth[0].uid}:' +\
-                ' can not handle unidir topology. TO DO.'
-            LOGGER.critical(msg)
+            msg = f'Error while handling reversed path {pth[-1].uid} to {pth[0].uid}:' \
+                + ' can not handle unidir topology. TO DO.'
             raise ValueError(msg)
     reversed_path.append(pth[0])
 
@@ -912,9 +937,7 @@ def find_reversed_path(pth):
 
 
 def ispart(ptha, pthb):
-    """ the functions takes two paths a and b and retrns True
-        if all a elements are part of b and in the same order
-    """
+    """the functions takes two paths a and b and retrns True if all a elements are part of b and in the same order"""
     j = 0
     for elem in ptha:
         if elem in pthb:
@@ -928,8 +951,7 @@ def ispart(ptha, pthb):
 
 
 def remove_candidate(candidates, allpaths, rqst, pth):
-    """ filter duplicate candidates
-    """
+    """filter duplicate candidates"""
     # print(f'coucou {rqst.request_id}')
     for key, candidate in candidates.items():
         temp = candidate.copy()
@@ -944,8 +966,7 @@ def remove_candidate(candidates, allpaths, rqst, pth):
 
 
 def compare_reqs(req1, req2, disjlist):
-    """ compare two requests: returns True or False
-    """
+    """compare two requests: returns True or False"""
     dis1 = [d for d in disjlist if req1.request_id in d.disjunctions_req]
     dis2 = [d for d in disjlist if req2.request_id in d.disjunctions_req]
     same_disj = False
@@ -978,28 +999,32 @@ def compare_reqs(req1, req2, disjlist):
             req1.format == req2.format and \
             req1.OSNR == req2.OSNR and \
             req1.roll_off == req2.roll_off and \
-            same_disj and \
-            getattr(req1, 'N', None) is None and getattr(req2, 'N', None) is None and \
-            getattr(req1, 'M', None) is None and getattr(req2, 'M', None) is None:
+            req1.tx_power == req2.tx_power and \
+            same_disj:
         return True
     else:
         return False
 
 
 def requests_aggregation(pathreqlist, disjlist):
-    """ this function aggregates requests so that if several requests
-        exist between same source and destination and with same transponder type
+    """this function aggregates requests so that if several requests
+    exist between same source and destination and with same transponder type
+    If transponder mode is defined and identical, then also agregates demands.
     """
     # todo maybe add conditions on mode ??, spacing ...
     # currently if undefined takes the default values
     local_list = pathreqlist.copy()
     for req in pathreqlist:
         for this_r in local_list:
-            if req.request_id != this_r.request_id and compare_reqs(req, this_r, disjlist):
+            if req.request_id != this_r.request_id and compare_reqs(req, this_r, disjlist) and\
+                    this_r.tsp_mode is not None:
                 # aggregate
                 this_r.path_bandwidth += req.path_bandwidth
+                this_r.N = this_r.N + req.N
+                this_r.M = this_r.M + req.M
                 temp_r_id = this_r.request_id
                 this_r.request_id = ' | '.join((this_r.request_id, req.request_id))
+
                 # remove request from list
                 local_list.remove(req)
                 # todo change also disjunction req with new demand
@@ -1016,23 +1041,22 @@ def requests_aggregation(pathreqlist, disjlist):
 
 
 def correct_json_route_list(network, pathreqlist):
-    """ all names in list should be exact name in the network, and there is no ambiguity
-        This function only checks that list is correct, warns user if the name is incorrect and
-        suppresses the constraint it it is loose or raises an error if it is strict
+    """all names in list should be exact name in the network, and there is no ambiguity
+
+    This function only checks that list is correct, warns user if the name is incorrect and
+    suppresses the constraint it it is loose or raises an error if it is strict
     """
     all_uid = [n.uid for n in network.nodes()]
     transponders = [n.uid for n in network.nodes() if isinstance(n, Transceiver)]
     for pathreq in pathreqlist:
         if pathreq.source not in transponders:
-            msg = f'{ansi_escapes.red}Request: {pathreq.request_id}: could not find transponder' +\
-                f' source : {pathreq.source}.{ansi_escapes.reset}'
-            LOGGER.critical(msg)
+            msg = f'Request: {pathreq.request_id}: could not find transponder' \
+                + f' source : {pathreq.source}.'
             raise ServiceError(msg)
 
         if pathreq.destination not in transponders:
-            msg = f'{ansi_escapes.red}Request: {pathreq.request_id}: could not find transponder' +\
-                f' destination : {pathreq.destination}.{ansi_escapes.reset}'
-            LOGGER.critical(msg)
+            msg = f'Request: {pathreq.request_id}: could not find transponder' \
+                + f' destination : {pathreq.destination}.'
             raise ServiceError(msg)
 
         # silently remove source and dest nodes from the list
@@ -1051,24 +1075,21 @@ def correct_json_route_list(network, pathreqlist):
                     # if no matching can be found in the network just ignore this constraint
                     # if it is a loose constraint
                     # warns the user that this node is not part of the topology
-                    msg = f'{ansi_escapes.yellow}invalid route node specified:\n\t\'{n_id}\',' +\
-                        f' could not use it as constraint, skipped!{ansi_escapes.reset}'
-                    print(msg)
-                    LOGGER.info(msg)
+                    msg = f'invalid route node specified:\n\t\'{n_id}\',' \
+                        + ' could not use it as constraint, skipped!'
+                    LOGGER.warning(msg)
                     pathreq.loose_list.pop(pathreq.nodes_list.index(n_id))
                     pathreq.nodes_list.remove(n_id)
                 else:
-                    msg = f'{ansi_escapes.red}could not find node:\n\t \'{n_id}\' in network' +\
-                        f' topology. Strict constraint can not be applied.{ansi_escapes.reset}'
-                    LOGGER.critical(msg)
+                    msg = f'could not find node:\n\t \'{n_id}\' in network' \
+                        + ' topology. Strict constraint can not be applied.'
                     raise ServiceError(msg)
 
     return pathreqlist
 
 
 def deduplicate_disjunctions(disjn):
-    """ clean disjunctions to remove possible repetition
-    """
+    """clean disjunctions to remove possible repetition"""
     local_disjn = disjn.copy()
     for elem in local_disjn:
         for dis_elem in local_disjn:
@@ -1078,23 +1099,28 @@ def deduplicate_disjunctions(disjn):
     return local_disjn
 
 
-def compute_path_with_disjunction(network, equipment, pathreqlist, pathlist):
-    """ use a list but a dictionnary might be helpful to find path based on request_id
-        TODO change all these req, dsjct, res lists into dict !
+def compute_path_with_disjunction(network, equipment, pathreqlist, pathlist, redesign=False):
+    """use a list but a dictionnary might be helpful to find path based on request_id
+
+    TODO change all these req, dsjct, res lists into dict !
     """
     path_res_list = []
     reversed_path_res_list = []
     propagated_reversed_path_res_list = []
 
+    total_nb_requests = len(pathreqlist)
+    if redesign:
+        LOGGER.warning('Redesign the network for each request channel, '
+                       + 'using the request channel as the reference channel for the design.')
     for i, pathreq in enumerate(pathreqlist):
 
         # use the power specified in requests but might be different from the one
         # specified for design the power is an optional parameter for requests
         # definition if optional, use the one defines in eqt_config.json
-        print(f'request {pathreq.request_id}')
-        print(f'Computing path from {pathreq.source} to {pathreq.destination}')
-        # adding first node to be clearer on the output
-        print(f'with path constraint: {[pathreq.source] + pathreq.nodes_list}')
+        msg = f'\n\trequest {pathreq.request_id}\n' \
+              + f'\tComputing path from {pathreq.source} to {pathreq.destination}\n' \
+              + f'\twith path constraint: {[pathreq.source] + pathreq.nodes_list}'
+        # # adding first node to be clearer on the output
 
         # pathlist[i] contains the whole path information for request i
         # last element is a transciver and where the result of the propagation is
@@ -1103,8 +1129,19 @@ def compute_path_with_disjunction(network, equipment, pathreqlist, pathlist):
         # elements to simulate performance, several demands having the same destination
         # may use the same transponder for the performance simulation. This is why
         # we use deepcopy: to ensure that each propagation is recorded and not overwritten
+        # reversed path is needed for correct spectrum assignment
+        if redesign:
+            # this is the legacy case where network was automatically redesigned using the
+            # request channel as reference (nb and power used for amplifiers total power out)
+            reversed_path = []
+            if pathlist[i]:
+                reversed_path = find_reversed_path(pathlist[i])
+            network_nodes_for_redesign = pathlist[i] + reversed_path
+            network_module.design_network(pathreq, network.subgraph(network_nodes_for_redesign), equipment,
+                                          set_connector_losses=False, verbose=False)
         total_path = deepcopy(pathlist[i])
-        print(f'Computed path (roadms):{[e.uid for e in total_path  if isinstance(e, Roadm)]}')
+        msg = msg + f'\n\tComputed path (roadms):{[e.uid for e in total_path  if isinstance(e, Roadm)]}'
+        LOGGER.info(msg)
         # for debug
         # print(f'{pathreq.baud_rate}   {pathreq.power}   {pathreq.spacing}   {pathreq.nb_channel}')
         if total_path:
@@ -1115,14 +1152,12 @@ def compute_path_with_disjunction(network, equipment, pathreqlist, pathlist):
                 snr01nm_with_penalty = total_path[-1].snr_01nm - total_path[-1].total_penalty
                 min_ind = argmin(snr01nm_with_penalty)
                 if round(snr01nm_with_penalty[min_ind], 2) < pathreq.OSNR + equipment['SI']['default'].sys_margins:
-                    msg = f'\tWarning! Request {pathreq.request_id} computed path from' +\
-                          f' {pathreq.source} to {pathreq.destination} does not pass with {pathreq.tsp_mode}' +\
-                          f'\n\tcomputed SNR in 0.1nm = {round(total_path[-1].snr_01nm[min_ind], 2)}' +\
-                          f'\n\tCD penalty = {round(total_path[-1].penalties["chromatic_dispersion"][min_ind], 2)}' +\
-                          f'\n\tPMD penalty = {round(total_path[-1].penalties["pmd"][min_ind], 2)}' +\
-                          f'\n\trequired osnr = {pathreq.OSNR}' +\
-                          f'\n\tsystem margin = {equipment["SI"]["default"].sys_margins}'
-                    print(msg)
+                    msg = f'\tWarning! Request {pathreq.request_id} computed path from' \
+                          + f' {pathreq.source} to {pathreq.destination} does not pass with {pathreq.tsp_mode}' \
+                          + f'\n\tcomputed SNR in 0.1nm = {round(total_path[-1].snr_01nm[min_ind], 2)}'
+                    msg = _penalty_msg(total_path, msg, min_ind) \
+                        + f'\n\trequired osnr = {pathreq.OSNR}' \
+                        + f'\n\tsystem margin = {equipment["SI"]["default"].sys_margins}'
                     LOGGER.warning(msg)
                     pathreq.blocking_reason = 'MODE_NOT_FEASIBLE'
             else:
@@ -1142,6 +1177,8 @@ 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']
+                        pathreq.offset_db = mode['equalization_offset_db']
                     # other blocking reason should not appear at this point
                 except AttributeError:
                     pathreq.baud_rate = mode['baud_rate']
@@ -1150,28 +1187,28 @@ 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']
+                    pathreq.offset_db = mode['equalization_offset_db']
 
             # reversed path is needed for correct spectrum assignment
             reversed_path = find_reversed_path(pathlist[i])
             if pathreq.bidir and pathreq.baud_rate is not None:
                 # Both directions requested, and a feasible mode was found
                 rev_p = deepcopy(reversed_path)
-
-                print(f'\n\tPropagating Z to A direction {pathreq.destination} to {pathreq.source}')
-                print(f'\tPath (roadsm) {[r.uid for r in rev_p if isinstance(r,Roadm)]}\n')
+                msg = f'\n\tPropagating Z to A direction {pathreq.destination} to {pathreq.source}\n' \
+                      + f'\tPath (roadms) {[r.uid for r in rev_p if isinstance(r,Roadm)]}\n'
+                LOGGER.info(msg)
                 propagate(rev_p, pathreq, equipment)
                 propagated_reversed_path = rev_p
                 snr01nm_with_penalty = rev_p[-1].snr_01nm - rev_p[-1].total_penalty
                 min_ind = argmin(snr01nm_with_penalty)
                 if round(snr01nm_with_penalty[min_ind], 2) < pathreq.OSNR + equipment['SI']['default'].sys_margins:
-                    msg = f'\tWarning! Request {pathreq.request_id} computed path from' +\
-                          f' {pathreq.source} to {pathreq.destination} does not pass with {pathreq.tsp_mode}' +\
-                          f'\n\tcomputed SNR in 0.1nm = {round(rev_p[-1].snr_01nm[min_ind], 2)}' +\
-                          f'\n\tCD penalty = {round(rev_p[-1].penalties["chromatic_dispersion"][min_ind], 2)}' +\
-                          f'\n\tPMD penalty = {round(rev_p[-1].penalties["pmd"][min_ind], 2)}' +\
-                          f'\n\trequired osnr = {pathreq.OSNR}' +\
-                          f'\n\tsystem margin = {equipment["SI"]["default"].sys_margins}'
-                    print(msg)
+                    msg = f'\tWarning! Request {pathreq.request_id} computed path from' \
+                          + f' {pathreq.destination} to {pathreq.source} does not pass with {pathreq.tsp_mode}' \
+                          + f'\n\tcomputed SNR in 0.1nm = {round(rev_p[-1].snr_01nm[min_ind], 2)}'
+                    msg = _penalty_msg(rev_p, msg, min_ind) \
+                        + f'\n\trequired osnr = {pathreq.OSNR}' \
+                        + f'\n\tsystem margin = {equipment["SI"]["default"].sys_margins}'
                     LOGGER.warning(msg)
                     # TODO selection of mode should also be on reversed direction !!
                     if not hasattr(pathreq, 'blocking_reason'):
@@ -1179,9 +1216,8 @@ def compute_path_with_disjunction(network, equipment, pathreqlist, pathlist):
             else:
                 propagated_reversed_path = []
         else:
-            msg = 'Total path is empty. No propagation'
-            print(msg)
-            LOGGER.info(msg)
+            msg = f'Request {pathreq.request_id}: Total path is empty. No propagation'
+            LOGGER.warning(msg)
             reversed_path = []
             propagated_reversed_path = []
 
@@ -1189,12 +1225,12 @@ def compute_path_with_disjunction(network, equipment, pathreqlist, pathlist):
         reversed_path_res_list.append(reversed_path)
         propagated_reversed_path_res_list.append(propagated_reversed_path)
         # print to have a nice output
-        print('')
     return path_res_list, reversed_path_res_list, propagated_reversed_path_res_list
 
 
 def compute_spectrum_slot_vs_bandwidth(bandwidth, spacing, bit_rate, slot_width=0.0125e12):
-    """ Compute the number of required wavelengths and the M value (number of consumed slots)
+    """Compute the number of required wavelengths and the M value (number of consumed slots)
+
     Each wavelength consumes one `spacing`, and the result is rounded up to consume a natural number of slots.
 
     >>> compute_spectrum_slot_vs_bandwidth(400e9, 50e9, 200e9)
@@ -1203,3 +1239,65 @@ def compute_spectrum_slot_vs_bandwidth(bandwidth, spacing, bit_rate, slot_width=
     number_of_wavelengths = ceil(bandwidth / bit_rate)
     total_number_of_slots = ceil(spacing / slot_width) * number_of_wavelengths
     return number_of_wavelengths, total_number_of_slots
+
+
+def _penalty_msg(total_path, msg, min_ind):
+    """formatting helper for reporting unfeasible paths
+
+    The penalty info are optional, so this checks that penalty exists before creating a message."""
+    penalty_dict = {
+        'pdl': 'PDL',
+        'chromatic_dispersion': 'CD',
+        'pmd': 'PMD'}
+    for key, pretty in penalty_dict.items():
+        if key in total_path[-1].penalties:
+            msg += f'\n\t{pretty} penalty = {round(total_path[-1].penalties[key][min_ind], 2)}'
+        else:
+            msg += f'\n\t{pretty} penalty not evaluated'
+    return msg
+
+
+def is_adjacent(oms1, oms2):
+    """ oms1's egress ROADM is oms2's ingress ROADM
+    """
+    return oms1.el_list[-1] == oms2.el_list[0]
+
+
+def explicit_path(node_list, source, destination, network):
+    """ if list of nodes leads to adjacent oms, then means that the path is explicit, and no need to compute
+    the function returns the explicit path (including source and destination ROADMs)
+    """
+    path_oms = []
+    for elem in node_list:
+        if hasattr(elem, 'oms'):
+            path_oms.append(elem.oms)
+    if not path_oms:
+        return None
+    path_oms = unique_ordered(path_oms)
+    try:
+        next_node = next(network.successors(source))
+        source_roadm = next_node if isinstance(next_node, Roadm) else source
+        previous_node = next(network.predecessors(destination))
+        destination_roadm = previous_node if isinstance(previous_node, Roadm) else destination
+        if not (path_oms[0].el_list[0] == source_roadm and path_oms[-1].el_list[-1] == destination_roadm):
+            return None
+    except StopIteration:
+        return None
+
+    oms0 = path_oms[0]
+    path = [source] + oms0.el_list
+    for oms in path_oms[1:]:
+        if not is_adjacent(oms0, oms):
+            return None
+        oms0 = oms
+        path.extend(oms.el_list)
+    path.append(destination)
+    return unique_ordered(path)
+
+
+def find_elements_common_range(el_list: list, equipment: dict) -> List[dict]:
+    """Find the common frequency range of amps of a given list of elements (for example an OMS or a path)
+    If there are no amplifiers in the path, then use the SI
+    """
+    amp_bands = [n.params.bands for n in el_list if isinstance(n, (Edfa, Multiband_amplifier))]
+    return find_common_range(amp_bands, equipment['SI']['default'].f_min, equipment['SI']['default'].f_max)

gnpy/topology/spectrum_assignment.py

@@ -15,28 +15,31 @@ element/oms correspondace
 
 from collections import namedtuple
 from logging import getLogger
-from gnpy.core.elements import Roadm, Transceiver
+
+from gnpy.core.elements import Roadm, Transceiver, Edfa, Multiband_amplifier
 from gnpy.core.exceptions import ServiceError, SpectrumError
-from gnpy.topology.request import compute_spectrum_slot_vs_bandwidth
+from gnpy.core.utils import order_slots, restore_order
+from gnpy.topology.request import compute_spectrum_slot_vs_bandwidth, find_elements_common_range
 
 LOGGER = getLogger(__name__)
+GUARDBAND = 25e9
 
 
 class Bitmap:
-    """ records the spectrum occupation
-    """
+    """records the spectrum occupation"""
 
-    def __init__(self, f_min, f_max, grid, guardband=0.15e12, bitmap=None):
-        # n is the min index including guardband. Guardband is require to be sure
+    def __init__(self, f_min, f_max, grid, guardband=GUARDBAND, bitmap=None):
+        # n is the min index including guardband. Guardband is required to be sure
         # that a channel can be assigned  with center frequency fmin (means that its
         # slot occupation goes below freq_index_min
-        n_min = frequency_to_n(f_min - guardband, grid)
-        n_max = frequency_to_n(f_max + guardband, grid) - 1
+        n_min = frequency_to_n(f_min, grid)
+        n_max = frequency_to_n(f_max, grid)
         self.n_min = n_min
         self.n_max = n_max
-        self.freq_index_min = frequency_to_n(f_min)
-        self.freq_index_max = frequency_to_n(f_max)
+        self.freq_index_min = frequency_to_n(f_min + guardband)
+        self.freq_index_max = frequency_to_n(f_max - guardband)
         self.freq_index = list(range(n_min, n_max + 1))
+        self.guardband = guardband
         if bitmap is None:
             self.bitmap = [1] * (n_max - n_min + 1)
         elif len(bitmap) == len(self.freq_index):
@@ -45,26 +48,22 @@ class Bitmap:
             raise SpectrumError(f'bitmap is not consistant with f_min{f_min} - n: {n_min} and f_max{f_max}- n :{n_max}')
 
     def getn(self, i):
-        """ converts the n (itu grid) into a local index
-        """
+        """converts the n (itu grid) into a local index"""
         return self.freq_index[i]
 
     def geti(self, nvalue):
-        """ converts the local index into n (itu grid)
-        """
+        """converts the local index into n (itu grid)"""
         return self.freq_index.index(nvalue)
 
     def insert_left(self, newbitmap):
-        """ insert bitmap on the left to align oms bitmaps if their start frequencies are different
-        """
+        """insert bitmap on the left to align oms bitmaps if their start frequencies are different"""
         self.bitmap = newbitmap + self.bitmap
         temp = list(range(self.n_min - len(newbitmap), self.n_min))
         self.freq_index = temp + self.freq_index
         self.n_min = self.freq_index[0]
 
     def insert_right(self, newbitmap):
-        """ insert bitmap on the right to align oms bitmaps if their stop frequencies are different
-        """
+        """insert bitmap on the right to align oms bitmaps if their stop frequencies are different"""
         self.bitmap = self.bitmap + newbitmap
         self.freq_index = self.freq_index + list(range(self.n_max, self.n_max + len(newbitmap)))
         self.n_max = self.freq_index[-1]
@@ -75,8 +74,8 @@ OMSParams = namedtuple('OMSParams', 'oms_id el_id_list el_list')
 
 
 class OMS:
-    """ OMS class is the logical container that represent a link between two adjacent ROADMs and
-        records the crossed elements and the occupied spectrum
+    """OMS class is the logical container that represent a link between two adjacent ROADMs and
+    records the crossed elements and the occupied spectrum
     """
 
     def __init__(self, *args, **params):
@@ -87,7 +86,6 @@ class OMS:
         self.spectrum_bitmap = []
         self.nb_channels = 0
         self.service_list = []
-    # TODO
 
     def __str__(self):
         return '\n\t'.join([f'{type(self).__name__} {self.oms_id}',
@@ -98,36 +96,28 @@ class OMS:
                             f'{self.el_id_list[0]} - {self.el_id_list[-1]}', '\n'])
 
     def add_element(self, elem):
-        """ records oms elements
-        """
+        """records oms elements"""
         self.el_id_list.append(elem.uid)
         self.el_list.append(elem)
 
-    def update_spectrum(self, f_min, f_max, guardband=0.15e12, existing_spectrum=None,
-                        grid=0.00625e12):
-        """ frequencies expressed in Hz
+    def update_spectrum(self, f_min, f_max, guardband=GUARDBAND, existing_spectrum=None, grid=0.00625e12):
+        """Frequencies expressed in Hz.
+        Add 150 GHz margin to enable a center channel on f_min
+        Use ITU-T G694.1 Flexible DWDM grid definition
+        For the flexible DWDM grid, the allowed frequency slots have a nominal central frequency (in THz) defined by:
+        193.1 + n × 0.00625 where n is a positive or negative integer including 0
+        and 0.00625 is the nominal central frequency granularity in THz
+        and a slot width defined by:
+        12.5 × m where m is a positive integer and 12.5 is the slot width granularity in GHz.
+        Any combination of frequency slots is allowed as long as no two frequency slots overlap.
+        If bitmap is not None, then use it: Bitmap checks its consistency with f_min f_max
+        else a brand new bitmap is created
         """
-        if existing_spectrum is None:
-            # add some 150 GHz margin to enable a center channel on f_min
-            # use ITU-T G694.1
-            # Flexible DWDM grid definition
-            # For the flexible DWDM grid, the allowed frequency slots have a nominal
-            # central frequency (in THz) defined by:
-            # 193.1 + n × 0.00625 where n is a positive or negative integer including 0
-            # and 0.00625 is the nominal central frequency granularity in THz
-            # and a slot width defined by:
-            # 12.5 × m where m is a positive integer and 12.5 is the slot width granularity in
-            # GHz.
-            # Any combination of frequency slots is allowed as long as no two frequency
-            # slots overlap.
-
-            # TODO : add explaination on that / parametrize ....
-            self.spectrum_bitmap = Bitmap(f_min, f_max, grid, guardband)
-            # print(len(self.spectrum_bitmap.bitmap))
+        self.spectrum_bitmap = Bitmap(f_min=f_min, f_max=f_max, grid=grid, guardband=guardband,
+                                      bitmap=existing_spectrum)
 
     def assign_spectrum(self, nvalue, mvalue):
-        """ change oms spectrum to mark spectrum assigned
-        """
+        """change oms spectrum to mark spectrum assigned"""
         if not isinstance(nvalue, int):
             raise SpectrumError(f'N must be a signed integer, got {nvalue}')
         if not isinstance(mvalue, int):
@@ -146,16 +136,16 @@ class OMS:
         self.spectrum_bitmap.bitmap[self.spectrum_bitmap.geti(startn):self.spectrum_bitmap.geti(stopn) + 1] = [0] * (stopn - startn + 1)
 
     def add_service(self, service_id, nb_wl):
-        """ record service and mark spectrum as occupied
-        """
+        """record service and mark spectrum as occupied"""
         self.service_list.append(service_id)
         self.nb_channels += nb_wl
 
 
 def frequency_to_n(freq, grid=0.00625e12):
-    """ converts frequency into the n value (ITU grid)
-        reference to Recommendation G.694.1 (02/12), Figure I.3
-        https://www.itu.int/rec/T-REC-G.694.1-201202-I/en
+    """converts frequency into the n value (ITU grid)
+
+    reference to Recommendation G.694.1 (02/12), Figure I.3
+    https://www.itu.int/rec/T-REC-G.694.1-201202-I/en
 
     >>> frequency_to_n(193.1375e12)
     6
@@ -167,9 +157,10 @@ def frequency_to_n(freq, grid=0.00625e12):
 
 
 def nvalue_to_frequency(nvalue, grid=0.00625e12):
-    """ converts n value into a frequency
-        reference to Recommendation G.694.1 (02/12), Table 1
-        https://www.itu.int/rec/T-REC-G.694.1-201202-I/en
+    """converts n value into a frequency
+
+    reference to Recommendation G.694.1 (02/12), Table 1
+    https://www.itu.int/rec/T-REC-G.694.1-201202-I/en
 
     >>> nvalue_to_frequency(6)
     193137500000000.0
@@ -181,17 +172,17 @@ def nvalue_to_frequency(nvalue, grid=0.00625e12):
 
 
 def mvalue_to_slots(nvalue, mvalue):
-    """ convert center n an m into start and stop n
-    """
+    """convert center n an m into start and stop n"""
     startn = nvalue - mvalue
     stopn = nvalue + mvalue - 1
     return startn, stopn
 
 
 def slots_to_m(startn, stopn):
-    """ converts the start and stop n values to the center n and m value
-        reference to Recommendation G.694.1 (02/12), Figure I.3
-        https://www.itu.int/rec/T-REC-G.694.1-201202-I/en
+    """converts the start and stop n values to the center n and m value
+
+    reference to Recommendation G.694.1 (02/12), Figure I.3
+    https://www.itu.int/rec/T-REC-G.694.1-201202-I/en
 
     >>> nval, mval = slots_to_m(6, 20)
     >>> nval
@@ -206,10 +197,11 @@ def slots_to_m(startn, stopn):
 
 
 def m_to_freq(nvalue, mvalue, grid=0.00625e12):
-    """ converts m into frequency range
-        spectrum(13,7) is (193137500000000.0, 193225000000000.0)
-        reference to Recommendation G.694.1 (02/12), Figure I.3
-        https://www.itu.int/rec/T-REC-G.694.1-201202-I/en
+    """converts m into frequency range
+
+    spectrum(13,7) is (193137500000000.0, 193225000000000.0)
+    reference to Recommendation G.694.1 (02/12), Figure I.3
+    https://www.itu.int/rec/T-REC-G.694.1-201202-I/en
 
     >>> fstart, fstop = m_to_freq(13, 7)
     >>> fstart
@@ -225,9 +217,7 @@ def m_to_freq(nvalue, mvalue, grid=0.00625e12):
 
 
 def align_grids(oms_list):
-    """ used to apply same grid to all oms : same starting n, stop n and slot size
-        out of grid slots are set to 0
-    """
+    """Used to apply same grid to all oms : same starting n, stop n and slot size. Out of grid slots are set to 0."""
     n_min = min([o.spectrum_bitmap.n_min for o in oms_list])
     n_max = max([o.spectrum_bitmap.n_max for o in oms_list])
     for this_o in oms_list:
@@ -238,17 +228,60 @@ def align_grids(oms_list):
     return oms_list
 
 
+def find_network_freq_range(network, equipment):
+    """Find the lowest freq from amps and highest freq among all amps to determine the resulting bitmap
+    """
+    amp_bands = [band for n in network.nodes() if isinstance(n, (Edfa, Multiband_amplifier)) for band in n.params.bands]
+    min_frequencies = [a['f_min'] for a in amp_bands]
+    max_frequencies = [a['f_max'] for a in amp_bands]
+    return min(min_frequencies), max(max_frequencies)
+
+
+def create_oms_bitmap(oms, equipment, f_min, f_max, guardband, grid):
+    """Find the highest low freq from oms amps and lowest high freq among oms amps to determine
+    the possible bitmap window.
+    f_min and f_max represent the useable spectrum (not the useable center frequencies)
+    ie n smaller than frequency_to_n(min_freq, grid) are not useable
+    """
+    n_min = frequency_to_n(f_min, grid)
+    n_max = frequency_to_n(f_max, grid) - 1
+    common_range = find_elements_common_range(oms.el_list, equipment)
+    band0 = common_range[0]
+    band0_n_min = frequency_to_n(band0['f_min'], grid)
+    band0_n_max = frequency_to_n(band0['f_max'], grid)
+    bitmap = [0] * (band0_n_min - n_min) + [1] * (band0_n_max - band0_n_min + 1)
+    i = 1
+    while i < len(common_range):
+        band = common_range[i]
+        band_n_min = frequency_to_n(band['f_min'], grid)
+        band_n_max = frequency_to_n(band['f_max'], grid)
+        bitmap = bitmap + [0] * (band_n_min - band0_n_max - 1) + [1] * (band_n_max - band_n_min + 1)
+        band0_n_max = band_n_max
+        i += 1
+    bitmap = bitmap + [0] * (n_max - band0_n_max)
+    return bitmap
+
+
 def build_oms_list(network, equipment):
-    """ initialization of OMS list in the network
-        an oms is build reading all intermediate nodes between two adjacent ROADMs
-        each element within the list is being added an oms and oms_id to record the
-        oms it belongs to.
-        the function supports different spectrum width and supposes that the whole network
-        works with the min range among OMSs
+    """initialization of OMS list in the network
+
+    an oms is build reading all intermediate nodes between two adjacent ROADMs
+    each element within the list is being added an oms and oms_id to record the
+    oms it belongs to.
+    the function supports different spectrum width and supposes that the whole network
+    works with the min range among OMSs
     """
     oms_id = 0
     oms_list = []
-    for node in [n for n in network.nodes() if isinstance(n, Roadm)]:
+    # identify all vertices of OMS: of course ROADM, but aso links to external chassis transponders
+    oms_vertices = [n for n in network.nodes() if isinstance(n, Roadm)] +\
+                   [n for n in network.nodes() if isinstance(n, Transceiver)
+                    and not isinstance(next(network.successors(n)), Roadm)]
+    # determine the size of the bitmap common to all the omses: find min and max frequencies of all amps
+    # in the network. These gives the band not the center frequency. Thhen we use a reference channel
+    # slot width (50GHz) to set the f_min, f_max
+    f_min, f_max = find_network_freq_range(network, equipment)
+    for node in oms_vertices:
         for edge in network.edges([node]):
             if not isinstance(edge[1], Transceiver):
                 nd_in = edge[0]  # nd_in is a Roadm
@@ -282,8 +315,9 @@ def build_oms_list(network, equipment):
                     nd_out.oms_list = []
                     nd_out.oms_list.append(oms_id)
 
-                oms.update_spectrum(equipment['SI']['default'].f_min,
-                                    equipment['SI']['default'].f_max, grid=0.00625e12)
+                bitmap = create_oms_bitmap(oms, equipment, f_min=f_min, f_max=f_max, guardband=GUARDBAND,
+                                           grid=0.00625e12)
+                oms.update_spectrum(f_min, f_max, guardband=GUARDBAND, grid=0.00625e12, existing_spectrum=bitmap)
                 # oms.assign_spectrum(13,7) gives back (193137500000000.0, 193225000000000.0)
                 # as in the example in the standard
                 # oms.assign_spectrum(13,7)
@@ -296,8 +330,9 @@ def build_oms_list(network, equipment):
 
 
 def reversed_oms(oms_list):
-    """ identifies reversed OMS
-        only applicable for non parallel OMS
+    """identifies reversed OMS
+
+    only applicable for non parallel OMS
     """
     for oms in oms_list:
         has_reversed = False
@@ -322,28 +357,42 @@ def bitmap_sum(band1, band2):
     return res
 
 
-def spectrum_selection(pth, oms_list, requested_m, requested_n=None):
-    """Collects spectrum availability and call the select_candidate function"""
-
-    # use indexes instead of ITU-T n values
+def build_path_oms_id_list(pth):
     path_oms = []
     for elem in pth:
         if not isinstance(elem, Roadm) and not isinstance(elem, Transceiver):
             # only edfa, fused and fibers have oms_id attribute
             path_oms.append(elem.oms_id)
     # remove duplicate oms_id, order is not important
-    path_oms = list(set(path_oms))
-    # assuming all oms have same freq index
-    if not path_oms:
-        candidate = (None, None, None)
-        return candidate, path_oms
-    freq_index = oms_list[path_oms[0]].spectrum_bitmap.freq_index
-    freq_index_min = oms_list[path_oms[0]].spectrum_bitmap.freq_index_min
-    freq_index_max = oms_list[path_oms[0]].spectrum_bitmap.freq_index_max
+    return list(set(path_oms))
 
-    freq_availability = oms_list[path_oms[0]].spectrum_bitmap.bitmap
+
+def aggregate_oms_bitmap(path_oms, oms_list):
+    spectrum = oms_list[path_oms[0]].spectrum_bitmap
+    bitmap = spectrum.bitmap
+    # assuming all oms have same freq indices
     for oms in path_oms[1:]:
-        freq_availability = bitmap_sum(oms_list[oms].spectrum_bitmap.bitmap, freq_availability)
+        bitmap = bitmap_sum(oms_list[oms].spectrum_bitmap.bitmap, bitmap)
+    params = {
+        'oms_id': 0,
+        'el_id_list': 0,
+        'el_list': []
+    }
+    freq_min = nvalue_to_frequency(spectrum.n_min)
+    freq_max = nvalue_to_frequency(spectrum.n_max)
+    aggregate_oms = OMS(**params)
+    aggregate_oms.update_spectrum(freq_min, freq_max, grid=0.00625e12, guardband=spectrum.guardband,
+                                  existing_spectrum=bitmap)
+    return aggregate_oms
+
+
+def spectrum_selection(test_oms, requested_m, requested_n=None):
+    """Collects spectrum availability and call the select_candidate function"""
+    freq_index = test_oms.spectrum_bitmap.freq_index
+    freq_index_min = test_oms.spectrum_bitmap.freq_index_min
+    freq_index_max = test_oms.spectrum_bitmap.freq_index_max
+    freq_availability = test_oms.spectrum_bitmap.bitmap
+
     if requested_n is None:
         # avoid slots reserved on the edge 0.15e-12 on both sides -> 24
         candidates = [(freq_index[i] + requested_m, freq_index[i], freq_index[i] + 2 * requested_m - 1)
@@ -354,29 +403,36 @@ def spectrum_selection(pth, oms_list, requested_m, requested_n=None):
 
         candidate = select_candidate(candidates, policy='first_fit')
     else:
-        i = oms_list[path_oms[0]].spectrum_bitmap.geti(requested_n)
-        # print(f'N {requested_n} i {i}')
-        # print(freq_availability[i-m:i+m] )
-        # print(freq_index[i-m:i+m])
-        if (freq_availability[i - requested_m:i + requested_m] == [1] * (2 * requested_m) and
-                freq_index[i - requested_m] >= freq_index_min
+        i = test_oms.spectrum_bitmap.geti(requested_n)
+        if (freq_availability[i - requested_m:i + requested_m] == [1] * (2 * requested_m)
+                and freq_index[i - requested_m] >= freq_index_min
                 and freq_index[i + requested_m - 1] <= freq_index_max):
             # candidate is the triplet center_n, startn and stopn
             candidate = (requested_n, requested_n - requested_m, requested_n + requested_m - 1)
         else:
             candidate = (None, None, None)
-        # print("coucou11")
-        # print(candidate)
-    # print(freq_availability[321:321+2*m])
-    # a = [i+321 for i in range(2*m)]
-    # print(a)
-    # print(candidate)
-    return candidate, path_oms
+    return candidate
+
+
+def determine_slot_numbers(test_oms, requested_n, required_m, per_channel_m):
+    """determines max availability around requested_n. requested_n should not be None"""
+    bitmap = test_oms.spectrum_bitmap
+    freq_index = bitmap.freq_index
+    freq_index_min = bitmap.freq_index_min
+    freq_index_max = bitmap.freq_index_max
+    freq_availability = bitmap.bitmap
+    center_i = bitmap.geti(requested_n)
+    i = per_channel_m
+    while (freq_availability[center_i - i:center_i + i] == [1] * (2 * i)
+           and freq_index[center_i - i] >= freq_index_min
+           and freq_index[center_i + i - 1] <= freq_index_max
+           and i <= required_m):
+        i += per_channel_m
+    return i - per_channel_m
 
 
 def select_candidate(candidates, policy):
-    """ selects a candidate among all available spectrum
-    """
+    """selects a candidate among all available spectrum"""
     if policy == 'first_fit':
         if candidates:
             return candidates[0]
@@ -386,44 +442,112 @@ def select_candidate(candidates, policy):
         raise ServiceError('Only first_fit spectrum assignment policy is implemented.')
 
 
+def compute_n_m(required_m, rq, path_oms, oms_list, per_channel_m, policy='first_fit'):
+    """ based on requested path_bandwidth fill in M=None values with uint values, using per_channel_m
+    and center frequency, with first fit strategy. The function checks the available spectrum but check
+    consistencies among M values of the request, but not with other requests.
+    For example, if request is for 32 slots corresponding to 8 x 4 slots of 32Gbauds channels,
+    the following frequency slots will result in the following assignment
+
+    N = 0, 8,    16, 32           -> 0,   8,   16,   32
+    M = 8, None, 8,  None         -> 8,   8,    8,    8
+
+    N = 0,    8,    16, 32        -> 0,   , 16
+    M = None, None, 8,  None      -> 24,  , 8
+    """
+    selected_m = []
+    selected_n = []
+    remaining_slots_to_serve = required_m
+    # order slots for the computation: assign biggest m first
+    rq_N, rq_M, order = order_slots([{'N': n, 'M': m} for n, m in zip(rq.N, rq.M)])
+    # Create an oms that represents current assignments of all oms listed in path_oms, and test N and M on it.
+    # If M is defined, checks that proposed N, M is free
+    test_oms = aggregate_oms_bitmap(path_oms, oms_list)
+    for n, m in zip(rq_N, rq_M):
+        if m is not None and n is not None:
+            # check availabilityfor this n, m
+            available_slots = determine_slot_numbers(test_oms, n, m, m)
+            if available_slots == 0:
+                # if n, m are not feasible, break at this point no have non zero remaining_slots_to_serve
+                # in order to blocks the request (even is other N,M where feasible)
+                break
+        elif m is not None and n is None:
+            # find a candidate n
+            n, _, _ = spectrum_selection(test_oms, m, None)
+            if n is None:
+                # if no n is feasible for the m, block the request
+                break
+        elif m is None and n is not None:
+            # find a feasible m for this n. If None is found, then block the request
+            m = determine_slot_numbers(test_oms, n, remaining_slots_to_serve, per_channel_m)
+            if m == 0 or remaining_slots_to_serve == 0:
+                break
+        else:
+            # if n and m are not defined, try to find a single  assignment to fits the remaining slots to serve
+            # (first fit strategy)
+            n, _, _ = spectrum_selection(test_oms, remaining_slots_to_serve, None)
+            if n is None or remaining_slots_to_serve == 0:
+                break
+            else:
+                m = remaining_slots_to_serve
+        selected_m.append(m)
+        selected_n.append(n)
+        test_oms.assign_spectrum(n, m)
+        remaining_slots_to_serve = remaining_slots_to_serve - m
+
+    # re-order selected_m and selected_n according to initial request N, M order, ignoring None values
+    not_selected = [None for i in range(len(rq_N) - len(selected_n))]
+    selected_m = restore_order(selected_m + not_selected, order)
+    selected_n = restore_order(selected_n + not_selected, order)
+    return selected_n, selected_m, remaining_slots_to_serve
+
+
 def pth_assign_spectrum(pths, rqs, oms_list, rpths):
-    """ basic first fit assignment
-        if reversed path are provided, means that occupation is bidir
+    """basic first fit assignment
+
+    if reversed path are provided, means that occupation is bidir
     """
     for pth, rq, rpth in zip(pths, rqs, rpths):
-        # computes the number of channels required
         if hasattr(rq, 'blocking_reason'):
             rq.N = None
             rq.M = None
         else:
-            nb_wl, requested_m = compute_spectrum_slot_vs_bandwidth(rq.path_bandwidth,
-                                                                    rq.spacing, rq.bit_rate)
-            if getattr(rq, 'M', None) is not None:
-                # Consistency check between the requested M and path_bandwidth
-                # M value should be bigger than the computed requested_m (simple estimate)
-                # TODO: elaborate a more accurate estimate with nb_wl * tx_osnr + possibly guardbands in case of
+            # computes the number of channels required for path_bandwidth and the min required nb of slots
+            # for one channel (corresponds to the spacing)
+            nb_wl, required_m = compute_spectrum_slot_vs_bandwidth(rq.path_bandwidth,
+                                                                   rq.spacing, rq.bit_rate)
+            _, per_channel_m = compute_spectrum_slot_vs_bandwidth(rq.bit_rate,
+                                                                  rq.spacing, rq.bit_rate)
+            # find oms ids that are concerned both by pth and rpth
+            path_oms = build_path_oms_id_list(pth + rpth)
+            if getattr(rq, 'M', None) is not None and all(rq.M):
+                # if all M are well defined: Consistency check that the requested M are enough to carry the nb_wl:
+                # check that the integer number of per_channel_m carried in each M value is enough to carry nb_wl.
+                # if not, blocks the demand
+                nb_channels_of_request = sum([m // per_channel_m for m in rq.M])
+                # TODO: elaborate a more accurate estimate with nb_wl * min_spacing + possibly guardbands in case of
                 # superchannel closed packing.
-                if requested_m > rq.M:
+                if nb_wl > nb_channels_of_request:
                     rq.N = None
                     rq.M = None
                     rq.blocking_reason = 'NOT_ENOUGH_RESERVED_SPECTRUM'
-                    # need to stop here for this request and not go though spectrum selection process with requested_m
+                    # need to stop here for this request and not go though spectrum selection process
                     continue
-                # use the req.M even if requested_m is smaller
-                requested_m = rq.M
-            requested_n = getattr(rq, 'N', None)
-            (center_n, startn, stopn), path_oms = spectrum_selection(pth + rpth, oms_list, requested_m,
-                                                                     requested_n)
-            # if requested n and m concern already occupied spectrum the previous function returns a None candidate
-            # if not None, center_n and start, stop frequencies are applicable to all oms of pth
-            # checks that spectrum is not None else indicate blocking reason
-            if center_n is not None:
-                for oms_elem in path_oms:
-                    oms_list[oms_elem].assign_spectrum(center_n, requested_m)
-                    oms_list[oms_elem].add_service(rq.request_id, nb_wl)
-                rq.N = center_n
-                rq.M = requested_m
-            else:
+            # Use the req.M even if nb_wl and required_m are smaller.
+            # first fit strategy: assign as many lambda as possible in the None remaining N, M values
+            selected_n, selected_m, remaining_slots_to_serve = \
+                compute_n_m(required_m, rq, path_oms, oms_list, per_channel_m)
+            # if there are some remaining_slots_to_serve, this means that provided rq.M and rq.N values were
+            # not possible. Then do not go though spectrum assignment process and blocks the demand
+            if remaining_slots_to_serve > 0:
                 rq.N = None
                 rq.M = None
                 rq.blocking_reason = 'NO_SPECTRUM'
+                continue
+            for oms_elem in path_oms:
+                for this_n, this_m in zip(selected_n, selected_m):
+                    if this_m is not None:
+                        oms_list[oms_elem].assign_spectrum(this_n, this_m)
+                oms_list[oms_elem].add_service(rq.request_id, nb_wl)
+            rq.N = selected_n
+            rq.M = selected_m

requirements.txt

@@ -1,7 +0,0 @@
-matplotlib>=3.5.1,<4
-networkx>=2.6,<3
-numpy>=1.22.0,<2
-pandas>=1.3.5,<2
-pbr>=5.7.0,<6
-scipy>=1.7.3,<2
-xlrd>=1.2.0,<2

setup.cfg

@@ -3,7 +3,7 @@ name = gnpy
 description-file = README.md
 description-content-type = text/markdown; variant=GFM
 author = Telecom Infra Project
-author-email = jan.kundrat@telecominfraproject.com
+author-email = jkt@jankundrat.com
 license = BSD-3-Clause
 home-page = https://github.com/Telecominfraproject/oopt-gnpy
 project_urls =
@@ -22,6 +22,8 @@ classifier =
     Programming Language :: Python :: 3.8
     Programming Language :: Python :: 3.9
     Programming Language :: Python :: 3.10
+    Programming Language :: Python :: 3.11
+    Programming Language :: Python :: 3.12
     Programming Language :: Python :: Implementation :: CPython
     Topic :: Scientific/Engineering
     Topic :: Scientific/Engineering :: Physics
@@ -47,3 +49,35 @@ console_scripts =
     gnpy-transmission-example = gnpy.tools.cli_examples:transmission_main_example
     gnpy-path-request = gnpy.tools.cli_examples:path_requests_run
     gnpy-convert-xls = gnpy.tools.convert:_do_convert
+
+[options]
+install_requires =
+	# matplotlib 3.8 removed support for Python 3.8
+	matplotlib>=3.7.3,<4
+	# networkx 3.2 removed support for Python 3.8
+	networkx>=3.1,<4
+	# numpy 1.25 removed support for Python 3.8
+	numpy>=1.24.4,<2
+	pbr>=6.0.0,<7
+	# scipy 1.11 removed support for Python 3.8
+	scipy>=1.10.1,<2
+	# xlrd 2.x removed support for .xlsx, it's only .xls now
+	xlrd>=1.2.0,<2
+
+[options.extras_require]
+tests =
+	build>=1.0.3,<2
+	pytest>=7.4.3,<8
+	# pandas 2.1 removed support for Python 3.8
+	pandas>=2.0.3,<3
+	# flake v6 killed the --diff option
+	flake8>=5.0.4,<6
+
+docs =
+	alabaster>=0.7.12,<1
+	docutils>=0.17.1,<1
+	myst-parser>=0.16.1,<1
+	Pygments>=2.11.2,<3
+	rstcheck
+	Sphinx>=5.3.0,<6
+	sphinxcontrib-bibtex>=2.4.1,<3

tests/data/CORONET_Global_Topology_auto_design_expected.json

@@ -1203,6 +1203,7 @@
     {
       "uid": "roadm Abilene",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1226,6 +1227,7 @@
     {
       "uid": "roadm Albany",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1249,6 +1251,7 @@
     {
       "uid": "roadm Albuquerque",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1274,6 +1277,7 @@
     {
       "uid": "roadm Atlanta",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1298,6 +1302,7 @@
     {
       "uid": "roadm Austin",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1321,6 +1326,7 @@
     {
       "uid": "roadm Baltimore",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1345,6 +1351,7 @@
     {
       "uid": "roadm Baton_Rouge",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1368,6 +1375,7 @@
     {
       "uid": "roadm Billings",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1392,6 +1400,7 @@
     {
       "uid": "roadm Birmingham",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1416,6 +1425,7 @@
     {
       "uid": "roadm Bismarck",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1439,6 +1449,7 @@
     {
       "uid": "roadm Boston",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1462,6 +1473,7 @@
     {
       "uid": "roadm Buffalo",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1485,6 +1497,7 @@
     {
       "uid": "roadm Charleston",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1508,6 +1521,7 @@
     {
       "uid": "roadm Charlotte",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1531,6 +1545,7 @@
     {
       "uid": "roadm Chicago",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1555,6 +1570,7 @@
     {
       "uid": "roadm Cincinnati",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1579,6 +1595,7 @@
     {
       "uid": "roadm Cleveland",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1603,6 +1620,7 @@
     {
       "uid": "roadm Columbus",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1627,6 +1645,7 @@
     {
       "uid": "roadm Dallas",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1653,6 +1672,7 @@
     {
       "uid": "roadm Denver",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1678,6 +1698,7 @@
     {
       "uid": "roadm Detroit",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1701,6 +1722,7 @@
     {
       "uid": "roadm El_Paso",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1726,6 +1748,7 @@
     {
       "uid": "roadm Fresno",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1750,6 +1773,7 @@
     {
       "uid": "roadm Greensboro",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1775,6 +1799,7 @@
     {
       "uid": "roadm Hartford",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1798,6 +1823,7 @@
     {
       "uid": "roadm Houston",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1822,6 +1848,7 @@
     {
       "uid": "roadm Jacksonville",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1846,6 +1873,7 @@
     {
       "uid": "roadm Kansas_City",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1870,6 +1898,7 @@
     {
       "uid": "roadm Las_Vegas",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1895,6 +1924,7 @@
     {
       "uid": "roadm Little_Rock",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1918,6 +1948,7 @@
     {
       "uid": "roadm Long_Island",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1941,6 +1972,7 @@
     {
       "uid": "roadm Los_Angeles",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1966,6 +1998,7 @@
     {
       "uid": "roadm Louisville",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1991,6 +2024,7 @@
     {
       "uid": "roadm Memphis",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2014,6 +2048,7 @@
     {
       "uid": "roadm Miami",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2038,6 +2073,7 @@
     {
       "uid": "roadm Milwaukee",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2061,6 +2097,7 @@
     {
       "uid": "roadm Minneapolis",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2085,6 +2122,7 @@
     {
       "uid": "roadm Nashville",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2109,6 +2147,7 @@
     {
       "uid": "roadm New_Orleans",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2133,6 +2172,7 @@
     {
       "uid": "roadm New_York",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2159,6 +2199,7 @@
     {
       "uid": "roadm Newark",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2182,6 +2223,7 @@
     {
       "uid": "roadm Norfolk",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2205,6 +2247,7 @@
     {
       "uid": "roadm Oakland",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2231,6 +2274,7 @@
     {
       "uid": "roadm Oklahoma_City",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2254,6 +2298,7 @@
     {
       "uid": "roadm Omaha",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2278,6 +2323,7 @@
     {
       "uid": "roadm Orlando",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2301,6 +2347,7 @@
     {
       "uid": "roadm Philadelphia",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2325,6 +2372,7 @@
     {
       "uid": "roadm Phoenix",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2349,6 +2397,7 @@
     {
       "uid": "roadm Pittsburgh",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2373,6 +2422,7 @@
     {
       "uid": "roadm Portland",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2398,6 +2448,7 @@
     {
       "uid": "roadm Providence",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2421,6 +2472,7 @@
     {
       "uid": "roadm Raleigh",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2445,6 +2497,7 @@
     {
       "uid": "roadm Richmond",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2468,6 +2521,7 @@
     {
       "uid": "roadm Rochester",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2491,6 +2545,7 @@
     {
       "uid": "roadm Sacramento",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2514,6 +2569,7 @@
     {
       "uid": "roadm Salt_Lake_City",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2539,6 +2595,7 @@
     {
       "uid": "roadm San_Antonio",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2562,6 +2619,7 @@
     {
       "uid": "roadm San_Diego",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2585,6 +2643,7 @@
     {
       "uid": "roadm San_Francisco",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2608,6 +2667,7 @@
     {
       "uid": "roadm San_Jose",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2631,6 +2691,7 @@
     {
       "uid": "roadm Santa_Barbara",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2654,6 +2715,7 @@
     {
       "uid": "roadm Scranton",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2679,6 +2741,7 @@
     {
       "uid": "roadm Seattle",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2702,6 +2765,7 @@
     {
       "uid": "roadm Spokane",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2725,6 +2789,7 @@
     {
       "uid": "roadm Springfield",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2748,6 +2813,7 @@
     {
       "uid": "roadm St_Louis",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2772,6 +2838,7 @@
     {
       "uid": "roadm Syracuse",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2796,6 +2863,7 @@
     {
       "uid": "roadm Tallahassee",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2819,6 +2887,7 @@
     {
       "uid": "roadm Tampa",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2842,6 +2911,7 @@
     {
       "uid": "roadm Toledo",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2865,6 +2935,7 @@
     {
       "uid": "roadm Tucson",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2888,6 +2959,7 @@
     {
       "uid": "roadm Tulsa",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2911,6 +2983,7 @@
     {
       "uid": "roadm Washington_DC",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2936,6 +3009,7 @@
     {
       "uid": "roadm West_Palm_Beach",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2959,6 +3033,7 @@
     {
       "uid": "roadm Wilmington",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -2982,6 +3057,7 @@
     {
       "uid": "roadm Amsterdam",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -3007,6 +3083,7 @@
     {
       "uid": "roadm Berlin",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -3030,6 +3107,7 @@
     {
       "uid": "roadm Brussels",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -3053,6 +3131,7 @@
     {
       "uid": "roadm Bucharest",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -3076,6 +3155,7 @@
     {
       "uid": "roadm Frankfurt",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -3099,6 +3179,7 @@
     {
       "uid": "roadm Istanbul",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -3123,6 +3204,7 @@
     {
       "uid": "roadm London",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -3147,6 +3229,7 @@
     {
       "uid": "roadm Madrid",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -3170,6 +3253,7 @@
     {
       "uid": "roadm Paris",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -3194,6 +3278,7 @@
     {
       "uid": "roadm Rome",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -3219,6 +3304,7 @@
     {
       "uid": "roadm Vienna",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -3243,6 +3329,7 @@
     {
       "uid": "roadm Warsaw",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -3267,6 +3354,7 @@
     {
       "uid": "roadm Zurich",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -3290,6 +3378,7 @@
     {
       "uid": "roadm Bangkok",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -3313,6 +3402,7 @@
     {
       "uid": "roadm Beijing",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -3336,6 +3426,7 @@
     {
       "uid": "roadm Delhi",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -3360,6 +3451,7 @@
     {
       "uid": "roadm Hong_Kong",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -3385,6 +3477,7 @@
     {
       "uid": "roadm Honolulu",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -3409,6 +3502,7 @@
     {
       "uid": "roadm Mumbai",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -3433,6 +3527,7 @@
     {
       "uid": "roadm Seoul",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -3456,6 +3551,7 @@
     {
       "uid": "roadm Shanghai",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -3479,6 +3575,7 @@
     {
       "uid": "roadm Singapore",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -3502,6 +3599,7 @@
     {
       "uid": "roadm Sydney",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -3526,6 +3624,7 @@
     {
       "uid": "roadm Taipei",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -3551,6 +3650,7 @@
     {
       "uid": "roadm Tokyo",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -83375,7 +83475,7 @@
       "type": "Edfa",
       "type_variety": "std_medium_gain",
       "operational": {
-        "gain_target": 28.5006,
+        "gain_target": 28.5,
         "delta_p": null,
         "tilt_target": 0,
         "out_voa": 0
@@ -88752,7 +88852,7 @@
       "type": "Edfa",
       "type_variety": "std_medium_gain",
       "operational": {
-        "gain_target": 28.5032,
+        "gain_target": 28.5,
         "delta_p": null,
         "tilt_target": 0,
         "out_voa": 0
@@ -89037,7 +89137,7 @@
       "type": "Edfa",
       "type_variety": "std_medium_gain",
       "operational": {
-        "gain_target": 28.5006,
+        "gain_target": 28.5,
         "delta_p": null,
         "tilt_target": 0,
         "out_voa": 0
@@ -89721,7 +89821,7 @@
       "type": "Edfa",
       "type_variety": "std_medium_gain",
       "operational": {
-        "gain_target": 28.502,
+        "gain_target": 28.5,
         "delta_p": null,
         "tilt_target": 0,
         "out_voa": 0
@@ -89797,7 +89897,7 @@
       "type": "Edfa",
       "type_variety": "std_medium_gain",
       "operational": {
-        "gain_target": 28.502,
+        "gain_target": 28.5,
         "delta_p": null,
         "tilt_target": 0,
         "out_voa": 0
@@ -89911,7 +90011,7 @@
       "type": "Edfa",
       "type_variety": "std_medium_gain",
       "operational": {
-        "gain_target": 28.5032,
+        "gain_target": 28.5,
         "delta_p": null,
         "tilt_target": 0,
         "out_voa": 0
@@ -193159,4 +193259,4 @@
       "to_node": "fiber (Warsaw → Vienna)-_(7/7)"
     }
   ]
-}
+}

tests/data/CORONET_services.json

@@ -0,0 +1,21 @@
+{
+  "path-request": [
+    {
+      "request-id": "0",
+      "source": "trx Abilene",
+      "destination": "trx Albany",
+      "src-tp-id": "trx Abilene",
+      "dst-tp-id": "trx Albany",
+      "bidirectional": false,
+      "path-constraints": {
+        "te-bandwidth": {
+          "technology": "flexi-grid",
+          "trx_type": "Voyager",
+          "trx_mode": "mode 3",
+          "spacing": 62500000000.0,
+          "path_bandwidth": 100000000000.0
+        }
+      }
+    }
+  ]
+}

tests/data/default_edfa_config.json

@@ -1,4 +1,6 @@
 {
+    "f_min": 191.275e12,
+    "f_max": 196.125e12,
     "nf_ripple": [
         0.0,
         0.0,
@@ -196,101 +198,101 @@
         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,
-        1.7137640932265894,
-        1.7057507692361864,
-        1.6918150918099673,
-        1.6719047669939942,
-        1.6460167077689267,
-        1.6201194134191075,
-        1.5986915141218316,
-        1.5817353179379183,
-        1.569199764184379,
-        1.5566577309558969,
-        1.545374152761467,
-        1.5353620432989845,
-        1.5266220576235803,
-        1.5178910621476225,
-        1.5097346239790443,
-        1.502153039909686,
-        1.495145456062699,
-        1.488134243479226,
-        1.48111939735681,
-        1.474100442252211,
-        1.4670307626366115,
-        1.4599103316162523,
-        1.45273959485914,
-        1.445565137158368,
-        1.4340878115214444,
-        1.418273806730323,
-        1.3981208704326855,
-        1.3779439775587023,
-        1.3598972673004606,
-        1.3439818461440451,
-        1.3301807335621048,
-        1.316383926863083,
-        1.3040618749785347,
-        1.2932153453410835,
-        1.2838336236692311,
-        1.2744470198196236,
-        1.2650555289898042,
-        1.2556591482982988,
-        1.2428104897182262,
-        1.2264996957264114,
-        1.2067249615595257,
-        1.1869318618366975,
-        1.1672278304018044,
-        1.1476135933863398,
-        1.1280891949729075,
-        1.108555289615659,
-        1.0895983485572227,
-        1.0712204022764056,
-        1.0534217504465226,
-        1.0356155337864215,
+        1.0,
         1.017807767853702,
-        1.0
+        1.0356155337864215,
+        1.0534217504465226,
+        1.0712204022764056,
+        1.0895983485572227,
+        1.108555289615659,
+        1.1280891949729075,
+        1.1476135933863398,
+        1.1672278304018044,
+        1.1869318618366975,
+        1.2067249615595257,
+        1.2264996957264114,
+        1.2428104897182262,
+        1.2556591482982988,
+        1.2650555289898042,
+        1.2744470198196236,
+        1.2838336236692311,
+        1.2932153453410835,
+        1.3040618749785347,
+        1.316383926863083,
+        1.3301807335621048,
+        1.3439818461440451,
+        1.3598972673004606,
+        1.3779439775587023,
+        1.3981208704326855,
+        1.418273806730323,
+        1.4340878115214444,
+        1.445565137158368,
+        1.45273959485914,
+        1.4599103316162523,
+        1.4670307626366115,
+        1.474100442252211,
+        1.48111939735681,
+        1.488134243479226,
+        1.495145456062699,
+        1.502153039909686,
+        1.5097346239790443,
+        1.5178910621476225,
+        1.5266220576235803,
+        1.5353620432989845,
+        1.545374152761467,
+        1.5566577309558969,
+        1.569199764184379,
+        1.5817353179379183,
+        1.5986915141218316,
+        1.6201194134191075,
+        1.6460167077689267,
+        1.6719047669939942,
+        1.6918150918099673,
+        1.7057507692361864,
+        1.7137640932265894,
+        1.7217732861435076,
+        1.7297783508684146,
+        1.737780757913635,
+        1.7459181197626403,
+        1.7541903672600494,
+        1.7625959636196327,
+        1.7709972329654864,
+        1.7793941781790852,
+        1.7877868031023945,
+        1.7961751115773796,
+        1.8045606557581335,
+        1.8139629377087627,
+        1.824381436842932,
+        1.835814081380705,
+        1.847275503201129,
+        1.862235672444246,
+        1.8806927939516411,
+        1.9026104247588487,
+        1.9245345552113182,
+        1.9482128147680253,
+        1.9736443063300082,
+        2.0008103857988204,
+        2.0279625371819305,
+        2.055100772005235,
+        2.082225099873648,
+        2.1183028432496016,
+        2.16337565384239,
+        2.2174389328192197,
+        2.271520771371253,
+        2.322373696229342,
+        2.3699990328716107,
+        2.414398437185221,
+        2.4587748041127506,
+        2.499446286796604,
+        2.5364027376452056,
+        2.5696460593920065,
+        2.602860350286428,
+        2.630396440815385,
+        2.6521732021128046,
+        2.6681935771243177,
+        2.6841217449620203,
+        2.6947834587664494,
+        2.705443819238505,
+        2.714526681131686
     ]
-}
+}

tests/data/eqpt_config.json

@@ -1,4 +1,5 @@
-{     "Edfa":[{
+{
+    "Edfa": [{
             "type_variety": "CienaDB_medium_gain",
             "type_def": "advanced_model",
             "gain_flatmax": 25,
@@ -7,10 +8,9 @@
             "advanced_config_from_json": "std_medium_gain_advanced_config.json",
             "out_voa_auto": false,
             "allowed_for_design": true
-            },
-            {
+        }, {
             "type_variety": "std_medium_gain",
-            "type_def": "variable_gain",            
+            "type_def": "variable_gain",
             "gain_flatmax": 26,
             "gain_min": 15,
             "p_max": 21,
@@ -18,10 +18,9 @@
             "nf_max": 10,
             "out_voa_auto": false,
             "allowed_for_design": true
-            },
-            {
+        }, {
             "type_variety": "std_low_gain",
-            "type_def": "variable_gain",            
+            "type_def": "variable_gain",
             "gain_flatmax": 16,
             "gain_min": 8,
             "p_max": 21,
@@ -29,8 +28,7 @@
             "nf_max": 11,
             "out_voa_auto": false,
             "allowed_for_design": true
-            },
-            {
+        }, {
             "type_variety": "test",
             "type_def": "variable_gain",
             "gain_flatmax": 25,
@@ -40,8 +38,7 @@
             "nf_max": 10,
             "out_voa_auto": false,
             "allowed_for_design": true
-            },
-            {
+        }, {
             "type_variety": "test_fixed_gain",
             "type_def": "fixed_gain",
             "gain_flatmax": 21,
@@ -49,8 +46,7 @@
             "p_max": 21,
             "nf0": 5,
             "allowed_for_design": true
-            },
-            {
+        }, {
             "type_variety": "std_booster",
             "type_def": "fixed_gain",
             "gain_flatmax": 21,
@@ -58,18 +54,18 @@
             "p_max": 21,
             "nf0": 5,
             "allowed_for_design": false
-            }            
-      ],
-      "Fiber":[{
+        }
+    ],
+    "Fiber": [{
             "type_variety": "SSMF",
             "dispersion": 1.67e-05,
             "effective_area": 83e-12,
             "pmd_coef": 1.265e-15
-            }
-      ],
-      "Span":[{
-            "power_mode":true,
-            "delta_power_range_db": [0,0,0.5],
+        }
+    ],
+    "Span": [{
+            "power_mode": true,
+            "delta_power_range_db": [0, 0, 0.5],
             "max_fiber_lineic_loss_for_raman": 0.25,
             "target_extended_gain": 2.5,
             "max_length": 150,
@@ -79,157 +75,232 @@
             "EOL": 0,
             "con_in": 0,
             "con_out": 0
-            }
-      ],
-      "Roadm":[{
+        }
+    ],
+    "Roadm": [{
+            "type_variety": "example_test",
+            "target_pch_out_db": -18,
+            "add_drop_osnr": 35,
+            "pmd": 1e-12,
+            "pdl": 0.5,
+            "restrictions": {
+                "preamp_variety_list": [],
+                "booster_variety_list": []
+            },
+            "roadm-path-impairments": []
+        }, {
+            "type_variety": "example_detailed_impairments",
+            "target_pch_out_db": -20,
+            "add_drop_osnr": 35,
+            "pmd": 0,
+            "pdl": 0,
+            "restrictions": {
+                "preamp_variety_list":[],
+                "booster_variety_list":[]
+                            },
+            "roadm-path-impairments": [
+                  {
+                    "roadm-path-impairments-id": 0,
+                    "roadm-express-path": [{
+                        "frequency-range": {
+                            "lower-frequency": 191.3e12,
+                            "upper-frequency": 196.1e12
+                            },
+                        "roadm-pmd": 0,
+                        "roadm-cd": 0,
+                        "roadm-pdl": 0,
+                        "roadm-inband-crosstalk": 0,
+                        "roadm-maxloss": 16.5
+                        }
+                    ]
+                }, {
+                    "roadm-path-impairments-id": 1,
+                    "roadm-add-path": [{
+                        "frequency-range": {
+                            "lower-frequency": 191.3e12,
+                            "upper-frequency": 196.1e12
+                        },
+                        "roadm-pmd": 0,
+                        "roadm-cd": 0,
+                        "roadm-pdl": 0,
+                        "roadm-inband-crosstalk": 0,
+                        "roadm-maxloss": 11.5,
+                        "roadm-pmax": 2.5,
+                        "roadm-osnr": 41,
+                        "roadm-noise-figure": 23
+                    }, {
+                        "frequency-range": {
+                            "lower-frequency": 186.3e12,
+                            "upper-frequency": 190.1e12
+                        },
+                        "roadm-pmd": 0,
+                        "roadm-cd": 0,
+                        "roadm-pdl": 0.5,
+                        "roadm-inband-crosstalk": 0,
+                        "roadm-maxloss": 5,
+                        "roadm-pmax": 0,
+                        "roadm-osnr": 35,
+                        "roadm-noise-figure": 6
+                    }]
+                }, {
+                    "roadm-path-impairments-id": 2,
+                    "roadm-drop-path": [{
+                        "frequency-range": {
+                            "lower-frequency": 191.3e12,
+                            "upper-frequency": 196.1e12
+                            },
+                        "roadm-pmd": 0,
+                        "roadm-cd": 0,
+                        "roadm-pdl": 0,
+                        "roadm-inband-crosstalk": 0,
+                        "roadm-maxloss": 11.5,
+                        "roadm-minloss": 7.5,
+                        "roadm-typloss": 10,
+                        "roadm-pmin": -13.5,
+                        "roadm-pmax": -9.5,
+                        "roadm-ptyp": -12,
+                        "roadm-osnr": 41,
+                        "roadm-noise-figure": 15
+                    }]
+                }]
+        }, {
             "target_pch_out_db": -20,
             "add_drop_osnr": 38,
             "pmd": 0,
             "pdl": 0,
             "restrictions": {
-                            "preamp_variety_list":[],
-                            "booster_variety_list":[]
-                            }    
-            }],
-      "SI":[{
+                "preamp_variety_list": [],
+                "booster_variety_list": []
+            }
+        }
+    ],
+    "SI": [{
+            "type_variety": "default",
             "f_min": 191.3e12,
-            "f_max":196.1e12,
+            "f_max": 196.1e12,
             "baud_rate": 32e9,
             "spacing": 50e9,
             "power_dbm": 0,
-            "power_range_db": [0,0,0.5],
+            "power_range_db": [0, 0, 0.5],
             "roll_off": 0.15,
             "tx_osnr": 100,
-            "sys_margins": 0            
-            }],
+            "sys_margins": 0
+        }],
       "Transceiver":[
             {
             "type_variety": "vendorA_trx-type1",
-            "frequency":{
-                        "min": 191.35e12,
-                        "max": 196.1e12
-                        },
-            "mode":[
-                       {
-                       "format": "PS_SP64_1",
-                       "baud_rate": 32e9,
-                       "OSNR": 11,
-                       "bit_rate": 100e9,
-                       "roll_off": 0.15,
-                       "tx_osnr": 100,
-                       "min_spacing": 50e9,
-                       "cost":1
-                       },
-                       {
-                       "format": "PS_SP64_2",
-                       "baud_rate": 64e9,
-                       "OSNR": 15,
-                       "bit_rate": 200e9,
-                       "roll_off": 0.15,
-                       "tx_osnr": 100,
-                       "min_spacing": 75e9,
-                       "cost":1                       
-                       },
-                       {
-                       "format": "mode 1",
-                       "baud_rate": 32e9,
-                       "OSNR": 11,
-                       "bit_rate": 100e9,
-                       "roll_off": 0.15,
-                       "tx_osnr": 100,
-                       "min_spacing": 50e9,
-                       "cost":1
-                       },
-                       {
-                       "format": "mode 2",
-                       "baud_rate": 64e9,
-                       "OSNR": 15,
-                       "bit_rate": 200e9,
-                       "roll_off": 0.15,
-                       "tx_osnr": 100,
-                       "min_spacing": 75e9,
-                       "cost":1                       
-                       }                       
-                   ]
+            "frequency": {
+                "min": 191.35e12,
+                "max": 196.1e12
             },
-            {
+            "mode": [{
+                    "format": "PS_SP64_1",
+                    "baud_rate": 32e9,
+                    "OSNR": 11,
+                    "bit_rate": 100e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 100,
+                    "min_spacing": 50e9,
+                    "cost": 1
+                }, {
+                    "format": "PS_SP64_2",
+                    "baud_rate": 64e9,
+                    "OSNR": 15,
+                    "bit_rate": 200e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 100,
+                    "min_spacing": 75e9,
+                    "cost": 1
+                }, {
+                    "format": "mode 1",
+                    "baud_rate": 32e9,
+                    "OSNR": 11,
+                    "bit_rate": 100e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 100,
+                    "min_spacing": 50e9,
+                    "cost": 1
+                }, {
+                    "format": "mode 2",
+                    "baud_rate": 64e9,
+                    "OSNR": 15,
+                    "bit_rate": 200e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 100,
+                    "min_spacing": 75e9,
+                    "cost": 1
+                }
+            ]
+        }, {
             "type_variety": "Voyager_16QAM",
-            "frequency":{
-                        "min": 191.35e12,
-                        "max": 196.1e12
-                        },
-            "mode":[
-                       {
-                       "format": "16QAM",
-                       "baud_rate": 32e9,
-                       "OSNR": 19,
-                       "bit_rate": 200e9,
-                       "roll_off": 0.15,
-                       "tx_osnr": 100,
-                       "min_spacing": 50e9,
-                       "cost":1
-                       }
-                   ]
+            "frequency": {
+                "min": 191.35e12,
+                "max": 196.1e12
             },
-            {
+            "mode": [{
+                    "format": "16QAM",
+                    "baud_rate": 32e9,
+                    "OSNR": 19,
+                    "bit_rate": 200e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 100,
+                    "min_spacing": 50e9,
+                    "cost": 1
+                }
+            ]
+        }, {
             "type_variety": "Voyager",
-            "frequency":{
-                        "min": 191.35e12,
-                        "max": 196.1e12
-                        },
-            "mode":[
-                       {
-                       "format": "mode 1",
-                       "baud_rate": 32e9,
-                       "OSNR": 12,
-                       "bit_rate": 100e9,
-                       "roll_off": 0.15,
-                       "tx_osnr": 45,
-                       "min_spacing": 50e9,
-                       "cost":1
-                       },
-                       {
-                       "format": "mode 3",
-                       "baud_rate": 44e9,
-                       "OSNR": 18,
-                       "bit_rate": 300e9,
-                       "roll_off": 0.15,
-                       "tx_osnr": 45,
-                       "min_spacing": 62.5e9,
-                       "cost":1
-                       },
-                       {
-                       "format": "mode 2",
-                       "baud_rate": 66e9,
-                       "OSNR": 21,
-                       "bit_rate": 400e9,
-                       "roll_off": 0.15,
-                       "tx_osnr": 45,
-                       "min_spacing": 75e9,
-                       "cost":1
-                       },
-                       {
-                       "format": "mode 2 - fake",
-                       "baud_rate": 66e9,
-                       "OSNR": 21,
-                       "bit_rate": 400e9,
-                       "roll_off": 0.15,
-                       "tx_osnr": 45,
-                       "min_spacing": 75e9,
-                       "cost":1
-                       },                       
-                       {
-                       "format": "mode 4",
-                       "baud_rate": 66e9,
-                       "OSNR": 16,
-                       "bit_rate": 200e9,
-                       "roll_off": 0.15,
-                       "tx_osnr": 45,
-                       "min_spacing": 75e9,
-                       "cost":1
-                       }
-                   ]
-            }
-      ]
-
+            "frequency": {
+                "min": 191.35e12,
+                "max": 196.1e12
+            },
+            "mode": [{
+                    "format": "mode 1",
+                    "baud_rate": 32e9,
+                    "OSNR": 12,
+                    "bit_rate": 100e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 45,
+                    "min_spacing": 50e9,
+                    "cost": 1
+                }, {
+                    "format": "mode 3",
+                    "baud_rate": 44e9,
+                    "OSNR": 18,
+                    "bit_rate": 300e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 45,
+                    "min_spacing": 62.5e9,
+                    "cost": 1
+                }, {
+                    "format": "mode 2",
+                    "baud_rate": 66e9,
+                    "OSNR": 21,
+                    "bit_rate": 400e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 45,
+                    "min_spacing": 75e9,
+                    "cost": 1
+                }, {
+                    "format": "mode 2 - fake",
+                    "baud_rate": 66e9,
+                    "OSNR": 21,
+                    "bit_rate": 400e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 45,
+                    "min_spacing": 75e9,
+                    "cost": 1
+                }, {
+                    "format": "mode 4",
+                    "baud_rate": 66e9,
+                    "OSNR": 16,
+                    "bit_rate": 200e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 45,
+                    "min_spacing": 75e9,
+                    "cost": 1
+                }
+            ]
+        }
+    ]
 }

tests/data/eqpt_config_multiband.json

@@ -0,0 +1,396 @@
+{     "Edfa":[{
+            "type_variety": "CienaDB_medium_gain",
+            "type_def": "advanced_model",
+            "gain_flatmax": 25,
+            "gain_min": 15,
+            "p_max": 21,
+            "advanced_config_from_json": "std_medium_gain_advanced_config.json",
+            "out_voa_auto": false,
+            "allowed_for_design": true
+            },
+            {
+            "type_variety": "std_medium_gain",
+            "f_min": 191.25e12,
+            "f_max": 196.15e12,
+            "type_def": "variable_gain",
+            "gain_flatmax": 26,
+            "gain_min": 15,
+            "p_max": 21,
+            "nf_min": 6,
+            "nf_max": 10,
+            "out_voa_auto": false,
+            "allowed_for_design": true
+            },
+            {
+            "type_variety": "std_medium_gain_L",
+            "f_min": 186.55e12,
+            "f_max": 190.05e12,
+            "type_def": "variable_gain",
+            "gain_flatmax": 26,
+            "gain_min": 15,
+            "p_max": 21,
+            "nf_min": 6,
+            "nf_max": 10,
+            "out_voa_auto": false,
+            "allowed_for_design": true
+            },
+            {
+            "type_variety": "std_low_gain",
+            "f_min": 191.25e12,
+            "f_max": 196.15e12,
+            "type_def": "variable_gain",
+            "gain_flatmax": 16,
+            "gain_min": 8,
+            "p_max": 21,
+            "nf_min": 7,
+            "nf_max": 11,
+            "out_voa_auto": false,
+            "allowed_for_design": true
+        },
+        {
+            "type_variety": "std_low_gain_reduced_band",
+            "f_min": 192.25e12,
+            "f_max": 196.15e12,
+            "type_def": "variable_gain",
+            "gain_flatmax": 16,
+            "gain_min": 8,
+            "p_max": 21,
+            "nf_min": 7,
+            "nf_max": 11,
+            "out_voa_auto": false,
+            "allowed_for_design": true
+        },
+        {
+        "type_variety": "std_low_gain_reduced",
+        "f_min": 192.25e12,
+        "f_max": 196.15e12,
+        "type_def": "variable_gain",
+        "gain_flatmax": 16,
+        "gain_min": 8,
+        "p_max": 21,
+        "nf_min": 7,
+        "nf_max": 11,
+        "out_voa_auto": false,
+        "allowed_for_design": true
+    },
+		{
+            "type_variety": "std_low_gain_bis",
+            "f_min": 191.25e12,
+            "f_max": 196.15e12,
+            "type_def": "variable_gain",
+            "gain_flatmax": 16,
+            "gain_min": 8,
+            "p_max": 21,
+            "nf_min": 6,
+            "nf_max": 10,
+            "out_voa_auto": false,
+            "allowed_for_design": true
+        }, {
+            "type_variety": "std_low_gain_L_ter",
+            "f_min": 186.55e12,
+            "f_max": 190.05e12,
+            "type_def": "variable_gain",
+            "gain_flatmax": 16,
+            "gain_min": 8,
+            "p_max": 16,
+            "nf_min": 7,
+            "nf_max": 11,
+            "out_voa_auto": false,
+            "allowed_for_design": true
+        },
+		{
+            "type_variety": "std_low_gain_L",
+            "f_min": 186.55e12,
+            "f_max": 190.05e12,
+            "type_def": "variable_gain",
+            "gain_flatmax": 16,
+            "gain_min": 8,
+            "p_max": 21,
+            "nf_min": 7,
+            "nf_max": 11,
+            "out_voa_auto": false,
+            "allowed_for_design": true
+            },
+            {
+                "type_variety": "std_low_gain_L_reduced_band",
+                "f_min": 187.3e12,
+                "f_max": 190.05e12,
+                "type_def": "variable_gain",
+                "gain_flatmax": 16,
+                "gain_min": 8,
+                "p_max": 21,
+                "nf_min": 7,
+                "nf_max": 11,
+                "out_voa_auto": false,
+                "allowed_for_design": true
+                },
+            {
+            "type_variety": "test",
+            "type_def": "variable_gain",
+            "gain_flatmax": 25,
+            "gain_min": 15,
+            "p_max": 21,
+            "nf_min": 5.8,
+            "nf_max": 10,
+            "out_voa_auto": false,
+            "allowed_for_design": true
+            },
+            {
+            "type_variety": "test_fixed_gain",
+            "type_def": "fixed_gain",
+            "gain_flatmax": 21,
+            "gain_min": 20,
+            "p_max": 21,
+            "nf0": 5,
+            "allowed_for_design": true
+            },
+            {
+            "type_variety": "std_booster",
+            "type_def": "fixed_gain",
+            "gain_flatmax": 21,
+            "gain_min": 20,
+            "p_max": 21,
+            "nf0": 5,
+            "allowed_for_design": false
+                    }, {
+            "type_variety": "std_booster_L",
+            "f_min": 186.55e12,
+            "f_max": 190.05e12,
+            "type_def": "fixed_gain",
+            "gain_flatmax": 21,
+            "gain_min": 20,
+            "p_max": 21,
+            "nf0": 5,
+            "allowed_for_design": false
+        }, {
+            "type_variety": "std_booster_multiband",
+            "type_def": "multi_band",
+            "amplifiers": [
+                "std_booster",
+                "std_booster_L"
+            ],
+            "allowed_for_design": false
+        }, {
+            "type_variety": "std_medium_gain_multiband",
+            "type_def": "multi_band",
+            "amplifiers": [
+                "std_medium_gain",
+                "std_medium_gain_L"
+            ],
+            "allowed_for_design": true
+            },
+            {
+            "type_variety": "std_low_gain_multiband",
+            "type_def": "multi_band",
+            "amplifiers": [
+                "std_low_gain",
+                "std_low_gain_L"
+            ],
+            "allowed_for_design": false
+        }, {
+            "type_variety": "std_low_gain_multiband_ter",
+            "type_def": "multi_band",
+            "amplifiers": [
+                "std_low_gain",
+                "std_low_gain_L_ter"
+            ],
+            "allowed_for_design": false
+        }, {
+            "type_variety": "std_low_gain_multiband_bis",
+            "type_def": "multi_band",
+            "amplifiers": [
+                "std_low_gain_bis",
+                "std_low_gain_L"
+            ],
+            "allowed_for_design": true
+        }, {
+            "type_variety": "std_low_gain_multiband_reduced_bis",
+            "type_def": "multi_band",
+            "amplifiers": [
+                "std_low_gain_reduced",
+                "std_low_gain_L"
+            ],
+            "allowed_for_design": true
+        }, {
+            "type_variety": "std_low_gain_multiband_reduced",
+            "type_def": "multi_band",
+            "amplifiers": [
+                "std_low_gain_bis",
+                "std_low_gain_L_reduced_band"
+            ],
+            "allowed_for_design": true
+        }
+      ],
+      "Fiber":[{
+            "type_variety": "SSMF",
+            "dispersion": 1.67e-05,
+            "effective_area": 83e-12,
+            "pmd_coef": 1.265e-15
+            }
+      ],
+      "Span":[{
+            "power_mode":true,
+            "delta_power_range_db": [0,0,0.5],
+            "max_fiber_lineic_loss_for_raman": 0.25,
+            "target_extended_gain": 2.5,
+            "max_length": 150,
+            "length_units": "km",
+            "max_loss": 28,
+            "padding": 10,
+            "EOL": 0,
+            "con_in": 0,
+            "con_out": 0
+            }
+      ],
+      "Roadm":[{
+            "target_pch_out_db": -20,
+            "add_drop_osnr": 38,
+            "pmd": 0,
+            "pdl": 0,
+            "restrictions": {
+                            "preamp_variety_list":[],
+                            "booster_variety_list":[]
+                            }
+            }],
+      "SI":[{
+            "f_min": 191.3e12,
+            "f_max":196.1e12,
+            "baud_rate": 32e9,
+            "spacing": 50e9,
+            "power_dbm": 0,
+            "power_range_db": [0,0,0.5],
+            "roll_off": 0.15,
+            "tx_osnr": 100,
+            "sys_margins": 0
+            }],
+      "Transceiver":[
+            {
+            "type_variety": "vendorA_trx-type1",
+            "frequency":{
+                        "min": 191.35e12,
+                        "max": 196.1e12
+                        },
+            "mode":[
+                       {
+                       "format": "PS_SP64_1",
+                       "baud_rate": 32e9,
+                       "OSNR": 11,
+                       "bit_rate": 100e9,
+                       "roll_off": 0.15,
+                       "tx_osnr": 100,
+                       "min_spacing": 50e9,
+                       "cost":1
+                       },
+                       {
+                       "format": "PS_SP64_2",
+                       "baud_rate": 64e9,
+                       "OSNR": 15,
+                       "bit_rate": 200e9,
+                       "roll_off": 0.15,
+                       "tx_osnr": 100,
+                       "min_spacing": 75e9,
+                       "cost":1
+                       },
+                       {
+                       "format": "mode 1",
+                       "baud_rate": 32e9,
+                       "OSNR": 11,
+                       "bit_rate": 100e9,
+                       "roll_off": 0.15,
+                       "tx_osnr": 100,
+                       "min_spacing": 50e9,
+                       "cost":1
+                       },
+                       {
+                       "format": "mode 2",
+                       "baud_rate": 64e9,
+                       "OSNR": 15,
+                       "bit_rate": 200e9,
+                       "roll_off": 0.15,
+                       "tx_osnr": 100,
+                       "min_spacing": 75e9,
+                       "cost":1
+                       }
+                   ]
+            },
+            {
+            "type_variety": "Voyager_16QAM",
+            "frequency":{
+                        "min": 191.35e12,
+                        "max": 196.1e12
+                        },
+            "mode":[
+                       {
+                       "format": "16QAM",
+                       "baud_rate": 32e9,
+                       "OSNR": 19,
+                       "bit_rate": 200e9,
+                       "roll_off": 0.15,
+                       "tx_osnr": 100,
+                       "min_spacing": 50e9,
+                       "cost":1
+                       }
+                   ]
+            },
+            {
+            "type_variety": "Voyager",
+            "frequency":{
+                        "min": 191.35e12,
+                        "max": 196.1e12
+                        },
+            "mode":[
+                       {
+                       "format": "mode 1",
+                       "baud_rate": 32e9,
+                       "OSNR": 12,
+                       "bit_rate": 100e9,
+                       "roll_off": 0.15,
+                       "tx_osnr": 45,
+                       "min_spacing": 50e9,
+                       "cost":1
+                       },
+                       {
+                       "format": "mode 3",
+                       "baud_rate": 44e9,
+                       "OSNR": 18,
+                       "bit_rate": 300e9,
+                       "roll_off": 0.15,
+                       "tx_osnr": 45,
+                       "min_spacing": 62.5e9,
+                       "cost":1
+                       },
+                       {
+                       "format": "mode 2",
+                       "baud_rate": 66e9,
+                       "OSNR": 21,
+                       "bit_rate": 400e9,
+                       "roll_off": 0.15,
+                       "tx_osnr": 45,
+                       "min_spacing": 75e9,
+                       "cost":1
+                       },
+                       {
+                       "format": "mode 2 - fake",
+                       "baud_rate": 66e9,
+                       "OSNR": 21,
+                       "bit_rate": 400e9,
+                       "roll_off": 0.15,
+                       "tx_osnr": 45,
+                       "min_spacing": 75e9,
+                       "cost":1
+                       },
+                       {
+                       "format": "mode 4",
+                       "baud_rate": 66e9,
+                       "OSNR": 16,
+                       "bit_rate": 200e9,
+                       "roll_off": 0.15,
+                       "tx_osnr": 45,
+                       "min_spacing": 75e9,
+                       "cost":1
+                       }
+                   ]
+            }
+      ]
+
+}

tests/data/eqpt_config_psd.json

@@ -0,0 +1,220 @@
+{
+    "Edfa": [{
+            "type_variety": "CienaDB_medium_gain",
+            "type_def": "advanced_model",
+            "gain_flatmax": 25,
+            "gain_min": 15,
+            "p_max": 21,
+            "advanced_config_from_json": "std_medium_gain_advanced_config.json",
+            "out_voa_auto": false,
+            "allowed_for_design": true
+        }, {
+            "type_variety": "std_medium_gain",
+            "type_def": "variable_gain",
+            "gain_flatmax": 26,
+            "gain_min": 15,
+            "p_max": 21,
+            "nf_min": 6,
+            "nf_max": 10,
+            "out_voa_auto": false,
+            "allowed_for_design": true
+        }, {
+            "type_variety": "std_low_gain",
+            "type_def": "variable_gain",
+            "gain_flatmax": 16,
+            "gain_min": 8,
+            "p_max": 21,
+            "nf_min": 7,
+            "nf_max": 11,
+            "out_voa_auto": false,
+            "allowed_for_design": true
+        }, {
+            "type_variety": "test",
+            "type_def": "variable_gain",
+            "gain_flatmax": 25,
+            "gain_min": 15,
+            "p_max": 21,
+            "nf_min": 5.8,
+            "nf_max": 10,
+            "out_voa_auto": false,
+            "allowed_for_design": true
+        }, {
+            "type_variety": "test_fixed_gain",
+            "type_def": "fixed_gain",
+            "gain_flatmax": 21,
+            "gain_min": 20,
+            "p_max": 21,
+            "nf0": 5,
+            "allowed_for_design": true
+        }, {
+            "type_variety": "std_booster",
+            "type_def": "fixed_gain",
+            "gain_flatmax": 21,
+            "gain_min": 20,
+            "p_max": 21,
+            "nf0": 5,
+            "allowed_for_design": false
+        }
+    ],
+    "Fiber": [{
+            "type_variety": "SSMF",
+            "dispersion": 1.67e-05,
+            "effective_area": 83e-12,
+            "pmd_coef": 1.265e-15
+        }
+    ],
+    "Span": [{
+            "power_mode": true,
+            "delta_power_range_db": [0, 0, 0.5],
+            "max_fiber_lineic_loss_for_raman": 0.25,
+            "target_extended_gain": 2.5,
+            "max_length": 150,
+            "length_units": "km",
+            "max_loss": 28,
+            "padding": 10,
+            "EOL": 0,
+            "con_in": 0,
+            "con_out": 0
+        }
+    ],
+    "Roadm": [{
+            "target_psd_out_mWperGHz": 3.125e-4,
+            "add_drop_osnr": 38,
+            "pmd": 0,
+            "pdl": 0,
+            "restrictions": {
+                "preamp_variety_list": [],
+                "booster_variety_list": []
+            }
+        }
+    ],
+    "SI": [{
+            "f_min": 191.35e12,
+            "f_max": 196.1e12,
+            "baud_rate": 32e9,
+            "spacing": 50e9,
+            "power_dbm": 0,
+            "power_range_db": [0, 0, 0.5],
+            "roll_off": 0.15,
+            "tx_osnr": 100,
+            "sys_margins": 0
+        }
+    ],
+    "Transceiver": [{
+            "type_variety": "vendorA_trx-type1",
+            "frequency": {
+                "min": 191.4e12,
+                "max": 196.1e12
+            },
+            "mode": [{
+                    "format": "PS_SP64_1",
+                    "baud_rate": 32e9,
+                    "OSNR": 11,
+                    "bit_rate": 100e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 100,
+                    "min_spacing": 50e9,
+                    "cost": 1
+                }, {
+                    "format": "PS_SP64_2",
+                    "baud_rate": 64e9,
+                    "OSNR": 15,
+                    "bit_rate": 200e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 100,
+                    "min_spacing": 75e9,
+                    "cost": 1
+                }, {
+                    "format": "mode 1",
+                    "baud_rate": 32e9,
+                    "OSNR": 11,
+                    "bit_rate": 100e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 100,
+                    "min_spacing": 50e9,
+                    "cost": 1
+                }, {
+                    "format": "mode 2",
+                    "baud_rate": 64e9,
+                    "OSNR": 15,
+                    "bit_rate": 200e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 100,
+                    "min_spacing": 75e9,
+                    "cost": 1
+                }
+            ]
+        }, {
+            "type_variety": "Voyager_16QAM",
+            "frequency": {
+                "min": 191.4e12,
+                "max": 196.1e12
+            },
+            "mode": [{
+                    "format": "16QAM",
+                    "baud_rate": 32e9,
+                    "OSNR": 19,
+                    "bit_rate": 200e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 100,
+                    "min_spacing": 50e9,
+                    "cost": 1
+                }
+            ]
+        }, {
+            "type_variety": "Voyager",
+            "frequency": {
+                "min": 191.4e12,
+                "max": 196.1e12
+            },
+            "mode": [{
+                    "format": "mode 1",
+                    "baud_rate": 32e9,
+                    "OSNR": 12,
+                    "bit_rate": 100e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 45,
+                    "min_spacing": 50e9,
+                    "cost": 1
+                }, {
+                    "format": "mode 3",
+                    "baud_rate": 44e9,
+                    "OSNR": 18,
+                    "bit_rate": 300e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 45,
+                    "min_spacing": 62.5e9,
+                    "cost": 1
+                }, {
+                    "format": "mode 2",
+                    "baud_rate": 66e9,
+                    "OSNR": 21,
+                    "bit_rate": 400e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 45,
+                    "min_spacing": 75e9,
+                    "cost": 1
+                }, {
+                    "format": "mode 2 - fake",
+                    "baud_rate": 66e9,
+                    "OSNR": 21,
+                    "bit_rate": 400e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 45,
+                    "min_spacing": 75e9,
+                    "cost": 1
+                }, {
+                    "format": "mode 4",
+                    "baud_rate": 66e9,
+                    "OSNR": 16,
+                    "bit_rate": 200e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 45,
+                    "min_spacing": 75e9,
+                    "cost": 1
+                }
+            ]
+        }
+    ]
+
+}

tests/data/eqpt_config_psw.json

@@ -0,0 +1,220 @@
+{
+    "Edfa": [{
+            "type_variety": "CienaDB_medium_gain",
+            "type_def": "advanced_model",
+            "gain_flatmax": 25,
+            "gain_min": 15,
+            "p_max": 21,
+            "advanced_config_from_json": "std_medium_gain_advanced_config.json",
+            "out_voa_auto": false,
+            "allowed_for_design": true
+        }, {
+            "type_variety": "std_medium_gain",
+            "type_def": "variable_gain",
+            "gain_flatmax": 26,
+            "gain_min": 15,
+            "p_max": 21,
+            "nf_min": 6,
+            "nf_max": 10,
+            "out_voa_auto": false,
+            "allowed_for_design": true
+        }, {
+            "type_variety": "std_low_gain",
+            "type_def": "variable_gain",
+            "gain_flatmax": 16,
+            "gain_min": 8,
+            "p_max": 21,
+            "nf_min": 7,
+            "nf_max": 11,
+            "out_voa_auto": false,
+            "allowed_for_design": true
+        }, {
+            "type_variety": "test",
+            "type_def": "variable_gain",
+            "gain_flatmax": 25,
+            "gain_min": 15,
+            "p_max": 21,
+            "nf_min": 5.8,
+            "nf_max": 10,
+            "out_voa_auto": false,
+            "allowed_for_design": true
+        }, {
+            "type_variety": "test_fixed_gain",
+            "type_def": "fixed_gain",
+            "gain_flatmax": 21,
+            "gain_min": 20,
+            "p_max": 21,
+            "nf0": 5,
+            "allowed_for_design": true
+        }, {
+            "type_variety": "std_booster",
+            "type_def": "fixed_gain",
+            "gain_flatmax": 21,
+            "gain_min": 20,
+            "p_max": 21,
+            "nf0": 5,
+            "allowed_for_design": false
+        }
+    ],
+    "Fiber": [{
+            "type_variety": "SSMF",
+            "dispersion": 1.67e-05,
+            "effective_area": 83e-12,
+            "pmd_coef": 1.265e-15
+        }
+    ],
+    "Span": [{
+            "power_mode": true,
+            "delta_power_range_db": [0, 0, 0.5],
+            "max_fiber_lineic_loss_for_raman": 0.25,
+            "target_extended_gain": 2.5,
+            "max_length": 150,
+            "length_units": "km",
+            "max_loss": 28,
+            "padding": 10,
+            "EOL": 0,
+            "con_in": 0,
+            "con_out": 0
+        }
+    ],
+    "Roadm": [{
+            "target_out_mWperSlotWidth": 2.0e-4,
+            "add_drop_osnr": 38,
+            "pmd": 0,
+            "pdl": 0,
+            "restrictions": {
+                "preamp_variety_list": [],
+                "booster_variety_list": []
+            }
+        }
+    ],
+    "SI": [{
+            "f_min": 191.35e12,
+            "f_max": 196.1e12,
+            "baud_rate": 32e9,
+            "spacing": 50e9,
+            "power_dbm": 0,
+            "power_range_db": [0, 0, 0.5],
+            "roll_off": 0.15,
+            "tx_osnr": 100,
+            "sys_margins": 0
+        }
+    ],
+    "Transceiver": [{
+            "type_variety": "vendorA_trx-type1",
+            "frequency": {
+                "min": 191.4e12,
+                "max": 196.1e12
+            },
+            "mode": [{
+                    "format": "PS_SP64_1",
+                    "baud_rate": 32e9,
+                    "OSNR": 11,
+                    "bit_rate": 100e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 100,
+                    "min_spacing": 50e9,
+                    "cost": 1
+                }, {
+                    "format": "PS_SP64_2",
+                    "baud_rate": 64e9,
+                    "OSNR": 15,
+                    "bit_rate": 200e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 100,
+                    "min_spacing": 75e9,
+                    "cost": 1
+                }, {
+                    "format": "mode 1",
+                    "baud_rate": 32e9,
+                    "OSNR": 11,
+                    "bit_rate": 100e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 100,
+                    "min_spacing": 50e9,
+                    "cost": 1
+                }, {
+                    "format": "mode 2",
+                    "baud_rate": 64e9,
+                    "OSNR": 15,
+                    "bit_rate": 200e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 100,
+                    "min_spacing": 75e9,
+                    "cost": 1
+                }
+            ]
+        }, {
+            "type_variety": "Voyager_16QAM",
+            "frequency": {
+                "min": 191.4e12,
+                "max": 196.1e12
+            },
+            "mode": [{
+                    "format": "16QAM",
+                    "baud_rate": 32e9,
+                    "OSNR": 19,
+                    "bit_rate": 200e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 100,
+                    "min_spacing": 50e9,
+                    "cost": 1
+                }
+            ]
+        }, {
+            "type_variety": "Voyager",
+            "frequency": {
+                "min": 191.4e12,
+                "max": 196.1e12
+            },
+            "mode": [{
+                    "format": "mode 1",
+                    "baud_rate": 32e9,
+                    "OSNR": 12,
+                    "bit_rate": 100e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 45,
+                    "min_spacing": 50e9,
+                    "cost": 1
+                }, {
+                    "format": "mode 3",
+                    "baud_rate": 44e9,
+                    "OSNR": 18,
+                    "bit_rate": 300e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 45,
+                    "min_spacing": 62.5e9,
+                    "cost": 1
+                }, {
+                    "format": "mode 2",
+                    "baud_rate": 66e9,
+                    "OSNR": 21,
+                    "bit_rate": 400e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 45,
+                    "min_spacing": 75e9,
+                    "cost": 1
+                }, {
+                    "format": "mode 2 - fake",
+                    "baud_rate": 66e9,
+                    "OSNR": 21,
+                    "bit_rate": 400e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 45,
+                    "min_spacing": 75e9,
+                    "cost": 1
+                }, {
+                    "format": "mode 4",
+                    "baud_rate": 66e9,
+                    "OSNR": 16,
+                    "bit_rate": 200e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 45,
+                    "min_spacing": 75e9,
+                    "cost": 1
+                }
+            ]
+        }
+    ]
+
+}

tests/data/eqpt_config_sweep.json

@@ -0,0 +1,238 @@
+{
+      "Edfa": [{
+                  "type_variety": "CienaDB_medium_gain",
+                  "type_def": "advanced_model",
+                  "gain_flatmax": 25,
+                  "gain_min": 15,
+                  "p_max": 21,
+                  "advanced_config_from_json": "std_medium_gain_advanced_config.json",
+                  "out_voa_auto": false,
+                  "allowed_for_design": true
+            },
+            {
+                  "type_variety": "std_medium_gain",
+                  "type_def": "variable_gain",
+                  "gain_flatmax": 26,
+                  "gain_min": 15,
+                  "p_max": 21,
+                  "nf_min": 6,
+                  "nf_max": 10,
+                  "out_voa_auto": false,
+                  "allowed_for_design": true
+            },
+            {
+                  "type_variety": "std_low_gain",
+                  "type_def": "variable_gain",
+                  "gain_flatmax": 16,
+                  "gain_min": 8,
+                  "p_max": 21,
+                  "nf_min": 7,
+                  "nf_max": 11,
+                  "out_voa_auto": false,
+                  "allowed_for_design": true
+            },
+            {
+                  "type_variety": "test",
+                  "type_def": "variable_gain",
+                  "gain_flatmax": 25,
+                  "gain_min": 15,
+                  "p_max": 21,
+                  "nf_min": 5.8,
+                  "nf_max": 10,
+                  "out_voa_auto": false,
+                  "allowed_for_design": true
+            },
+            {
+                  "type_variety": "test_fixed_gain",
+                  "type_def": "fixed_gain",
+                  "gain_flatmax": 21,
+                  "gain_min": 20,
+                  "p_max": 21,
+                  "nf0": 5,
+                  "allowed_for_design": true
+            },
+            {
+                  "type_variety": "std_booster",
+                  "type_def": "fixed_gain",
+                  "gain_flatmax": 21,
+                  "gain_min": 20,
+                  "p_max": 21,
+                  "nf0": 5,
+                  "allowed_for_design": false
+            }
+      ],
+      "Fiber": [{
+                  "type_variety": "SSMF",
+                  "dispersion": 1.67e-05,
+                  "effective_area": 83e-12,
+                  "pmd_coef": 1.265e-15
+            }
+      ],
+      "Span": [{
+                  "power_mode":true,
+                  "delta_power_range_db": [0,0,0.5],
+                  "max_fiber_lineic_loss_for_raman": 0.25,
+                  "target_extended_gain": 2.5,
+                  "max_length": 150,
+                  "length_units": "km",
+                  "max_loss": 28,
+                  "padding": 10,
+                  "EOL": 0,
+                  "con_in": 0,
+                  "con_out": 0
+            }
+      ],
+      "Roadm": [{
+                  "target_pch_out_db": -20,
+                  "add_drop_osnr": 38,
+                  "pmd": 0,
+                  "pdl": 0,
+                  "restrictions": {
+                                  "preamp_variety_list":[],
+                                  "booster_variety_list":[]
+                  }
+            }
+      ],
+      "SI": [{
+                  "f_min": 191.35e12,
+                  "f_max": 196.1e12,
+                  "baud_rate": 32e9,
+                  "spacing": 50e9,
+                  "power_dbm": 0,
+                  "power_range_db": [-6,0,0.5],
+                  "roll_off": 0.15,
+                  "tx_osnr": 100,
+                  "sys_margins": 0
+            }
+      ],
+      "Transceiver":[
+            {
+            "type_variety": "vendorA_trx-type1",
+            "frequency":{
+                  "min": 191.4e12,
+                  "max": 196.1e12
+            },
+            "mode":[
+                       {
+                             "format": "PS_SP64_1",
+                             "baud_rate": 32e9,
+                             "OSNR": 11,
+                             "bit_rate": 100e9,
+                             "roll_off": 0.15,
+                             "tx_osnr": 100,
+                             "min_spacing": 50e9,
+                             "cost": 1
+                       },
+                       {
+                             "format": "PS_SP64_2",
+                             "baud_rate": 64e9,
+                             "OSNR": 15,
+                             "bit_rate": 200e9,
+                             "roll_off": 0.15,
+                             "tx_osnr": 100,
+                             "min_spacing": 75e9,
+                             "cost": 1
+                       },
+                       {
+                             "format": "mode 1",
+                             "baud_rate": 32e9,
+                             "OSNR": 11,
+                             "bit_rate": 100e9,
+                             "roll_off": 0.15,
+                             "tx_osnr": 100,
+                             "min_spacing": 50e9,
+                             "cost": 1
+                       },
+                       {
+                             "format": "mode 2",
+                             "baud_rate": 64e9,
+                             "OSNR": 15,
+                             "bit_rate": 200e9,
+                             "roll_off": 0.15,
+                             "tx_osnr": 100,
+                             "min_spacing": 75e9,
+                             "cost": 1
+                       }
+                  ]
+            },
+            {
+            "type_variety": "Voyager_16QAM",
+            "frequency": {
+                  "min": 191.4e12,
+                  "max": 196.1e12
+            },
+            "mode": [
+                       {
+                             "format": "16QAM",
+                             "baud_rate": 32e9,
+                             "OSNR": 19,
+                             "bit_rate": 200e9,
+                             "roll_off": 0.15,
+                             "tx_osnr": 100,
+                             "min_spacing": 50e9,
+                             "cost": 1
+                       }
+                  ]
+            },
+            {
+            "type_variety": "Voyager",
+            "frequency": {
+                  "min": 191.4e12,
+                  "max": 196.1e12
+            },
+            "mode": [
+                       {
+                             "format": "mode 1",
+                             "baud_rate": 32e9,
+                             "OSNR": 12,
+                             "bit_rate": 100e9,
+                             "roll_off": 0.15,
+                             "tx_osnr": 45,
+                             "min_spacing": 50e9,
+                             "cost": 1
+                       },
+                       {
+                             "format": "mode 3",
+                             "baud_rate": 44e9,
+                             "OSNR": 18,
+                             "bit_rate": 300e9,
+                             "roll_off": 0.15,
+                             "tx_osnr": 45,
+                             "min_spacing": 62.5e9,
+                             "cost": 1
+                       },
+                       {
+                             "format": "mode 2",
+                             "baud_rate": 66e9,
+                             "OSNR": 21,
+                             "bit_rate": 400e9,
+                             "roll_off": 0.15,
+                             "tx_osnr": 45,
+                             "min_spacing": 75e9,
+                             "cost": 1
+                       },
+                       {
+                             "format": "mode 2 - fake",
+                             "baud_rate": 66e9,
+                             "OSNR": 21,
+                             "bit_rate": 400e9,
+                             "roll_off": 0.15,
+                             "tx_osnr": 45,
+                             "min_spacing": 75e9,
+                             "cost": 1
+                       },
+                       {
+                             "format": "mode 4",
+                             "baud_rate": 66e9,
+                             "OSNR": 16,
+                             "bit_rate": 200e9,
+                             "roll_off": 0.15,
+                             "tx_osnr": 45,
+                             "min_spacing": 75e9,
+                             "cost": 1
+                       }
+                  ]
+            }
+      ]
+
+}

tests/data/perdegreemeshTopologyExampleV2_auto_design_expected.json

@@ -63,6 +63,7 @@
     {
       "uid": "roadm Lannion_CAS",
       "type": "Roadm",
+      "type_variety": "example_detailed_impairments",
       "params": {
         "target_pch_out_db": -18.6,
         "restrictions": {
@@ -73,7 +74,14 @@
           "east edfa in Lannion_CAS to Corlay": -18.6,
           "east edfa in Lannion_CAS to Morlaix": -23.0,
           "east edfa in Lannion_CAS to Stbrieuc": -20
-        }
+        },
+        "per_degree_impairments": [
+          {
+            "from_degree": "west edfa in Lannion_CAS to Morlaix",
+            "to_degree": "east edfa in Lannion_CAS to Stbrieuc",
+            "impairment_id": 0
+          }
+        ]
       },
       "metadata": {
         "location": {
@@ -87,6 +95,7 @@
     {
       "uid": "roadm Lorient_KMA",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -111,6 +120,7 @@
     {
       "uid": "roadm Vannes_KBE",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -134,6 +144,7 @@
     {
       "uid": "roadm Rennes_STA",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -157,6 +168,7 @@
     {
       "uid": "roadm Brest_KLA",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1668,4 +1680,4 @@
       "to_node": "fiber (Ploermel → Vannes_KBE)-"
     }
   ]
-}
+}

tests/data/perdegreemeshTopologyExampleV2_expected.json

@@ -62,7 +62,12 @@
     },
     {
       "uid": "roadm Lannion_CAS",
+      "type_variety": "example_detailed_impairments",
       "params": {
+        "per_degree_impairments": [{
+              "from_degree": "west edfa in Lannion_CAS to Morlaix",
+              "impairment_id": 0,
+              "to_degree": "east edfa in Lannion_CAS to Stbrieuc"}],
         "per_degree_pch_out_db": {
           "east edfa in Lannion_CAS to Corlay": -18.6,
           "east edfa in Lannion_CAS to Morlaix": -23.0
@@ -661,7 +666,7 @@
       "type": "Edfa",
       "operational": {
         "gain_target": null,
-        "tilt_target": 0
+        "tilt_target": null
       }
     },
     {
@@ -677,7 +682,7 @@
       "type": "Edfa",
       "operational": {
         "gain_target": null,
-        "tilt_target": 0
+        "tilt_target": null
       }
     },
     {
@@ -693,7 +698,7 @@
       "type": "Edfa",
       "operational": {
         "gain_target": null,
-        "tilt_target": 0
+        "tilt_target": null
       }
     },
     {
@@ -709,7 +714,7 @@
       "type": "Edfa",
       "operational": {
         "gain_target": null,
-        "tilt_target": 0
+        "tilt_target": null
       }
     },
     {
@@ -727,7 +732,7 @@
       "operational": {
         "gain_target": 20.0,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -761,7 +766,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -779,7 +784,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -797,7 +802,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -815,7 +820,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -833,7 +838,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -851,7 +856,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -869,7 +874,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -887,7 +892,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -905,7 +910,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -923,7 +928,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -941,7 +946,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -959,7 +964,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -978,7 +983,7 @@
       "operational": {
         "gain_target": 18.0,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -996,7 +1001,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1014,7 +1019,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1032,7 +1037,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     }

tests/data/sim_params.json

@@ -2,7 +2,7 @@
   "raman_params": {
     "flag": true,
     "result_spatial_resolution": 10e3,
-    "solver_spatial_resolution": 50
+    "solver_spatial_resolution": 10e3
   },
   "nli_params": {
     "method": "ggn_spectrally_separated",

tests/data/std_medium_gain_advanced_config.json

@@ -1,4 +1,7 @@
-{     "nf_fit_coeff": [
+{
+      "f_min": 191.275e12,
+      "f_max": 196.125e12,
+      "nf_fit_coeff": [
       0.000168241,
       0.0469961,
       0.0359549,

tests/data/testTopology_auto_design_expected.json

@@ -159,6 +159,7 @@
     {
       "uid": "roadm Lannion_CAS",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -183,6 +184,7 @@
     {
       "uid": "roadm Lorient_KMA",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -207,6 +209,7 @@
     {
       "uid": "roadm Vannes_KBE",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -230,6 +233,7 @@
     {
       "uid": "roadm Rennes_STA",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -240,7 +244,6 @@
           "east edfa in Rennes_STA to Stbrieuc": -20,
           "east edfa in Rennes_STA to Ploermel": -20
         }
-
       },
       "metadata": {
         "location": {
@@ -254,6 +257,7 @@
     {
       "uid": "roadm Brest_KLA",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -277,6 +281,7 @@
     {
       "uid": "roadm a",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -302,6 +307,7 @@
     {
       "uid": "roadm b",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -310,7 +316,7 @@
           ],
           "booster_variety_list": []
         },
-        "per_degree_pch_out_db":{
+        "per_degree_pch_out_db": {
           "east edfa in b to a": -20,
           "east edfa in b to f": -20
         }
@@ -327,13 +333,14 @@
     {
       "uid": "roadm c",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
           "preamp_variety_list": [],
           "booster_variety_list": []
         },
-        "per_degree_pch_out_db":{
+        "per_degree_pch_out_db": {
           "east edfa in c to a": -20,
           "east edfa in c to d": -20,
           "east edfa in c to f": -20
@@ -351,6 +358,7 @@
     {
       "uid": "roadm d",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -374,6 +382,7 @@
     {
       "uid": "roadm e",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -397,6 +406,7 @@
     {
       "uid": "roadm f",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -421,6 +431,7 @@
     {
       "uid": "roadm g",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -430,7 +441,7 @@
         "per_degree_pch_out_db": {
           "east edfa in g to e": -20,
           "east edfa in g to h": -20
-         }
+        }
       },
       "metadata": {
         "location": {
@@ -444,6 +455,7 @@
     {
       "uid": "roadm h",
       "type": "Roadm",
+      "type_variety": "default",
       "params": {
         "target_pch_out_db": -20,
         "restrictions": {
@@ -1593,7 +1605,7 @@
       "type": "Edfa",
       "type_variety": "std_medium_gain",
       "operational": {
-        "gain_target": 18.5,
+        "gain_target": 13.177288,
         "delta_p": null,
         "tilt_target": 0,
         "out_voa": 0
@@ -2235,7 +2247,7 @@
       "type": "Edfa",
       "type_variety": "std_low_gain",
       "operational": {
-        "gain_target": 6.5,
+        "gain_target": 11.822712,
         "delta_p": null,
         "tilt_target": 0,
         "out_voa": 0
@@ -2292,7 +2304,7 @@
       "type": "Edfa",
       "type_variety": "std_low_gain",
       "operational": {
-        "gain_target": 13.82,
+        "gain_target": 13.822712,
         "delta_p": null,
         "tilt_target": 0,
         "out_voa": 0
@@ -2311,7 +2323,7 @@
       "type": "Edfa",
       "type_variety": "test_fixed_gain",
       "operational": {
-        "gain_target": 15.18,
+        "gain_target": 15.177288,
         "delta_p": null,
         "tilt_target": 0,
         "out_voa": 0

tests/data/testTopology_expected.json

@@ -1171,7 +1171,7 @@
       "operational": {
         "gain_target": 19.0,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1190,7 +1190,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1209,7 +1209,7 @@
       "operational": {
         "gain_target": 18.0,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": 0.5
       }
     },
@@ -1227,7 +1227,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1245,7 +1245,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1263,7 +1263,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1281,7 +1281,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1299,7 +1299,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1318,7 +1318,7 @@
       "operational": {
         "gain_target": 18.5,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1336,7 +1336,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1354,7 +1354,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1372,7 +1372,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1391,7 +1391,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1409,7 +1409,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1428,7 +1428,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1446,7 +1446,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1464,7 +1464,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1482,7 +1482,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1500,7 +1500,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1518,7 +1518,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1551,7 +1551,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1569,7 +1569,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1587,7 +1587,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1605,7 +1605,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1623,7 +1623,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1641,7 +1641,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1659,7 +1659,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1677,7 +1677,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1695,7 +1695,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1713,7 +1713,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1731,7 +1731,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1749,7 +1749,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1768,7 +1768,7 @@
       "operational": {
         "gain_target": 18.0,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1786,7 +1786,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1805,7 +1805,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1823,7 +1823,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1841,7 +1841,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1859,7 +1859,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1877,7 +1877,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1895,7 +1895,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1913,7 +1913,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1931,7 +1931,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1949,7 +1949,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1967,7 +1967,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -1985,7 +1985,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -2003,7 +2003,7 @@
       "operational": {
         "gain_target": 19.0,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -2021,7 +2021,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -2039,7 +2039,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -2057,7 +2057,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -2075,7 +2075,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -2093,7 +2093,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -2111,7 +2111,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -2129,7 +2129,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -2147,7 +2147,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -2165,7 +2165,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -2183,7 +2183,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -2201,7 +2201,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -2219,7 +2219,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -2237,7 +2237,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -2255,7 +2255,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -2273,7 +2273,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -2291,7 +2291,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -2309,7 +2309,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -2327,7 +2327,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     },
@@ -2345,7 +2345,7 @@
       "operational": {
         "gain_target": null,
         "delta_p": null,
-        "tilt_target": 0,
+        "tilt_target": null,
         "out_voa": null
       }
     }

tests/data/testTopology_response_expected.csv

@@ -1,7 +1,7 @@
 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
-0,trx Lorient_KMA,trx Vannes_KBE,100.0,True,1,1,Voyager,mode 1,30.84,30.84,26.75,32.0,0.0,trx Lorient_KMA | 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,"-284, 4",,,
-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",,,
+0,trx Lorient_KMA,trx Vannes_KBE,100.0,True,1,1,Voyager,mode 1,30.84,30.84,26.75,32.0,0.0,trx Lorient_KMA | 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,"[-284], [4]",,,
+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 2,22.27,22.14,15.05,64.0,0.0,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 2,30.79,28.76,21.67,64.0,3.0,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,,,,,,,,,,,,,,

tests/data/test_fiber_fix_expected_results.csv

@@ -1,97 +1,97 @@
 signal,nli
-1.9952623149688793e-05,1.1158426495504604e-08
-1.9952623149688793e-05,1.263949624403159e-08
-1.9952623149688793e-05,1.3358478621325285e-08
-1.9952623149688793e-05,1.3830775406251184e-08
-1.9952623149688793e-05,1.4180462471172083e-08
-1.9952623149688793e-05,1.4456701012984246e-08
-1.9952623149688793e-05,1.4683973899785875e-08
-1.9952623149688793e-05,1.487624147046227e-08
-1.9952623149688793e-05,1.5042217041806274e-08
-1.9952623149688793e-05,1.5187703614492153e-08
-1.9952623149688793e-05,1.5316759790785317e-08
-1.9952623149688793e-05,1.543233485150211e-08
-1.9952623149688793e-05,1.553663885878994e-08
-1.9952623149688793e-05,1.5631370249579246e-08
-1.9952623149688793e-05,1.5717862065800704e-08
-1.9952623149688793e-05,1.57971793985894e-08
-1.9952623149688793e-05,1.5870186356579704e-08
-1.9952623149688793e-05,1.593759332223716e-08
-1.9952623149688793e-05,1.5999991070923486e-08
-1.9952623149688793e-05,1.6057875903450682e-08
-1.9952623149688793e-05,1.6111668489205982e-08
-1.9952623149688793e-05,1.6161728217386366e-08
-1.9952623149688793e-05,1.6208364281630228e-08
-1.9952623149688793e-05,1.6251844350226973e-08
-1.9952623149688793e-05,1.629240142540359e-08
-1.9952623149688793e-05,1.6330239326114482e-08
-1.9952623149688793e-05,1.6365537111728e-08
-1.9952623149688793e-05,1.6398452681655655e-08
-1.9952623149688793e-05,1.642912572715412e-08
-1.9952623149688793e-05,1.6457680168940455e-08
-1.9952623149688793e-05,1.6484226183026747e-08
-1.9952623149688793e-05,1.6508861894003893e-08
-1.9952623149688793e-05,1.6531674797617433e-08
-1.9952623149688793e-05,1.655274296130114e-08
-1.9952623149688793e-05,1.657213604125123e-08
-1.9952623149688793e-05,1.6589916146838222e-08
-1.9952623149688793e-05,1.660613857708963e-08
-1.9952623149688793e-05,1.6620852449214096e-08
-1.9952623149688793e-05,1.6634101235366932e-08
-1.9952623149688793e-05,1.664592322084737e-08
-1.9952623149688793e-05,1.6656351894496074e-08
-1.9952623149688793e-05,1.666541628009631e-08
-1.9952623149688793e-05,1.6673141215973025e-08
-1.9952623149688793e-05,1.6679547588653583e-08
-1.9952623149688793e-05,1.6684652525341145e-08
-1.9952623149688793e-05,1.668846954900963e-08
-1.9952623149688793e-05,1.66910086991187e-08
-1.9952623149688793e-05,1.6692276620238304e-08
-1.9952623149688793e-05,1.6692276620238304e-08
-1.9952623149688793e-05,1.6691008699118703e-08
-1.9952623149688793e-05,1.6688469549009633e-08
-1.9952623149688793e-05,1.6684652525341148e-08
-1.9952623149688793e-05,1.6679547588653586e-08
-1.9952623149688793e-05,1.6673141215973028e-08
-1.9952623149688793e-05,1.666541628009631e-08
-1.9952623149688793e-05,1.6656351894496084e-08
-1.9952623149688793e-05,1.6645923220847374e-08
-1.9952623149688793e-05,1.6634101235366935e-08
-1.9952623149688793e-05,1.66208524492141e-08
-1.9952623149688793e-05,1.6606138577089633e-08
-1.9952623149688793e-05,1.6589916146838225e-08
-1.9952623149688793e-05,1.6572136041251237e-08
-1.9952623149688793e-05,1.6552742961301146e-08
-1.9952623149688793e-05,1.653167479761744e-08
-1.9952623149688793e-05,1.6508861894003893e-08
-1.9952623149688793e-05,1.648422618302675e-08
-1.9952623149688793e-05,1.645768016894046e-08
-1.9952623149688793e-05,1.6429125727154126e-08
-1.9952623149688793e-05,1.6398452681655658e-08
-1.9952623149688793e-05,1.6365537111728004e-08
-1.9952623149688793e-05,1.6330239326114482e-08
-1.9952623149688793e-05,1.6292401425403594e-08
-1.9952623149688793e-05,1.6251844350226973e-08
-1.9952623149688793e-05,1.6208364281630228e-08
-1.9952623149688793e-05,1.616172821738637e-08
-1.9952623149688793e-05,1.6111668489205982e-08
-1.9952623149688793e-05,1.605787590345069e-08
-1.9952623149688793e-05,1.5999991070923493e-08
-1.9952623149688793e-05,1.5937593322237167e-08
-1.9952623149688793e-05,1.5870186356579704e-08
-1.9952623149688793e-05,1.5797179398589402e-08
-1.9952623149688793e-05,1.571786206580071e-08
-1.9952623149688793e-05,1.5631370249579252e-08
-1.9952623149688793e-05,1.5536638858789946e-08
-1.9952623149688793e-05,1.5432334851502114e-08
-1.9952623149688793e-05,1.531675979078532e-08
-1.9952623149688793e-05,1.5187703614492156e-08
-1.9952623149688793e-05,1.5042217041806274e-08
-1.9952623149688793e-05,1.4876241470462273e-08
-1.9952623149688793e-05,1.4683973899785879e-08
-1.9952623149688793e-05,1.4456701012984246e-08
-1.9952623149688793e-05,1.4180462471172086e-08
-1.9952623149688793e-05,1.3830775406251184e-08
-1.9952623149688793e-05,1.3358478621325285e-08
-1.9952623149688793e-05,1.2639496244031593e-08
-1.9952623149688793e-05,1.1158426495504613e-08
+1.9952623149688796e-05,1.0570305869494063e-08
+1.9952623149688796e-05,1.1989102199581664e-08
+1.9952623149688796e-05,1.2687787891259665e-08
+1.9952623149688796e-05,1.3153676763101585e-08
+1.9952623149688796e-05,1.3504001312414315e-08
+1.9952623149688796e-05,1.378517965356758e-08
+1.9952623149688796e-05,1.4020312829929705e-08
+1.9952623149688796e-05,1.4222564206194578e-08
+1.9952623149688796e-05,1.440014394542033e-08
+1.9952623149688796e-05,1.4558516068269932e-08
+1.9952623149688796e-05,1.4701499315172012e-08
+1.9952623149688796e-05,1.4831866587815758e-08
+1.9952623149688796e-05,1.4951694168451522e-08
+1.9952623149688796e-05,1.506257639956634e-08
+1.9952623149688796e-05,1.5165763570833366e-08
+1.9952623149688796e-05,1.5262253772723937e-08
+1.9952623149688796e-05,1.535285600134073e-08
+1.9952623149688796e-05,1.543823467328411e-08
+1.9952623149688796e-05,1.551894175425445e-08
+1.9952623149688796e-05,1.5595440417063968e-08
+1.9952623149688796e-05,1.5668122772822936e-08
+1.9952623149688796e-05,1.5737323370281063e-08
+1.9952623149688796e-05,1.5803329618444796e-08
+1.9952623149688796e-05,1.5866389935670908e-08
+1.9952623149688796e-05,1.592672019391794e-08
+1.9952623149688796e-05,1.598450886742589e-08
+1.9952623149688796e-05,1.6039921184766554e-08
+1.9952623149688796e-05,1.609310250559421e-08
+1.9952623149688796e-05,1.61441810880001e-08
+1.9952623149688796e-05,1.6193270372246937e-08
+1.9952623149688796e-05,1.6240470877236143e-08
+1.9952623149688796e-05,1.6285871784230113e-08
+1.9952623149688796e-05,1.6329552265978812e-08
+1.9952623149688796e-05,1.6371582606990462e-08
+1.9952623149688796e-05,1.641202515119326e-08
+1.9952623149688796e-05,1.6450935105904177e-08
+1.9952623149688796e-05,1.6488361225310858e-08
+1.9952623149688796e-05,1.6524346392188574e-08
+1.9952623149688796e-05,1.6558928113022246e-08
+1.9952623149688796e-05,1.6592138938867027e-08
+1.9952623149688796e-05,1.6624006821997905e-08
+1.9952623149688796e-05,1.665455541654349e-08
+1.9952623149688796e-05,1.668380432977811e-08
+1.9952623149688796e-05,1.6711769329485368e-08
+1.9952623149688796e-05,1.6738462511750264e-08
+1.9952623149688796e-05,1.6763892432637406e-08
+1.9952623149688796e-05,1.6788064206436675e-08
+1.9952623149688796e-05,1.681097957247311e-08
+1.9952623149688796e-05,1.6832636931862217e-08
+1.9952623149688796e-05,1.6853031355021186e-08
+1.9952623149688796e-05,1.687215456020574e-08
+1.9952623149688796e-05,1.688999486281053e-08
+1.9952623149688796e-05,1.690653709463382e-08
+1.9952623149688796e-05,1.6921762491746848e-08
+1.9952623149688796e-05,1.6935648549006222e-08
+1.9952623149688796e-05,1.6948168838584662e-08
+1.9952623149688796e-05,1.695929278914847e-08
+1.9952623149688796e-05,1.696898542145055e-08
+1.9952623149688796e-05,1.6977207035104874e-08
+1.9952623149688796e-05,1.6983912840119302e-08
+1.9952623149688796e-05,1.6989052525338295e-08
+1.9952623149688796e-05,1.699256975421989e-08
+1.9952623149688796e-05,1.6994401576260005e-08
+1.9952623149688796e-05,1.6994477739772384e-08
+1.9952623149688796e-05,1.699271988849318e-08
+1.9952623149688796e-05,1.6989040620420942e-08
+1.9952623149688796e-05,1.6983342382173362e-08
+1.9952623149688796e-05,1.6975516165613388e-08
+1.9952623149688796e-05,1.6965439965112504e-08
+1.9952623149688796e-05,1.6952976942961452e-08
+1.9952623149688796e-05,1.693797323625088e-08
+1.9952623149688796e-05,1.6920255319826076e-08
+1.9952623149688796e-05,1.6899626814970685e-08
+1.9952623149688796e-05,1.6875864599859146e-08
+1.9952623149688796e-05,1.6848714031984708e-08
+1.9952623149688796e-05,1.6817883029489423e-08
+1.9952623149688796e-05,1.6783034669737056e-08
+1.9952623149688796e-05,1.674377783759437e-08
+1.9952623149688796e-05,1.669965527407164e-08
+1.9952623149688796e-05,1.6650128108596616e-08
+1.9952623149688796e-05,1.6594555557112625e-08
+1.9952623149688796e-05,1.6532167853144137e-08
+1.9952623149688796e-05,1.6462029512327026e-08
+1.9952623149688796e-05,1.6382988469074245e-08
+1.9952623149688796e-05,1.6293604020234886e-08
+1.9952623149688796e-05,1.619204201130206e-08
+1.9952623149688796e-05,1.607591759753518e-08
+1.9952623149688796e-05,1.5942050594874486e-08
+1.9952623149688796e-05,1.578606776895102e-08
+1.9952623149688796e-05,1.5601720601345105e-08
+1.9952623149688796e-05,1.5379633197361104e-08
+1.9952623149688796e-05,1.5104793671989548e-08
+1.9952623149688796e-05,1.4750892345803154e-08
+1.9952623149688796e-05,1.4265134295425351e-08
+1.9952623149688796e-05,1.3514359541675816e-08
+1.9952623149688796e-05,1.194579997186553e-08

tests/data/test_fiber_flex_expected_results.csv

@@ -1,6 +1,6 @@
 signal,nli
-1.9952623149688793e-05,5.522326183599433e-09
-1.7957360834719913e-05,4.5606601423111315e-09
-2.593841009459543e-05,6.633717697038881e-09
-1.5962098519751036e-05,4.3237017878447286e-09
-2.3943147779626553e-05,8.311382502260195e-09
+1.9952623149688796e-05,5.183134799604202e-09
+1.7957360834719913e-05,4.286200408629989e-09
+2.593841009459543e-05,6.2510001955285065e-09
+1.5962098519751036e-05,4.082332034495425e-09
+2.3943147779626556e-05,7.857762167195498e-09

tests/data/test_fiber_flex_expected_with_raman_results.csv

@@ -0,0 +1,6 @@
+,signal,nli
+0,3.9807550201531427e-07,1.0345007661598643e-10
+1,3.582672964406964e-07,8.554802579711129e-11
+2,5.174953110822109e-07,1.2476289414924814e-10
+3,3.1845777742391393e-07,8.147850636202276e-11
+4,4.776857897651456e-07,1.5683132697931042e-10

tests/data/test_long_network.json

@@ -0,0 +1,809 @@
+{
+    "network_name": "Example Network - long path",
+    "elements": [{
+            "uid": "Site_A",
+            "type": "Transceiver",
+            "metadata": {
+                "location": {
+                    "city": "Site A",
+                    "region": "",
+                    "latitude": 0,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+          "uid": "roadm Site A",
+          "metadata": {
+            "location": {
+              "city": "Site A",
+              "region": "RLD",
+              "latitude": 0.0,
+              "longitude": 0.0
+            }
+          },
+          "type": "Roadm"
+        },
+        {
+            "uid": "booster A",
+            "type": "Edfa",
+            "type_variety": "std_medium_gain",
+            "operational": {
+                "gain_target": 16,
+                "tilt_target": 0
+            },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 0,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Span1",
+            "type": "Fiber",
+            "type_variety": "SSMF",
+            "params": {
+                "length": 80,
+                "loss_coef": 0.2,
+                "length_units": "km"
+                },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 1,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Edfa1",
+            "type": "Edfa",
+            "type_variety": "test",
+            "operational": {
+                "gain_target": 16,
+                "tilt_target": 0
+            },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 2,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Span2",
+            "type": "Fiber",
+            "type_variety": "SSMF",
+            "params": {
+                "length": 80,
+                "loss_coef": 0.2,
+                "length_units": "km"
+                },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 1,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Edfa2",
+            "type": "Edfa",
+            "type_variety": "test_fixed_gain",
+            "operational": {
+                "gain_target": 16,
+                "tilt_target": 0
+            },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 2,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Span3",
+            "type": "Fiber",
+            "type_variety": "SSMF",
+            "params": {
+                "length": 80,
+                "loss_coef": 0.2,
+                "length_units": "km"
+                },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 1,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Edfa3",
+            "type": "Edfa",
+            "type_variety": "test",
+            "operational": {
+                "gain_target": 16,
+                "tilt_target": 0
+            },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 2,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Span4",
+            "type": "Fiber",
+            "type_variety": "SSMF",
+            "params": {
+                "length": 80,
+                "loss_coef": 0.2,
+                "length_units": "km"
+                },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 1,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Edfa4",
+            "type": "Edfa",
+            "type_variety": "test_fixed_gain",
+            "operational": {
+                "gain_target": 16,
+                "tilt_target": 0
+            },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 2,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Span5",
+            "type": "Fiber",
+            "type_variety": "SSMF",
+            "params": {
+                "length": 80,
+                "loss_coef": 0.2,
+                "length_units": "km"
+                },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 1,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Edfa5",
+            "type": "Edfa",
+            "type_variety": "test",
+            "operational": {
+                "gain_target": 16,
+                "tilt_target": 0
+            },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 2,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+          "uid": "roadm Site C",
+          "metadata": {
+            "location": {
+              "city": "Site A",
+              "region": "RLD",
+              "latitude": 0.0,
+              "longitude": 0.0
+            }
+          },
+          "type": "Roadm"
+        },
+        {
+            "uid": "booster C",
+            "type": "Edfa",
+            "type_variety": "std_medium_gain",
+            "operational": {
+                "gain_target": 16,
+                "tilt_target": 0
+            },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 0,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Span6",
+            "type": "Fiber",
+            "type_variety": "SSMF",
+            "params": {
+                "length": 80,
+                "loss_coef": 0.2,
+                "length_units": "km"
+                },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 1,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Edfa6",
+            "type": "Edfa",
+            "type_variety": "test_fixed_gain",
+            "operational": {
+                "gain_target": 16,
+                "tilt_target": 0
+            },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 2,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Span7",
+            "type": "Fiber",
+            "type_variety": "SSMF",
+            "params": {
+                "length": 80,
+                "loss_coef": 0.2,
+                "length_units": "km"
+                },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 1,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Edfa7",
+            "type": "Edfa",
+            "type_variety": "test",
+            "operational": {
+                "gain_target": 16,
+                "tilt_target": 0
+            },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 2,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Span8",
+            "type": "Fiber",
+            "type_variety": "SSMF",
+            "params": {
+                "length": 80,
+                "loss_coef": 0.2,
+                "length_units": "km"
+                },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 1,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Edfa8",
+            "type": "Edfa",
+            "type_variety": "test_fixed_gain",
+            "operational": {
+                "gain_target": 16,
+                "tilt_target": 0
+            },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 2,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Span9",
+            "type": "Fiber",
+            "type_variety": "SSMF",
+            "params": {
+                "length": 80,
+                "loss_coef": 0.2,
+                "length_units": "km"
+                },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 1,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Edfa9",
+            "type": "Edfa",
+            "type_variety": "test",
+            "operational": {
+                "gain_target": 16,
+                "tilt_target": 0
+            },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 2,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Span10",
+            "type": "Fiber",
+            "type_variety": "SSMF",
+            "params": {
+                "length": 80,
+                "loss_coef": 0.2,
+                "length_units": "km"
+                },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 1,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Edfa10",
+            "type": "Edfa",
+            "type_variety": "test_fixed_gain",
+            "operational": {
+                "gain_target": 16,
+                "tilt_target": 0
+            },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 2,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+          "uid": "roadm Site D",
+          "metadata": {
+            "location": {
+              "city": "Site A",
+              "region": "RLD",
+              "latitude": 0.0,
+              "longitude": 0.0
+            }
+          },
+          "type": "Roadm"
+        },
+        {
+            "uid": "booster D",
+            "type": "Edfa",
+            "type_variety": "std_medium_gain",
+            "operational": {
+                "gain_target": 16,
+                "tilt_target": 0
+            },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 0,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Span11",
+            "type": "Fiber",
+            "type_variety": "SSMF",
+            "params": {
+                "length": 80,
+                "loss_coef": 0.2,
+                "length_units": "km"
+                },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 1,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Edfa11",
+            "type": "Edfa",
+            "type_variety": "test",
+            "operational": {
+                "gain_target": 16,
+                "tilt_target": 0
+            },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 2,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Span12",
+            "type": "Fiber",
+            "type_variety": "SSMF",
+            "params": {
+                "length": 80,
+                "loss_coef": 0.2,
+                "length_units": "km"
+                },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 1,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Edfa12",
+            "type": "Edfa",
+            "type_variety": "test_fixed_gain",
+            "operational": {
+                "gain_target": 16,
+                "tilt_target": 0
+            },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 2,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+          "uid": "roadm Site E",
+          "metadata": {
+            "location": {
+              "city": "Site A",
+              "region": "RLD",
+              "latitude": 0.0,
+              "longitude": 0.0
+            }
+          },
+          "type": "Roadm"
+        },
+        {
+            "uid": "booster E",
+            "type": "Edfa",
+            "type_variety": "std_medium_gain",
+            "operational": {
+                "gain_target": 16,
+                "tilt_target": 0
+            },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 0,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Span13",
+            "type": "Fiber",
+            "type_variety": "SSMF",
+            "params": {
+                "length": 80,
+                "loss_coef": 0.2,
+                "length_units": "km"
+                },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 1,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Edfa13",
+            "type": "Edfa",
+            "type_variety": "test",
+            "operational": {
+                "gain_target": 16,
+                "tilt_target": 0
+            },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 2,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Span14",
+            "type": "Fiber",
+            "type_variety": "SSMF",
+            "params": {
+                "length": 80,
+                "loss_coef": 0.2,
+                "length_units": "km"
+                },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 1,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Edfa14",
+            "type": "Edfa",
+            "type_variety": "test_fixed_gain",
+            "operational": {
+                "gain_target": 16,
+                "tilt_target": 0
+            },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 2,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Span15",
+            "type": "Fiber",
+            "type_variety": "SSMF",
+            "params": {
+                "length": 80,
+                "loss_coef": 0.2,
+                "length_units": "km"
+                },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 1,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Edfa15",
+            "type": "Edfa",
+            "type_variety": "test",
+            "operational": {
+                "gain_target": 16,
+                "tilt_target": 0
+            },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 2,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+          "uid": "roadm Site B",
+          "metadata": {
+            "location": {
+              "city": "Site B",
+              "region": "RLD",
+              "latitude": 0.0,
+              "longitude": 0.0
+            }
+          },
+          "type": "Roadm"
+        },
+        {
+            "uid": "Site_B",
+            "type": "Transceiver",
+            "metadata": {
+                "location": {
+                    "city": "Site B",
+                    "region": "",
+                    "latitude": 3,
+                    "longitude": 0
+                }
+            }
+        }
+
+    ],
+    "connections": [{
+            "from_node": "Site_A",
+            "to_node": "roadm Site A"
+        },
+        {
+            "from_node": "roadm Site A",
+            "to_node": "booster A"
+        },
+        {
+            "from_node": "booster A",
+            "to_node": "Span1"
+        },
+        {
+            "from_node": "Span1",
+            "to_node": "Edfa1"
+        },
+        {
+            "from_node": "Edfa1",
+            "to_node": "Span2"
+        },
+        {
+            "from_node": "Span2",
+            "to_node": "Edfa2"
+        },       
+        {
+            "from_node": "Edfa2",
+            "to_node": "Span3"
+        },
+        {
+            "from_node": "Span3",
+            "to_node": "Edfa3"
+        },       
+        {
+            "from_node": "Edfa3",
+            "to_node": "Span4"
+        },
+        {
+            "from_node": "Span4",
+            "to_node": "Edfa4"
+        },       
+        {
+            "from_node": "Edfa4",
+            "to_node": "Span5"
+        },
+        {
+            "from_node": "Span5",
+            "to_node": "Edfa5"
+        },       
+        {
+            "from_node": "Edfa5",
+            "to_node": "roadm Site C"
+        },
+        {
+            "from_node": "roadm Site C",
+            "to_node": "booster C"
+        },
+        {
+            "from_node": "booster C",
+            "to_node": "Span6"
+        },
+        {
+            "from_node": "Span6",
+            "to_node": "Edfa6"
+        },
+        {
+            "from_node": "Edfa6",
+            "to_node": "Span7"
+        },
+        {
+            "from_node": "Span7",
+            "to_node": "Edfa7"
+        },       
+        {
+            "from_node": "Edfa7",
+            "to_node": "Span8"
+        },
+        {
+            "from_node": "Span8",
+            "to_node": "Edfa8"
+        },       
+        {
+            "from_node": "Edfa8",
+            "to_node": "Span9"
+        },
+        {
+            "from_node": "Span9",
+            "to_node": "Edfa9"
+        },       
+        {
+            "from_node": "Edfa9",
+            "to_node": "Span10"
+        },
+        {
+            "from_node": "Span10",
+            "to_node": "Edfa10"
+        },       
+        {
+            "from_node": "Edfa10",
+            "to_node": "roadm Site D"
+        },
+        {
+            "from_node": "roadm Site D",
+            "to_node": "booster D"
+        },
+        {
+            "from_node": "booster D",
+            "to_node": "Span11"
+        },
+        {
+            "from_node": "Span11",
+            "to_node": "Edfa11"
+        },
+        {
+            "from_node": "Edfa11",
+            "to_node": "Span12"
+        },
+        {
+            "from_node": "Span12",
+            "to_node": "Edfa12"
+        },       
+        {
+            "from_node": "Edfa12",
+            "to_node": "roadm Site E"
+        },
+        {
+            "from_node": "roadm Site E",
+            "to_node": "booster E"
+        },
+        {
+            "from_node": "booster E",
+            "to_node": "Span13"
+        },
+        {
+            "from_node": "Span13",
+            "to_node": "Edfa13"
+        },       
+        {
+            "from_node": "Edfa13",
+            "to_node": "Span14"
+        },
+        {
+            "from_node": "Span14",
+            "to_node": "Edfa14"
+        },       
+        {
+            "from_node": "Edfa14",
+            "to_node": "Span15"
+        },
+        {
+            "from_node": "Span15",
+            "to_node": "Edfa15"
+        },       
+        {
+            "from_node": "Edfa15",
+            "to_node": "roadm Site B"
+        },
+        {
+            "from_node": "roadm Site B",
+            "to_node": "Site_B"
+        }
+    ]
+}

tests/data/test_lumped_losses_fiber_no_pumps.csv

@@ -1,96 +1,97 @@
-1.000000000000000021e-03,5.915557166626927424e-04,3.840877221245049653e-04,2.466727384040633977e-04,1.573474629975438242e-04,9.994300566924636483e-05,6.331217828438720550e-05,4.004003600460594289e-05,2.529553013238426405e-05
-1.000000000000000021e-03,5.910087140881509866e-04,3.835259923737136521e-04,2.462279833344210639e-04,1.570298910751132091e-04,9.972770370845923681e-05,6.317035443958771254e-05,3.994817502675088248e-05,2.523663218966317481e-05
-1.000000000000000021e-03,5.904624672772724710e-04,3.829653846278134621e-04,2.457842903404500092e-04,1.567131560243821098e-04,9.951300275831494635e-05,6.302894048828063141e-05,3.985658526347281646e-05,2.517791045472703461e-05
-1.000000000000000021e-03,5.899169734839467818e-04,3.824058950962419133e-04,2.453416557234028707e-04,1.563972548287988369e-04,9.929890059644241613e-05,6.288793488850426760e-05,3.976526568300959219e-05,2.511936425240675192e-05
-1.000000000000000021e-03,5.893722299298780965e-04,3.818475199355363483e-04,2.449000757332959262e-04,1.560821844312877747e-04,9.908539497140619857e-05,6.274733607851797545e-05,3.967421524053715842e-05,2.506099289905929403e-05
-1.000000000000000021e-03,5.888282338048204554e-04,3.812902552499899305e-04,2.444595465697163919e-04,1.557679417349789946e-04,9.887248360327203632e-05,6.260714247720703990e-05,3.958343287844485109e-05,2.500279570274922987e-05
-1.000000000000000021e-03,5.882849822668304897e-04,3.807340970923119234e-04,2.440200643826254091e-04,1.554545236039297650e-04,9.866016418416755746e-05,6.246735248448248958e-05,3.949291752660743311e-05,2.494477196342878550e-05
-1.000000000000000021e-03,5.877403241312353140e-04,3.801768826891299322e-04,2.435799392795756472e-04,1.551407333476893054e-04,9.844762961169771281e-05,6.232743618144160702e-05,3.940232665622856909e-05,2.488670234861411131e-05
-1.000000000000000021e-03,5.871942634186000593e-04,3.796186190022559433e-04,2.431391779090945110e-04,1.548265761463755335e-04,9.823488356157069126e-05,6.218739605181586088e-05,3.931166190013138108e-05,2.482858791458364441e-05
-1.000000000000000021e-03,5.866468041173980141e-04,3.790593129257516128e-04,2.426977868451053722e-04,1.545120571165502460e-04,9.802192966180839372e-05,6.204723454596646772e-05,3.922092486871354988e-05,2.477042970290643410e-05
-1.000000000000000021e-03,5.860979501842715923e-04,3.784989712866057847e-04,2.422557725877665796e-04,1.541971813119806349e-04,9.780877149334107738e-05,6.190695408131255699e-05,3.913011715023943305e-05,2.471222874063606835e-05
-1.000000000000000021e-03,5.855428054911818777e-04,3.779325362214322600e-04,2.418091152720060536e-04,1.538790714407033914e-04,9.759345536126727376e-05,6.176526646718061210e-05,3.903840385562048950e-05,2.465344951928800295e-05
-1.000000000000000021e-03,5.849813875456967485e-04,3.773600382625630725e-04,2.413578451141304101e-04,1.535577517356502032e-04,9.737599881379701964e-05,6.162218372343696363e-05,3.894579295722817337e-05,2.459409722576532997e-05
-1.000000000000000021e-03,5.844137140093277807e-04,3.767815081401627831e-04,2.409019924922280132e-04,1.532332465435331354e-04,9.715641947446165209e-05,6.147771791853056712e-05,3.885229245840823961e-05,2.453417706661341544e-05
-1.000000000000000021e-03,5.838398026969793654e-04,3.761969767804803125e-04,2.404415879440078890e-04,1.529055803229076760e-04,9.693473504062415536e-05,6.133188116843621776e-05,3.875791039276530608e-05,2.447369426754818325e-05
-1.000000000000000021e-03,5.832596715764015120e-04,3.756064753041117783e-04,2.399766621646476654e-04,1.525747776422469490e-04,9.671096328199675835e-05,6.118468563560254894e-05,3.866265482344967213e-05,2.441265407298631344e-05
-1.000000000000000021e-03,5.826733387676383139e-04,3.750100350242446291e-04,2.395072460046293692e-04,1.522408631780071723e-04,9.648512203915507620e-05,6.103614352789890607e-05,3.856653384244465896e-05,2.435106174557538284e-05
-1.000000000000000021e-03,5.820810441807425326e-04,3.744079089136845458e-04,2.390335428197917333e-04,1.519039834472631588e-04,9.625731118926498641e-05,6.088632085792882825e-05,3.846959029618343478e-05,2.428894479270876788e-05
-1.000000000000000021e-03,5.814828054675655030e-04,3.738001274474016776e-04,2.385555825972570808e-04,1.515641624143126585e-04,9.602754804766522702e-05,6.073522946956701240e-05,3.837183203343020677e-05,2.422630831805138899e-05
-1.000000000000000021e-03,5.808786404289665358e-04,3.731867212872194738e-04,2.380733954737781241e-04,1.512214241469207320e-04,9.579584999731260704e-05,6.058288124990010364e-05,3.827326693032387752e-05,2.416315744255338838e-05
-1.000000000000000021e-03,5.802685670142343878e-04,3.725677212800349654e-04,2.375870117335689917e-04,1.508757928143868079e-04,9.556223448729982074e-05,6.042928812817623899e-05,3.817390288966712009e-05,2.409949730398197492e-05
-1.000000000000000021e-03,5.796526968519212725e-04,3.719432545624587992e-04,2.370965379259533010e-04,1.505273470495264228e-04,9.532675589286383365e-05,6.027448635812982831e-05,3.807376356963088358e-05,2.403534314190221437e-05
-1.000000000000000021e-03,5.790310479093865609e-04,3.713133518665244839e-04,2.366020041460938778e-04,1.501761108339581341e-04,9.508943151110322721e-05,6.011848775803337427e-05,3.797285679655886708e-05,2.397070004317041725e-05
-1.000000000000000021e-03,5.784036383121742876e-04,3.706780441238009577e-04,2.361034406501071276e-04,1.498221082613785163e-04,9.485027871279472060e-05,5.996130419345521918e-05,3.787119042685209972e-05,2.390557311366359151e-05
-1.000000000000000021e-03,5.777704863433433301e-04,3.700373624633735868e-04,2.356008778526011383e-04,1.494653635353745394e-04,9.460931494071504256e-05,5.980294757607067669e-05,3.776877234616485572e-05,2.383996747774973522e-05
-1.000000000000000021e-03,5.771316104427772061e-04,3.693913382097967039e-04,2.350943463241932639e-04,1.491059009672212558e-04,9.436655770795449559e-05,5.964342986246977532e-05,3.766561046859827219e-05,2.377388827775711285e-05
-1.000000000000000021e-03,5.764870292064976651e-04,3.687400028810397501e-04,2.345838767890191692e-04,1.487437449736715462e-04,9.412202459622686095e-05,5.948276305296108525e-05,3.756171273589169023e-05,2.370734067344167480e-05
-1.000000000000000021e-03,5.758375946234993610e-04,3.680842155919215072e-04,2.340701414876563216e-04,1.483793719498717782e-04,9.387603703510192111e-05,5.932115823520561543e-05,3.745721560837426419e-05,2.364041205155829344e-05
-1.000000000000000021e-03,5.751833223951592329e-04,3.674240032104748388e-04,2.335531666416808738e-04,1.480128027327277474e-04,9.362861002461524574e-05,5.915862564500113025e-05,3.735212585887714390e-05,2.357310681190604310e-05
-1.000000000000000021e-03,5.745242283439769499e-04,3.667593927483133397e-04,2.330329785818580578e-04,1.476440582313571256e-04,9.337975861033522300e-05,5.899517554647101613e-05,3.724645027783519551e-05,2.350542936525477225e-05
-1.000000000000000021e-03,5.738603284130473379e-04,3.660904113590355527e-04,2.325096037462197861e-04,1.472731594253864234e-04,9.312949788206356194e-05,5.883081823114703288e-05,3.714019567266727830e-05,2.343738413293735195e-05
-1.000000000000000021e-03,5.731917314000997948e-04,3.654171810067180655e-04,2.319831433530600859e-04,1.469001805571756218e-04,9.287787898176902846e-05,5.866558771451522606e-05,3.703338420706373603e-05,2.336898537810763873e-05
-1.000000000000000021e-03,5.725184531760584700e-04,3.647397285916569422e-04,2.314536235223777264e-04,1.465251423190001693e-04,9.262491677678542706e-05,5.849949412959568328e-05,3.692602258076439168e-05,2.330023745100291859e-05
-1.000000000000000021e-03,5.718405097346714646e-04,3.640580811583470273e-04,2.309210704829702432e-04,1.461480654746279682e-04,9.237062617930094123e-05,5.833254763720848617e-05,3.681811751074023914e-05,2.323114471257928647e-05
-1.000000000000000021e-03,5.711579171919053044e-04,3.633722658937428313e-04,2.303855105703487465e-04,1.457689708574830614e-04,9.211502214495855044e-05,5.816475842498649012e-05,3.670967573052717390e-05,2.316171153407365905e-05
-1.000000000000000021e-03,5.704706917853448337e-04,3.626823101255147543e-04,2.298469702246577707e-04,1.453878793688169360e-04,9.185811967146480901e-05,5.799613670639434487e-05,3.660070398956385041e-05,2.309194229656875218e-05
-1.000000000000000021e-03,5.697788498735841109e-04,3.619882413202942504e-04,2.293054759885765847e-04,1.450048119758659754e-04,9.159993379718755254e-05,5.782669271974067121e-05,3.649120905252569737e-05,2.302184139055488024e-05
-1.000000000000000021e-03,5.690839249361781711e-04,3.612915831986411477e-04,2.287622092282637863e-04,1.446206010588695675e-04,9.134102410651873409e-05,5.765679311500335815e-05,3.638142760462866287e-05,2.295156024783083523e-05
-1.000000000000000021e-03,5.683859273133331363e-04,3.605923537461036641e-04,2.282171874164802170e-04,1.442352604283837097e-04,9.108140050118301682e-05,5.748644463023418617e-05,3.627136409615889481e-05,2.288110175599186700e-05
-1.000000000000000021e-03,5.676848674169825831e-04,3.598905710243090802e-04,2.276704280772651710e-04,1.438488039249314222e-04,9.082107289971861372e-05,5.731565401294060850e-05,3.616102298282374168e-05,2.281046880581413228e-05
-1.000000000000000021e-03,5.669807557304629327e-04,3.591862531699841626e-04,2.271219487847658911e-04,1.434612454179789358e-04,9.056005123670258442e-05,5.714442801953895405e-05,3.605040872538299508e-05,2.273966429101268041e-05
-1.000000000000000021e-03,5.662756696284717943e-04,3.584815135619230891e-04,2.265734124911083147e-04,1.430737675597642188e-04,9.029913524493678829e-05,5.697329258726415482e-05,3.593986162522089478e-05,2.266890625682277028e-05
-1.000000000000000021e-03,5.655696121370722921e-04,3.577763576655677066e-04,2.260248242651153531e-04,1.426863741861267136e-04,9.003832758694041048e-05,5.680224948786493348e-05,3.582938283523433764e-05,2.259819544408493697e-05
-1.000000000000000021e-03,5.648625863181645518e-04,3.570707909872514684e-04,2.254761892066869196e-04,1.422990691538204475e-04,8.977763093872482745e-05,5.663130050166089515e-05,3.571897351373497692e-05,2.252753259705453904e-05
-1.000000000000000021e-03,5.641545952693012783e-04,3.563648190736888295e-04,2.249275124461922067e-04,1.419118563399853763e-04,8.951704798938974166e-05,5.646044741725799208e-05,3.560863482425828067e-05,2.245691846327665519e-05
-1.000000000000000021e-03,5.634456421235028989e-04,3.556584475114634488e-04,2.243787991438712611e-04,1.415247396416227112e-04,8.925658144072659398e-05,5.628969203126926030e-05,3.549836793537420297e-05,2.238635379346144160e-05
-1.000000000000000021e-03,5.627357300490819225e-04,3.549516819265270164e-04,2.238300544892383743e-04,1.411377229750731741e-04,8.899623400682166435e-05,5.611903614803531905e-05,3.538817402049950172e-05,2.231583934136070575e-05
-1.000000000000000021e-03,5.620265745740795878e-04,3.542462045793823072e-04,2.232825718355581747e-04,1.407517127660701999e-04,8.873661298955798349e-05,5.594887683102661642e-05,3.527830905094599801e-05,2.224553873809700179e-05
-1.000000000000000021e-03,5.613181716914067464e-04,3.535420099674467737e-04,2.227363458317740963e-04,1.403667046661989041e-04,8.847771519474716161e-05,5.577921186868756777e-05,3.516877154903826080e-05,2.217545101761858074e-05
-1.000000000000000021e-03,5.606105174362018646e-04,3.528390926567629869e-04,2.221913711948466166e-04,1.399826943822104249e-04,8.821953746856249804e-05,5.561003907731979445e-05,3.505956005567386572e-05,2.210557522599888898e-05
-1.000000000000000021e-03,5.599036078855734169e-04,3.521374472813410678e-04,2.216476427089692435e-04,1.395996776753266513e-04,8.796207669700402236e-05,5.544135630070159596e-05,3.495067313006486022e-05,2.203591042126611129e-05
-1.000000000000000021e-03,5.591992581819900257e-04,3.514388991464684675e-04,2.211065862108586844e-04,1.392186639199167455e-04,8.770601346645835294e-05,5.527361030479937100e-05,3.484239951455165907e-05,2.196664147854661471e-05
-1.000000000000000021e-03,5.584974577369657654e-04,3.507434317312190116e-04,2.205681855748304113e-04,1.388396401724235928e-04,8.745133837595373525e-05,5.510679463338290224e-05,3.473473491875524963e-05,2.189776560286087024e-05
-1.000000000000000021e-03,5.577981960279812567e-04,3.500510286412654524e-04,2.200324248112712805e-04,1.384625936044855835e-04,8.719804211084999142e-05,5.494090289064881491e-05,3.462767509292537455e-05,2.182928002589032757e-05
-1.000000000000000021e-03,5.571014625980537805e-04,3.493616736077873519e-04,2.194992880653100025e-04,1.380875115017372141e-04,8.694611544190507637e-05,5.477592874055209164e-05,3.452121582748434684e-05,2.176118200567564087e-05
-1.000000000000000021e-03,5.564072470552914543e-04,3.486753504863699662e-04,2.189687596154907018e-04,1.377143812626113696e-04,8.669554922434540153e-05,5.461186590614181566e-05,3.441535295257522900e-05,2.169346882631773742e-05
-1.000000000000000021e-03,5.557155390724777657e-04,3.479920432559456848e-04,2.184408238724854000e-04,1.373431903971786034e-04,8.644633439696230146e-05,5.444870816891464747e-05,3.431008233762035268e-05,2.162613779768559453e-05
-1.000000000000000021e-03,5.550282308874212147e-04,3.473135853761227104e-04,2.179168797741835634e-04,1.369749146289257855e-04,8.619912270820690987e-05,5.428688083545962664e-05,3.420567782931384528e-05,2.155936384410790980e-05
-1.000000000000000021e-03,5.543453044039096641e-04,3.466399489498413807e-04,2.173969005859271932e-04,1.366095327799803094e-04,8.595389890905566596e-05,5.412637349122350694e-05,3.410213253115887156e-05,2.149314248283782383e-05
-1.000000000000000021e-03,5.536667416543260278e-04,3.459711063207645686e-04,2.168808598269340415e-04,1.362470238846985658e-04,8.571064790824490891e-05,5.396717583150281386e-05,3.399943962027434491e-05,2.142746927933548718e-05
-1.000000000000000021e-03,5.529925247987736454e-04,3.453070300711248903e-04,2.163687312677279435e-04,1.358873671873677324e-04,8.546935477049620155e-05,5.380927766017944798e-05,3.389759234653519571e-05,2.136233984669994517e-05
-1.000000000000000021e-03,5.523242123557685823e-04,3.446492679875326503e-04,2.158617145497228296e-04,1.355314077800207832e-04,8.523058755788532964e-05,5.365305115434923267e-05,3.379683095199963850e-05,2.129790788796336886e-05
-1.000000000000000021e-03,5.516617811459008413e-04,3.439977838203789025e-04,2.153597748770509342e-04,1.351791181086447731e-04,8.499432644189092710e-05,5.349848277838686991e-05,3.369714647650299112e-05,2.123416758034315385e-05
-1.000000000000000021e-03,5.510052081904127453e-04,3.433525416882428740e-04,2.148628778379304833e-04,1.348304709382760146e-04,8.476055183025699440e-05,5.334555916079864168e-05,3.359853006971443923e-05,2.117111317291617240e-05
-1.000000000000000021e-03,5.503544707098636342e-04,3.427135060745518090e-04,2.143709894006651793e-04,1.344854393494213974e-04,8.452924436422564868e-05,5.319426709225049006e-05,3.350097298979682009e-05,2.110873898573355285e-05
-1.000000000000000021e-03,5.497095461228062706e-04,3.420806418242862412e-04,2.138840759097000895e-04,1.341439967345312348e-04,8.430038491581448882e-05,5.304459352362807473e-05,3.340446660208796169e-05,2.104703940894962140e-05
-1.000000000000000021e-03,5.490704120444732210e-04,3.414539141407250565e-04,2.134021040817290626e-04,1.338061167945246396e-04,8.407395458513211085e-05,5.289652556412503305e-05,3.330900237780223545e-05,2.098600890196449472e-05
-1.000000000000000021e-03,5.484371298422446297e-04,3.408333692467681113e-04,2.129251024270059081e-04,1.334718163299163942e-04,8.384996326436759272e-05,5.275006911363530105e-05,3.321458388816914869e-05,2.092564965374723430e-05
-1.000000000000000021e-03,5.478096771889333057e-04,3.402189725469309967e-04,2.124530379214265854e-04,1.331410692793207290e-04,8.362839223924152716e-05,5.260521141428185532e-05,3.312120269498457291e-05,2.086595618361951373e-05
-1.000000000000000021e-03,5.471880319478338028e-04,3.396106897916775086e-04,2.119858778995733334e-04,1.328138498781884043e-04,8.340922301478033581e-05,5.246193986031627375e-05,3.302885046175365648e-05,2.080692307740899109e-05
-1.000000000000000021e-03,5.465721721714859958e-04,3.390084870743713592e-04,2.115235900510876890e-04,1.324901326555763298e-04,8.319243731282516567e-05,5.232024199634728476e-05,3.293751895248773966e-05,2.074854498665493433e-05
-1.000000000000000021e-03,5.459629241404544706e-04,3.384131725428774743e-04,2.110667946787101129e-04,1.321703518944146796e-04,8.297832591380873519e-05,5.218030786215170435e-05,3.284733064718059383e-05,2.069090019482566404e-05
-1.000000000000000021e-03,5.453602635114507746e-04,3.378247083723790616e-04,2.106154557101209946e-04,1.318544791589579420e-04,8.276686841524559784e-05,5.204212355675735553e-05,3.275827635494999894e-05,2.063398273378693265e-05
-1.000000000000000021e-03,5.447641661727002649e-04,3.372430571518834691e-04,2.101695374983407439e-04,1.315424863636820575e-04,8.255804467261116894e-05,5.190567535780167048e-05,3.267034700418964161e-05,2.057778671334094953e-05
-1.000000000000000021e-03,5.441746082424759419e-04,3.366681818805845774e-04,2.097290048174083239e-04,1.312343457694375884e-04,8.235183479637930588e-05,5.177094971942625129e-05,3.258353364113958107e-05,2.052230632028256941e-05
-1.000000000000000021e-03,5.435915660676414983e-04,3.361000459642789163e-04,2.092938228581225045e-04,1.309300299796617602e-04,8.214821914910500468e-05,5.163793327020207291e-05,3.249782742847867687e-05,2.046753581747058517e-05
-1.000000000000000021e-03,5.430150162222109568e-04,3.355386132118161487e-04,2.088639572238308215e-04,1.306295119366350911e-04,8.194717834254394587e-05,5.150661281108238069e-05,3.241321964393507540e-05,2.041346954291065543e-05
-1.000000000000000021e-03,5.424450181544462771e-04,3.349839271155603902e-04,2.084394340599780252e-04,1.303328067075743425e-04,8.174872108664715700e-05,5.137699346328779477e-05,3.232971335519106785e-05,2.036010936323692981e-05
-1.000000000000000021e-03,5.418815485793469040e-04,3.344359518126675877e-04,2.080202193468776877e-04,1.300398875631312602e-04,8.155282824274543232e-05,5.124906220377890154e-05,3.224729995882103797e-05,2.030744969463951815e-05
-1.000000000000000021e-03,5.413245844265015877e-04,3.338946518213946552e-04,2.076062794549138172e-04,1.297507280942760690e-04,8.135948090770286575e-05,5.112280617242197348e-05,3.216597096011364272e-05,2.025548502431296445e-05
-1.000000000000000021e-03,5.407741028387587183e-04,3.333599920378552692e-04,2.071975811406991498e-04,1.294653022088876294e-04,8.116866041129419294e-05,5.099821267012491396e-05,3.208571797180734118e-05,2.020420990962221080e-05
-1.000000000000000021e-03,5.402303025747014556e-04,3.328321561526745862e-04,2.067942601549091974e-04,1.291837026153304184e-04,8.098042783792958120e-05,5.087532120941394917e-05,3.200656629292941073e-05,2.015364045325785942e-05
-1.000000000000000021e-03,5.396931605409548225e-04,3.323111087173311550e-04,2.063962829591101838e-04,1.289059030082810651e-04,8.079476437365657295e-05,5.075411899733799342e-05,3.192850747587867838e-05,2.010377117411100997e-05
-1.000000000000000021e-03,5.391626538584482192e-04,3.317968146613820609e-04,2.060036164003212873e-04,1.286318773981824648e-04,8.061165143636152362e-05,5.063459340114707095e-05,3.185153317990099749e-05,2.005459666083220688e-05
-1.000000000000000021e-03,5.386387598611086340e-04,3.312892392892789490e-04,2.056162277072543561e-04,1.283616001079104806e-04,8.043107067321031531e-05,5.051673194647494236e-05,3.177563516985718072e-05,2.000611157101859271e-05
-1.000000000000000021e-03,5.381214560945620949e-04,3.307883482772016118e-04,2.052340844865845515e-04,1.280950457694747437e-04,8.025300395811590449e-05,5.040052231554306949e-05,3.170080531500530551e-05,1.995831063041139523e-05
-1.000000000000000021e-03,5.376107203148404326e-04,3.302941076699326679e-04,2.048571547192701043e-04,1.278321893207613302e-04,8.007743338923888358e-05,5.028595234538581714e-05,3.162703558779804923e-05,1.991118863210237610e-05
-1.000000000000000021e-03,5.371022748594453389e-04,3.298024246812891291e-04,2.044823390252625600e-04,1.275708788891961756e-04,7.990292563076895462e-05,5.017208832604374468e-05,3.155372544493714152e-05,1.986436223028797206e-05
-1.000000000000000021e-03,5.365961117498912114e-04,3.293132869081722108e-04,2.041096254707852251e-04,1.273111050031557620e-04,7.972947385597059739e-05,5.005892559260894715e-05,3.148087179640124731e-05,1.981782941609088261e-05
-1.000000000000000021e-03,5.360922230056120808e-04,3.288266819633119922e-04,2.037390021489647173e-04,1.270528582181514683e-04,7.955707126027152540e-05,4.994645949643470906e-05,3.140847156340457124e-05,1.977158818812822943e-05
-1.000000000000000021e-03,5.355906006439992324e-04,3.283425974753378354e-04,2.033704571798752555e-04,1.267961291168465381e-04,7.938571106126265876e-05,4.983468540512894492e-05,3.133652167839010846e-05,1.972563655250571181e-05
-1.000000000000000021e-03,5.350891259362026411e-04,3.278589502113704612e-04,2.030023855884717748e-04,1.265397912200834861e-04,7.921463777328526478e-05,4.972310904903239575e-05,3.126470337114675132e-05,1.967977067875568528e-05
-1.000000000000000021e-03,5.345877966909636306e-04,3.273757374134148111e-04,2.026347848040474173e-04,1.262838424862170036e-04,7.904384991646645146e-05,4.961172941171763403e-05,3.119301596587686584e-05,1.963399012641590455e-05
-1.000000000000000021e-03,5.340866106918126799e-04,3.268929562877650259e-04,2.022676522245876859e-04,1.260282808504260116e-04,7.887334599501782170e-05,4.950054546622813158e-05,3.112145877995456692e-05,1.958829445064508602e-05
-1.000000000000000021e-03,5.335855656972701092e-04,3.264106040054970534e-04,2.019009852173301103e-04,1.257731042251948573e-04,7.870312449759831505e-05,4.938955617533215942e-05,3.105003112409614148e-05,1.954268320233478379e-05
-1.000000000000000021e-03,5.330846594410641130e-04,3.259286777029715004e-04,2.015347811193284109e-04,1.255183105007985753e-04,7.853318389767883849e-05,4.927876049177821896e-05,3.097873230253197569e-05,1.949715592822227211e-05
+0 km,7 km,10 km,20 km,30 km,40 km,50 km,60 km,70 km,80 km
+0.001,0.0007539177767305848,0.0005922648034750569,0.00038490617279658137,0.000247392320543982,0.00015791555306607623,0.0001003671566373715,6.361853747449743e-05,4.0256607010916584e-05,2.5446360651215025e-05
+0.001,0.0007533902419958185,0.0005917244917926066,0.0003843504806873478,0.0002469518485683003,0.0001576007478372822,0.00010015356028336005,6.347773727057954e-05,4.016534974386181e-05,2.5387814123899557e-05
+0.001,0.0007528628792251897,0.0005911844879166931,0.0003837954443648265,0.0002465120671477305,0.00015728651344926157,9.994038442151031e-05,6.333722806950528e-05,4.007428674216739e-05,2.5329394519652018e-05
+0.001,0.0007523356872805944,0.0005906447907925071,0.0003832410626787123,0.0002460729751307637,0.00015697284891103737,9.972762829117227e-05,6.319700932883725e-05,3.9983417636365086e-05,2.5271101593906498e-05
+0.001,0.0007518086649950493,0.0005901053993262767,0.0003826873344129807,0.000245634571300401,0.00015665975317873245,9.95152910932062e-05,6.305708047968383e-05,3.9892742039402306e-05,2.5212935090640287e-05
+0.001,0.0007512818111727442,0.0005895663123854124,0.00038213425828639575,0.0002451968543747938,0.0001563472251561599,9.930337199043154e-05,6.291744092702573e-05,3.980225954686196e-05,2.5154894742516358e-05
+0.001,0.0007507551245890757,0.0005890275287986403,0.0003815818329529933,0.00024475982300791935,0.00015603526369546326,9.909187010814736e-05,6.277809005009466e-05,3.9711969737226656e-05,2.5096980271060998e-05
+0.001,0.0007502264452226824,0.0005884868559085152,0.00038102785262825065,0.00024432175277214066,0.00015572264698517112,9.88799609470651e-05,6.26384862087587e-05,3.9621522312922004e-05,2.5038967246912867e-05
+0.001,0.000749695774901522,0.0005879442972710813,0.00038047232480109013,0.00024388265123925605,0.00015540938109456906,9.866764888521257e-05,6.249863239143498e-05,3.953091925128679e-05,2.498085695464162e-05
+0.001,0.0007491631154430714,0.0005873998564236413,0.00037991525691100856,0.00024344252592185564,0.00015509547204033507,9.845493826021018e-05,6.235853155787194e-05,3.944016251022327e-05,2.4922650665997884e-05
+0.001,0.0007486284686543365,0.0005868535368848297,0.00037935665634847824,0.00024300138427382333,0.00015478092578703268,9.824183336966692e-05,6.2218186639435e-05,3.9349254028404254e-05,2.4864349640052216e-05
+0.001,0.0007480870957031576,0.000586300474032492,0.00037879149123861057,0.0002425552230413961,0.00015446287448415177,9.80263854642033e-05,6.207631182945079e-05,3.925735993392708e-05,2.4805418700342044e-05
+0.001,0.0007475390029192987,0.0005857406797529387,0.0003782197872436036,0.00024210406905347345,0.00015414134016629157,9.780861070114894e-05,6.193291827596257e-05,3.9164487654400794e-05,2.4745862693628148e-05
+0.001,0.0007469841968019674,0.0005851741661454001,0.0003776415703174324,0.00024164794939077438,0.0001538163450528885,9.758852536415706e-05,6.178801721036571e-05,3.907064467141267e-05,2.4685686501265697e-05
+0.001,0.0007464226840200367,0.0005846009455219824,0.00037705686670463337,0.0002411868913840224,0.0001534879115464833,9.736614586183658e-05,6.164161994641891e-05,3.897583851984993e-05,2.462489503874857e-05
+0.001,0.0007458544714121565,0.0005840210304075477,0.00037646570293897594,0.00024072092261200308,0.00015315606223087128,9.714148872627883e-05,6.149373787917076e-05,3.888007678716513e-05,2.45634932552282e-05
+0.001,0.0007452795659868886,0.0005834344335395587,0.00037586810584214404,0.0002402500708997022,0.000152820819869329,9.691457061165131e-05,6.134438248396139e-05,3.878336711269497e-05,2.450148613305654e-05
+0.001,0.0007446982091229053,0.0005828414050804809,0.00037526433980072227,0.0002397745491251193,0.0001524823380310375,9.668549630724793e-05,6.119362307821724e-05,3.868575452092861e-05,2.4438902597094615e-05
+0.001,0.0007441104077793266,0.0005822419576041865,0.000374654431136991,0.00023929438452844706,0.0001521406389697796,9.645428208068799e-05,6.10414708650461e-05,3.858724646752558e-05,2.4375747508899864e-05
+0.001,0.0007435161690815189,0.0005816361038919765,0.0003740384064517259,0.00023880960458647735,0.00015179574511101,9.622094431596755e-05,6.0887937123913e-05,3.848785045741014e-05,2.4312025761525238e-05
+0.001,0.0007429155003212045,0.0005810238569324259,0.0003734162926228523,0.00023832023701067176,0.00015144767905002232,9.598549951201614e-05,6.073303320960556e-05,3.83875740440667e-05,2.42477422790523e-05
+0.001,0.0007423085305088799,0.0005804053544492479,0.00037278824495395505,0.0002378264113599139,0.00015109653618969462,9.574801357389848e-05,6.057680304696976e-05,3.828644589147927e-05,2.4182915529390252e-05
+0.001,0.0007416952671281527,0.0005797806095975203,0.0003721542903540935,0.000237328155265129,0.00015074233901078615,9.550850289594038e-05,6.041925791001213e-05,3.8184473495775654e-05,2.411755039775751e-05
+0.001,0.0007410757178351933,0.0005791496357472955,0.0003715144560199718,0.00023682549660121693,0.00015038511017095082,9.526698399235154e-05,6.026040915139781e-05,3.8081664403831414e-05,2.4051651801815565e-05
+0.001,0.0007404498904588522,0.0005785124464833875,0.0003708687694343879,0.00023631846348487102,0.00015002487250269267,9.502347349561174e-05,6.0100268201280124e-05,3.797802621247342e-05,2.3985224691142176e-05
+0.001,0.000739817793000736,0.0005778690556051271,0.0003702172583646133,0.00023580708427235972,0.00014966164901128063,9.477798815483971e-05,5.9938846566140114e-05,3.787356656768574e-05,2.3918274046705576e-05
+0.001,0.0007391794336352683,0.0005772194771261406,0.00036955995086080306,0.00023529138755728292,0.00014929546287266413,9.453054483414699e-05,5.977615582759686e-05,3.776829316380097e-05,2.385080488032797e-05
+0.001,0.0007385356837401969,0.0005765645972068202,0.00036889774196575574,0.0002347720745464529,0.00014892681149659162,9.42814794200372e-05,5.9612416728092556e-05,3.7662348796962334e-05,2.378290869241134e-05
+0.001,0.0007378865501365786,0.0005759044277808471,0.0003682306558404449,0.0002342491698918279,0.00014855571485941296,9.403080644324396e-05,5.944763924389705e-05,3.755574009362634e-05,2.371458979861065e-05
+0.001,0.0007372320397833855,0.0005752389809515306,0.00036755871686373843,0.00023372269842386576,0.0001481821930634036,9.377854051813779e-05,5.92818334053861e-05,3.74484737148236e-05,2.364585253654512e-05
+0.001,0.0007365721597776102,0.0005745682689916729,0.0003668819496311701,0.00023319268514975972,0.00014780626633511263,9.352469634142839e-05,5.911500929610015e-05,3.734055635551828e-05,2.35767012653732e-05
+0.001,0.0007359070382096695,0.0005738924278329212,0.00036620050520582223,0.0002326592549483615,0.00014742802610623578,9.326933684741753e-05,5.8947208765596136e-05,3.723201528626343e-05,2.3507153539330125e-05
+0.001,0.0007352366822744919,0.0005732114697907263,0.0003655144080205807,0.0002321224325628545,0.00014704749234532768,9.30124765259338e-05,5.877844174725213e-05,3.712285709891949e-05,2.3437213649054356e-05
+0.001,0.0007345610993055855,0.0005725254073502257,0.0003648236827235576,0.00023158224291094424,0.00014666468514311576,9.275412994749425e-05,5.860871822644595e-05,3.701308841848994e-05,2.336688590620212e-05
+0.001,0.0007338802967751745,0.0005718342531661053,0.0003641283541767086,0.00023103871108296522,0.00014627962471074844,9.249431176193324e-05,5.843804823954878e-05,3.690271590243443e-05,2.32961746429929e-05
+0.001,0.0007331942822941306,0.0005711380200622195,0.000363428447454292,0.00023049186233985435,0.00014589233137793237,9.223303669696582e-05,5.826644187292669e-05,3.679174623998899e-05,2.3225084211761806e-05
+0.001,0.0007325030636119942,0.000570436721031382,0.0003627239878414387,0.00022994172211121704,0.00014550282559116622,9.197031955679165e-05,5.809390926192732e-05,3.66801861514784e-05,2.3153618984504423e-05
+0.001,0.0007318082249846354,0.000569731957456593,0.0003620165686744435,0.0002293895269980944,0.0001451119793900387,9.170674702405544e-05,5.792083507086079e-05,3.656828410642266e-05,2.3081938026911105e-05
+0.001,0.0007311097709589727,0.0005690237376093709,0.0003613062068501535,0.00022883529410468975,0.00014471980653999792,9.14423290636205e-05,5.774722611940683e-05,3.6456044625219865e-05,2.3010044279099164e-05
+0.001,0.0007304077061664716,0.0005683120698629669,0.00036059291938656614,0.00022827904062761942,0.00014432632086784533,9.117707567918865e-05,5.757308925149413e-05,3.6343472243382366e-05,2.293794069062729e-05
+0.001,0.0007297020353231152,0.0005675969626922158,0.0003598767234219355,0.00022772078385475435,0.00014393153626068395,9.091099691249356e-05,5.739843133472259e-05,3.6230571511141116e-05,2.2865630220232028e-05
+0.001,0.0007289948155254126,0.0005668805157203741,0.00035915975887038315,0.00022716220982550104,0.00014353665302637602,9.064490513205597e-05,5.722378701487135e-05,3.611768859725593e-05,2.279333481077374e-05
+0.001,0.0007282860481242219,0.0005661627319510184,0.0003584420321019746,0.00022660332474955334,0.0001431416759965279,9.037880375071969e-05,5.7049158588914064e-05,3.6004825007582225e-05,2.2721055434706506e-05
+0.001,0.0007275757345080665,0.0005654436144326382,0.00035772354953912867,0.0002260441348764214,0.00014274661002939483,9.011269619839399e-05,5.687454836459578e-05,3.5891982254752056e-05,2.2648793068745655e-05
+0.001,0.0007268638761030398,0.0005647231662585116,0.0003570043176561602,0.0002254846464948739,0.0001423514600093734,8.984658592167178e-05,5.6699958660169715e-05,3.5779161857992474e-05,2.2576548693746487e-05
+0.001,0.0007261504743729061,0.0005640013905666712,0.00035628434297892044,0.0002249248659324104,0.00014195623084652692,8.958047638345721e-05,5.652539180412445e-05,3.5666365342942064e-05,2.250432329458613e-05
+0.001,0.0007254355308190145,0.0005632782905397587,0.00035556363208430486,0.00022436479955468554,0.0001415609274760624,8.931437106256734e-05,5.635085013490999e-05,3.5553594241465086e-05,2.2432117860037706e-05
+0.001,0.0007247208411865314,0.0005625556717890254,0.0003548439578507697,0.00022380581171795803,0.00014116650687104502,8.904891159204534e-05,5.61767534392646e-05,3.544111934056619e-05,2.2360105593797506e-05
+0.001,0.00072400640290786,0.000561833531609933,0.00035412531668185007,0.00022324789882467925,0.00014077296603347102,8.878409573173543e-05,5.600310014889176e-05,3.5328939584811035e-05,2.228828580259237e-05
+0.001,0.0007232922134419361,0.000561111867333509,0.00035340770503880037,0.00022269105733400152,0.00014038030201108317,8.8519921274859e-05,5.5829888718494674e-05,3.521705393409239e-05,2.221665780315407e-05
+0.001,0.0007225782702742098,0.0005603906763262448,0.0003526911194401051,0.00022213528376118449,0.00013998851189683077,8.825638604758897e-05,5.5657117625479614e-05,3.510546136342301e-05,2.214522092208301e-05
+0.001,0.0007218663790349207,0.0005596717927826706,0.0003519774072686018,0.00022158202285215626,0.0001395986194011808,8.79941808494171e-05,5.5485240659247415e-05,3.499445534348073e-05,2.207416317565271e-05
+0.001,0.0007211565340671307,0.000558955209053062,0.0003512665554090523,0.0002210312613239567,0.0001392106136609281,8.773329770591506e-05,5.531425230770577e-05,3.4884032196140665e-05,2.200348216114185e-05
+0.001,0.0007204487297429265,0.0005582409175317231,0.00035055855083818805,0.00022048298599609717,0.00013882448390129787,8.747372870968443e-05,5.514414710600884e-05,3.477418827518741e-05,2.1933175496831414e-05
+0.001,0.0007197429604633029,0.0005575289106567639,0.00034985338062403464,0.00021993718378969884,0.00013844021943515845,8.721546601972457e-05,5.497491963609735e-05,3.466491996599965e-05,2.1863240821795316e-05
+0.001,0.0007190392206580874,0.0005568191809099186,0.0003491510319252488,0.00021939384172660586,0.00013805780966217872,8.695850186076947e-05,5.480656452622117e-05,3.455622368522282e-05,2.1793675795681418e-05
+0.001,0.0007183375047857581,0.0005561117208162645,0.0003484514919904163,0.00021885294692850114,0.00013767724406801954,8.670282852265318e-05,5.463907645048003e-05,3.444809588045186e-05,2.172447809850241e-05
+0.001,0.0007176398171146462,0.0005554085359032386,0.00034775670623974134,0.0002183159850558065,0.00013729955964329822,8.644913914890404e-05,5.447290801376681e-05,3.4340828149382966e-05,2.165583410030806e-05
+0.001,0.0007169461476104001,0.0005547096122322624,0.0003470666509123452,0.00021778293254731739,0.00013692473741724035,8.61974199558567e-05,5.430804975101775e-05,3.423441420247442e-05,2.15877397033261e-05
+0.001,0.0007162564863033777,0.0005540149359610161,0.00034638130244054916,0.00021725376605180745,0.00013655275859741984,8.594765729366703e-05,5.4144492290891566e-05,3.412884781322755e-05,2.152019085117201e-05
+0.001,0.0007155708232884887,0.0005533244933430584,0.0003457006374485156,0.00021672846242622396,0.00013618360456808432,8.569983764497563e-05,5.3982226354801364e-05,3.40241228175218e-05,2.1453183528407172e-05
+0.001,0.0007148907167777357,0.0005526398589811358,0.0003450262214786548,0.00021620823613271921,0.00013581813150424676,8.545453691240582e-05,5.3821629494988794e-05,3.392048307301558e-05,2.1386873841072776e-05
+0.001,0.0007142161543980302,0.0005519610151552632,0.0003443580235484482,0.00021569305634208547,0.00013545631457844443,8.521173706397784e-05,5.366268933409295e-05,3.381792035746172e-05,2.1321256433336487e-05
+0.001,0.0007135471238866448,0.0005512879443055973,0.0003436960129838173,0.0002151828925541902,0.0001350981292399717,8.497142027417112e-05,5.350539363884576e-05,3.371642654533412e-05,2.1256326012778444e-05
+0.001,0.0007128836130908273,0.0005506206290316834,0.00034304015941680915,0.00021467771459500972,0.00013474355121214092,8.473356892178238e-05,5.3349730318534455e-05,3.361599360677474e-05,2.1192077349695405e-05
+0.001,0.0007122256099675188,0.0005499590520918091,0.0003423904327834519,0.00021417749261385993,0.00013439255648970008,8.449816558787534e-05,5.319568742351694e-05,3.35166136065783e-05,2.1128505276425763e-05
+0.001,0.0007115731025829938,0.0005493031964022937,0.0003417468033215544,0.0002136821970805245,0.000134045121336186,8.426519305371512e-05,5.304325314373989e-05,3.341827870317759e-05,2.106560468667675e-05
+0.001,0.0007109261960066054,0.0005486531617838624,0.0003411093543337103,0.00021319188469384628,0.00013370128216574169,8.403467429429362e-05,5.289244191063335e-05,3.3320997960119286e-05,2.1003381278303992e-05
+0.001,0.0007102848782749661,0.0005480089311096272,0.00034047805604549785,0.00021270652596543237,0.00013336101530442401,8.380659214968773e-05,5.274324196034732e-05,3.3224763568979684e-05,2.0941829967612013e-05
+0.001,0.0007096491375319776,0.0005473704874075498,0.00033985287897724074,0.00021222609171923644,0.00013302429733989257,8.358092965463258e-05,5.25956416646764e-05,3.312956781229619e-05,2.088094573050621e-05
+0.001,0.0007090189620285115,0.0005467378138598193,0.00033923379394201156,0.00021175055308892372,0.0001326911051189623,8.335767003660405e-05,5.244962952967474e-05,3.3035403062615115e-05,2.0820723601860106e-05
+0.001,0.0007083951884833238,0.0005461117507284293,0.0003386216233705354,0.00021128054172409337,0.00013236188112429926,8.31371097341464e-05,5.230539940125761e-05,3.294239432192892e-05,2.076124352066865e-05
+0.001,0.0007077778041323896,0.000545492279490185,0.00033801633474888146,0.00021081602540817874,0.00013203659950671687,8.291923002160208e-05,5.216293844781483e-05,3.285053307562121e-05,2.0702499944560498e-05
+0.001,0.0007071667963482699,0.0005448793818164008,0.0003374178959240363,0.00021035697230232543,0.00013171523473119512,8.270401240613943e-05,5.202223399957759e-05,3.2759810917439646e-05,2.064448740210184e-05
+0.001,0.0007065621526397901,0.000544273039572169,0.0003368262751014692,0.00020990335094223987,0.0001313977615739679,8.249143862546502e-05,5.188327354697137e-05,3.267021954836586e-05,2.0587200492045967e-05
+0.001,0.0007059638606517004,0.0005436732348155853,0.0003362414408426963,0.00020945513023505862,0.00013108415511962746,8.228149064554578e-05,5.174604473897535e-05,3.258175077549588e-05,2.053063388259103e-05
+0.001,0.0007053719081643225,0.0005430799497970297,0.00033566336206289793,0.00020901227945626015,0.00013077439075827684,8.20741506583782e-05,5.161053538152209e-05,3.249439651094783e-05,2.047478231065629e-05
+0.001,0.0007047863993885064,0.0005424932828039034,0.0003350921191971394,0.0002085748525953015,0.00013046850282623395,8.186944018288875e-05,5.147675893062123e-05,3.240816518060804e-05,2.0419651063027786e-05
+0.001,0.0007042073221500205,0.000541913216142546,0.0003345276813035574,0.00020814281911618103,0.00013016646689064054,8.166734155142674e-05,5.134470329367606e-05,3.232304876615549e-05,2.0365234922821375e-05
+0.001,0.0007036346644028755,0.0005413397323015136,0.0003339700177764314,0.00020771614883326046,0.00012986825880927205,8.146783731136239e-05,5.121435652738549e-05,3.2239039349161415e-05,2.0311528738515718e-05
+0.001,0.0007030684142289592,0.0005407728139509093,0.00033341909834398085,0.00020729481190839792,0.00012957385472790145,8.12709102230124e-05,5.1085706836262186e-05,3.215612911007561e-05,2.0258527423278803e-05
+0.001,0.0007025087877125606,0.0005402126735385767,0.0003328751197405316,0.00020687895394469412,0.00012928335419699167,8.107662593993734e-05,5.095879672189383e-05,3.2074345280702784e-05,2.0206248321016987e-05
+0.001,0.0007019557729111361,0.0005396592936234858,0.0003323380514212155,0.00020646854484108668,0.0001289967331620245,8.088496708343894e-05,5.083361429641059e-05,3.1993679981452625e-05,2.0154686306347033e-05
+0.001,0.0007014093580122255,0.0005391126569487217,0.00033180786317787807,0.00020606355484577673,0.00012871396785738722,8.069591648818549e-05,5.071014781993386e-05,3.191412543168724e-05,2.010383631860697e-05
+0.001,0.0007008695313331482,0.0005385727464408199,0.0003312845251369297,0.00020566395455347188,0.0001284350348038258,8.050945720021523e-05,5.058838569914163e-05,3.1835673948741706e-05,2.0053693361205354e-05
+0.001,0.0007003362813206957,0.0005380395452091099,0.00033076800775717,0.00020526971490259695,0.00012815991080588224,8.032557247492852e-05,5.046831648582724e-05,3.175831794694013e-05,2.0004252500969603e-05
+0.001,0.0006998095965508554,0.0005375130365450592,0.00033025828182767485,0.00020488080717254625,0.00012788857294937395,8.014424577511121e-05,5.0349928875481843e-05,3.168204993662801e-05,1.9955508867504938e-05
+0.001,0.0006992849713647436,0.000536988737498668,0.0003297510574308719,0.00020449398184933928,0.0001276187643023316,7.996397313508213e-05,5.023224268320585e-05,3.160623916862529e-05,1.9907059627792014e-05
+0.001,0.0006987624017369506,0.0005364666423066755,0.0003292463243531191,0.00020410922866516542,0.000127350476544274,7.978474848904117e-05,5.011525373583123e-05,3.153088286679667e-05,1.985890297191875e-05
+0.001,0.0006982418836227756,0.0005359467451858974,0.000328744072370431,0.00020372653735498857,0.00012708370136324496,7.960656578019794e-05,4.9998957867578574e-05,3.1455978260416835e-05,1.9811037093701456e-05
+0.001,0.0006977234129582083,0.0005354290403332512,0.00032824429124863073,0.00020334589765671668,0.0001268184304559618,7.942941896086744e-05,4.988335092011608e-05,3.138152258420484e-05,1.976346019070644e-05
+0.001,0.0006972049031024503,0.0005349114284143175,0.00032774491556384407,0.00020296571741865816,0.00012655354571184963,7.925255773427056e-05,4.976794174339877e-05,3.130719890433304e-05,1.971596949574029e-05
+0.001,0.0006966863521517465,0.000534393907423076,0.000327245942795149,0.00020258599425811892,0.00012628904521745982,7.907598070306232e-05,4.965272937294146e-05,3.123300657741713e-05,1.966856458850977e-05
+0.001,0.000696167758177353,0.0005338764753221901,0.0003267473703778833,0.00020220672575398438,0.00012602492703077633,7.889968645020544e-05,4.95377128311886e-05,3.1158944951577065e-05,1.9621245043263798e-05
+0.001,0.0006956491192256274,0.0005333591300431401,0.0003262491957040711,0.00020182790944721542,0.00012576118918167348,7.872367353931904e-05,4.942289112776892e-05,3.108501336660957e-05,1.957401042890837e-05
+0.001,0.0006951304333180151,0.0005328418694862771,0.0003257514161227785,0.00020144954284132072,0.00012549782967232552,7.854794051499734e-05,4.9308263259716635e-05,3.1011211154138606e-05,1.9526860309103978e-05

tests/data/test_lumped_losses_raman_fiber.csv

@@ -1,98 +1,99 @@
-1.000000000000000021e-03,6.059428294303585358e-04,4.124436878765448634e-04,2.874462507798667202e-04,2.103923932407651741e-04,1.678414792051835720e-04,1.547359756068614396e-04,1.816771264024174659e-04,3.223716208702616772e-04
-1.000000000000000021e-03,6.053134417926274479e-04,4.117055522849718272e-04,2.867345754340725936e-04,2.097129658661858059e-04,1.671408382188599656e-04,1.538980727463440737e-04,1.804148897825019347e-04,3.196786917797407482e-04
-1.000000000000000021e-03,6.046849734433535663e-04,4.109690875237402238e-04,2.860250085842350751e-04,2.090360511584246736e-04,1.664434237997556567e-04,1.530650313352837862e-04,1.791618737991747007e-04,3.170092948646618079e-04
-1.000000000000000021e-03,6.040574211994215528e-04,4.102342878398261821e-04,2.853175420730359193e-04,2.083616379079277403e-04,1.657492189637676920e-04,1.522368203677859210e-04,1.779180061767344980e-04,3.143632101370558260e-04
-1.000000000000000021e-03,6.034068048547004811e-04,4.094522032835132145e-04,2.845379038962591819e-04,2.075849237814657072e-04,1.649065028354379026e-04,1.511673145690444625e-04,1.761800891032866087e-04,3.101696013411107001e-04
-1.000000000000000021e-03,6.027571485450614400e-04,4.086719523190792173e-04,2.837607352174946669e-04,2.068114158243908936e-04,1.640683639230874002e-04,1.501056331894667201e-04,1.744595725998800725e-04,3.060328853710948794e-04
-1.000000000000000021e-03,6.021084488423111532e-04,4.078935283235860985e-04,2.829860258399657913e-04,2.060410983394430761e-04,1.632347745793864737e-04,1.490517152618393740e-04,1.727562758069566841e-04,3.019522704750103072e-04
-1.000000000000000021e-03,6.014584946769776846e-04,4.071145901524401327e-04,2.822117789926256531e-04,2.052723360542508288e-04,1.624043320606193702e-04,1.480041851535097400e-04,1.710684425093964716e-04,2.979241342510260699e-04
-1.000000000000000021e-03,6.008072907871274187e-04,4.063351463691671850e-04,2.814380028310788616e-04,2.045051334722086352e-04,1.615770319309204608e-04,1.469630126571907884e-04,1.693959447695294284e-04,2.939478272988644745e-04
-1.000000000000000021e-03,6.001497788207915121e-04,4.055445944921290597e-04,2.806482406689155018e-04,2.037157793952982566e-04,1.607178158735042268e-04,1.458699426620449350e-04,1.676159912510077627e-04,2.896237141368235984e-04
-1.000000000000000021e-03,5.994910374944025042e-04,4.047535948218318100e-04,2.798590565363265966e-04,2.029281739019909579e-04,1.598620980598530251e-04,1.447840125437445142e-04,1.658536074234625343e-04,2.853613427461420497e-04
-1.000000000000000021e-03,5.988260139740232213e-04,4.039566207302157830e-04,2.790656268334378021e-04,2.021383019157014865e-04,1.590064003284783227e-04,1.437018088215471850e-04,1.641044980680633682e-04,2.811520890672289147e-04
-1.000000000000000021e-03,5.981547295973556680e-04,4.031537149116132062e-04,2.782680029999613086e-04,2.013462168991827844e-04,1.581507747250400562e-04,1.426233730149128554e-04,1.623686402996199669e-04,2.769954674673250896e-04
-1.000000000000000021e-03,5.974772058480589884e-04,4.023449202032754039e-04,2.774662364916018848e-04,2.005519721420980883e-04,1.572952728586730247e-04,1.415487458023807841e-04,1.606460099014084351e-04,2.728909933446289080e-04
-1.000000000000000021e-03,5.967924602151245417e-04,4.015280354275974962e-04,2.766567170422079059e-04,1.997501120648868605e-04,1.564314572956195643e-04,1.404632320601022135e-04,1.589039550526354799e-04,2.687258582080010155e-04
-1.000000000000000021e-03,5.961015210291329552e-04,4.007053572943599577e-04,2.758431797434220318e-04,1.989462429758611403e-04,1.555679612182393453e-04,1.393818318435550562e-04,1.571757793000612348e-04,2.646152969711955188e-04
-1.000000000000000021e-03,5.954044103428367566e-04,3.998769294251378363e-04,2.750256766566040972e-04,1.981404183299118591e-04,1.547048355449558708e-04,1.383045830163869136e-04,1.554614480121203164e-04,2.605587686095252363e-04
-1.000000000000000021e-03,5.947013777400173224e-04,3.990430339362700481e-04,2.742044609070656662e-04,1.973328535702731791e-04,1.538422664307762401e-04,1.372316495983985922e-04,1.537610729441031576e-04,2.565559887984316962e-04
-1.000000000000000021e-03,5.939924446448691022e-04,3.982037130272156897e-04,2.733795825341983144e-04,1.965235995659166514e-04,1.529803013963573505e-04,1.361630645638145173e-04,1.520746117923915490e-04,2.526064078268458078e-04
-1.000000000000000021e-03,5.932708072008613994e-04,3.973448313523968048e-04,2.725292879585060313e-04,1.956816407574392804e-04,1.520737532268283138e-04,1.350253536692922732e-04,1.502520417395671493e-04,2.482429369311983419e-04
-1.000000000000000021e-03,5.925433291275937313e-04,3.964806700993557770e-04,2.716755655440644093e-04,1.948383564731119815e-04,1.511684275731327428e-04,1.338932464840604031e-04,1.484467502756363724e-04,2.439468427807235304e-04
-1.000000000000000021e-03,5.918101288584805405e-04,3.956113769664689763e-04,2.708185566039283476e-04,1.939938721213093850e-04,1.502644330979066827e-04,1.327668256867655240e-04,1.466587172007617287e-04,2.397173806066686273e-04
-1.000000000000000021e-03,5.910712282476807177e-04,3.947369945531709078e-04,2.699583111402765676e-04,1.931482375169864133e-04,1.493618135415682949e-04,1.316461114156967569e-04,1.448878476419733323e-04,2.355536787532183434e-04
-1.000000000000000021e-03,5.903266492974269808e-04,3.938575655934699066e-04,2.690948791496987824e-04,1.923015022699072797e-04,1.484606121781560119e-04,1.305311230155930472e-04,1.431340460611192763e-04,2.314548735305102924e-04
-1.000000000000000021e-03,5.895916361077759233e-04,3.930015889157450278e-04,2.682679585446747167e-04,1.915046725018171748e-04,1.476265343831741983e-04,1.295122987364192039e-04,1.415403000102364922e-04,2.277048762028301403e-04
-1.000000000000000021e-03,5.888509505821538188e-04,3.921405251235485093e-04,2.674377005486313469e-04,1.907064029438769275e-04,1.467931904457939435e-04,1.284977589807231727e-04,1.399600943498011466e-04,2.240084844157824847e-04
-1.000000000000000021e-03,5.881046147353881284e-04,3.912744162777674595e-04,2.666041537474170709e-04,1.899067416692393098e-04,1.459606238177286390e-04,1.274875313353729649e-04,1.383933775516596422e-04,2.203650745808937968e-04
-1.000000000000000021e-03,5.873535041835053691e-04,3.904041908037584924e-04,2.657681073335506784e-04,1.891063272435359547e-04,1.451293640595985746e-04,1.264820875947550167e-04,1.368405966876043531e-04,2.167748460671411873e-04
-1.000000000000000021e-03,5.865976375297720377e-04,3.895298843642901903e-04,2.649296021207307814e-04,1.883051991594097421e-04,1.442994447916011967e-04,1.254814426573682665e-04,1.353016807452276011e-04,2.132371421364682767e-04
-1.000000000000000021e-03,5.858370334908887412e-04,3.886515327161476187e-04,2.640886789067201177e-04,1.875033967487228748e-04,1.434708992929061271e-04,1.244856108614830043e-04,1.337765582492128895e-04,2.097513117892475961e-04
-1.000000000000000021e-03,5.850717109173866415e-04,3.877691717638952012e-04,2.632453785571107932e-04,1.867009592919541521e-04,1.426437606415217971e-04,1.234946061839636009e-04,1.322651576281378186e-04,2.063167108712882474e-04
-1.000000000000000021e-03,5.843017841779564719e-04,3.868829399849727933e-04,2.623998297381095117e-04,1.858979973489535589e-04,1.418181214209275826e-04,1.225084976697060233e-04,1.307674705482058720e-04,2.029328092689827875e-04
-1.000000000000000021e-03,5.835272719783185817e-04,3.859928727352694559e-04,2.615520723807220407e-04,1.850945488934469029e-04,1.409940129261724197e-04,1.215272967897141737e-04,1.292834223062770812e-04,1.995989685833853672e-04
-1.000000000000000021e-03,5.827481931390192209e-04,3.850990054645316799e-04,2.607021463995574332e-04,1.842906517410432266e-04,1.401714661235729505e-04,1.205510144929276286e-04,1.278129378421479238e-04,1.963145562589016025e-04
-1.000000000000000021e-03,5.819741190680906414e-04,3.842186528659996786e-04,2.598733678747139103e-04,1.835150990942780922e-04,1.393856353878371876e-04,1.196241043053028848e-04,1.264157249384978508e-04,1.931533527071859933e-04
-1.000000000000000021e-03,5.811954906857532679e-04,3.833344911530978875e-04,2.590423495637437460e-04,1.827389251971546448e-04,1.386010231640464696e-04,1.187014313463915350e-04,1.250305871218520724e-04,1.900379005692562904e-04
-1.000000000000000021e-03,5.804123268686601621e-04,3.824465553881609072e-04,2.582091305240798466e-04,1.819621668943112273e-04,1.378176602140172575e-04,1.177830097537161525e-04,1.236574657124121280e-04,1.869676369318762604e-04
-1.000000000000000021e-03,5.796261981805348861e-04,3.815564763513873891e-04,2.573750710547324729e-04,1.811859034357266680e-04,1.370364232713841633e-04,1.168696105278517803e-04,1.222971239749574140e-04,1.839432835829867073e-04
-1.000000000000000021e-03,5.788371166298537929e-04,3.806642767897665215e-04,2.565401959245502722e-04,1.804101566728638260e-04,1.362573268495191094e-04,1.159612279572367423e-04,1.209494737333099285e-04,1.809642226433420603e-04
-1.000000000000000021e-03,5.780450942923127944e-04,3.797699794962239458e-04,2.557045298790712744e-04,1.796349483543752492e-04,1.354803852765658562e-04,1.150578560981921263e-04,1.196144270262388971e-04,1.780298433107154344e-04
-1.000000000000000021e-03,5.772501433097143196e-04,3.788736073065085246e-04,2.548680976361037374e-04,1.788603001211915217e-04,1.347056126906417029e-04,1.141594887707218423e-04,1.182918961012392148e-04,1.751395417672447184e-04
-1.000000000000000021e-03,5.764543993312114430e-04,3.779774418763772749e-04,2.540328419678222955e-04,1.780877776040980329e-04,1.339342982068885400e-04,1.132672798320007770e-04,1.169830787460522149e-04,1.722947974180976999e-04
-1.000000000000000021e-03,5.756578658699793754e-04,3.770814893242558957e-04,2.531987675065141240e-04,1.773173809352787060e-04,1.331664323570320042e-04,1.123811939733057949e-04,1.156878429947367658e-04,1.694949148917899182e-04
-1.000000000000000021e-03,5.748605464740858808e-04,3.761857558021953151e-04,2.523658789003789082e-04,1.765491102506614579e-04,1.324020056947932127e-04,1.115011960644554309e-04,1.144060580710201219e-04,1.667392091004694466e-04
-1.000000000000000021e-03,5.740605208264367093e-04,3.752860221900411030e-04,2.515274179172670150e-04,1.757730484194969538e-04,1.316262968484167275e-04,1.106031805667756002e-04,1.130890327345630944e-04,1.638824360782746060e-04
-1.000000000000000021e-03,5.732597217139582440e-04,3.743865400085843336e-04,2.506901966657434509e-04,1.749991992264989145e-04,1.308541788026545852e-04,1.097115653341001649e-04,1.117862964715498958e-04,1.610733229285059244e-04
-1.000000000000000021e-03,5.724581527307928295e-04,3.734873153380866809e-04,2.498542195054800734e-04,1.742275621656188764e-04,1.300856406740597557e-04,1.088263113284397853e-04,1.104977038827086933e-04,1.583110954216201049e-04
-1.000000000000000021e-03,5.716575664778975139e-04,3.725901351223118402e-04,2.490209518882956956e-04,1.734592774731394025e-04,1.293215843757922266e-04,1.079481792373105579e-04,1.092239503861538297e-04,1.555962288273640454e-04
-1.000000000000000021e-03,5.708579584178212615e-04,3.716949912014026899e-04,2.481903820156593069e-04,1.726943281050015661e-04,1.285619816677677630e-04,1.070771097820554131e-04,1.079648627815312081e-04,1.529279069228532445e-04
-1.000000000000000021e-03,5.700593240595020362e-04,3.708018755033176054e-04,2.473624982033243466e-04,1.719326971657234189e-04,1.278068045492097274e-04,1.062130442456755448e-04,1.067202699871894330e-04,1.503053277730467872e-04
-1.000000000000000021e-03,5.692616589508734098e-04,3.699107800243283187e-04,2.465372888514933574e-04,1.711743678703278799e-04,1.270560252111956913e-04,1.053559244063845252e-04,1.054900029091859618e-04,1.477277031714797602e-04
-1.000000000000000021e-03,5.684668249800580104e-04,3.690236622787805977e-04,2.457163959794073484e-04,1.704206404911772779e-04,1.263106878677109810e-04,1.045066466916437628e-04,1.042749156168111009e-04,1.451958181660296986e-04
-1.000000000000000021e-03,5.676748100054150635e-04,3.681404996529084395e-04,2.448997907163580324e-04,1.696714800685054925e-04,1.255707452244580493e-04,1.036651297187654374e-04,1.030748062779383314e-04,1.427088285865420875e-04
-1.000000000000000021e-03,5.668856019606317766e-04,3.672612697075048303e-04,2.440874444423345342e-04,1.689268519744038289e-04,1.248361504800705925e-04,1.028312930702206035e-04,1.018894759794533339e-04,1.402659065748177792e-04
-1.000000000000000021e-03,5.660978860040193896e-04,3.663833351346186431e-04,2.432754355983019401e-04,1.681813812071888568e-04,1.240994738987429935e-04,1.019939514788640786e-04,1.006986246362622330e-04,1.378144424939918523e-04
-1.000000000000000021e-03,5.653129566917403656e-04,3.655093013788168949e-04,2.424676550876819162e-04,1.674404214250226010e-04,1.233681396595080271e-04,1.011643116013039457e-04,9.952262825359122704e-05,1.354072260989254840e-04
-1.000000000000000021e-03,5.645308021917731225e-04,3.646391464999218838e-04,2.416640749146854532e-04,1.667039386446287719e-04,1.226421015627000995e-04,1.003422934745905857e-04,9.836128786277046102e-05,1.330434286087782230e-04
-1.000000000000000021e-03,5.637533512715803535e-04,3.637748048842355184e-04,2.408662573039999728e-04,1.659731272165147797e-04,1.219222820499958832e-04,9.952864742131557339e-05,9.721524794079934454e-05,1.307234068834026641e-04
-1.000000000000000021e-03,5.629805832911210584e-04,3.629162394326295588e-04,2.400741573155354143e-04,1.652479360999081315e-04,1.212086174287677493e-04,9.872327382660381703e-05,9.608428430098295324e-05,1.284463004684426848e-04
-1.000000000000000021e-03,5.622124777547171659e-04,3.620634133660168507e-04,2.392877304473862261e-04,1.645283148026117340e-04,1.205010447638324016e-04,9.792607442008048754e-05,9.496817636981473815e-05,1.262112668660531425e-04
-1.000000000000000021e-03,5.614490142952067260e-04,3.612162901840461500e-04,2.385069325732311319e-04,1.638142133018547766e-04,1.197995017798251111e-04,9.713695214164584071e-05,9.386670693673759554e-05,1.240174806289624668e-04
-1.000000000000000021e-03,5.606917876274927293e-04,3.603765175920537237e-04,2.377331235014221514e-04,1.631066878653574946e-04,1.191048135885559178e-04,9.635658300529022745e-05,9.278045936752733660e-05,1.218652818497198865e-04
-1.000000000000000021e-03,5.599407711882442139e-04,3.595440474626148916e-04,2.369662455139816704e-04,1.624056743525901679e-04,1.184169030927857740e-04,9.558485302446070387e-05,9.170919453133945438e-05,1.197538170918552259e-04
-1.000000000000000021e-03,5.591959386362445371e-04,3.587188321446053963e-04,2.362062415256148103e-04,1.617111093879153761e-04,1.177356942042864595e-04,9.482164989506947420e-05,9.065267746151001182e-05,1.176822517560764241e-04
-1.000000000000000021e-03,5.584679347085343051e-04,3.579204847030152616e-04,2.354801209260217886e-04,1.610571916563278928e-04,1.171042314833950021e-04,9.412413419793498636e-05,8.969710985766721223e-05,1.158171674873524301e-04
-1.000000000000000021e-03,5.577460350040716044e-04,3.571292104088583889e-04,2.347605938090600918e-04,1.604093154405005178e-04,1.164788453357124725e-04,9.343405219685640224e-05,8.875393403591714169e-05,1.139848385910069905e-04
-1.000000000000000021e-03,5.570302142000897652e-04,3.563449642017089015e-04,2.340476072540025119e-04,1.597674235279283352e-04,1.158594695166401697e-04,9.275131062844492946e-05,8.782296490203555085e-05,1.121846347113116888e-04
-1.000000000000000021e-03,5.563205323021954991e-04,3.555677864531773679e-04,2.333411773810724506e-04,1.591315117132938270e-04,1.152460793074575203e-04,9.207585166649024939e-05,8.690405384945894284e-05,1.104159825692847303e-04
-1.000000000000000021e-03,5.556169640414684949e-04,3.547976323593042547e-04,2.326412517183593022e-04,1.585015234268918286e-04,1.146386094395640093e-04,9.140758389001004606e-05,8.599702078916069998e-05,1.086782756746748250e-04
-1.000000000000000021e-03,5.549194843577821444e-04,3.540344575531629255e-04,2.319477783590319386e-04,1.578774027587701891e-04,1.140369954748351631e-04,9.074641718074756330e-05,8.510168863112195785e-05,1.069709201912467553e-04
-1.000000000000000021e-03,5.542280683854123842e-04,3.532782180675540329e-04,2.312607059053527040e-04,1.572590943887706505e-04,1.134411737206307899e-04,9.009226260907148270e-05,8.421788308143062266e-05,1.052933342707317640e-04
-1.000000000000000021e-03,5.535435592528614506e-04,3.525297669260542646e-04,2.305807244353639423e-04,1.566471216497641210e-04,1.128515385449576329e-04,8.944542244330133641e-05,8.334582247054855841e-05,1.036454797753234207e-04
-1.000000000000000021e-03,5.528659293179593237e-04,3.517890550472092793e-04,2.299077767025847687e-04,1.560414233076215052e-04,1.122680202866636689e-04,8.880580140054516926e-05,8.248532556564376009e-05,1.020267728319692934e-04
-1.000000000000000021e-03,5.521951511907441769e-04,3.510560338657343622e-04,2.292418061160320900e-04,1.554419388795865483e-04,1.116905502099227850e-04,8.817330560855271690e-05,8.163621425685595255e-05,1.004366420798761639e-04
-1.000000000000000021e-03,5.515357570680511773e-04,3.503384240935171457e-04,2.285926195257032088e-04,1.548599664871607589e-04,1.111316359132737570e-04,8.756139732097920178e-05,8.081143045355856274e-05,9.887558842870256459e-05
-1.000000000000000021e-03,5.508831499695134287e-04,3.496283778737561509e-04,2.279502431225978031e-04,1.542840041018178729e-04,1.105785086338880569e-04,8.695624145470242240e-05,7.999741899881025698e-05,9.734197099681332458e-05
-1.000000000000000021e-03,5.502373034395177318e-04,3.489258487042028742e-04,2.273146231686837553e-04,1.537139948092133693e-04,1.100311043442569531e-04,8.635775141128424154e-05,7.919401605398894981e-05,9.583525507692383649e-05
-1.000000000000000021e-03,5.495982753581815606e-04,3.482308738101717264e-04,2.266857730549029851e-04,1.531499327576744506e-04,1.094893985055618503e-04,8.576587362490598130e-05,7.840109070036870779e-05,9.435495486246966317e-05
-1.000000000000000021e-03,5.489660394111083985e-04,3.475434070850742167e-04,2.260636396270461598e-04,1.525917617808240120e-04,1.089533281238570496e-04,8.518052329278025009e-05,7.761848365092111231e-05,9.290055631012179708e-05
-1.000000000000000021e-03,5.483405695169031721e-04,3.468634028939242666e-04,2.254481703234268038e-04,1.520394263837190755e-04,1.084228310216190315e-04,8.460161684084565312e-05,7.684603831721497692e-05,9.147155629123523643e-05
-1.000000000000000021e-03,5.477218398160970184e-04,3.461908160447295835e-04,2.248393131323322177e-04,1.514928716901747189e-04,1.078978457743454979e-04,8.402907183940372035e-05,7.608360066029366266e-05,9.006746210081428210e-05
-1.000000000000000021e-03,5.471100509830739483e-04,3.455258336641454844e-04,2.242372067336963117e-04,1.509521904405796073e-04,1.073784265457052923e-04,8.346290305268591516e-05,7.533111218717854750e-05,8.868791149478115541e-05
-1.000000000000000021e-03,5.465051768578450733e-04,3.448684100639811626e-04,2.236417986238402995e-04,1.504173273640432558e-04,1.068645117117092864e-04,8.290302835464549746e-05,7.458842111919879593e-05,8.733242665255502488e-05
-1.000000000000000021e-03,5.459071915129861232e-04,3.442185000233075119e-04,2.230530368831633737e-04,1.498882278486614187e-04,1.063560404456327061e-04,8.234936680962616474e-05,7.385537825478157199e-05,8.600053991717240256e-05
-1.000000000000000021e-03,5.453277270883248422e-04,3.435968835063559607e-04,2.224987019123045480e-04,1.493989071766017103e-04,1.058939742146122178e-04,8.185283432112488435e-05,7.320083692872619591e-05,8.480120525436834187e-05
-1.000000000000000021e-03,5.447550756300442850e-04,3.429826158632724655e-04,2.219507707465509202e-04,1.489150144895505707e-04,1.054368625228528346e-04,8.136171358212484941e-05,7.255437084770181432e-05,8.362138199130290570e-05
-1.000000000000000021e-03,5.441892122953419404e-04,3.423756544954533452e-04,2.214091954721005766e-04,1.484365007267826825e-04,1.049846526338566485e-04,8.087593753205775274e-05,7.191586230794716592e-05,8.246071125248328093e-05
-1.000000000000000021e-03,5.436257869834306257e-04,3.417717090501741430e-04,2.208705585733095858e-04,1.479607869104501987e-04,1.045353737048878776e-04,8.039389156994272538e-05,7.128377081045688692e-05,8.131715986317171246e-05
-1.000000000000000021e-03,5.430647907230928004e-04,3.411707635569704330e-04,2.203348411819341208e-04,1.474878525008424264e-04,1.040890016129762040e-04,7.991554062444546221e-05,7.065802258502740848e-05,8.019045690600123673e-05
-1.000000000000000021e-03,5.425062145452035425e-04,3.405728020832017003e-04,2.198020245085192449e-04,1.470176770797095381e-04,1.036455124224165388e-04,7.944084996986793814e-05,7.003854478529189314e-05,7.908033605497744685e-05
-1.000000000000000021e-03,5.419500494783734655e-04,3.399778087230425684e-04,2.192720898259863408e-04,1.465502403299397984e-04,1.032048823605461293e-04,7.896978519468913008e-05,6.942526543493823701e-05,7.798653539704048551e-05
-1.000000000000000021e-03,5.413941311127808435e-04,3.393835756573481376e-04,2.187432334857416756e-04,1.460841531649274296e-04,1.027660304092932323e-04,7.850144313387483587e-05,6.881729624423716459e-05,7.690779566810491681e-05
-1.000000000000000021e-03,5.408384570747234218e-04,3.387900992487282684e-04,2.182154508640741402e-04,1.456194096560940569e-04,1.023289477092565169e-04,7.803580657419308998e-05,6.821458972979072498e-05,7.584390730667668775e-05
-1.000000000000000021e-03,5.402830249660243826e-04,3.381973758275843189e-04,2.176887373150658610e-04,1.451560038709627043e-04,1.018936254270842219e-04,7.757285840469518032e-05,6.761709884283491779e-05,7.479466373034050742e-05
-1.000000000000000021e-03,5.397131798668998448e-04,3.375789411508863902e-04,2.171273238640323091e-04,1.446496280566233409e-04,1.014059885387841374e-04,7.704415358894297196e-05,6.692941074150365747e-05,7.359880640935691578e-05
-1.000000000000000021e-03,5.391436022217508317e-04,3.369613465901344558e-04,2.165671485747253328e-04,1.441448734692186333e-04,1.009205829600201576e-04,7.651897498112265308e-05,6.624864922679121694e-05,7.242198835523922060e-05
-1.223599761281599736e-03,2.870574988594920204e-03,5.410558860144323162e-03,9.740321680379944794e-03,1.718226620812626088e-02,3.032327552654904765e-02,5.460894525232919822e-02,1.025522470341594938e-01,2.059999999999999887e-01
-4.083745694274921144e-03,8.369042825128880300e-03,1.439530071705045523e-02,2.397223043565064812e-02,3.897486419880552555e-02,6.202545653148217042e-02,9.626510461383173956e-02,1.437785279168776742e-01,2.000000000000000111e-01
+0 km,7 km,10 km,20 km,30 km,40 km,50 km,60 km,70 km,80 km
+0.001,0.0007654185419972524,0.0006073276056093792,0.0004146475603472041,0.0002902671662170687,0.00021391771777705748,0.0001725335650783217,0.0001619576961300996,0.00019613348481478226,0.00036856555154569
+0.001,0.0007648265274364283,0.0006066999211698721,0.00041390447843586643,0.00028954214777674505,0.00021321488386807227,0.000171794621873672,0.00016105212098646378,0.00019472570335261124,0.00036543839103292923
+0.001,0.00076423475082742,0.0006060726714187721,0.00041316253063080743,0.0002888187769396436,0.00021251421079625184,0.0001710586847350929,0.00016015140443848455,0.0001933277019274264,0.0003623371672096105
+0.001,0.0007636432110143774,0.0006054458552058583,0.0004124217148997164,0.0002880970500399001,0.00021181569222935437,0.00017032574197864756,0.00015925552136261768,0.00019193941431515274,0.0003592616661738057
+0.001,0.00076303338083269,0.0006047950045143256,0.0004116319711720434,0.0002873006965446473,0.00021101094555209704,0.00016943701776024394,0.00015810165194844262,0.00019000410483730154,0.00035435344277520014
+0.001,0.0007624238065102939,0.0006041446264165162,0.00041084352013847123,0.0002865063493191246,0.0002102090638489634,0.00016855270795343295,0.00015695583271993784,0.000188087762219128,0.00034951092824224374
+0.001,0.0007618144867783217,0.0006034947195801519,0.00041005635912737644,0.00028571400313117073,0.000209410037075066,0.00016767279125308264,0.000155818009114819,0.0001861902048930817,0.00034473325633896016
+0.001,0.0007612032230964268,0.0006028430274261829,0.0004092680923152776,0.00028492160640338084,0.00020861217469969167,0.00016679580316719344,0.00015468672086849377,0.00018430951195784153,0.0003400162401556392
+0.001,0.0007605900180176352,0.0006021895546863309,0.000408478730269415,0.00028412917095061395,0.00020781548628341263,0.00016592174578786424,0.0001535619455802766,0.00018244555944842464,0.000335359176825339
+0.001,0.0007599713504055552,0.0006015295991965402,0.0004076783487865176,0.0002833211587561824,0.00020699731581240013,0.00016501652229006483,0.00015238542164450143,0.000180469716663273,0.00033030742335106797
+0.001,0.0007593507507343548,0.0006008678808679657,0.00040687692763931326,0.0002825132124386522,0.00020618050489600874,0.00016411458466993175,0.0001512162128542595,0.00017851301079839705,0.00032532712476693115
+0.001,0.0007587233815624382,0.0006001993719156019,0.00040606894689472706,0.0002817004901671922,0.0002053609939218913,0.0001632123763493078,0.00015005076311129325,0.00017657081573262424,0.0003204083683965919
+0.001,0.0007580892517126006,0.0005995240885208491,0.0004052544459439752,0.0002808830430533993,0.00020453883912382008,0.0001623099555953223,0.0001488891244397257,0.00017464312199372379,0.0003155506430768726
+0.001,0.000757448370179369,0.0005988420470784952,0.00040443346443882024,0.0002800609223493252,0.00020371409666633197,0.00016140738029416416,0.00014773134796391561,0.00017272991838834854,0.0003107534371554913
+0.001,0.0007567998780637095,0.00059815208713495,0.0004036034596839685,0.0002792300099339312,0.0002028805779162198,0.0001604950646725177,0.00014656055358564036,0.0001707926621492761,0.00030587527063737443
+0.001,0.0007561446539823046,0.0005974554049330684,0.00040276706435836743,0.0002783945504517164,0.00020204463288750894,0.00015958281494627715,0.00014539398266818048,0.00016887071348766508,0.00030106092293455183
+0.001,0.0007554827075416216,0.0005967520176577599,0.00040192431930056663,0.00027755459621831854,0.00020120631834502002,0.0001586706886366497,0.00014423168344504801,0.00016696404870095068,0.0002963098069760517
+0.001,0.0007548142866478835,0.0005960421864035,0.00040107552190864405,0.000276710416864016,0.00020036586735025836,0.00015775889202640647,0.00014307384532883863,0.00016507281278915244,0.00029162164835553234
+0.001,0.0007541394007727726,0.0005953259280357876,0.00040022071194032425,0.0002758620629396957,0.00019952333420039184,0.0001568474791446172,0.00014192051114461952,0.000163196972716382,0.0002869958456117749
+0.001,0.000753453261276124,0.0005945968692177485,0.0003993465628625302,0.0002749888554209862,0.00019864889698783253,0.00015589225679197793,0.00014069786108208423,0.00016117883466097707,0.0002818995386546663
+0.001,0.0007527606830746849,0.0005938614307668164,0.00039846653446453577,0.00027411169702333635,0.00019777273283424246,0.00015493803621995225,0.00013948100714945055,0.00015917971666279146,0.00027688213338452353
+0.001,0.000752061800251107,0.000593119758904669,0.0003975808078042764,0.0002732307613051971,0.00019689499757748,0.00015398495670235495,0.00013827006619590224,0.00015719963654088831,0.0002719428500764955
+0.001,0.0007513566228984858,0.0005923718712186205,0.00039668942343994645,0.00027234609917798486,0.0001960157448213401,0.0001530330687596212,0.0001370650679700252,0.0001552385042294358,0.000267080708365827
+0.001,0.0007506451612836396,0.000591617785508762,0.0003957924221725467,0.000271457761656054,0.00019513502804513515,0.00015208242246717618,0.00013586604131538964,0.00015329622856198138,0.00026229473523033993
+0.001,0.0007499410764512782,0.0005908750616328752,0.0003949229270536377,0.00027061232560569935,0.0001943133661054677,0.00015121214860141992,0.00013478433495717895,0.0001515553239579192,0.0002579720674546429
+0.001,0.0007492307077939692,0.0005901261380362599,0.0003940477612542949,0.0002697630351391033,0.0001934898396676657,0.00015034229582385722,0.00013370682839182124,0.00014982878466439454,0.0002537103994437239
+0.001,0.0007485140657428474,0.0005893710325813375,0.00039316696481225496,0.0002689099395032721,0.00019266450005914697,0.0001494729137699088,0.00013263355942168804,0.00014811657325897344,0.00024950907434075407
+0.001,0.0007477920374988207,0.0005886106575770754,0.00039228150952041026,0.0002680538698732277,0.00019183802609277662,0.00014860457389983926,0.00013156505071301006,0.00014641921273032577,0.00024536841469387607
+0.001,0.0007470646318417684,0.000587845028280153,0.00039139142972943226,0.00026719486815718823,0.00019101046063687028,0.0001477373157547231,0.00013050132642612272,0.00014473664329931894,0.00024128771968956965
+0.001,0.000746331857690334,0.0005870741601150847,0.0003904967599732611,0.0002663329763359202,0.00019018184646592208,0.0001468711785583918,0.00012944241008601634,0.0001430688043627681,0.00023726629318772418
+0.001,0.000745593724114763,0.0005862980686936137,0.0003895975350198339,0.0002654682365419898,0.00018935222636504533,0.00014600620135329296,0.00012838832478192589,0.0001414156348862137,0.00023330344508078714
+0.001,0.0007448503634609417,0.0005855168970231048,0.00038869392700388675,0.0002646008091121948,0.00018852173980288115,0.00014514250481571783,0.00012733917050730646,0.00013977716467553823,0.00022939865638927297
+0.001,0.0007441017848559784,0.0005847306606783223,0.0003877859701983709,0.00026373073520765555,0.0001876904281105713,0.0001442801258881357,0.00012629496716723198,0.0001381533278738466,0.00022555123779529113
+0.001,0.000743347997566111,0.0005839393754013221,0.00038687369905472224,0.00026285805605533836,0.00018685833252122952,0.0001434191012016364,0.000125255734067765,0.0001365440579330561,0.0002217605049922782
+0.001,0.0007425980122165197,0.0005831545190886928,0.0003859781684512971,0.00026201154253776947,0.00018606159531590292,0.00014260456502787665,0.00012428053620243385,0.0001350337902936029,0.00021815009586241928
+0.001,0.0007418428222422646,0.0005823646190291856,0.00038507830193089523,0.00026116232813230903,0.0001852638537460305,0.00014179094397584395,0.00012330941740530514,0.00013353605951814913,0.000214590937375532
+0.001,0.0007410824370750857,0.0005815696910727474,0.00038417413352571667,0.0002603104530145326,0.00018446514774054143,0.00014097827417406666,0.00012234240064855456,0.00013205082038162874,0.00021108244611026217
+0.001,0.0007403184660014679,0.0005807713776603622,0.0003832673750805624,0.0002594573523562161,0.0001836666246937272,0.00014016749938812904,0.00012138033426810311,0.00013057895028626532,0.00020762556561303502
+0.001,0.0007395509147560407,0.0005799696890514251,0.0003823580493090658,0.0002586030525652665,0.00018286830960336872,0.00013935863904919696,0.00012042322007081454,0.00012912037080446074,0.00020421964337946413
+0.001,0.0007387797891584386,0.0005791646356057247,0.0003814461790231548,0.0002577475800751799,0.00018207022739706005,0.00013855171240478834,0.00011947105956106815,0.0001276750034397535,0.00020086403326178727
+0.001,0.0007380050951127038,0.000578356227782297,0.00038053178712941366,0.00025689096133950617,0.0001812724029256328,0.00013774673851177748,0.00011852385393388548,0.0001262427696188339,0.00019755809538624924
+0.001,0.0007372289278765602,0.0005775466274590334,0.00037961719344922816,0.00025603518246549605,0.00018047644930614432,0.00013694506185961904,0.00011758283148187963,0.00012482499369662246,0.0001943036135491255
+0.001,0.0007364512893389424,0.0005767358384619004,0.0003787024060562486,0.0002551802511391958,0.00017968237057615404,0.0001361466779099092,0.00011664796319397039,0.00012342154884218158,0.00019109983808810176
+0.001,0.0007356721814267267,0.0005759238646613067,0.0003777874330677233,0.0002543261750635963,0.00017889017076266666,0.00013535158210437542,0.00011571922013388507,0.00012203230915507028,0.00018794602945024216
+0.001,0.0007348903474707337,0.0005751089853685418,0.00037686841880625214,0.0002534666634479025,0.00017809042928748983,0.00013454542482553396,0.00011477223754870921,0.00012060527355137842,0.0001846722184263047
+0.001,0.0007341070502230424,0.0005742929332546843,0.0003759492523595486,0.00025260806222015907,0.0001772926545344648,0.0001337427099649026,0.00011383170595735486,0.00011919336498115804,0.00018145260378874068
+0.001,0.0007333222916846016,0.0005734757122629472,0.0003750299418068227,0.0002517503788238516,0.00017649684991694604,0.00013294343155185963,0.00011289759243683735,0.00011779644164652425,0.00017828633792123003
+0.001,0.0007325378931581444,0.0005726591695542582,0.00037411237735305734,0.00025089517102575937,0.00017570423657326553,0.00013214856737581396,0.00011197073980433406,0.0001164153090011861,0.00017517406255147756
+0.001,0.0007317538520017976,0.0005718433021851622,0.00037319655361857124,0.00025004243032543557,0.00017491480109484205,0.00013135809328305347,0.0001110510931582075,0.00011504979334922632,0.00017211487548789828
+0.001,0.0007309701656016873,0.0005710281072505973,0.0003722824652942578,0.0002491921483105776,0.0001741285301842189,0.000130571985300518,0.00011013859805243972,0.00011369972292133188,0.00016910788945367707
+0.001,0.0007301868313673829,0.0005702135818768351,0.00037137010712276296,0.0002483443166280691,0.00017334541061775574,0.0001297902195887095,0.00010923320042963165,0.00011236492773944517,0.00016615223146969552
+0.001,0.0007294056858799279,0.0005694016103111181,0.00037046146816096116,0.0002475006124214696,0.00017256678052747222,0.0001290138836109195,0.0001083358528422491,0.00011104635018836138,0.0001632488500459303
+0.001,0.0007286267228265342,0.0005685921836368309,0.00036955653001307385,0.0002466610110284357,0.00017179260894632618,0.00012824293390221287,0.00010744647704539295,0.00010974378427192081,0.00016039680402653037
+0.001,0.0007278499359259242,0.0005677852929874075,0.0003686552744110329,0.000245825487979439,0.00017102286517266867,0.00012747732740860064,0.00010656499564424898,0.00010845702677323144,0.00015759516965436614
+0.001,0.0007270741396329974,0.0005669793834682747,0.00036775459243596013,0.0002449894231786493,0.00017025120181982169,0.00012670823171859124,0.00010567792500018758,0.00010716091111285436,0.00015477474449137119
+0.001,0.000726300509089127,0.0005661759961906673,0.0003668575707603942,0.00024415741777526555,0.00016948395998088835,0.00012594449635600327,0.000104798812682607,0.00010588078664841973,0.00015200532201362785
+0.001,0.0007255290381246482,0.0005653751224576382,0.0003659641914980988,0.00024332944777626794,0.00016872110941070693,0.00012518607857232714,0.00010392758109688598,0.00010461644792633948,0.00014928596543342006
+0.001,0.0007247617567123682,0.0005645788092050789,0.0003650765144822908,0.0002425071844547339,0.00016796393694001768,0.00012443398389681975,0.0001030650652169494,0.00010336864256044427,0.00014661715384500229
+0.001,0.0007239986535454607,0.0005637870401634551,0.0003641945072810511,0.0002416905868396016,0.00016721239470100498,0.00012368815096486452,0.00010221116577265065,0.00010213713571582715,0.00014399790845116419
+0.001,0.0007232397173862652,0.0005629997991705343,0.0003633181377200508,0.00024087961432750444,0.00016646643530137355,0.0001229485190910589,0.00010136578475280116,0.00010092169616032506,0.00014142727026178776
+0.001,0.00072248493705675,0.0005622170701566371,0.0003624473738436701,0.00024007422662323307,0.0001657260117481481,0.0001222150281737581,0.00010052882527084345,9.972209600365954e-05,0.000138904299062937
+0.001,0.0007217358950348228,0.000561440466555814,0.00036158388844431124,0.00023927581064585389,0.0001649922088518066,0.00012148853502428482,9.97010025298723e-05,9.853897375975792e-05,0.00013642939611266722
+0.001,0.0007209925771752469,0.0005606699674539418,0.0003607276390906435,0.00023848431321443392,0.00016426496576549843,0.00012076896452456461,9.888220185750974e-05,9.737207727303015e-05,0.00013400158668478175
+0.001,0.0007202549694492843,0.0005599055521123883,0.0003598785837495114,0.00023769968169456287,0.0001635442223215586,0.00012005624248053424,9.807231018575002e-05,9.622115859912247e-05,0.0001316199170239902
+0.001,0.0007195323462520794,0.0005591590509921624,0.0003590585920958888,0.00023695215697134095,0.00016286848728581805,0.00011939925459233543,9.733714368639992e-05,9.518785816120323e-05,0.00012948993319015237
+0.001,0.0007188153904868624,0.0005584185656809617,0.00035824561959240325,0.0002362112130369942,0.00016219882234612553,0.00011874843483873407,9.660966052802588e-05,9.416779973181015e-05,0.00012739732395025585
+0.001,0.0007181040886926203,0.0005576840763431772,0.00035743962664792336,0.00023547680133784063,0.00016153517356182933,0.00011810371926813217,9.588976814961258e-05,9.316079161517574e-05,0.00012534138272701054
+0.001,0.0007173985458519898,0.0005569556823713467,0.0003566406932084415,0.00023474897015812488,0.0001608775616408074,0.00011746510277705029,9.517742467544867e-05,9.216669491688869e-05,0.00012332148630987257
+0.001,0.0007166987484521553,0.0005562333638566226,0.00035584877964252954,0.00023402767102794237,0.00016022593289484238,0.00011683252196707568,9.447253888812898e-05,9.118532235085734e-05,0.00012133695300052025
+0.001,0.0007160046830928351,0.0005555171010581067,0.00035506384668924896,0.00023331285597027454,0.00015958023422419515,0.00011620591419889337,9.377502080118898e-05,9.021648961473911e-05,0.0001193871149139895
+0.001,0.0007153163364777631,0.0005548068743897313,0.0003542858554234702,0.00023260447744820426,0.00015894041305074621,0.00011558521750971036,9.308478154551746e-05,8.926001517936315e-05,0.00011747131722415646
+0.001,0.0007146345569433116,0.0005541035424399441,0.0003535156771553805,0.00023190324345165713,0.00015830701004295562,0.00011497084410199294,9.240214376206843e-05,8.831614266750221e-05,0.00011558953011398195
+0.001,0.0007139593299513231,0.0005534070835269872,0.0003527532683936443,0.00023120910091256188,0.0001576799668132194,0.0001143627259393922,9.172701183766888e-05,8.738468264977303e-05,0.00011374109491336212
+0.001,0.0007132906411062882,0.000552717476178626,0.00035199858609393853,0.00023052199734406963,0.00015705922565321226,0.00011376079584113645,9.105929150577112e-05,8.646544883197101e-05,0.0001119253667056674
+0.001,0.0007126330184449588,0.0005520403753247726,0.0003512614302897459,0.0002298546155988536,0.00015645973111213718,0.0001131821199461313,9.041832213305081e-05,8.557937250804453e-05,0.00011015252685143603
+0.001,0.000711981898998139,0.0005513700720769836,0.0003505318788510462,0.00022919410061472063,0.0001558663157908185,0.00011260933085632868,8.978431049296584e-05,8.47047300887107e-05,0.00010841080451458245
+0.001,0.0007113372689341813,0.0005507065458152156,0.0003498098907291114,0.00022854040278969112,0.00015527892573799634,0.00011204236655473188,8.91571706762786e-05,8.384135267939557e-05,0.00010669960309782175
+0.001,0.0007106992322019796,0.0005500498941226498,0.0003490955423571875,0.00022789356692450827,0.00015469757908825738,0.00011148122111140715,8.85368641132709e-05,8.298911908997276e-05,0.00010501839736063321
+0.001,0.0007100677749962943,0.0005494000964108962,0.0003483887928208825,0.00022725354367970012,0.00015412222229517684,0.0001109258331719869,8.792330625417495e-05,8.214786445649906e-05,0.00010336661223747767
+0.001,0.0007094428836454011,0.0005487571322870243,0.0003476896016176379,0.00022662028424420426,0.00015355280242059424,0.00011037614213504096,8.731641371338838e-05,8.131742659415625e-05,0.00010174368455214035
+0.001,0.000708824544604701,0.0005481209815436059,0.0003469979286303459,0.00022599374029548395,0.00015298926708448145,0.00010983208809074389,8.671610418600222e-05,8.049764584384383e-05,0.0001001490624678555
+0.001,0.0007082129757140411,0.0005474918591435615,0.0003463139755716002,0.00022537406278014643,0.00015243171907300512,0.00010929373376222763,8.612240008214866e-05,7.968846833775761e-05,9.858234685897841e-05
+0.001,0.000707608163356494,0.0005468697446924577,0.00034563770186245746,0.00022476120284855867,0.000151880105523236,0.00010876101889898551,8.55352191851817e-05,7.888973637819775e-05,9.704300162684373e-05
+0.001,0.0007070100940503908,0.0005462546179930401,0.0003449690673354809,0.0002241551121757662,0.0001513343741731098,0.00010823388399029377,8.495448041312896e-05,7.810129484091103e-05,9.553050179700698e-05
+0.001,0.0007064294568015821,0.0005456599791780414,0.0003443323098548347,0.00022358838010950836,0.00015083471987353974,0.00010776122599922914,8.444190686735427e-05,7.7408734986085e-05,9.418640444056476e-05
+0.001,0.0007058555225588962,0.0005450722639773927,0.0003437030311737585,0.0002230281662249932,0.00015034058581471813,0.00010729360256509277,8.39348527119689e-05,7.67245852554126e-05,9.28639870067709e-05
+0.001,0.0007052882785030611,0.0005444914532267761,0.0003430811937591523,0.00022247442636251605,0.00014985192565331347,0.00010683096298015644,8.343325230960875e-05,7.604872690242875e-05,9.156285904414627e-05
+0.001,0.0007047231683832071,0.0005439129831785586,0.00034246228783126086,0.00022192355689618922,0.00014936600614401393,0.00010637120139586071,8.293535717958106e-05,7.537945268589432e-05,9.028066182205625e-05
+0.001,0.0007041601877072604,0.0005433368472132987,0.00034184629997988753,0.0002213755412469509,0.00014888280881520869,0.00010591429577334988,8.244113465160048e-05,7.471669094184598e-05,8.901710564172274e-05
+0.001,0.0007035993319644686,0.0005427630386936696,0.0003412332167993905,0.00022083036287728357,0.0001484023152778868,0.00010546022421726945,8.195055234184008e-05,7.406037083397318e-05,8.777190547391773e-05
+0.001,0.0007030405966226531,0.0005421915509602746,0.00034062302487816926,0.00022028800527546067,0.00014792450720603156,0.00010500896495215848,8.146357812174912e-05,7.341042229889045e-05,8.65447807653603e-05
+0.001,0.000702481864866424,0.0005416202367428493,0.000340013528083159,0.00021974666745789773,0.00014744798951136416,0.00010455942245749231,8.097928325582809e-05,7.276590831707151e-05,8.53343290345859e-05
+0.001,0.0007019231347536531,0.000541049093933996,0.00033940472324505365,0.0002192063454080809,0.00014697275707368812,0.00010411158905958576,8.049765241221972e-05,7.212678380437272e-05,8.414032725111873e-05
+0.001,0.0007013644043173649,0.0005404781203952333,0.00033879660715245314,0.0002186670350763442,0.0001464988047567028,0.00010366545709431046,8.001867033124847e-05,7.149300404124133e-05,8.296255535497e-05
+0.001,0.0007007923380838431,0.0005398904209305891,0.0003381585602637609,0.00021808716235465733,0.0001459743062317574,0.00010315717126101123,7.946027734643345e-05,7.0747191644443e-05,8.159291401029592e-05
+0.001,0.0007002202839606736,0.0005393029161815334,0.0003375212965814363,0.0002175084901000057,0.0001454514108138073,0.00010265113449578299,7.890553328521924e-05,7.000884795811205e-05,8.02453392500888e-05
+0.0010564983206625808,0.0020323156209598047,0.0024972245821602065,0.004735193374982383,0.008578320915529132,0.015253393937469889,0.027225142859919765,0.049861478431656114,0.09601482521493776,0.2
+0.004249939542299966,0.007357791565894658,0.008737301696751656,0.01505607870396766,0.02510093603015811,0.040835074437955504,0.0649892582783015,0.10076225398432796,0.14991538310282454,0.206

tests/data/test_old_parameters_fiber_config.json

@@ -0,0 +1,224 @@
+{
+  "uid": "Span1",
+  "params": {
+    "length": 80,
+    "loss_coef": 0.2,
+    "length_units": "km",
+    "att_in": 0,
+    "con_in": 0.5,
+    "con_out": 0.5,
+    "type_variety": "SSMF",
+    "dispersion": 0.0000167,
+    "effective_area": 83e-12,
+    "pmd_coef": 1.265e-15,
+    "raman_efficiency": {
+      "cr": [
+        0.00000000e+00,
+        1.12351610e-05,
+        3.47838074e-05,
+        5.79356636e-05,
+        8.06921680e-05,
+        9.79845709e-05,
+        1.10454361e-04,
+        1.18735302e-04,
+        1.24736889e-04,
+        1.30110053e-04,
+        1.41001273e-04,
+        1.46383247e-04,
+        1.57011792e-04,
+        1.70765865e-04,
+        1.88408911e-04,
+        2.05914127e-04,
+        2.24074028e-04,
+        2.47508283e-04,
+        2.77729174e-04,
+        3.08044243e-04,
+        3.34764439e-04,
+        3.56481704e-04,
+        3.77127256e-04,
+        3.96269124e-04,
+        4.10955175e-04,
+        4.18718761e-04,
+        4.19511263e-04,
+        4.17025384e-04,
+        4.13565369e-04,
+        4.07726048e-04,
+        3.83671291e-04,
+        4.08564283e-04,
+        3.69571936e-04,
+        3.14442090e-04,
+        2.16074535e-04,
+        1.23097823e-04,
+        8.95457457e-05,
+        7.52470400e-05,
+        7.19806145e-05,
+        8.87961158e-05,
+        9.30812065e-05,
+        9.37058268e-05,
+        8.45719619e-05,
+        6.90585286e-05,
+        4.50407159e-05,
+        3.36521245e-05,
+        3.02292475e-05,
+        2.69376939e-05,
+        2.60020897e-05,
+        2.82958958e-05,
+        3.08667558e-05,
+        3.66024657e-05,
+        5.80610307e-05,
+        6.54797937e-05,
+        6.25022715e-05,
+        5.37806442e-05,
+        3.94996621e-05,
+        2.68120644e-05,
+        2.33038554e-05,
+        1.79140757e-05,
+        1.52472424e-05,
+        1.32707565e-05,
+        1.06541760e-05,
+        9.84649374e-06,
+        9.13999627e-06,
+        9.08971012e-06,
+        1.04227525e-05,
+        1.50419271e-05,
+        1.77838232e-05,
+        2.15810815e-05,
+        2.03744008e-05,
+        1.81939341e-05,
+        1.31862121e-05,
+        9.65352116e-06,
+        8.62698322e-06,
+        9.18688016e-06,
+        1.01737784e-05,
+        1.08017817e-05,
+        1.03903588e-05,
+        9.30040333e-06,
+        8.30809173e-06,
+        6.90650401e-06,
+        5.52238029e-06,
+        3.90648708e-06,
+        2.22908227e-06,
+        1.55796177e-06,
+        9.77218716e-07,
+        3.23477236e-07,
+        1.60602454e-07,
+        7.97306386e-08
+      ],
+      "frequency_offset": [
+        0.0e12,
+        0.5e12,
+        1.0e12,
+        1.5e12,
+        2.0e12,
+        2.5e12,
+        3.0e12,
+        3.5e12,
+        4.0e12,
+        4.5e12,
+        5.0e12,
+        5.5e12,
+        6.0e12,
+        6.5e12,
+        7.0e12,
+        7.5e12,
+        8.0e12,
+        8.5e12,
+        9.0e12,
+        9.5e12,
+        10.0e12,
+        10.5e12,
+        11.0e12,
+        11.5e12,
+        12.0e12,
+        12.5e12,
+        12.75e12,
+        13.0e12,
+        13.25e12,
+        13.5e12,
+        14.0e12,
+        14.5e12,
+        14.75e12,
+        15.0e12,
+        15.5e12,
+        16.0e12,
+        16.5e12,
+        17.0e12,
+        17.5e12,
+        18.0e12,
+        18.25e12,
+        18.5e12,
+        18.75e12,
+        19.0e12,
+        19.5e12,
+        20.0e12,
+        20.5e12,
+        21.0e12,
+        21.5e12,
+        22.0e12,
+        22.5e12,
+        23.0e12,
+        23.5e12,
+        24.0e12,
+        24.5e12,
+        25.0e12,
+        25.5e12,
+        26.0e12,
+        26.5e12,
+        27.0e12,
+        27.5e12,
+        28.0e12,
+        28.5e12,
+        29.0e12,
+        29.5e12,
+        30.0e12,
+        30.5e12,
+        31.0e12,
+        31.5e12,
+        32.0e12,
+        32.5e12,
+        33.0e12,
+        33.5e12,
+        34.0e12,
+        34.5e12,
+        35.0e12,
+        35.5e12,
+        36.0e12,
+        36.5e12,
+        37.0e12,
+        37.5e12,
+        38.0e12,
+        38.5e12,
+        39.0e12,
+        39.5e12,
+        40.0e12,
+        40.5e12,
+        41.0e12,
+        41.5e12,
+        42.0e12
+      ]
+    }
+  },
+  "operational": {
+        "temperature": 283,
+        "raman_pumps": [
+          {
+            "power": 224.403e-3,
+            "frequency": 205e12,
+            "propagation_direction": "counterprop"
+          },
+          {
+            "power": 231.135e-3,
+            "frequency": 201e12,
+            "propagation_direction": "counterprop"
+          }
+        ]
+      },
+  "metadata": {
+    "location": {
+      "latitude": 1,
+      "longitude": 0,
+      "city": null,
+      "region": ""
+    }
+  }
+}

tests/data/test_parameters_fiber_config.json

@@ -0,0 +1,225 @@
+{
+  "uid": "Span1",
+  "params": {
+    "length": 80,
+    "loss_coef": 0.2,
+    "length_units": "km",
+    "att_in": 0,
+    "con_in": 0.5,
+    "con_out": 0.5,
+    "type_variety": "SSMF",
+    "dispersion": 0.0000167,
+    "effective_area": 83e-12,
+    "pmd_coef": 1.265e-15,
+    "raman_coefficient": {
+      "g0": [
+        0.00000000e+00,
+        1.12351610e-05,
+        3.47838074e-05,
+        5.79356636e-05,
+        8.06921680e-05,
+        9.79845709e-05,
+        1.10454361e-04,
+        1.18735302e-04,
+        1.24736889e-04,
+        1.30110053e-04,
+        1.41001273e-04,
+        1.46383247e-04,
+        1.57011792e-04,
+        1.70765865e-04,
+        1.88408911e-04,
+        2.05914127e-04,
+        2.24074028e-04,
+        2.47508283e-04,
+        2.77729174e-04,
+        3.08044243e-04,
+        3.34764439e-04,
+        3.56481704e-04,
+        3.77127256e-04,
+        3.96269124e-04,
+        4.10955175e-04,
+        4.18718761e-04,
+        4.19511263e-04,
+        4.17025384e-04,
+        4.13565369e-04,
+        4.07726048e-04,
+        3.83671291e-04,
+        4.08564283e-04,
+        3.69571936e-04,
+        3.14442090e-04,
+        2.16074535e-04,
+        1.23097823e-04,
+        8.95457457e-05,
+        7.52470400e-05,
+        7.19806145e-05,
+        8.87961158e-05,
+        9.30812065e-05,
+        9.37058268e-05,
+        8.45719619e-05,
+        6.90585286e-05,
+        4.50407159e-05,
+        3.36521245e-05,
+        3.02292475e-05,
+        2.69376939e-05,
+        2.60020897e-05,
+        2.82958958e-05,
+        3.08667558e-05,
+        3.66024657e-05,
+        5.80610307e-05,
+        6.54797937e-05,
+        6.25022715e-05,
+        5.37806442e-05,
+        3.94996621e-05,
+        2.68120644e-05,
+        2.33038554e-05,
+        1.79140757e-05,
+        1.52472424e-05,
+        1.32707565e-05,
+        1.06541760e-05,
+        9.84649374e-06,
+        9.13999627e-06,
+        9.08971012e-06,
+        1.04227525e-05,
+        1.50419271e-05,
+        1.77838232e-05,
+        2.15810815e-05,
+        2.03744008e-05,
+        1.81939341e-05,
+        1.31862121e-05,
+        9.65352116e-06,
+        8.62698322e-06,
+        9.18688016e-06,
+        1.01737784e-05,
+        1.08017817e-05,
+        1.03903588e-05,
+        9.30040333e-06,
+        8.30809173e-06,
+        6.90650401e-06,
+        5.52238029e-06,
+        3.90648708e-06,
+        2.22908227e-06,
+        1.55796177e-06,
+        9.77218716e-07,
+        3.23477236e-07,
+        1.60602454e-07,
+        7.97306386e-08
+      ],
+      "frequency_offset": [
+        0.0e12,
+        0.5e12,
+        1.0e12,
+        1.5e12,
+        2.0e12,
+        2.5e12,
+        3.0e12,
+        3.5e12,
+        4.0e12,
+        4.5e12,
+        5.0e12,
+        5.5e12,
+        6.0e12,
+        6.5e12,
+        7.0e12,
+        7.5e12,
+        8.0e12,
+        8.5e12,
+        9.0e12,
+        9.5e12,
+        10.0e12,
+        10.5e12,
+        11.0e12,
+        11.5e12,
+        12.0e12,
+        12.5e12,
+        12.75e12,
+        13.0e12,
+        13.25e12,
+        13.5e12,
+        14.0e12,
+        14.5e12,
+        14.75e12,
+        15.0e12,
+        15.5e12,
+        16.0e12,
+        16.5e12,
+        17.0e12,
+        17.5e12,
+        18.0e12,
+        18.25e12,
+        18.5e12,
+        18.75e12,
+        19.0e12,
+        19.5e12,
+        20.0e12,
+        20.5e12,
+        21.0e12,
+        21.5e12,
+        22.0e12,
+        22.5e12,
+        23.0e12,
+        23.5e12,
+        24.0e12,
+        24.5e12,
+        25.0e12,
+        25.5e12,
+        26.0e12,
+        26.5e12,
+        27.0e12,
+        27.5e12,
+        28.0e12,
+        28.5e12,
+        29.0e12,
+        29.5e12,
+        30.0e12,
+        30.5e12,
+        31.0e12,
+        31.5e12,
+        32.0e12,
+        32.5e12,
+        33.0e12,
+        33.5e12,
+        34.0e12,
+        34.5e12,
+        35.0e12,
+        35.5e12,
+        36.0e12,
+        36.5e12,
+        37.0e12,
+        37.5e12,
+        38.0e12,
+        38.5e12,
+        39.0e12,
+        39.5e12,
+        40.0e12,
+        40.5e12,
+        41.0e12,
+        41.5e12,
+        42.0e12
+      ],
+      "reference_frequency": 206184634112792
+    }
+  },
+  "operational": {
+        "temperature": 283,
+        "raman_pumps": [
+          {
+            "power": 224.403e-3,
+            "frequency": 205e12,
+            "propagation_direction": "counterprop"
+          },
+          {
+            "power": 231.135e-3,
+            "frequency": 201e12,
+            "propagation_direction": "counterprop"
+          }
+        ]
+      },
+  "metadata": {
+    "location": {
+      "latitude": 1,
+      "longitude": 0,
+      "city": null,
+      "region": ""
+    }
+  }
+}

tests/data/test_raman_fiber_expected_results.csv

@@ -1,97 +1,97 @@
-,signal,ase,nli
-0,0.0002866683470642085,3.455694800734997e-08,2.1767706055953313e-07
-1,0.0002842930902246378,3.4445260342151434e-08,2.20064716108892e-07
-2,0.00028193841273409963,3.4334217950641774e-08,2.2239856929822977e-07
-3,0.0002796041237984927,3.422381587730477e-08,2.2467937700272344e-07
-4,0.00027589218358262,3.3956266402003705e-08,2.2576401766047814e-07
-5,0.0002722303444814487,3.3690926476196685e-08,2.2678094635121413e-07
-6,0.00026861791294303266,3.342777134334201e-08,2.2773179097640802e-07
-7,0.00026505174756069215,3.316675429137828e-08,2.2861602718115889e-07
-8,0.0002615312775878486,3.290785186664305e-08,2.294352005376256e-07
-9,0.0002577007690018081,3.26092154155734e-08,2.2987401053105823e-07
-10,0.00025392474812815994,3.231329178154223e-08,2.3024928325076607e-07
-11,0.0002501957390130402,3.201993265117851e-08,2.3055653947072044e-07
-12,0.0002465133077961936,3.172911082648897e-08,2.3079745224174315e-07
-13,0.00024287702172285261,3.144079924487205e-08,2.309736700807475e-07
-14,0.00023918644496802598,3.1142660565561954e-08,2.3099023972100006e-07
-15,0.00023554415781363666,3.084719002003063e-08,2.3094533745656626e-07
-16,0.0002319496781605366,3.0554358283826426e-08,2.3084061926161906e-07
-17,0.000228402746264896,3.026413819712743e-08,2.306779372506828e-07
-18,0.00022490287297566154,2.997650061885732e-08,2.2679314039447925e-07
-19,0.0002210339853226993,2.9639081336421986e-08,2.225476971173533e-07
-20,0.00021722472675673681,2.9305156366940595e-08,2.1837424228396343e-07
-21,0.00021347443350916938,2.8974683300060073e-08,2.1427183120915025e-07
-22,0.00020978233224910872,2.864761802749998e-08,2.1023941353936252e-07
-23,0.0002061476568412488,2.832391679540816e-08,2.0627595093585302e-07
-24,0.0002028237056285935,2.8034565895618217e-08,2.0263423697267893e-07
-25,0.00019954715529254185,2.7748013284124615e-08,1.9905015325521452e-07
-26,0.0001963174528000437,2.7464226779716075e-08,1.9552292765090665e-07
-27,0.00019313475803109547,2.718318103861899e-08,1.920525003885138e-07
-28,0.00018999848980183525,2.6904843987797665e-08,1.8863807494364378e-07
-29,0.00018690807208013476,2.6629183784477213e-08,1.852788635171717e-07
-30,0.00018386293497138034,2.635616888672288e-08,1.8197408797080072e-07
-31,0.0001808626075954048,2.608576967648626e-08,1.7872307155753016e-07
-32,0.00017790652540681915,2.5817954962418864e-08,1.7552504805064169e-07
-33,0.00017499412908771533,2.5552693765056307e-08,1.723792598876346e-07
-34,0.0001721914205512116,2.529821177538242e-08,1.69350416018518e-07
-35,0.00016942913344260413,2.5046172734840803e-08,1.663699885487624e-07
-36,0.0001667067703020692,2.4796549229968703e-08,1.6343730102750106e-07
-37,0.00016402494737034808,2.4549324625938814e-08,1.602954566731582e-07
-38,0.0001613831201060569,2.430447139995257e-08,1.5720933628441338e-07
-39,0.00015878075021906192,2.406196225171638e-08,1.541780421866172e-07
-40,0.00015621730558753943,2.3821770097360405e-08,1.5120068940449393e-07
-41,0.000153694061439545,2.3583901792213434e-08,1.4827814327673852e-07
-42,0.00015121040694331307,2.3348329147104765e-08,1.4540943949658482e-07
-43,0.00014876574026315321,2.3115024224465226e-08,1.425936300160931e-07
-44,0.00014623043935025647,2.2864250993313975e-08,1.397065102093322e-07
-45,0.00014373723010448477,2.2616046400344574e-08,1.3687531950007013e-07
-46,0.0001412854316913198,2.2370377299191064e-08,1.3409901704421416e-07
-47,0.00013887544742801196,2.2127221340618422e-08,1.313775961274423e-07
-48,0.0001365065605420479,2.1886545482453933e-08,1.2870998887773554e-07
-49,0.00013417806673897108,2.164831702445933e-08,1.2609514810979993e-07
-50,0.00013188927370907155,2.1412503578881085e-08,1.2353204666803017e-07
-51,0.00012964085531237725,2.117909919147538e-08,1.2102094138348867e-07
-52,0.00012743207116500861,2.094807084934547e-08,1.1856076487337955e-07
-53,0.0001252621950917354,2.0719385892834214e-08,1.161504721855161e-07
-54,0.00012308423338164536,2.0485363383535514e-08,1.1374627006340893e-07
-55,0.00012094535834842106,2.0253755300794944e-08,1.1139168040879032e-07
-56,0.00011884484242431182,2.0024527468430072e-08,1.0897675288127846e-07
-57,0.00011678298769107047,1.9797656169961167e-08,1.0661409941810817e-07
-58,0.0001147590394591346,1.9573107382367898e-08,1.0430256532408603e-07
-59,0.00011277225867179729,1.935084744632288e-08,1.0204102225018314e-07
-60,0.00011082192110664448,1.913084301808743e-08,9.982836715827351e-08
-61,0.00010890831555726861,1.8913080844667903e-08,9.76644171489201e-08
-62,0.00010703069380321927,1.8697527263494167e-08,9.554805889434612e-08
-63,0.00010518832400867466,1.8484148971365717e-08,9.347820572370307e-08
-64,0.00010353027948847247,1.8300360477604286e-08,9.158630538270034e-08
-65,0.00010190114820620951,1.8118339508838893e-08,8.97332684173061e-08
-66,0.00010030037817345079,1.7938063167097722e-08,8.791826040657302e-08
-67,9.872746699919663e-05,1.775950920143011e-08,8.614049912759125e-08
-68,9.718188342878233e-05,1.7582655209782296e-08,8.439918541238176e-08
-69,9.566310719955732e-05,1.740747903977101e-08,8.269353818341506e-08
-70,9.417062845393912e-05,1.7233958752991638e-08,8.102279372648069e-08
-71,9.27044102709892e-05,1.7062082036004708e-08,7.938660156523957e-08
-72,9.12639411274833e-05,1.6891826998956218e-08,7.778420731697601e-08
-73,8.984872036936478e-05,1.6723171993235663e-08,7.621487408851861e-08
-74,8.845926525396718e-05,1.6557077218868872e-08,7.467873241780861e-08
-75,8.709405706837696e-05,1.6392620086127443e-08,7.352620174175576e-08
-76,8.575262707251024e-05,1.6229781230847938e-08,7.239374499535451e-08
-77,8.44345490553445e-05,1.6068541919248203e-08,7.128100236441453e-08
-78,8.313937224726923e-05,1.5908883354530017e-08,7.018759330199112e-08
-79,8.18666553697718e-05,1.5750787028460176e-08,6.91131452736768e-08
-80,8.061596620368467e-05,1.5594234699428168e-08,6.80572933931087e-08
-81,7.93869860927298e-05,1.54392105845737e-08,6.701976864553917e-08
-82,7.81792957880057e-05,1.528569691328632e-08,6.600021709431229e-08
-83,7.69924848842345e-05,1.5133676199095668e-08,6.499829226870119e-08
-84,7.592423495462984e-05,1.5006007729453082e-08,6.40964585216171e-08
-85,7.487323130273564e-05,1.4879695488874347e-08,6.320918435915818e-08
-86,7.383915942693816e-05,1.475473179887786e-08,6.233620427401105e-08
-87,7.282024738915393e-05,1.4631093950979863e-08,6.147602236758762e-08
-88,7.181625694043332e-05,1.4508775744557438e-08,6.062843750629826e-08
-89,7.082695384072487e-05,1.4387771381868581e-08,5.979325194092954e-08
-90,6.985210770121701e-05,1.4268075471622408e-08,5.8970271173546604e-08
-91,6.889061932028676e-05,1.414966436041678e-08,5.8158567240485706e-08
-92,6.79423037538828e-05,1.4032534237451406e-08,5.7357984009008465e-08
-93,6.700697867481953e-05,1.3916681725635683e-08,5.656836755557654e-08
-94,6.594003301527265e-05,1.3765768410137306e-08,5.5667634894222326e-08
-95,6.489000516837228e-05,1.3616343300622314e-08,5.4781184522010985e-08
+signal,ase,nli
+0.0003277611420723,3.899470345438449e-08,2.4249796960234704e-07
+0.0003250017686895,3.88695612548288e-08,2.454439759471401e-07
+0.0003222650868463,3.8745084795940443e-08,2.483227752372772e-07
+0.0003195509110308,3.8621269402790665e-08,2.5113526636955305e-07
+0.0003152051917212,3.831511482545576e-08,2.525571834385986e-07
+0.0003109173643927,3.801142847546096e-08,2.538929936309988e-07
+0.0003066866698276,3.7710184357421544e-08,2.551447305997623e-07
+0.0003025094755105,3.74113291159896e-08,2.5631194688927884e-07
+0.0002983851632227,3.711483823464807e-08,2.57396572290435e-07
+0.0002939090370085,3.677384148134545e-08,2.580457272005108e-07
+0.0002894959951166,3.6435889805735655e-08,2.586138532899247e-07
+0.0002851374388563,3.6100804786136576e-08,2.590960415051975e-07
+0.0002808329106728,3.5768558450009235e-08,2.594943775098954e-07
+0.0002765819528091,3.543912291177373e-08,2.5981091869063053e-07
+0.0002722585970856,3.509728654683229e-08,2.599278683420713e-07
+0.0002679917286135,3.475846792019279e-08,2.5996691909916645e-07
+0.0002637808235807,3.44226362592242e-08,2.599301554005377e-07
+0.000259625629125,3.4089763516669025e-08,2.598199006832183e-07
+0.0002555256094681,3.375981897519707e-08,2.555756892447018e-07
+0.0002510061132968,3.3373930606403456e-08,2.509166795385623e-07
+0.0002465564627785,3.299202134196193e-08,2.463324441551975e-07
+0.0002421759612383,3.2614046686258965e-08,2.418221717126139e-07
+0.0002378637384678,3.2239959043845714e-08,2.373848791838885e-07
+0.0002336189309368,3.18697111382095e-08,2.3301959212527745e-07
+0.000229786907623,3.154429519674801e-08,2.290704910449234e-07
+0.0002260088301865,3.1221956560459585e-08,2.251793706228873e-07
+0.0002222841156937,3.090266251951521e-08,2.213455610060874e-07
+0.0002186130300924,3.058638893853768e-08,2.175692384572231e-07
+0.000214994952946,3.027310301755256e-08,2.138496969887665e-07
+0.0002114292680468,2.9962772135295886e-08,2.1018623618806562e-07
+0.0002079153646262,2.9655363939172943e-08,2.0657816240778285e-07
+0.0002044527805171,2.9350849083358333e-08,2.0302493091347645e-07
+0.0002010409060047,2.904919541568693e-08,1.995258515561229e-07
+0.0001976791358816,2.8750370949174832e-08,1.9608024000424985e-07
+0.0001944779969388,2.8467714690234763e-08,1.9279758501200657e-07
+0.0001913221363037,2.8187696076865265e-08,1.895633279917293e-07
+0.0001882110383135,2.7910287978744905e-08,1.8637688328585562e-07
+0.0001851455117447,2.763547687941092e-08,1.8294394600063082e-07
+0.000182124981625,2.7363235313116972e-08,1.795685295609545e-07
+0.0001791488786026,2.7093535992876882e-08,1.762497841686646e-07
+0.0001762166388716,2.6826351803791374e-08,1.729868703567211e-07
+0.0001733298028653,2.6561698509687352e-08,1.6978101465406346e-07
+0.0001704877122406,2.6299547455845844e-08,1.666312813274778e-07
+0.0001676897174922,2.603987020688793e-08,1.635367489137303e-07
+0.0001647841340631,2.575975887106765e-08,1.603495310656704e-07
+0.0001619264140112,2.5482476734612484e-08,1.57221255401216e-07
+0.0001591158121254,2.520798936957748e-08,1.541508918468439e-07
+0.0001563528761692,2.493627607244877e-08,1.5113866658054666e-07
+0.0001536368140911,2.4667302328210823e-08,1.4818350881997277e-07
+0.0001509668468559,2.4401033945674264e-08,1.452843671077152e-07
+0.0001483422079002,2.4137437022238145e-08,1.4244020864846993e-07
+0.0001457637135497,2.3876511018332856e-08,1.3965152374161321e-07
+0.0001432305385497,2.3618221073161605e-08,1.369172264569389e-07
+0.0001407418727912,2.3362532671027093e-08,1.342362523931659e-07
+0.0001382358967136,2.3099449050292612e-08,1.3154948565189317e-07
+0.0001357749763134,2.283907541163823e-08,1.2891625322154978e-07
+0.0001333582893299,2.2581374993581207e-08,1.2619656354429104e-07
+0.0001309862438042,2.232632442642508e-08,1.2353438214155152e-07
+0.0001286579813897,2.2073886950323847e-08,1.2092847534724114e-07
+0.000126372660974,2.182402617504561e-08,1.1837763658356932e-07
+0.0001241294577704,2.1576706021416435e-08,1.1588068523754277e-07
+0.0001219287137845,2.1331915734067192e-08,1.1343753674855828e-07
+0.0001197695745451,2.108961868593061e-08,1.110469928559736e-07
+0.0001176512038143,2.08497786224886e-08,1.0870788304064741e-07
+0.0001157575507964,2.064536482973696e-08,1.0658919731348233e-07
+0.0001138968874049,2.0442910777630443e-08,1.0451285530046546e-07
+0.0001120685950893,2.0242392369145183e-08,1.0247797592009348e-07
+0.0001102721277733,2.0043786431072003e-08,1.004837520181078e-07
+0.0001085068885057,1.9847069357675e-08,9.852933720632749e-08
+0.0001067722923259,1.96522177979178e-08,9.661390355735894e-08
+0.0001050677655983,1.9459208611917452e-08,9.473664081358061e-08
+0.0001033932787473,1.9268030831168163e-08,9.289723486055733e-08
+0.000101748254869,1.9078661277163014e-08,9.109488193990664e-08
+0.0001001321289996,1.8891077021770737e-08,8.932879630040843e-08
+9.855410305682533e-05,1.8707685556144628e-08,8.760688138727519e-08
+9.70035750524988e-05,1.8526106268805162e-08,8.622858338902384e-08
+9.54800221910602e-05,1.834631916025987e-08,8.487426418079828e-08
+9.398298376195116e-05,1.816830511796191e-08,8.35435142269833e-08
+9.251195567959074e-05,1.7992044730451886e-08,8.223588543496443e-08
+9.106644418852578e-05,1.7817518892375448e-08,8.095093889480209e-08
+8.964596537975902e-05,1.7644708778076547e-08,7.968824444928503e-08
+8.825016836609303e-05,1.7473598709850392e-08,7.844749018716727e-08
+8.687858293497841e-05,1.7304170348281186e-08,7.722825812630736e-08
+8.553074854577241e-05,1.7136405660531677e-08,7.603013888213162e-08
+8.433358305815234e-05,1.6998940860777966e-08,7.496595249494088e-08
+8.315557511358139e-05,1.6862959916000355e-08,7.39187956635932e-08
+8.199638277812437e-05,1.6728456488525712e-08,7.28883644355918e-08
+8.085394966108952e-05,1.6595404781975636e-08,7.187283084061544e-08
+7.972802118918304e-05,1.6463800186495995e-08,7.087196858293762e-08
+7.861834686065792e-05,1.6333638547854104e-08,6.988555498611813e-08
+7.752468007410017e-05,1.6204916172056103e-08,6.891337084080734e-08
+7.644579608395804e-05,1.607760634807453e-08,6.795432744410053e-08
+7.538149823706338e-05,1.5951707212505497e-08,6.700824998672802e-08
+7.433159248509273e-05,1.5827217400105257e-08,6.607496597492307e-08
+7.310944660054646e-05,1.5658417045863033e-08,6.498857397068902e-08
+7.190690836035234e-05,1.5491319571073834e-08,6.391961162711614e-08

tests/invocation/logs_path_request

@@ -0,0 +1,156 @@
+INFO     gnpy.tools.cli_examples:cli_examples.py Computing path requests meshTopologyExampleV2.xls into JSON format
+WARNING  gnpy.tools.json_io:json_io.py 
+	WARNING missing type_variety attribute in eqpt_config.json[Roadm]
+	default value is type_variety = default
+
+INFO     gnpy.tools.json_io:json_io.py Automatically converting requests from XLS to JSON
+INFO     gnpy.tools.worker_utils:worker_utils.py List of disjunctions:
+[Disjunction 3
+	relaxable:    false
+	link-diverse: True
+	node-diverse: True
+	request-id-numbers: ['3', '1']
+, Disjunction 4
+	relaxable:    false
+	link-diverse: True
+	node-diverse: True
+	request-id-numbers: ['4', '5']
+]
+INFO     gnpy.tools.worker_utils:worker_utils.py Aggregating similar requests
+INFO     gnpy.tools.worker_utils:worker_utils.py The following services have been requested:
+[PathRequest 0
+	source: 	trx Lorient_KMA
+	destination:	trx Vannes_KBE
+	trx type:	Voyager
+	trx mode:	None
+	baud_rate:	None Gbaud
+	bit_rate:	None Gb/s
+	spacing:	50.0 GHz
+	power:  	1.0 dBm
+	tx_power_dbm:  	0.0 dBm
+	nb channels: 	80
+	path_bandwidth: 	100.0 Gbit/s
+	nodes-list:	[]
+	loose-list:	[]
+, PathRequest 1
+	source: 	trx Brest_KLA
+	destination:	trx Vannes_KBE
+	trx type:	Voyager
+	trx mode:	mode 1
+	baud_rate:	32.0 Gbaud
+	bit_rate:	100.0 Gb/s
+	spacing:	50.0 GHz
+	power:  	1.0 dBm
+	tx_power_dbm:  	0.0 dBm
+	nb channels: 	95
+	path_bandwidth: 	200.0 Gbit/s
+	nodes-list:	['roadm Brest_KLA', 'roadm Lannion_CAS', 'roadm Lorient_KMA', 'roadm Vannes_KBE']
+	loose-list:	['LOOSE', 'LOOSE', 'LOOSE', 'LOOSE']
+, PathRequest 3
+	source: 	trx Lannion_CAS
+	destination:	trx Rennes_STA
+	trx type:	vendorA_trx-type1
+	trx mode:	mode 1
+	baud_rate:	32.0 Gbaud
+	bit_rate:	100.0 Gb/s
+	spacing:	50.0 GHz
+	power:  	0.0 dBm
+	tx_power_dbm:  	0.0 dBm
+	nb channels: 	95
+	path_bandwidth: 	60.0 Gbit/s
+	nodes-list:	[]
+	loose-list:	[]
+, PathRequest 4
+	source: 	trx Rennes_STA
+	destination:	trx Lannion_CAS
+	trx type:	vendorA_trx-type1
+	trx mode:	None
+	baud_rate:	None Gbaud
+	bit_rate:	None Gb/s
+	spacing:	75.0 GHz
+	power:  	3.0 dBm
+	tx_power_dbm:  	0.0 dBm
+	nb channels: 	63
+	path_bandwidth: 	150.0 Gbit/s
+	nodes-list:	[]
+	loose-list:	[]
+, PathRequest 5
+	source: 	trx Rennes_STA
+	destination:	trx Lannion_CAS
+	trx type:	vendorA_trx-type1
+	trx mode:	mode 2
+	baud_rate:	66.0 Gbaud
+	bit_rate:	200.0 Gb/s
+	spacing:	75.0 GHz
+	power:  	3.0 dBm
+	tx_power_dbm:  	0.0 dBm
+	nb channels: 	63
+	path_bandwidth: 	20.0 Gbit/s
+	nodes-list:	[]
+	loose-list:	[]
+, PathRequest 7 | 6
+	source: 	trx Lannion_CAS
+	destination:	trx Lorient_KMA
+	trx type:	Voyager
+	trx mode:	mode 1
+	baud_rate:	32.0 Gbaud
+	bit_rate:	100.0 Gb/s
+	spacing:	50.0 GHz
+	power:  	0.0 dBm
+	tx_power_dbm:  	0.0 dBm
+	nb channels: 	76
+	path_bandwidth: 	700.0 Gbit/s
+	nodes-list:	[]
+	loose-list:	[]
+, PathRequest 7b
+	source: 	trx Lannion_CAS
+	destination:	trx Lorient_KMA
+	trx type:	Voyager
+	trx mode:	mode 1
+	baud_rate:	32.0 Gbaud
+	bit_rate:	100.0 Gb/s
+	spacing:	75.0 GHz
+	power:  	0.0 dBm
+	tx_power_dbm:  	0.0 dBm
+	nb channels: 	50
+	path_bandwidth: 	400.0 Gbit/s
+	nodes-list:	[]
+	loose-list:	[]
+]
+INFO     gnpy.tools.worker_utils:worker_utils.py Propagating on selected path
+WARNING  gnpy.topology.request:request.py Redesign the network for each request channel, using the request channel as the reference channel for the design.
+INFO     gnpy.topology.request:request.py 
+	request 0
+	Computing path from trx Lorient_KMA to trx Vannes_KBE
+	with path constraint: ['trx Lorient_KMA', 'trx Vannes_KBE']
+	Computed path (roadms):['roadm Lorient_KMA', 'roadm Vannes_KBE']
+INFO     gnpy.topology.request:request.py 
+	request 1
+	Computing path from trx Brest_KLA to trx Vannes_KBE
+	with path constraint: ['trx Brest_KLA', 'roadm Brest_KLA', 'roadm Lannion_CAS', 'roadm Lorient_KMA', 'roadm Vannes_KBE', 'trx Vannes_KBE']
+	Computed path (roadms):['roadm Brest_KLA', 'roadm Lannion_CAS', 'roadm Lorient_KMA', 'roadm Vannes_KBE']
+INFO     gnpy.topology.request:request.py 
+	request 3
+	Computing path from trx Lannion_CAS to trx Rennes_STA
+	with path constraint: ['trx Lannion_CAS', 'trx Rennes_STA']
+	Computed path (roadms):['roadm Lannion_CAS', 'roadm Rennes_STA']
+INFO     gnpy.topology.request:request.py 
+	request 4
+	Computing path from trx Rennes_STA to trx Lannion_CAS
+	with path constraint: ['trx Rennes_STA', 'trx Lannion_CAS']
+	Computed path (roadms):['roadm Rennes_STA', 'roadm Vannes_KBE', 'roadm Lorient_KMA', 'roadm Lannion_CAS']
+INFO     gnpy.topology.request:request.py 
+	request 5
+	Computing path from trx Rennes_STA to trx Lannion_CAS
+	with path constraint: ['trx Rennes_STA', 'trx Lannion_CAS']
+	Computed path (roadms):['roadm Rennes_STA', 'roadm Lannion_CAS']
+INFO     gnpy.topology.request:request.py 
+	request 7 | 6
+	Computing path from trx Lannion_CAS to trx Lorient_KMA
+	with path constraint: ['trx Lannion_CAS', 'trx Lorient_KMA']
+	Computed path (roadms):['roadm Lannion_CAS', 'roadm Lorient_KMA']
+INFO     gnpy.topology.request:request.py 
+	request 7b
+	Computing path from trx Lannion_CAS to trx Lorient_KMA
+	with path constraint: ['trx Lannion_CAS', 'trx Lorient_KMA']
+	Computed path (roadms):['roadm Lannion_CAS', 'roadm Lorient_KMA']

tests/invocation/logs_path_requests_run_CD_PMD_PDL_missing

@@ -0,0 +1,37 @@
+INFO     gnpy.tools.cli_examples:cli_examples.py Computing path requests CORONET_services.json into JSON format
+WARNING  gnpy.tools.json_io:json_io.py 
+	WARNING missing type_variety attribute in eqpt_config.json[Roadm]
+	default value is type_variety = default
+
+INFO     gnpy.tools.worker_utils:worker_utils.py List of disjunctions:
+[]
+INFO     gnpy.tools.worker_utils:worker_utils.py Aggregating similar requests
+INFO     gnpy.tools.worker_utils:worker_utils.py The following services have been requested:
+[PathRequest 0
+	source: 	trx Abilene
+	destination:	trx Albany
+	trx type:	Voyager
+	trx mode:	mode 3
+	baud_rate:	44.0 Gbaud
+	bit_rate:	300.0 Gb/s
+	spacing:	62.50000000000001 GHz
+	power:  	0.0 dBm
+	tx_power_dbm:  	0.0 dBm
+	nb channels: 	76
+	path_bandwidth: 	100.0 Gbit/s
+	nodes-list:	[]
+	loose-list:	[]
+]
+INFO     gnpy.tools.worker_utils:worker_utils.py Propagating on selected path
+INFO     gnpy.topology.request:request.py 
+	request 0
+	Computing path from trx Abilene to trx Albany
+	with path constraint: ['trx Abilene', 'trx Albany']
+	Computed path (roadms):['roadm Abilene', 'roadm Dallas', 'roadm Little_Rock', 'roadm Memphis', 'roadm Nashville', 'roadm Louisville', 'roadm Cincinnati', 'roadm Columbus', 'roadm Cleveland', 'roadm Buffalo', 'roadm Rochester', 'roadm Syracuse', 'roadm Albany']
+WARNING  gnpy.topology.request:request.py 	Warning! Request 0 computed path from trx Abilene to trx Albany does not pass with mode 3
+	computed SNR in 0.1nm = 14.44
+	PDL penalty not evaluated
+	CD penalty not evaluated
+	PMD penalty not evaluated
+	required osnr = 18
+	system margin = 2

tests/invocation/logs_power_sweep_example

@@ -0,0 +1,383 @@
+WARNING  gnpy.tools.json_io:json_io.py 
+	WARNING missing type_variety attribute in eqpt_config.json[Roadm]
+	default value is type_variety = default
+
+INFO     gnpy.tools.cli_examples:cli_examples.py source = 'trx Brest_KLA'
+INFO     gnpy.tools.cli_examples:cli_examples.py destination = 'trx Rennes_STA'
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in Lorient_KMA to Loudeac
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: effective gain in Node east edfa in Lannion_CAS to Stbrieuc
+	is above user specified amplifier std_low_gain
+	max flat gain: 16dB ; required gain: 21.22dB. Please check amplifier type.
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: effective gain in Node east edfa in Stbrieuc to Rennes_STA
+	is below user specified amplifier std_medium_gain
+	min gain: 15dB ; required gain: 12.0dB. Please check amplifier type.
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in Rennes_STA to Stbrieuc
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: effective gain in Node east edfa in Lannion_CAS to Morlaix
+	is above user specified amplifier std_low_gain
+	max flat gain: 16dB ; required gain: 21.22dB. Please check amplifier type.
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in Brest_KLA to Morlaix
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in Lorient_KMA to Loudeac
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: effective gain in Node west edfa in Lannion_CAS to Corlay
+	is above user specified amplifier test
+	max flat gain: 25dB ; required gain: 28.0dB. Please check amplifier type.
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in Lorient_KMA to Vannes_KBE
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in Vannes_KBE to Lorient_KMA
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in Lorient_KMA to Quimper
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in Quimper to Lorient_KMA
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in Brest_KLA to Quimper
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in Vannes_KBE to Lorient_KMA
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in Lorient_KMA to Vannes_KBE
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in Vannes_KBE to Ploermel
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in Ploermel to Vannes_KBE
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in Rennes_STA to Ploermel
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in Rennes_STA to Stbrieuc
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in Stbrieuc to Rennes_STA
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in Lannion_CAS to Stbrieuc
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in Rennes_STA to Ploermel
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in Vannes_KBE to Ploermel
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in Brest_KLA to Morlaix
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: effective gain in Node east edfa in Brest_KLA to Quimper
+	is above user specified amplifier std_low_gain
+	max flat gain: 16dB ; required gain: 21.22dB. Please check amplifier type.
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in Quimper to Lorient_KMA
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in Lorient_KMA to Quimper
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in a to b
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in b to a
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in a to c
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in c to a
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in b to a
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in a to b
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in b to f
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in f to b
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in c to a
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in a to c
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in d to c
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in c to f
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in f to c
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in d to c
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in c to d
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in d to e
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in e to d
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in e to d
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in d to e
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in e to g
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in g to e
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in f to c
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in c to f
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in f to b
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in b to f
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in f to h
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in h to f
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in g to e
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in e to g
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in g to h
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in h to g
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in h to f
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in f to h
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in h to g
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in g to h
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.78 is applied
+
+INFO     gnpy.tools.worker_utils:worker_utils.py Power mode is set to True=> it can be modified in eqpt_config.json - Span
+INFO     gnpy.tools.worker_utils:worker_utils.py Now propagating between trx Brest_KLA and trx Rennes_STA
+INFO     gnpy.tools.worker_utils:worker_utils.py 
+Channels propagating: (Input optical power deviation in span = 0.00dB,
+                       spacing = 50.00GHz,
+                       transceiver output power = 3.00dBm,
+                       nb_channels = 95)
+INFO     gnpy.tools.worker_utils:worker_utils.py 
+Channels propagating: (Input optical power deviation in span = 0.00dB,
+                       spacing = 50.00GHz,
+                       transceiver output power = 3.00dBm,
+                       nb_channels = 95)
+INFO     gnpy.tools.worker_utils:worker_utils.py 
+Channels propagating: (Input optical power deviation in span = 0.00dB,
+                       spacing = 50.00GHz,
+                       transceiver output power = 3.00dBm,
+                       nb_channels = 95)
+INFO     gnpy.tools.worker_utils:worker_utils.py 
+Channels propagating: (Input optical power deviation in span = 0.00dB,
+                       spacing = 50.00GHz,
+                       transceiver output power = 3.00dBm,
+                       nb_channels = 95)
+INFO     gnpy.tools.worker_utils:worker_utils.py 
+Channels propagating: (Input optical power deviation in span = 0.00dB,
+                       spacing = 50.00GHz,
+                       transceiver output power = 3.00dBm,
+                       nb_channels = 95)
+INFO     gnpy.tools.worker_utils:worker_utils.py 
+Channels propagating: (Input optical power deviation in span = 0.00dB,
+                       spacing = 50.00GHz,
+                       transceiver output power = 3.00dBm,
+                       nb_channels = 95)
+INFO     gnpy.tools.worker_utils:worker_utils.py 
+Channels propagating: (Input optical power deviation in span = 0.00dB,
+                       spacing = 50.00GHz,
+                       transceiver output power = 3.00dBm,
+                       nb_channels = 95)
+INFO     gnpy.tools.worker_utils:worker_utils.py 
+Channels propagating: (Input optical power deviation in span = 0.00dB,
+                       spacing = 50.00GHz,
+                       transceiver output power = 3.00dBm,
+                       nb_channels = 95)
+INFO     gnpy.tools.worker_utils:worker_utils.py 
+Channels propagating: (Input optical power deviation in span = 0.00dB,
+                       spacing = 50.00GHz,
+                       transceiver output power = 3.00dBm,
+                       nb_channels = 95)
+INFO     gnpy.tools.worker_utils:worker_utils.py 
+Channels propagating: (Input optical power deviation in span = 0.00dB,
+                       spacing = 50.00GHz,
+                       transceiver output power = 3.00dBm,
+                       nb_channels = 95)
+INFO     gnpy.tools.worker_utils:worker_utils.py 
+Channels propagating: (Input optical power deviation in span = 0.00dB,
+                       spacing = 50.00GHz,
+                       transceiver output power = 3.00dBm,
+                       nb_channels = 95)
+INFO     gnpy.tools.worker_utils:worker_utils.py 
+Channels propagating: (Input optical power deviation in span = 0.00dB,
+                       spacing = 50.00GHz,
+                       transceiver output power = 3.00dBm,
+                       nb_channels = 95)
+INFO     gnpy.tools.worker_utils:worker_utils.py 
+Channels propagating: (Input optical power deviation in span = 0.00dB,
+                       spacing = 50.00GHz,
+                       transceiver output power = 3.00dBm,
+                       nb_channels = 95)

tests/invocation/logs_transmission_saturated

@@ -0,0 +1,323 @@
+WARNING  gnpy.tools.json_io:json_io.py 
+	WARNING missing type_variety attribute in eqpt_config.json[Roadm]
+	default value is type_variety = default
+
+INFO     gnpy.tools.cli_examples:cli_examples.py source = 'trx Lannion_CAS'
+INFO     gnpy.tools.cli_examples:cli_examples.py destination = 'trx Lorient_KMA'
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in Lorient_KMA to Loudeac
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: effective gain in Node east edfa in Lannion_CAS to Stbrieuc
+	is above user specified amplifier std_low_gain
+	max flat gain: 16dB ; required gain: 21.18dB. Please check amplifier type.
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: effective gain in Node east edfa in Stbrieuc to Rennes_STA
+	is below user specified amplifier std_medium_gain
+	min gain: 15dB ; required gain: 12.0dB. Please check amplifier type.
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in Rennes_STA to Stbrieuc
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: effective gain in Node east edfa in Lannion_CAS to Morlaix
+	is above user specified amplifier std_low_gain
+	max flat gain: 16dB ; required gain: 21.18dB. Please check amplifier type.
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in Brest_KLA to Morlaix
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in Lorient_KMA to Loudeac
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: effective gain in Node west edfa in Lannion_CAS to Corlay
+	is above user specified amplifier test
+	max flat gain: 25dB ; required gain: 28.0dB. Please check amplifier type.
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in Lorient_KMA to Vannes_KBE
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in Vannes_KBE to Lorient_KMA
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in Lorient_KMA to Quimper
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in Quimper to Lorient_KMA
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in Brest_KLA to Quimper
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in Vannes_KBE to Lorient_KMA
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in Lorient_KMA to Vannes_KBE
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in Vannes_KBE to Ploermel
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in Ploermel to Vannes_KBE
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in Rennes_STA to Ploermel
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in Rennes_STA to Stbrieuc
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in Stbrieuc to Rennes_STA
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in Lannion_CAS to Stbrieuc
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in Rennes_STA to Ploermel
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in Vannes_KBE to Ploermel
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in Brest_KLA to Morlaix
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: effective gain in Node east edfa in Brest_KLA to Quimper
+	is above user specified amplifier std_low_gain
+	max flat gain: 16dB ; required gain: 21.18dB. Please check amplifier type.
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in Quimper to Lorient_KMA
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in Lorient_KMA to Quimper
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in a to b
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in b to a
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in a to c
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in c to a
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in b to a
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in a to b
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in b to f
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in f to b
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in c to a
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in a to c
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in d to c
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in c to f
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in f to c
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in d to c
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in c to d
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in d to e
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in e to d
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in e to d
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in d to e
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in e to g
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in g to e
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in f to c
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in c to f
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in f to b
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in b to f
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in f to h
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in h to f
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in g to e
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in e to g
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in g to h
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in h to g
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in h to f
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in f to h
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node east edfa in h to g
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+WARNING  gnpy.core.network:network.py 
+	WARNING: target gain and power in node west edfa in g to h
+	is beyond all available amplifiers capabilities and/or extended_gain_range:
+	a power reduction of -1.82 is applied
+
+INFO     gnpy.tools.worker_utils:worker_utils.py Power mode is set to True=> it can be modified in eqpt_config.json - Span
+INFO     gnpy.tools.worker_utils:worker_utils.py Now propagating between trx Lannion_CAS and trx Lorient_KMA
+INFO     gnpy.tools.worker_utils:worker_utils.py 
+Channels propagating: (Input optical power deviation in span = 0.00dB,
+                       spacing = 50.00GHz,
+                       transceiver output power = 3.00dBm,
+                       nb_channels = 96)

tests/invocation/multiband_transmission

@@ -0,0 +1,294 @@
+User input for spectrum used for propagation instead of SI
+Power mode is set to True
+=> it can be modified in eqpt_config.json - Span
+
+There are 3 fiber spans over 240 km between trx Site_A and trx Site_D
+
+Now propagating between trx Site_A and trx Site_D:
+Reference used for design: (Input optical power reference in span = 0.00dBm,
+                            spacing = 50.00GHz
+                            nb_channels = 76)
+
+Channels propagating: (Input optical power deviation in span = cband: 0.00, lband: 0.00dB,
+                       spacing = cband: 50.00, lband: 50.00GHz,
+                       transceiver output power = cband: 0.00, lband: 0.00dBm,
+                       nb_channels = 149)
+Input optical power reference in span = 0.00 dBm:
+Transceiver trx Site_A
+  GSNR (0.1nm, dB):          cband: 40.00, lband: 40.00
+  GSNR (signal bw, dB):      cband: 35.92, lband: 35.92
+  OSNR ASE (0.1nm, dB):      cband: 40.00, lband: 40.00
+  OSNR ASE (signal bw, dB):  cband: 35.92, lband: 35.92
+  CD (ps/nm):                0.00
+  PMD (ps):                  0.00
+  PDL (dB):                  0.00
+  Latency (ms):              0.00
+  Actual pch out (dBm):      cband: 0.00, lband: 0.00
+Roadm roadm Site_A
+  Type_variety:            default
+  Reference loss (dB):     20.00
+  Actual loss (dB):        cband: 20.00, lband: 20.00
+  Reference pch out (dBm): -20.00
+  Actual pch out (dBm):    cband: -20.00, lband: -20.00
+Multiband_amplifier east edfa in Site_A to Site_B
+  type_variety:           std_medium_gain_multiband
+  type_variety:           std_medium_gain_C    type_variety:           std_medium_gain_L  
+  effective gain(dB):     20.90                effective gain(dB):     22.19              
+  (before att_in and before output VOA)        (before att_in and before output VOA)      
+  tilt-target(dB)         0.00                 tilt-target(dB)         0.00               
+  noise figure (dB):      6.38                 noise figure (dB):      6.19               
+  (including att_in)                           (including att_in)                         
+  pad att_in (dB):        0.00                 pad att_in (dB):        0.00               
+  Power In (dBm):         -1.08                Power In (dBm):         -1.49              
+  Power Out (dBm):        19.83                Power Out (dBm):        20.71              
+  Delta_P (dB):           0.90                 Delta_P (dB):           2.19               
+  target pch (dBm):       0.90                 target pch (dBm):       3.00               
+  actual pch out (dBm):   -2.09                actual pch out (dBm):   -0.80              
+  output VOA (dB):        3.00                 output VOA (dB):        3.00               
+Fiber          fiber (Site_A → Site_B)-
+  type_variety:                SSMF
+  length (km):                 75.00
+  pad att_in (dB):             0.00
+  total loss (dB):             15.00
+  (includes conn loss (dB) in: 0.00 out: 0.00)
+  (conn loss out includes EOL margin defined in eqpt_config.json)
+  reference pch out (dBm):     -17.10
+  actual pch out (dBm):        cband: -17.09, lband: -15.80
+Multiband_amplifier east edfa in Site_B to Site_C
+  type_variety:           std_medium_gain_multiband
+  type_variety:           std_medium_gain_C    type_variety:           std_medium_gain_L  
+  effective gain(dB):     18.00                effective gain(dB):     18.00              
+  (before att_in and before output VOA)        (before att_in and before output VOA)      
+  tilt-target(dB)         0.00                 tilt-target(dB)         0.00               
+  noise figure (dB):      7.38                 noise figure (dB):      7.38               
+  (including att_in)                           (including att_in)                         
+  pad att_in (dB):        0.00                 pad att_in (dB):        0.00               
+  Power In (dBm):         1.83                 Power In (dBm):         2.71               
+  Power Out (dBm):        19.84                Power Out (dBm):        20.72              
+  Delta_P (dB):           0.90                 Delta_P (dB):           2.19               
+  target pch (dBm):       0.90                 target pch (dBm):       3.00               
+  actual pch out (dBm):   -2.09                actual pch out (dBm):   -0.79              
+  output VOA (dB):        3.00                 output VOA (dB):        3.00               
+Fiber          fiber (Site_B → Site_C)-
+  type_variety:                SSMF
+  length (km):                 80.00
+  pad att_in (dB):             0.00
+  total loss (dB):             16.80
+  (includes conn loss (dB) in: 0.00 out: 0.00)
+  (conn loss out includes EOL margin defined in eqpt_config.json)
+  reference pch out (dBm):     -18.90
+  actual pch out (dBm):        cband: -18.88, lband: -17.59
+Multiband_amplifier east edfa in Site_C to Site_D
+  type_variety:           std_medium_gain_multiband
+  type_variety:           std_medium_gain_C    type_variety:           std_medium_gain_L  
+  effective gain(dB):     19.80                effective gain(dB):     19.80              
+  (before att_in and before output VOA)        (before att_in and before output VOA)      
+  tilt-target(dB)         0.00                 tilt-target(dB)         0.00               
+  noise figure (dB):      6.63                 noise figure (dB):      6.63               
+  (including att_in)                           (including att_in)                         
+  pad att_in (dB):        0.00                 pad att_in (dB):        0.00               
+  Power In (dBm):         0.04                 Power In (dBm):         0.92               
+  Power Out (dBm):        19.84                Power Out (dBm):        20.72              
+  Delta_P (dB):           0.90                 Delta_P (dB):           2.19               
+  target pch (dBm):       0.90                 target pch (dBm):       3.00               
+  actual pch out (dBm):   -2.08                actual pch out (dBm):   -0.79              
+  output VOA (dB):        3.00                 output VOA (dB):        3.00               
+Fiber          fiber (Site_C → Site_D)-
+  type_variety:                SSMF
+  length (km):                 85.00
+  pad att_in (dB):             0.00
+  total loss (dB):             18.70
+  (includes conn loss (dB) in: 0.00 out: 0.00)
+  (conn loss out includes EOL margin defined in eqpt_config.json)
+  reference pch out (dBm):     -20.80
+  actual pch out (dBm):        cband: -20.78, lband: -19.49
+Multiband_amplifier west edfa in Site_D to Site_C
+  type_variety:           std_medium_gain_multiband
+  type_variety:           std_medium_gain_C    type_variety:           std_medium_gain_L  
+  effective gain(dB):     21.70                effective gain(dB):     21.70              
+  (before att_in and before output VOA)        (before att_in and before output VOA)      
+  tilt-target(dB)         0.00                 tilt-target(dB)         0.00               
+  noise figure (dB):      6.25                 noise figure (dB):      6.25               
+  (including att_in)                           (including att_in)                         
+  pad att_in (dB):        0.00                 pad att_in (dB):        0.00               
+  Power In (dBm):         -1.85                Power In (dBm):         -0.97              
+  Power Out (dBm):        19.85                Power Out (dBm):        20.73              
+  Delta_P (dB):           0.90                 Delta_P (dB):           2.19               
+  target pch (dBm):       0.90                 target pch (dBm):       3.00               
+  actual pch out (dBm):   -2.07                actual pch out (dBm):   -0.78              
+  output VOA (dB):        3.00                 output VOA (dB):        3.00               
+Roadm roadm Site_D
+  Type_variety:            default
+  Reference loss (dB):     17.90
+  Actual loss (dB):        cband: 17.93, lband: 19.22
+  Reference pch out (dBm): -20.00
+  Actual pch out (dBm):    cband: -20.00, lband: -20.00
+Transceiver trx Site_D
+  GSNR (0.1nm, dB):          cband: 24.89, lband: 25.36
+  GSNR (signal bw, dB):      cband: 20.80, lband: 21.28
+  OSNR ASE (0.1nm, dB):      cband: 25.55, lband: 26.51
+  OSNR ASE (signal bw, dB):  cband: 21.47, lband: 22.43
+  CD (ps/nm):                4008.00
+  PMD (ps):                  0.62
+  PDL (dB):                  0.00
+  Latency (ms):              1.18
+  Actual pch out (dBm):      cband: 0.00, lband: 0.00
+
+Transmission result for input optical power reference in span = 0.00 dBm:
+  Final GSNR (0.1 nm): 25.11 dB
+
+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                 186.55000                    -20.03                       22.46                       29.10                       21.61
+    2                 186.60000                    -20.03                       22.46                       28.59                       21.51
+    3                 186.65000                    -20.03                       22.46                       28.36                       21.47
+    4                 186.70000                    -20.03                       22.46                       28.22                       21.44
+    5                 186.75000                    -20.03                       22.46                       28.12                       21.41
+    6                 186.80000                    -20.03                       22.46                       28.04                       21.40
+    7                 186.85000                    -20.03                       22.46                       27.98                       21.38
+    8                 186.90000                    -20.03                       22.45                       27.92                       21.37
+    9                 186.95000                    -20.03                       22.45                       27.88                       21.36
+   10                 187.00000                    -20.03                       22.45                       27.84                       21.35
+   11                 187.05000                    -20.03                       22.45                       27.80                       21.34
+   12                 187.10000                    -20.03                       22.45                       27.77                       21.33
+   13                 187.15000                    -20.03                       22.45                       27.74                       21.32
+   14                 187.20000                    -20.03                       22.45                       27.71                       21.32
+   15                 187.25000                    -20.03                       22.45                       27.69                       21.31
+   16                 187.30000                    -20.03                       22.45                       27.67                       21.31
+   17                 187.35000                    -20.03                       22.45                       27.65                       21.30
+   18                 187.40000                    -20.03                       22.44                       27.63                       21.29
+   19                 187.45000                    -20.03                       22.44                       27.61                       21.29
+   20                 187.50000                    -20.03                       22.44                       27.60                       21.29
+   21                 187.55000                    -20.03                       22.44                       27.58                       21.28
+   22                 187.60000                    -20.03                       22.44                       27.57                       21.28
+   23                 187.65000                    -20.03                       22.44                       27.55                       21.27
+   24                 187.70000                    -20.03                       22.44                       27.54                       21.27
+   25                 187.75000                    -20.03                       22.44                       27.53                       21.27
+   26                 187.80000                    -20.03                       22.44                       27.52                       21.26
+   27                 187.85000                    -20.03                       22.44                       27.51                       21.26
+   28                 187.90000                    -20.03                       22.43                       27.50                       21.26
+   29                 187.95000                    -20.03                       22.43                       27.49                       21.25
+   30                 188.00000                    -20.03                       22.43                       27.48                       21.25
+   31                 188.05000                    -20.03                       22.43                       27.47                       21.25
+   32                 188.10000                    -20.03                       22.43                       27.47                       21.25
+   33                 188.15000                    -20.03                       22.43                       27.46                       21.24
+   34                 188.20000                    -20.03                       22.43                       27.45                       21.24
+   35                 188.25000                    -20.03                       22.43                       27.45                       21.24
+   36                 188.30000                    -20.03                       22.43                       27.44                       21.24
+   37                 188.35000                    -20.03                       22.43                       27.44                       21.23
+   38                 188.40000                    -20.03                       22.42                       27.43                       21.23
+   39                 188.45000                    -20.03                       22.42                       27.43                       21.23
+   40                 188.50000                    -20.03                       22.42                       27.43                       21.23
+   41                 188.55000                    -20.03                       22.42                       27.42                       21.23
+   42                 188.60000                    -20.03                       22.42                       27.42                       21.23
+   43                 188.65000                    -20.03                       22.42                       27.42                       21.23
+   44                 188.70000                    -20.03                       22.42                       27.42                       21.22
+   45                 188.75000                    -20.03                       22.42                       27.42                       21.22
+   46                 188.80000                    -20.03                       22.42                       27.42                       21.22
+   47                 188.85000                    -20.03                       22.41                       27.42                       21.22
+   48                 188.90000                    -20.03                       22.41                       27.42                       21.22
+   49                 188.95000                    -20.03                       22.41                       27.42                       21.22
+   50                 189.00000                    -20.03                       22.41                       27.43                       21.22
+   51                 189.05000                    -20.03                       22.41                       27.43                       21.22
+   52                 189.10000                    -20.03                       22.41                       27.44                       21.22
+   53                 189.15000                    -20.03                       22.41                       27.44                       21.22
+   54                 189.20000                    -20.03                       22.41                       27.45                       21.22
+   55                 189.25000                    -20.03                       22.41                       27.46                       21.23
+   56                 189.30000                    -20.03                       22.41                       27.46                       21.23
+   57                 189.35000                    -20.03                       22.40                       27.47                       21.23
+   58                 189.40000                    -20.03                       22.40                       27.49                       21.23
+   59                 189.45000                    -20.03                       22.40                       27.50                       21.23
+   60                 189.50000                    -20.03                       22.40                       27.51                       21.24
+   61                 189.55000                    -20.03                       22.40                       27.53                       21.24
+   62                 189.60000                    -20.03                       22.40                       27.55                       21.24
+   63                 189.65000                    -20.03                       22.40                       27.58                       21.25
+   64                 189.70000                    -20.03                       22.40                       27.60                       21.25
+   65                 189.75000                    -20.03                       22.40                       27.63                       21.26
+   66                 189.80000                    -20.03                       22.40                       27.67                       21.27
+   67                 189.85000                    -20.03                       22.39                       27.72                       21.28
+   68                 189.90000                    -20.03                       22.39                       27.78                       21.29
+   69                 189.95000                    -20.03                       22.39                       27.86                       21.31
+   70                 190.00000                    -20.03                       22.39                       27.97                       21.33
+   71                 190.05000                    -20.03                       22.39                       28.21                       21.38
+   72                 191.25000                    -20.03                       21.51                       28.66                       20.74
+   73                 191.30000                    -20.03                       21.51                       28.96                       20.79
+   74                 191.35000                    -20.03                       21.51                       29.16                       20.82
+   75                 191.40000                    -20.03                       21.50                       29.32                       20.84
+   76                 191.45000                    -20.03                       21.50                       29.51                       20.87
+   77                 191.50000                    -20.03                       21.50                       29.75                       20.90
+   78                 191.55000                    -20.03                       21.50                       30.22                       20.95
+   79                 191.60000                    -20.03                       21.50                       30.96                       21.03
+   80                 191.65000                    -20.03                       21.50                       30.46                       20.98
+   81                 191.70000                    -20.03                       21.50                       30.23                       20.95
+   82                 191.75000                    -20.03                       21.50                       30.08                       20.93
+   83                 191.80000                    -20.03                       21.50                       29.97                       20.92
+   84                 191.85000                    -20.03                       21.50                       29.88                       20.91
+   85                 191.90000                    -20.03                       21.49                       29.81                       20.90
+   86                 191.95000                    -20.03                       21.49                       29.75                       20.89
+   87                 192.00000                    -20.03                       21.49                       29.70                       20.88
+   88                 192.05000                    -20.03                       21.49                       29.65                       20.87
+   89                 192.10000                    -20.03                       21.49                       29.61                       20.87
+   90                 192.15000                    -20.03                       21.49                       29.57                       20.86
+   91                 192.20000                    -20.03                       21.49                       29.54                       20.86
+   92                 192.25000                    -20.03                       21.49                       29.50                       20.85
+   93                 192.30000                    -20.03                       21.49                       29.47                       20.85
+   94                 192.35000                    -20.03                       21.49                       29.44                       20.84
+   95                 192.40000                    -20.03                       21.48                       29.42                       20.84
+   96                 192.45000                    -20.03                       21.48                       29.39                       20.83
+   97                 192.50000                    -20.03                       21.48                       29.37                       20.83
+   98                 192.55000                    -20.03                       21.48                       29.35                       20.82
+   99                 192.60000                    -20.03                       21.48                       29.33                       20.82
+  100                 192.65000                    -20.03                       21.48                       29.31                       20.82
+  101                 192.70000                    -20.03                       21.48                       29.29                       20.81
+  102                 192.75000                    -20.03                       21.48                       29.27                       20.81
+  103                 192.80000                    -20.03                       21.48                       29.25                       20.81
+  104                 192.85000                    -20.03                       21.47                       29.23                       20.80
+  105                 192.90000                    -20.03                       21.47                       29.22                       20.80
+  106                 192.95000                    -20.03                       21.47                       29.20                       20.80
+  107                 193.00000                    -20.03                       21.47                       29.18                       20.79
+  108                 193.05000                    -20.03                       21.47                       29.17                       20.79
+  109                 193.10000                    -20.03                       21.47                       29.16                       20.79
+  110                 193.15000                    -20.03                       21.47                       29.14                       20.78
+  111                 193.20000                    -20.03                       21.47                       29.13                       20.78
+  112                 193.25000                    -20.03                       21.47                       29.12                       20.78
+  113                 193.30000                    -20.03                       21.47                       29.10                       20.78
+  114                 193.35000                    -20.03                       21.46                       29.09                       20.77
+  115                 193.40000                    -20.03                       21.46                       29.08                       20.77
+  116                 193.45000                    -20.03                       21.46                       29.07                       20.77
+  117                 193.50000                    -20.03                       21.46                       29.06                       20.77
+  118                 193.55000                    -20.03                       21.46                       29.05                       20.76
+  119                 193.60000                    -20.03                       21.46                       29.04                       20.76
+  120                 193.65000                    -20.03                       21.46                       29.03                       20.76
+  121                 193.70000                    -20.03                       21.46                       29.02                       20.76
+  122                 193.75000                    -20.03                       21.46                       29.01                       20.75
+  123                 193.80000                    -20.03                       21.46                       29.00                       20.75
+  124                 193.85000                    -20.03                       21.45                       28.99                       20.75
+  125                 193.90000                    -20.03                       21.45                       28.99                       20.75
+  126                 193.95000                    -20.03                       21.45                       28.98                       20.75
+  127                 194.00000                    -20.03                       21.45                       28.97                       20.74
+  128                 194.05000                    -20.03                       21.45                       28.97                       20.74
+  129                 194.10000                    -20.03                       21.45                       28.96                       20.74
+  130                 194.15000                    -20.03                       21.45                       28.96                       20.74
+  131                 194.20000                    -20.03                       21.45                       28.96                       20.74
+  132                 194.25000                    -20.03                       21.45                       28.95                       20.74
+  133                 194.30000                    -20.03                       21.45                       28.95                       20.74
+  134                 194.35000                    -20.03                       21.44                       28.95                       20.73
+  135                 194.40000                    -20.03                       21.44                       28.95                       20.73
+  136                 194.45000                    -20.03                       21.44                       28.95                       20.73
+  137                 194.50000                    -20.03                       21.44                       28.96                       20.73
+  138                 194.55000                    -20.03                       21.44                       28.96                       20.73
+  139                 194.60000                    -20.03                       21.44                       28.97                       20.73
+  140                 194.65000                    -20.03                       21.44                       28.98                       20.73
+  141                 194.70000                    -20.03                       21.44                       28.99                       20.74
+  142                 194.75000                    -20.03                       21.44                       29.01                       20.74
+  143                 194.80000                    -20.03                       21.44                       29.04                       20.74
+  144                 194.85000                    -20.03                       21.43                       29.07                       20.74
+  145                 194.90000                    -20.03                       21.43                       29.12                       20.75
+  146                 194.95000                    -20.03                       21.43                       29.18                       20.76
+  147                 195.00000                    -20.03                       21.43                       29.28                       20.77
+  148                 195.05000                    -20.03                       21.43                       29.44                       20.79
+  149                 195.10000                    -20.03                       21.43                       29.84                       20.84
+
+(Invalid source node 'Site_A' replaced with trx Site_A)
+
+(Invalid destination node 'Site_D' replaced with trx Site_D)