Merge pull request #302 from Telecominfraproject/develop

Merge develop into master in preparation for the 1.8 release

Highlights for the upcoming release:

- Raman simulation
- automatic building of Docker images
- bugfixes, refactoring, CI and docs improvements

.codecov.yml

@@ -0,0 +1 @@
+comment: off

.docker-entry.sh

@@ -0,0 +1,3 @@
+#!/bin/bash
+cp -nr /oopt-gnpy/examples /shared
+exec "$@"

.docker-travis.sh

@@ -0,0 +1,47 @@
+#!/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} ./transmission_main_example.py
+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/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,29 @@
+---
+name: Bug report
+about: Create a report to help us improve
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Steps to reproduce the behavior:
+1. Go to '...'
+2. Click on '....'
+3. Scroll down to '....'
+4. See error
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+**Environment:**
+ - OS: [e.g. Windows]
+ - Python Version [e.g, 3.7]
+ - Anaconda Version [e.g. 3.7]
+
+**Additional context**
+Add any other context about the problem here.

.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,17 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.

.gitignore

@@ -2,6 +2,7 @@
 __pycache__/
 *.py[cod]
 *$py.class
+.ipynb_checkpoints
 
 # C extensions
 *.so

.readthedocs.yml

@@ -0,0 +1,4 @@
+build:
+    image: latest
+python:
+    version: 3.6

.travis.yml

@@ -1,9 +1,27 @@
+dist: xenial
+sudo: false
 language: python
+services: docker
 python:
   - "3.6"
-# command to install dependencies
-install:
-  - pip install -r requirements.txt
-# command to run tests
+  - "3.7"
+install: skip
 script:
-  - pytest
+  - python setup.py install
+  - pip install pytest-cov rstcheck
+  - pytest --cov-report=xml --cov=gnpy
+  - rstcheck --ignore-roles cite --ignore-directives automodule --recursive --ignore-messages '(Duplicate explicit target name.*)' .
+  - ./examples/transmission_main_example.py
+  - ./examples/path_requests_run.py
+  - ./examples/transmission_main_example.py examples/raman_edfa_example_network.json --sim examples/sim_params.json --show-channels
+  - sphinx-build 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

AUTHORS.rst

@@ -0,0 +1,29 @@
+gnpy is written and maintained by the Telecom Infra Project with work
+contributed by the following TIP members.
+
+To learn how to contribute, please see CONTRIBUTING.md
+
+(*in alphabetical order*)
+
+- Alessio Ferrari (Politecnico di Torino) <alessio.ferrari@polito.it>
+- Anders Lindgren (Telia Company) <Anders.X.Lindgren@teliacompany.com>
+- Andrea d'Amico (Politecnico di Torino) <andrea.damico@polito.it>
+- Brian Taylor (Facebook) <briantaylor@fb.com>
+- David Boertjes (Ciena) <dboertje@ciena.com>
+- Diego Landa (Facebook) <dlanda@fb.com>
+- Esther Le Rouzic (Orange) <esther.lerouzic@orange.com>
+- Gabriele Galimberti (Cisco) <ggalimbe@cisco.com>
+- Gert Grammel (Juniper Networks) <ggrammel@juniper.net>
+- Gilad Goldfarb (Facebook) <giladg@fb.com>
+- James Powell (Telecom Infra Project) <james.powell@telecominfraproject.com>
+- Jan Kundrát (Telecom Infra Project) <jan.kundrat@telecominfraproject.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>
+- 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>
+- Xufeng Liu (Jabil) <xufeng_liu@jabil.com>

Contributors.md

@@ -1,17 +0,0 @@
-Contributors in alphabetical order
-==================================
-
-Name | Surname | Affiliation | Contact
------|---------|-------------|--------
-Alessio | Ferrari | Politecnico di Torino | alessio.ferrari@polito.it
-Brian | Taylor | Facebook | briantaylor@fb.com
-David | Boertjes | Ciena | dboertje@ciena.com
-Esther | Le Rouzic | Orange | esther.lerouzic@orange.com
-Gabriele | Galimberti | Cisco | ggalimbe@cisco.com
-Gert | Grammel | Juniper Networks | ggrammel@juniper.net
-Gilad | Goldfarb | Facebook | giladg@fb.com
-James | Powell | Consultant | james@dontusethiscode.com
-Jeanluc | Auge | Orange | jeanluc.auge@orange.com
-Liu | Xufeng | Jabil | Xufeng_Liu@jabil.com
-Mattia | Cantono | Politecnico di Torino | mattia.cantono@polito.it
-Vittorio | Curri | Politecnico di Torino | vittorio.curri@polito.it

Dockerfile

@@ -0,0 +1,7 @@
+FROM python:3.7-slim
+COPY . /oopt-gnpy
+WORKDIR /oopt-gnpy
+RUN python setup.py install
+WORKDIR /shared/examples
+ENTRYPOINT ["/oopt-gnpy/.docker-entry.sh"]
+CMD ["/bin/bash"]

Excel_userguide.rst

@@ -0,0 +1,251 @@
+
+How to prepare the Excel input file
+-----------------------------------
+
+`examples/transmission_main_example.py <examples/transmission_main_example.py>`_ gives the possibility to use an excel input file instead of a json file. The program then will generate the corresponding json file for you.
+
+The file named 'meshTopologyExampleV2.xls' is an example.
+
+In order to work the excel file MUST contain at least 2 sheets:
+
+- Nodes
+- Links
+
+(In progress) The File MAY contain an additional sheet:
+
+- Eqt
+- Service
+
+Nodes sheet
+-----------
+
+Nodes sheet contains nine columns.
+Each line represents a 'node' (ROADM site or an in line amplifier site ILA or a Fused)::
+
+  City (Mandatory) ; State ; Country ; Region ; Latitude ; Longitude ; Type
+
+- **City** is used for the name of a node of the graph. It accepts letters, numbers,underscore,dash, blank... (not exhaustive). The user may want to avoid commas for future CSV exports.
+
+   **City name MUST be unique** 
+
+- **Type** is not mandatory. 
+
+  - If not filled, it will be interpreted as an 'ILA' site if node degree is 2 and as a ROADM otherwise.
+  - If filled, it can take "ROADM", "FUSED" or "ILA" values. If another string is used, it will be considered as not filled. FUSED means that ingress and egress spans will be fused together.  
+
+- *State*, *Country*, *Region* are not mandatory.
+  "Region" is a holdover from the CORONET topology reference file `CORONET_Global_Topology.xls <examples/CORONET_Global_Topology.xls>`_. CORONET separates its network into geographical regions (Europe, Asia, Continental US.) This information is not used by gnpy.
+
+- *Longitude*, *Latitude* are not mandatory. If filled they should contain numbers.
+
+- **Booster_restriction** and **Preamp_restriction** are not mandatory.
+  If used, they must contain one or several amplifier type_variety names separated by ' | '. This information is used to restrict types of amplifiers used in a ROADM node during autodesign. If a ROADM booster or preamp is already specified in the Eqpt sheet , the field is ignored. The field is also ignored if the node is not a ROADM node.
+
+**There MUST NOT be empty line(s) between two nodes lines**
+
+
+Links sheet
+-----------
+
+Links sheet must contain sixteen columns::
+
+                   <--           east cable from a to z                                   --> <--                  west from z to                                   -->
+   NodeA ; NodeZ ; Distance km ; Fiber type ; Lineic att ; Con_in ; Con_out ; PMD ; Cable Id ; Distance km ; Fiber type ; Lineic att ; Con_in ; Con_out ; PMD ; Cable Id
+
+
+Links sheets MUST contain all links between nodes defined in Nodes sheet.
+Each line represents a 'bidir link' between two nodes. The two directions are represented on a single line with "east cable from a to z" fields and "west from z to a" fields. Values for 'a to z' may be different from values from 'z to a'. 
+Since both direction of a bidir 'a-z' link are described on the same line (east and west), 'z to a' direction MUST NOT be repeated in a different line. If repeated, it will generate another parrallel bidir link between the same end nodes.
+
+
+Parameters for "east cable from a to z" and "west from z to a" are detailed in 2x7 columns. If not filled, "west from z to a" is copied from "east cable from a to z".
+
+For example, a line filled with::
+
+  node6 ; node3 ; 80 ; SSMF ; 0.2 ; 0.5 ; 0.5 ; 0.1 ; cableB ;  ;  ; 0.21 ; 0.2 ;  ;  ;  
+
+will generate a unidir fiber span from node6 to node3 with::
+ 
+  [node6 node3 80 SSMF 0.2 0.5 0.5 0.1 cableB] 
+
+and a fiber span from node3 to node6::
+
+ [node6 node3 80 SSMF 0.21 0.2 0.5 0.1 cableB] attributes. 
+
+- **NodeA** and **NodeZ** are Mandatory. 
+  They are the two endpoints of the link. They MUST contain a node name from the **City** names listed in Nodes sheet.
+
+- **Distance km** is not mandatory. 
+  It is the link length.
+
+  - If filled it MUST contain numbers. If empty it is replaced by a default "80" km value. 
+  - If value is below 150 km, it is considered as a single (bidirectional) fiber span.
+  - If value is over 150 km the `transmission_main_example.py <examples/transmission_main_example.py>`_ program will automatically suppose that intermediate span description are required and will generate fiber spans elements with "_1","_2", ... trailing strings which are not visible in the json output. The reason for the splitting is that current edfa usually do not support large span loss. The current assumption is that links larger than 150km will require intermediate amplification. This value will be revisited when Raman amplification is added”
+
+- **Fiber type** is not mandatory. 
+
+  If filled it must contain types listed in `eqpt_config.json <examples/eqpt_config.json>`_ in "Fiber" list "type_variety".
+  If not filled it takes "SSMF" as default value.
+
+- **Lineic att** is not mandatory. 
+
+  It is the lineic attenuation expressed in dB/km.
+  If filled it must contain positive numbers.
+  If not filled it takes "0.2" dB/km value
+
+- *Con_in*, *Con_out* are not mandatory. 
+
+  They are the connector loss in dB at ingress and egress of the fiber spans.
+  If filled they must contain positive numbers.
+  If not filled they take "0.5" dB default value.
+
+- *PMD* is not mandatory and and is not used yet. 
+
+  It is the PMD value of the link in ps.
+  If filled they must contain positive numbers.
+  If not filled, it takes "0.1" ps value.
+
+- *Cable Id* is not mandatory. 
+  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)
+
+Eqpt sheet 
+----------
+
+Eqt sheet is optional. It lists the amplifiers types and characteristics on each degree of the *Node A* line.
+Eqpt sheet must contain twelve columns::
+
+                   <--           east cable from a to z        --> <--        west from z to a                 -->
+  Node A ; Node Z ; amp type ; att_in ; amp gain ; tilt ; att_out ; amp type ; att_in ; amp gain ; tilt ; att_out
+
+If the sheet is present, it MUST have as many lines as egress directions of ROADMs defined in Links Sheet. 
+
+For example, consider the following list of links (A,B and C being a ROADM and amp# ILAs)
+
+::
+
+  A    - amp1
+  amp1 - amp2
+  Amp2 - B
+  A    - amp3
+  amp3 - C
+
+then Eqpt sheet should contain:
+  - one line for each ILAs: amp1, amp2, amp3 
+  - one line for each degree 1 ROADMs B and C  
+  - two lines for ROADM A  which is a degree 2 ROADM 
+
+::
+
+  A    - amp1
+  amp1 - amp2
+  Amp2 - B
+  A    - amp3
+  amp3 - C
+  B    - amp2
+  C    - amp3
+
+
+In case you already have filled Nodes and Links sheets `create_eqpt_sheet.py <examples/create_eqpt_sheet.py>`_  can be used to automatically create a template for the mandatory entries of the list.
+
+.. code-block:: shell
+
+    $ cd examples
+    $ python create_eqpt_sheet.py meshTopologyExampleV2.xls
+
+This generates a text file meshTopologyExampleV2_eqt_sheet.txt  whose content can be directly copied into the Eqt sheet of the excel file. The user then can fill the values in the rest of the columns.
+
+
+- **Node A** is mandatory. It is the name of the node (as listed in Nodes sheet).
+  If Node A is a 'ROADM' (Type attribute in sheet Node), its number of occurence must be equal to its degree.
+  If Node A is an 'ILA' it should appear only once.
+
+- **Node Z** is mandatory. It is the egress direction from the *Node A* site. Multiple Links between the same Node A and NodeZ is not supported.
+
+- **amp type** is not mandatory. 
+  If filled it must contain types listed in `eqpt_config.json <examples/eqpt_config.json>`_ in "Edfa" list "type_variety".
+  If not filled it takes "std_medium_gain" as default value.
+  If filled with fused, a fused element with 0.0 dB loss will be placed instead of an amplifier. This might be used to avoid booster amplifier on a ROADM direction.
+
+- **amp_gain** is not mandatory. It is the value to be set on the amplifier (in dB).
+  If not filled, it will be determined with design rules in the convert.py file.
+  If filled, it must contain positive numbers.
+
+- *att_in* and *att_out* are not mandatory and are not used yet. They are the value of the attenautor at input and output of amplifier (in dB).
+  If filled they must contain positive numbers.
+
+- *tilt* --TODO--
+
+# to be completed #
+
+(in progress)
+
+Service sheet 
+-------------
+
+Service sheet is optional. It lists the services for which path and feasibility must be computed with path_requests_run.py.
+
+Service sheet must contain 11 columns::  
+
+   route id ; Source ; Destination ; TRX type ; Mode ; System: spacing ; System: input power (dBm) ; System: nb of channels ;  routing: disjoint from ; routing: path ; routing: is loose?
+
+- **route id** is mandatory. It must be unique. It is the identifier of the request. It can be an integer or a string (do not  use blank or dash or coma)
+
+- **Source** is mandatory. It is the name of the source node (as listed in Nodes sheet). Source MUST be a ROADM node. (TODO: relax this and accept trx entries)
+
+- **Destination** is mandatory. It is the name of the destination node (as listed in Nodes sheet). Source MUST be a ROADM node. (TODO: relax this and accept trx entries)
+
+- **TRX type** is mandatory. They are the variety type and selected mode of the transceiver to be used for the propagation simulation. These modes MUST be defined in the equipment library. The format of the mode is used as the name of the mode. (TODO: maybe add another  mode id on Transceiver library ?). In particular the mode selection defines the channel baudrate to be used for the propagation simulation.
+
+- **mode** is optional. If not specified, the program will search for the mode of the defined transponder with the highest baudrate fitting within the spacing value. 
+
+- **System: spacing** is mandatory. Spacing is the channel spacing defined in GHz difined for the feasibility propagation simulation, assuming system full load.
+
+- **System: input power (dBm) ; System: nb of channels** are optional input defining the system parameters for the propagation simulation.
+
+  - input power is the channel optical input power in dBm
+  - nb of channels is the number of channels to be used for the simulation.
+
+- **routing: disjoint from ; routing: path ; routing: is loose?** are optional.
+
+  - disjoint from: identifies the requests from which this request must be disjoint. If filled it must contain request ids separated by ' | ' 
+  - path: is the set of ROADM nodes that must be used by the path. It must contain the list of ROADM names that the path must cross. TODO : only ROADM nodes are accepted in this release. Relax this with any type of nodes. If filled it must contain ROADM ids separated by ' | '. Exact names are required. 
+  - is loose?  'no' value means that the list of nodes should be strictly followed, while any other value means that the constraint may be relaxed if the node is not reachable. 
+
+- ** path bandwidth** is optional. It is the amount of capacity required between source and destination in Gbit/s. Default value is 0.0 Gbit/s. 
+
+path_requests_run.py
+------------------------
+
+**Usage**: path_requests_run.py [-h] [-v] [-o OUTPUT]
+                            [network_filename xls or json] [service_filename xls or json] [eqpt_filename json]
+
+.. code-block:: shell
+
+    $ cd examples
+    $ python path_requests_run.py meshTopologyExampleV2.xls service_file.json eqpt_file -o output_file.json
+
+A function that computes performances for a list of services provided in the service file (accepts json or excel format.
+
+if the service <file.xls> is in xls format, path_requests_run.py converts it to a json file <file_services.json> following the Yang model for requesting Path Computation defined in `draft-ietf-teas-yang-path-computation-01.txt <https://www.ietf.org/id/draft-ietf-teas-yang-path-computation-01.pdf>`_. For PSE use, additional fields with trx type and mode have been added to the te-bandwidth field. 
+
+A template for the json file can be found here: `service_template.json <service_template.json>`_
+
+
+If no output file is given, the computation is shown on standard output for demo.
+If a file is specified with the optional -o argument, the result of the computation is converted into a json format following  the Yang model for requesting Path Computation defined in `draft-ietf-teas-yang-path-computation-01.txt <https://www.ietf.org/id/draft-ietf-teas-yang-path-computation-01.pdf>`_. TODO: verify that this implementation is correct + give feedback to ietf on what is missing for our specific application.
+
+A template for the result of computation json file can be found here: `path_result_template.json <path_result_template.json>`_
+
+Important note: path_requests_run.py is not a network dimensionning tool : each service does not reserve spectrum, or occupy ressources such as transponders. It only computes path feasibility assuming the spectrum (between defined frequencies) is loaded with "nb of channels" spaced by "spacing" values as specified in the system parameters input in the service file, each cannel having the same characteristics in terms of baudrate, format, ... as the service transponder. The transceiver element acts as a "logical starting/stopping point" for the spectral information propagation. At that point it is not meant to represent the capacity of add drop ports
+As a result transponder type is not part of the network info. it is related to the list of services requests.
+
+In a next step we plan to provide required features to enable dimensionning : alocation of ressources, counting channels, limitation of the number of channels, ...
+
+(in progress)
+
+

README.rst

@@ -1,15 +1,21 @@
-====
+.. image:: docs/images/GNPy-banner.png
+   :width: 100%
+   :align: left
+   :alt: GNPy with an OLS system
+
+====================================================================
 `gnpy`: mesh optical network route planning and optimization library
-====
+====================================================================
 
 |docs| |build|
 
-**gnpy is an open-source, community-developed library for building route planning
-and optimization tools in real-world mesh optical networks.**
+**`gnpy` is an open-source, community-developed library for building route
+planning and optimization tools in real-world mesh optical networks.**
 
-`gnpy <http://github.com/telecominfraproject/gnpy>`_ is:
+`gnpy <http://github.com/telecominfraproject/oopt-gnpy>`__ is:
+--------------------------------------------------------------
 
-- a sponsored project of the `OOPT/PSE <http://telecominfraproject.com/project-groups-2/backhaul-projects/open-optical-packet-transport/>`_ working group of the `Telecom Infra Project <http://telecominfraproject.com>`_.
+- a sponsored project of the `OOPT/PSE <https://telecominfraproject.com/open-optical-packet-transport/>`_ working group of the `Telecom Infra Project <http://telecominfraproject.com>`_
 - fully community-driven, fully open source library
 - driven by a consortium of operators, vendors, and academic researchers
 - intended for rapid development of production-grade route planning tools
@@ -18,63 +24,159 @@ and optimization tools in real-world mesh optical networks.**
 
 Documentation: https://gnpy.readthedocs.io
 
-Installation
-------------
+Get In Touch
+~~~~~~~~~~~~
 
-``gnpy`` is hosted in the `Python Package Index <http://pypi.org/>`_ (`gnpy <https://pypi.org/project/gnpy/>`_). It can be installed via:
+There are `weekly calls <https://telecominfraproject.workplace.com/events/458339931322799/>`__ about our progress.
+Newcomers, users and telecom operators are especially welcome there.
+We encourage all interested people outside the TIP to `join the project <https://telecominfraproject.com/apply-for-membership/>`__.
+
+Branches and Tagged Releases
+----------------------------
+
+- all releases are `available via GitHub <https://github.com/Telecominfraproject/oopt-gnpy/releases>`_
+- the `master <https://github.com/Telecominfraproject/oopt-gnpy/tree/master>`_ branch contains stable, `validated code <https://github.com/Telecominfraproject/oopt-gnpy/wiki/Testing-for-Quality>`_. It is updated from develop on a release schedule determined by the OOPT-PSE Working Group.
+- the `develop <https://github.com/Telecominfraproject/oopt-gnpy/tree/develop>`_ branch contains the latest code under active development, which may not be fully validated and tested.
+
+How to Install
+--------------
+
+Using prebuilt Docker images
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Our `Docker images <https://hub.docker.com/r/telecominfraproject/oopt-gnpy>`_ contain everything needed to run all examples from this guide.
+Docker transparently fetches the image over the network upon first use.
+On Linux and Mac, run:
+
+
+.. code-block:: shell-session
+
+    $ docker run -it --rm --volume $(pwd):/shared telecominfraproject/oopt-gnpy
+    root@bea050f186f7:/shared/examples#
+
+On Windows, launch from Powershell as:
+
+.. code-block:: powershell
+
+    PS C:\> docker run -it --rm --volume ${PWD}:/shared telecominfraproject/oopt-gnpy
+    root@89784e577d44:/shared/examples#
+
+In both cases, a directory named ``examples/`` will appear in your current working directory.
+GNPy automaticallly populates it with example files from the current release.
+Remove that directory if you want to start from scratch.
+
+Using Python on your computer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+   **Note**: `gnpy` supports Python 3 only. Python 2 is not supported.
+   `gnpy` requires Python ≥3.6
+
+   **Note**: the `gnpy` maintainers strongly recommend the use of Anaconda for
+   managing dependencies.
+
+It is recommended that you use a "virtual environment" when installing `gnpy`.
+Do not install `gnpy` on your system Python.
+
+We recommend the use of the `Anaconda Python distribution <https://www.anaconda.com/download>`_ which comes with many scientific computing
+dependencies pre-installed. Anaconda creates a base "virtual environment" for
+you automatically. You can also create and manage your ``conda`` "virtual
+environments" yourself (see:
+https://conda.io/docs/user-guide/tasks/manage-environments.html)
+
+To activate your Anaconda virtual environment, you may need to do the
+following:
 
 .. code-block:: shell
 
-    $ pip install gnpy
+    $ source /path/to/anaconda/bin/activate # activate Anaconda base environment
+    (base) $                                # note the change to the prompt
 
-It can also be installed directly from the repo.
+You can check which Anaconda environment you are using with:
 
 .. code-block:: shell
 
-    $ git clone https://github.com/telecominfraproject/gnpy
-    $ cd gnpy
-    $ python setup.py install
+    (base) $ conda env list                          # list all environments
+    # conda environments:
+    #
+    base                  *  /src/install/anaconda3
 
-Both approaches above will handle installing any additional software dependencies.
+    (base) $ echo $CONDA_DEFAULT_ENV                 # show default environment
+    base
 
-    **Note**: *We recommend the use of the Anaconda Python distribution
-    (https://www.anaconda.com/download) which comes with many scientific
-    computing dependencies pre-installed.*
+You can check your version of Python with the following. If you are using
+Anaconda's Python 3, you should see similar output as below. Your results may
+be slightly different depending on your Anaconda installation path and the
+exact version of Python you are using.
 
-Instructions for Use
---------------------
+.. code-block:: shell
+
+    $ which python                   # check which Python executable is used
+    /path/to/anaconda/bin/python
+    $ python -V                      # check your Python version
+    Python 3.6.5 :: Anaconda, Inc.
+
+From within your Anaconda Python 3 environment, you can clone the master branch
+of the `gnpy` repo and install it with:
+
+.. code-block:: shell
+
+    $ git clone https://github.com/Telecominfraproject/oopt-gnpy # clone the repo
+    $ cd oopt-gnpy
+    $ python setup.py install                                    # install
+
+To test that `gnpy` was successfully installed, you can run this command. If it
+executes without a ``ModuleNotFoundError``, you have successfully installed
+`gnpy`.
+
+.. code-block:: shell
+
+    $ python -c 'import gnpy' # attempt to import gnpy
+
+    $ pytest                  # run tests
+
+Instructions for First Use
+--------------------------
 
 ``gnpy`` is a library for building route planning and optimization tools.
 
 It ships with a number of example programs. Release versions will ship with
 fully-functional programs.
 
-
     **Note**: *If you are a network operator or involved in route planning and
-    optimization for your organization, please contact project maintainer James
-    Powell <james.powell@telecominfraproject>. gnpy is looking for users with
+    optimization for your organization, please contact project maintainer Jan
+    Kundrát <jan.kundrat@telecominfraproject.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:
 
-**To get started, run the transmission example:**
+.. image:: https://telecominfraproject.github.io/oopt-gnpy/docs/images/transmission_main_example.svg
+   :width: 100%
+   :align: left
+   :alt: Running a simple simulation example
+   :target: https://asciinema.org/a/252295
 
-.. code-block:: shell
-
-    $ python examples/transmission_main_example.py
-
-By default, this script operates on a single span network defined in `examples/edfa/edfa_example_network.json <examples/edfa/edfa_example_network.json>`_
+By default, this script operates on a single span network defined in
+`examples/edfa_example_network.json <examples/edfa_example_network.json>`_
 
 You can specify a different network at the command line as follows. For
-example, to use the CORONET Continental US (CONUS) network defined in `examples/coronet_conus_example.json <examples/coronet_conus_example.json>`_:
+example, to use the CORONET Global network defined in
+`examples/CORONET_Global_Topology.json <examples/CORONET_Global_Topology.json>`_:
 
-.. code-block:: shell
+.. code-block:: shell-session
 
-    $ python examples/transmission_main_example.py examples/coronet_conus_example.json
+    $ ./examples/transmission_main_example.py examples/CORONET_Global_Topology.json
 
-This script will calculate the average signal osnr and snr across 93 network
-elements (transceiver, ROADMs, fibers, and amplifiers) between Abilene, Texas
-and Albany, New York.
+It is also possible to use an Excel file input (for example
+`examples/CORONET_Global_Topology.xls <examples/CORONET_Global_Topology.xls>`_).
+The Excel file will be processed into a JSON file with the same prefix. For
+further instructions on how to prepare the Excel input file, see
+`Excel_userguide.rst <Excel_userguide.rst>`_.
+
+The main transmission example will calculate the average signal OSNR and SNR
+across network elements (transceiver, ROADMs, fibers, and amplifiers)
+between two transceivers selected by the user. Additional details are provided by doing ``transmission_main_example.py -h``. (By default, for the CORONET Global
+network, it will show the transmission of spectral information between Abilene and Albany)
 
 This script calculates the average signal OSNR = |OSNR| and SNR = |SNR|.
 
@@ -87,16 +189,352 @@ interference noise.
 .. |Pase| replace:: P\ :sub:`ase`
 .. |Pnli| replace:: P\ :sub:`nli`
 
-The `transmission_main_example.py <examples/transmission_main_example.py>`_
-script propagates a specrum of 96 channels at 32 Gbaud, 50 GHz spacing and 0
-dBm/channel. These are not yet parametrized but can be modified directly in the
-script (via the SpectralInformation tuple) to accomodate any baud rate,
-spacing, power or channel count demand.
+Further Instructions for Use (`transmission_main_example.py`, `path_requests_run.py`)
+-------------------------------------------------------------------------------------
 
-The amplifier's gain is set to exactly compsenate for the loss in each network
-element. The amplifier is currently defined with gain range of 15 dB to 25 dB
-and 21 dBm max output power. Ripple and NF models are defined in
-`examples/edfa_config.json <examples/edfa_config.json>`_
+Design and transmission parameters are defined in a dedicated json file. By
+default, this information is read from `examples/eqpt_config.json
+<examples/eqpt_config.json>`_. This file defines the equipment libraries that
+can be customized (EDFAs, fibers, and transceivers).
+
+It also defines the simulation parameters (spans, ROADMs, and the spectral
+information to transmit.)
+
+The EDFA equipment library is a list of supported amplifiers. New amplifiers
+can be added and existing ones removed. Three different noise models are available:
+
+1. ``'type_def': 'variable_gain'`` is a simplified model simulating a 2-coil EDFA with internal, input and output VOAs. The NF vs gain response is calculated accordingly based on the input parameters: ``nf_min``, ``nf_max``, and ``gain_flatmax``. It is not a simple interpolation but a 2-stage NF calculation.
+2. ``'type_def': 'fixed_gain'`` is a fixed gain model.  `NF == Cte == nf0` if `gain_min < gain < gain_flatmax`
+3. ``'type_def': None`` is an advanced model. A detailed JSON configuration file is required (by default `examples/std_medium_gain_advanced_config.json <examples/std_medium_gain_advanced_config.json>`_). It uses a 3rd order polynomial where NF = f(gain), NF_ripple = f(frequency), gain_ripple = f(frequency), N-array dgt = f(frequency). Compared to the previous models, NF ripple and gain ripple are modelled.
+
+For all amplifier models:
+
++------------------------+-----------+-----------------------------------------+
+| field                  |   type    | description                             |
++========================+===========+=========================================+
+| ``type_variety``       | (string)  | a unique name to ID the amplifier in the|
+|                        |           | JSON/Excel template topology input file |
++------------------------+-----------+-----------------------------------------+
+| ``out_voa_auto``       | (boolean) | auto_design feature to optimize the     |
+|                        |           | amplifier output VOA. If true, output   |
+|                        |           | VOA is present and will be used to push |
+|                        |           | amplifier gain to its maximum, within   |
+|                        |           | EOL power margins.                      |
++------------------------+-----------+-----------------------------------------+
+| ``allowed_for_design`` | (boolean) | If false, the amplifier will not be     |
+|                        |           | picked by auto-design but it can still  |
+|                        |           | be used as a manual input (from JSON or |
+|                        |           | Excel template topology files.)         |
++------------------------+-----------+-----------------------------------------+
+
+The fiber library currently describes SSMF and NZDF but additional fiber types can be entered by the user following the same model:
+
++----------------------+-----------+-----------------------------------------+
+| field                | type      | description                             |
++======================+===========+=========================================+
+| ``type_variety``     | (string)  | a unique name to ID the fiber in the    |
+|                      |           | JSON or Excel template topology input   |
+|                      |           | file                                    |
++----------------------+-----------+-----------------------------------------+
+| ``dispersion``       | (number)  | (s.m-1.m-1)                             |
++----------------------+-----------+-----------------------------------------+
+| ``gamma``            | (number)  | 2pi.n2/(lambda*Aeff) (w-2.m-1)          |
++----------------------+-----------+-----------------------------------------+
+
+The transceiver equipment library is a list of supported transceivers. New
+transceivers can be added and existing ones removed at will by the user. It is
+used to determine the service list path feasibility when running the
+`path_request_run.py routine <examples/path_request_run.py>`_.
+
++----------------------+-----------+-----------------------------------------+
+| field                | type      | description                             |
++======================+===========+=========================================+
+| ``type_variety``     | (string)  | A unique name to ID the transceiver in  |
+|                      |           | the JSON or Excel template topology     |
+|                      |           | input file                              |
++----------------------+-----------+-----------------------------------------+
+| ``frequency``        | (number)  | Min/max as below.                       |
++----------------------+-----------+-----------------------------------------+
+| ``mode``             | (number)  | A list of modes supported by the        |
+|                      |           | transponder. New modes can be added at  |
+|                      |           | will by the user. The modes are specific|
+|                      |           | to each transponder type_variety.       |
+|                      |           | Each mode is described as below.        |
++----------------------+-----------+-----------------------------------------+
+
+The modes are defined as follows:
+
++----------------------+-----------+-----------------------------------------+
+| field                | type      | description                             |
++======================+===========+=========================================+
+| ``format``           | (string)  | a unique name to ID the mode            |
++----------------------+-----------+-----------------------------------------+
+| ``baud_rate``        | (number)  | in Hz                                   |
++----------------------+-----------+-----------------------------------------+
+| ``OSNR``             | (number)  | min required OSNR in 0.1nm (dB)         |
++----------------------+-----------+-----------------------------------------+
+| ``bit_rate``         | (number)  | in bit/s                                |
++----------------------+-----------+-----------------------------------------+
+| ``roll_off``         | (number)  | Not used.                               |
++----------------------+-----------+-----------------------------------------+
+| ``tx_osnr``          | (number)  | In dB. OSNR out from transponder.       |
++----------------------+-----------+-----------------------------------------+
+| ``cost``             | (number)  | Arbitrary unit                          |
++----------------------+-----------+-----------------------------------------+
+
+Simulation parameters are defined as follows.
+
+Auto-design automatically creates EDFA amplifier network elements when they are
+missing, after a fiber, or between a ROADM and a fiber. This auto-design
+functionality can be manually and locally deactivated by introducing a ``Fused``
+network element after a ``Fiber`` or a ``Roadm`` that doesn't need amplification.
+The amplifier is chosen in the EDFA list of the equipment library based on
+gain, power, and NF criteria. Only the EDFA that are marked
+``'allowed_for_design': true`` are considered.
+
+For amplifiers defined in the topology JSON input but whose ``gain = 0``
+(placeholder), auto-design will set its gain automatically: see ``power_mode`` in
+the ``Spans`` library to find out how the gain is calculated.
+
+Span configuration is performed as follows. It is not a list (which may change
+in later releases) and the user can only modify the value of existing
+parameters:
+
++-------------------------------------+-----------+---------------------------------------------+
+| field                               | type      | description                                 |
++=====================================+===========+=============================================+
+| ``power_mode``                      | (boolean) | If false, gain mode. Auto-design sets       |
+|                                     |           | amplifier gain = preceding span loss,       |
+|                                     |           | unless the amplifier exists and its         |
+|                                     |           | gain > 0 in the topology input JSON.        |
+|                                     |           | If true, power mode (recommended for        |
+|                                     |           | auto-design and power sweep.)               |
+|                                     |           | Auto-design sets amplifier power            |
+|                                     |           | according to delta_power_range. If the      |
+|                                     |           | amplifier exists with gain > 0 in the       |
+|                                     |           | topology JSON input, then its gain is       |
+|                                     |           | translated into a power target/channel.     |
+|                                     |           | Moreover, when performing a power sweep     |
+|                                     |           | (see ``power_range_db`` in the SI           |
+|                                     |           | configuration library) the power sweep      |
+|                                     |           | is performed w/r/t this power target,       |
+|                                     |           | regardless of preceding amplifiers          |
+|                                     |           | power saturation/limitations.               |
++-------------------------------------+-----------+---------------------------------------------+
+| ``delta_power_range_db``            | (number)  | Auto-design only, power-mode                |
+|                                     |           | only. Specifies the [min, max, step]        |
+|                                     |           | power excursion/span. It is a relative      |
+|                                     |           | power excursion w/r/t the                   |
+|                                     |           | power_dbm + power_range_db                  |
+|                                     |           | (power sweep if applicable) defined in      |
+|                                     |           | the SI configuration library. This          |
+|                                     |           | relative power excursion is = 1/3 of        |
+|                                     |           | the span loss difference with the           |
+|                                     |           | reference 20 dB span. The 1/3 slope is      |
+|                                     |           | derived from the GN model equations.        |
+|                                     |           | For example, a 23 dB span loss will be      |
+|                                     |           | set to 1 dB more power than a 20 dB         |
+|                                     |           | span loss. The 20 dB reference spans        |
+|                                     |           | will *always* be set to                     |
+|                                     |           | power = power_dbm + power_range_db.         |
+|                                     |           | To configure the same power in all          |
+|                                     |           | spans, use `[0, 0, 0]`. All spans will      |
+|                                     |           | be set to                                   |
+|                                     |           | power = power_dbm + power_range_db.         |
+|                                     |           | To configure the same power in all spans    |
+|                                     |           | and 3 dB more power just for the longest    |
+|                                     |           | spans: `[0, 3, 3]`. The longest spans are   |
+|                                     |           | set to                                      |
+|                                     |           | power = power_dbm + power_range_db + 3.     |
+|                                     |           | To configure a 4 dB power range across      |
+|                                     |           | all spans in 0.5 dB steps: `[-2, 2, 0.5]`.  |
+|                                     |           | A 17 dB span is set to                      |
+|                                     |           | power = power_dbm + power_range_db - 1,     |
+|                                     |           | a 20 dB span to                             |
+|                                     |           | power = power_dbm + power_range_db and      |
+|                                     |           | a 23 dB span to                             |
+|                                     |           | power = power_dbm + power_range_db + 1      |
++-------------------------------------+-----------+---------------------------------------------+
+| ``max_fiber_lineic_loss_for_raman`` | (number)  | Maximum linear fiber loss for Raman         |
+|                                     |           | amplification use.                          |
++-------------------------------------+-----------+---------------------------------------------+
+| ``max_length``                      | (number)  | Split fiber lengths > max_length.           |
+|                                     |           | Interest to support high level              |
+|                                     |           | topologies that do not specify in line      |
+|                                     |           | amplification sites. For example the        |
+|                                     |           | CORONET_Global_Topology.xls defines         |
+|                                     |           | links > 1000km between 2 sites: it          |
+|                                     |           | couldn't be simulated if these links        |
+|                                     |           | were not split in shorter span lengths.     |
++-------------------------------------+-----------+---------------------------------------------+
+| ``length_unit``                     | "m"/"km"  | Unit for ``max_length``.                    |
++-------------------------------------+-----------+---------------------------------------------+
+| ``max_loss``                        | (number)  | Not used in the current code                |
+|                                     |           | implementation.                             |
++-------------------------------------+-----------+---------------------------------------------+
+| ``padding``                         | (number)  | In dB. Min span loss before putting an      |
+|                                     |           | attenuator before fiber. Attenuator         |
+|                                     |           | value                                       |
+|                                     |           | Fiber.att_in = max(0, padding - span_loss). |
+|                                     |           | Padding can be set manually to reach a      |
+|                                     |           | higher padding value for a given fiber      |
+|                                     |           | by filling in the Fiber/params/att_in       |
+|                                     |           | field in the topology json input [1]        |
+|                                     |           | but if span_loss = length * loss_coef       |
+|                                     |           | + att_in + con_in + con_out < padding,      |
+|                                     |           | the specified att_in value will be          |
+|                                     |           | completed to have span_loss = padding.      |
+|                                     |           | Therefore it is not possible to set         |
+|                                     |           | span_loss < padding.                        |
++-------------------------------------+-----------+---------------------------------------------+
+| ``EOL``                             | (number)  | All fiber span loss ageing. The value       |
+|                                     |           | is added to the con_out (fiber output       |
+|                                     |           | connector). So the design and the path      |
+|                                     |           | feasibility are performed with              |
+|                                     |           | span_loss + EOL. EOL cannot be set          |
+|                                     |           | manually for a given fiber span             |
+|                                     |           | (workaround is to specify higher            |
+|                                     |           | ``con_out`` loss for this fiber).           |
++-------------------------------------+-----------+---------------------------------------------+
+| ``con_in``,                         | (number)  | Default values if Fiber/params/con_in/out   |
+| ``con_out``                         |           | is None in the topology input               |
+|                                     |           | description. This default value is          |
+|                                     |           | ignored if a Fiber/params/con_in/out        |
+|                                     |           | value is input in the topology for a        |
+|                                     |           | given Fiber.                                |
++-------------------------------------+-----------+---------------------------------------------+
+
+.. code-block:: json
+
+    {
+        "uid": "fiber (A1->A2)",
+        "type": "Fiber",
+        "type_variety": "SSMF",
+        "params":
+        {
+              "type_variety": "SSMF",
+              "length": 120.0,
+              "loss_coef": 0.2,
+              "length_units": "km",
+              "att_in": 0,
+              "con_in": 0,
+              "con_out": 0
+        }
+    }
+
+ROADMs can be configured as follows. The user can only modify the value of
+existing parameters:
+
++--------------------------+-----------+---------------------------------------------+
+| field                    |   type    | description                                 |
++==========================+===========+=============================================+
+| ``target_pch_out_db``    | (number)  | Auto-design sets the ROADM egress channel   |
+|                          |           | power. This reflects typical control loop   |
+|                          |           | algorithms that adjust ROADM losses to      |
+|                          |           | equalize channels (eg coming from different |
+|                          |           | ingress direction or add ports)             |
+|                          |           | This is the default value                   |
+|                          |           | Roadm/params/target_pch_out_db if no value  |
+|                          |           | is given in the ``Roadm`` element in the    |
+|                          |           | topology input description.                 |
+|                          |           | This default value is ignored if a          |
+|                          |           | params/target_pch_out_db value is input in  |
+|                          |           | the topology for a given ROADM.             |
++--------------------------+-----------+---------------------------------------------+
+| ``add_drop_osnr``        | (number)  | OSNR contribution from the add/drop ports   |
++--------------------------+-----------+---------------------------------------------+
+| ``restrictions``         | (dict of  | If non-empty, keys ``preamp_variety_list``  |
+|                          |  strings) | and ``booster_variety_list`` represent      |
+|                          |           | list of ``type_variety`` amplifiers which   |
+|                          |           | are allowed for auto-design within ROADM's  |
+|                          |           | line degrees.                               |
+|                          |           |                                             |
+|                          |           | If no booster should be placed on a degree, |
+|                          |           | insert a ``Fused`` node on the degree       |
+|                          |           | output.                                     |
++--------------------------+-----------+---------------------------------------------+
+
+The ``SpectralInformation`` object can be configured as follows. The user can
+only modify the value of existing parameters. It defines a spectrum of N
+identical carriers. While the code libraries allow for different carriers and
+power levels, the current user parametrization only allows one carrier type and
+one power/channel definition.
+
++----------------------+-----------+-------------------------------------------+
+| field                |   type    | description                               |
++======================+===========+===========================================+
+| ``f_min``,           | (number)  | In Hz. Carrier min max excursion.         |
+| ``f_max``            |           |                                           |
++----------------------+-----------+-------------------------------------------+
+| ``baud_rate``        | (number)  | In Hz. Simulated baud rate.               |
++----------------------+-----------+-------------------------------------------+
+| ``spacing``          | (number)  | In Hz. Carrier spacing.                   |
++----------------------+-----------+-------------------------------------------+
+| ``roll_off``         | (number)  | Not used.                                 |
++----------------------+-----------+-------------------------------------------+
+| ``tx_osnr``          | (number)  | In dB. OSNR out from transponder.         |
++----------------------+-----------+-------------------------------------------+
+| ``power_dbm``        | (number)  | Reference channel power. In gain mode     |
+|                      |           | (see spans/power_mode = false), all gain  |
+|                      |           | settings are offset w/r/t this reference  |
+|                      |           | power. In power mode, it is the           |
+|                      |           | reference power for                       |
+|                      |           | Spans/delta_power_range_db. For example,  |
+|                      |           | if delta_power_range_db = `[0,0,0]`, the  |
+|                      |           | same power=power_dbm is launched in every |
+|                      |           | spans. The network design is performed    |
+|                      |           | with the power_dbm value: even if a       |
+|                      |           | power sweep is defined (see after) the    |
+|                      |           | design is not repeated.                   |
++----------------------+-----------+-------------------------------------------+
+| ``power_range_db``   | (number)  | Power sweep excursion around power_dbm.   |
+|                      |           | It is not the min and max channel power   |
+|                      |           | values! The reference power becomes:      |
+|                      |           | power_range_db + power_dbm.               |
++----------------------+-----------+-------------------------------------------+
+| ``sys_margins``      | (number)  | In dB. Added margin on min required       |
+|                      |           | transceiver OSNR.                         |         
++----------------------+-----------+-------------------------------------------+
+
+The `transmission_main_example.py <examples/transmission_main_example.py>`_ 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.
+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.
+
+An experimental support for Raman amplification is available:
+
+.. code-block:: shell
+
+     $ ./examples/transmission_main_example.py \
+       examples/raman_edfa_example_network.json \
+       --sim examples/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 <examples/raman_edfa_example_network.json>`_.
+General numeric parameters for simulaiton control are provided in the `examples/sim_params.json <examples/sim_params.json>`_.
+
+Use `examples/path_requests_run.py <examples/path_requests_run.py>`_ to run multiple optimizations as follows:
+
+.. code-block:: shell
+
+     $ python path_requests_run.py -h
+     Usage: path_requests_run.py [-h] [-v] [-o OUTPUT] [network_filename] [service_filename] [eqpt_filename]
+
+The ``network_filename`` and ``service_filename`` can be an XLS or JSON file. The ``eqpt_filename`` must be a JSON file.
+
+To see an example of it, run:
+
+.. code-block:: shell
+
+    $ cd examples
+    $ python path_requests_run.py meshTopologyExampleV2.xls meshTopologyExampleV2_services.json eqpt_config.json -o output_file.json
+
+This program requires a list of connections to be estimated and the equipment
+library. The program computes performances for the list of services (accepts
+JSON or Excel format) using the same spectrum propagation modules as
+``transmission_main_example.py``. Explanation on the Excel template is provided in
+the `Excel_userguide.rst <Excel_userguide.rst#service-sheet>`_. Template for
+the JSON format can be found here: `service-template.json
+<service-template.json>`_.
 
 Contributing
 ------------
@@ -104,17 +542,17 @@ Contributing
 ``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 James Powell
-<james.powell@telecominfraproject.com> or Gert Grammel <ggrammel@juniper.net>.
+To get involved, please contact Jan Kundrát
+<jan.kundrat@telecominfraproject.com> or Gert Grammel <ggrammel@juniper.net>.
 
 ``gnpy`` contributions are currently limited to members of `TIP
 <http://telecominfraproject.com>`_. Membership is free and open to all.
 
 See the `Onboarding Guide
 <https://github.com/Telecominfraproject/gnpy/wiki/Onboarding-Guide>`_ for
-specific details on code contribtions.
+specific details on code contributions.
 
-See `AUTHORS.Md <AUTHORS.Md>`_ for past and present contributors.
+See `AUTHORS.rst <AUTHORS.rst>`_ for past and present contributors.
 
 Project Background
 ------------------
@@ -122,7 +560,7 @@ Project Background
 Data Centers are built upon interchangeable, highly standardized node and
 network architectures rather than a sum of isolated solutions. This also
 translates to optical networking. It leads to a push in enabling multi-vendor
-optical network by disaggregating HW and SW functions and focussing on
+optical network by disaggregating HW and SW functions and focusing on
 interoperability. In this paradigm, the burden of responsibility for ensuring
 the performance of such disaggregated open optical systems falls on the
 operators. Consequently, operators and vendors are collaborating in defining
@@ -158,8 +596,8 @@ implementations.
   :alt: Documentation Status
   :scale: 100%
 
-.. |build| image:: https://travis-ci.org/mcantono/gnpy.svg?branch=develop
-  :target: https://travis-ci.org/mcantono/gnpy
+.. |build| image:: https://travis-ci.com/Telecominfraproject/oopt-gnpy.svg?branch=develop
+  :target: https://travis-ci.com/Telecominfraproject/oopt-gnpy
   :alt: Build Status
   :scale: 100%
 
@@ -187,5 +625,4 @@ License
 
 ``gnpy`` is distributed under a standard BSD 3-Clause License.
 
-See `LICENSE <LICENSE>`_ for more details.
-
+See `LICENSE <LICENSE>`__ for more details.

contributors.md

@@ -1,17 +0,0 @@
-Contributors in alphabetical order
-==================================
-
-Name | Surname | Affiliation | Contact
------|---------|-------------|--------
-Alessio | Ferrari | Politecnico di Torino | alessio.ferrari@polito.it
-Brian | Taylor | Facebook | briantaylor@fb.com
-David | Boertjes | Ciena | dboertje@ciena.com
-Esther | Le Rouzic | Orange | esther.lerouzic@orange.com
-Gabriele | Galimberti | Cisco | ggalimbe@cisco.com
-Gert | Grammel | Juniper Networks | ggrammel@juniper.net
-Gilad | Goldfarb | Facebook | giladg@fb.com
-James | Powell | Consultant | james@dontusethiscode.com
-Jeanluc | Auge | Orange | jeanluc.auge@orange.com
-Liu | Xufeng | Jabil | Xufeng_Liu@jabil.com
-Mattia | Cantono | Politecnico di Torino | mattia.cantono@polito.it
-Vittorio | Curri | Politecnico di Torino | vittorio.curri@polito.it

docs/biblio.bib

@@ -874,7 +874,7 @@ month={Sept},}
   number = {7},
   journal = {Optics Express},
   urlyear = {2017-11-14},
-  year = {2012-03-26},
+  date = {2012-03-26},
   year = {2012},
   pages = {7777},
   author = {Bononi, A. and Serena, P. and Rossi, N. and Grellier, E. and Vacondio, F.}
@@ -1114,7 +1114,7 @@ month={Sept},}
   number = {26},
   journal = {Optics Express},
   urlyear = {2017-11-16},
-  year = {2013-12-30},
+  date = {2013-12-30},
   year = {2013},
   pages = {32254},
   author = {Bononi, Alberto and Beucher, Ottmar and Serena, Paolo}

docs/conf.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 #
-# GNpy documentation build configuration file, created by
+# gnpy documentation build configuration file, created by
 # sphinx-quickstart on Mon Dec 18 14:41:01 2017.
 #
 # This file is execfile()d with the current directory set to its
@@ -47,8 +47,8 @@ source_suffix = ['.rst', '.md']
 master_doc = 'index'
 
 # General information about the project.
-project = 'GNpy'
-copyright = '2017, Telecom InfraProject - OOPT PSE Group'
+project = 'gnpy'
+copyright = '2018, Telecom InfraProject - OOPT PSE Group'
 author = 'Telecom InfraProject - OOPT PSE Group'
 
 # The version info for the project you're documenting, acts as replacement for
@@ -120,7 +120,7 @@ html_sidebars = {
 # -- Options for HTMLHelp output ------------------------------------------
 
 # Output file base name for HTML help builder.
-htmlhelp_basename = 'GNpydoc'
+htmlhelp_basename = 'gnpydoc'
 
 
 # -- Options for LaTeX output ---------------------------------------------
@@ -147,7 +147,7 @@ latex_elements = {
 # (source start file, target name, title,
 #  author, documentclass [howto, manual, or own class]).
 latex_documents = [
-    (master_doc, 'GNpy.tex', 'GNpy Documentation',
+    (master_doc, 'gnpy.tex', 'gnpy Documentation',
      'Telecom InfraProject - OOPT PSE Group', 'manual'),
 ]
 
@@ -157,7 +157,7 @@ latex_documents = [
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
 man_pages = [
-    (master_doc, 'gnpy', 'GNpy Documentation',
+    (master_doc, 'gnpy', 'gnpy Documentation',
      [author], 1)
 ]
 
@@ -168,10 +168,9 @@ man_pages = [
 # (source start file, target name, title, author,
 #  dir menu entry, description, category)
 texinfo_documents = [
-    (master_doc, 'GNpy', 'GNpy Documentation',
-     author, 'GNpy', 'One line description of project.',
+    (master_doc, 'gnpy', 'gnpy Documentation',
+     author, 'gnpy', 'One line description of project.',
      'Miscellaneous'),
 ]
 
-
-
+autodoc_default_flags = ['members', 'undoc-members', 'private-members', 'show-inheritance']

docs/index.rst

@@ -1,37 +1,33 @@
-.. GNpy documentation master file, created by
+.. gnpy documentation master file, created by
    sphinx-quickstart on Mon Dec 18 14:41:01 2017.
    You can adapt this file completely to your liking, but it should at least
    contain the root `toctree` directive.
 
-Welcome to GNpy's documentation!
+Welcome to gnpy's documentation!
 ================================
 
-Gaussian Noise (GN) based modeling library for physical layer impairment evaluation in optical networks.
+**gnpy is an open-source, community-developed library for building route planning
+and optimization tools in real-world mesh optical networks.**
 
-Summary
---------
-
-We believe that openly sharing ideas, specifications, and other intellectual property is the key to maximizing innovation and reducing complexity
-
-PSE WG Charter
---------------
-
-- Goal is to build an end-to-end simulation environment which defines the network models of the optical device transfer functions and their parameters. This environment will provide validation of the optical performance requirements for the TIP OLS building blocks.   
-- The model may be approximate or complete depending on the network complexity. Each model shall be validated against the proposed network scenario. 
-- The environment must be able to process network models from multiple vendors, and also allow users to pick any implementation in an open source framework. 
-- The PSE will influence and benefit from the innovation of the DTC, API, and OLS working groups.
-- The PSE represents a step along the journey towards multi-layer optimization.
+`gnpy <http://github.com/telecominfraproject/gnpy>`_ is:
 
+- a sponsored project of the `OOPT/PSE <http://telecominfraproject.com/project-groups-2/backhaul-projects/open-optical-packet-transport/>`_ working group of the `Telecom Infra Project <http://telecominfraproject.com>`_.
+- fully community-driven, fully open source library
+- driven by a consortium of operators, vendors, and academic researchers
+- intended for rapid development of production-grade route planning tools
+- easily extensible to include custom network elements
+- performant to the scale of real-world mesh optical networks
 
 Documentation
 =============
 
-The following pages are meant to describe specific implementation details and modeling assumptions behind GNpy. 
+The following pages are meant to describe specific implementation details and
+modeling assumptions behind gnpy. 
 
 .. toctree::
    :maxdepth: 2
 
-   gn_model
+   model
 
 Indices and tables
 ==================
@@ -42,29 +38,65 @@ Indices and tables
 
 Contributors in alphabetical order
 ==================================
-+----------+------------+-----------------------+----------------------------+
-| Name     | Surname    | Affiliation           | Contact                    |
-+==========+============+=======================+============================+
-| Alessio  | Ferrari    | Politecnico di Torino | alessio.ferrari@polito.it  |
-+----------+------------+-----------------------+----------------------------+
-| Brian    | Taylor     | Facebook              | briantaylor@fb.com         |
-+----------+------------+-----------------------+----------------------------+
-| David    | Boertjes   | Ciena                 | dboertje@ciena.com         |
-+----------+------------+-----------------------+----------------------------+
-| Esther   | Le Rouzic  | Orange                | esther.lerouzic@orange.com |
-+----------+------------+-----------------------+----------------------------+
-| Gabriele | Galimberti | Cisco                 | ggalimbe@cisco.com         |
-+----------+------------+-----------------------+----------------------------+
-| Gert     | Grammel    | Juniper Networks      | ggrammel@juniper.net       |
-+----------+------------+-----------------------+----------------------------+
-| Gilad    | Goldfarb   | Facebook              | giladg@fb.com              |
-+----------+------------+-----------------------+----------------------------+
-| James    | Powell     | Consultant            | james@dontusethiscode.com  |
-+----------+------------+-----------------------+----------------------------+
-| Jeanluc  | Auge       | Orange                | jeanluc.auge@orange.com    |
-+----------+------------+-----------------------+----------------------------+
-| Mattia   | Cantono    | Politecnico di Torino | mattia.cantono@polito.it   |
-+----------+------------+-----------------------+----------------------------+
-| Vittorio | Curri      | Politecnico di Torino | vittorio.curri@polito.it   |
-+----------+------------+-----------------------+----------------------------+
++----------+------------+-----------------------+--------------------------------------+
+| Name     | Surname    | Affiliation           | Contact                              |
++==========+============+=======================+======================================+
+| Alessio  | Ferrari    | Politecnico di Torino | alessio.ferrari@polito.it            |
++----------+------------+-----------------------+--------------------------------------+
+| Anders   | Lindgren   | Telia Company         | Anders.X.Lindgren@teliacompany.com   |
++----------+------------+-----------------------+--------------------------------------+
+| Andrea   | d'Amico    | Politecnico di Torino | andrea.damico@polito.it              |
++----------+------------+-----------------------+--------------------------------------+
+| Brian    | Taylor     | Facebook              | briantaylor@fb.com                   |
++----------+------------+-----------------------+--------------------------------------+
+| David    | Boertjes   | Ciena                 | dboertje@ciena.com                   |
++----------+------------+-----------------------+--------------------------------------+
+| Diego    | Landa      | Facebook              | dlanda@fb.com                        |
++----------+------------+-----------------------+--------------------------------------+
+| Esther   | Le Rouzic  | Orange                | esther.lerouzic@orange.com           |
++----------+------------+-----------------------+--------------------------------------+
+| Gabriele | Galimberti | Cisco                 | ggalimbe@cisco.com                   |
++----------+------------+-----------------------+--------------------------------------+
+| Gert     | Grammel    | Juniper Networks      | ggrammel@juniper.net                 |
++----------+------------+-----------------------+--------------------------------------+
+| Gilad    | Goldfarb   | Facebook              | giladg@fb.com                        |
++----------+------------+-----------------------+--------------------------------------+
+| James    | Powell     | Telecom Infra Project | james.powell@telecominfraproject.com |
++----------+------------+-----------------------+--------------------------------------+
+| Jan      | Kundrát    | Telecom Infra Project | jan.kundrat@telecominfraproject.com  |
++----------+------------+-----------------------+--------------------------------------+
+| Jeanluc  | Augé       | Orange                | jeanluc.auge@orange.com              |
++----------+------------+-----------------------+--------------------------------------+
+| Jonas    | Mårtensson | RISE Research Sweden  | 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       |
++----------+------------+-----------------------+--------------------------------------+
+| 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             |
++----------+------------+-----------------------+--------------------------------------+
+| Xufeng   | Liu        | Jabil                 | xufeng_liu@jabil.com                 |
++----------+------------+-----------------------+--------------------------------------+
+
+--------------
+
+- Goal is to build an end-to-end simulation environment which defines the
+  network models of the optical device transfer functions and their parameters.
+  This environment will provide validation of the optical performance
+  requirements for the TIP OLS building blocks.   
+- The model may be approximate or complete depending on the network complexity.
+  Each model shall be validated against the proposed network scenario. 
+- The environment must be able to process network models from multiple vendors,
+  and also allow users to pick any implementation in an open source framework. 
+- The PSE will influence and benefit from the innovation of the DTC, API, and
+  OLS working groups.
+- The PSE represents a step along the journey towards multi-layer optimization.
 

docs/model.rst

@@ -4,15 +4,15 @@ The QoT estimation in the PSE framework of TIP-OOPT
 QoT-E including ASE noise and NLI accumulation 
 ----------------------------------------------
 
-The operations of PSE simulative framework are based on the capability to estimate the QoT of one
-or more channels operating lightpaths over a given network route. For
-backbone transport networks, we can suppose that transceivers are
-operating polarization-division-multiplexed multilevel modulation
-formats with DSP-based coherent receivers, including equalization. For
-the optical links, we focus on state-of-the-art amplified and
-uncompensated fiber links, connecting network nodes including ROADMs,
-where add and drop operations on data traffic are performed. In such a
-transmission scenario, it is well accepted
+The operations of PSE simulative framework are based on the capability to
+estimate the QoT of one or more channels operating lightpaths over a given
+network route. For backbone transport networks, we can suppose that
+transceivers are operating polarization-division-multiplexed multilevel
+modulation formats with DSP-based coherent receivers, including equalization.
+For the optical links, we focus on state-of-the-art amplified and uncompensated
+fiber links, connecting network nodes including ROADMs, where add and drop
+operations on data traffic are performed. In such a transmission scenario, it
+is well accepted
 :cite:`vacondio_nonlinear_2012,bononi_modeling_2012,carena_modeling_2012,mecozzi_nonlinear_2012,secondini_analytical_2012,johannisson_perturbation_2013,dar_properties_2013,serena_alternative_2013,secondini_achievable_2013,poggiolini_gn-model_2014,dar_accumulation_2014,poggiolini_analytical_2011,savory_approximations_2013,bononi_single-_2013,johannisson_modeling_2014`
 to assume that transmission performances are limited by the amplified
 spontaneous emission (ASE) noise generated by optical amplifiers and and
@@ -49,7 +49,6 @@ filtering effects. Note that for state-of-the art equipment, filtering
 effects can be typically neglected over routes with few hops
 :cite:`rahman_mitigation_2014,foggi_overcoming_2015`.
 
-
 To properly estimate :math:`P_{\text{ch}}` and :math:`P_{\text{ASE}}`
 the transmitted power at the beginning of the considered route must be
 known, and losses and amplifiers gain and noise figure, including their
@@ -62,8 +61,10 @@ models have been proposed and validated in the technical literature
 The decision about which model to test within the PSE activities was
 driven by requirements of the entire PSE framework:
 
-i. the model must be *local*, i.e., related individually to each network element (i.e. fiber span) generating NLI, independently of preceding and subsequent elements; and 
-ii. the related computational time must be compatible with interactive operations. 
+i. the model must be *local*, i.e., related individually to each network
+element (i.e. fiber span) generating NLI, independently of preceding and
+subsequent elements; and ii. the related computational time must be compatible
+with interactive operations. 
 
 So, the choice fell on the Gaussian Noise
 (GN) model with incoherent accumulation of NLI over fiber spans
@@ -79,46 +80,67 @@ for fiber types with chromatic dispersion roughly larger than 4
 ps/nm/km, the analytical approximation ensures an excellent accuracy
 with a computational time compatible with real-time operations.
 
-
-
 The Gaussian Noise Model to evaluate the NLI
 --------------------------------------------
-As previously stated, fiber propagation of multilevel modulation formats relying on the polarization-division-multiplexing  
-generates impairments that can be summarized as  a disturbance called nonlinear interference (NLI),
-when exploiting a DSP-based coherent receiver, as in all state-of-the-art equipment.
-From a practical point of view, the NLI can be modeled as an additive 
-Gaussian random process added by each fiber span, and whose strength depends on the cube of the input power spectral density and 
-on the fiber-span parameters. 
 
-Since the introduction in the market in 2007 of the first transponder based on such a transmission technique, the scientific
-community has intensively worked to define the propagation behavior of such a trasnmission technique.
-First, the role of in-line chromatic dispersion compensation has been investigated, deducing that besides being 
-not essential, it is indeed detrimental for performances :cite:`curri_dispersion_2008`.
-Then, it has been observed that the fiber propagation impairments are practically summarized by the sole NLI, being all the other 
-phenomena compensated for by the blind equalizer implemented in the receiver DSP :cite:`carena_statistical_2010`.
-Once these assessments have been accepted by the community, several prestigious research groups have started to work 
-on deriving analytical models able to estimating the NLI accumulation, and consequentially the generalized SNR that sets the BER,
-according to the transponder BER vs. SNR performance.
-Many models delivering different levels of accuracy have been developed and validated. As previously clarified, for the purposes
-of the PSE framework, the  GN-model with incoherent accumulation of NLI over fiber spans has been selected as adequate.
-The reason for such a choice is first such a model being a "local" model, so related to each fiber spans, independently of
-the preceding and succeeding network elements. The other model characteristic driving the choice is 
-the availability of a closed form for the model, so permitting a real-time evaluation, as required by the PSE framework.
-For a detailed derivation of the model, please refer to :cite:`poggiolini_analytical_2011`, while a qualitative description
-can be summarized as in the following.
-The GN-model assumes that the channel comb propagating in the fiber is well approximated by unpolarized spectrally shaped
-Gaussian noise. In such a scenario, supposing to rely - as in state-of-the-art equipment - on a receiver entirely compensating for linear propagation effects, propagation in the fiber only excites the four-wave mixing (FWM) process among the continuity of
-the tones occupying the bandwidth. Such a FWM generates an unpolarized complex Gaussian disturbance in each spectral slot
-that can be easily evaluated extending the FWM theory from a set of discrete tones - the standard FWM theory introduced back in the 90s by Inoue :cite:`Innoue-FWM`- to a continuity of tones, possibly spectrally shaped.
-Signals propagating in the fiber are not equivalent to Gaussian noise, but thanks to the absence of in-line compensation for choromatic dispersion,
-the become so, over short distances. 
-So, the Gaussian noise model with incoherent accumulation of NLI has estensively proved to be a quick yet accurate and conservative tool
-to estimate propagation impairments of fiber propagation.
-Note that the GN-model has not been derived with the aim of an *exact* performance estimation, but to pursue a conservative performance prediction. So, considering these characteristics, and the fact that the NLI is always a secondary effect with respect to the ASE noise accumulation, and - most importantly - that typically linear propagation parameters (losses, gains and noise figures) are known within 
-a variation range, a QoT estimator based on the GN model is adequate to deliver performance predictions in terms of a reasonable SNR range, rather than an exact value.
-As final remark, it must be clarified that the GN-model is adequate to be used when relying on a relatively narrow bandwidth up to few THz. When exceeding such a bandwidth occupation, the GN-model must be generalized introducing the interaction with the Stimulated 
-Raman Scattering in order to give a proper estimation for all channels :cite:`cantono2018modeling`. 
-This will be the main upgrade required within the PSE framework.
+As previously stated, fiber propagation of multilevel modulation formats
+relying on the polarization-division-multiplexing  generates impairments that
+can be summarized as  a disturbance called nonlinear interference (NLI), when
+exploiting a DSP-based coherent receiver, as in all state-of-the-art equipment.
+From a practical point of view, the NLI can be modeled as an additive Gaussian
+random process added by each fiber span, and whose strength depends on the cube
+of the input power spectral density and on the fiber-span parameters. 
 
+Since the introduction in the market in 2007 of the first transponder based on
+such a transmission technique, the scientific community has intensively worked
+to define the propagation behavior of such a trasnmission technique.  First,
+the role of in-line chromatic dispersion compensation has been investigated,
+deducing that besides being not essential, it is indeed detrimental for
+performances :cite:`curri_dispersion_2008`.  Then, it has been observed that
+the fiber propagation impairments are practically summarized by the sole NLI,
+being all the other phenomena compensated for by the blind equalizer
+implemented in the receiver DSP :cite:`carena_statistical_2010`.  Once these
+assessments have been accepted by the community, several prestigious research
+groups have started to work on deriving analytical models able to estimating
+the NLI accumulation, and consequentially the generalized SNR that sets the
+BER, according to the transponder BER vs. SNR performance.  Many models
+delivering different levels of accuracy have been developed and validated. As
+previously clarified, for the purposes of the PSE framework, the  GN-model with
+incoherent accumulation of NLI over fiber spans has been selected as adequate.
+The reason for such a choice is first such a model being a "local" model, so
+related to each fiber spans, independently of the preceding and succeeding
+network elements. The other model characteristic driving the choice is the
+availability of a closed form for the model, so permitting a real-time
+evaluation, as required by the PSE framework.  For a detailed derivation of the
+model, please refer to :cite:`poggiolini_analytical_2011`, while a qualitative
+description can be summarized as in the following.  The GN-model assumes that
+the channel comb propagating in the fiber is well approximated by unpolarized
+spectrally shaped Gaussian noise. In such a scenario, supposing to rely - as in
+state-of-the-art equipment - on a receiver entirely compensating for linear
+propagation effects, propagation in the fiber only excites the four-wave mixing
+(FWM) process among the continuity of the tones occupying the bandwidth. Such a
+FWM generates an unpolarized complex Gaussian disturbance in each spectral slot
+that can be easily evaluated extending the FWM theory from a set of discrete
+tones - the standard FWM theory introduced back in the 90s by Inoue
+:cite:`Innoue-FWM`- to a continuity of tones, possibly spectrally shaped.
+Signals propagating in the fiber are not equivalent to Gaussian noise, but
+thanks to the absence of in-line compensation for choromatic dispersion, the
+become so, over short distances.  So, the Gaussian noise model with incoherent
+accumulation of NLI has estensively proved to be a quick yet accurate and
+conservative tool to estimate propagation impairments of fiber propagation.
+Note that the GN-model has not been derived with the aim of an *exact*
+performance estimation, but to pursue a conservative performance prediction.
+So, considering these characteristics, and the fact that the NLI is always a
+secondary effect with respect to the ASE noise accumulation, and - most
+importantly - that typically linear propagation parameters (losses, gains and
+noise figures) are known within a variation range, a QoT estimator based on the
+GN model is adequate to deliver performance predictions in terms of a
+reasonable SNR range, rather than an exact value.  As final remark, it must be
+clarified that the GN-model is adequate to be used when relying on a relatively
+narrow bandwidth up to few THz. When exceeding such a bandwidth occupation, the
+GN-model must be generalized introducing the interaction with the Stimulated
+Raman Scattering in order to give a proper estimation for all channels
+:cite:`cantono2018modeling`.  This will be the main upgrade required within the
+PSE framework.
 
-.. bibliography:: biblio.bib  
+.. bibliography:: biblio.bib  

docs/source/gnpy.core.rst

@@ -4,10 +4,39 @@ gnpy\.core package
 Submodules
 ----------
 
+gnpy\.core\.ansi_escapes module
+-------------------------------
+
+.. automodule:: gnpy.core.ansi_escapes
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+gnpy\.core\.convert module
+--------------------------
+
+.. automodule:: gnpy.core.convert
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 gnpy\.core\.elements module
 ---------------------------
 
 .. automodule:: gnpy.core.elements
+
+gnpy\.core\.equipment module
+----------------------------
+
+.. automodule:: gnpy.core.equipment
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+gnpy\.core\.exceptions module
+-----------------------------
+
+.. automodule:: gnpy.core.exceptions
     :members:
     :undoc-members:
     :show-inheritance:
@@ -16,30 +45,34 @@ gnpy\.core\.execute module
 --------------------------
 
 .. automodule:: gnpy.core.execute
-    :members:
-    :undoc-members:
-    :show-inheritance:
 
 gnpy\.core\.info module
 -----------------------
 
 .. automodule:: gnpy.core.info
-    :members:
-    :undoc-members:
-    :show-inheritance:
 
 gnpy\.core\.network module
 --------------------------
 
 .. automodule:: gnpy.core.network
-    :members:
-    :undoc-members:
-    :show-inheritance:
 
 gnpy\.core\.node module
 -----------------------
 
 .. automodule:: gnpy.core.node
+
+gnpy\.core\.request module
+--------------------------
+
+.. automodule:: gnpy.core.request
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+gnpy\.core\.service_sheet module
+--------------------------------
+
+.. automodule:: gnpy.core.service_sheet
     :members:
     :undoc-members:
     :show-inheritance:
@@ -48,23 +81,14 @@ gnpy\.core\.units module
 ------------------------
 
 .. automodule:: gnpy.core.units
-    :members:
-    :undoc-members:
-    :show-inheritance:
 
 gnpy\.core\.utils module
 ------------------------
 
 .. automodule:: gnpy.core.utils
-    :members:
-    :undoc-members:
-    :show-inheritance:
 
 
 Module contents
 ---------------
 
 .. automodule:: gnpy.core
-    :members:
-    :undoc-members:
-    :show-inheritance:

docs/source/gnpy.rst

@@ -12,6 +12,3 @@ Module contents
 ---------------
 
 .. automodule:: gnpy
-    :members:
-    :undoc-members:
-    :show-inheritance:

examples/Juniper-BoosterHG.json

@@ -0,0 +1,160 @@
+{
+      "nf_fit_coeff": [
+        0.0008,
+        0.0272,
+        -0.2249,
+        6.4902
+       ],
+      "f_min": 191.35e12,
+      "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,
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+        0.0,
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+        0.0,
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+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0
+    ],
+    "gain_ripple": [
+        0.15017064489112,
+        0.14157768006701,
+        0.00223094639866,
+        -0.06701528475711,
+        -0.05982935510889,
+        -0.01028161641541,
+        0.02740682579566,
+        0.02795958961474,
+        0.00107516750419,
+        -0.02199015912898,
+        -0.00877407872698,
+        0.0453465242881,
+        0.1204721524288,
+        0.18936662479061,
+        0.23826109715241,
+        0.26956762981574,
+        0.27836159966498,
+        0.26941687604691,
+        0.23579878559464,
+        0.18147717755444,
+        0.1191656197655,
+        0.05921587102177,
+        0.01509526800668,
+        -0.01053287269681,
+        -0.02475397822447,
+        -0.01847257118928,
+        -0.00420121440538,
+        0.01584903685091,
+        0.0399193886097,
+        0.04494451423784,
+        0.04961788107202,
+        0.03378873534338,
+        0.01027114740367,
+        -0.01319618927973,
+        -0.04962835008375,
+        -0.0765630234506,
+        -0.10606051088777,
+        -0.13550774706866,
+        -0.15460322445561,
+        -0.17113588777219,
+        -0.18053287269681,
+        -0.18324644053602,
+        -0.19440221943049,
+        -0.20897508375209,
+        -0.23575900335007,
+        -0.25188965661642,
+        -0.22244242043552,
+        -0.15656302345061
+    ],
+    "dgt": [
+        2.4553191172498,
+        2.44342862248888,
+        2.41879254989742,
+        2.38192717604575,
+        2.33147727493671,
+        2.26678136721453,
+        2.19013043016015,
+        2.10336369905543,
+        2.01414465424155,
+        1.92915262384742,
+        1.85543800978691,
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+        1.54578500307573,
+        1.5242627235492,
+        1.50335352244996,
+        1.48420288841848,
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+        1.3966294751726,
+        1.38430337205545,
+        1.3710092503689,
+        1.35690844654118,
+        1.3405812000038,
+        1.32210817897091,
+        1.30069883494415,
+        1.27657903892303,
+        1.24931318255134,
+        1.21911100318577,
+        1.18632744096844,
+        1.15209185089701,
+        1.11575888725852,
+        1.07773189112355,
+        1.03941448941778,
+        1.0
+    ]
+}

examples/Makefile

@@ -1,11 +0,0 @@
-REGIONS = europe asia conus
-TARGETS = $(foreach region,$(REGIONS),coronet.$(region).json)
-
-all: $(TARGETS)
-
-$(TARGETS): convert.py CORONET_Global_Topology.xls
-	python $< -f $(subst .json,,$(subst coronet.,,$@)) > $@
-
-.PHONY: clean
-clean:
-	-rm $(TARGETS) -f

examples/OperatorInputNetworkv3-xufeng.json

@@ -1,143 +0,0 @@
-{
-  "networks": {
-    "network": [
-      {
-        "network-types": {
-          "tip-oopt-pse": {}
-        },
-        "network-id": "pt-to-pt",
-        "node": [
-          {
-            "node-id": "M_KMA",
-            "type":"roadm",
-            "termination-point": [
-              {
-                "tp-id": "1-2-1"
-              }
-            ]
-          },
-          {
-            "node-id": "T_CAS",
-            "type":"roadm",
-            "termination-point": [
-              {
-                "tp-id": "2-1-1"
-              },
-              {
-                "tp-id": "2-3-1"
-              }
-            ]
-          },
-          {
-            "node-id": "LA",
-            "type":"ila",
-            "termination-point": [
-              {
-                "tp-id": "3-2-1"
-              },
-              {
-                "tp-id": "3-4-1"
-              }
-            ]
-          },
-          {
-            "node-id": "SR",
-            "type":"fused",
-            "termination-point": [
-              {
-                "tp-id": "4-3-1"
-              }
-            ]
-          },
-          {
-            "node-id": "C",
-            "type":"ila",
-            "termination-point": [
-              {
-                "tp-id": "5-6-1"
-              }
-            ]
-          },
-          {
-            "node-id": "N_KBE",
-            "type":"roadm",
-            "termination-point": [
-              {
-                "tp-id": "6-5-1"
-              },
-              {
-                "tp-id": "6-7-1"
-              }
-            ]
-          },
-          {
-            "node-id": "N_KBA",
-            "type":"roadm",
-            "termination-point": [
-              {
-                "tp-id": "7-6-1"
-              }
-            ]
-          }
-        ],
-        "link": [
-          {
-            "link-id": "M_KMA,1-2-1,T_CAS,2-1-1",
-            "source": {
-              "source-node": "M_KMA",
-              "source-tp": "1-2-1"
-            }
-            "destination": {
-              "dest-node": "T_CAS",
-              "dest-tp": "2-1-1"
-            }
-          },
-          {
-            "link-id": "T_CAS,2-3-1,LA,3-2-1",
-            "source": {
-              "source-node": "T_CAS",
-              "source-tp": "2-3-1"
-            }
-            "destination": {
-              "dest-node": "LA",
-              "dest-tp": "3-2-1"
-            }
-          },
-          {
-            "link-id": "LA,3-4-1,SR,4-3-1",
-            "source": {
-              "source-node": "LA",
-              "source-tp": "3-4-1"
-            }
-            "destination": {
-              "dest-node": "SR",
-              "dest-tp": "4-3-1"
-            }
-          },
-          {
-            "link-id": "C,5-6-1,N_KBE,6-5-1",
-            "source": {
-              "source-node": "C",
-              "source-tp": "5-6-1"
-            }
-            "destination": {
-              "dest-node": "N_KBE",
-              "dest-tp": "6-5-1"
-            }
-          },
-          {
-            "link-id": "N_KBE,6-7-1,N_KBA,7-6-1",
-            "source": {
-              "source-node": "N_KBE",
-              "source-tp": "6-7-1"
-            }
-            "destination": {
-              "dest-node": "N_KBA",
-              "dest-tp": "7-6-1"
-            }
-          }
-        ]
-      }
-    ]
-  }
-}

examples/OperatorInputNetworkv3.json

@@ -1,157 +0,0 @@
-{
-"network_name": "pt to pt",
-"nodes_elements": 
-[
-	{
-        "id":"M_KMA",
-        "type":"ROADM",
-        "metadata": {
-        	"city":"M",
-        	"region":"RLD",
-        	"latitude":0,
-        	"longitude":0
-        }
-    },
-    {
-        "id":"T_CAS",
-        "type":"ROADM",
-        "metadata": {
-        	"city":"T",
-        	"region":"RLD",
-        	"latitude":0,
-        	"longitude":0
-        }
-    },
-    {
-        "id":"LA",
-        "type":"ILA",
-        "metadata": {
-        	"city":"LA",
-        	"region":"RLD",
-        	"latitude":0,
-        	"longitude":0
-        }
-    },
-    {
-        "id":"SR",
-        "type":"fused",
-        "metadata": {
-        	"city":"SR",
-        	"region":"RLD",
-        	"latitude":0,
-        	"longitude":0
-        }
-    },
-    {
-        "id":"C",
-        "type":"ILA",
-        "metadata": {
-        	"city":"C",
-        	"region":"RLD",
-        	"latitude":0,
-        	"longitude":0
-        }
-    },
-    {
-        "id":"N_KBE",
-        "type":"ROADM",
-        "metadata": {
-        	"city":"N",
-        	"region":"RLD",
-        	"latitude":0,
-        	"longitude":0
-        }
-    },
-    {
-        "id":"N_KBA",
-        "type":"ROADM",
-        "metadata": {
-        	"city":"N",
-        	"region":"RLD",
-        	"latitude":0,
-        	"longitude":0
-        }
-    },
-],
-"OTS_elements":[
-	{
-		"id":1,
-		"source_id":"M_KMA",
-		"dest_id":"T_CAS",
-		"parameters_cable":{
-			"units":"km",
-			"length":60,
-			"id":"F060",
-			"type":"G652"
-		},
-		"parameters_east":{
-			"con_in":0.5,
-			"con_out":0.5,
-			"loss":16,
-			"pmd":2,
-			"fo_id":5
-		},
-		"parameters_west":{
-			"con_in":0.5,
-			"con_out":0.5,
-			"loss":15,
-			"pmd":2,
-			"fo_id":6			
-		}
-	},
-	{
-		"id":2,
-		"source_id":"T_CAS",
-		"dest_id":"LA",
-		"parameters_cable":{
-		},
-		"parameters_east":{
-		},
-		"parameters_west":{
-		}
-	},
-	{
-		"id":3,
-		"source_id":"LA",
-		"dest_id":"SR",
-		"parameters_cable":{
-		},
-		"parameters_east":{
-		},
-		"parameters_west":{
-		}
-	},
-	{
-		"id":3,
-		"source_id":"SR",
-		"dest_id":"C",
-		"parameters_cable":{
-		},
-		"parameters_east":{
-		},
-		"parameters_west":{
-		}
-	},
-	{
-		"id":5,
-		"source_id":"C",
-		"dest_id":"N_KBE",
-		"parameters_cable":{
-		},
-		"parameters_east":{
-		},
-		"parameters_west":{
-		}
-	},
-	{
-		"id":6,
-		"source_id":"N_KBE",
-		"dest_id":"N_KBA",
-		"parameters_cable":{
-		},
-		"parameters_east":{
-		},
-		"parameters_west":{
-		}
-	},							
-]}

examples/convert.py

@@ -1,162 +0,0 @@
-#!/usr/bin/env python3
-
-from sys import exit
-try:
-    from xlrd import open_workbook
-except ModuleNotFoundError:
-    exit('Required: `pip install xlrd`')
-from argparse import ArgumentParser
-from collections import namedtuple, Counter
-from itertools import chain
-from json import dumps
-from uuid import uuid4
-import math
-import numpy as np
-
-output_json_file_name = 'coronet_conus_example.json'
-Node = namedtuple('Node', 'city state country region latitude longitude')
-class Link(namedtuple('Link', 'from_city to_city distance distance_units')):
-    def __new__(cls, from_city, to_city, distance, distance_units='km'):
-        return super().__new__(cls, from_city, to_city, distance, distance_units)
-
-def define_span_range(min_span, max_span, nspans):
-    srange = (max_span - min_span) + min_span*np.random.rand(nspans)
-    return srange
-
-def amp_spacings(min_span,max_span,length):
-    nspans =  math.ceil(length/100)
-    spans = define_span_range(min_span, max_span, nspans)
-    tot = spans.sum()
-    delta = length -tot
-    if delta > 0 and delta < 25:
-        ind  = np.where(np.min(spans))
-        spans[ind] = spans[ind] + delta
-    elif delta >= 25 and delta < 40:
-        spans = spans + delta/float(nspans)
-    elif delta > 40 and delta < 100:
-        spans = np.append(spans,delta)
-    elif delta > 100:
-        spans  = np.append(spans, [delta/2, delta/2])
-    elif delta < 0:
-        spans = spans + delta/float(nspans)
-    return list(spans)
-
-def parse_excel(args):
-    with open_workbook(args.workbook) as wb:
-        nodes_sheet = wb.sheet_by_name('Nodes')
-        links_sheet = wb.sheet_by_name('Links')
-
-        # sanity check
-        header = [x.value.strip() for x in nodes_sheet.row(4)]
-        expected = ['City', 'State', 'Country', 'Region', 'Latitude', 'Longitude']
-        if header != expected:
-            raise ValueError(f'Malformed header on Nodes sheet: {header} != {expected}')
-
-        nodes = []
-        for row in all_rows(nodes_sheet, start=5):
-            nodes.append(Node(*(x.value for x in row)))
-
-        # sanity check
-        header = [x.value.strip() for x in links_sheet.row(4)]
-        expected = ['Node A', 'Node Z', 'Distance (km)']
-        if header != expected:
-            raise ValueError(f'Malformed header on Nodes sheet: {header} != {expected}')
-
-        links = []
-        for row in all_rows(links_sheet, start=5):
-            links.append(Link(*(x.value for x in row)))
-
-    # sanity check
-    all_cities = Counter(n.city for n in nodes)
-    if len(all_cities) != len(nodes):
-        ValueError(f'Duplicate city: {all_cities}')
-    if any(ln.from_city not in all_cities or
-           ln.to_city   not in all_cities for ln in links):
-        ValueError(f'Bad link.')
-
-    return nodes, links
-
-parser = ArgumentParser()
-parser.add_argument('workbook', nargs='?', default='CORONET_Global_Topology.xls')
-parser.add_argument('-f', '--filter-region', action='append', default=[])
-
-all_rows = lambda sh, start=0: (sh.row(x) for x in range(start, sh.nrows))
-
-def midpoint(city_a, city_b):
-    lats  = city_a.latitude, city_b.latitude
-    longs = city_a.longitude, city_b.longitude
-    return {
-        'latitude':  sum(lats)  / 2,
-        'longitude': sum(longs) / 2,
-    }
-
-if __name__ == '__main__':
-    args = parser.parse_args()
-    nodes, links = parse_excel(args)
-
-    if args.filter_region:
-        nodes = [n for n in nodes if n.region.lower() in args.filter_region]
-        cities = {n.city for n in nodes}
-        links = [lnk for lnk in links if lnk.from_city in cities and
-                                         lnk.to_city in cities]
-        cities = {lnk.from_city for lnk in links} | {lnk.to_city for lnk in links}
-        nodes = [n for n in nodes if n.city in cities]
-
-    nodes_by_city = {n.city: n for n in nodes}
-
-    data = {
-        'elements':
-            [{'uid': f'trx {x.city}',
-              'metadata': {'location': {'city':      x.city,
-                                        'region':    x.region,
-                                        'latitude':  x.latitude,
-                                        'longitude': x.longitude}},
-              'type': 'Transceiver'}
-             for x in nodes] +
-            [{'uid': f'roadm {x.city}',
-              'metadata': {'location': {'city':      x.city,
-                                        'region':    x.region,
-                                        'latitude':  x.latitude,
-                                        'longitude': x.longitude}},
-              'type': 'Roadm'}
-             for x in nodes] +             
-            [{'uid': f'fiber ({x.from_city} → {x.to_city})',
-              'metadata': {'location': midpoint(nodes_by_city[x.from_city],
-                                                nodes_by_city[x.to_city])},
-              'type': 'Fiber',
-              'params': {'length':   round(x.distance, 3),
-                         'length_units':    x.distance_units,
-                         'loss_coef': 0.2,
-                         'dispersion': 16.7E-6,
-                         'gamma': 1.27E-3}
-              }
-             for x in links],
-        'connections':
-            list(chain.from_iterable(zip( # put bidi next to each other
-            [{'from_node': f'roadm {x.from_city}',
-              'to_node':   f'fiber ({x.from_city} → {x.to_city})'}
-             for x in links],
-            [{'from_node': f'fiber ({x.from_city} → {x.to_city})',
-              'to_node':   f'roadm {x.from_city}'}
-             for x in links])))
-            +
-            list(chain.from_iterable(zip(
-            [{'from_node': f'fiber ({x.from_city} → {x.to_city})',
-              'to_node':   f'roadm {x.to_city}'}
-             for x in links],
-            [{'from_node': f'roadm {x.to_city}',
-              'to_node':   f'fiber ({x.from_city} → {x.to_city})'}
-             for x in links])))
-            +
-            list(chain.from_iterable(zip(
-            [{'from_node': f'trx {x.city}',
-              'to_node':   f'roadm {x.city}'}
-             for x in nodes],
-            [{'from_node': f'roadm {x.city}',
-              'to_node':   f'trx {x.city}'}
-             for x in nodes])))            
-    }
-
-    print(dumps(data, indent=2))
-    with  open(output_json_file_name,'w') as edfa_json_file:
-        edfa_json_file.write(dumps(data, indent=2))

examples/coronet.asia.json

@@ -1,542 +0,0 @@
-{
-  "elements": [
-    {
-      "uid": "Bangkok",
-      "metadata": {
-        "location": {
-          "city": "Bangkok",
-          "region": "Asia",
-          "latitude": 13.73,
-          "longitude": 100.5
-        }
-      },
-      "type": "Transceiver"
-    },
-    {
-      "uid": "Beijing",
-      "metadata": {
-        "location": {
-          "city": "Beijing",
-          "region": "Asia",
-          "latitude": 39.92999979,
-          "longitude": 116.4000013
-        }
-      },
-      "type": "Transceiver"
-    },
-    {
-      "uid": "Delhi",
-      "metadata": {
-        "location": {
-          "city": "Delhi",
-          "region": "Asia",
-          "latitude": 28.6700003,
-          "longitude": 77.2099989
-        }
-      },
-      "type": "Transceiver"
-    },
-    {
-      "uid": "Hong_Kong",
-      "metadata": {
-        "location": {
-          "city": "Hong_Kong",
-          "region": "Asia",
-          "latitude": 22.267,
-          "longitude": 114.14
-        }
-      },
-      "type": "Transceiver"
-    },
-    {
-      "uid": "Honolulu",
-      "metadata": {
-        "location": {
-          "city": "Honolulu",
-          "region": "Asia",
-          "latitude": 21.3199996,
-          "longitude": -157.800003
-        }
-      },
-      "type": "Transceiver"
-    },
-    {
-      "uid": "Mumbai",
-      "metadata": {
-        "location": {
-          "city": "Mumbai",
-          "region": "Asia",
-          "latitude": 18.9599987,
-          "longitude": 72.8199999
-        }
-      },
-      "type": "Transceiver"
-    },
-    {
-      "uid": "Seoul",
-      "metadata": {
-        "location": {
-          "city": "Seoul",
-          "region": "Asia",
-          "latitude": 37.56000108,
-          "longitude": 126.9899988
-        }
-      },
-      "type": "Transceiver"
-    },
-    {
-      "uid": "Shanghai",
-      "metadata": {
-        "location": {
-          "city": "Shanghai",
-          "region": "Asia",
-          "latitude": 31.23,
-          "longitude": 121.47
-        }
-      },
-      "type": "Transceiver"
-    },
-    {
-      "uid": "Singapore",
-      "metadata": {
-        "location": {
-          "city": "Singapore",
-          "region": "Asia",
-          "latitude": 1.299999907,
-          "longitude": 103.8499992
-        }
-      },
-      "type": "Transceiver"
-    },
-    {
-      "uid": "Sydney",
-      "metadata": {
-        "location": {
-          "city": "Sydney",
-          "region": "Asia",
-          "latitude": -33.86999896,
-          "longitude": 151.2100066
-        }
-      },
-      "type": "Transceiver"
-    },
-    {
-      "uid": "Taipei",
-      "metadata": {
-        "location": {
-          "city": "Taipei",
-          "region": "Asia",
-          "latitude": 25.0200005,
-          "longitude": 121.449997
-        }
-      },
-      "type": "Transceiver"
-    },
-    {
-      "uid": "Tokyo",
-      "metadata": {
-        "location": {
-          "city": "Tokyo",
-          "region": "Asia",
-          "latitude": 35.6699986,
-          "longitude": 139.770004
-        }
-      },
-      "type": "Transceiver"
-    },
-    {
-      "uid": "fiber (Bangkok \u2192 Delhi)",
-      "metadata": {
-        "length": 3505.95,
-        "units": "km",
-        "location": {
-          "latitude": 21.20000015,
-          "longitude": 88.85499945000001
-        }
-      },
-      "type": "Fiber"
-    },
-    {
-      "uid": "fiber (Bangkok \u2192 Hong_Kong)",
-      "metadata": {
-        "length": 2070.724,
-        "units": "km",
-        "location": {
-          "latitude": 17.9985,
-          "longitude": 107.32
-        }
-      },
-      "type": "Fiber"
-    },
-    {
-      "uid": "fiber (Beijing \u2192 Seoul)",
-      "metadata": {
-        "length": 1146.124,
-        "units": "km",
-        "location": {
-          "latitude": 38.745000434999994,
-          "longitude": 121.69500005
-        }
-      },
-      "type": "Fiber"
-    },
-    {
-      "uid": "fiber (Beijing \u2192 Shanghai)",
-      "metadata": {
-        "length": 1284.465,
-        "units": "km",
-        "location": {
-          "latitude": 35.579999895,
-          "longitude": 118.93500065
-        }
-      },
-      "type": "Fiber"
-    },
-    {
-      "uid": "fiber (Delhi \u2192 Mumbai)",
-      "metadata": {
-        "length": 1402.141,
-        "units": "km",
-        "location": {
-          "latitude": 23.8149995,
-          "longitude": 75.0149994
-        }
-      },
-      "type": "Fiber"
-    },
-    {
-      "uid": "fiber (Hong_Kong \u2192 Shanghai)",
-      "metadata": {
-        "length": 1480.406,
-        "units": "km",
-        "location": {
-          "latitude": 26.7485,
-          "longitude": 117.805
-        }
-      },
-      "type": "Fiber"
-    },
-    {
-      "uid": "fiber (Hong_Kong \u2192 Sydney)",
-      "metadata": {
-        "length": 8856.6,
-        "units": "km",
-        "location": {
-          "latitude": -5.801499479999999,
-          "longitude": 132.67500330000001
-        }
-      },
-      "type": "Fiber"
-    },
-    {
-      "uid": "fiber (Hong_Kong \u2192 Taipei)",
-      "metadata": {
-        "length": 966.177,
-        "units": "km",
-        "location": {
-          "latitude": 23.64350025,
-          "longitude": 117.79499849999999
-        }
-      },
-      "type": "Fiber"
-    },
-    {
-      "uid": "fiber (Honolulu \u2192 Sydney)",
-      "metadata": {
-        "length": 9808.616,
-        "units": "km",
-        "location": {
-          "latitude": -6.274999679999999,
-          "longitude": -3.294998199999995
-        }
-      },
-      "type": "Fiber"
-    },
-    {
-      "uid": "fiber (Honolulu \u2192 Taipei)",
-      "metadata": {
-        "length": 9767.013,
-        "units": "km",
-        "location": {
-          "latitude": 23.17000005,
-          "longitude": -18.175003000000004
-        }
-      },
-      "type": "Fiber"
-    },
-    {
-      "uid": "fiber (Mumbai \u2192 Singapore)",
-      "metadata": {
-        "length": 4692.708,
-        "units": "km",
-        "location": {
-          "latitude": 10.1299993035,
-          "longitude": 88.33499954999999
-        }
-      },
-      "type": "Fiber"
-    },
-    {
-      "uid": "fiber (Seoul \u2192 Tokyo)",
-      "metadata": {
-        "length": 1391.085,
-        "units": "km",
-        "location": {
-          "latitude": 36.614999839999996,
-          "longitude": 133.3800014
-        }
-      },
-      "type": "Fiber"
-    },
-    {
-      "uid": "fiber (Singapore \u2192 Sydney)",
-      "metadata": {
-        "length": 7562.331,
-        "units": "km",
-        "location": {
-          "latitude": -16.2849995265,
-          "longitude": 127.5300029
-        }
-      },
-      "type": "Fiber"
-    },
-    {
-      "uid": "fiber (Taipei \u2192 Tokyo)",
-      "metadata": {
-        "length": 2537.345,
-        "units": "km",
-        "location": {
-          "latitude": 30.344999549999997,
-          "longitude": 130.6100005
-        }
-      },
-      "type": "Fiber"
-    }
-  ],
-  "connections": [
-    {
-      "from_node": "Bangkok",
-      "to_node": "fiber (Bangkok \u2192 Delhi)"
-    },
-    {
-      "from_node": "fiber (Bangkok \u2192 Delhi)",
-      "to_node": "Bangkok"
-    },
-    {
-      "from_node": "Bangkok",
-      "to_node": "fiber (Bangkok \u2192 Hong_Kong)"
-    },
-    {
-      "from_node": "fiber (Bangkok \u2192 Hong_Kong)",
-      "to_node": "Bangkok"
-    },
-    {
-      "from_node": "Beijing",
-      "to_node": "fiber (Beijing \u2192 Seoul)"
-    },
-    {
-      "from_node": "fiber (Beijing \u2192 Seoul)",
-      "to_node": "Beijing"
-    },
-    {
-      "from_node": "Beijing",
-      "to_node": "fiber (Beijing \u2192 Shanghai)"
-    },
-    {
-      "from_node": "fiber (Beijing \u2192 Shanghai)",
-      "to_node": "Beijing"
-    },
-    {
-      "from_node": "Delhi",
-      "to_node": "fiber (Delhi \u2192 Mumbai)"
-    },
-    {
-      "from_node": "fiber (Delhi \u2192 Mumbai)",
-      "to_node": "Delhi"
-    },
-    {
-      "from_node": "Hong_Kong",
-      "to_node": "fiber (Hong_Kong \u2192 Shanghai)"
-    },
-    {
-      "from_node": "fiber (Hong_Kong \u2192 Shanghai)",
-      "to_node": "Hong_Kong"
-    },
-    {
-      "from_node": "Hong_Kong",
-      "to_node": "fiber (Hong_Kong \u2192 Sydney)"
-    },
-    {
-      "from_node": "fiber (Hong_Kong \u2192 Sydney)",
-      "to_node": "Hong_Kong"
-    },
-    {
-      "from_node": "Hong_Kong",
-      "to_node": "fiber (Hong_Kong \u2192 Taipei)"
-    },
-    {
-      "from_node": "fiber (Hong_Kong \u2192 Taipei)",
-      "to_node": "Hong_Kong"
-    },
-    {
-      "from_node": "Honolulu",
-      "to_node": "fiber (Honolulu \u2192 Sydney)"
-    },
-    {
-      "from_node": "fiber (Honolulu \u2192 Sydney)",
-      "to_node": "Honolulu"
-    },
-    {
-      "from_node": "Honolulu",
-      "to_node": "fiber (Honolulu \u2192 Taipei)"
-    },
-    {
-      "from_node": "fiber (Honolulu \u2192 Taipei)",
-      "to_node": "Honolulu"
-    },
-    {
-      "from_node": "Mumbai",
-      "to_node": "fiber (Mumbai \u2192 Singapore)"
-    },
-    {
-      "from_node": "fiber (Mumbai \u2192 Singapore)",
-      "to_node": "Mumbai"
-    },
-    {
-      "from_node": "Seoul",
-      "to_node": "fiber (Seoul \u2192 Tokyo)"
-    },
-    {
-      "from_node": "fiber (Seoul \u2192 Tokyo)",
-      "to_node": "Seoul"
-    },
-    {
-      "from_node": "Singapore",
-      "to_node": "fiber (Singapore \u2192 Sydney)"
-    },
-    {
-      "from_node": "fiber (Singapore \u2192 Sydney)",
-      "to_node": "Singapore"
-    },
-    {
-      "from_node": "Taipei",
-      "to_node": "fiber (Taipei \u2192 Tokyo)"
-    },
-    {
-      "from_node": "fiber (Taipei \u2192 Tokyo)",
-      "to_node": "Taipei"
-    },
-    {
-      "from_node": "fiber (Bangkok \u2192 Delhi)",
-      "to_node": "Delhi"
-    },
-    {
-      "from_node": "Delhi",
-      "to_node": "fiber (Bangkok \u2192 Delhi)"
-    },
-    {
-      "from_node": "fiber (Bangkok \u2192 Hong_Kong)",
-      "to_node": "Hong_Kong"
-    },
-    {
-      "from_node": "Hong_Kong",
-      "to_node": "fiber (Bangkok \u2192 Hong_Kong)"
-    },
-    {
-      "from_node": "fiber (Beijing \u2192 Seoul)",
-      "to_node": "Seoul"
-    },
-    {
-      "from_node": "Seoul",
-      "to_node": "fiber (Beijing \u2192 Seoul)"
-    },
-    {
-      "from_node": "fiber (Beijing \u2192 Shanghai)",
-      "to_node": "Shanghai"
-    },
-    {
-      "from_node": "Shanghai",
-      "to_node": "fiber (Beijing \u2192 Shanghai)"
-    },
-    {
-      "from_node": "fiber (Delhi \u2192 Mumbai)",
-      "to_node": "Mumbai"
-    },
-    {
-      "from_node": "Mumbai",
-      "to_node": "fiber (Delhi \u2192 Mumbai)"
-    },
-    {
-      "from_node": "fiber (Hong_Kong \u2192 Shanghai)",
-      "to_node": "Shanghai"
-    },
-    {
-      "from_node": "Shanghai",
-      "to_node": "fiber (Hong_Kong \u2192 Shanghai)"
-    },
-    {
-      "from_node": "fiber (Hong_Kong \u2192 Sydney)",
-      "to_node": "Sydney"
-    },
-    {
-      "from_node": "Sydney",
-      "to_node": "fiber (Hong_Kong \u2192 Sydney)"
-    },
-    {
-      "from_node": "fiber (Hong_Kong \u2192 Taipei)",
-      "to_node": "Taipei"
-    },
-    {
-      "from_node": "Taipei",
-      "to_node": "fiber (Hong_Kong \u2192 Taipei)"
-    },
-    {
-      "from_node": "fiber (Honolulu \u2192 Sydney)",
-      "to_node": "Sydney"
-    },
-    {
-      "from_node": "Sydney",
-      "to_node": "fiber (Honolulu \u2192 Sydney)"
-    },
-    {
-      "from_node": "fiber (Honolulu \u2192 Taipei)",
-      "to_node": "Taipei"
-    },
-    {
-      "from_node": "Taipei",
-      "to_node": "fiber (Honolulu \u2192 Taipei)"
-    },
-    {
-      "from_node": "fiber (Mumbai \u2192 Singapore)",
-      "to_node": "Singapore"
-    },
-    {
-      "from_node": "Singapore",
-      "to_node": "fiber (Mumbai \u2192 Singapore)"
-    },
-    {
-      "from_node": "fiber (Seoul \u2192 Tokyo)",
-      "to_node": "Tokyo"
-    },
-    {
-      "from_node": "Tokyo",
-      "to_node": "fiber (Seoul \u2192 Tokyo)"
-    },
-    {
-      "from_node": "fiber (Singapore \u2192 Sydney)",
-      "to_node": "Sydney"
-    },
-    {
-      "from_node": "Sydney",
-      "to_node": "fiber (Singapore \u2192 Sydney)"
-    },
-    {
-      "from_node": "fiber (Taipei \u2192 Tokyo)",
-      "to_node": "Tokyo"
-    },
-    {
-      "from_node": "Tokyo",
-      "to_node": "fiber (Taipei \u2192 Tokyo)"
-    }
-  ]
-}

examples/coronet.europe.json

@@ -1,582 +0,0 @@
-{
-  "elements": [
-    {
-      "uid": "Amsterdam",
-      "metadata": {
-        "location": {
-          "city": "Amsterdam",
-          "region": "Europe",
-          "latitude": 52.3699996,
-          "longitude": 4.88999915
-        }
-      },
-      "type": "Transceiver"
-    },
-    {
-      "uid": "Berlin",
-      "metadata": {
-        "location": {
-          "city": "Berlin",
-          "region": "Europe",
-          "latitude": 52.520002,
-          "longitude": 13.379995
-        }
-      },
-      "type": "Transceiver"
-    },
-    {
-      "uid": "Brussels",
-      "metadata": {
-        "location": {
-          "city": "Brussels",
-          "region": "Europe",
-          "latitude": 50.830002,
-          "longitude": 4.330002
-        }
-      },
-      "type": "Transceiver"
-    },
-    {
-      "uid": "Bucharest",
-      "metadata": {
-        "location": {
-          "city": "Bucharest",
-          "region": "Europe",
-          "latitude": 44.44,
-          "longitude": 26.1
-        }
-      },
-      "type": "Transceiver"
-    },
-    {
-      "uid": "Frankfurt",
-      "metadata": {
-        "location": {
-          "city": "Frankfurt",
-          "region": "Europe",
-          "latitude": 50.1199992,
-          "longitude": 8.68000104
-        }
-      },
-      "type": "Transceiver"
-    },
-    {
-      "uid": "Istanbul",
-      "metadata": {
-        "location": {
-          "city": "Istanbul",
-          "region": "Europe",
-          "latitude": 41.1,
-          "longitude": 29.0
-        }
-      },
-      "type": "Transceiver"
-    },
-    {
-      "uid": "London",
-      "metadata": {
-        "location": {
-          "city": "London",
-          "region": "Europe",
-          "latitude": 51.5200005,
-          "longitude": -0.100000296
-        }
-      },
-      "type": "Transceiver"
-    },
-    {
-      "uid": "Madrid",
-      "metadata": {
-        "location": {
-          "city": "Madrid",
-          "region": "Europe",
-          "latitude": 40.419998,
-          "longitude": -3.7100002
-        }
-      },
-      "type": "Transceiver"
-    },
-    {
-      "uid": "Paris",
-      "metadata": {
-        "location": {
-          "city": "Paris",
-          "region": "Europe",
-          "latitude": 48.86,
-          "longitude": 2.3399995
-        }
-      },
-      "type": "Transceiver"
-    },
-    {
-      "uid": "Rome",
-      "metadata": {
-        "location": {
-          "city": "Rome",
-          "region": "Europe",
-          "latitude": 41.8899996,
-          "longitude": 12.5000004
-        }
-      },
-      "type": "Transceiver"
-    },
-    {
-      "uid": "Vienna",
-      "metadata": {
-        "location": {
-          "city": "Vienna",
-          "region": "Europe",
-          "latitude": 48.2200024,
-          "longitude": 16.3700005
-        }
-      },
-      "type": "Transceiver"
-    },
-    {
-      "uid": "Warsaw",
-      "metadata": {
-        "location": {
-          "city": "Warsaw",
-          "region": "Europe",
-          "latitude": 52.2599987,
-          "longitude": 21.0200005
-        }
-      },
-      "type": "Transceiver"
-    },
-    {
-      "uid": "Zurich",
-      "metadata": {
-        "location": {
-          "city": "Zurich",
-          "region": "Europe",
-          "latitude": 47.3800015,
-          "longitude": 8.5399996
-        }
-      },
-      "type": "Transceiver"
-    },
-    {
-      "uid": "fiber (Amsterdam \u2192 Berlin)",
-      "metadata": {
-        "length": 690.608,
-        "units": "km",
-        "location": {
-          "latitude": 52.4450008,
-          "longitude": 9.134997075
-        }
-      },
-      "type": "Fiber"
-    },
-    {
-      "uid": "fiber (Amsterdam \u2192 Brussels)",
-      "metadata": {
-        "length": 210.729,
-        "units": "km",
-        "location": {
-          "latitude": 51.600000800000004,
-          "longitude": 4.610000575000001
-        }
-      },
-      "type": "Fiber"
-    },
-    {
-      "uid": "fiber (Amsterdam \u2192 Frankfurt)",
-      "metadata": {
-        "length": 436.324,
-        "units": "km",
-        "location": {
-          "latitude": 51.2449994,
-          "longitude": 6.785000095000001
-        }
-      },
-      "type": "Fiber"
-    },
-    {
-      "uid": "fiber (Berlin \u2192 Warsaw)",
-      "metadata": {
-        "length": 623.015,
-        "units": "km",
-        "location": {
-          "latitude": 52.390000349999994,
-          "longitude": 17.199997749999998
-        }
-      },
-      "type": "Fiber"
-    },
-    {
-      "uid": "fiber (Brussels \u2192 London)",
-      "metadata": {
-        "length": 381.913,
-        "units": "km",
-        "location": {
-          "latitude": 51.17500125,
-          "longitude": 2.115000852
-        }
-      },
-      "type": "Fiber"
-    },
-    {
-      "uid": "fiber (Bucharest \u2192 Istanbul)",
-      "metadata": {
-        "length": 528.58,
-        "units": "km",
-        "location": {
-          "latitude": 42.769999999999996,
-          "longitude": 27.55
-        }
-      },
-      "type": "Fiber"
-    },
-    {
-      "uid": "fiber (Bucharest \u2192 Warsaw)",
-      "metadata": {
-        "length": 1136.2,
-        "units": "km",
-        "location": {
-          "latitude": 48.34999935,
-          "longitude": 23.56000025
-        }
-      },
-      "type": "Fiber"
-    },
-    {
-      "uid": "fiber (Frankfurt \u2192 Vienna)",
-      "metadata": {
-        "length": 717.001,
-        "units": "km",
-        "location": {
-          "latitude": 49.1700008,
-          "longitude": 12.52500077
-        }
-      },
-      "type": "Fiber"
-    },
-    {
-      "uid": "fiber (Istanbul \u2192 Rome)",
-      "metadata": {
-        "length": 1650.406,
-        "units": "km",
-        "location": {
-          "latitude": 41.4949998,
-          "longitude": 20.7500002
-        }
-      },
-      "type": "Fiber"
-    },
-    {
-      "uid": "fiber (London \u2192 Paris)",
-      "metadata": {
-        "length": 411.692,
-        "units": "km",
-        "location": {
-          "latitude": 50.19000025,
-          "longitude": 1.1199996019999998
-        }
-      },
-      "type": "Fiber"
-    },
-    {
-      "uid": "fiber (Madrid \u2192 Paris)",
-      "metadata": {
-        "length": 1263.619,
-        "units": "km",
-        "location": {
-          "latitude": 44.639999,
-          "longitude": -0.6850003500000001
-        }
-      },
-      "type": "Fiber"
-    },
-    {
-      "uid": "fiber (Madrid \u2192 Zurich)",
-      "metadata": {
-        "length": 1497.358,
-        "units": "km",
-        "location": {
-          "latitude": 43.89999975,
-          "longitude": 2.4149997
-        }
-      },
-      "type": "Fiber"
-    },
-    {
-      "uid": "fiber (Rome \u2192 Vienna)",
-      "metadata": {
-        "length": 920.026,
-        "units": "km",
-        "location": {
-          "latitude": 45.055001000000004,
-          "longitude": 14.43500045
-        }
-      },
-      "type": "Fiber"
-    },
-    {
-      "uid": "fiber (Rome \u2192 Zurich)",
-      "metadata": {
-        "length": 823.4,
-        "units": "km",
-        "location": {
-          "latitude": 44.63500055,
-          "longitude": 10.52
-        }
-      },
-      "type": "Fiber"
-    },
-    {
-      "uid": "fiber (Vienna \u2192 Warsaw)",
-      "metadata": {
-        "length": 669.297,
-        "units": "km",
-        "location": {
-          "latitude": 50.24000055,
-          "longitude": 18.6950005
-        }
-      },
-      "type": "Fiber"
-    }
-  ],
-  "connections": [
-    {
-      "from_node": "Amsterdam",
-      "to_node": "fiber (Amsterdam \u2192 Berlin)"
-    },
-    {
-      "from_node": "fiber (Amsterdam \u2192 Berlin)",
-      "to_node": "Amsterdam"
-    },
-    {
-      "from_node": "Amsterdam",
-      "to_node": "fiber (Amsterdam \u2192 Brussels)"
-    },
-    {
-      "from_node": "fiber (Amsterdam \u2192 Brussels)",
-      "to_node": "Amsterdam"
-    },
-    {
-      "from_node": "Amsterdam",
-      "to_node": "fiber (Amsterdam \u2192 Frankfurt)"
-    },
-    {
-      "from_node": "fiber (Amsterdam \u2192 Frankfurt)",
-      "to_node": "Amsterdam"
-    },
-    {
-      "from_node": "Berlin",
-      "to_node": "fiber (Berlin \u2192 Warsaw)"
-    },
-    {
-      "from_node": "fiber (Berlin \u2192 Warsaw)",
-      "to_node": "Berlin"
-    },
-    {
-      "from_node": "Brussels",
-      "to_node": "fiber (Brussels \u2192 London)"
-    },
-    {
-      "from_node": "fiber (Brussels \u2192 London)",
-      "to_node": "Brussels"
-    },
-    {
-      "from_node": "Bucharest",
-      "to_node": "fiber (Bucharest \u2192 Istanbul)"
-    },
-    {
-      "from_node": "fiber (Bucharest \u2192 Istanbul)",
-      "to_node": "Bucharest"
-    },
-    {
-      "from_node": "Bucharest",
-      "to_node": "fiber (Bucharest \u2192 Warsaw)"
-    },
-    {
-      "from_node": "fiber (Bucharest \u2192 Warsaw)",
-      "to_node": "Bucharest"
-    },
-    {
-      "from_node": "Frankfurt",
-      "to_node": "fiber (Frankfurt \u2192 Vienna)"
-    },
-    {
-      "from_node": "fiber (Frankfurt \u2192 Vienna)",
-      "to_node": "Frankfurt"
-    },
-    {
-      "from_node": "Istanbul",
-      "to_node": "fiber (Istanbul \u2192 Rome)"
-    },
-    {
-      "from_node": "fiber (Istanbul \u2192 Rome)",
-      "to_node": "Istanbul"
-    },
-    {
-      "from_node": "London",
-      "to_node": "fiber (London \u2192 Paris)"
-    },
-    {
-      "from_node": "fiber (London \u2192 Paris)",
-      "to_node": "London"
-    },
-    {
-      "from_node": "Madrid",
-      "to_node": "fiber (Madrid \u2192 Paris)"
-    },
-    {
-      "from_node": "fiber (Madrid \u2192 Paris)",
-      "to_node": "Madrid"
-    },
-    {
-      "from_node": "Madrid",
-      "to_node": "fiber (Madrid \u2192 Zurich)"
-    },
-    {
-      "from_node": "fiber (Madrid \u2192 Zurich)",
-      "to_node": "Madrid"
-    },
-    {
-      "from_node": "Rome",
-      "to_node": "fiber (Rome \u2192 Vienna)"
-    },
-    {
-      "from_node": "fiber (Rome \u2192 Vienna)",
-      "to_node": "Rome"
-    },
-    {
-      "from_node": "Rome",
-      "to_node": "fiber (Rome \u2192 Zurich)"
-    },
-    {
-      "from_node": "fiber (Rome \u2192 Zurich)",
-      "to_node": "Rome"
-    },
-    {
-      "from_node": "Vienna",
-      "to_node": "fiber (Vienna \u2192 Warsaw)"
-    },
-    {
-      "from_node": "fiber (Vienna \u2192 Warsaw)",
-      "to_node": "Vienna"
-    },
-    {
-      "from_node": "fiber (Amsterdam \u2192 Berlin)",
-      "to_node": "Berlin"
-    },
-    {
-      "from_node": "Berlin",
-      "to_node": "fiber (Amsterdam \u2192 Berlin)"
-    },
-    {
-      "from_node": "fiber (Amsterdam \u2192 Brussels)",
-      "to_node": "Brussels"
-    },
-    {
-      "from_node": "Brussels",
-      "to_node": "fiber (Amsterdam \u2192 Brussels)"
-    },
-    {
-      "from_node": "fiber (Amsterdam \u2192 Frankfurt)",
-      "to_node": "Frankfurt"
-    },
-    {
-      "from_node": "Frankfurt",
-      "to_node": "fiber (Amsterdam \u2192 Frankfurt)"
-    },
-    {
-      "from_node": "fiber (Berlin \u2192 Warsaw)",
-      "to_node": "Warsaw"
-    },
-    {
-      "from_node": "Warsaw",
-      "to_node": "fiber (Berlin \u2192 Warsaw)"
-    },
-    {
-      "from_node": "fiber (Brussels \u2192 London)",
-      "to_node": "London"
-    },
-    {
-      "from_node": "London",
-      "to_node": "fiber (Brussels \u2192 London)"
-    },
-    {
-      "from_node": "fiber (Bucharest \u2192 Istanbul)",
-      "to_node": "Istanbul"
-    },
-    {
-      "from_node": "Istanbul",
-      "to_node": "fiber (Bucharest \u2192 Istanbul)"
-    },
-    {
-      "from_node": "fiber (Bucharest \u2192 Warsaw)",
-      "to_node": "Warsaw"
-    },
-    {
-      "from_node": "Warsaw",
-      "to_node": "fiber (Bucharest \u2192 Warsaw)"
-    },
-    {
-      "from_node": "fiber (Frankfurt \u2192 Vienna)",
-      "to_node": "Vienna"
-    },
-    {
-      "from_node": "Vienna",
-      "to_node": "fiber (Frankfurt \u2192 Vienna)"
-    },
-    {
-      "from_node": "fiber (Istanbul \u2192 Rome)",
-      "to_node": "Rome"
-    },
-    {
-      "from_node": "Rome",
-      "to_node": "fiber (Istanbul \u2192 Rome)"
-    },
-    {
-      "from_node": "fiber (London \u2192 Paris)",
-      "to_node": "Paris"
-    },
-    {
-      "from_node": "Paris",
-      "to_node": "fiber (London \u2192 Paris)"
-    },
-    {
-      "from_node": "fiber (Madrid \u2192 Paris)",
-      "to_node": "Paris"
-    },
-    {
-      "from_node": "Paris",
-      "to_node": "fiber (Madrid \u2192 Paris)"
-    },
-    {
-      "from_node": "fiber (Madrid \u2192 Zurich)",
-      "to_node": "Zurich"
-    },
-    {
-      "from_node": "Zurich",
-      "to_node": "fiber (Madrid \u2192 Zurich)"
-    },
-    {
-      "from_node": "fiber (Rome \u2192 Vienna)",
-      "to_node": "Vienna"
-    },
-    {
-      "from_node": "Vienna",
-      "to_node": "fiber (Rome \u2192 Vienna)"
-    },
-    {
-      "from_node": "fiber (Rome \u2192 Zurich)",
-      "to_node": "Zurich"
-    },
-    {
-      "from_node": "Zurich",
-      "to_node": "fiber (Rome \u2192 Zurich)"
-    },
-    {
-      "from_node": "fiber (Vienna \u2192 Warsaw)",
-      "to_node": "Warsaw"
-    },
-    {
-      "from_node": "Warsaw",
-      "to_node": "fiber (Vienna \u2192 Warsaw)"
-    }
-  ]
-}

examples/create_eqpt_sheet.py

@@ -0,0 +1,101 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+create_eqpt_sheet.py
+====================
+
+XLS parser that can be called to create a "City" column in the "Eqpt" sheet.
+
+If not present in the "Nodes" sheet, the "Type" column will be implicitly
+determined based on the topology.
+"""
+
+try:
+    from xlrd import open_workbook
+except ModuleNotFoundError:
+    exit('Required: `pip install xlrd`')
+from argparse import ArgumentParser
+
+PARSER = ArgumentParser()
+PARSER.add_argument('workbook', nargs='?', default='meshTopologyExampleV2.xls',
+                    help='create the mandatory columns in Eqpt sheet')
+ALL_ROWS = lambda sh, start=0: (sh.row(x) for x in range(start, sh.nrows))
+
+class Node:
+    """ Node element contains uid, list of connected nodes and eqpt type
+    """
+    def __init__(self, uid, to_node):
+        self.uid = uid
+        self.to_node = to_node
+        self.eqpt = None
+
+    def __repr__(self):
+        return f'uid {self.uid} \nto_node {[node for node in self.to_node]}\neqpt {self.eqpt}\n'
+
+    def __str__(self):
+        return f'uid {self.uid} \nto_node {[node for node in self.to_node]}\neqpt {self.eqpt}\n'
+
+def read_excel(input_filename):
+    """ read excel Nodes and Links sheets and create a dict of nodes with
+    their to_nodes and type of eqpt
+    """
+    with open_workbook(input_filename) as wobo:
+        # reading Links sheet
+        links_sheet = wobo.sheet_by_name('Links')
+        nodes = {}
+        for row in ALL_ROWS(links_sheet, start=5):
+            try:
+                nodes[row[0].value].to_node.append(row[1].value)
+            except KeyError:
+                nodes[row[0].value] = Node(row[0].value, [row[1].value])
+            try:
+                nodes[row[1].value].to_node.append(row[0].value)
+            except KeyError:
+                nodes[row[1].value] = Node(row[1].value, [row[0].value])
+
+        nodes_sheet = wobo.sheet_by_name('Nodes')
+        for row in ALL_ROWS(nodes_sheet, start=5):
+            node = row[0].value
+            eqpt = row[6].value
+            try:
+                if eqpt == 'ILA' and len(nodes[node].to_node) != 2:
+                    print(f'Inconsistancy ILA node with degree > 2: {node} ')
+                    exit()
+                if eqpt == '' and len(nodes[node].to_node) == 2:
+                    nodes[node].eqpt = 'ILA'
+                elif eqpt == '' and len(nodes[node].to_node) != 2:
+                    nodes[node].eqpt = 'ROADM'
+                else:
+                    nodes[node].eqpt = eqpt
+            except KeyError:
+                print(f'inconsistancy between nodes and links sheet: {node} is not listed in links')
+                exit()
+        return nodes
+
+def create_eqt_template(nodes, input_filename):
+    """ writes list of node A node Z corresponding to Nodes and Links sheets in order
+    to help user populating Eqpt
+    """
+    output_filename = f'{input_filename[:-4]}_eqpt_sheet.txt'
+    with open(output_filename, 'w', encoding='utf-8') as my_file:
+        # print header similar to excel
+        my_file.write('OPTIONAL\n\n\n\
+           \t\tNode a egress amp (from a to z)\t\t\t\t\tNode a ingress amp (from z to a) \
+           \nNode A \tNode Z \tamp type \tatt_in \tamp gain \ttilt \tatt_out\
+           amp type   \tatt_in \tamp gain   \ttilt   \tatt_out\n')
+
+
+        for node in nodes.values():
+            if node.eqpt == 'ILA':
+                my_file.write(f'{node.uid}\t{node.to_node[0]}\n')
+            if node.eqpt == 'ROADM':
+                for to_node in node.to_node:
+                    my_file.write(f'{node.uid}\t{to_node}\n')
+
+        print(f'File {output_filename} successfully created with Node A - Node Z ' +
+              ' entries for Eqpt sheet in excel file.')
+
+if __name__ == '__main__':
+    ARGS = PARSER.parse_args()
+    create_eqt_template(read_excel(ARGS.workbook), ARGS.workbook)

examples/default_edfa_config.json

@@ -0,0 +1,296 @@
+{
+    "nf_ripple": [
+        0.0,
+        0.0,
+        0.0,
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+        0.0
+    ],
+    "gain_ripple": [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
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+    ],
+    "dgt": [
+        2.714526681131686,
+        2.705443819238505,
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+        1.1869318618366975,
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+        1.0712204022764056,
+        1.0534217504465226,
+        1.0356155337864215,
+        1.017807767853702,
+        1.0
+    ]
+}

examples/edfa/Pchan2D.txt

@@ -1,8 +0,0 @@
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examples/edfa/Pchan2DLegend.txt

@@ -1,8 +0,0 @@
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examples/edfa/edfa_config.json

@@ -1,9 +0,0 @@
-{
-    "params": {
-        "dfg": [25.13596985, 25.11822814, 25.09542133, 25.06245771, 25.02602765, 24.99637953, 24.98167255, 24.97530668, 24.98320726, 24.99718565, 25.01757247, 25.03832781, 25.05495585, 25.0670719, 25.07091411, 25.07094365, 25.07114324, 25.07533627, 25.08731018, 25.10313936, 25.12276204, 25.14239479, 25.15945633, 25.17392704, 25.17673767, 25.17037141, 25.15216254, 25.1311431, 25.10802335, 25.08548777, 25.06916675, 25.05848176, 25.05447313, 25.05154441, 25.04946059, 25.04717849, 25.04551656, 25.04467649, 25.0407292, 25.03285408, 25.0234883, 25.01659234, 25.01332136, 25.01123434, 25.01030015, 25.00936548, 25.00873964, 25.00842535, 25.00696466, 25.0040431, 25.00070998, 24.9984232, 24.99306332, 24.98352421, 24.97125103, 24.96038108, 24.94888721, 24.93531489, 24.92131927, 24.90898697, 24.89896514, 24.88958463, 24.8808387, 24.87210092, 24.86462026, 24.85839773, 24.85445838, 24.85155443, 24.85176601, 24.85408014, 24.85909624, 24.86474458, 24.87203486, 24.8803652, 24.88910669, 24.89721313, 24.90282604, 24.9065669, 24.9086508, 24.91093944, 24.91343079, 24.91592344, 24.92155351, 24.93031861, 24.94052812, 24.94904669, 24.95757123, 24.96781845, 24.98180093, 24.99782686, 25.01393183, 25.02809846, 25.04032575, 25.05256981, 25.06479701, 25.07704697],
-        "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, 1.017807767853702, 1.0],
-        "nf_fit_coeff": [0.000168241, 0.0469961, 0.0359549, 5.82851],
-        "nf_ripple": [-0.315374332, -0.315374332, -0.3154009157100272, -0.3184914611751095, -0.32158358425400546, -0.3246772861549999, -0.32762368641496226, -0.3205413846123276, -0.31345546385118733, -0.3063659213569748, -0.29920267890990127, -0.27061972852631744, -0.24202215770774693, -0.21340995523361256, -0.18478227130158695, -0.14809761118389625, -0.11139416731807622, -0.07467192527357988, -0.038026748965679924, -0.019958469399422092, -0.0018809287980157928, 0.01620587996057356, 0.03430196400570967, 0.05240733047405406, 0.07052198650959736, 0.079578036683472, 0.08854664736190952, 0.0975198632319653, 0.10649768784154924, 0.0977413804499074, 0.08880343717266004, 0.07986089973284587, 0.0709137645874038, 0.06333589274056531, 0.055756212252058776, 0.04817263174786321, 0.04058514821716236, 0.03338159167571013, 0.026178308595650738, 0.018971315351761126, 0.011760609076833628, 0.01695029492275999, 0.02227499135770144, 0.02760243318910433, 0.03293262254079026, 0.038265561538776145, 0.04360125231127117, 0.03485699074348155, 0.025991055149117932, 0.017120541224980364, 0.008275758735920322, 0.0019423214065246042, -0.004394389017104359, -0.010734375072893196, -0.017077639301414434, -0.02467970289957285, -0.03229797040382168, -0.03992018009047725, -0.04753456632753024, -0.049234003141433724, -0.05093432003654719, -0.05263551769669225, -0.05433759680640246, -0.0560405580509193, -0.057718452237076875, -0.056840590379175944, -0.055962273198734966, -0.05508350034141658, -0.054204271452516814, -0.05839608872695511, -0.06262733016971533, -0.0668607690892037, -0.07090173625606945, -0.05209609730905224, -0.03328068412141294, -0.014455489070928059, 0.004315038757905716, 0.014839202394482527, 0.025368841662503576, 0.03590396083646565, 0.0464445641953214, 0.05699065602246746, 0.06754224060577406, 0.10002709623672751, 0.13258013095133617, 0.1651501336277331, 0.1977371175359939, 0.23194802687829724, 0.26618779883837107, 0.3004454365808535, 0.33472095409250663, 0.35929034770587287, 0.38384389188855605, 0.40841026111391787, 0.43298946543290784, 0.43298946543290784],
-        "frequencies": []
-    }
-}

examples/edfa/example.py

@@ -1,72 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-import matplotlib.pyplot as plt
-import numpy as np
-
-from gnpy.core.utils import (load_json,
-                             itufs,
-                             freq2wavelength,
-                             lin2db,
-                             db2lin)
-from gnpy.core import network
-
-topology = load_json('edfa_example_network.json')
-nw = network.network_from_json(topology)
-pch2d_legend_data = np.loadtxt('Pchan2DLegend.txt')
-pch2d = np.loadtxt('Pchan2D.txt')
-
-ch_spacing = 0.05
-fc = itufs(ch_spacing)
-lc = freq2wavelength(fc) / 1000
-nchan = np.arange(len(lc))
-df = np.ones(len(lc)) * ch_spacing
-
-edfa1 = [n for n in nw.nodes() if n.uid == 'Edfa1'][0]
-edfa1.gain_target = 20.0
-edfa1.tilt_target = -0.7
-edfa1.calc_nf()
-
-results = []
-for Pin in pch2d:
-    chgain = edfa1.gain_profile(Pin)
-    pase = edfa1.noise_profile(chgain, fc, df)
-    pout = lin2db(db2lin(Pin + chgain) + db2lin(pase))
-    results.append(pout)
-
-# Generate legend text
-
-pch2d_legend = []
-for ea in pch2d_legend_data:
-    s = ''.join([chr(xx) for xx in ea.astype(dtype=int)]).strip()
-    pch2d_legend.append(s)
-
-# Plot
-axis_font = {'fontname': 'Arial', 'size': '16', 'fontweight': 'bold'}
-title_font = {'fontname': 'Arial', 'size': '17', 'fontweight': 'bold'}
-tic_font = {'fontname': 'Arial', 'size': '12'}
-plt.rcParams["font.family"] = "Arial"
-plt.figure()
-plt.plot(nchan, pch2d.T, '.-', lw=2)
-plt.xlabel('Channel Number', **axis_font)
-plt.ylabel('Channel Power [dBm]', **axis_font)
-plt.title('Input Power Profiles for Different Channel Loading', **title_font)
-plt.legend(pch2d_legend, loc=5)
-plt.grid()
-plt.ylim((-100, -10))
-plt.xlim((0, 110))
-plt.xticks(np.arange(0, 100, 10), **tic_font)
-plt.yticks(np.arange(-110, -10, 10), **tic_font)
-plt.figure()
-for result in results:
-    plt.plot(nchan, result, '.-', lw=2)
-plt.title('Output Power w/ ASE for Different Channel Loading', **title_font)
-plt.xlabel('Channel Number', **axis_font)
-plt.ylabel('Channel Power [dBm]', **axis_font)
-plt.grid()
-plt.ylim((-50, 10))
-plt.xlim((0, 100))
-plt.xticks(np.arange(0, 100, 10), **tic_font)
-plt.yticks(np.arange(-50, 10, 10), **tic_font)
-plt.legend(pch2d_legend, loc=5)
-plt.show()

examples/edfa_example_network.json

@@ -1,7 +1,7 @@
 {
     "network_name": "EDFA Example Network - P2P",
     "elements": [{
-            "uid": "Site A",
+            "uid": "Site_A",
             "type": "Transceiver",
             "metadata": {
                 "location": {
@@ -15,13 +15,15 @@
         {
             "uid": "Span1",
             "type": "Fiber",
+            "type_variety": "SSMF",
             "params": {
                 "length": 80,
                 "loss_coef": 0.2,
                 "length_units": "km",
-		"dispersion": 16.7E-6,
-		"gamma": 1.27E-3
-            },
+                "att_in": 0,
+                "con_in": 0.5,
+                "con_out": 0.5                
+                },
             "metadata": {
                 "location": {
                     "region": "",
@@ -33,11 +35,12 @@
         {
             "uid": "Edfa1",
             "type": "Edfa",
+            "type_variety": "std_low_gain",            
             "operational": {
-                "gain_target": 16,
-                "tilt_target": 0
+                "gain_target": 17,
+                "tilt_target": 0,
+                "out_voa": 0
             },
-            "config_from_json": "edfa_config.json",
             "metadata": {
                 "location": {
                     "region": "",
@@ -47,13 +50,13 @@
             }
         },
         {
-            "uid": "Site B",
+            "uid": "Site_B",
             "type": "Transceiver",
             "metadata": {
                 "location": {
                     "city": "Site B",
                     "region": "",
-                    "latitude": 3,
+                    "latitude": 2,
                     "longitude": 0
                 }
             }
@@ -61,7 +64,7 @@
 
     ],
     "connections": [{
-            "from_node": "Site A",
+            "from_node": "Site_A",
             "to_node": "Span1"
         },
         {
@@ -70,7 +73,7 @@
         },
         {
             "from_node": "Edfa1",
-            "to_node": "Site B"
+            "to_node": "Site_B"
         }
 
     ]

examples/edfa_model/OA.json

@@ -1,15 +1,6 @@
 {
-    "gain_flatmax": 25,
-    "gain_min": 15,
-    "p_max": 21,
-    "nf_fit_coeff": "pNFfit3.txt",
     "nf_ripple": "NFR_96.txt", 
-    "dfg": "DFG_96.txt",
+    "gain_ripple": "DFG_96.txt",
     "dgt": "DGT_96.txt",
-    "nf_model": 
-        {
-        "enabled": true,
-    	"nf_min": 5.8,
-    	"nf_max": 10
-        }     
+    "nf_fit_coeff": "pNFfit3.txt"
 }

examples/edfa_model/Pchan2D.txt

@@ -1,8 +0,0 @@
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examples/edfa_model/Pchan2DLegend.txt

@@ -1,8 +0,0 @@
-   7.0000000000000000e+01	   1.1700000000000000e+02	   1.0800000000000000e+02	   1.0800000000000000e+02	   3.2000000000000000e+01	   7.0000000000000000e+01	   1.0800000000000000e+02	   9.7000000000000000e+01	   1.1600000000000000e+02	   3.2000000000000000e+01	   3.2000000000000000e+01	   3.2000000000000000e+01	   3.2000000000000000e+01	   3.2000000000000000e+01	   3.2000000000000000e+01	
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-   1.1900000000000000e+02	   1.1100000000000000e+02	   1.1400000000000000e+02	   1.1500000000000000e+02	   1.1600000000000000e+02	   3.2000000000000000e+01	   9.9000000000000000e+01	   9.7000000000000000e+01	   1.1500000000000000e+02	   1.0100000000000000e+02	   3.2000000000000000e+01	   3.2000000000000000e+01	   3.2000000000000000e+01	   3.2000000000000000e+01	   3.2000000000000000e+01	

examples/edfa_model/amplifier.py

@@ -1,301 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Mon Nov 27 12:32:04 2017
-
-@author: briantaylor
-"""
-import numpy as np
-from numpy import polyfit, polyval, mean
-from utilities import lin2db, db2lin, itufs, freq2wavelength
-import matplotlib.pyplot as plt
-from scipy.constants import h
-
-
-def noise_profile(nf, gain, ffs, df):
-    """ noise_profile(nf, gain, ffs, df) computes amplifier ase
-
-    :param nf: Noise figure in dB
-    :param gain: Actual gain calculated for the EDFA in dB units
-    :param ffs: A numpy array of frequencies
-    :param df: the reference bw in THz
-    :type nf: numpy.ndarray
-    :type gain: numpy.ndarray
-    :type ffs: numpy.ndarray
-    :type df: float
-    :return: the asepower in dBm
-    :rtype: numpy.ndarray
-
-    ASE POWER USING PER CHANNEL GAIN PROFILE
-    INPUTS:
-    NF_dB - Noise figure in dB, vector of length number of channels or
-            spectral slices
-    G_dB  - Actual gain calculated for the EDFA, vector of length number of
-            channels or spectral slices
-    ffs     - Center frequency grid of the channels or spectral slices in THz,
-            vector of length number of channels or spectral slices
-    dF    - width of each channel or spectral slice in THz,
-            vector of length number of channels or spectral slices
-    OUTPUT:
-        ase_dBm - ase in dBm per channel or spectral slice
-    NOTE: the output is the total ASE in the channel or spectral slice. For
-    50GHz channels the ASE BW is effectively 0.4nm. To get to noise power in
-    0.1nm, subtract 6dB.
-
-    ONSR is usually quoted as channel power divided by
-    the ASE power in 0.1nm RBW, regardless of the width of the actual
-    channel.  This is a historical convention from the days when optical
-    signals were much smaller (155Mbps, 2.5Gbps, ... 10Gbps) than the
-    resolution of the OSAs that were used to measure spectral power which
-    were set to 0.1nm resolution for convenience.  Moving forward into
-    flexible grid and high baud rate signals, it may be convenient to begin
-    quoting power spectral density in the same BW for both signal and ASE,
-    e.g. 12.5GHz."""
-
-    h_mWThz = 1e-3 * h * (1e14)**2
-    nf_lin = db2lin(nf)
-    g_lin = db2lin(gain)
-    ase = h_mWThz * df * ffs * (nf_lin * g_lin - 1)
-    asedb = lin2db(ase)
-
-    return asedb
-
-
-def gain_profile(dfg, dgt, Pin, gp, gtp):
-    """
-    :param dfg: design flat gain
-    :param dgt: design gain tilt
-    :param Pin: channing input power profile
-    :param gp: Average gain setpoint in dB units
-    :param gtp: gain tilt setting
-    :type dfg: numpy.ndarray
-    :type dgt: numpy.ndarray
-    :type Pin: numpy.ndarray
-    :type gp: float
-    :type gtp: float
-    :return: gain profile in dBm
-    :rtype: numpy.ndarray
-
-    AMPLIFICATION USING INPUT PROFILE
-    INPUTS:
-        DFG - vector of length number of channels or spectral slices
-        DGT - vector of length number of channels or spectral slices
-        Pin - input powers vector of length number of channels or
-        spectral slices
-        Gp  - provisioned gain length 1
-        GTp - provisioned tilt length 1
-
-    OUTPUT:
-        amp gain per channel or spectral slice
-    NOTE: there is no checking done for violations of the total output power
-        capability of the amp.
-        Ported from Matlab version written by David Boerges at Ciena.
-    Based on:
-        R. di Muro, "The Er3+ fiber gain coefficient derived from a dynamic
-        gain
-        tilt technique", Journal of Lightwave Technology, Vol. 18, Iss. 3,
-        Pp. 343-347, 2000.
-    """
-    err_tolerance = 1.0e-11
-    simple_opt = True
-
-    # TODO make all values linear unit and convert to dB units as needed within
-    # this function.
-    nchan = list(range(len(Pin)))
-
-    # TODO find a way to use these or lose them.  Primarily we should have a
-    # way to determine if exceeding the gain or output power of the amp
-    tot_in_power_db = lin2db(np.sum(db2lin(Pin)))
-    avg_gain_db = lin2db(mean(db2lin(dfg)))
-
-    # Linear fit to get the
-    p = polyfit(nchan, dgt, 1)
-    dgt_slope = p[0]
-
-    # Calculate the target slope-  Currently assumes equal spaced channels
-    # TODO make it so that supports arbitrary channel spacing.
-    targ_slope = gtp / (len(nchan) - 1)
-
-    # 1st estimate of DGT scaling
-    dgts1 = targ_slope / dgt_slope
-
-    # when simple_opt is true code makes 2 attempts to compute gain and
-    # the internal voa value.  This is currently here to provide direct
-    # comparison with original Matlab code.  Will be removed.
-    # TODO replace with loop
-
-    if simple_opt:
-
-        # 1st estimate of Er gain & voa loss
-        g1st = dfg + dgt * dgts1
-        voa = lin2db(mean(db2lin(g1st))) - gp
-
-        # 2nd estimate of Amp ch gain using the channel input profile
-        g2nd = g1st - voa
-        pout_db = lin2db(np.sum(db2lin(Pin + g2nd)))
-        dgts2 = gp - (pout_db - tot_in_power_db)
-
-        # Center estimate of amp ch gain
-        xcent = dgts2
-        gcent = g1st - voa + dgt * xcent
-        pout_db = lin2db(np.sum(db2lin(Pin + gcent)))
-        gavg_cent = pout_db - tot_in_power_db
-
-        # Lower estimate of Amp ch gain
-        deltax = np.max(g1st) - np.min(g1st)
-        xlow = dgts2 - deltax
-        glow = g1st - voa + xlow * dgt
-        pout_db = lin2db(np.sum(db2lin(Pin + glow)))
-        gavg_low = pout_db - tot_in_power_db
-
-        # Upper gain estimate
-        xhigh = dgts2 + deltax
-        ghigh = g1st - voa + xhigh * dgt
-        pout_db = lin2db(np.sum(db2lin(Pin + ghigh)))
-        gavg_high = pout_db - tot_in_power_db
-
-        # compute slope
-        slope1 = (gavg_low - gavg_cent) / (xlow - xcent)
-        slope2 = (gavg_cent - gavg_high) / (xcent - xhigh)
-
-        if np.abs(gp - gavg_cent) <= err_tolerance:
-            dgts3 = xcent
-        elif gp < gavg_cent:
-            dgts3 = xcent - (gavg_cent - gp) / slope1
-        else:
-            dgts3 = xcent + (-gavg_cent + gp) / slope2
-
-        gprofile = g1st - voa + dgt * dgts3
-    else:
-        gprofile = None
-
-    return gprofile
-
-
-if __name__ == '__main__':
-
-    plt.close('all')
-    fc = itufs(0.05)
-    lc = freq2wavelength(fc) / 1000
-    nchan = list(range(len(lc)))
-    df = np.array([0.05] * (nchan[-1] + 1))
-    # TODO remove path dependence
-    path = ''
-
-    """
-    DFG_96:  Design flat gain at each wavelength in the 96 channel 50GHz ITU
-    grid in dB.  This can be experimentally determined by measuring the gain
-    at each wavelength using a full, flat channel (or ASE) load at the input.
-    The amplifier should be set to its maximum flat gain (tilt = 0dB).  This
-    measurement captures the ripple of the amplifier.  If the amplifier was
-    designed to be mimimum ripple at some other tilt value, then the ripple
-    reflected in this measurement will not be that minimum.  However, when
-    the DGT gets applied through the provisioning of tilt, the model should
-    accurately reproduce the expected ripple at that tilt value.  One could
-    also do the measurement at some expected tilt value and back-calculate
-    this vector using the DGT method.  Alternatively, one could re-write the
-    algorithm to accept a nominal tilt and a tiled version of this vector.
-    """
-
-    dfg_96 = np.loadtxt(path + 'DFG_96.txt')
-
-    """maximum gain for flat operation - the amp in the data file was designed
-    for 25dB gain and has an internal VOA for setting the external gain
-    """
-
-    avg_dfg = dfg_96.mean()
-
-    """
-    DGT_96:  This is the so-called Dynamic Gain Tilt of the EDFA in dB/dB. It
-    is the change in gain at each wavelength corresponding to a 1dB change at
-    the longest wavelength supported.  The value can be obtained
-    experimentally or through analysis of the cross sections or Giles
-    parameters of the Er fibre.  This is experimentally measured by changing
-    the gain of the amplifier above the maximum flat gain while not changing
-    the internal VOA (i.e. the mid-stage VOA is set to minimum and does not
-    change during the measurement). Note that the measurement can change the
-    gain by an arbitrary amount and divide by the gain change (in dB) which
-    is measured at the reference wavelength (the red end of the band).
-    """
-
-    dgt_96 = np.loadtxt(path + 'DGT_96.txt')
-
-    """
-    pNFfit3:  Cubic polynomial fit coefficients to noise figure in dB
-    averaged across wavelength as a function of gain change from design flat:
-
-        NFavg = pNFfit3(1)*dG^3 + pNFfit3(2)*dG^2 pNFfit3(3)*dG + pNFfit3(4)
-    where
-        dG = GainTarget - average(DFG_96)
-    note that dG will normally be a negative value.
-    """
-
-    nf_fitco = np.loadtxt(path + 'pNFfit3.txt')
-
-    """NFR_96:  Noise figure ripple in dB away from the average noise figure
-    across the band.  This captures the wavelength dependence of the NF.  To
-    calculate the NF across channels, one uses the cubic fit coefficients
-    with the external gain target to get the average nosie figure, NFavg and
-    then adds this to NFR_96:
-    NF_96 = NFR_96 + NFavg
-    """
-
-    nf_ripple = np.loadtxt(path + 'NFR_96.txt')
-
-    # This is an example to set the provisionable gain and gain-tilt values
-    # Tilt is in units of dB/THz
-    gain_target = 20.0
-    tilt_target = -0.7
-
-    # calculate the NF for the EDFA at this gain setting
-    dg = gain_target - avg_dfg
-    nf_avg = polyval(nf_fitco, dg)
-    nf_96 = nf_ripple + nf_avg
-
-    # get the input power profiles to show
-    pch2d = np.loadtxt(path + 'Pchan2D.txt')
-
-    # Load legend and assemble legend text
-    pch2d_legend_data = np.loadtxt(path + 'Pchan2DLegend.txt')
-    pch2d_legend = []
-    for ea in pch2d_legend_data:
-        s = ''.join([chr(xx) for xx in ea.astype(dtype=int)]).strip()
-        pch2d_legend.append(s)
-
-    # assemble plot
-    axis_font = {'fontname': 'Arial', 'size': '16', 'fontweight': 'bold'}
-    title_font = {'fontname': 'Arial', 'size': '17', 'fontweight': 'bold'}
-    tic_font = {'fontname': 'Arial', 'size': '12'}
-
-    plt.rcParams["font.family"] = "Arial"
-    plt.figure()
-    plt.plot(nchan, pch2d.T, '.-', lw=2)
-    plt.xlabel('Channel Number', **axis_font)
-    plt.ylabel('Channel Power [dBm]', **axis_font)
-    plt.title('Input Power Profiles for Different Channel Loading',
-              **title_font)
-    plt.legend(pch2d_legend, loc=5)
-    plt.grid()
-    plt.ylim((-100, -10))
-    plt.xlim((0, 110))
-    plt.xticks(np.arange(0, 100, 10), **tic_font)
-    plt.yticks(np.arange(-110, -10, 10), **tic_font)
-
-    plt.figure()
-    ea = pch2d[1, :]
-    for ea in pch2d:
-        chgain = gain_profile(dfg_96, dgt_96, ea, gain_target, tilt_target)
-        pase = noise_profile(nf_96, chgain, fc, df)
-        pout = lin2db(db2lin(ea + chgain) + db2lin(pase))
-        plt.plot(nchan, pout, '.-', lw=2)
-    plt.title('Output Power with ASE for Different Channel Loading',
-              **title_font)
-    plt.xlabel('Channel Number', **axis_font)
-    plt.ylabel('Channel Power [dBm]', **axis_font)
-    plt.grid()
-    plt.ylim((-50, 10))
-    plt.xlim((0, 100))
-    plt.xticks(np.arange(0, 100, 10), **tic_font)
-    plt.yticks(np.arange(-50, 10, 10), **tic_font)
-    plt.legend(pch2d_legend, loc=5)
-    plt.show()

examples/edfa_model/amplifier_models_description.rst

@@ -0,0 +1,300 @@
+*********************************************
+Amplifier models and configuration
+*********************************************
+
+
+1. Equipment configuration description
+#######################################
+
+Equipment description defines equipment types and parameters.
+It takes place in the default **eqpt_config.json** file. 
+By default **transmission_main_example.py** uses **eqpt_config.json** file and that
+can be changed with **-e** or **--equipment** command line parameter.
+
+2. Amplifier parameters and subtypes
+#######################################
+
+Several amplifiers can be used by GNpy, so they are defined as an array of equipment parameters in **eqpt_config.json** file.
+
+- *"type_variety"*:
+    Each amplifier is identified by its unique *"type_variety"*, which is used in the topology files input to reference a specific amplifier. It is a user free defined id.
+    
+    For each amplifier *type_variety*, specific parameters are describing its attributes and performance:
+
+- *"type_def"*:
+    Sets the amplifier model that the simulation will use to calculate the ase noise contribution. 5 models are defined with reserved words:
+
+    - *"advanced_model"*
+    - *"variable_gain"*
+    - *"fixed_gain"*
+    - *"dual_stage"*
+    - *"openroadm"*
+        *see next section for a full description of these models*
+
+- *"advanced_config_from_json"*:
+    **This parameter is only applicable to the _"advanced_model"_ model**
+    
+    json file name describing:
+
+    - nf_fit_coeff
+    - f_min/max
+    - gain_ripple
+    - nf_ripple 
+    - dgt
+    
+    *see next section for a full description*
+
+- *"gain_flatmax"*: 
+    amplifier maximum gain in dB before its extended gain range: flat or nominal tilt output. 
+    
+    If gain > gain_flatmax, the amplifier will tilt, based on its dgt function
+
+    If gain > gain_flatmax + target_extended_gain, the amplifier output power is reduced to  not exceed the extended gain range.
+
+- *"gain_min"*: 
+    amplifier minimum gain in dB.
+
+    If gain < gain_min, the amplifier input is automatically padded, which results in
+
+    NF += gain_min - gain 
+
+- *"p_max"*: 
+    amplifier max output power, full load
+
+    Total signal output power will not be allowed beyond this value
+
+- *"nf_min/max"*:
+    **These parameters are only applicable to the _"variable_gain"_ model**
+
+    min & max NF values in dB
+
+    NF_min is the amplifier NF @ gain_max  
+
+    NF_max is the amplifier NF @ gain_min  
+
+- *"nf_coef"*: 
+    **This parameter is only applicable to the *"openroadm"* model**
+
+    [a, b, c, d] 3rd order polynomial coefficients list to define the incremental OSNR vs Pin
+    
+    Incremental OSNR is the amplifier OSNR contribution
+    
+    Pin is the amplifier channel input power defined in a 50GHz bandwidth
+    
+    Incremental OSNR = a*Pin³ + b*Pin² + c*Pin + d
+
+- *"preamp_variety"*: 
+    **This parameter is only applicable to the _"dual_stage"_ model**
+
+    1st stage type_variety
+
+- *"booster_variety"*: 
+    **This parameter is only applicable to the *"dual_stage"* model**
+
+    2nd stage type_variety
+
+- *"out_voa_auto"*: true/false
+    **power_mode only**
+
+    **This parameter is only applicable to the *"advanced_model"* and *"variable_gain"* models**
+
+    If "out_voa_auto": true, auto_design will chose the output_VOA value that maximizes the amplifier gain within its power capability and therefore minimizes its NF.
+
+- *"allowed_for_design"*: true/false
+    **auto_design only**
+
+    Tells auto_design if this amplifier can be picked for the design (deactivates unwanted amplifiers)
+
+    It does not prevent the use of an amplifier if it is placed in the topology input.
+
+    .. code-block:: json
+
+        {"Edfa": [{
+                    "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
+                    }
+            ]}
+
+
+3. Amplifier models
+#######################################
+
+In an opensource and multi-vendor environnement, it is needed to support different use cases and context. Therefore several models are supported for amplifiers.
+
+5 types of EDFA definition are possible and referenced by the *"type_def"* parameter with the following reserved words:
+
+-  *"advanced_model"* 
+    This model is refered as a whitebox model because of the detailed level of knowledge that is required. The amplifier NF model and ripple definition are described by a json file referenced with *"advanced_config_from_json"*: json filename. This json file contains:
+
+    - nf_fit_coeff: [a,b,c,d]
+         
+        3rd order polynomial NF = f(-dg) coeficients list
+
+        dg = gain - gain_max
+
+    - f_min/max: amplifier frequency range in Hz
+    - gain_ripple : [...]
+
+        amplifier gain ripple excursion comb list in dB across the frequency range.
+    - nf_ripple : [...]
+        
+        amplifier nf ripple excursion comb list in dB across the frequency range. 
+    - dgt : [...]
+        amplifier dynamic gain tilt comb list across the frequency range.
+            
+        *See next section for the generation of this json file*
+
+    .. code-block:: json-object
+
+        "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
+                }
+            ]
+
+- *"variable_gain"* 
+    This model is refered as an operator model because a lower level of knowledge is required. A full polynomial description of the NF cross the gain range is not required. Instead, NF_min and NF_max values are required and used by the code to model a dual stage amplifier with an internal mid stage VOA. NF_min and NF_max values are typically available from equipment suppliers data-sheet.
+
+    There is a default JSON file ”default_edfa_config.json”* to enforce 0 tilt and ripple values because GNpy core algorithm is a multi-carrier propogation.
+    - gain_ripple =[0,...,0]
+    - nf_ripple = [0,...,0]
+    - dgt = [...] generic dgt comb
+
+    .. code-block:: json-object
+
+        "Edfa":[{
+                "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
+                }
+            ]
+
+-  *"fixed_gain"* 
+    This model is also an operator model with a single NF value that emulates basic single coil amplifiers without internal VOA.
+
+    if gain_min < gain < gain_max, NF == nf0
+    
+    if gain < gain_min, the amplifier input is automatically padded, which results in 
+
+    NF += gain_min - gain
+
+    .. code-block:: json-object
+
+        "Edfa":[{
+                "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
+                }
+            ]
+
+- *"openroadm"* 
+    This model is a black box model replicating OpenRoadm MSA spec for ILA.
+
+    .. code-block:: json-object
+
+        "Edfa":[{
+                "type_variety": "low_noise",
+                "type_def": "openroadm",
+                "gain_flatmax": 27,
+                "gain_min": 12,
+                "p_max": 22,
+                "nf_coef": [-8.104e-4,-6.221e-2,-5.889e-1,37.62],
+                "allowed_for_design": false
+                }
+            ]
+
+- *"dual_stage"* 
+    This model allows the cascade (pre-defined combination) of any 2 amplifiers already described in the eqpt_config.json library.
+    
+    - preamp_variety defines the 1st stge type variety
+    
+    - booster variety defines the 2nd stage type variety
+    
+    Both preamp and booster variety must exist in the eqpt libray
+    The resulting NF is the sum of the 2 amplifiers 
+    The preamp is operated to its maximum gain
+    
+    - gain_min indicates to auto_design when this dual_stage should be used
+    
+    But unlike other models the 1st stage input will not be padded: it is always operated to its maximu gain and min NF. Therefore if gain adaptation and padding is needed it will be performed by the 2nd stage.
+
+    .. code-block:: json
+
+                {
+                "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
+                }
+
+4. advanced_config_from_json 
+#######################################
+
+The build_oa_json.py library in gnpy/examples/edfa_model can be used to build the json file required for the amplifier advanced_model type_def:
+
+Update an existing json file with all the 96ch txt files for a given amplifier type
+amplifier type 'OA_type1' is hard coded but can be modified and other types added
+returns an updated amplifier json file: output_json_file_name = 'edfa_config.json'
+amplifier file names
+
+Convert a set of amplifier files + input json definiton file into a valid edfa_json_file:
+
+nf_fit_coeff: NF 3rd order polynomial coefficients txt file
+
+nf = f(dg) with dg = gain_operational - gain_max
+
+nf_ripple: NF ripple excursion txt file
+
+gain_ripple: gain ripple txt file
+
+dgt: dynamic gain txt file
+
+input json file in argument (defult = 'OA.json')
+
+the json input file should have the following fields:
+
+    .. code-block:: json
+
+        {
+            "nf_fit_coeff": "nf_filename.txt",
+            "nf_ripple": "nf_ripple_filename.txt", 
+            "gain_ripple": "DFG_filename.txt",
+            "dgt": "DGT_filename.txt"
+        }
+

examples/edfa_model/build_oa_json.py

@@ -4,7 +4,6 @@
 Created on Tue Jan 30 12:32:00 2018
 
 @author: jeanluc-auge
-@comments about amplifier input files from Brian Taylor & Dave Boertjes
 
 update an existing json file with all the 96ch txt files for a given amplifier type
 amplifier type 'OA_type1' is hard coded but can be modified and other types added
@@ -18,46 +17,29 @@ from gnpy.core.utils import lin2db, db2lin
 
 """amplifier file names
 convert a set of amplifier files + input json definiton file into a valid edfa_json_file:
-nf_fit_coeff: NF polynomial coefficients txt file (optional)
+nf_fit_coeff: NF 3rd order polynomial coefficients txt file
+    nf = f(dg)
+    with dg = gain_operational - gain_max
 nf_ripple: NF ripple excursion txt file
-dfg: gain txt file
+gain_ripple: gain ripple txt file
 dgt: dynamic gain txt file
 input json file in argument (defult = 'OA.json')
+
 the json input file should have the following fields:
 {
-    "gain_flatmax": 25,
-    "gain_min": 15,
-    "p_max": 21,
-    "nf_fit_coeff": "pNFfit3.txt",
-    "nf_ripple": "NFR_96.txt", 
-    "dfg": "DFG_96.txt",
-    "dgt": "DGT_96.txt",
-    "nf_model": 
-        {
-        "enabled": true,
-        "nf_min": 5.8,
-        "nf_max": 10
-        }
+    "nf_fit_coeff": "nf_filename.txt",
+    "nf_ripple": "nf_ripple_filename.txt", 
+    "gain_ripple": "DFG_filename.txt",
+    "dgt": "DGT_filename.txt",
 }
-gain_flat = max flat gain (dB)
-gain_min = min gain (dB) : will consider an input VOA if below (TBD vs throwing an exception)
-p_max = max power (dBm)
-nf_fit = boolean (True, False) : 
-        if False nf_fit_coeff are ignored and nf_model fields are used
+
 """
 
 input_json_file_name = "OA.json" #default path
-output_json_file_name = "edfa_config.json"
-param_field  ="params"
-gain_min_field = "gain_min"
-gain_max_field = "gain_flatmax"
-gain_ripple_field = "dfg"
+output_json_file_name = "default_edfa_config.json"
+gain_ripple_field = "gain_ripple"
 nf_ripple_field = "nf_ripple"
 nf_fit_coeff = "nf_fit_coeff"
-nf_model_field = "nf_model"
-nf_model_enabled_field = "enabled"
-nf_min_field  ="nf_min"
-nf_max_field = "nf_max"
 
 def read_file(field, file_name):
     """read and format the 96 channels txt files describing the amplifier NF and ripple
@@ -71,62 +53,16 @@ def read_file(field, file_name):
     #data = list(data.split(","))
     #data = [float(x) for x in data]
     data = np.loadtxt(file_name)
+    print(len(data), file_name)
     if field == gain_ripple_field or field == nf_ripple_field:
         #consider ripple excursion only to avoid redundant information
         #because the max flat_gain is already given by the 'gain_flat' field in json
         #remove the mean component
+        print(file_name, ', mean value =', data.mean(), ' is substracted')
         data = data - data.mean()
     data = data.tolist()
     return data
 
-def nf_model(amp_dict):
-    if amp_dict[nf_model_field][nf_model_enabled_field] == True:
-        gain_min = amp_dict[gain_min_field]
-        gain_max = amp_dict[gain_max_field]
-        nf_min = amp_dict[nf_model_field][nf_min_field]
-        nf_max = amp_dict[nf_model_field][nf_max_field]
-        #use NF estimation model based on NFmin and NFmax in json OA file
-        delta_p = 5 #max power dB difference between 1st and 2nd stage coils
-        #dB g1a = (1st stage gain) - (internal voa attenuation)
-        g1a_min = gain_min - (gain_max-gain_min) - delta_p
-        g1a_max = gain_max - delta_p
-        #nf1 and nf2 are the nf of the 1st and 2nd stage coils
-        #calculate nf1 and nf2 values that solve nf_[min/max] = nf1 + nf2 / g1a[min/max]
-        nf2 = lin2db((db2lin(nf_min) - db2lin(nf_max)) / (1/db2lin(g1a_max)-1/db2lin(g1a_min)))
-        nf1 = lin2db(db2lin(nf_min)- db2lin(nf2)/db2lin(g1a_max)) #expression (1)
-
-        """ now checking and recalculating the results:
-        recalculate delta_p to check it is within [1-6] boundaries
-        This is to check that the nf_min and nf_max values from the json file
-        make sense. If not a warning is printed """
-        if nf1 < 4:
-            print('1st coil nf calculated value {} is too low: revise inputs'.format(nf1))
-        if nf2 < nf1 + 0.3 or nf2 > nf1 + 2: 
-            """nf2 should be with [nf1+0.5 - nf1 +2] boundaries
-            there shouldn't be very high nf differences between 2 coils
-            => recalculate delta_p 
-            """            
-            nf2 = max(nf2, nf1+0.3)
-            nf2 = min(nf2, nf1+2)
-            g1a_max = lin2db(db2lin(nf2) / (db2lin(nf_min) - db2lin(nf1))) #use expression (1)
-            delta_p = gain_max - g1a_max
-            g1a_min = gain_min - (gain_max-gain_min) - delta_p
-            if delta_p < 1 or delta_p > 6:
-                #delta_p should be > 1dB and < 6dB => consider user warning if not
-                print('1st coil vs 2nd coil calculated DeltaP {} is not valid: revise inputs'
-                            .format(delta_p))
-        #check the calculated values for nf1 & nf2:
-        nf_min_calc = lin2db(db2lin(nf1) + db2lin(nf2)/db2lin(g1a_max))
-        nf_max_calc = lin2db(db2lin(nf1) + db2lin(nf2)/db2lin(g1a_min))
-        if (abs(nf_min_calc-nf_min) > 0.01) or (abs(nf_max_calc-nf_max) > 0.01):
-            print('nf model calculation failed with nf_min {} and nf_max {} calculated'
-                    .format(nf_min_calc, nf_max_calc))
-            print('do not use the generated edfa_config.json file')
-    else :
-        (nf1, nf2, delta_p) = (0, 0, 0)
-
-    return (nf1, nf2, delta_p)
-
 def input_json(path):
     """read the json input file and add all the 96 channels txt files
     create the output json file with output_json_file_name"""
@@ -138,20 +74,7 @@ def input_json(path):
         if re.search(r'.txt$',str(v)) :
             amp_dict[k] = read_file(k, v)
 
-    #calculate nf of 1st and 2nd coil for the nf_model if 'enabled'==true
-    (nf1, nf2, delta_p) = nf_model(amp_dict)
-    #rename nf_min and nf_max in nf1 and nf2 after the nf model calculation:
-    del amp_dict[nf_model_field][nf_min_field]
-    del amp_dict[nf_model_field][nf_max_field]
-    amp_dict[nf_model_field]['nf1'] = nf1
-    amp_dict[nf_model_field]['nf2'] = nf2
-    amp_dict[nf_model_field]['delta_p'] = delta_p
-    #rename dfg into gain_ripple after removing the average part:
-    amp_dict['gain_ripple'] = amp_dict.pop(gain_ripple_field)
-
-    new_amp_dict = {}
-    new_amp_dict[param_field] = amp_dict
-    amp_text = json.dumps(new_amp_dict, indent=4)
+    amp_text = json.dumps(amp_dict, indent=4)
     #print(amp_text)
     with  open(output_json_file_name,'w') as edfa_json_file:
         edfa_json_file.write(amp_text)

examples/edfa_model/edfa_config.json

@@ -1,313 +0,0 @@
-{
-    "params": {
-        "gain_flatmax": 25,
-        "gain_min": 15,
-        "p_max": 21,
-        "nf_fit_coeff": [
-            0.000168241,
-            0.0469961,
-            0.0359549,
-            5.82851
-        ],
-        "nf_ripple": [
-            -0.3110761646066259,
-            -0.3110761646066259,
-            -0.31110274831665313,
-            -0.31419329378173544,
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-            0.012573926129294415,
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-        ],
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-            2.714526681131686,
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-            1.445565137158368,
-            1.4340878115214444,
-            1.418273806730323,
-            1.3981208704326855,
-            1.3779439775587023,
-            1.3598972673004606,
-            1.3439818461440451,
-            1.3301807335621048,
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-            1.0
-        ],
-        "nf_model": {
-            "enabled": true,
-            "nf1": 5.727887800964238,
-            "nf2": 7.727887800964238,
-            "delta_p": 5.238350271545567
-        },
-        "gain_ripple": [
-            0.1359703369791596,
-            0.11822862697916037,
-            0.09542181697916163,
-            0.06245819697916133,
-            0.02602813697916062,
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-            0.06479749697916048,
-            0.07704745697916238
-        ]
-    }
-}

examples/eqpt_config.json

@@ -0,0 +1,307 @@
+{     "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": "low_noise",
+            "type_def": "openroadm",
+            "gain_flatmax": 27,
+            "gain_min": 12,
+            "p_max": 22,
+            "nf_coef": [-8.104e-4,-6.221e-2,-5.889e-1,37.62],
+            "allowed_for_design": false
+            },          
+            {
+            "type_variety": "standard",
+            "type_def": "openroadm",
+            "gain_flatmax": 27,
+            "gain_min": 12,
+            "p_max": 22,
+            "nf_coef": [-5.952e-4,-6.250e-2,-1.071,28.99],
+            "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,
+            "gamma": 0.00127
+            },
+            {
+            "type_variety": "NZDF",
+            "dispersion": 0.5e-05,
+            "gamma": 0.00146
+            },
+            {
+            "type_variety": "LOF",
+            "dispersion": 2.2e-05,
+            "gamma": 0.000843
+            }
+      ],
+      "RamanFiber":[{
+            "type_variety": "SSMF",
+            "dispersion": 1.67e-05,
+            "gamma": 0.00127,
+            "raman_efficiency": {
+              "cr":[
+                  0, 9.4E-06, 2.92E-05, 4.88E-05, 6.82E-05, 8.31E-05, 9.4E-05, 0.0001014, 0.0001069, 0.0001119,
+                  0.0001217, 0.0001268, 0.0001365, 0.000149, 0.000165, 0.000181, 0.0001977, 0.0002192, 0.0002469,
+                  0.0002749, 0.0002999, 0.0003206, 0.0003405, 0.0003592, 0.000374, 0.0003826, 0.0003841, 0.0003826,
+                  0.0003802, 0.0003756, 0.0003549, 0.0003795, 0.000344, 0.0002933, 0.0002024, 0.0001158, 8.46E-05,
+                  7.14E-05, 6.86E-05, 8.5E-05, 8.93E-05, 9.01E-05, 8.15E-05, 6.67E-05, 4.37E-05, 3.28E-05, 2.96E-05,
+                  2.65E-05, 2.57E-05, 2.81E-05, 3.08E-05, 3.67E-05, 5.85E-05, 6.63E-05, 6.36E-05, 5.5E-05, 4.06E-05,
+                  2.77E-05, 2.42E-05, 1.87E-05, 1.6E-05, 1.4E-05, 1.13E-05, 1.05E-05, 9.8E-06, 9.8E-06, 1.13E-05,
+                  1.64E-05, 1.95E-05, 2.38E-05, 2.26E-05, 2.03E-05, 1.48E-05, 1.09E-05, 9.8E-06, 1.05E-05, 1.17E-05,
+                  1.25E-05, 1.21E-05, 1.09E-05, 9.8E-06, 8.2E-06, 6.6E-06, 4.7E-06, 2.7E-06, 1.9E-06, 1.2E-06, 4E-07,
+                  2E-07, 1E-07
+              ],
+              "frequency_offset":[
+                0, 0.5e12, 1e12, 1.5e12, 2e12, 2.5e12, 3e12, 3.5e12, 4e12, 4.5e12, 5e12, 5.5e12, 6e12, 6.5e12, 7e12,
+                7.5e12, 8e12, 8.5e12, 9e12, 9.5e12, 10e12, 10.5e12, 11e12, 11.5e12, 12e12, 12.5e12, 12.75e12,
+                13e12, 13.25e12, 13.5e12, 14e12, 14.5e12, 14.75e12, 15e12, 15.5e12, 16e12, 16.5e12, 17e12,
+                17.5e12, 18e12, 18.25e12, 18.5e12, 18.75e12, 19e12, 19.5e12, 20e12, 20.5e12, 21e12, 21.5e12,
+                22e12, 22.5e12, 23e12, 23.5e12, 24e12, 24.5e12, 25e12, 25.5e12, 26e12, 26.5e12, 27e12, 27.5e12, 28e12,
+                28.5e12, 29e12, 29.5e12, 30e12, 30.5e12, 31e12, 31.5e12, 32e12, 32.5e12, 33e12, 33.5e12, 34e12, 34.5e12,
+                35e12, 35.5e12, 36e12, 36.5e12, 37e12, 37.5e12, 38e12, 38.5e12, 39e12, 39.5e12, 40e12, 40.5e12, 41e12,
+                41.5e12, 42e12
+              ]
+              }
+            }
+      ],
+      "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,
+            "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":[
+            {
+            "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
+                       }
+                   ]
+            }
+      ]
+
+}

examples/fused_roadm_example_network.json

@@ -0,0 +1,196 @@
+{
+  "elements": [
+    {
+      "uid": "trx Site_A",
+      "metadata": {
+        "location": {
+          "city": "Site_A",
+          "region": "",
+          "latitude": 0,
+          "longitude": 0
+        }
+      },
+      "type": "Transceiver"
+    },
+    {
+      "uid": "trx Site_C",
+      "metadata": {
+        "location": {
+          "city": "Site_C",
+          "region": "",
+          "latitude": 0,
+          "longitude": 0
+        }
+      },
+      "type": "Transceiver"
+    },
+    {
+      "uid": "roadm Site_A",
+      "metadata": {
+        "location": {
+          "city": "Site_A",
+          "region": "",
+          "latitude": 0,
+          "longitude": 0
+        }
+      },
+      "type": "Roadm",
+      "params": {
+        "loss": 17
+      }
+    },
+    {
+      "uid": "roadm Site_C",
+      "metadata": {
+        "location": {
+          "city": "Site_C",
+          "region": "",
+          "latitude": 0,
+          "longitude": 0
+        }
+      },
+      "type": "Roadm"
+    },
+    {
+      "uid": "ingress fused spans in Site_B",
+      "metadata": {
+        "location": {
+          "city": "Site_B",
+          "region": "",
+          "latitude": 0,
+          "longitude": 0
+        }
+      },
+      "type": "Fused",
+      "params": {
+        "loss": 0.5
+      }
+    },
+    {
+      "uid": "egress fused spans in Site_B",
+      "metadata": {
+        "location": {
+          "city": "Site_B",
+          "region": "",
+          "latitude": 0,
+          "longitude": 0
+        }
+      },
+      "type": "Fused"
+    },
+    {
+      "uid": "fiber (Site_A \u2192 Site_B)-",
+      "metadata": {
+        "location": {
+          "latitude": 0.0,
+          "longitude": 0.0
+        }
+      },
+      "type": "Fiber",
+      "type_variety": "SSMF",
+      "params": {
+        "length": 40.0,
+        "length_units": "km",
+        "loss_coef": 0.2
+      }
+    },
+    {
+      "uid": "fiber (Site_B \u2192 Site_C)-",
+      "metadata": {
+        "location": {
+          "latitude": 0.0,
+          "longitude": 0.0
+        }
+      },
+      "type": "Fiber",
+      "type_variety": "SSMF",
+      "params": {
+        "length": 50.0,
+        "length_units": "km",
+        "loss_coef": 0.2
+      }
+    },
+    {
+      "uid": "fiber (Site_B \u2192 Site_A)-",
+      "metadata": {
+        "location": {
+          "latitude": 0.0,
+          "longitude": 0.0
+        }
+      },
+      "type": "Fiber",
+      "type_variety": "SSMF",
+      "params": {
+        "length": 40.0,
+        "length_units": "km",
+        "loss_coef": 0.2
+      }
+    },
+    {
+      "uid": "fiber (Site_C \u2192 Site_B)-",
+      "metadata": {
+        "location": {
+          "latitude": 0.0,
+          "longitude": 0.0
+        }
+      },
+      "type": "Fiber",
+      "type_variety": "SSMF",
+      "params": {
+        "length": 50.0,
+        "length_units": "km",
+        "loss_coef": 0.2
+      }
+    }
+  ],
+  "connections": [
+    {
+      "from_node": "roadm Site_A",
+      "to_node": "fiber (Site_A \u2192 Site_B)-"
+    },
+    {
+      "from_node": "fiber (Site_B \u2192 Site_A)-",
+      "to_node": "roadm Site_A"
+    },
+    {
+      "from_node": "fiber (Site_A \u2192 Site_B)-",
+      "to_node": "ingress fused spans in Site_B"
+    },
+    {
+      "from_node": "ingress fused spans in Site_B",
+      "to_node": "fiber (Site_B \u2192 Site_C)-"
+    },
+    {
+      "from_node": "fiber (Site_C \u2192 Site_B)-",
+      "to_node": "egress fused spans in Site_B"
+    },
+    {
+      "from_node": "egress fused spans in Site_B",
+      "to_node": "fiber (Site_B \u2192 Site_A)-"
+    },
+    {
+      "from_node": "roadm Site_C",
+      "to_node": "fiber (Site_C \u2192 Site_B)-"
+    },
+    {
+      "from_node": "fiber (Site_B \u2192 Site_C)-",
+      "to_node": "roadm Site_C"
+    },
+    {
+      "from_node": "trx Site_A",
+      "to_node": "roadm Site_A"
+    },
+    {
+      "from_node": "roadm Site_A",
+      "to_node": "trx Site_A"
+    },
+    {
+      "from_node": "trx Site_C",
+      "to_node": "roadm Site_C"
+    },
+    {
+      "from_node": "roadm Site_C",
+      "to_node": "trx Site_C"
+    }
+  ]
+}

examples/meshTopologyExampleV2_services.json

@@ -0,0 +1,307 @@
+{
+  "path-request": [
+    {
+      "request-id": "0",
+      "source": "Lorient_KMA",
+      "destination": "Vannes_KBE",
+      "src-tp-id": "trx Lorient_KMA",
+      "dst-tp-id": "trx Vannes_KBE",
+      "path-constraints": {
+        "te-bandwidth": {
+          "technology": "flexi-grid",
+          "trx_type": "Voyager",
+          "trx_mode": null,
+          "effective-freq-slot": [
+            {
+              "n": "null",
+              "m": "null"
+            }
+          ],
+          "spacing": 50000000000.0,
+          "max-nb-of-channel": 80,
+          "output-power": 0.0012589254117941673,
+          "path_bandwidth": 100000000000.0
+        }
+      },
+      "optimizations": {
+        "explicit-route-include-objects": []
+      }
+    },
+    {
+      "request-id": "1",
+      "source": "Brest_KLA",
+      "destination": "Vannes_KBE",
+      "src-tp-id": "trx Brest_KLA",
+      "dst-tp-id": "trx Vannes_KBE",
+      "path-constraints": {
+        "te-bandwidth": {
+          "technology": "flexi-grid",
+          "trx_type": "Voyager",
+          "trx_mode": "mode 1",
+          "effective-freq-slot": [
+            {
+              "n": "null",
+              "m": "null"
+            }
+          ],
+          "spacing": 50000000000.0,
+          "max-nb-of-channel": null,
+          "output-power": 0.0012589254117941673,
+          "path_bandwidth": 200000000000.0
+        }
+      },
+      "optimizations": {
+        "explicit-route-include-objects": [
+          {
+            "index": 0,
+            "unnumbered-hop": {
+              "node-id": "roadm Brest_KLA",
+              "link-tp-id": "link-tp-id is not used",
+              "hop-type": "loose",
+              "direction": "direction is not used"
+            },
+            "label-hop": {
+              "te-label": {
+                "generic": "generic is not used",
+                "direction": "direction is not used"
+              }
+            }
+          },
+          {
+            "index": 1,
+            "unnumbered-hop": {
+              "node-id": "roadm Lannion_CAS",
+              "link-tp-id": "link-tp-id is not used",
+              "hop-type": "loose",
+              "direction": "direction is not used"
+            },
+            "label-hop": {
+              "te-label": {
+                "generic": "generic is not used",
+                "direction": "direction is not used"
+              }
+            }
+          },
+          {
+            "index": 2,
+            "unnumbered-hop": {
+              "node-id": "roadm Lorient_KMA",
+              "link-tp-id": "link-tp-id is not used",
+              "hop-type": "loose",
+              "direction": "direction is not used"
+            },
+            "label-hop": {
+              "te-label": {
+                "generic": "generic is not used",
+                "direction": "direction is not used"
+              }
+            }
+          },
+          {
+            "index": 3,
+            "unnumbered-hop": {
+              "node-id": "roadm Vannes_KBE",
+              "link-tp-id": "link-tp-id is not used",
+              "hop-type": "loose",
+              "direction": "direction is not used"
+            },
+            "label-hop": {
+              "te-label": {
+                "generic": "generic is not used",
+                "direction": "direction is not used"
+              }
+            }
+          }
+        ]
+      }
+    },
+    {
+      "request-id": "3",
+      "source": "Lannion_CAS",
+      "destination": "Rennes_STA",
+      "src-tp-id": "trx Lannion_CAS",
+      "dst-tp-id": "trx Rennes_STA",
+      "path-constraints": {
+        "te-bandwidth": {
+          "technology": "flexi-grid",
+          "trx_type": "vendorA_trx-type1",
+          "trx_mode": "mode 1",
+          "effective-freq-slot": [
+            {
+              "n": "null",
+              "m": "null"
+            }
+          ],
+          "spacing": 50000000000.0,
+          "max-nb-of-channel": null,
+          "output-power": null,
+          "path_bandwidth": 60000000000.0
+        }
+      },
+      "optimizations": {
+        "explicit-route-include-objects": []
+      }
+    },
+    {
+      "request-id": "4",
+      "source": "Rennes_STA",
+      "destination": "Lannion_CAS",
+      "src-tp-id": "trx Rennes_STA",
+      "dst-tp-id": "trx Lannion_CAS",
+      "path-constraints": {
+        "te-bandwidth": {
+          "technology": "flexi-grid",
+          "trx_type": "vendorA_trx-type1",
+          "trx_mode": null,
+          "effective-freq-slot": [
+            {
+              "n": "null",
+              "m": "null"
+            }
+          ],
+          "spacing": 75000000000.0,
+          "max-nb-of-channel": null,
+          "output-power": 0.0019952623149688794,
+          "path_bandwidth": 150000000000.0
+        }
+      },
+      "optimizations": {
+        "explicit-route-include-objects": []
+      }
+    },
+    {
+      "request-id": "5",
+      "source": "Rennes_STA",
+      "destination": "Lannion_CAS",
+      "src-tp-id": "trx Rennes_STA",
+      "dst-tp-id": "trx Lannion_CAS",
+      "path-constraints": {
+        "te-bandwidth": {
+          "technology": "flexi-grid",
+          "trx_type": "vendorA_trx-type1",
+          "trx_mode": "mode 2",
+          "effective-freq-slot": [
+            {
+              "n": "null",
+              "m": "null"
+            }
+          ],
+          "spacing": 75000000000.0,
+          "max-nb-of-channel": 63,
+          "output-power": 0.0019952623149688794,
+          "path_bandwidth": 20000000000.0
+        }
+      },
+      "optimizations": {
+        "explicit-route-include-objects": []
+      }
+    },
+    {
+      "request-id": "6",
+      "source": "Lannion_CAS",
+      "destination": "Lorient_KMA",
+      "src-tp-id": "trx Lannion_CAS",
+      "dst-tp-id": "trx Lorient_KMA",
+      "path-constraints": {
+        "te-bandwidth": {
+          "technology": "flexi-grid",
+          "trx_type": "Voyager",
+          "trx_mode": "mode 1",
+          "effective-freq-slot": [
+            {
+              "n": "null",
+              "m": "null"
+            }
+          ],
+          "spacing": 50000000000.0,
+          "max-nb-of-channel": 76,
+          "output-power": 0.001,
+          "path_bandwidth": 300000000000.0
+        }
+      },
+      "optimizations": {
+        "explicit-route-include-objects": []
+      }
+    },
+    {
+      "request-id": "7",
+      "source": "Lannion_CAS",
+      "destination": "Lorient_KMA",
+      "src-tp-id": "trx Lannion_CAS",
+      "dst-tp-id": "trx Lorient_KMA",
+      "path-constraints": {
+        "te-bandwidth": {
+          "technology": "flexi-grid",
+          "trx_type": "Voyager",
+          "trx_mode": "mode 1",
+          "effective-freq-slot": [
+            {
+              "n": "null",
+              "m": "null"
+            }
+          ],
+          "spacing": 50000000000.0,
+          "max-nb-of-channel": 76,
+          "output-power": 0.001,
+          "path_bandwidth": 400000000000.0
+        }
+      },
+      "optimizations": {
+        "explicit-route-include-objects": []
+      }
+    },
+    {
+      "request-id": "7b",
+      "source": "Lannion_CAS",
+      "destination": "Lorient_KMA",
+      "src-tp-id": "trx Lannion_CAS",
+      "dst-tp-id": "trx Lorient_KMA",
+      "path-constraints": {
+        "te-bandwidth": {
+          "technology": "flexi-grid",
+          "trx_type": "Voyager",
+          "trx_mode": "mode 1",
+          "effective-freq-slot": [
+            {
+              "n": "null",
+              "m": "null"
+            }
+          ],
+          "spacing": 75000000000.0,
+          "max-nb-of-channel": 50,
+          "output-power": 0.001,
+          "path_bandwidth": 400000000000.0
+        }
+      },
+      "optimizations": {
+        "explicit-route-include-objects": []
+      }
+    }
+  ],
+  "synchronization": [
+    {
+      "synchronization-id": "3",
+      "svec": {
+        "relaxable": "False",
+        "link-diverse": "True",
+        "node-diverse": "True",
+        "request-id-number": [
+          "3",
+          "1"
+        ]
+      }
+    },
+    {
+      "synchronization-id": "4",
+      "svec": {
+        "relaxable": "False",
+        "link-diverse": "True",
+        "node-diverse": "True",
+        "request-id-number": [
+          "4",
+          "5"
+        ]
+      }
+    }
+  ]
+}

examples/path_requests_run.py

@@ -0,0 +1,369 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+path_requests_run.py
+====================
+
+Reads a JSON request file in accordance with the Yang model
+for requesting path computation and returns path results in terms
+of path and feasibilty.
+
+See: draft-ietf-teas-yang-path-computation-01.txt
+"""
+
+from sys import exit
+from argparse import ArgumentParser
+from pathlib import Path
+from collections import namedtuple
+from logging import getLogger, basicConfig, CRITICAL, DEBUG, INFO
+from json import dumps, loads
+from networkx import (draw_networkx_nodes, draw_networkx_edges,
+                      draw_networkx_labels)
+from numpy import mean
+from gnpy.core.service_sheet import convert_service_sheet, Request_element, Element
+from gnpy.core.utils import load_json
+from gnpy.core.network import load_network, build_network, save_network
+from gnpy.core.equipment import load_equipment, trx_mode_params, automatic_nch, automatic_spacing
+from gnpy.core.elements import Transceiver, Roadm, Edfa, Fused, Fiber
+from gnpy.core.utils import db2lin, lin2db
+from gnpy.core.request import (Path_request, Result_element, compute_constrained_path,
+                              propagate, jsontocsv, Disjunction, compute_path_dsjctn, requests_aggregation,
+                              propagate_and_optimize_mode)
+from gnpy.core.exceptions import ConfigurationError, EquipmentConfigError, NetworkTopologyError
+import gnpy.core.ansi_escapes as ansi_escapes
+from copy import copy, deepcopy
+from textwrap import dedent
+from math import ceil
+
+#EQPT_LIBRARY_FILENAME = Path(__file__).parent / 'eqpt_config.json'
+
+logger = getLogger(__name__)
+
+parser = ArgumentParser(description = 'A function that computes performances for a list of services provided in a json file or an excel sheet.')
+parser.add_argument('network_filename', nargs='?', type = Path, default= Path(__file__).parent / 'meshTopologyExampleV2.xls')
+parser.add_argument('service_filename', nargs='?', type = Path, default= Path(__file__).parent / 'meshTopologyExampleV2.xls')
+parser.add_argument('eqpt_filename', nargs='?', type = Path, default=Path(__file__).parent / 'eqpt_config.json')
+parser.add_argument('-v', '--verbose', action='count', default=0, help='increases verbosity for each occurence')
+parser.add_argument('-o', '--output', type = Path)
+
+
+def requests_from_json(json_data,equipment):
+    requests_list = []
+
+    for req in json_data['path-request']:
+        # init all params from request
+        params = {}
+        params['request_id'] = req['request-id']
+        params['source'] = req['src-tp-id']
+        params['destination'] = req['dst-tp-id']
+        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']
+        nd_list = req['optimizations']['explicit-route-include-objects']
+        params['nodes_list'] = [n['unnumbered-hop']['node-id'] for n in nd_list]
+        params['loose_list'] = [n['unnumbered-hop']['hop-type'] for n in nd_list]
+        params['spacing'] = req['path-constraints']['te-bandwidth']['spacing']
+
+        # 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
+        if req['path-constraints']['te-bandwidth']['output-power']:
+            params['power'] = req['path-constraints']['te-bandwidth']['output-power']
+
+        # same process for nb-channel
+        f_min = params['f_min']
+        f_max_from_si = params['f_max']
+        if req['path-constraints']['te-bandwidth']['max-nb-of-channel'] is not None :
+            nch = req['path-constraints']['te-bandwidth']['max-nb-of-channel'] 
+            params['nb_channel'] = nch         
+            spacing = params['spacing']
+            params['f_max'] = f_min + nch*spacing
+        else :
+            params['nb_channel'] = automatic_nch(f_min,f_max_from_si,params['spacing'])
+
+        consistency_check(params, f_max_from_si)
+
+        try :
+            params['path_bandwidth'] = req['path-constraints']['te-bandwidth']['path_bandwidth']
+        except KeyError:
+            pass
+        requests_list.append(Path_request(**params))
+    return requests_list
+
+def consistency_check(params, f_max_from_si):
+    f_min = params['f_min']
+    f_max = params['f_max']
+    max_recommanded_nb_channels = automatic_nch(f_min,f_max,
+                params['spacing'])
+    if params['baud_rate'] is not None:
+        #implicitely 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 {params["trx_type"]}'+\
+                f' {params["trx_mode"]} min spacing value {params["min_spacing"]*1e-9}GHz.\n'+\
+                'Computation stopped'
+            print(msg)
+            logger.critical(msg)
+            exit()
+        if f_max>f_max_from_si:
+            msg = dedent(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}
+            Computation stopped.''')
+            logger.critical(msg)
+            exit()    
+
+
+def disjunctions_from_json(json_data):
+    disjunctions_list = []
+
+    for snc in json_data['synchronization']:
+        params = {}
+        params['disjunction_id'] = snc['synchronization-id']
+        params['relaxable'] = snc['svec']['relaxable']
+        params['link_diverse'] = snc['svec']['link-diverse']
+        params['node_diverse'] = snc['svec']['node-diverse']
+        params['disjunctions_req'] = snc['svec']['request-id-number']
+        disjunctions_list.append(Disjunction(**params))
+    return disjunctions_list
+
+
+def load_requests(filename,eqpt_filename):
+    if filename.suffix.lower() == '.xls':
+        logger.info('Automatically converting requests from XLS to JSON')
+        json_data = convert_service_sheet(filename,eqpt_filename)
+    else:
+        with open(filename, encoding='utf-8') as f:
+            json_data = loads(f.read())
+    return json_data
+
+def compute_path_with_disjunction(network, equipment, pathreqlist, pathlist):
+    
+    # use a list but a dictionnary might be helpful to find path bathsed on request_id
+    # TODO change all these req, dsjct, res lists into dict !
+    path_res_list = []
+
+    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
+        p_db = lin2db(pathreq.power*1e3)
+        p_total_db = p_db + lin2db(pathreq.nb_channel)
+        print(f'request {pathreq.request_id}')
+        print(f'Computing path from {pathreq.source} to {pathreq.destination}')
+        print(f'with path constraint: {[pathreq.source]+pathreq.nodes_list}') #adding first node to be clearer on the output
+
+        total_path = pathlist[i]
+        print(f'Computed path (roadms):{[e.uid for e in total_path  if isinstance(e, Roadm)]}\n')
+        # for debug
+        # print(f'{pathreq.baud_rate}   {pathreq.power}   {pathreq.spacing}   {pathreq.nb_channel}')
+        if total_path :
+            if pathreq.baud_rate is not None:
+                total_path = propagate(total_path,pathreq,equipment)
+                # for el in total_path: print(el)
+                temp_snr01nm = round(mean(total_path[-1].snr+lin2db(pathreq.baud_rate/(12.5e9))),2)
+                if temp_snr01nm < pathreq.OSNR :
+                    msg = f'\tWarning! Request {pathreq.request_id} computed path from {pathreq.source} to {pathreq.destination} does not pass with {pathreq.tsp_mode}\n' +\
+                    f'\tcomputedSNR in 0.1nm = {temp_snr01nm} - required osnr {pathreq.OSNR}\n'
+                    print(msg)
+                    logger.warning(msg)
+                    total_path = []
+            else:
+                total_path,mode = propagate_and_optimize_mode(total_path,pathreq,equipment)
+                # if no baudrate satisfies spacing, no mode is returned and an empty path is returned
+                # a warning is shown in the propagate_and_optimize_mode
+                if mode is not None :
+                    # propagate_and_optimize_mode function returns the mode with the highest bitrate
+                    # that passes. if no mode passes, then it returns an empty path
+                    pathreq.baud_rate = mode['baud_rate']
+                    pathreq.tsp_mode = mode['format']
+                    pathreq.format = mode['format']
+                    pathreq.OSNR = mode['OSNR']
+                    pathreq.tx_osnr = mode['tx_osnr']
+                    pathreq.bit_rate = mode['bit_rate']
+                else :
+                    total_path = []
+        # we record the last tranceiver object in order to have th whole 
+        # information about spectrum. Important Note: since transceivers 
+        # attached to roadms are actually logical elements to simulate
+        # performance, several demands having the same destination may use 
+        # the same transponder for the performance simaulation. This is why 
+        # we use deepcopy: to ensure each propagation is recorded and not 
+        # overwritten 
+        
+        path_res_list.append(deepcopy(total_path))
+    return path_res_list
+
+def correct_route_list(network, pathreqlist):
+    # prepares the format of route list of nodes to be consistant
+    # remove wrong names, remove endpoints
+    # also correct source and destination
+    anytype = [n.uid for n in network.nodes() if not isinstance(n, Transceiver) and not isinstance(n, Fiber)]
+    # TODO there is a problem of identification of fibers in case of parallel fibers bitween 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:
+        for i,n_id in enumerate(pathreq.nodes_list):
+            # replace possibly wrong name with a formated roadm name
+            # print(n_id)
+            if n_id not in anytype :
+                nodes_suggestion = [uid for uid in anytype \
+                    if n_id.lower() in uid.lower()]
+                if pathreq.loose_list[i] == 'loose':
+                    if len(nodes_suggestion)>0 :
+                        new_n = nodes_suggestion[0]
+                        print(f'invalid route node specified:\
+                        \n\'{n_id}\', replaced with \'{new_n}\'')
+                        pathreq.nodes_list[i] = new_n
+                    else:
+                        print(f'\x1b[1;33;40m'+f'invalid route node specified \'{n_id}\', could not use it as constraint, skipped!'+'\x1b[0m')
+                        pathreq.nodes_list.remove(n_id)
+                        pathreq.loose_list.pop(i)
+                else:
+                    msg = f'\x1b[1;33;40m'+f'could not find node : {n_id} in network topology. Strict constraint can not be applied.'+'\x1b[0m'
+                    logger.critical(msg)
+                    raise ValueError(msg)
+        if pathreq.source not in transponders:
+            msg = f'\x1b[1;31;40m'+f'Request: {pathreq.request_id}: could not find transponder source : {pathreq.source}.'+'\x1b[0m'
+            logger.critical(msg)
+            print(f'{msg}\nComputation stopped.')
+            exit()
+            
+        if pathreq.destination not in transponders:
+            msg = f'\x1b[1;31;40m'+f'Request: {pathreq.request_id}: could not find transponder destination : {pathreq.destination}.'+'\x1b[0m'
+            logger.critical(msg)
+            print(f'{msg}\nComputation stopped.')
+            exit()
+
+        # TODO remove endpoints from this list in case they were added by the user in the xls or json files
+    return pathreqlist
+
+def correct_disjn(disjn):
+    local_disjn = disjn.copy()
+    for el in local_disjn:
+        for d in local_disjn:
+            if set(el.disjunctions_req) == set(d.disjunctions_req) and\
+             el.disjunction_id != d.disjunction_id:
+                local_disjn.remove(d)
+    return local_disjn
+
+
+def path_result_json(pathresult):
+    data = {
+        'path': [n.json for n in pathresult]
+    }
+    return data
+
+
+if __name__ == '__main__':
+    args = parser.parse_args()
+    basicConfig(level={2: DEBUG, 1: INFO, 0: CRITICAL}.get(args.verbose, DEBUG))
+    logger.info(f'Computing path requests {args.service_filename} into JSON format')
+    print('\x1b[1;34;40m'+f'Computing path requests {args.service_filename} into JSON format'+ '\x1b[0m')
+    # for debug
+    # print( args.eqpt_filename)
+    try:
+        data = load_requests(args.service_filename,args.eqpt_filename)
+        equipment = load_equipment(args.eqpt_filename)
+        network = load_network(args.network_filename,equipment)
+    except EquipmentConfigError as e:
+        print(f'{ansi_escapes.red}Configuration error in the equipment library:{ansi_escapes.reset} {e}')
+        exit(1)
+    except NetworkTopologyError as e:
+        print(f'{ansi_escapes.red}Invalid network definition:{ansi_escapes.reset} {e}')
+        exit(1)
+    except ConfigurationError as e:
+        print(f'{ansi_escapes.red}Configuration error:{ansi_escapes.reset} {e}')
+        exit(1)
+
+    # 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))
+    build_network(network, equipment, p_db, p_total_db)
+    save_network(args.network_filename, network)
+
+    rqs = requests_from_json(data, equipment)
+
+    # 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 a in list(set(all_ids)):
+            all_ids.remove(a)
+        msg = f'Requests id {all_ids} are not unique'
+        logger.critical(msg)
+        exit()
+    rqs = correct_route_list(network, rqs)
+
+    # pths = compute_path(network, equipment, rqs)
+    dsjn = disjunctions_from_json(data)
+
+    print('\x1b[1;34;40m'+f'List of disjunctions'+ '\x1b[0m')
+    print(dsjn)
+    # need to warn or correct in case of wrong disjunction form
+    # disjunction must not be repeated with same or different ids
+    dsjn = correct_disjn(dsjn)
+
+    # Aggregate demands with same exact constraints
+    print('\x1b[1;34;40m'+f'Aggregating similar requests'+ '\x1b[0m')
+
+    rqs,dsjn = requests_aggregation(rqs,dsjn)
+    # TODO export novel set of aggregated demands in a json file
+
+    print('\x1b[1;34;40m'+'The following services have been requested:'+ '\x1b[0m')
+    print(rqs)
+
+    print('\x1b[1;34;40m'+f'Computing all paths with constraints'+ '\x1b[0m')
+    pths = compute_path_dsjctn(network, equipment, rqs, dsjn)
+
+    print('\x1b[1;34;40m'+f'Propagating on selected path'+ '\x1b[0m')
+    propagatedpths = compute_path_with_disjunction(network, equipment, rqs, pths)
+
+    print('\x1b[1;34;40m'+f'Result summary'+ '\x1b[0m')
+
+    header = ['req id', '  demand','  snr@bandwidth','  snr@0.1nm','  Receiver minOSNR', '  mode', '  Gbit/s' , '  nb of tsp pairs']
+    data = []
+    data.append(header)
+    for i, p in enumerate(propagatedpths):
+        if p:
+            line = [f'{rqs[i].request_id}', f' {rqs[i].source} to {rqs[i].destination} : ', f'{round(mean(p[-1].snr),2)}',\
+                f'{round(mean(p[-1].snr+lin2db(rqs[i].baud_rate/(12.5e9))),2)}',\
+                f'{rqs[i].OSNR}', f'{rqs[i].tsp_mode}' , f'{round(rqs[i].path_bandwidth * 1e-9,2)}' , f'{ceil(rqs[i].path_bandwidth / rqs[i].bit_rate) }']
+        else:
+            line = [f'{rqs[i].request_id}',f' {rqs[i].source} to {rqs[i].destination} : not feasible ']
+        data.append(line)
+
+    col_width = max(len(word) for row in data for word in row[2:])   # padding
+    firstcol_width = max(len(row[0]) for row in data )   # padding
+    secondcol_width = max(len(row[1]) for row in data )   # padding
+    for row in data:
+        firstcol = ''.join(row[0].ljust(firstcol_width))
+        secondcol = ''.join(row[1].ljust(secondcol_width))
+        remainingcols = ''.join(word.center(col_width,' ') for word in row[2:])
+        print(f'{firstcol} {secondcol} {remainingcols}')
+
+
+    if args.output :
+        result = []
+        # assumes that list of rqs and list of propgatedpths have same order
+        for i,p in enumerate(propagatedpths):
+            result.append(Result_element(rqs[i],p))
+        temp = path_result_json(result)
+        fnamecsv = f'{str(args.output)[0:len(str(args.output))-len(str(args.output.suffix))]}.csv'
+        fnamejson = f'{str(args.output)[0:len(str(args.output))-len(str(args.output.suffix))]}.json'
+        with open(fnamejson, 'w', encoding='utf-8') as f:
+            f.write(dumps(path_result_json(result), indent=2, ensure_ascii=False))
+            with open(fnamecsv,"w", encoding='utf-8') as fcsv :
+                jsontocsv(temp,equipment,fcsv)
+                print('\x1b[1;34;40m'+f'saving in {args.output} and {fnamecsv}'+ '\x1b[0m')

examples/raman_edfa_example_network.json

@@ -0,0 +1,98 @@
+{
+  "elements": [
+    {
+      "uid": "Site_A",
+      "type": "Transceiver",
+      "metadata": {
+        "location": {
+          "latitude": 0,
+          "longitude": 0,
+          "city": "Site A",
+          "region": ""
+        }
+      }
+    },
+    {
+      "uid": "Span1",
+      "type": "RamanFiber",
+      "type_variety": "SSMF",
+      "operational": {
+        "temperature": 283,
+        "raman_pumps": [
+          {
+            "power": 200e-3,
+            "frequency": 205e12,
+            "propagation_direction": "counterprop"
+          },
+          {
+            "power": 206e-3,
+            "frequency": 201e12,
+            "propagation_direction": "counterprop"
+          }
+        ]
+      },
+      "params": {
+        "type_variety": "SSMF",
+        "length": 80.0,
+        "loss_coef": 0.2,
+        "length_units": "km",
+        "att_in": 0,
+        "con_in": 0.5,
+        "con_out": 0.5
+      },
+      "metadata": {
+        "location": {
+          "latitude": 1,
+          "longitude": 0,
+          "city": null,
+          "region": ""
+        }
+      }
+    },
+    {
+      "uid": "Edfa1",
+      "type": "Edfa",
+      "type_variety": "std_low_gain",
+      "operational": {
+        "gain_target": 15.0,
+        "delta_p": -2,
+        "tilt_target": 0,
+        "out_voa": 0
+      },
+      "metadata": {
+        "location": {
+          "latitude": 2,
+          "longitude": 0,
+          "city": null,
+          "region": ""
+        }
+      }
+    },
+    {
+      "uid": "Site_B",
+      "type": "Transceiver",
+      "metadata": {
+        "location": {
+          "latitude": 2,
+          "longitude": 0,
+          "city": "Site B",
+          "region": ""
+        }
+      }
+    }
+  ],
+  "connections": [
+    {
+      "from_node": "Site_A",
+      "to_node": "Span1"
+    },
+    {
+      "from_node": "Span1",
+      "to_node": "Edfa1"
+    },
+    {
+      "from_node": "Edfa1",
+      "to_node": "Site_B"
+    }
+  ]
+}

examples/sim_params.json

@@ -0,0 +1,14 @@
+{
+  "raman_computed_channels": [1, 18, 37, 56, 75],
+  "raman_parameters": {
+    "flag_raman": true,
+    "space_resolution": 10e3,
+    "tolerance": 1e-8
+  },
+  "nli_parameters": {
+  	"nli_method_name": "ggn_spectrally_separated",
+  	"wdm_grid_size": 50e9,
+  	"dispersion_tolerance": 1,
+  	"phase_shift_tollerance": 0.1
+  }
+}

examples/std_medium_gain_advanced_config.json

@@ -0,0 +1,303 @@
+{     "nf_fit_coeff": [
+      0.000168241,
+      0.0469961,
+      0.0359549,
+      5.82851
+      ],
+      "f_min": 191.35e12,
+      "f_max": 196.1e12,
+      "nf_ripple": [
+      -0.3110761646066259,
+      -0.3110761646066259,
+      -0.31110274831665313,
+      -0.31419329378173544,
+      -0.3172854168606314,
+      -0.32037911876162584,
+      -0.3233255190215882,
+      -0.31624321721895354,
+      -0.30915729645781326,
+      -0.30206775396360075,
+      -0.2949045115165272,
+      -0.26632156113294336,
+      -0.23772399031437283,
+      -0.20911178784023846,
+      -0.18048410390821285,
+      -0.14379944379052215,
+      -0.10709599992470213,
+      -0.07037375788020579,
+      -0.03372858157230583,
+      -0.015660302006048,
+      0.0024172385953583004,
+      0.020504047353947653,
+      0.03860013139908377,
+      0.05670549786742816,
+      0.07482015390297145,
+      0.0838762040768461,
+      0.09284481475528361,
+      0.1018180306253394,
+      0.11079585523492333,
+      0.1020395478432815,
+      0.09310160456603413,
+      0.08415906712621996,
+      0.07521193198077789,
+      0.0676340601339394,
+      0.06005437964543287,
+      0.052470799141237305,
+      0.044883315610536455,
+      0.037679759069084225,
+      0.03047647598902483,
+      0.02326948274513522,
+      0.01605877647020772,
+      0.021248462316134083,
+      0.02657315875107553,
+      0.03190060058247842,
+      0.03723078993416436,
+      0.04256372893215024,
+      0.047899419704645264,
+      0.03915515813685565,
+      0.030289222542492025,
+      0.021418708618354456,
+      0.012573926129294415,
+      0.006240488799898697,
+      -9.622162373026585e-05,
+      -0.006436207679519103,
+      -0.012779471908040341,
+      -0.02038153550619876,
+      -0.027999803010447587,
+      -0.035622012697103154,
+      -0.043236398934156144,
+      -0.04493583574805963,
+      -0.04663615264317309,
+      -0.048337350303318156,
+      -0.050039429413028365,
+      -0.051742390657545205,
+      -0.05342028484370278,
+      -0.05254242298580185,
+      -0.05166410580536087,
+      -0.05078533294804249,
+      -0.04990610405914272,
+      -0.05409792133358102,
+      -0.05832916277634124,
+      -0.06256260169582961,
+      -0.06660356886269536,
+      -0.04779792991567815,
+      -0.028982516728038848,
+      -0.010157321677553965,
+      0.00861320615127981,
+      0.01913736978785662,
+      0.029667009055877668,
+      0.04020212822983975,
+      0.050742731588695494,
+      0.061288823415841555,
+      0.07184040799914815,
+      0.1043252636301016,
+      0.13687829834471027,
+      0.1694483010211072,
+      0.202035284929368,
+      0.23624619427167134,
+      0.27048596623174515,
+      0.30474360397422756,
+      0.3390191214858807,
+      0.36358851509924695,
+      0.38814205928193013,
+      0.41270842850729195,
+      0.4372876328262819,
+      0.4372876328262819
+      ],
+      "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,
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+      0.06479749697916048,
+      0.07704745697916238
+      ]
+}

examples/transmission_main_example.py

@@ -1,90 +1,316 @@
-#!/usr/bin/env 
-"""
-@author: briantaylor
-@author: giladgoldfarb
-@author: jeanluc-auge
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
 
-Transmission setup example: 
-reads from network json (default = examples/edfa/edfa_example_network.json)
-propagates a 96 channels comb 
-"""
+'''
+transmission_main_example.py
+============================
+
+Main example for transmission simulation.
+
+Reads from network JSON (by default, `edfa_example_network.json`)
+'''
+
+from gnpy.core.equipment import load_equipment, trx_mode_params
+from gnpy.core.utils import db2lin, lin2db, write_csv
 from argparse import ArgumentParser
-from json import load
 from sys import exit
 from pathlib import Path
+from json import loads
+from collections import Counter
 from logging import getLogger, basicConfig, INFO, ERROR, DEBUG
-
-from matplotlib.pyplot import show, axis, figure, title
+from numpy import linspace, mean, log10
+from matplotlib.pyplot import show, axis, figure, title, text
 from networkx import (draw_networkx_nodes, draw_networkx_edges,
                       draw_networkx_labels, dijkstra_path)
+from gnpy.core.network import load_network, build_network, save_network, load_sim_params, configure_network
+from gnpy.core.elements import Transceiver, Fiber, RamanFiber, Edfa, Roadm
+from gnpy.core.info import create_input_spectral_information, SpectralInformation, Channel, Power, Pref
+from gnpy.core.request import Path_request, RequestParams, compute_constrained_path, propagate2
+from gnpy.core.exceptions import ConfigurationError, EquipmentConfigError, NetworkTopologyError
+import gnpy.core.ansi_escapes as ansi_escapes
 
-from gnpy.core import network_from_json, build_network
-from gnpy.core.elements import Transceiver, Fiber, Edfa
-from gnpy.core.info import SpectralInformation, Channel, Power
-#from gnpy.core.algorithms import closed_paths
+logger = getLogger(__name__)
 
-logger = getLogger(__package__ or __file__)
-
-def format_si(spectral_infos):
-    return '\n'.join([
-        f'#{idx} Carrier(frequency={c.frequency},\n  power=Power(signal={c.power.signal}, nli={c.power.nli}, ase={c.power.ase}))'
-        for idx, si in sorted(set(spectral_infos))
-        for c in set(si.carriers)
-    ])
-
-logger = getLogger('gnpy.core')
-
-def main(args):
-    with open(args.filename) as f:
-        json_data = load(f)
-
-    network = network_from_json(json_data)
-    build_network(network)
-
-    spacing = 0.05 #THz
-    si = SpectralInformation() # !! SI units W, Hz
-    si = si.update(carriers=tuple(Channel(f, (191.3+spacing*f)*1e12, 
-            32e9, 0.15, Power(1e-3, 0, 0)) for f in range(1,97)))
-
-    trx = [n for n in network.nodes() if isinstance(n, Transceiver)]
-    source, sink = trx[0], trx[1]
- 
-    path = dijkstra_path(network, source, sink)
-    print(f'There are {len(path)} network elements between {source} and {sink}')
-
-    for el in path:
-        si = el(si)
-        print(el)
-
-    nodelist = [n for n in network.nodes() if isinstance(n, (Transceiver, Fiber))]
-    pathnodes = [n for n in path if isinstance(n, (Transceiver, Fiber))]
-    edgelist = [(u, v) for u, v in zip(pathnodes, pathnodes[1:])]
-    node_color = ['#ff0000' if n is source or n is sink else
-                  '#900000' if n in path else '#ffdfdf'
-                  for n in nodelist]
-    edge_color = ['#ff9090' if u in path and v in path else '#ababab'
-                  for u, v in edgelist]
-    labels = {n: n.location.city if isinstance(n, Transceiver) else ''
-              for n in pathnodes}
+def plot_baseline(network):
+    edges = set(network.edges())
+    pos = {n: (n.lng, n.lat) for n in network.nodes()}
+    labels = {n: n.location.city for n in network.nodes() if isinstance(n, Transceiver)}
+    city_labels = set(labels.values())
+    for n in network.nodes():
+        if n.location.city and n.location.city not in city_labels:
+            labels[n] = n.location.city
+            city_labels.add(n.location.city)
+    label_pos = pos
 
     fig = figure()
-    pos = {n: (n.lng, n.lat) for n in nodelist}
     kwargs = {'figure': fig, 'pos': pos}
-    plot = draw_networkx_nodes(network, nodelist=nodelist, node_color=node_color, **kwargs)
-    draw_networkx_edges(network, edgelist=edgelist, edge_color=edge_color, **kwargs)
-    draw_networkx_labels(network, labels=labels, font_size=14, **kwargs)
-    title(f'Propagating from {source.loc.city} to {sink.loc.city}')
+    plot = draw_networkx_nodes(network, nodelist=network.nodes(), node_color='#ababab', **kwargs)
+    draw_networkx_edges(network, edgelist=edges, edge_color='#ababab', **kwargs)
+    draw_networkx_labels(network, labels=labels, font_size=14, **{**kwargs, 'pos': label_pos})
     axis('off')
     show()
 
+def plot_results(network, path, source, destination, infos):
+    path_edges = set(zip(path[:-1], path[1:]))
+    edges = set(network.edges()) - path_edges
+    pos = {n: (n.lng, n.lat) for n in network.nodes()}
+    nodes = {}
+    for k, (x, y) in pos.items():
+        nodes.setdefault((round(x, 1), round(y, 1)), []).append(k)
+    labels = {n: n.location.city for n in network.nodes() if isinstance(n, Transceiver)}
+    city_labels = set(labels.values())
+    for n in network.nodes():
+        if n.location.city and n.location.city not in city_labels:
+            labels[n] = n.location.city
+            city_labels.add(n.location.city)
+    label_pos = pos
+
+    fig = figure()
+    kwargs = {'figure': fig, 'pos': pos}
+    all_nodes = [n for n in network.nodes() if n not in path]
+    plot = draw_networkx_nodes(network, nodelist=all_nodes, node_color='#ababab', node_size=50, **kwargs)
+    draw_networkx_nodes(network, nodelist=path, node_color='#ff0000', node_size=55, **kwargs)
+    draw_networkx_edges(network, edgelist=edges, edge_color='#ababab', **kwargs)
+    draw_networkx_edges(network, edgelist=path_edges, edge_color='#ff0000', **kwargs)
+    draw_networkx_labels(network, labels=labels, font_size=14, **{**kwargs, 'pos': label_pos})
+    title(f'Propagating from {source.loc.city} to {destination.loc.city}')
+    axis('off')
+
+    heading = 'Spectral Information\n\n'
+    textbox = text(0.85, 0.20, heading, fontsize=14, fontname='Ubuntu Mono',
+                   verticalalignment='top', transform=fig.axes[0].transAxes,
+                   bbox={'boxstyle': 'round', 'facecolor': 'wheat', 'alpha': 0.5})
+
+    msgs = {(x, y): heading + '\n\n'.join(str(n) for n in ns if n in path)
+            for (x, y), ns in nodes.items()}
+
+    def hover(event):
+        if event.xdata is None or event.ydata is None:
+            return
+        if fig.contains(event):
+            x, y = round(event.xdata, 1), round(event.ydata, 1)
+            if (x, y) in msgs:
+                textbox.set_text(msgs[x, y])
+            else:
+                textbox.set_text(heading)
+            fig.canvas.draw_idle()
+
+    fig.canvas.mpl_connect('motion_notify_event', hover)
+    show()
+
+
+def main(network, equipment, source, destination, sim_params, req=None):
+    result_dicts = {}
+    network_data = [{
+                    'network_name'  : str(args.filename),
+                    'source'        : source.uid,
+                    'destination'   : destination.uid
+                    }]
+    result_dicts.update({'network': network_data})
+    design_data = [{
+                    'power_mode'        : equipment['Span']['default'].power_mode,
+                    'span_power_range'  : equipment['Span']['default'].delta_power_range_db,
+                    'design_pch'        : equipment['SI']['default'].power_dbm,
+                    'baud_rate'         : equipment['SI']['default'].baud_rate
+                    }]
+    result_dicts.update({'design': design_data})
+    simulation_data = []
+    result_dicts.update({'simulation results': simulation_data})
+
+    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']))
+
+    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)
+    build_network(network, equipment, pref_ch_db, pref_total_db)
+    path = compute_constrained_path(network, req)
+
+    if len([s.length for s in path if isinstance(s, RamanFiber)]):
+        if sim_params is None:
+            print(f'{ansi_escapes.red}Invocation error:{ansi_escapes.reset} RamanFiber requires passing simulation params via --sim-params')
+            exit(1)
+        configure_network(network, sim_params)
+
+    spans = [s.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} and {destination.uid}')
+    print(f'\nNow propagating between {source.uid} and {destination.uid}:')
+
+    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]')
+        power_range = [0]
+
+    if not power_mode:
+        #power cannot be changed in gain mode
+        power_range = [0]
+    for dp_db in power_range:
+        req.power = db2lin(pref_ch_db + dp_db)*1e-3
+        if power_mode:
+            print(f'\nPropagating with input power = {ansi_escapes.cyan}{lin2db(req.power*1e3):.2f} dBm{ansi_escapes.reset}:')
+        else:
+            print(f'\nPropagating in {ansi_escapes.cyan}gain mode{ansi_escapes.reset}: power cannot be set manually')
+        infos = propagate2(path, req, equipment)
+        if len(power_range) == 1:
+            for elem in path:
+                print(elem)
+        if power_mode:
+            print(f'\nTransmission result for input power = {lin2db(req.power*1e3):.2f} dBm:')
+        else:
+            print(f'\nTransmission results:')
+        print(f'  Final SNR total (signal bw): {ansi_escapes.cyan}{mean(destination.snr):.02f} dB{ansi_escapes.reset}')
+
+        #print(f'\n !!!!!!!!!!!!!!!!!     TEST POINT         !!!!!!!!!!!!!!!!!!!!!')
+        #print(f'carriers ase output of {path[1]} =\n {list(path[1].carriers("out", "nli"))}')
+        # => use "in" or "out" parameter
+        # => use "nli" or "ase" or "signal" or "total" parameter        
+        if power_mode:
+            simulation_data.append({
+                        'Pch_dBm'               : pref_ch_db + dp_db,
+                        'OSNR_ASE_0.1nm'        : round(mean(destination.osnr_ase_01nm),2),
+                        'OSNR_ASE_signal_bw'    : round(mean(destination.osnr_ase),2),
+                        'SNR_nli_signal_bw'     : round(mean(destination.osnr_nli),2),
+                        'SNR_total_signal_bw'   : round(mean(destination.snr),2)
+                                })
+        else:
+            simulation_data.append({
+                        'gain_mode'             : 'power canot be set',
+                        'OSNR_ASE_0.1nm'        : round(mean(destination.osnr_ase_01nm),2),
+                        'OSNR_ASE_signal_bw'    : round(mean(destination.osnr_ase),2),
+                        'SNR_nli_signal_bw'     : round(mean(destination.osnr_nli),2),
+                        'SNR_total_signal_bw'   : round(mean(destination.snr),2)
+                                })
+    write_csv(result_dicts, 'simulation_result.csv')
+    return path, infos
+
+
 parser = ArgumentParser()
+parser.add_argument('-e', '--equipment', type=Path,
+                    default=Path(__file__).parent / 'eqpt_config.json')
+parser.add_argument('--sim-params', type=Path,
+                    default=None, help='Path to the JSON containing simulation parameters (required for Raman)')
+parser.add_argument('--show-channels', action='store_true', help='Show final per-channel OSNR summary')
+parser.add_argument('-pl', '--plot', action='store_true')
+parser.add_argument('-v', '--verbose', action='count', default=0, help='increases verbosity for each occurence')
+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('-names', '--names-matching', action='store_true', help='display network names that are closed matches')
 parser.add_argument('filename', nargs='?', type=Path,
-  default= Path(__file__).parent / 'edfa/edfa_example_network.json')
-parser.add_argument('-v', '--verbose', action='count')
+                    default=Path(__file__).parent / 'edfa_example_network.json')
+parser.add_argument('source', nargs='?', help='source node')
+parser.add_argument('destination',   nargs='?', help='destination node')
+
 
 if __name__ == '__main__':
     args = parser.parse_args()
-    level = {1: INFO, 2: DEBUG}.get(args.verbose, ERROR)
-    logger.setLevel(level)
-    basicConfig()
-    exit(main(args))
+    basicConfig(level={0: ERROR, 1: INFO, 2: DEBUG}.get(args.verbose, DEBUG))
+
+    try:
+        equipment = load_equipment(args.equipment)
+        network = load_network(args.filename, equipment, args.names_matching)
+        sim_params = load_sim_params(args.sim_params) if args.sim_params is not None else None
+    except EquipmentConfigError as e:
+        print(f'{ansi_escapes.red}Configuration error in the equipment library:{ansi_escapes.reset} {e}')
+        exit(1)
+    except NetworkTopologyError as e:
+        print(f'{ansi_escapes.red}Invalid network definition:{ansi_escapes.reset} {e}')
+        exit(1)
+    except ConfigurationError as e:
+        print(f'{ansi_escapes.red}Configuration error:{ansi_escapes.reset} {e}')
+        exit(1)
+
+    if args.plot:
+        plot_baseline(network)
+
+    transceivers = {n.uid: n for n in network.nodes() if isinstance(n, Transceiver)}
+
+    if not transceivers:
+        exit('Network has no transceivers!')
+    if len(transceivers) < 2:
+        exit('Network has only one transceiver!')
+
+    if args.list_nodes:
+        for uid in transceivers:
+            print(uid)
+        exit()
+
+    #First try to find exact match if source/destination provided
+    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')
+
+    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')
+
+    #If no exact match try to find partial match
+    if args.source and not source:
+        #TODO code a more advanced regex to find nodes match
+        source = next((transceivers.pop(uid) for uid in transceivers \
+                  if args.source.lower() in uid.lower()), None)
+
+    if args.destination and not destination:
+        #TODO code a more advanced regex to find nodes match
+        destination = next((transceivers.pop(uid) for uid in transceivers \
+                  if args.destination.lower() in uid.lower()), None)
+
+    #If no partial match or no source/destination provided pick random
+    if not source:
+        source = list(transceivers.values())[0]
+        del transceivers[source.uid]
+
+    if not destination:
+        destination = list(transceivers.values())[0]
+
+    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['nodes_list'] = [destination.uid]
+    params['loose_list'] = ['strict']
+    params['format'] = ''
+    params['path_bandwidth'] = 0
+    trx_params = trx_mode_params(equipment)
+    if args.power:
+        trx_params['power'] = db2lin(float(args.power))*1e-3
+    params.update(trx_params)
+    req = Path_request(**params)
+    path, infos = main(network, equipment, source, destination, sim_params, req)
+    save_network(args.filename, network)
+
+    if args.show_channels:
+        print('\nThe total SNR per channel at the end of the line is:')
+        print('{:>5}{:>26}{:>26}{:>28}{:>28}{:>28}' \
+            .format('Ch. #', 'Channel frequency (THz)', 'Channel power (dBm)', 'OSNR ASE (signal bw, dB)', 'SNR NLI (signal bw, dB)', 'SNR total (signal bw, dB)'))
+        for final_carrier, ch_osnr, ch_snr_nl, ch_snr in zip(infos[path[-1]][1].carriers, path[-1].osnr_ase, path[-1].osnr_nli, path[-1].snr):
+            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(final_carrier.channel_number, round(ch_freq, 2), round(ch_power, 2), round(ch_osnr, 2), round(ch_snr_nl, 2), round(ch_snr, 2)))
+
+    if not args.source:
+        print(f'\n(No source node specified: picked {source.uid})')
+    elif not valid_source:
+        print(f'\n(Invalid source node {args.source!r} replaced with {source.uid})')
+
+    if not args.destination:
+        print(f'\n(No destination node specified: picked {destination.uid})')
+    elif not valid_destination:
+        print(f'\n(Invalid destination node {args.destination!r} replaced with {destination.uid})')
+
+    if args.plot:
+        plot_results(network, path, source, destination, infos)

examples/write_path_jsontocsv.py

@@ -0,0 +1,36 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+write_path_jsontocsv.py
+========================
+
+Reads JSON path result file in accordance with the Yang model for requesting
+path computation and writes results to a CSV file.
+
+See: draft-ietf-teas-yang-path-computation-01.txt
+"""
+
+from argparse import ArgumentParser
+from pathlib import Path
+from json import loads
+from gnpy.core.equipment  import load_equipment
+from gnpy.core.request  import jsontocsv
+
+
+parser = ArgumentParser(description = 'A function that writes json path results in an excel sheet.')
+parser.add_argument('filename', nargs='?', type = Path)
+parser.add_argument('output_filename', nargs='?', type = Path)
+parser.add_argument('eqpt_filename', nargs='?', type = Path, default=Path(__file__).parent / 'eqpt_config.json')
+
+if __name__ == '__main__':
+    args = parser.parse_args()
+
+    with open(args.output_filename, 'w', encoding='utf-8') as file:
+        with open(args.filename, encoding='utf-8') as f:
+            print(f'Reading {args.filename}')
+            json_data = loads(f.read())
+            equipment = load_equipment(args.eqpt_filename)
+            print(f'Writing in {args.output_filename}')
+            jsontocsv(json_data,equipment,file)
+

gnpy/core/__init__.py

@@ -1,4 +1,28 @@
 #!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+########################################################################
+#                _____ ___ ____    ____  ____  _____                   #
+#               |_   _|_ _|  _ \  |  _ \/ ___|| ____|                  #
+#                 | |  | || |_) | | |_) \___ \|  _|                    #
+#                 | |  | ||  __/  |  __/ ___) | |___                   #
+#                 |_| |___|_|     |_|   |____/|_____|                  #
+#                                                                      #
+#                == Physical Simulation Environment ==                 #
+#                                                                      #
+########################################################################
+
+
+'''
+gnpy route planning and optimization library
+============================================
+
+gnpy is a route planning and optimization library, written in Python, for
+operators of large-scale mesh optical networks.
+
+:copyright: © 2018, Telecom Infra Project
+:license: BSD 3-Clause, see LICENSE for more details.
+'''
 
 from . import elements
 from .execute import *

gnpy/core/ansi_escapes.py

@@ -0,0 +1,13 @@
+#!/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'
+cyan = '\x1b[1;36;40m'
+reset = '\x1b[0m'

gnpy/core/convert.py

@@ -0,0 +1,627 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+gnpy.core.convert
+=================
+
+This module contains utilities for converting between XLS and JSON.
+
+The input XLS file must contain sheets named "Nodes" and "Links".
+It may optionally contain a sheet named "Eqpt".
+
+In the "Nodes" sheet, only the "City" column is mandatory. The column "Type"
+can be determined automatically given the topology (e.g., if degree 2, ILA;
+otherwise, ROADM.) Incorrectly specified types (e.g., ILA for node of
+degree ≠ 2) will be automatically corrected.
+
+In the "Links" sheet, only the first three columns ("Node A", "Node Z" and
+"east Distance (km)") are mandatory.  Missing "west" information is copied from
+the "east" information so that it is possible to input undirected data.
+"""
+
+from sys import exit
+try:
+    from xlrd import open_workbook
+except ModuleNotFoundError:
+    exit('Required: `pip install xlrd`')
+from argparse import ArgumentParser
+from collections import namedtuple, Counter, defaultdict
+from itertools import chain
+from json import dumps
+from pathlib import Path
+from difflib import get_close_matches
+from gnpy.core.utils import silent_remove
+import time
+
+all_rows = lambda sh, start=0: (sh.row(x) for x in range(start, sh.nrows))
+
+class Node(object):
+    def __init__(self, **kwargs):
+        super(Node, self).__init__()
+        self.update_attr(kwargs)
+
+    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():
+            v = clean_kwargs.get(k,v)
+            setattr(self, k, v)
+
+    default_values = \
+    {
+        'city':         '',
+        'state':        '',
+        'country':      '',
+        'region':       '',
+        'latitude':     0,
+        'longitude':    0,
+        'node_type':    'ILA',
+        'booster_restriction' : '',
+        'preamp_restriction'  : ''
+    }
+
+class Link(object):
+    """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__()
+        self.update_attr(kwargs)
+        self.distance_units = 'km'
+
+    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():
+            v = clean_kwargs.get(k,v)
+            setattr(self, k, v)
+            k = 'west' + k.split('east')[-1]
+            v = clean_kwargs.get(k,v)
+            setattr(self, k, v)
+
+    def __eq__(self, link):
+        return (self.from_city == link.from_city and self.to_city == link.to_city) \
+                or (self.from_city == link.to_city and self.to_city == link.from_city)
+
+    default_values = \
+    {
+            'from_city':            '',
+            'to_city':              '',
+            'east_distance':        80,
+            'east_fiber':           'SSMF',
+            'east_lineic':          0.2,
+            'east_con_in':          None,
+            'east_con_out':         None,
+            'east_pmd':             0.1,
+            'east_cable':           ''
+    }
+
+
+class Eqpt(object):
+    def __init__(self, **kwargs):
+        super(Eqpt, self).__init__()
+        self.update_attr(kwargs)
+
+    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():
+            v_east = clean_kwargs.get(k,v)
+            setattr(self, k, v_east)
+            k = 'west' + k.split('east')[-1]
+            v_west = clean_kwargs.get(k,v)
+            setattr(self, k, v_west)
+
+    default_values = \
+    {
+            'from_city':        '',
+            'to_city':          '',
+            'east_amp_type':    '',
+            'east_att_in':      0,
+            'east_amp_gain':    None,
+            'east_amp_dp':      None,
+            'east_tilt':        0,
+            'east_att_out':     None
+    }
+
+
+def read_header(my_sheet, line, slice_):
+    """ return the list of headers !:= ''
+    header_i = [(header, header_column_index), ...]
+    in a {line, slice1_x, slice_y} range
+    """
+    Param_header = namedtuple('Param_header', 'header colindex')
+    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:
+        header_i = []
+    if header_i != [] and header_i[-1].colindex != slice_[1]:
+        header_i.append(Param_header('',slice_[1]))
+    return header_i
+
+def read_slice(my_sheet, line, slice_, header):
+    """return the slice range of a given header
+    in a defined range {line, slice_x, slice_y}"""
+    header_i = read_header(my_sheet, line, slice_)
+    slice_range = (-1,-1)
+    if header_i != []:
+        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:
+            pass
+    return slice_range
+
+
+def parse_headers(my_sheet, input_headers_dict, headers, start_line, slice_in):
+    """return a dict of header_slice
+    key = column index
+    value = header name"""
+
+
+    for h0 in input_headers_dict:
+        slice_out = read_slice(my_sheet, start_line, slice_in, h0)
+        iteration = 1
+        while slice_out == (-1,-1) and iteration < 10:
+            #try next lines
+            #print(h0, iteration)
+            slice_out = read_slice(my_sheet, start_line+iteration, slice_in, h0)
+            iteration += 1
+        if slice_out == (-1, -1):
+            if h0 in ('east', 'Node A', 'Node Z', 'City') :
+                print(f'\x1b[1;31;40m'+f'CRITICAL: missing _{h0}_ header: EXECUTION ENDS'+ '\x1b[0m')
+                exit()
+            else:
+                print(f'missing header {h0}')
+        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'\x1b[1;31;40m'+f'CRITICAL ERROR: could not find any header to read _ ABORT'+ '\x1b[0m')
+        exit()
+    return headers
+
+def parse_row(row, headers):
+    #print([label for label in ept.values()])
+    #print([i for i in ept.keys()])
+    #print(row[i for i in ept.keys()])
+    return {f: r.value for f, r in \
+            zip([label for label in headers.values()], [row[i] for i in headers])}
+            #if r.ctype != XL_CELL_EMPTY}
+
+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 sanity_check(nodes, links, nodes_by_city, links_by_city, eqpts_by_city):
+
+    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 \
+                    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)
+    #if duplicate_links != []:
+        #time.sleep(3)
+    for l in duplicate_links:
+        links.remove(l)
+
+    try :
+        test_nodes = [n for n in nodes_by_city if not n in links_by_city]
+        test_links = [n for n in links_by_city if not n in nodes_by_city]
+        test_eqpts = [n for n in eqpts_by_city if not n in nodes_by_city]
+        assert (test_nodes == [] or test_nodes == [''])\
+                and (test_links == [] or test_links ==[''])\
+                and (test_eqpts == [] or test_eqpts ==[''])
+    except AssertionError:
+        print(f'CRITICAL error: \nNames in Nodes and Links sheets do no match, check:\
+            \n{test_nodes} in Nodes sheet\
+            \n{test_links} in Links sheet\
+            \n{test_eqpts} in Eqpt sheet')
+        exit(1)
+
+    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')
+            nodes_by_city[city].node_type = 'ROADM'
+            for n in nodes:
+                if n.city==city:
+                    n.node_type='ROADM'
+    return nodes, links
+
+def convert_file(input_filename, names_matching=False, filter_region=[]):
+    nodes, links, eqpts = parse_excel(input_filename)
+    if filter_region:
+        nodes = [n for n in nodes if n.region.lower() in filter_region]
+        cities = {n.city for n in nodes}
+        links = [lnk for lnk in links if lnk.from_city in cities and
+                                         lnk.to_city in cities]
+        cities = {lnk.from_city for lnk in links} | {lnk.to_city for lnk in links}
+        nodes = [n for n in nodes if n.city in cities]
+
+    global nodes_by_city
+    nodes_by_city = {n.city: n for n in nodes}
+    #create matching dictionary for node name mismatch analysis
+
+    cities = {''.join(c.strip() for c in n.city.split('C+L')).lower(): n.city for n in nodes}
+    cities_to_match = [k for k in cities]
+    city_match_dic = defaultdict(list)
+    for city in cities:
+        if city in cities_to_match:
+            cities_to_match.remove(city)
+        matches = get_close_matches(city, cities_to_match, 4, 0.85)
+        for m in matches:
+            city_match_dic[cities[city]].append(cities[m])
+    #check lower case/upper case
+    for city in nodes_by_city:
+        for match_city in nodes_by_city:
+            if match_city.lower() == city.lower() and match_city != city:
+                city_match_dic[city].append(match_city)
+
+    if names_matching:
+        print('\ncity match dictionary:',city_match_dic)
+    with  open('name_match_dictionary.json', 'w', encoding='utf-8') as city_match_dic_file:
+        city_match_dic_file.write(dumps(city_match_dic, indent=2, ensure_ascii=False))
+
+    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)
+
+    nodes, links = sanity_check(nodes, links, nodes_by_city, links_by_city, eqpts_by_city)
+
+    data = {
+        'elements':
+            [{'uid': f'trx {x.city}',
+              'metadata': {'location': {'city':      x.city,
+                                        'region':    x.region,
+                                        'latitude':  x.latitude,
+                                        'longitude': x.longitude}},
+              'type': 'Transceiver'}
+             for x in nodes_by_city.values() if x.node_type.lower() == 'roadm'] +
+            [{'uid': f'roadm {x.city}',
+              'metadata': {'location': {'city':      x.city,
+                                        'region':    x.region,
+                                        'latitude':  x.latitude,
+                                        'longitude': x.longitude}},
+              'type': 'Roadm'}
+             for x in nodes_by_city.values() if x.node_type.lower() == 'roadm' \
+                 and x.booster_restriction == '' and x.preamp_restriction == ''] +
+            [{'uid': f'roadm {x.city}',
+              'params' : {
+                'restrictions': {
+                  'preamp_variety_list': silent_remove(x.preamp_restriction.split(' | '),''),
+                  'booster_variety_list': silent_remove(x.booster_restriction.split(' | '),'')
+                  }
+              },
+              'metadata': {'location': {'city':      x.city,
+                                        'region':    x.region,
+                                        'latitude':  x.latitude,
+                                        'longitude': x.longitude}},
+              'type': 'Roadm'}
+             for x in nodes_by_city.values() if x.node_type.lower() == 'roadm' and \
+                 (x.booster_restriction != '' or x.preamp_restriction != '')] +
+            [{'uid': f'west fused spans in {x.city}',
+              'metadata': {'location': {'city':      x.city,
+                                        'region':    x.region,
+                                        'latitude':  x.latitude,
+                                        'longitude': x.longitude}},
+              'type': 'Fused'}
+             for x in nodes_by_city.values() if x.node_type.lower() == 'fused'] +
+            [{'uid': f'east fused spans in {x.city}',
+              'metadata': {'location': {'city':      x.city,
+                                        'region':    x.region,
+                                        'latitude':  x.latitude,
+                                        'longitude': x.longitude}},
+              'type': 'Fused'}
+             for x in nodes_by_city.values() if x.node_type.lower() == 'fused'] +
+            [{'uid': f'fiber ({x.from_city} \u2192 {x.to_city})-{x.east_cable}',
+              'metadata': {'location': midpoint(nodes_by_city[x.from_city],
+                                                nodes_by_city[x.to_city])},
+              'type': 'Fiber',
+              'type_variety': x.east_fiber,
+              'params': {'length':   round(x.east_distance, 3),
+                         'length_units':    x.distance_units,
+                         'loss_coef': x.east_lineic,
+                         'con_in':x.east_con_in,
+                         'con_out':x.east_con_out}
+            }
+              for x in links] +
+            [{'uid': f'fiber ({x.to_city} \u2192 {x.from_city})-{x.west_cable}',
+              'metadata': {'location': midpoint(nodes_by_city[x.from_city],
+                                                nodes_by_city[x.to_city])},
+              'type': 'Fiber',
+              'type_variety': x.west_fiber,
+              'params': {'length':   round(x.west_distance, 3),
+                         'length_units':    x.distance_units,
+                         'loss_coef': x.west_lineic,
+                         'con_in':x.west_con_in,
+                         'con_out':x.west_con_out}
+            } # missing ILA construction
+              for x in links] +
+            [{'uid': f'east edfa in {e.from_city} to {e.to_city}',
+              'metadata': {'location': {'city':      nodes_by_city[e.from_city].city,
+                                        'region':    nodes_by_city[e.from_city].region,
+                                        'latitude':  nodes_by_city[e.from_city].latitude,
+                                        'longitude': nodes_by_city[e.from_city].longitude}},
+              'type': 'Edfa',
+              'type_variety': e.east_amp_type,
+              'operational': {'gain_target': e.east_amp_gain,
+                              'delta_p':     e.east_amp_dp,
+                              'tilt_target': e.east_tilt,
+                              'out_voa'    : e.east_att_out}
+             }
+             for e in eqpts if (e.east_amp_type.lower() != '' and \
+                                e.east_amp_type.lower() != 'fused')] +
+            [{'uid': f'west edfa in {e.from_city} to {e.to_city}',
+              'metadata': {'location': {'city':      nodes_by_city[e.from_city].city,
+                                        'region':    nodes_by_city[e.from_city].region,
+                                        'latitude':  nodes_by_city[e.from_city].latitude,
+                                        'longitude': nodes_by_city[e.from_city].longitude}},
+              'type': 'Edfa',
+              'type_variety': e.west_amp_type,
+              'operational': {'gain_target': e.west_amp_gain,
+                              'delta_p':     e.west_amp_dp,
+                              'tilt_target': e.west_tilt,
+                              'out_voa'    : e.west_att_out}
+             }
+             for e in eqpts if (e.west_amp_type.lower() != '' and \
+                                e.west_amp_type.lower() != 'fused')] +
+            # fused edfa variety is a hack to indicate that there should not be
+            # booster amplifier out the roadm.
+            # If user specifies ILA in Nodes sheet and fused in Eqpt sheet, then assumes that
+            # this is a fused nodes.
+            [{'uid': f'east edfa in {e.from_city} to {e.to_city}',
+              'metadata': {'location': {'city':      nodes_by_city[e.from_city].city,
+                                        'region':    nodes_by_city[e.from_city].region,
+                                        'latitude':  nodes_by_city[e.from_city].latitude,
+                                        'longitude': nodes_by_city[e.from_city].longitude}},
+              'type': 'Fused',
+              'params': {'loss': 0}
+             }
+             for e in eqpts if e.east_amp_type.lower() == 'fused'] +
+            [{'uid': f'west edfa in {e.from_city} to {e.to_city}',
+              'metadata': {'location': {'city':      nodes_by_city[e.from_city].city,
+                                        'region':    nodes_by_city[e.from_city].region,
+                                        'latitude':  nodes_by_city[e.from_city].latitude,
+                                        'longitude': nodes_by_city[e.from_city].longitude}},
+              'type': 'Fused',
+              'params': {'loss': 0}
+             }
+             for e in eqpts if e.west_amp_type.lower() == 'fused'],
+        'connections':
+            list(chain.from_iterable([eqpt_connection_by_city(n.city)
+            for n in nodes]))
+            +
+            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}'}
+             for x in nodes_by_city.values() if x.node_type.lower()=='roadm'])))
+    }
+
+    suffix_filename = str(input_filename.suffixes[0])
+    full_input_filename = str(input_filename)
+    split_filename = [full_input_filename[0:len(full_input_filename)-len(suffix_filename)] , suffix_filename[1:]]
+    output_json_file_name = split_filename[0]+'.json'
+    with  open(output_json_file_name, 'w', encoding='utf-8') as edfa_json_file:
+        edfa_json_file.write(dumps(data, indent=2, ensure_ascii=False))
+    return output_json_file_name
+
+def parse_excel(input_filename):
+    link_headers = \
+    {  'Node A': 'from_city',
+       'Node Z': 'to_city',
+       'east':{
+            'Distance (km)':        'east_distance',
+            'Fiber type':           'east_fiber',
+            'lineic att':           'east_lineic',
+            'Con_in':               'east_con_in',
+            'Con_out':              'east_con_out',
+            'PMD':                  'east_pmd',
+            'Cable id':             'east_cable'
+        },
+        'west':{
+            'Distance (km)':        'west_distance',
+            'Fiber type':           'west_fiber',
+            'lineic att':           'west_lineic',
+            'Con_in':               'west_con_in',
+            'Con_out':              'west_con_out',
+            'PMD':                  'west_pmd',
+            'Cable id':             'west_cable'
+        }
+    }
+    node_headers = \
+    {   'City':         'city',
+        'State':        'state',
+        'Country':      'country',
+        'Region':       'region',
+        'Latitude':     'latitude',
+        'Longitude':    'longitude',
+        'Type':         'node_type',
+        'Booster_restriction': 'booster_restriction',
+        'Preamp_restriction': 'preamp_restriction'
+    }
+    eqpt_headers = \
+    {  'Node A': 'from_city',
+       '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',
+            'att_out':          'east_att_out'
+       },
+       'west':{
+            'amp type':         'west_amp_type',
+            'att_in':           'west_att_in',
+            'amp gain':         'west_amp_gain',
+            'delta p':          'west_amp_dp',
+            'tilt':             'west_tilt',
+            'att_out':          'west_att_out'
+       }
+    }
+
+    with open_workbook(input_filename) as wb:
+        nodes_sheet = wb.sheet_by_name('Nodes')
+        links_sheet = wb.sheet_by_name('Links')
+        try:
+            eqpt_sheet = wb.sheet_by_name('Eqpt')
+        except Exception:
+            #eqpt_sheet is optional
+            eqpt_sheet = None
+
+        nodes = []
+        for node in parse_sheet(nodes_sheet, node_headers, NODES_LINE, NODES_LINE+1, NODES_COLUMN):
+            nodes.append(Node(**node))
+        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))
+        #print('\n', [l.__dict__ for l in links])
+
+        eqpts = []
+        if eqpt_sheet != None:
+            for eqpt in parse_sheet(eqpt_sheet, eqpt_headers, EQPTS_LINE, EQPTS_LINE+2, EQPTS_COLUMN):
+                eqpts.append(Eqpt(**eqpt))
+
+    # sanity check
+    all_cities = Counter(n.city for n in nodes)
+    if len(all_cities) != len(nodes):
+        raise ValueError(f'Duplicate city: {all_cities}')
+    if any(ln.from_city not in all_cities or
+           ln.to_city   not in all_cities for ln in links):
+        raise ValueError(f'Bad link.')
+
+    return nodes, links, eqpts
+
+
+def eqpt_connection_by_city(city_name):
+    other_cities = fiber_dest_from_source(city_name)
+    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])
+            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)
+            subdata += connect_eqpt(from_, in_, to_)
+
+            from_ = fiber_link(other_city, city_name)
+            in_ = eqpt_in_city_to_city(city_name, other_city, "west")
+            to_ = f'roadm {city_name}'
+            subdata += connect_eqpt(from_, in_, to_)
+    return subdata
+
+
+def connect_eqpt(from_, in_, to_):
+    connections = []
+    if in_ !='':
+        connections = [{'from_node': from_, 'to_node': in_},
+                      {'from_node': in_, 'to_node': to_}]
+    else:
+        connections = [{'from_node': from_, 'to_node': to_}]
+    return connections
+
+
+def eqpt_in_city_to_city(in_city, to_city, direction='east'):
+    rev_direction = 'west' if direction == 'east' else 'east'
+    amp_direction = f'{direction}_amp_type'
+    amp_rev_direction = f'{rev_direction}_amp_type'
+    return_eqpt = ''
+    if in_city in eqpts_by_city:
+        for e in eqpts_by_city[in_city]:
+            if nodes_by_city[in_city].node_type.lower() == 'roadm':
+                if e.to_city == to_city and getattr(e, amp_direction) != '':
+                    return_eqpt = f'{direction} edfa in {e.from_city} to {e.to_city}'
+            elif nodes_by_city[in_city].node_type.lower() == 'ila':
+                if e.to_city != to_city:
+                    direction = rev_direction
+                    amp_direction = amp_rev_direction
+                if getattr(e, amp_direction) != '':
+                    return_eqpt = f'{direction} edfa in {e.from_city} to {e.to_city}'
+    if nodes_by_city[in_city].node_type.lower() == 'fused':
+        return_eqpt = f'{direction} fused spans in {in_city}'
+    return return_eqpt
+
+
+def fiber_dest_from_source(city_name):
+    destinations = []
+    links_from_city = links_by_city[city_name]
+    for l in links_from_city:
+        if l.from_city == city_name:
+            destinations.append(l.to_city)
+        else:
+            destinations.append(l.from_city)
+    return destinations
+
+
+def fiber_link(from_city, to_city):
+    source_dest = (from_city, to_city)
+    link = links_by_city[from_city]
+    l = next(l for l in link if l.from_city in source_dest and l.to_city in source_dest)
+    if l.from_city == from_city:
+        fiber = f'fiber ({l.from_city} \u2192 {l.to_city})-{l.east_cable}'
+    else:
+        fiber = f'fiber ({l.to_city} \u2192 {l.from_city})-{l.west_cable}'
+    return fiber
+
+
+def midpoint(city_a, city_b):
+    lats  = city_a.latitude, city_b.latitude
+    longs = city_a.longitude, city_b.longitude
+    try:
+        result = {
+        'latitude':  sum(lats)  / 2,
+        'longitude': sum(longs) / 2
+                }
+    except :
+        result = {
+        'latitude':  0,
+        'longitude': 0
+                }
+    return result
+
+#output_json_file_name = 'coronet_conus_example.json'
+#TODO get column size automatically from tupple size
+
+NODES_COLUMN = 10
+NODES_LINE = 4
+LINKS_COLUMN = 16
+LINKS_LINE = 3
+EQPTS_LINE = 3
+EQPTS_COLUMN = 14
+parser = ArgumentParser()
+parser.add_argument('workbook', nargs='?', type=Path , default='meshTopologyExampleV2.xls')
+parser.add_argument('-f', '--filter-region', action='append', default=[])
+
+if __name__ == '__main__':
+    args = parser.parse_args()
+    convert_file(args.workbook, args.filter_region)

gnpy/core/equipment.py

@@ -0,0 +1,401 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+'''
+gnpy.core.equipment
+===================
+
+This module contains functionality for specifying equipment.
+'''
+
+from numpy import clip, polyval
+from operator import itemgetter
+from math import isclose
+from pathlib import Path
+from json import load
+from gnpy.core.utils import lin2db, db2lin, load_json
+from collections import namedtuple
+from gnpy.core.elements import Edfa
+from gnpy.core.exceptions import EquipmentConfigError
+import time
+
+Model_vg = namedtuple('Model_vg', 'nf1 nf2 delta_p')
+Model_fg = namedtuple('Model_fg', 'nf0')
+Model_openroadm = namedtuple('Model_openroadm', 'nf_coef')
+Model_hybrid = namedtuple('Model_hybrid', 'nf_ram gain_ram edfa_variety')
+Model_dual_stage = namedtuple('Model_dual_stage', 'preamp_variety booster_variety')
+
+class common:
+    def update_attr(self, default_values, kwargs, name):
+        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(f'\x1b[1;31;40m'+
+                      f'\n WARNING missing {k} attribute in eqpt_config.json[{name}]'+
+                      f'\n default value is {k} = {v}'+
+                      f'\x1b[0m')
+                time.sleep(1)
+
+class SI(common):
+    default_values =\
+    {
+        "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":          45,
+        "sys_margins":      0
+    }
+
+    def __init__(self, **kwargs):
+        self.update_attr(self.default_values, kwargs, 'SI')
+
+class Span(common):
+    default_values = \
+    {
+        'power_mode':                       True,
+        'delta_power_range_db':             None,
+        'max_fiber_lineic_loss_for_raman':  0.25,
+        'target_extended_gain':             2.5,
+        'max_length':                       150,
+        'length_units':                     'km',
+        'max_loss':                         None,
+        'padding':                          10,
+        'EOL':                              0,
+        'con_in':                           0,
+        'con_out':                          0
+    }
+
+    def __init__(self, **kwargs):
+        self.update_attr(self.default_values, kwargs, 'Span')
+
+class Roadm(common):
+    default_values = \
+    {
+        'target_pch_out_db':   -17,
+        'add_drop_osnr':       100,
+        'restrictions': {
+            'preamp_variety_list':[],
+            'booster_variety_list':[]
+            }
+    }
+
+    def __init__(self, **kwargs):
+        self.update_attr(self.default_values, kwargs, 'Roadm')
+
+class Transceiver(common):
+    default_values = \
+    {
+        'type_variety': None,
+        'frequency':    None,
+        'mode':         {}
+    }
+
+    def __init__(self, **kwargs):
+        self.update_attr(self.default_values, kwargs, 'Transceiver')
+
+class Fiber(common):
+    default_values = \
+    {
+        'type_variety':  '',
+        'dispersion':    None,
+        'gamma':         0
+    }
+
+    def __init__(self, **kwargs):
+        self.update_attr(self.default_values, kwargs, 'Fiber')
+
+class RamanFiber(common):
+    default_values = \
+    {
+        'type_variety':  '',
+        'dispersion':    None,
+        'gamma':         0,
+        'raman_efficiency': None
+    }
+
+    def __init__(self, **kwargs):
+        self.update_attr(self.default_values, kwargs, 'RamanFiber')
+        for param in ('cr', 'frequency_offset'):
+            if param not in self.raman_efficiency:
+                raise EquipmentConfigError(f'RamanFiber.raman_efficiency: missing "{param}" parameter')
+        if self.raman_efficiency['frequency_offset'] != sorted(self.raman_efficiency['frequency_offset']):
+            raise EquipmentConfigError(f'RamanFiber.raman_efficiency.frequency_offset is not sorted')
+
+class Amp(common):
+    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
+    }
+
+    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'
+
+        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
+
+        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')
+            for k in ('nf_min', 'nf_max'):
+                try:
+                    del kwargs[k]
+                except KeyError:
+                    pass
+            nf_def = Model_fg(nf0)
+        elif type_def == 'advanced_model':
+            config = Path(filename).parent / kwargs.pop('advanced_config_from_json')
+        elif type_def == 'variable_gain':
+            gain_min, gain_max = kwargs['gain_min'], kwargs['gain_flatmax']
+            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')
+            try: #remove all remaining nf inputs
+                del kwargs['nf0']
+            except KeyError: pass #nf0 is not needed for variable gain amp
+            nf1, nf2, delta_p = nf_model(type_variety, gain_min, gain_max, nf_min, nf_max)
+            nf_def = Model_vg(nf1, nf2, delta_p)
+        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')
+            nf_def = Model_openroadm(nf_coef)
+        elif type_def == 'dual_stage':
+            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')
+            dual_stage_def = Model_dual_stage(preamp_variety, booster_variety)
+
+        with open(config, encoding='utf-8') as f:
+            json_data = load(f)
+
+        return cls(**{**kwargs, **json_data,
+            'nf_model': nf_def, 'dual_stage_model': dual_stage_def})
+
+
+def nf_model(type_variety, gain_min, gain_max, nf_min, nf_max):
+    if nf_min < -10:
+        raise EquipmentConfigError(f'Invalid nf_min value {nf_min!r} for amplifier {type_variety}')
+    if nf_max < -10:
+        raise EquipmentConfigError(f'Invalid nf_max value {nf_max!r} for amplifier {type_variety}')
+
+    # NF estimation model based on nf_min and nf_max
+    # delta_p:  max power dB difference between first and second stage coils
+    # dB g1a:   first stage gain - internal VOA attenuation
+    # nf1, nf2: first and second stage coils
+    #           calculated by solving nf_{min,max} = nf1 + nf2 / g1a{min,max}
+    delta_p = 5
+    g1a_min = gain_min - (gain_max - gain_min) - delta_p
+    g1a_max = gain_max - delta_p
+    nf2 = lin2db((db2lin(nf_min) - db2lin(nf_max)) /
+                 (1/db2lin(g1a_max) - 1/db2lin(g1a_min)))
+    nf1 = lin2db(db2lin(nf_min) - db2lin(nf2)/db2lin(g1a_max))
+
+    if nf1 < 4:
+        raise EquipmentConfigError(f'First coil value too low {nf1} for amplifier {type_variety}')
+
+    # Check 1 dB < delta_p < 6 dB to ensure nf_min and nf_max values make sense.
+    # There shouldn't be high nf differences between the two coils:
+    #    nf2 should be nf1 + 0.3 < nf2 < nf1 + 2
+    # If not, recompute and check delta_p
+    if not nf1 + 0.3 < nf2 < nf1 + 2:
+        nf2 = clip(nf2, nf1 + 0.3, nf1 + 2)
+        g1a_max = lin2db(db2lin(nf2) / (db2lin(nf_min) - db2lin(nf1)))
+        delta_p = gain_max - g1a_max
+        g1a_min = gain_min - (gain_max-gain_min) - delta_p
+        if not 1 < delta_p < 11:
+            raise EquipmentConfigError(f'Computed \N{greek capital letter delta}P invalid \
+                \n 1st coil vs 2nd coil calculated DeltaP {delta_p:.2f} for \
+                \n amplifier {type_variety} is not valid: revise inputs \
+                \n calculated 1st coil NF = {nf1:.2f}, 2nd coil NF = {nf2:.2f}')
+    # Check calculated values for nf1 and nf2
+    calc_nf_min = lin2db(db2lin(nf1) + db2lin(nf2)/db2lin(g1a_max))
+    if not isclose(nf_min, calc_nf_min, abs_tol=0.01):
+        raise EquipmentConfigError(f'nf_min does not match calc_nf_min, {nf_min} vs {calc_nf_min} for amp {type_variety}')
+    calc_nf_max = lin2db(db2lin(nf1) + db2lin(nf2)/db2lin(g1a_min))
+    if not isclose(nf_max, calc_nf_max, abs_tol=0.01):
+        raise EquipmentConfigError(f'nf_max does not match calc_nf_max, {nf_max} vs {calc_nf_max} for amp {type_variety}')
+
+    return nf1, nf2, delta_p
+
+def edfa_nf(gain_target, variety_type, equipment):
+    amp_params = equipment['Edfa'][variety_type]
+    amp = Edfa(
+            uid = f'calc_NF',
+            params = amp_params.__dict__,
+            operational = {
+                'gain_target': gain_target,
+                'tilt_target': 0
+                        }
+            )
+    amp.pin_db = 0
+    amp.nch = 88
+    return amp._calc_nf(True)
+
+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)"""
+    trx_params = {}
+    default_si_data = equipment['SI']['default']
+
+    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
+            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,
+                           "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'Computation stoped: could not find tsp : {trx_type_variety} with mode: {trx_mode} in eqpt 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['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
+
+    return trx_params
+
+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 automatic_nch(f_min, f_max, spacing):
+    return int((f_max - f_min)//spacing)
+
+def automatic_fmax(f_min, spacing, nch):
+    return f_min + spacing * nch
+
+def load_equipment(filename):
+    json_data = load_json(filename)
+    return equipment_from_json(json_data, filename)
+
+def update_trx_osnr(equipment):
+    """add sys_margins to all Transceivers OSNR values"""
+    for trx in equipment['Transceiver'].values():
+        for m in trx.mode:
+            m['OSNR'] = m['OSNR'] + equipment['SI']['default'].sys_margins
+    return equipment
+
+def update_dual_stage(equipment):
+    edfa_dict = equipment['Edfa']
+    for edfa in edfa_dict.values():
+        if edfa.type_def == 'dual_stage':
+            edfa_preamp = edfa_dict[edfa.dual_stage_model.preamp_variety]
+            edfa_booster = edfa_dict[edfa.dual_stage_model.booster_variety]
+            for key, value in edfa_preamp.__dict__.items():
+                attr_k = 'preamp_' + key
+                setattr(edfa, attr_k, value)
+            for key, value in edfa_booster.__dict__.items():
+                attr_k = 'booster_' + key
+                setattr(edfa, attr_k, value)
+            edfa.p_max = edfa_booster.p_max
+            edfa.gain_flatmax = edfa_booster.gain_flatmax + edfa_preamp.gain_flatmax
+            if edfa.gain_min < edfa_preamp.gain_min:
+                raise EquipmentConfigError(f'Dual stage {edfa.type_variety} min gain is lower than its preamp min gain')
+    return equipment
+
+def roadm_restrictions_sanity_check(equipment):
+    """ verifies that booster and preamp restrictions specified in roadm equipment are listed
+    in the edfa.
+    """
+    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')
+
+def equipment_from_json(json_data, filename):
+    """build global dictionnary eqpt_library that stores all eqpt characteristics:
+    edfa type type_variety, fiber type_variety
+    from the eqpt_config.json (filename parameter)
+    also read advanced_config_from_json file parameters for edfa if they are available:
+    typically nf_ripple, dfg gain ripple, dgt and nf polynomial nf_fit_coeff
+    if advanced_config_from_json file parameter is not present: use nf_model:
+    requires nf_min and nf_max values boundaries of the edfa gain range
+    """
+    equipment = {}
+    for key, entries in json_data.items():
+        equipment[key] = {}
+        typ = globals()[key]
+        for entry in entries:
+            subkey = entry.get('type_variety', 'default')
+            if key == 'Edfa':
+                equipment[key][subkey] = Amp.from_json(filename, **entry)
+            else:
+                equipment[key][subkey] = typ(**entry)
+    equipment = update_trx_osnr(equipment)
+    equipment = update_dual_stage(equipment)
+    roadm_restrictions_sanity_check(equipment)
+    return equipment

gnpy/core/exceptions.py

@@ -0,0 +1,19 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+'''
+gnpy.core.exceptions
+====================
+
+Exceptions thrown by other gnpy modules
+'''
+
+
+class ConfigurationError(Exception):
+    '''User-provided configuration contains an error'''
+
+class EquipmentConfigError(ConfigurationError):
+    '''Incomplete or wrong configuration within the equipment library'''
+
+class NetworkTopologyError(ConfigurationError):
+    '''Topology of user-provided network is wrong'''

gnpy/core/execute.py

@@ -1,2 +1,10 @@
 #!/usr/bin/env python3
+# -*- coding: utf-8 -*-
 
+'''
+gnpy.core.execute
+=================
+
+This module contains functions for executing the propogation of
+spectral information on a `gnpy` network.
+'''

gnpy/core/info.py

@@ -1,34 +1,58 @@
 #!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+'''
+gnpy.core.info
+==============
+
+This module contains classes for modelling :class:`SpectralInformation`.
+'''
+
 
 from collections import namedtuple
+from numpy import array
+from gnpy.core.utils import lin2db, db2lin
+from json import loads
+from gnpy.core.utils import load_json
+from gnpy.core.equipment import automatic_nch, automatic_spacing
 
-class ConvenienceAccess:
-    def __init_subclass__(cls):
-        for abbrev, field in getattr(cls, '_ABBREVS', {}).items():
-            setattr(cls, abbrev, property(lambda self, f=field: getattr(self, f)))
+class Power(namedtuple('Power', 'signal nli ase')):
+    """carriers power in W"""
 
-    def update(self, **kwargs):
-        for abbrev, field in getattr(self, '_ABBREVS', {}).items():
-            if abbrev in kwargs:
-                kwargs[field] = kwargs.pop(abbrev)
-        return self._replace(**kwargs)
 
-class Power(namedtuple('Power', 'signal nonlinear_interference amplified_spontaneous_emission'), ConvenienceAccess):
-    _ABBREVS = {'nli': 'nonlinear_interference',
-                'ase': 'amplified_spontaneous_emission',}
+class Channel(namedtuple('Channel', 'channel_number frequency baud_rate roll_off power')):
+    pass
 
-class Channel(namedtuple('Channel', 'channel_number frequency baud_rate roll_off power'), ConvenienceAccess):
-    _ABBREVS = {'channel': 'channel_number',
-                'num_chan': 'channel_number',
-                'ffs':     'frequency',
-                'freq':    'frequency',}
 
-class SpectralInformation(namedtuple('SpectralInformation', 'carriers'), ConvenienceAccess):
-    def __new__(cls, *carriers):
-        return super().__new__(cls, carriers)
+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(namedtuple('SpectralInformation', 'pref carriers')):
+
+    def __new__(cls, pref, carriers):
+        return super().__new__(cls, pref, carriers)
+
+
+def create_input_spectral_information(f_min, f_max, roll_off, baud_rate, power, spacing):
+    # pref in dB : convert power lin into power in dB
+    pref = lin2db(power * 1e3)
+    nb_channel = automatic_nch(f_min, f_max, spacing)
+    si = SpectralInformation(
+        pref=Pref(pref, pref, lin2db(nb_channel)),
+        carriers=[
+            Channel(f, (f_min+spacing*f),
+            baud_rate, roll_off, Power(power, 0, 0)) for f in range(1,nb_channel+1)
+            ])
+    return si
 
 if __name__ == '__main__':
+    pref = lin2db(power * 1e3)
     si = SpectralInformation(
+        Pref(pref, pref),
         Channel(1, 193.95e12, 32e9, 0.15,  # 193.95 THz, 32 Gbaud
             Power(1e-3, 1e-6, 1e-6)),             # 1 mW, 1uW, 1uW
         Channel(1, 195.95e12, 32e9, 0.15,  # 195.95 THz, 32 Gbaud
@@ -36,15 +60,13 @@ if __name__ == '__main__':
     )
 
     si = SpectralInformation()
-    spacing = 0.05 #THz
+    spacing = 0.05 # THz
+
+    si = si._replace(carriers=tuple(Channel(f+1, 191.3+spacing*(f+1), 32e9, 0.15, Power(1e-3, f, 1)) for f in range(96)))
 
-    si = si.update(carriers=tuple(Channel(f+1, 191.3+spacing*(f+1), 32e9, 0.15, Power(1e-3, f, 1)) for f in range(96)))
-    
     print(f'si = {si}')
     print(f'si = {si.carriers[0].power.nli}')
     print(f'si = {si.carriers[20].power.nli}')
-    """
-    si2 = si.update(carriers=tuple(c.update(power = c.power.update(nli = c.power.nli * 1e5))
+    si2 = si._replace(carriers=tuple(c._replace(power = c.power._replace(nli = c.power.nli * 1e5))
                               for c in si.carriers))
     print(f'si2 = {si2}')
-    """

gnpy/core/network.py

@@ -1,114 +1,560 @@
 #!/usr/bin/env python3
+# -*- coding: utf-8 -*-
 
+'''
+gnpy.core.network
+=================
+
+This module contains functions for constructing networks of network elements.
+'''
+
+from gnpy.core.convert import convert_file
 from networkx import DiGraph
-
+from numpy import arange
+from scipy.interpolate import interp1d
+from logging import getLogger
+from os import path
+from operator import itemgetter, attrgetter
 from gnpy.core import elements
-from gnpy.core.elements import Fiber, Edfa, Transceiver, Roadm
+from gnpy.core.elements import Fiber, Edfa, Transceiver, Roadm, Fused, RamanFiber
+from gnpy.core.equipment import edfa_nf
+from gnpy.core.exceptions import ConfigurationError, NetworkTopologyError
 from gnpy.core.units import UNITS
+from gnpy.core.utils import (load_json, save_json, round2float, db2lin,
+                            merge_amplifier_restrictions)
+from gnpy.core.science_utils import SimParams
+from collections import namedtuple
 
-MAX_SPAN_LENGTH = 125000
-TARGET_SPAN_LENGTH = 100000
-MIN_SPAN_LENGTH = 75000
+logger = getLogger(__name__)
 
-def network_from_json(json_data):
+def load_network(filename, equipment, name_matching = False):
+    json_filename = ''
+    if filename.suffix.lower() == '.xls':
+        logger.info('Automatically generating topology JSON file')
+        json_filename = convert_file(filename, name_matching)
+    elif filename.suffix.lower() == '.json':
+        json_filename = filename
+    else:
+        raise ValueError(f'unsuported topology filename extension {filename.suffix.lower()}')
+    json_data = load_json(json_filename)
+    return network_from_json(json_data, equipment)
+
+def save_network(filename, network):
+    filename_output = path.splitext(filename)[0] + '_auto_design.json'
+    json_data = network_to_json(network)
+    save_json(json_data, filename_output)
+
+def network_from_json(json_data, equipment):
     # 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
     g = DiGraph()
     for el_config in json_data['elements']:
-        g.add_node(getattr(elements, el_config['type'])(el_config))
+        typ = el_config.pop('type')
+        variety = el_config.pop('type_variety', 'default')
+        if typ in equipment and variety in equipment[typ]:
+            extra_params = equipment[typ][variety]
+            temp = el_config.setdefault('params', {})
+            temp = merge_amplifier_restrictions(temp, extra_params.__dict__)
+            el_config['params'] = temp
+        elif typ in ['Edfa', 'Fiber']: # catch it now because the code will crash later!
+            raise ConfigurationError(f'The {typ} of variety type {variety} was not recognized:'
+                    '\nplease check it is properly defined in the eqpt_config json file')
+        cls = getattr(elements, typ)
+        el = cls(**el_config)
+        g.add_node(el)
 
     nodes = {k.uid: k for k in g.nodes()}
 
     for cx in json_data['connections']:
         from_node, to_node = cx['from_node'], cx['to_node']
-        g.add_edge(nodes[from_node], nodes[to_node])
+        try:
+            if isinstance(nodes[from_node], Fiber):
+                edge_length = nodes[from_node].params.length
+            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}')
 
     return g
 
-def calculate_new_length(fiber_length):
-    result = (fiber_length, 1)
-    if fiber_length > MAX_SPAN_LENGTH:
-        n_spans = int(fiber_length // TARGET_SPAN_LENGTH)
-        
-        length1 = fiber_length / (n_spans+1)
-        result1 = (length1, n_spans+1)
-        delta1 = TARGET_SPAN_LENGTH-length1
-        
-        length2 = fiber_length / n_spans
-        delta2 = length2-TARGET_SPAN_LENGTH
-        result2 = (length2, n_spans)
-        
-        if length1<MIN_SPAN_LENGTH and length2<MAX_SPAN_LENGTH:
-            result = result2
-        elif length2>MAX_SPAN_LENGTH and length1>MIN_SPAN_LENGTH:
-            result = result1
+def network_to_json(network):
+    data = {
+        'elements': [n.to_json for n in network]
+        }
+    connections = {
+        'connections': [{"from_node": n.uid,
+                         "to_node": next_n.uid}
+                        for n in network
+                        for next_n in network.successors(n) if next_n is not None]
+        }
+    data.update(connections)
+    return data
+
+def select_edfa(raman_allowed, gain_target, power_target, equipment, uid, restrictions=None):
+    """amplifer selection algorithm
+    @Orange Jean-Luc Augé
+    """
+    Edfa_list = namedtuple('Edfa_list', 'variety power gain_min nf')
+    TARGET_EXTENDED_GAIN = equipment['Span']['default'].target_extended_gain
+
+    # for roadm restriction only: create a dict including not allowed for design amps
+    # because main use case is to have specific radm amp which are not allowed for ILA
+    # with the auto design
+    edfa_dict = {name: amp for (name, amp) in equipment['Edfa'].items()
+        if restrictions is None or name in restrictions}
+
+    pin = power_target - gain_target
+
+    # create 2 list of available amplifiers with relevant attributes for their selection
+
+    # edfa list with:
+    # extended gain min allowance of 3dB: could be parametrized, but a bit complex
+    # extended gain max allowance TARGET_EXTENDED_GAIN is coming from eqpt_config.json
+    # power attribut include power AND gain limitations
+    edfa_list = [Edfa_list(
+                variety=edfa_variety,
+                power=min(
+                    pin
+                    +edfa.gain_flatmax
+                    +TARGET_EXTENDED_GAIN,
+                    edfa.p_max
+                    )
+                    -power_target,
+                gain_min=
+                    gain_target+3
+                    -edfa.gain_min,
+                nf=edfa_nf(gain_target, edfa_variety, equipment)) \
+                for edfa_variety, edfa in edfa_dict.items()
+                if ((edfa.allowed_for_design or restrictions is not None) and not edfa.raman)]
+
+    #consider a Raman list because of different gain_min requirement: 
+    #do not allow extended gain min for Raman
+    raman_list = [Edfa_list(
+                variety=edfa_variety,
+                power=min(
+                    pin
+                    +edfa.gain_flatmax
+                    +TARGET_EXTENDED_GAIN,
+                    edfa.p_max
+                    )
+                    -power_target,
+                gain_min=
+                    gain_target
+                    -edfa.gain_min,
+                nf=edfa_nf(gain_target, edfa_variety, equipment))
+                for edfa_variety, edfa in edfa_dict.items()
+                if (edfa.allowed_for_design and edfa.raman)] \
+                if raman_allowed else []
+
+    #merge raman and edfa lists
+    amp_list = edfa_list + raman_list
+
+    #filter on min gain limitation: 
+    acceptable_gain_min_list = [x for x in amp_list if x.gain_min>0]
+
+    if len(acceptable_gain_min_list) < 1:
+        #do not take this empty list into account for the rest of the code
+        #but issue a warning to the user and do not consider Raman
+        #Raman below min gain should not be allowed because i is meant to be a design requirement
+        #and raman padding at the amplifier input is impossible!
+
+        if len(edfa_list) < 1:
+            raise ConfigurationError(f'auto_design could not find any amplifier \
+                    to satisfy min gain requirement in node {uid} \
+                    please increase span fiber padding')
         else:
-            if delta1 < delta2: 
-                result = result1
-            else:
-                result = result2
+            # TODO: convert to logging
+            print(
+                f'\x1b[1;31;40m'\
+                + f'WARNING: target gain in node {uid} is below all available amplifiers min gain: \
+                    amplifier input padding will be assumed, consider increase span fiber padding instead'\
+                + '\x1b[0m'
+                )
+            acceptable_gain_min_list = edfa_list
+
+    #filter on gain+power limitation:
+    #this list checks both the gain and the power requirement
+    #because of the way .power is calculated in the list
+    acceptable_power_list = [x for x in acceptable_gain_min_list if x.power>0]
+    if len(acceptable_power_list) < 1:
+        #no amplifier satisfies the required power, so pick the highest power(s):
+        power_max = max(acceptable_gain_min_list, key=attrgetter('power')).power
+        #check and pick if other amplifiers may have a similar gain/power
+        #allow a 0.3dB power range 
+        #this allows to chose an amplifier with a better NF subsequentely
+        acceptable_power_list = [x for x in acceptable_gain_min_list
+                                 if x.power-power_max>-0.3]
+
+    
+    # gain and power requirements are resolved,
+    #       =>chose the amp with the best NF among the acceptable ones:
+    selected_edfa = min(acceptable_power_list, key=attrgetter('nf')) #filter on NF
+    #check what are the gain and power limitations of this amp
+    power_reduction = round(min(selected_edfa.power, 0),2)
+    if power_reduction < -0.5:
+        print(
+            f'\x1b[1;31;40m'\
+            + f'WARNING: target gain and power in node {uid}\n \
+    is beyond all available amplifiers capabilities and/or extended_gain_range:\n\
+    a power reduction of {power_reduction} is applied\n'\
+            + '\x1b[0m'
+            )
+
+
+    return selected_edfa.variety, power_reduction
+
+def target_power(network, node, equipment): #get_fiber_dp
+    SPAN_LOSS_REF = 20
+    POWER_SLOPE = 0.3
+    power_mode = equipment['Span']['default'].power_mode
+    dp_range = list(equipment['Span']['default'].delta_power_range_db)
+    node_loss = span_loss(network, node)
+
+    try:
+        dp = round2float((node_loss - SPAN_LOSS_REF) * POWER_SLOPE, dp_range[2])
+        dp = max(dp_range[0], dp)
+        dp = min(dp_range[1], dp)
+    except KeyError:
+        raise ConfigurationError(f'invalid delta_power_range_db definition in eqpt_config[Span]'
+              f'delta_power_range_db: [lower_bound, upper_bound, step]')
+
+    if isinstance(node, Roadm):
+        dp = 0
+
+    return dp
+
+def prev_node_generator(network, node):
+    """fused spans interest:
+    iterate over all predecessors while they are Fused or Fiber type"""
+    try:
+        prev_node = next(n for n in network.predecessors(node))
+    except StopIteration:
+        raise NetworkTopologyError(f'Node {node.uid} is not properly connected, please check network topology')
+    # yield and re-iterate
+    if isinstance(prev_node, Fused) or isinstance(node, Fused):
+        yield prev_node
+        yield from prev_node_generator(network, prev_node)
+    else:
+        StopIteration
+
+def next_node_generator(network, node):
+    """fused spans interest:
+    iterate over all successors while they are Fused or Fiber type"""
+    try:
+        next_node = next(n for n in network.successors(node))
+    except StopIteration:
+        raise NetworkTopologyError('Node {node.uid} is not properly connected, please check network topology')
+    # yield and re-iterate
+    if isinstance(next_node, Fused) or isinstance(node, Fused):
+        yield next_node
+        yield from next_node_generator(network, next_node)
+    else:
+        StopIteration
+
+def span_loss(network, node):
+    """Fused span interest:
+    return the total span loss of all the fibers spliced by a Fused node"""
+    loss = node.loss if node.passive else 0
+    try:
+        prev_node = next(n for n in network.predecessors(node))
+        if isinstance(prev_node, Fused):
+            loss += sum(n.loss for n in prev_node_generator(network, node))
+    except StopIteration:
+        pass
+    try:
+        next_node = next(n for n in network.successors(node))
+        if isinstance(next_node, Fused):
+            loss += sum(n.loss for n in next_node_generator(network, node))
+    except StopIteration:
+        pass
+    return loss
+
+def find_first_node(network, node):
+    """Fused node interest:
+    returns the 1st node at the origin of a succession of fused nodes
+    (aka no amp in between)"""
+    this_node = node
+    for this_node in prev_node_generator(network, node):
+        pass
+    return this_node
+
+def find_last_node(network, node):
+    """Fused node interest:
+    returns the last node in a succession of fused nodes
+    (aka no amp in between)"""
+    this_node = node
+    for this_node in next_node_generator(network, node):
+        pass
+    return this_node
+
+def set_amplifier_voa(amp, power_target, power_mode):
+    VOA_MARGIN = 1 #do not maximize the VOA optimization
+    if amp.out_voa is None:
+        if power_mode:
+            gain_target = amp.effective_gain
+            voa = min(amp.params.p_max-power_target,
+                      amp.params.gain_flatmax-amp.effective_gain)
+            voa = max(round2float(max(voa, 0), 0.5) - VOA_MARGIN, 0) if amp.params.out_voa_auto else 0
+            amp.delta_p = amp.delta_p + voa
+            amp.effective_gain = amp.effective_gain + voa
+        else:
+            voa = 0 # no output voa optimization in gain mode
+        amp.out_voa = voa
+
+def set_egress_amplifier(network, roadm, equipment, pref_total_db):
+    power_mode = equipment['Span']['default'].power_mode
+    next_oms = (n for n in network.successors(roadm) if not isinstance(n, Transceiver))
+    for oms in next_oms:
+        #go through all the OMS departing from the Roadm
+        node = roadm
+        prev_node = roadm
+        next_node = oms
+        # if isinstance(next_node, Fused): #support ROADM wo egress amp for metro applications
+        #     node = find_last_node(next_node)
+        #     next_node = next(n for n in network.successors(node))
+        #     next_node = find_last_node(next_node)
+        prev_dp = getattr(node.params, 'target_pch_out_db', 0)
+        dp = prev_dp
+        prev_voa = 0
+        voa = 0
+        while True:
+        #go through all nodes in the OMS (loop until next Roadm instance)
+            if isinstance(node, Edfa):
+                node_loss = span_loss(network, prev_node)
+                voa = node.out_voa if node.out_voa else 0
+                if node.delta_p is None:
+                    dp = target_power(network, next_node, equipment)
+                else:
+                    dp = node.delta_p
+                gain_from_dp = node_loss + dp - prev_dp + prev_voa
+                if node.effective_gain is None or power_mode:
+                    gain_target = gain_from_dp
+                else: #gain mode with effective_gain 
+                    gain_target = node.effective_gain
+                    dp = prev_dp - node_loss + gain_target
+
+                power_target = pref_total_db + dp         
+
+                raman_allowed = False
+                if isinstance(prev_node, Fiber):
+                    max_fiber_lineic_loss_for_raman = \
+                            equipment['Span']['default'].max_fiber_lineic_loss_for_raman
+                    raman_allowed = prev_node.params.loss_coef < max_fiber_lineic_loss_for_raman
+
+                # implementation of restrictions on roadm boosters
+                if isinstance(prev_node,Roadm):
+                    if prev_node.restrictions['booster_variety_list']:
+                        restrictions = prev_node.restrictions['booster_variety_list']
+                    else:
+                        restrictions = None
+                elif isinstance(next_node,Roadm):
+                    # implementation of restrictions on roadm preamp
+                    if next_node.restrictions['preamp_variety_list']:
+                        restrictions = next_node.restrictions['preamp_variety_list']
+                    else:
+                        restrictions = None
+                else:
+                    restrictions = None
+
+                if node.params.type_variety == '':                   
+                    edfa_variety, power_reduction = select_edfa(raman_allowed, 
+                                   gain_target, power_target, equipment, node.uid, restrictions)
+                    extra_params = equipment['Edfa'][edfa_variety]
+                    node.params.update_params(extra_params.__dict__)
+                    dp += power_reduction
+                    gain_target += power_reduction
+                elif node.params.raman and not raman_allowed:
+                    print(
+                        f'\x1b[1;31;40m'\
+                        + f'WARNING: raman is used in node {node.uid}\n \
+                but fiber lineic loss is above threshold\n'\
+                        + '\x1b[0m'
+                        )                    
+                                
+                node.delta_p = dp if power_mode else None
+                node.effective_gain = gain_target
+                set_amplifier_voa(node, power_target, power_mode)
+            if isinstance(next_node, Roadm) or isinstance(next_node, Transceiver):
+                break
+            prev_dp = dp
+            prev_voa = voa
+            prev_node = node
+            node = next_node
+            # print(f'{node.uid}')
+            next_node = next(n for n in network.successors(node))
+
+
+def add_egress_amplifier(network, node):
+    next_nodes = [n for n in network.successors(node)
+        if not (isinstance(n, Transceiver) or isinstance(n, Fused) or isinstance(n, Edfa))]
+        #no amplification for fused spans or TRX
+    for i, next_node in enumerate(next_nodes):
+        network.remove_edge(node, next_node)
+        amp = Edfa(
+                    uid = f'Edfa{i}_{node.uid}',
+                    params = {},
+                    metadata = {
+                        'location': {
+                            'latitude':  (node.lat * 2 + next_node.lat * 2) / 4,
+                            'longitude': (node.lng * 2 + next_node.lng * 2) / 4,
+                            'city':      node.loc.city,
+                            'region':    node.loc.region,
+                        }
+                    },
+                    operational = {
+                        'gain_target': None,
+                        'tilt_target': 0,
+                    })
+        network.add_node(amp)
+        if isinstance(node,Fiber):
+            edgeweight = node.params.length
+        else:
+            edgeweight = 0.01
+        network.add_edge(node, amp, weight = edgeweight)
+        network.add_edge(amp, next_node, weight = 0.01)
+
+
+def calculate_new_length(fiber_length, bounds, target_length):
+    if fiber_length < bounds.stop:
+        return fiber_length, 1
+
+    n_spans = int(fiber_length // target_length)
+
+    length1 = fiber_length / (n_spans+1)
+    delta1 = target_length-length1
+    result1 = (length1, n_spans+1)
+
+    length2 = fiber_length / n_spans
+    delta2 = length2-target_length
+    result2 = (length2, n_spans)
+
+    if (bounds.start<=length1<=bounds.stop) and not(bounds.start<=length2<=bounds.stop):
+        result = result1
+    elif (bounds.start<=length2<=bounds.stop) and not(bounds.start<=length1<=bounds.stop):
+        result = result2
+    else:
+        result = result1 if delta1 < delta2 else result2
 
     return result
 
-def split_fiber(network, fiber):
-    new_length, n_spans = calculate_new_length(fiber.length)
-    prev_node = fiber
-    if n_spans > 1:
-        next_nodes = [_ for _ in network.successors(fiber)]
-        for next_node in next_nodes:
-            network.remove_edge(fiber, next_node)
 
-        new_params_length = new_length / UNITS[fiber.params.length_units]
-        config = {'uid':fiber.uid, 'type': 'Fiber', 'metadata': fiber.__dict__['metadata'], \
-            'params': fiber.__dict__['params']}
-        fiber.uid = config['uid'] + '_1'
-        fiber.length = new_length
-        fiber.loss = fiber.loss_coef * fiber.length
+def split_fiber(network, fiber, bounds, target_length, equipment):
+    new_length, n_spans = calculate_new_length(fiber.length, bounds, target_length)
+    if n_spans == 1:
+        return
 
-        for i in range(2, n_spans+1):
-            new_config = dict(config)
-            new_config['uid'] = new_config['uid'] + '_' + str(i)
-            new_config['params'].length = new_params_length
-            new_node = Fiber(new_config)
-            network.add_node(new_node)
-            network.add_edge(prev_node, new_node)
-            network = add_egress_amplifier(network, prev_node)
-            prev_node = new_node
+    try:
+        next_node = next(network.successors(fiber))
+        prev_node = next(network.predecessors(fiber))
+    except StopIteration:
+        raise NetworkTopologyError(f'Fiber {fiber.uid} is not properly connected, please check network topology')
 
-        for next_node in next_nodes:
-            network.add_edge(prev_node, next_node)
-        
-    network = add_egress_amplifier(network, prev_node)
-    return network
+    network.remove_node(fiber)
 
-def add_egress_amplifier(network, node):
-    next_nodes = [n for n in network.successors(node) 
-        if not (isinstance(n, Edfa) or isinstance(n, Transceiver))]
-    i = 1
-    for next_node in next_nodes:
-        network.remove_edge(node, next_node)
-        
-        uid = 'Edfa' + str(i)+ '_' + str(node.uid)
-        metadata = next_node.metadata
-        operational = {'gain_target': node.loss, 'tilt_target': 0}
-        edfa_config_json = 'edfa_config.json'
-        config = {'uid':uid, 'type': 'Edfa', 'metadata': metadata, \
-                    'config_from_json': edfa_config_json, 'operational': operational}
-        new_edfa = Edfa(config)
-        network.add_node(new_edfa)
-        network.add_edge(node,new_edfa)
-        network.add_edge(new_edfa, next_node)
-        i +=1
+    fiber_params = fiber.params._asdict()
+    fiber_params['length'] = new_length / UNITS[fiber.params.length_units]
+    fiber_params['con_in'] = fiber.con_in
+    fiber_params['con_out'] = fiber.con_out
 
-    return network
+    f = interp1d([prev_node.lng, next_node.lng], [prev_node.lat, next_node.lat])
+    xpos = [prev_node.lng + (next_node.lng - prev_node.lng) * (n+1)/(n_spans+1) for n in range(n_spans)]
+    ypos = f(xpos)
+    for span, lng, lat in zip(range(n_spans), xpos, ypos):
+        new_span = Fiber(uid = f'{fiber.uid}_({span+1}/{n_spans})',
+                          metadata = {
+                            'location': {
+                                'latitude':  lat,
+                                'longitude': lng,
+                                'city':      fiber.loc.city,
+                                'region':    fiber.loc.region,
+                            }
+                          },
+                          params = fiber_params)
+        if isinstance(prev_node,Fiber):
+            edgeweight = prev_node.params.length
+        else:
+            edgeweight = 0.01
+        network.add_edge(prev_node, new_span, weight = edgeweight)
+        prev_node = new_span
+    if isinstance(prev_node,Fiber):
+        edgeweight = prev_node.params.length
+    else:
+        edgeweight = 0.01    
+    network.add_edge(prev_node, next_node, weight = edgeweight)
 
-def build_network(network):
-    fibers = [f for f in network.nodes() if isinstance(f, Fiber)]
+def add_connector_loss(network, fibers, default_con_in, default_con_out, EOL):
     for fiber in fibers:
-        network = split_fiber(network, fiber)
+        if fiber.con_in is None: fiber.con_in = default_con_in
+        if fiber.con_out is None: fiber.con_out = default_con_out
+        next_node = next(n for n in network.successors(fiber))
+        if not isinstance(next_node, Fused):
+            fiber.con_out += EOL
+
+def add_fiber_padding(network, fibers, padding):
+    """last_fibers = (fiber for n in network.nodes()
+                         if not (isinstance(n, Fiber) or isinstance(n, Fused))
+                         for fiber in network.predecessors(n)
+                         if isinstance(fiber, Fiber))"""
+    for fiber in fibers:
+        this_span_loss = span_loss(network, fiber)
+        try:
+            next_node = next(network.successors(fiber))
+        except StopIteration:
+            raise NetworkTopologyError(f'Fiber {fiber.uid} is not properly connected, please check network topology')
+        if this_span_loss < padding and not (isinstance(next_node, Fused)):
+            #add a padding att_in at the input of the 1st fiber:
+            #address the case when several fibers are spliced together
+            first_fiber = find_first_node(network, fiber)
+            # in order to support no booster , fused might be placed
+            # just after a roadm: need to check that first_fiber is really a fiber
+            if isinstance(first_fiber,Fiber):
+                if first_fiber.att_in is None:
+                    first_fiber.att_in = padding - this_span_loss
+                else:
+                    first_fiber.att_in = first_fiber.att_in + padding - this_span_loss
+
+def build_network(network, equipment, pref_ch_db, pref_total_db):
+    default_span_data = equipment['Span']['default']
+    max_length = int(default_span_data.max_length * UNITS[default_span_data.length_units])
+    min_length = max(int(default_span_data.padding/0.2*1e3),50_000)
+    bounds = range(min_length, max_length)
+    target_length = max(min_length, 90_000)
+    default_con_in = default_span_data.con_in
+    default_con_out = default_span_data.con_out
+    padding = default_span_data.padding
+
+    #set roadm loss for gain_mode before to build network
+    fibers = [f for f in network.nodes() if isinstance(f, Fiber)]
+    add_connector_loss(network, fibers, default_con_in, default_con_out, default_span_data.EOL)
+    add_fiber_padding(network, fibers, padding)
+    # don't group split fiber and add amp in the same loop
+    # =>for code clarity (at the expense of speed):
+    for fiber in fibers:
+        split_fiber(network, fiber, bounds, target_length, equipment)
+
+    amplified_nodes = [n for n in network.nodes()
+                        if isinstance(n, Fiber) or isinstance(n, Roadm)]
+
+    for node in amplified_nodes:
+        add_egress_amplifier(network, node)
 
     roadms = [r for r in network.nodes() if isinstance(r, Roadm)]
     for roadm in roadms:
-        add_egress_amplifier(network, roadm)
+        set_egress_amplifier(network, roadm, equipment, pref_total_db)
 
+    #support older json input topology wo Roadms:
+    if len(roadms) == 0:
+        trx = [t for t in network.nodes() if isinstance(t, Transceiver)]
+        for t in trx:
+            set_egress_amplifier(network, t, equipment, pref_total_db)
+
+def load_sim_params(filename):
+    sim_params = load_json(filename)
+    return SimParams(params=sim_params)
+
+def configure_network(network, sim_params):
+    for node in network.nodes:
+        if isinstance(node, RamanFiber):
+            node.sim_params = sim_params

gnpy/core/node.py

@@ -1,60 +1,56 @@
 #! /bin/usr/python3
+# -*- coding: utf-8 -*-
+
+'''
+gnpy.core.node
+==============
+
+This module contains the base class for a network element.
+
+Strictly, a network element is any callable which accepts an immutable
+:class:`.info.SpectralInformation` object and returns an :class:`.info.SpectralInformation` object
+(a copy).
+
+Network elements MUST implement two attributes .uid and .name representing a
+unique identifier and a printable name.
+
+This base class provides a more convenient way to define a network element
+via subclassing.
+'''
 
 from uuid import uuid4
-from gnpy.core.utils import load_json
-
-
-class ConfigStruct:
-
-    def __init__(self, **config):
-        if config is None:
-            return None
-        if 'config_from_json' in config:
-            json_config = load_json(config['config_from_json'])
-            self.set_config_attr(json_config)
-
-        self.set_config_attr(config)
-
-    def set_config_attr(self, config):
-        for k, v in config.items():
-            setattr(self, k, ConfigStruct(**v)
-                    if isinstance(v, dict) else v)
-
-    def __repr__(self):
-        return f'{self.__dict__}'
+from collections import namedtuple
 
+class Location(namedtuple('Location', 'latitude longitude city region')):
+    def __new__(cls, latitude=0, longitude=0, city=None, region=None):
+        return super().__new__(cls, latitude, longitude, city, region)
 
 class Node:
-
-    def __init__(self, config=None):
-        self.config = ConfigStruct(**config)
-        if self.config is None or not hasattr(self.config, 'uid'):
-            self.uid = uuid4()
-        else:
-            self.uid = self.config.uid
-        if hasattr(self.config, 'params'):
-            self.params = self.config.params     
-        if hasattr(self.config, 'metadata'):
-            self.metadata = self.config.metadata
-        if hasattr(self.config, 'operational'):
-            self.operational = self.config.operational            
+    def __init__(self, uid, name=None, params=None, metadata=None, operational=None):
+        if name is None:
+            name = uid
+        self.uid, self.name = uid, name
+        if metadata is None:
+            metadata = {'location': {}}
+        if metadata and not isinstance(metadata.get('location'), Location):
+            metadata['location'] = Location(**metadata.pop('location', {}))
+        self.params, self.metadata, self.operational = params, metadata, operational
 
     @property
     def coords(self):
-        return tuple(self.lng, self.lat)
+        return self.lng, self.lat
 
     @property
     def location(self):
-        return self.config.metadata.location
+        return self.metadata['location']
+    loc = location
 
     @property
-    def loc(self):  # Aliases .location
-        return self.location
+    def longitude(self):
+        return self.location.longitude
+    lng = longitude
 
     @property
-    def lng(self):
-        return self.config.metadata.location.longitude
-
-    @property
-    def lat(self):
-        return self.config.metadata.location.latitude
+    def latitude(self):
+        return self.location.latitude
+    lat = latitude

gnpy/core/request.py

@@ -0,0 +1,923 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+gnpy.core.request
+=================
+
+This module contains path request functionality.
+
+This functionality allows the user to provide a JSON request
+file in accordance with a Yang model for requesting path
+computations and returns path results in terms of path
+and feasibility
+
+See: draft-ietf-teas-yang-path-computation-01.txt
+"""
+
+from sys import exit
+from collections import namedtuple
+from logging import getLogger, basicConfig, CRITICAL, DEBUG, INFO
+from networkx import (dijkstra_path, NetworkXNoPath, all_simple_paths,shortest_path_length)
+from networkx.utils import pairwise 
+from numpy import mean
+from gnpy.core.service_sheet import convert_service_sheet, Request_element, Element
+from gnpy.core.elements import Transceiver, Roadm, Edfa, Fused
+from gnpy.core.utils import db2lin, lin2db
+from gnpy.core.info import create_input_spectral_information, SpectralInformation, Channel, Power
+from copy import copy, deepcopy
+from csv import writer
+from math import ceil
+
+logger = getLogger(__name__)
+
+
+RequestParams = namedtuple('RequestParams','request_id source destination trx_type'+
+' trx_mode nodes_list loose_list spacing power nb_channel f_min f_max format baud_rate OSNR bit_rate roll_off tx_osnr min_spacing cost path_bandwidth')
+DisjunctionParams = namedtuple('DisjunctionParams','disjunction_id relaxable link_diverse node_diverse disjunctions_req')
+
+class Path_request:
+    def __init__(self, *args, **params):
+        params = RequestParams(**params)
+        self.request_id = params.request_id
+        self.source     = params.source
+        self.destination = params.destination
+        self.tsp        = params.trx_type
+        self.tsp_mode   = params.trx_mode
+        self.baud_rate  = params.baud_rate
+        self.nodes_list = params.nodes_list
+        self.loose_list = params.loose_list
+        self.spacing    = params.spacing
+        self.power      = params.power
+        self.nb_channel = params.nb_channel
+        self.f_min       = params.f_min
+        self.f_max       = params.f_max
+        self.format     = params.format
+        self.OSNR       = params.OSNR
+        self.bit_rate   = params.bit_rate
+        self.roll_off   = params.roll_off
+        self.tx_osnr    = params.tx_osnr
+        self.min_spacing = params.min_spacing
+        self.cost       = params.cost
+        self.path_bandwidth  = params.path_bandwidth
+
+    def __str__(self):
+        return '\n\t'.join([  f'{type(self).__name__} {self.request_id}',
+                            f'source:       {self.source}',
+                            f'destination:  {self.destination}'])
+    def __repr__(self):
+        if self.baud_rate is not None:
+            temp = self.baud_rate * 1e-9
+            temp2 = self.bit_rate * 1e-9
+        else:
+            temp = self.baud_rate
+            temp2 = self.bit_rate
+
+        return '\n\t'.join([  f'{type(self).__name__} {self.request_id}',
+                            f'source: \t{self.source}',
+                            f'destination:\t{self.destination}',
+                            f'trx type:\t{self.tsp}',
+                            f'trx mode:\t{self.tsp_mode}',
+                            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'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}',
+                            f'loose-list:\t{self.loose_list}'
+                            '\n'])
+class Disjunction:
+    def __init__(self, *args, **params):
+        params = DisjunctionParams(**params)
+        self.disjunction_id = params.disjunction_id
+        self.relaxable = params.relaxable
+        self.link_diverse = params.link_diverse
+        self.node_diverse = params.node_diverse
+        self.disjunctions_req = params.disjunctions_req
+        
+    def __str__(self):
+        return '\n\t'.join([f'relaxable:    {self.relaxable}',
+                            f'link-diverse:       {self.link_diverse}',
+                            f'node-diverse:  {self.node_diverse}',
+                            f'request-id-numbers: {self.disjunctions_req}']
+                            )
+    def __repr__(self):
+        return '\n\t'.join([  f'{type(self).__name__} {self.disjunction_id}',
+                            f'relaxable:    {self.relaxable}',
+                            f'link-diverse:       {self.link_diverse}',
+                            f'node-diverse:  {self.node_diverse}',
+                            f'request-id-numbers: {self.disjunctions_req}'
+                            '\n'])
+
+class Result_element(Element):
+    def __init__(self,path_request,computed_path):
+        self.path_id = path_request.request_id
+        self.path_request = path_request
+        self.computed_path = computed_path
+        hop_type = []
+        if len(computed_path)>0 :
+            for e in computed_path :
+                if isinstance(e, Transceiver) :
+                    hop_type.append(' - '.join([path_request.tsp,path_request.tsp_mode]))
+                else:
+                    hop_type.append('not recorded')
+        else:
+            # TODO differentiate empty path in case not feasible because of tsp or not feasible because
+            # ther is no path connecting the nodes (whatever the tsp)
+            mode = 'not feasible with this transponder'
+            hop_type = ' - '.join([path_request.tsp,mode])
+        self.hop_type = hop_type
+    uid = property(lambda self: repr(self))
+    @property
+    def pathresult(self):
+        if not self.computed_path:
+            return {
+                   'path-id': self.path_id,
+                   'path-properties':{
+                       'path-metric': [
+                           {
+                           'metric-type': 'SNR@bandwidth',
+                           'accumulative-value': 'None'
+                           },
+                           {
+                           'metric-type': 'SNR@0.1nm',
+                           'accumulative-value': 'None'
+                           },
+                           {
+                           'metric-type': 'OSNR@bandwidth',
+                           'accumulative-value': 'None'
+                           },
+                           {
+                           'metric-type': 'OSNR@0.1nm',
+                           'accumulative-value': 'None'
+                           },
+                           {
+                           'metric-type': 'reference_power',
+                           'accumulative-value': self.path_request.power
+                           },
+                           {
+                           'metric-type': 'path_bandwidth',
+                           'accumulative-value': self.path_request.path_bandwidth
+                           }
+                        ],
+                        'path-srlgs': {
+                            'usage': 'not used yet',
+                            'values': 'not used yet'
+                        },
+                        'path-route-objects': [
+                            {
+                            'path-route-object': {
+                                'index': 0,
+                                'unnumbered-hop': {
+                                    'node-id': self.path_request.source,
+                                    'link-tp-id': self.path_request.source,
+                                    'hop-type':  self.hop_type,
+                                    'direction': 'not used'
+                                },
+                                'label-hop': {
+                                    'te-label': {
+                                        'generic': 'not used yet',
+                                        'direction': 'not used yet'
+                                        }
+                                    }
+                                }
+                            },
+                            {
+                            'path-route-object': {
+                                'index': 1,
+                                'unnumbered-hop': {
+                                    'node-id': self.path_request.destination,
+                                    'link-tp-id': self.path_request.destination,
+                                    'hop-type':  self.hop_type,
+                                    'direction': 'not used'
+                                },
+                                'label-hop': {
+                                    'te-label': {
+                                        'generic': 'not used yet',
+                                        'direction': 'not used yet'
+                                        }
+                                    }
+                                }
+                            }
+                            ]
+                    }
+                }
+        else:
+            return {
+                   'path-id': self.path_id,
+                   'path-properties':{
+                       'path-metric': [
+                           {
+                           'metric-type': 'SNR@bandwidth',
+                           'accumulative-value': round(mean(self.computed_path[-1].snr),2)
+                           },
+                           {
+                           'metric-type': 'SNR@0.1nm',
+                           'accumulative-value': round(mean(self.computed_path[-1].snr+lin2db(self.path_request.baud_rate/12.5e9)),2)
+                           },
+                           {
+                           'metric-type': 'OSNR@bandwidth',
+                           'accumulative-value': round(mean(self.computed_path[-1].osnr_ase),2)
+                           },
+                           {
+                           'metric-type': 'OSNR@0.1nm',
+                           'accumulative-value': round(mean(self.computed_path[-1].osnr_ase_01nm),2)
+                           },
+                           {
+                           'metric-type': 'reference_power',
+                           'accumulative-value': self.path_request.power
+                           },
+                           {
+                           'metric-type': 'path_bandwidth',
+                           'accumulative-value': self.path_request.path_bandwidth
+                           }
+                        ],
+                        'path-srlgs': {
+                            'usage': 'not used yet',
+                            'values': 'not used yet'
+                        },
+                        'path-route-objects': [
+                            {
+                            'path-route-object': {
+                                'index': self.computed_path.index(n),
+                                'unnumbered-hop': {
+                                    'node-id': n.uid,
+                                    'link-tp-id': n.uid,
+                                    'hop-type': self.hop_type[self.computed_path.index(n)],
+                                    'direction': 'not used'
+                                },
+                                'label-hop': {
+                                    'te-label': {
+                                        'generic': 'not used yet',
+                                        'direction': 'not used yet'
+                                        }
+                                    }
+                                }
+                            } for n in self.computed_path
+                            ]
+                    }
+                }
+
+    @property
+    def json(self):
+        return self.pathresult
+
+def compute_constrained_path(network, req):
+    trx = [n for n in network.nodes() if isinstance(n, Transceiver)]
+    roadm = [n for n in network.nodes() if isinstance(n, Roadm)]
+    edfa = [n for n in network.nodes() if isinstance(n, Edfa)]
+    anytypenode = [n for n in network.nodes()]
+
+    source = next(el for el in trx if el.uid == req.source)
+
+    # This method ensures that the constraint can be satisfied without loops
+    # except when it is not possible : eg if constraints makes a loop
+    # It requires that the source, dest and nodes are correct (no error in the names)
+    destination = next(el for el in trx if el.uid == req.destination)
+    nodes_list = []
+    for n in req.nodes_list :
+        # for debug excel print(n)
+        nodes_list.append(next(el for el in anytypenode if el.uid == n))
+    # nodes_list contains at least the destination
+    if nodes_list is None :
+        msg = f'Request {req.request_id} problem in the constitution of nodes_list: should at least include destination'
+        logger.critical(msg)
+        exit()
+    if req.nodes_list[-1] != req.destination:
+        msg = f'Request {req.request_id} malformed list of nodes: last node should be destination trx'
+        logger.critical(msg)
+        exit()
+
+    if len(nodes_list) == 1  :
+        try :
+            total_path = dijkstra_path(network, source, destination, weight = 'weight')
+            # print('checking edges length is correct')
+            # print(shortest_path_length(network,source,destination))
+            # print(shortest_path_length(network,source,destination,weight ='weight'))
+            # s = total_path[0]
+            # for e in total_path[1:]:
+            #     print(s.uid)
+            #     print(network.get_edge_data(s,e))
+            #     s = e
+        except NetworkXNoPath:
+            msg = f'\x1b[1;33;40m'+f'Request {req.request_id} could not find a path from {source.uid} to node : {destination.uid} in network topology'+ '\x1b[0m'
+            logger.critical(msg)
+            print(msg)
+            total_path = []        
+    else : 
+        all_simp_pths = list(all_simple_paths(network,source=source,\
+            target=destination, cutoff=120))
+        candidate = []
+        for p in all_simp_pths :
+            if ispart(nodes_list, p) :
+                # print(f'selection{[el.uid for el in p if el in roadm]}')
+                candidate.append(p)
+        # select the shortest path (in nb of hops) -> changed to shortest path in km length
+        if len(candidate)>0 :
+            # candidate.sort(key=lambda x: len(x))
+            candidate.sort(key=lambda x: sum(network.get_edge_data(x[i],x[i+1])['weight'] for i in range(len(x)-2)))
+            total_path = candidate[0]
+        else:
+            if req.loose_list[req.nodes_list.index(n)] == 'loose':
+                print(f'\x1b[1;33;40m'+f'Request {req.request_id} could not find a path crossing {nodes_list} in network topology'+ '\x1b[0m')
+                print(f'constraint ignored')
+                total_path = dijkstra_path(network, source, destination, weight = 'weight')
+            else:
+                msg = f'\x1b[1;33;40m'+f'Request {req.request_id} could not find a path crossing {nodes_list}.\nNo path computed'+ '\x1b[0m'
+                logger.critical(msg)
+                print(msg)
+                total_path = []
+
+    # obsolete method: this does not guaranty to avoid loops or correct results
+    # Here is the demonstration :
+    #         1     1
+    # eg    a----b-----c
+    #       |1   |0.5  |1
+    #       e----f--h--g
+    #         1  0.5 0.5
+    # if I have to compute a to g with constraint f-c
+    # result will be a concatenation of: a-b-f and f-b-c and c-g
+    # which means a loop. 
+    # if to avoid loops I iteratively suppress edges of the segmenst in the topo
+    # segment 1 = a-b-f
+    #               1
+    # eg    a    b-----c
+    #       |1         |1
+    #       e----f--h--g
+    #         1  0.5 0.5
+    # then 
+    # segment 2 = f-h-g-c
+    #               1
+    # eg    a    b-----c
+    #       |1          
+    #       e----f  h  g
+    #         1  
+    # then there is no more path to g destination
+    #
+    # 
+    # total_path = [source]
+
+    # for n in req.nodes_list:
+    #     try :
+    #         node = next(el for el in trx if el.uid == n)
+    #     except StopIteration:
+    #         try:
+    #             node = next(el for el in anytypenode if el.uid == n)
+    #         except StopIteration:
+    #             try:
+    #                 # TODO this test is not giving good results: full name of the 
+    #                 # amp is required to avoid ambiguity on the direction
+    #                 node = next(el for el in anytypenode 
+    #                     if n in el.uid)
+    #             except StopIteration:
+    #                 msg = f'could not find node : {n} in network topology: \
+    #                     not a trx, roadm, edfa, fiber or fused element'
+    #                 logger.critical(msg)
+    #                 raise ValueError(msg)
+    #     # extend path list without repeating source -> skip first element in the list
+    #     try:
+    #         # to avoid looping back: use an alternate graph were current path edges and vertex are suppressed
+
+    #         total_path.extend(dijkstra_path(network, source, node)[1:])
+    #         source = node
+    #     except NetworkXNoPath:
+    #         if req.loose_list[req.nodes_list.index(n)] == 'loose':
+    #             print(f'could not find a path from {source.uid} to loose node : {n} in network topology')
+    #             print(f'node  {n} is skipped')
+    #         else:
+    #             msg = f'could not find a path from {source.uid} to node : {n} in network topology'
+    #             logger.critical(msg)
+    #             print(msg)
+    #             total_path = []
+
+    return total_path
+
+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)
+    for el in path:
+        si = el(si)
+    path[-1].update_snr(req.tx_osnr, equipment['Roadm']['default'].add_drop_osnr)
+    return path
+
+def propagate2(path, req, equipment):
+    si = create_input_spectral_information(
+        req.f_min, req.f_max, req.roll_off, req.baud_rate,
+        req.power, req.spacing)
+    infos = {}
+    for el in path:
+        before_si = si
+        after_si  = si = el(si)
+        infos[el] = before_si, after_si
+    path[-1].update_snr(req.tx_osnr, equipment['Roadm']['default'].add_drop_osnr)
+    return infos
+
+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([m['baud_rate'] for m in equipment['Transceiver'][req.tsp].mode 
+        if float(m['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 : 
+        # at least 1 baudrate can be tested wrt spacing
+        for b in baudrate_to_explore :
+            modes_to_explore = [m for m in equipment['Transceiver'][req.tsp].mode 
+                if m['baud_rate'] == b and float(m['min_spacing'])<= req.spacing]
+            modes_to_explore = sorted(modes_to_explore, 
+                key = lambda x: x['bit_rate'], reverse=True)
+            # print(modes_to_explore)
+            # step2 : computes propagation for each baudrate: stop and select the first that passes
+            found_a_feasible_mode = False
+            # TODO : the case of roll of is not included: for now use SI one
+            # TODO : if the loop in mode optimization does not have a feasible path, then bugs
+            si = create_input_spectral_information(
+            req.f_min, req.f_max, equipment['SI']['default'].roll_off,
+            b, req.power, req.spacing)
+            for el in path:
+                si = el(si)
+            for m in modes_to_explore :
+                if path[-1].snr is not None:
+                    path[-1].update_snr(m['tx_osnr'], equipment['Roadm']['default'].add_drop_osnr)
+                    if round(min(path[-1].snr+lin2db(b/(12.5e9))),2) > m['OSNR'] :
+                        found_a_feasible_mode = True
+                        return path, m
+                else:  
+                    return [], None
+        # only get to this point if no baudrate/mode satisfies OSNR requirement
+
+        # 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)
+        return [],None
+    else :
+    #  no baudrate satisfying spacing
+        msg = f'\tWarning! Request {req.request_id}: no baudrate satisfies spacing requirement.\n'
+        print(msg)
+        logger.info(msg)
+        return [], None
+
+
+def jsontocsv(json_data,equipment,fileout):
+    # read 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(('path-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'))
+    tspjsondata = equipment['Transceiver']
+    #print(tspjsondata)
+    for p in json_data['path']:
+        path_id     = p['path-id']
+        source      = p['path-properties']['path-route-objects'][0]\
+        ['path-route-object']['unnumbered-hop']['node-id']
+        destination = p['path-properties']['path-route-objects'][-1]\
+        ['path-route-object']['unnumbered-hop']['node-id']
+        # selects only roadm nodes
+        pth        = ' | '.join([ e['path-route-object']['unnumbered-hop']['node-id']
+                 for e in p['path-properties']['path-route-objects'] 
+                 if e['path-route-object']['unnumbered-hop']['node-id'].startswith('roadm') or e['path-route-object']['unnumbered-hop']['node-id'].startswith('Edfa')])
+
+        [tsp,mode] = p['path-properties']['path-route-objects'][0]\
+        ['path-route-object']['unnumbered-hop']['hop-type'].split(' - ')
+
+        # find the min  acceptable OSNR, baud rate from the eqpt library based on tsp (tupe) and mode (format)
+        # loading equipment already tests the existence of tsp type and mode:
+        if mode !='not feasible with this transponder' :
+            [minosnr, baud_rate, bit_rate, cost] = next([m['OSNR'] , m['baud_rate'] , m['bit_rate'], m['cost']]  
+                for m in equipment['Transceiver'][tsp].mode if  m['format']==mode)
+        # else:
+        #     [minosnr, baud_rate, bit_rate] = ['','','','']
+        output_snr = next(e['accumulative-value'] 
+            for e in p['path-properties']['path-metric'] if e['metric-type'] == 'SNR@0.1nm')
+        output_snrbandwidth = next(e['accumulative-value']
+            for e in p['path-properties']['path-metric'] if e['metric-type'] == 'SNR@bandwidth')
+        output_osnr = next(e['accumulative-value']
+            for e in p['path-properties']['path-metric'] if e['metric-type'] == 'OSNR@0.1nm')
+        output_osnrbandwidth = next(e['accumulative-value']
+            for e in p['path-properties']['path-metric'] if e['metric-type'] == 'OSNR@bandwidth')
+        power = next(e['accumulative-value']
+            for e in p['path-properties']['path-metric'] if e['metric-type'] == 'reference_power')
+        path_bandwidth = next(e['accumulative-value']
+            for e in p['path-properties']['path-metric'] if e['metric-type'] == 'path_bandwidth')
+        if isinstance(output_snr, str):
+            isok = False
+            nb_tsp = 0
+            pthbdbw = round(path_bandwidth*1e-9,2)
+            rosnr = ''
+            rsnr = ''
+            rsnrb = ''
+            br = ''
+            pw = ''
+            total_cost = ''
+        else:
+            isok   = output_snr >= minosnr
+            nb_tsp = ceil(path_bandwidth / bit_rate)
+            pthbdbw = round(path_bandwidth*1e-9,2)
+            rosnr  = round(output_osnr,2)
+            rsnr   = round(output_snr,2)
+            rsnrb  = round(output_snrbandwidth,2)
+            br     = round(baud_rate*1e-9,2) 
+            pw     = round(lin2db(power)+30,2)
+            total_cost = nb_tsp * cost
+        mywriter.writerow((path_id,
+            source,
+            destination,
+            pthbdbw,
+            isok,
+            nb_tsp,
+            total_cost,
+            tsp,
+            mode,
+            rosnr,
+            rsnr,
+            rsnrb,
+            br,
+            pw,
+            pth
+            ))
+
+
+def compute_path_dsjctn(network, equipment, pathreqlist, disjunctions_list):
+    # pathreqlist is a list of Path_request objects
+    # disjunctions_list a list of Disjunction objects
+    
+    # given a network, a list of requests with the set of disjunction features between
+    # request, the function computes the set of path satisfying : first the disjunction
+    # constraint and second the routing constraint if the request include an explicit 
+    # set of elements to pass through.
+    # the algorithm used allows to specify disjunction for demands not sharing source or
+    # destination.
+    # a request might be declared as disjoint from several requests
+    # it is a iterative process:
+    # first computes a list of all shortest path (this may add computation time)
+    # second elaborate the set of path solution for each synchronization vector
+    # third select only the candidates that satisfy all synchronization vectors they belong to
+    # fourth apply route constraints : remove candidate path that do not satisfy the constraint
+    # fifth select the first candidate among the set of candidates.
+    # the example network used in comments has been added to the set of data tests files
+
+    # define the list to be returned
+    path_res_list = []
+
+    # all disjctn must be computed at once together to avoid blocking
+    #         1     1
+    # eg    a----b-----c
+    #       |1   |0.5  |1
+    #       e----f--h--g
+    #         1  0.5 0.5
+    # if I have to compute a to g and a to h 
+    # I must not compute a-b-f-h-g, otherwise there is no disjoint path remaining for a to h
+    # instead I should list all most disjoint path and select the one that have the less
+    # number of commonalities
+    #     \     path abfh  aefh   abcgh 
+    #      \___cost   2     2.5    3.5
+    #   path| cost  
+    #  abfhg|  2.5    x      x      x
+    #  abcg |  3      x             x
+    #  aefhg|  3      x      x      x
+    # from this table abcg and aefh have no common links and should be preferred 
+    # even they are not the shortest paths
+
+    # build the list of pathreqlist elements not concerned by disjunction
+    global_disjunctions_list = [e for d in disjunctions_list for e in d.disjunctions_req ]
+    pathreqlist_simple = [e for e in pathreqlist if e.request_id not in global_disjunctions_list]
+    pathreqlist_disjt = [e for e in pathreqlist if e.request_id in global_disjunctions_list]
+
+    # use a mirror class to record path and the corresponding requests
+    class Pth:
+        def __init__(self, req, pth, simplepth):
+            self.req = req
+            self.pth = pth
+            self.simplepth = simplepth
+
+    # step 1
+    # for each remaining request compute a set of simple path
+    allpaths = {}
+    rqs = {}
+    simple_rqs = {}
+    simple_rqs_reversed = {}
+    for pathreq in pathreqlist_disjt :
+        all_simp_pths = list(all_simple_paths(network,\
+            source=next(el for el in network.nodes() if el.uid == pathreq.source),\
+            target=next(el for el in network.nodes() if el.uid == pathreq.destination),\
+            cutoff=80))
+        # sort them in km length instead of hop
+        # all_simp_pths = sorted(all_simp_pths, key=lambda path: len(path))
+        all_simp_pths = sorted(all_simp_pths, key=lambda \
+            x: sum(network.get_edge_data(x[i],x[i+1])['weight'] for i in range(len(x)-2)))
+        # reversed direction paths required to check disjunction on both direction
+        all_simp_pths_reversed = []
+        for pth in all_simp_pths:
+            all_simp_pths_reversed.append(find_reversed_path(pth,network))
+        rqs[pathreq.request_id] = all_simp_pths 
+        temp =[]
+        for p in all_simp_pths :
+            # build a short list representing each roadm+direction with the first item
+            # start enumeration at 1 to avoid Trx in the list
+            s = [e.uid for i,e in enumerate(p[1:-1]) \
+                if (isinstance(e,Roadm) | (isinstance(p[i],Roadm) ))] 
+            temp.append(s)
+            # id(s) is unique even if path is the same: two objects with same 
+            # path have two different ids
+            allpaths[id(s)] = Pth(pathreq,p,s)
+        simple_rqs[pathreq.request_id] = temp
+        temp =[]
+        for p in all_simp_pths_reversed :
+            # build a short list representing each roadm+direction with the first item
+            # start enumeration at 1 to avoid Trx in the list
+            temp.append([e.uid for i,e in enumerate(p[1:-1]) \
+                if (isinstance(e,Roadm) | (isinstance(p[i],Roadm) ))] )
+        simple_rqs_reversed[pathreq.request_id] = temp
+    # step 2 
+    # for each set of requests that need to be disjoint
+    # select the disjoint path combination
+
+    candidates = {}
+    for d in disjunctions_list :
+        dlist = d.disjunctions_req.copy()
+        # each line of dpath is one combination of path that satisfies disjunction
+        dpath = []
+        for i,p in enumerate(simple_rqs[dlist[0]]):
+            dpath.append([p])
+            # allpaths[id(p)].d_id = d.disjunction_id
+        # in each loop, dpath is updated with a path for rq that satisfies 
+        # disjunction with each path in dpath
+        # for example, assume set of requests in the vector (disjunction_list) is  {rq1,rq2, rq3}
+        # rq1  p1: abfhg
+        #      p2: aefhg
+        #      p3: abcg
+        # rq2  p8: bf
+        # rq3  p4: abcgh
+        #      p6: aefh
+        #      p7: abfh
+        # initiate with rq1
+        #  dpath = [[p1]
+        #           [p2]
+        #           [p3]]
+        #  after first loop:
+        #  dpath = [[p1 p8]
+        #           [p3 p8]]
+        #  since p2 and p8 are not disjoint
+        #  after second loop:
+        #  dpath = [ p3 p8 p6 ]
+        #  since p1 and p4 are not disjoint 
+        #        p1 and p7 are not disjoint
+        #        p3 and p4 are not disjoint
+        #        p3 and p7 are not disjoint
+
+        for e1 in dlist[1:] :
+            temp = []
+            for j,p1 in enumerate(simple_rqs[e1]):
+                # allpaths[id(p1)].d_id = d.disjunction_id
+                # can use index j in simple_rqs_reversed because index 
+                # of direct and reversed paths have been kept identical
+                p1_reversed = simple_rqs_reversed[e1][j]
+                # print(p1_reversed)
+                # print('\n\n')
+                for k,c in enumerate(dpath) :
+                    # print(f' c: \t{c}')
+                    temp2 = c.copy()
+                    all_disjoint = 0
+                    for p in c :
+                        all_disjoint += isdisjoint(p1,p)+ isdisjoint(p1_reversed,p)
+                    if all_disjoint ==0:
+                        temp2.append(p1)
+                        temp.append(temp2)
+                            # print(f' coucou {e1}: \t{temp}')
+            dpath = temp
+        # print(dpath)
+        candidates[d.disjunction_id] = dpath
+
+    # for i in disjunctions_list  :
+    #     print(f'\n{candidates[i.disjunction_id]}')
+
+    # step 3
+    # now for each request, select the path that satisfies all disjunctions
+    # path must be in candidates[id] for all concerned ids
+    # for example, assume set of sync vectors (disjunction groups) is
+    #   s1 = {rq1 rq2}   s2 = {rq1 rq3}
+    #   candidate[s1] = [[p1 p8]
+    #                    [p3 p8]]
+    #   candidate[s2] = [[p3 p6]]
+    #   for rq1 p3 should be preferred
+
+
+    for pathreq in pathreqlist_disjt:
+        concerned_d_id = [d.disjunction_id for d in disjunctions_list if pathreq.request_id in d.disjunctions_req]
+        # for each set of solution, verify that the same path is used for the same request
+        candidate_paths = simple_rqs[pathreq.request_id]
+        # print('coucou')
+        # print(pathreq.request_id)
+        for p in candidate_paths :
+            iscandidate = 0
+            for sol in concerned_d_id :
+                test = 1
+                # for each solution test if p is part of the solution
+                # if yes, then p can remain a candidate
+                for i,m in enumerate(candidates[sol]) :
+                    if p in m:
+                        if allpaths[id(m[m.index(p)])].req.request_id == pathreq.request_id :
+                            test = 0
+                            break
+                iscandidate += test
+            if iscandidate != 0:
+                for l in concerned_d_id :
+                    for m in candidates[l] :
+                        if p in m :
+                            candidates[l].remove(m)
+
+#    for i in disjunctions_list  :
+#        print(i.disjunction_id)
+#        print(f'\n{candidates[i.disjunction_id]}')
+
+    # step 4 apply route constraints : remove candidate path that do not satisfy the constraint
+    # only in  the case of disjounction: the simple path is processed in request.compute_constrained_path
+    # TODO : keep a version without the loose constraint
+    for d in disjunctions_list  :
+        temp = []
+        for j,sol in enumerate(candidates[d.disjunction_id]) :
+            testispartok = True
+            for i,p in enumerate(sol) :
+                # print(f'test {allpaths[id(p)].req.request_id}')
+                # print(f'length of route {len(allpaths[id(p)].req.nodes_list)}')
+                if allpaths[id(p)].req.nodes_list :
+                    # if p does not containt the ordered list node, remove sol from the candidate
+                    # except if this was the last solution: then check if the constraint is loose or not
+                    if not ispart(allpaths[id(p)].req.nodes_list, p) : 
+                        # print(f'nb of solutions {len(temp)}')
+                        if j < len(candidates[d.disjunction_id])-1 :
+                            msg = f'removing {sol}'
+                            logger.info(msg)
+                            testispartok = False
+                            #break
+                        else:
+                            if 'loose' in allpaths[id(p)].req.loose_list:
+                                logger.info(f'Could not apply route constraint'+
+                                    f'{allpaths[id(p)].req.nodes_list} on request {allpaths[id(p)].req.request_id}')
+                            else :
+                                logger.info(f'removing last solution from candidate paths\n{sol}')
+                                testispartok = False
+            if testispartok :
+                temp.append(sol)
+        candidates[d.disjunction_id] = temp
+
+    # step 5 select the first combination that works
+    pathreslist_disjoint = {}
+    for d in disjunctions_list  :
+        test_sol = True
+        while test_sol:
+            # print('coucou')
+            if candidates[d.disjunction_id] :
+                for p in candidates[d.disjunction_id][0]:
+                    if allpaths[id(p)].req in pathreqlist_disjt: 
+                        # print(f'selected path :{p} for req {allpaths[id(p)].req.request_id}')
+                        pathreslist_disjoint[allpaths[id(p)].req] = allpaths[id(p)].pth
+                        pathreqlist_disjt.remove(allpaths[id(p)].req)
+                        candidates = remove_candidate(candidates, allpaths, allpaths[id(p)].req, p)
+                        test_sol = False
+            else:
+                msg = f'No disjoint path found with added constraint'
+                logger.critical(msg)
+                print(f'{msg}\nComputation stopped.')
+                # TODO in this case: replay step 5  with the candidate without constraints
+                exit()
+    
+    # for i in disjunctions_list  :
+    #     print(i.disjunction_id)
+    #     print(f'\n{candidates[i.disjunction_id]}')
+
+    # list the results in the same order as initial pathreqlist        
+    for req in pathreqlist :
+        req.nodes_list.append(req.destination)
+        # we assume that the destination is a strict constraint
+        req.loose_list.append('strict')
+        if req in pathreqlist_simple:
+            path_res_list.append(compute_constrained_path(network, req))
+        else:
+            path_res_list.append(pathreslist_disjoint[req])
+    return path_res_list
+
+def isdisjoint(p1,p2) :
+    # returns 0 if disjoint
+    edge1 = list(pairwise(p1))
+    edge2 = list(pairwise(p2))
+    for e in edge1 :
+        if e in edge2 :
+            return 1
+    return 0
+
+def find_reversed_path(p,network) :
+    # 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
+    reversed_roadm_path = list(reversed([e for e in p if isinstance (e,Roadm)]))
+    source = p[-1]
+    destination = p[0]
+    total_path = [source]
+    for node in reversed_roadm_path :
+        total_path.extend(dijkstra_path(network, source, node, weight = 'weight')[1:])
+        source = node
+    total_path.append(destination)
+    return total_path
+
+def ispart(a,b) :
+    # 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 i, el in enumerate(a):
+        if el in b :
+            if b.index(el) >= j :
+                j = b.index(el)
+            else: 
+                return False
+        else:
+            return False
+    return True
+
+def remove_candidate(candidates, allpaths, rq, pth) :
+    # print(f'coucou {rq.request_id}')
+    for key, candidate  in candidates.items() :
+        temp = candidate.copy()
+        for i,sol in enumerate(candidate) :
+            for p in sol :
+                if allpaths[id(p)].req.request_id == rq.request_id :
+                    if id(p) != id(pth) :
+                        temp.remove(sol)
+                        break
+        candidates[key] = temp
+    return candidates
+
+def compare_reqs(req1,req2,disjlist) :
+    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
+    if dis1 and dis2 :
+        temp1 = []
+        for d in dis1:
+            temp1.extend(d.disjunctions_req)
+            temp1.remove(req1.request_id)
+        temp2 = []
+        for d in dis2:
+            temp2.extend(d.disjunctions_req)
+            temp2.remove(req2.request_id)
+        if set(temp1) == set(temp2) :
+            same_disj = True
+    elif not dis2 and not dis1:
+        same_disj = True
+
+    if req1.source     == req2.source and \
+        req1.destination == req2.destination and  \
+        req1.tsp        == req2.tsp and \
+        req1.tsp_mode   == req2.tsp_mode and \
+        req1.baud_rate  == req2.baud_rate and \
+        req1.nodes_list == req2.nodes_list and \
+        req1.loose_list == req2.loose_list and \
+        req1.spacing    == req2.spacing and \
+        req1.power      == req2.power and \
+        req1.nb_channel == req2.nb_channel and \
+        req1.f_min  == req2.f_min and \
+        req1.f_max  == req2.f_max and \
+        req1.format     == req2.format and \
+        req1.OSNR       == req2.OSNR and \
+        req1.roll_off   == req2.roll_off 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
+    # todo maybe add conditions on mode ??, spacing ...
+    # currently if undefined takes the default values
+    local_list = pathreqlist.copy()
+    for req in pathreqlist:
+        for r in local_list : 
+            if  req.request_id != r.request_id and compare_reqs(req, r, disjlist):
+                # aggregate
+                r.path_bandwidth += req.path_bandwidth
+                temp_r_id = r.request_id
+                r.request_id = ' | '.join((r.request_id,req.request_id))
+                # remove request from list
+                local_list.remove(req)
+                # todo change also disjunction req with new demand
+
+                for d in disjlist :
+                    if req.request_id in d.disjunctions_req :
+                        d.disjunctions_req.remove(req.request_id)
+                        d.disjunctions_req.append(r.request_id)
+                for d in disjlist :        
+                    if temp_r_id in d.disjunctions_req :
+                        disjlist.remove(d)
+                break
+    return local_list, disjlist

gnpy/core/science_utils.py

@@ -0,0 +1,820 @@
+import numpy as np
+from operator import attrgetter
+from collections import namedtuple
+from logging import getLogger
+import scipy.constants as ph
+from scipy.integrate import solve_bvp
+from scipy.integrate import cumtrapz
+from scipy.interpolate import interp1d
+from scipy.optimize import OptimizeResult
+
+from gnpy.core.utils import db2lin
+
+
+logger = getLogger(__name__)
+
+
+class RamanParams():
+    def __init__(self, params):
+        self._flag_raman = params['flag_raman']
+        self._space_resolution = params['space_resolution']
+        self._tolerance = params['tolerance']
+
+    @property
+    def flag_raman(self):
+        return self._flag_raman
+
+    @property
+    def space_resolution(self):
+        return self._space_resolution
+
+    @property
+    def tolerance(self):
+        return self._tolerance
+
+class NLIParams():
+    def __init__(self, params):
+        self._nli_method_name = params['nli_method_name']
+        self._wdm_grid_size = params['wdm_grid_size']
+        self._dispersion_tolerance = params['dispersion_tolerance']
+        self._phase_shift_tollerance = params['phase_shift_tollerance']
+        self._f_cut_resolution = None
+        self._f_pump_resolution = None
+
+    @property
+    def nli_method_name(self):
+        return self._nli_method_name
+
+    @property
+    def wdm_grid_size(self):
+        return self._wdm_grid_size
+
+    @property
+    def dispersion_tolerance(self):
+        return self._dispersion_tolerance
+
+    @property
+    def phase_shift_tollerance(self):
+        return self._phase_shift_tollerance
+
+    @property
+    def f_cut_resolution(self):
+        return self._f_cut_resolution
+
+    @f_cut_resolution.setter
+    def f_cut_resolution(self, f_cut_resolution):
+        self._f_cut_resolution = f_cut_resolution
+
+    @property
+    def f_pump_resolution(self):
+        return self._f_pump_resolution
+
+    @f_pump_resolution.setter
+    def f_pump_resolution(self, f_pump_resolution):
+        self._f_pump_resolution = f_pump_resolution
+
+class SimParams():
+    def __init__(self, params):
+        self._raman_computed_channels = params['raman_computed_channels']
+        self._raman_params = RamanParams(params=params['raman_parameters'])
+        self._nli_params = NLIParams(params=params['nli_parameters'])
+
+    @property
+    def raman_computed_channels(self):
+        return self._raman_computed_channels
+
+    @property
+    def raman_params(self):
+        return self._raman_params
+
+    @property
+    def nli_params(self):
+        return self._nli_params
+
+class FiberParams():
+    def __init__(self, fiber):
+        self._loss_coef = 2 * fiber.dbkm_2_lin()[1]
+        self._length = fiber.length
+        self._gamma = fiber.gamma
+        self._beta2 = fiber.beta2()
+        self._beta3 = fiber.beta3 if hasattr(fiber, 'beta3') else 0
+        self._f_ref_beta = fiber.f_ref_beta if hasattr(fiber, 'f_ref_beta') else 0
+        self._raman_efficiency = fiber.params.raman_efficiency
+        self._temperature = fiber.operational['temperature']
+
+    @property
+    def loss_coef(self):
+        return self._loss_coef
+
+    @property
+    def length(self):
+        return self._length
+
+    @property
+    def gamma(self):
+        return self._gamma
+
+    @property
+    def beta2(self):
+        return self._beta2
+
+    @property
+    def beta3(self):
+        return self._beta3
+
+    @property
+    def f_ref_beta(self):
+        return self._f_ref_beta
+
+    @property
+    def raman_efficiency(self):
+        return self._raman_efficiency
+
+    @property
+    def temperature(self):
+        return self._temperature
+
+    def alpha0(self, f_ref=193.5e12):
+        """ It returns the zero element of the series expansion of attenuation coefficient alpha(f) in the
+        reference frequency f_ref
+
+        :param f_ref: reference frequency of series expansion [Hz]
+        :return: alpha0: power attenuation coefficient in f_ref [Neper/m]
+        """
+        if not hasattr(self.loss_coef, 'alpha_power'):
+            alpha0 = self.loss_coef
+        else:
+            alpha_interp = interp1d(self.loss_coef['frequency'],
+                                    self.loss_coef['alpha_power'])
+            alpha0 = alpha_interp(f_ref)
+        return alpha0
+
+pump = namedtuple('RamanPump', 'power frequency propagation_direction')
+
+def propagate_raman_fiber(fiber, *carriers):
+    sim_params = fiber.sim_params
+    raman_params = fiber.sim_params.raman_params
+    nli_params = fiber.sim_params.nli_params
+    # apply input attenuation to carriers
+    attenuation_in = db2lin(fiber.con_in + fiber.att_in)
+    chan = []
+    for carrier in carriers:
+        pwr = carrier.power
+        pwr = pwr._replace(signal=pwr.signal / attenuation_in,
+                           nli=pwr.nli / attenuation_in,
+                           ase=pwr.ase / attenuation_in)
+        carrier = carrier._replace(power=pwr)
+        chan.append(carrier)
+    carriers = tuple(f for f in chan)
+    fiber_params = FiberParams(fiber)
+
+    # evaluate fiber attenuation involving also SRS if required by sim_params
+    if 'raman_pumps' in fiber.operational:
+        raman_pumps = tuple(pump(p['power'], p['frequency'], p['propagation_direction'])
+                            for p in fiber.operational['raman_pumps'])
+    else:
+        raman_pumps = None
+    raman_solver = RamanSolver(raman_params=raman_params, fiber_params=fiber_params)
+    stimulated_raman_scattering = raman_solver.stimulated_raman_scattering(carriers=carriers,
+                                                                           raman_pumps=raman_pumps)
+    fiber_attenuation = (stimulated_raman_scattering.rho[:, -1])**-2
+    if not raman_params.flag_raman:
+        fiber_attenuation = tuple(fiber.lin_attenuation for _ in carriers)
+
+    # evaluate Raman ASE noise if required by sim_params and if raman pumps are present
+    if raman_params.flag_raman and raman_pumps:
+        raman_ase = raman_solver.spontaneous_raman_scattering.power[:, -1]
+    else:
+        raman_ase = tuple(0 for _ in carriers)
+
+    # evaluate nli and propagate in fiber
+    attenuation_out = db2lin(fiber.con_out)
+    nli_solver = NliSolver(nli_params=nli_params, fiber_params=fiber_params)
+    nli_solver.stimulated_raman_scattering = stimulated_raman_scattering
+
+    nli_frequencies = []
+    computed_nli = []
+    for carrier in (c for c in carriers if c.channel_number in sim_params.raman_computed_channels):
+        resolution_param = frequency_resolution(carrier, carriers, sim_params, fiber_params)
+        f_cut_resolution, f_pump_resolution, _, _ = resolution_param
+        nli_params.f_cut_resolution = f_cut_resolution
+        nli_params.f_pump_resolution = f_pump_resolution
+        nli_frequencies.append(carrier.frequency)
+        computed_nli.append(nli_solver.compute_nli(carrier, *carriers))
+
+    new_carriers = []
+    for carrier, attenuation, rmn_ase in zip(carriers, fiber_attenuation, raman_ase):
+        carrier_nli = np.interp(carrier.frequency, nli_frequencies, computed_nli)
+        pwr = carrier.power
+        pwr = pwr._replace(signal=pwr.signal/attenuation/attenuation_out,
+                           nli=(pwr.nli+carrier_nli)/attenuation/attenuation_out,
+                           ase=((pwr.ase/attenuation)+rmn_ase)/attenuation_out)
+        new_carriers.append(carrier._replace(power=pwr))
+    return new_carriers
+
+def frequency_resolution(carrier, carriers, sim_params, fiber_params):
+    def _get_freq_res_k_phi(delta_count, grid_size, alpha0, delta_z, beta2, k_tol, phi_tol):
+        res_phi = _get_freq_res_phase_rotation(delta_count, grid_size, delta_z, beta2, phi_tol)
+        res_k = _get_freq_res_dispersion_attenuation(delta_count, grid_size, alpha0, beta2, k_tol)
+        res_dict = {'res_phi': res_phi, 'res_k': res_k}
+        method = min(res_dict, key=res_dict.get)
+        return res_dict[method], method, res_dict
+
+    def _get_freq_res_dispersion_attenuation(delta_count, grid_size, alpha0, beta2, k_tol):
+        return k_tol * abs(alpha0) / abs(beta2) / (1 + delta_count) / (4 * np.pi ** 2 * grid_size)
+
+    def _get_freq_res_phase_rotation(delta_count, grid_size, delta_z, beta2, phi_tol):
+        return phi_tol / abs(beta2) / (1 + delta_count) / delta_z / (4 * np.pi ** 2 * grid_size)
+
+    grid_size = sim_params.nli_params.wdm_grid_size
+    delta_z = sim_params.raman_params.space_resolution
+    alpha0 = fiber_params.alpha0()
+    beta2 = fiber_params.beta2
+    k_tol = sim_params.nli_params.dispersion_tolerance
+    phi_tol = sim_params.nli_params.phase_shift_tollerance
+    f_pump_resolution, method_f_pump, res_dict_pump = \
+        _get_freq_res_k_phi(0, grid_size, alpha0, delta_z, beta2, k_tol, phi_tol)
+    f_cut_resolution = {}
+    method_f_cut = {}
+    res_dict_cut = {}
+    for cut_carrier in carriers:
+        delta_number = cut_carrier.channel_number - carrier.channel_number
+        delta_count = abs(delta_number)
+        f_res, method, res_dict = \
+            _get_freq_res_k_phi(delta_count, grid_size, alpha0, delta_z, beta2, k_tol, phi_tol)
+        f_cut_resolution[f'delta_{delta_number}'] = f_res
+        method_f_cut[delta_number] = method
+        res_dict_cut[delta_number] = res_dict
+    return [f_cut_resolution, f_pump_resolution, (method_f_cut, method_f_pump), (res_dict_cut, res_dict_pump)]
+
+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
+    """
+    psd = np.zeros(np.shape(f))
+    for carrier in carriers:
+        f_nch = carrier.frequency
+        g_ch = carrier.power.signal / carrier.baud_rate
+        ts = 1 / carrier.baud_rate
+        passband = (1 - carrier.roll_off) / (2 / carrier.baud_rate)
+        stopband = (1 + carrier.roll_off) / (2 / carrier.baud_rate)
+        ff = np.abs(f - f_nch)
+        tf = ff - passband
+        if carrier.roll_off == 0:
+            psd = np.where(tf <= 0, g_ch, 0.) + psd
+        else:
+            psd = g_ch * (np.where(tf <= 0, 1., 0.) + 1 / 2 * (1 + np.cos(np.pi * ts / carrier.roll_off * tf)) *
+                          np.where(tf > 0, 1., 0.) * np.where(np.abs(ff) <= stopband, 1., 0.)) + psd
+    return psd
+
+class RamanSolver:
+    def __init__(self, raman_params=None, fiber_params=None):
+        """ Initialize the fiber object with its physical parameters
+        :param length: fiber length in m.
+        :param alphap: fiber power attenuation coefficient vs frequency in 1/m. numpy array
+        :param freq_alpha: frequency axis of alphap in Hz. numpy array
+        :param cr_raman: Raman efficiency vs frequency offset in 1/W/m. numpy array
+        :param freq_cr: reference frequency offset axis for cr_raman. numpy array
+        :param raman_params: namedtuple containing the solver parameters (optional).
+        """
+        self.fiber_params = fiber_params
+        self.raman_params = raman_params
+        self._carriers = None
+        self._stimulated_raman_scattering = None
+        self._spontaneous_raman_scattering = None
+
+    @property
+    def fiber_params(self):
+        return self._fiber_params
+
+    @fiber_params.setter
+    def fiber_params(self, fiber_params):
+        self._stimulated_raman_scattering = None
+        self._fiber_params = fiber_params
+
+    @property
+    def carriers(self):
+        return self._carriers
+
+    @carriers.setter
+    def carriers(self, carriers):
+        """
+        :param carriers: tuple of namedtuples containing information about carriers
+        :return:
+        """
+        self._carriers = carriers
+        self._stimulated_raman_scattering = None
+
+    @property
+    def raman_pumps(self):
+        return self._raman_pumps
+
+    @raman_pumps.setter
+    def raman_pumps(self, raman_pumps):
+        self._raman_pumps = raman_pumps
+        self._stimulated_raman_scattering = None
+
+    @property
+    def raman_params(self):
+        return self._raman_params
+
+    @raman_params.setter
+    def raman_params(self, raman_params):
+        """
+        :param raman_params: namedtuple containing the solver parameters (optional).
+        :return:
+        """
+        self._raman_params = raman_params
+        self._stimulated_raman_scattering = None
+        self._spontaneous_raman_scattering = None
+
+    @property
+    def spontaneous_raman_scattering(self):
+        if self._spontaneous_raman_scattering is None:
+            # SET STUFF
+            loss_coef = self.fiber_params.loss_coef
+            raman_efficiency = self.fiber_params.raman_efficiency
+            temperature = self.fiber_params.temperature
+            carriers = self.carriers
+            raman_pumps = self.raman_pumps
+
+            logger.debug('Start computing fiber Spontaneous Raman Scattering')
+            power_spectrum, freq_array, prop_direct, bn_array = self._compute_power_spectrum(carriers, raman_pumps)
+
+            if not hasattr(loss_coef, 'alpha_power'):
+                alphap_fiber = loss_coef * np.ones(freq_array.shape)
+            else:
+                interp_alphap = interp1d(loss_coef['frequency'], loss_coef['alpha_power'])
+                alphap_fiber = interp_alphap(freq_array)
+
+            freq_diff = abs(freq_array - np.reshape(freq_array, (len(freq_array), 1)))
+            interp_cr = interp1d(raman_efficiency['frequency_offset'], raman_efficiency['cr'])
+            cr = interp_cr(freq_diff)
+
+            # z propagation axis
+            z_array = self._stimulated_raman_scattering.z
+            ase_bc = np.zeros(freq_array.shape)
+
+            # calculate ase power
+            spontaneous_raman_scattering = self._int_spontaneous_raman(z_array, self._stimulated_raman_scattering.power,
+                                                                       alphap_fiber, freq_array, cr, freq_diff, ase_bc,
+                                                                       bn_array, temperature)
+
+            setattr(spontaneous_raman_scattering, 'frequency', freq_array)
+            setattr(spontaneous_raman_scattering, 'z', z_array)
+            setattr(spontaneous_raman_scattering, 'power', spontaneous_raman_scattering.x)
+            delattr(spontaneous_raman_scattering, 'x')
+
+            logger.debug(spontaneous_raman_scattering.message)
+
+            self._spontaneous_raman_scattering = spontaneous_raman_scattering
+
+        return self._spontaneous_raman_scattering
+
+    @staticmethod
+    def _compute_power_spectrum(carriers, raman_pumps=None):
+        """
+        Rearrangement of spectral and Raman pump information to make them compatible with Raman solver
+        :param carriers: a tuple of namedtuples describing the transmitted channels
+        :param raman_pumps: a namedtuple describing the Raman pumps
+        :return:
+        """
+
+        # Signal power spectrum
+        pow_array = np.array([])
+        f_array = np.array([])
+        noise_bandwidth_array = np.array([])
+        for carrier in sorted(carriers, key=attrgetter('frequency')):
+            f_array = np.append(f_array, carrier.frequency)
+            pow_array = np.append(pow_array, carrier.power.signal)
+            ref_bw = carrier.baud_rate
+            noise_bandwidth_array = np.append(noise_bandwidth_array, ref_bw)
+
+        propagation_direction = np.ones(len(f_array))
+
+        # Raman pump power spectrum
+        if raman_pumps:
+            for pump in raman_pumps:
+                pow_array = np.append(pow_array, pump.power)
+                f_array = np.append(f_array, pump.frequency)
+                direction = +1 if pump.propagation_direction.lower() == 'coprop' else -1
+                propagation_direction = np.append(propagation_direction, direction)
+                noise_bandwidth_array = np.append(noise_bandwidth_array, ref_bw)
+
+        # Final sorting
+        ind = np.argsort(f_array)
+        f_array = f_array[ind]
+        pow_array = pow_array[ind]
+        propagation_direction = propagation_direction[ind]
+
+        return pow_array, f_array, propagation_direction, noise_bandwidth_array
+
+    def _int_spontaneous_raman(self, z_array, raman_matrix, alphap_fiber, freq_array, cr_raman_matrix, freq_diff, ase_bc, bn_array, temperature):
+        spontaneous_raman_scattering = OptimizeResult()
+
+        dx = self.raman_params.space_resolution
+        h = ph.value('Planck constant')
+        kb = ph.value('Boltzmann constant')
+
+        power_ase = np.nan * np.ones(raman_matrix.shape)
+        int_pump = cumtrapz(raman_matrix, z_array, dx=dx, axis=1, initial=0)
+
+        for f_ind, f_ase in enumerate(freq_array):
+            cr_raman = cr_raman_matrix[f_ind, :]
+            vibrational_loss = f_ase / freq_array[:f_ind]
+            eta = 1/(np.exp((h*freq_diff[f_ind, f_ind+1:])/(kb*temperature)) - 1)
+
+            int_fiber_loss = -alphap_fiber[f_ind] * z_array
+            int_raman_loss = np.sum((cr_raman[:f_ind] * vibrational_loss * int_pump[:f_ind, :].transpose()).transpose(), axis=0)
+            int_raman_gain = np.sum((cr_raman[f_ind + 1:] * int_pump[f_ind + 1:, :].transpose()).transpose(), axis=0)
+
+            int_gain_loss = int_fiber_loss + int_raman_gain + int_raman_loss
+
+            new_ase = np.sum((cr_raman[f_ind+1:] * (1 + eta) * raman_matrix[f_ind+1:, :].transpose()).transpose() * h * f_ase * bn_array[f_ind], axis=0)
+
+            bc_evolution = ase_bc[f_ind] * np.exp(int_gain_loss)
+            ase_evolution = np.exp(int_gain_loss) * cumtrapz(new_ase*np.exp(-int_gain_loss), z_array, dx=dx, initial=0)
+
+            power_ase[f_ind, :] = bc_evolution + ase_evolution
+
+        spontaneous_raman_scattering.x = 2 * power_ase
+        spontaneous_raman_scattering.success = True
+        spontaneous_raman_scattering.message = "Spontaneous Raman Scattering evaluated successfully"
+
+        return spontaneous_raman_scattering
+
+    def stimulated_raman_scattering(self, carriers, raman_pumps=None):
+        """ Returns stimulated Raman scattering solution including 
+        fiber gain/loss profile.
+        :return: self._stimulated_raman_scattering: the SRS problem solution.
+        scipy.interpolate.PPoly instance
+        """
+
+        if self._stimulated_raman_scattering is None:
+            # fiber parameters
+            fiber_length = self.fiber_params.length
+            loss_coef = self.fiber_params.loss_coef
+            if self.raman_params.flag_raman:
+                raman_efficiency = self.fiber_params.raman_efficiency
+            else:
+                raman_efficiency = self.fiber_params.raman_efficiency
+                raman_efficiency['cr'] = np.array(raman_efficiency['cr']) * 0
+            # raman solver parameters
+            z_resolution = self.raman_params.space_resolution
+            tolerance = self.raman_params.tolerance
+
+            logger.debug('Start computing fiber Stimulated Raman Scattering')
+
+            power_spectrum, freq_array, prop_direct, _ = self._compute_power_spectrum(carriers, raman_pumps)
+
+            if not hasattr(loss_coef, 'alpha_power'):
+                alphap_fiber = loss_coef * np.ones(freq_array.shape)
+            else:
+                interp_alphap = interp1d(loss_coef['frequency'], loss_coef['alpha_power'])
+                alphap_fiber = interp_alphap(freq_array)
+
+            freq_diff = abs(freq_array - np.reshape(freq_array, (len(freq_array), 1)))
+            interp_cr = interp1d(raman_efficiency['frequency_offset'], raman_efficiency['cr'])
+            cr = interp_cr(freq_diff)
+
+            # z propagation axis
+            z = np.arange(0, fiber_length+1, z_resolution)
+
+            ode_function = lambda z, p: self._ode_stimulated_raman(z, p, alphap_fiber, freq_array, cr, prop_direct)
+            boundary_residual = lambda ya, yb: self._residuals_stimulated_raman(ya, yb, power_spectrum, prop_direct)
+            initial_guess_conditions = self._initial_guess_stimulated_raman(z, power_spectrum, alphap_fiber, prop_direct)
+
+            # ODE SOLVER
+            stimulated_raman_scattering = solve_bvp(ode_function, boundary_residual, z, initial_guess_conditions, tol=tolerance)
+
+            rho = (stimulated_raman_scattering.y.transpose() / power_spectrum).transpose()
+            rho = np.sqrt(rho)    # From power attenuation to field attenuation
+            setattr(stimulated_raman_scattering, 'frequency', freq_array)
+            setattr(stimulated_raman_scattering, 'z', stimulated_raman_scattering.x)
+            setattr(stimulated_raman_scattering, 'rho', rho)
+            setattr(stimulated_raman_scattering, 'power', stimulated_raman_scattering.y)
+            delattr(stimulated_raman_scattering, 'x')
+            delattr(stimulated_raman_scattering, 'y')
+
+            self.carriers = carriers
+            self.raman_pumps = raman_pumps
+            self._stimulated_raman_scattering = stimulated_raman_scattering
+
+        return self._stimulated_raman_scattering
+
+    def _residuals_stimulated_raman(self, ya, yb, power_spectrum, prop_direct):
+
+        computed_boundary_value = np.zeros(ya.size)
+
+        for index, direction in enumerate(prop_direct):
+            if direction == +1:
+                computed_boundary_value[index] = ya[index]
+            else:
+                computed_boundary_value[index] = yb[index]
+
+        return power_spectrum - computed_boundary_value
+
+    def _initial_guess_stimulated_raman(self, z, power_spectrum, alphap_fiber, prop_direct):
+        """ Computes the initial guess knowing the boundary conditions
+        :param z: patial axis [m]. numpy array
+        :param power_spectrum: power in each frequency slice [W].    Frequency axis is defined by freq_array. numpy array
+        :param alphap_fiber: frequency dependent fiber attenuation of signal power [1/m]. Frequency defined by freq_array. numpy array
+        :param prop_direct: indicates the propagation direction of each power slice in power_spectrum:
+        +1 for forward propagation and -1 for backward propagation. Frequency defined by freq_array. numpy array
+        :return: power_guess: guess on the initial conditions [W]. The first ndarray index identifies the frequency slice,
+        the second ndarray index identifies the step in z. ndarray
+        """
+
+        power_guess = np.empty((power_spectrum.size, z.size))
+        for f_index, power_slice in enumerate(power_spectrum):
+            if prop_direct[f_index] == +1:
+                power_guess[f_index, :] = np.exp(-alphap_fiber[f_index] * z) * power_slice
+            else:
+                power_guess[f_index, :] = np.exp(-alphap_fiber[f_index] * z[::-1]) * power_slice
+
+        return power_guess
+
+    def _ode_stimulated_raman(self, z, power_spectrum, alphap_fiber, freq_array, cr_raman_matrix, prop_direct):
+        """ Aim of ode_raman is to implement the set of ordinary differential equations (ODEs) describing the Raman effect.
+        :param z: spatial axis (unused).
+        :param power_spectrum: power in each frequency slice [W].    Frequency axis is defined by freq_array. numpy array. Size n
+        :param alphap_fiber: frequency dependent fiber attenuation of signal power [1/m]. Frequency defined by freq_array. numpy array. Size n
+        :param freq_array: reference frequency axis [Hz]. numpy array. Size n
+        :param cr_raman: Cr(f) Raman gain efficiency variation in frequency [1/W/m]. Frequency defined by freq_array. numpy ndarray. Size nxn
+        :param prop_direct: indicates the propagation direction of each power slice in power_spectrum:
+        +1 for forward propagation and -1 for backward propagation. Frequency defined by freq_array. numpy array. Size n
+        :return: dP/dz: the power variation in dz [W/m]. numpy array. Size n
+        """
+
+        dpdz = np.nan * np.ones(power_spectrum.shape)
+        for f_ind, power in enumerate(power_spectrum):
+            cr_raman = cr_raman_matrix[f_ind, :]
+            vibrational_loss = freq_array[f_ind] / freq_array[:f_ind]
+
+            for z_ind, power_sample in enumerate(power):
+                raman_gain = np.sum(cr_raman[f_ind+1:] * power_spectrum[f_ind+1:, z_ind])
+                raman_loss = np.sum(vibrational_loss * cr_raman[:f_ind] * power_spectrum[:f_ind, z_ind])
+
+                dpdz_element = prop_direct[f_ind] * (-alphap_fiber[f_ind] + raman_gain - raman_loss) * power_sample
+                dpdz[f_ind][z_ind] = dpdz_element
+
+        return np.vstack(dpdz)
+
+class NliSolver:
+    """ This class implements the NLI models.
+        Model and method can be specified in `self.nli_params.method`.
+        List of implemented methods:
+        'gn_model_analytic': brute force triple integral solution
+        'ggn_spectrally_separated_xpm_spm': XPM plus SPM
+    """
+
+    def __init__(self, nli_params=None, fiber_params=None):
+        """ Initialize the fiber object with its physical parameters
+        """
+        self.fiber_params = fiber_params
+        self.nli_params = nli_params
+        self.stimulated_raman_scattering = None
+
+    @property
+    def fiber_params(self):
+        return self._fiber_params
+
+    @fiber_params.setter
+    def fiber_params(self, fiber_params):
+        self._fiber_params = fiber_params
+
+    @property
+    def stimulated_raman_scattering(self):
+        return self._stimulated_raman_scattering
+
+    @stimulated_raman_scattering.setter
+    def stimulated_raman_scattering(self, stimulated_raman_scattering):
+        self._stimulated_raman_scattering = stimulated_raman_scattering
+
+    @property
+    def nli_params(self):
+        return self._nli_params
+
+    @nli_params.setter
+    def nli_params(self, nli_params):
+        """
+        :param model_params: namedtuple containing the parameters used to compute the NLI.
+        """
+        self._nli_params = nli_params
+
+    def compute_nli(self, carrier, *carriers):
+        """ Compute NLI power generated by the WDM comb `*carriers` on the channel under test `carrier`
+        at the end of the fiber span.
+        """
+        if 'gn_model_analytic' == self.nli_params.nli_method_name.lower():
+            carrier_nli = self._gn_analytic(carrier, *carriers)
+        elif 'ggn_spectrally_separated' in self.nli_params.nli_method_name.lower():
+            eta_matrix = self._compute_eta_matrix(carrier, *carriers)
+            carrier_nli = self._carrier_nli_from_eta_matrix(eta_matrix, carrier, *carriers)
+        else:
+            raise ValueError(f'Method {self.nli_params.method_nli} not implemented.')
+
+        return carrier_nli
+
+    @staticmethod
+    def _carrier_nli_from_eta_matrix(eta_matrix, carrier, *carriers):
+        carrier_nli = 0
+        for pump_carrier_1 in carriers:
+            for pump_carrier_2 in carriers:
+                carrier_nli += eta_matrix[pump_carrier_1.channel_number-1, pump_carrier_2.channel_number-1] * \
+                               pump_carrier_1.power.signal * pump_carrier_2.power.signal
+        carrier_nli *= carrier.power.signal
+
+        return carrier_nli
+
+    def _compute_eta_matrix(self, carrier_cut, *carriers):
+        cut_index = carrier_cut.channel_number - 1
+        # Matrix initialization
+        matrix_size = max(carriers, key=lambda x: getattr(x, 'channel_number')).channel_number
+        eta_matrix = np.zeros(shape=(matrix_size, matrix_size))
+
+        # SPM
+        logger.debug(f'Start computing SPM on channel #{carrier_cut.channel_number}')
+        # SPM GGN
+        if 'ggn' in self.nli_params.nli_method_name.lower():
+            partial_nli = self._generalized_spectrally_separated_spm(carrier_cut)
+        # SPM GN
+        elif 'gn' in self.nli_params.nli_method_name.lower():
+            partial_nli = self._gn_analytic(carrier_cut, *[carrier_cut])
+        eta_matrix[cut_index, cut_index] = partial_nli / (carrier_cut.power.signal**3)
+
+        # XPM
+        for pump_carrier in carriers:
+            pump_index = pump_carrier.channel_number - 1
+            if not (cut_index == pump_index):
+                logger.debug(f'Start computing XPM on channel #{carrier_cut.channel_number} '
+                             f'from channel #{pump_carrier.channel_number}')
+                # XPM GGN
+                if 'ggn' in self.nli_params.nli_method_name.lower():
+                    partial_nli = self._generalized_spectrally_separated_xpm(carrier_cut, pump_carrier)
+                # XPM GGN
+                elif 'gn' in self.nli_params.nli_method_name.lower():
+                    partial_nli = self._gn_analytic(carrier_cut, *[pump_carrier])
+                eta_matrix[pump_index, pump_index] = partial_nli /\
+                                                     (carrier_cut.power.signal * pump_carrier.power.signal**2)
+        return eta_matrix
+
+    # Methods for computing GN-model
+    def _gn_analytic(self, carrier, *carriers):
+        """ Computes the nonlinear interference power on a single carrier.
+        The method uses eq. 120 from arXiv:1209.0394.
+        :param carrier: the signal under analysis
+        :param carriers: the full WDM comb
+        :return: carrier_nli: the amount of nonlinear interference in W on the carrier under analysis
+        """
+        alpha = self.fiber_params.alpha0() / 2
+        beta2 = self.fiber_params.beta2
+        gamma = self.fiber_params.gamma
+        length = self.fiber_params.length
+        effective_length = (1 - np.exp(-2 * alpha * length)) / (2 * alpha)
+        asymptotic_length = 1 / (2 * alpha)
+
+        g_nli = 0
+        for interfering_carrier in carriers:
+            g_interfearing = interfering_carrier.power.signal / interfering_carrier.baud_rate
+            g_signal = carrier.power.signal / carrier.baud_rate
+            g_nli += g_interfearing**2 * g_signal \
+                * _psi(carrier, interfering_carrier, beta2=self.fiber_params.beta2, asymptotic_length=1/self.fiber_params.alpha0())
+        g_nli *= (16.0 / 27.0) * (gamma * effective_length)**2 /\
+                 (2 * np.pi * abs(beta2) * asymptotic_length)
+        carrier_nli = carrier.baud_rate * g_nli
+        return carrier_nli
+
+    # Methods for computing the GGN-model
+    def _generalized_spectrally_separated_spm(self, carrier):
+        f_cut_resolution = self.nli_params.f_cut_resolution['delta_0']
+        f_eval = carrier.frequency
+        g_cut = (carrier.power.signal / carrier.baud_rate)
+
+        spm_nli = carrier.baud_rate * (16.0 / 27.0) * self.fiber_params.gamma**2 * g_cut**3 * \
+                  self._generalized_psi(carrier, carrier, f_eval, f_cut_resolution, f_cut_resolution)
+        return spm_nli
+
+    def _generalized_spectrally_separated_xpm(self, carrier_cut, pump_carrier):
+        delta_index = pump_carrier.channel_number - carrier_cut.channel_number
+        f_cut_resolution = self.nli_params.f_cut_resolution[f'delta_{delta_index}']
+        f_pump_resolution = self.nli_params.f_pump_resolution
+        f_eval = carrier_cut.frequency
+        g_pump = (pump_carrier.power.signal / pump_carrier.baud_rate)
+        g_cut = (carrier_cut.power.signal / carrier_cut.baud_rate)
+        frequency_offset_threshold = self._frequency_offset_threshold(pump_carrier.baud_rate)
+        if abs(carrier_cut.frequency - pump_carrier.frequency) <= frequency_offset_threshold:
+            xpm_nli = carrier_cut.baud_rate * (16.0 / 27.0) * self.fiber_params.gamma**2 * g_pump**2 * g_cut * \
+                      2 * self._generalized_psi(carrier_cut, pump_carrier, f_eval, f_cut_resolution, f_pump_resolution)
+        else:
+            xpm_nli = carrier_cut.baud_rate * (16.0 / 27.0) * self.fiber_params.gamma**2 * g_pump**2 * g_cut * \
+                      2 * self._fast_generalized_psi(carrier_cut, pump_carrier, f_eval, f_cut_resolution)
+        return xpm_nli
+
+    def _fast_generalized_psi(self, carrier_cut, pump_carrier, f_eval, f_cut_resolution):
+        """ It computes the generalized psi function similarly to the one used in the GN model
+        :return: generalized_psi
+        """
+        # Fiber parameters
+        alpha0 = self.fiber_params.alpha0(f_eval)
+        beta2 = self.fiber_params.beta2
+        beta3 = self.fiber_params.beta3
+        f_ref_beta = self.fiber_params.f_ref_beta
+        z = self.stimulated_raman_scattering.z
+        frequency_rho = self.stimulated_raman_scattering.frequency
+        rho_norm = self.stimulated_raman_scattering.rho * np.exp(np.abs(alpha0) * z / 2)
+        if len(frequency_rho) == 1:
+            rho_function = lambda f: rho_norm[0, :]
+        else:
+            rho_function = interp1d(frequency_rho, rho_norm, axis=0, fill_value='extrapolate')
+        rho_norm_pump = rho_function(pump_carrier.frequency)
+
+        f1_array = np.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 = np.arange(carrier_cut.frequency,
+                             carrier_cut.frequency + (carrier_cut.baud_rate * (1 + carrier_cut.roll_off) / 2),
+                             f_cut_resolution)  # Only positive f2 is used since integrand_f2 is symmetric
+
+        integrand_f1 = np.zeros(len(f1_array))
+        for f1_index, f1 in enumerate(f1_array):
+            delta_beta = 4 * np.pi**2 * (f1 - f_eval) * (f2_array - f_eval) * \
+                         (beta2 + np.pi * beta3 * (f1 + f2_array - 2 * f_ref_beta))
+            integrand_f2 = self._generalized_rho_nli(delta_beta, rho_norm_pump, z, alpha0)
+            integrand_f1[f1_index] = 2 * np.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
+
+    def _generalized_psi(self, carrier_cut, pump_carrier, f_eval, f_cut_resolution, f_pump_resolution):
+        """ It computes the generalized psi function similarly to the one used in the GN model
+        :return: generalized_psi
+        """
+        # Fiber parameters
+        alpha0 = self.fiber_params.alpha0(f_eval)
+        beta2 = self.fiber_params.beta2
+        beta3 = self.fiber_params.beta3
+        f_ref_beta = self.fiber_params.f_ref_beta
+        z = self.stimulated_raman_scattering.z
+        frequency_rho = self.stimulated_raman_scattering.frequency
+        rho_norm = self.stimulated_raman_scattering.rho * np.exp(np.abs(alpha0) * z / 2)
+        if len(frequency_rho) == 1:
+            rho_function = lambda f: rho_norm[0, :]
+        else:
+            rho_function = interp1d(frequency_rho, rho_norm, axis=0, fill_value='extrapolate')
+        rho_norm_pump = rho_function(pump_carrier.frequency)
+
+        f1_array = np.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),
+                             f_pump_resolution)
+        f2_array = np.arange(carrier_cut.frequency - (carrier_cut.baud_rate * (1 + carrier_cut.roll_off) / 2),
+                             carrier_cut.frequency + (carrier_cut.baud_rate * (1 + carrier_cut.roll_off) / 2),
+                             f_cut_resolution)
+        psd1 = raised_cosine_comb(f1_array, pump_carrier) * (pump_carrier.baud_rate / pump_carrier.power.signal)
+
+        integrand_f1 = np.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, carrier_cut) * (carrier_cut.baud_rate / carrier_cut.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 * np.pi**2 * (f1 - f_eval) * (f2_array - f_eval) * \
+                         (beta2 + np.pi * beta3 * (f1 + f2_array - 2 * f_ref_beta))
+
+            integrand_f2 = ggg * self._generalized_rho_nli(delta_beta, rho_norm_pump, z, alpha0)
+            integrand_f1[f1_index] = np.trapz(integrand_f2, f2_array)
+        generalized_psi = np.trapz(integrand_f1, f1_array)
+        return generalized_psi
+
+    @staticmethod
+    def _generalized_rho_nli(delta_beta, rho_norm_pump, z, alpha0):
+        w = 1j * delta_beta - alpha0
+        generalized_rho_nli = (rho_norm_pump[-1]**2 * np.exp(w * z[-1]) - rho_norm_pump[0]**2 * np.exp(w * z[0])) / w
+        for z_ind in range(0, len(z) - 1):
+            derivative_rho = (rho_norm_pump[z_ind + 1]**2 - rho_norm_pump[z_ind]**2) / (z[z_ind + 1] - z[z_ind])
+            generalized_rho_nli -= derivative_rho * (np.exp(w * z[z_ind + 1]) - np.exp(w * z[z_ind])) / (w**2)
+        generalized_rho_nli = np.abs(generalized_rho_nli)**2
+        return generalized_rho_nli
+
+    def _frequency_offset_threshold(self, symbol_rate):
+        k_ref = 5
+        beta2_ref = 21.3e-27
+        delta_f_ref = 50e9
+        rs_ref = 32e9
+        freq_offset_th = ((k_ref * delta_f_ref) * rs_ref * beta2_ref) / (self.fiber_params.beta2 * symbol_rate)
+        return freq_offset_th
+
+def _psi(carrier, interfering_carrier, beta2, asymptotic_length):
+    """Calculates eq. 123 from `arXiv:1209.0394 <https://arxiv.org/abs/1209.0394>`__"""
+
+    if carrier.channel_number == interfering_carrier.channel_number: # SCI, SPM
+        psi = np.arcsinh(0.5 * np.pi**2 * asymptotic_length * abs(beta2) * carrier.baud_rate**2)
+    else: # XCI, XPM
+        delta_f = carrier.frequency - interfering_carrier.frequency
+        psi = np.arcsinh(np.pi**2 * asymptotic_length * abs(beta2) *
+                         carrier.baud_rate * (delta_f + 0.5 * interfering_carrier.baud_rate))
+        psi -= np.arcsinh(np.pi**2 * asymptotic_length * abs(beta2) *
+                          carrier.baud_rate * (delta_f - 0.5 * interfering_carrier.baud_rate))
+    return psi

gnpy/core/service_sheet.py

@@ -0,0 +1,256 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+gnpy.core.service_sheet
+========================
+
+XLS parser that can be called to create a JSON request file in accordance with
+Yang model for requesting path computation.
+
+See: draft-ietf-teas-yang-path-computation-01.txt
+"""
+
+from sys import exit
+try:
+    from xlrd import open_workbook, XL_CELL_EMPTY
+except ModuleNotFoundError:
+    exit('Required: `pip install xlrd`')
+from collections import namedtuple
+from logging import getLogger, basicConfig, CRITICAL, DEBUG, INFO
+from json import dumps
+from pathlib import Path
+from gnpy.core.equipment import load_equipment
+from gnpy.core.utils import db2lin, lin2db
+
+SERVICES_COLUMN = 12
+#EQPT_LIBRARY_FILENAME = Path(__file__).parent / 'eqpt_config.json'
+
+all_rows = lambda sheet, start=0: (sheet.row(x) for x in range(start, sheet.nrows))
+logger = getLogger(__name__)
+
+# Type for input data
+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)
+
+# Type for output data:  // from dutc
+class Element:
+    def __eq__(self, other):
+        return type(self) == type(other) and self.uid == other.uid
+    def __hash__(self):
+        return hash((type(self), self.uid))
+
+class Request_element(Element):
+    def __init__(self,Request,eqpt_filename):
+        # 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 = Request.source
+        self.destination = Request.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}'
+        # test that trx_type belongs to eqpt_config.json
+        # if not replace it with a default
+        equipment = load_equipment(eqpt_filename)
+        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
+                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)
+                    exit(1)
+            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)
+            exit()
+        # excel input are in GHz and dBm
+        if Request.spacing is not None:
+            self.spacing = Request.spacing * 1e9
+        else:
+            msg = f'Request {self.request_id} missing spacing: spacing is mandatory.\ncomputation stopped'
+            logger.critical(msg)
+            exit()
+        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
+        
+        value = correct_xlrd_int_to_str_reading(Request.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(' | ')
+
+        # cleaning the list of nodes to remove source and destination
+        # (because the remaining of the program assumes that the nodes list are nodes 
+        # on the path and should not include source and destination)
+        try :
+            self.nodes_list.remove(self.source)
+            msg = f'{self.source} removed from explicit path node-list'
+            logger.info(msg)
+        except ValueError:
+            msg = f'{self.source} already removed from explicit path node-list'
+            logger.info(msg)
+
+        try :
+            self.nodes_list.remove(self.destination)
+            msg = f'{self.destination} removed from explicit path node-list'
+            logger.info(msg)
+        except ValueError:
+            msg = f'{self.destination} already removed from explicit path node-list'
+            logger.info(msg)
+
+        # the excel parser applies the same hop-type to all nodes in the route nodes_list.
+        # user can change this per node in the generated json
+        self.loose = 'loose'
+        if Request.is_loose == '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))
+    @property
+    def pathrequest(self):
+        req_dictionnary = {
+                    'request-id':self.request_id,
+                    'source':    self.source,
+                    'destination':  self.destination,
+                    'src-tp-id': self.srctpid,
+                    'dst-tp-id': self.dsttpid,
+                    'path-constraints':{
+                        'te-bandwidth': {
+                            'technology': 'flexi-grid',
+                            'trx_type'  : self.trx_type,
+                            'trx_mode'  : self.mode,
+                            'effective-freq-slot':[{'n': 'null','m': 'null'}] ,
+                            'spacing'   : self.spacing,
+                            'max-nb-of-channel'  : self.nb_channel,
+                            'output-power'       : self.power
+                            # 'path_bandwidth'       : self.path_bandwidth 
+                        }
+                    },
+                    'optimizations': {
+                        'explicit-route-include-objects': [
+                        {
+                            'index': self.nodes_list.index(node),
+                            'unnumbered-hop':{
+                                'node-id': f'{node}',
+                                'link-tp-id': 'link-tp-id is not used',
+                                'hop-type': f'{self.loose}',
+                                'direction': 'direction is not used'
+                            },
+                            'label-hop':{
+                                'te-label': {
+                                    'generic': 'generic is not used',
+                                    'direction': 'direction is not used'
+                                }
+                            }
+                        }
+                        for node in self.nodes_list
+                    ]
+
+                }
+            }
+        if self.path_bandwidth is not None:
+            req_dictionnary['path-constraints']['te-bandwidth']['path_bandwidth'] = self.path_bandwidth
+            
+        return req_dictionnary
+    @property
+    def pathsync(self):
+        if self.disjoint_from :
+            return {'synchronization-id':self.request_id,
+                'svec': {
+                    'relaxable' : 'False',
+                    'link-diverse': 'True',
+                    'node-diverse': 'True',
+                    'request-id-number': [self.request_id]+ [n for n in self.disjoint_from]
+                }
+            }
+        # TO-DO: avoid multiple entries with same synchronisation vectors
+    @property
+    def json(self):
+        return self.pathrequest , self.pathsync
+
+def convert_service_sheet(input_filename, eqpt_filename, output_filename='', filter_region=[]):
+    service = parse_excel(input_filename)
+    req = [Request_element(n,eqpt_filename) for n in service]
+    # dumps the output into a json file with name
+    # split_filename = [input_filename[0:len(input_filename)-len(suffix_filename)] , suffix_filename[1:]]
+    if output_filename=='':
+        output_filename = f'{str(input_filename)[0:len(str(input_filename))-len(str(input_filename.suffixes[0]))]}_services.json'
+    # for debug
+    # print(json_filename)
+    data = {
+        'path-request': [n.json[0] for n in req],
+        'synchronization': [n.json[1] for n in req
+        if n.json[1] is not None]
+    }
+    with open(output_filename, 'w', encoding='utf-8') as f:
+            f.write(dumps(data, indent=2, ensure_ascii=False))
+    return data
+
+def correct_xlrd_int_to_str_reading(v) :
+    if not isinstance(v,str):
+        value = str(int(v))
+        if value.endswith('.0'):
+            value = value[:-2]
+    else:
+        value = v
+    return value
+
+# to be used from dutc
+def parse_row(row, fieldnames):
+    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):
+    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):
+        logger.info(f'Validating headers on {service_sheet.name!r}')
+        # 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] if len(x.value.strip())>0]
+
+        # create a service_fieldname independant from the excel column order
+        # to be compatible with any version of the sheet
+        # the following dictionnary records the excel field names and the corresponding parameter's name
+
+        authorized_fieldnames = {'route id':'request_id', 'Source':'source', 'Destination':'destination', \
+         'TRX type':'trx_type', 'Mode' : 'mode', 'System: spacing':'spacing', \
+         'System: input power (dBm)':'power', 'System: nb of channels':'nb_channel',\
+         'routing: disjoint from': 'disjoint_from', 'routing: path':'nodes_list',\
+         'routing: is loose?':'is_loose', 'path bandwidth':'path_bandwidth'}
+        try :
+            service_fieldnames = [authorized_fieldnames[e] for e in header]
+        except KeyError:
+            msg = f'Malformed header on Service sheet: {header} field not in {authorized_fieldnames}'
+            logger.critical(msg)
+            raise ValueError(msg)
+        for row in all_rows(service_sheet, start=5):
+            yield Request(**parse_row(row[0:SERVICES_COLUMN], service_fieldnames))

gnpy/core/units.py

@@ -1,2 +1,5 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
 UNITS = {'m': 1,
          'km': 1E3}

gnpy/core/utils.py

@@ -1,28 +1,76 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 
+'''
+gnpy.core.utils
+===============
+
+This module contains utility functions that are used with gnpy.
+'''
+
+
 import json
 
+from csv import writer
 import numpy as np
 from numpy import pi, cos, sqrt, log10
+from scipy import constants
 
 
 def load_json(filename):
-    with open(filename, 'r') as f:
+    with open(filename, 'r', encoding='utf-8') as f:
         data = json.load(f)
     return data
 
 
 def save_json(obj, filename):
-    with open(filename, 'w') as f:
-        json.dump(obj, f)
+    with open(filename, 'w', encoding='utf-8') as f:
+        json.dump(obj, f, indent=2, ensure_ascii=False)
 
+def write_csv(obj, filename):
+    """
+    convert dictionary items to a csv file
+    the dictionary format :
+
+    {'result category 1':
+                        [
+                        # 1st line of results
+                        {'header 1' : value_xxx,
+                         'header 2' : value_yyy},
+                         # 2nd line of results: same headers, different results
+                        {'header 1' : value_www,
+                         'header 2' : value_zzz}
+                        ],
+    'result_category 2':
+                        [
+                        {},{}
+                        ]
+    }
+
+    the generated csv file will be:
+    result_category 1
+    header 1    header 2
+    value_xxx   value_yyy
+    value_www   value_zzz
+    result_category 2
+    ...
+    """
+    with open(filename, 'w', encoding='utf-8') as f:
+        w = writer(f)
+        for data_key, data_list in obj.items():
+            #main header
+            w.writerow([data_key])
+            #sub headers:
+            headers = [_ for _ in data_list[0].keys()]
+            w.writerow(headers)
+            for data_dict in data_list:
+                w.writerow([_ for _ in data_dict.values()])
 
 def c():
     """
     Returns the speed of light in meters per second
     """
-    return 299792458.0
+    return constants.c
 
 
 def itufs(spacing, startf=191.35, stopf=196.10):
@@ -40,12 +88,26 @@ def itufs(spacing, startf=191.35, stopf=196.10):
     """
     return np.arange(startf, stopf + spacing / 2, spacing)
 
+def itufl(length, startf=191.35, stopf=196.10):
+    """Creates an array of frequencies whose default range is
+    191.35-196.10 THz
+
+    :param length: number of elements
+    :param starf: Start frequency in THz
+    :param stopf: Stop frequency in THz
+    :type length: integer
+    :type startf: float
+    :type stopf: float
+    :return an array of frequnecies determined by the spacing parameter
+    :rtype: numpy.ndarray
+    """
+    return np.linspace(startf, stopf, length)
 
 def h():
     """
     Returns plank's constant in J*s
     """
-    return 6.62607004e-34
+    return constants.h
 
 
 def lin2db(value):
@@ -55,18 +117,27 @@ def lin2db(value):
 def db2lin(value):
     return 10**(value / 10)
 
+def round2float(number, step):
+    step = round(step, 1)
+    if step >= 0.01:
+        number = round(number / step, 0)
+        number = round(number * step, 1)
+    else:
+        number = round(number, 2)
+    return number
 
-def wavelength2freq(value):
-    """ Converts wavelength units to frequeuncy units.
-    """
-    return c() / value
-
+wavelength2freq = constants.lambda2nu
+freq2wavelength = constants.nu2lambda
 
 def freq2wavelength(value):
     """ Converts frequency units to wavelength units.
     """
     return 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 deltawl2deltaf(delta_wl, wavelength):
     """ deltawl2deltaf(delta_wl, wavelength):
@@ -128,3 +199,43 @@ def rrc(ffs, baud_rate, alpha):
     p_inds = np.where(np.logical_and(np.abs(ffs) > 0, np.abs(ffs) < l_lim))
     hf[p_inds] = 1
     return sqrt(hf)
+
+def merge_amplifier_restrictions(dict1, dict2):
+    """Updates contents of dicts recursively
+
+    >>> d1 = {'params': {'restrictions': {'preamp_variety_list': [], 'booster_variety_list': []}}}
+    >>> d2 = {'params': {'target_pch_out_db': -20}}
+    >>> merge_amplifier_restrictions(d1, d2)
+    {'params': {'restrictions': {'preamp_variety_list': [], 'booster_variety_list': []}, 'target_pch_out_db': -20}}
+
+    >>> d3 = {'params': {'restrictions': {'preamp_variety_list': ['foo'], 'booster_variety_list': ['bar']}}}
+    >>> merge_amplifier_restrictions(d1, d3)
+    {'params': {'restrictions': {'preamp_variety_list': [], 'booster_variety_list': []}}}
+    """
+
+    copy_dict1 = dict1.copy()
+    for key in dict2:
+        if key in dict1:
+            if isinstance(dict1[key], dict):
+                copy_dict1[key] = merge_amplifier_restrictions(copy_dict1[key], dict2[key])
+        else:
+            copy_dict1[key] = dict2[key]
+    return copy_dict1
+
+def silent_remove(this_list, elem):
+    """Remove matching elements from a list without raising ValueError
+
+    >>> li = [0, 1]
+    >>> li = silent_remove(li, 1)
+    >>> li
+    [0]
+    >>> li = silent_remove(li, 1)
+    >>> li
+    [0]
+    """
+
+    try:
+        this_list.remove(elem)
+    except ValueError:
+        pass
+    return this_list

hooks/pre-commit

@@ -0,0 +1,3 @@
+#!/bin/sh
+
+exec pytest

json_structure_description.rst

@@ -0,0 +1,559 @@
+*********************************************
+Equipment and Network description definitions
+*********************************************
+
+1. Equipment description
+########################
+
+Equipment description defines equipment types and those parameters.
+Description is made in JSON file with predefined structure. By default
+**transmission_main_example.py** uses **eqpt_config.json** file and that
+can be changed with **-e** or **--equipment** command line parameter.
+Parsing of JSON file is made with
+**gnpy.core.equipment.load_equipment(equipment_description)** and return
+value is a dictionary of format **dict[‘equipment
+type’][‘subtype’]=object**
+
+1.1. Structure definition
+*************************
+
+1.1.1. Equipment types
+*************************
+
+Every equipment type is defined in JSON root with according name and
+array of parameters as value.
+
+.. code-block:: none
+
+    {"Edfa": [...],
+    "Fiber": [...]
+    }
+
+
+1.1.2. Equipment parameters and subtypes
+*****************************************
+
+
+Array of parameters is a list of objects with unordered parameter name
+and its value definition. In case of multiple equipment subtypes each
+object contains **"type_variety":”type name”** name:value combination,
+if only one subtype exists **"type_variety"** name is not mandatory and
+it will be marked with **”default”** value.
+
+.. code-block:: json
+
+    {"Edfa": [{
+                "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
+                }
+        ],
+    "Fiber": [{
+                "type_variety": "SSMF",
+                "dispersion": 1.67e-05,
+                "gamma": 0.00127
+                }
+        ]
+    }
+
+
+
+1.2. Equipment parameters by type
+*********************************
+
+1.2.1. EDFA element
+*******************
+
+Four types of EDFA definition are possible. Description JSON file
+location is in **transmission_main_example.py** folder:
+
+-  Advanced – with JSON file describing gain/noise figure tilt and
+   gain/noise figure ripple. **"advanced_config_from_json"** value
+   contains filename.
+
+.. code-block:: json-object
+
+    "Edfa":[{
+            "type_variety": "high_detail_model_example",
+            "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
+            }
+        ]
+
+-  Variable gain – with JSON file describing gain figure tilt and gain/noise
+   figure ripple. **”default_edfa_config.json”** as source file.
+
+.. code-block:: json-object
+
+    "Edfa":[{
+            "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
+            }
+        ]
+
+-  Fixed gain – with JSON file describing gain figure tilt and gain/noise
+   figure ripple. **”default_edfa_config.json”** as source file.
+
+.. code-block:: json-object
+
+    "Edfa":[{
+            "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
+            }
+        ]
+
+- openroadm – with JSON file describing gain figure tilt and gain/noise
+   figure ripple. **”default_edfa_config.json”** as source file. 
+
+.. code-block:: json-object
+
+    "Edfa":[{
+            "type_variety": "low_noise",
+            "type_def": "openroadm",
+            "gain_flatmax": 27,
+            "gain_min": 12,
+            "p_max": 22,
+            "nf_coef": [-8.104e-4,-6.221e-2,-5.889e-1,37.62],
+            "allowed_for_design": false
+            }
+        ]
+
+1.2.2. Fiber element
+********************
+
+Fiber element with its parameters:
+
+.. code-block:: json-object
+
+    "Fiber":[{
+            "type_variety": "SSMF",
+            "dispersion": 1.67e-05,
+            "gamma": 0.00127
+            }
+        ]
+
+RamanFiber element
+******************
+
+A special variant of the regular ``Fiber`` where the simulation engine accounts for the Raman effect.
+The newly added parameters are nested in the ``raman_efficiency`` dictionary.
+Its shape corresponds to typical properties of silica.
+More details are available from :cite:`curri_merit_2016`.
+
+The ``cr`` property is the normailzed Raman efficiency, so it is is (almost) independent of the fiber type, while the coefficient actually giving Raman gain is g_R=C_R/Aeff.
+
+The ``frequency_offset`` represents the spectral difference between the pumping photon and the one receiving energy.
+
+.. code-block:: json-object
+
+    "RamanFiber":[{
+      "type_variety": "SSMF",
+      "dispersion": 1.67e-05,
+      "gamma": 0.00127,
+      "raman_efficiency": {
+        "cr":[
+            0, 9.4E-06, 2.92E-05, 4.88E-05, 6.82E-05, 8.31E-05, 9.4E-05, 0.0001014, 0.0001069, 0.0001119,
+            0.0001217, 0.0001268, 0.0001365, 0.000149, 0.000165, 0.000181, 0.0001977, 0.0002192, 0.0002469,
+            0.0002749, 0.0002999, 0.0003206, 0.0003405, 0.0003592, 0.000374, 0.0003826, 0.0003841, 0.0003826,
+            0.0003802, 0.0003756, 0.0003549, 0.0003795, 0.000344, 0.0002933, 0.0002024, 0.0001158, 8.46E-05,
+            7.14E-05, 6.86E-05, 8.5E-05, 8.93E-05, 9.01E-05, 8.15E-05, 6.67E-05, 4.37E-05, 3.28E-05, 2.96E-05,
+            2.65E-05, 2.57E-05, 2.81E-05, 3.08E-05, 3.67E-05, 5.85E-05, 6.63E-05, 6.36E-05, 5.5E-05, 4.06E-05,
+            2.77E-05, 2.42E-05, 1.87E-05, 1.6E-05, 1.4E-05, 1.13E-05, 1.05E-05, 9.8E-06, 9.8E-06, 1.13E-05,
+            1.64E-05, 1.95E-05, 2.38E-05, 2.26E-05, 2.03E-05, 1.48E-05, 1.09E-05, 9.8E-06, 1.05E-05, 1.17E-05,
+            1.25E-05, 1.21E-05, 1.09E-05, 9.8E-06, 8.2E-06, 6.6E-06, 4.7E-06, 2.7E-06, 1.9E-06, 1.2E-06, 4E-07,
+            2E-07, 1E-07
+        ],
+        "frequency_offset":[
+          0, 0.5e12, 1e12, 1.5e12, 2e12, 2.5e12, 3e12, 3.5e12, 4e12, 4.5e12, 5e12, 5.5e12, 6e12, 6.5e12, 7e12,
+          7.5e12, 8e12, 8.5e12, 9e12, 9.5e12, 10e12, 10.5e12, 11e12, 11.5e12, 12e12, 12.5e12, 12.75e12,
+          13e12, 13.25e12, 13.5e12, 14e12, 14.5e12, 14.75e12, 15e12, 15.5e12, 16e12, 16.5e12, 17e12,
+          17.5e12, 18e12, 18.25e12, 18.5e12, 18.75e12, 19e12, 19.5e12, 20e12, 20.5e12, 21e12, 21.5e12,
+          22e12, 22.5e12, 23e12, 23.5e12, 24e12, 24.5e12, 25e12, 25.5e12, 26e12, 26.5e12, 27e12, 27.5e12, 28e12,
+          28.5e12, 29e12, 29.5e12, 30e12, 30.5e12, 31e12, 31.5e12, 32e12, 32.5e12, 33e12, 33.5e12, 34e12, 34.5e12,
+          35e12, 35.5e12, 36e12, 36.5e12, 37e12, 37.5e12, 38e12, 38.5e12, 39e12, 39.5e12, 40e12, 40.5e12, 41e12,
+          41.5e12, 42e12
+        ]
+        }
+      }
+    ]
+
+
+1.2.3 Roadm element
+*******************
+
+Roadm element with its parameters:
+
+.. code-block:: json-object
+
+      "Roadms":[{
+            "gain_mode_default_loss": 20,
+            "power_mode_pout_target": -20,
+            "add_drop_osnr": 38
+            }
+        ]
+
+1.2.3. Spans element
+********************
+
+Spans element with its parameters:
+
+.. code-block:: json-object
+
+    "Spans":[{
+            "power_mode":true,
+            "delta_power_range_db": [0,0,0.5],
+            "max_length": 150,
+            "length_units": "km",
+            "max_loss": 28,
+            "padding": 10,
+            "EOL": 0,
+            "con_in": 0,
+            "con_out": 0
+            }
+        ]
+
+
+1.2.4. Spectral Information
+***************************
+
+Spectral information with its parameters:
+
+.. code-block:: json-object
+
+    "SI":[{
+            "f_min": 191.3e12,
+            "baud_rate": 32e9,
+            "f_max":195.1e12,
+            "spacing": 50e9,
+            "power_dbm": 0,
+            "power_range_db": [0,0,0.5],
+            "roll_off": 0.15,
+            "tx_osnr": 40,
+            "sys_margins": 0
+            }
+        ]
+
+
+1.2.5. Transceiver element
+**************************
+
+Transceiver element with its parameters. **”mode”** can contain multiple
+Transceiver operation formats.
+
+Note that ``OSNR`` parameter refers to the receiver's minimal OSNR threshold for a given mode.
+
+.. code-block:: json-object
+
+    "Transceiver":[{
+                    "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
+                            }
+                    ]
+                }
+        ]
+
+***********************
+2. Network description
+***********************
+
+Network description defines network elements with additional to
+equipment description parameters, metadata and elements interconnection.
+Description is made in JSON file with predefined structure. By default
+**transmission_main_example.py** uses **edfa_example_network.json** file
+and can be changed from command line. Parsing of JSON file is made with
+**gnpy.core.network.load_network(network_description,
+equipment_description)** and return value is **DiGraph** object which
+mimics network description.
+
+2.1. Structure definition
+##########################
+
+2.1.1. File root structure
+***************************
+
+Network description JSON file root consist of three unordered parts:
+
+-  network_name – name of described network or service, is not used as
+   of now
+
+-  elements - contains array of network element objects with their
+   respective parameters
+
+-  connections – contains array of unidirectional connection objects
+
+.. code-block:: none
+
+    {"network_name": "Example Network",
+    "elements": [{...},
+                {...}
+                ],
+    "connections": [{...},
+                    {...}
+                    ]
+    }
+
+
+2.1.2. Elements parameters and subtypes
+****************************************
+
+Array of network element objects consist of unordered parameter names
+and those values. In case of **"type_variety"** absence
+**"type_variety":”default”** name:value combination is used. As of the
+moment, existence of used **"type_variety"** in equipment description is
+obligatory.
+
+2.2. Element parameters by type
+*********************************
+
+2.2.1. Transceiver element
+***************************
+
+Transceiver element with its parameters.
+
+.. code-block:: json
+
+    {"uid": "trx Site_A",
+    "metadata": {
+                "location": {
+                            "city": "Site_A",
+                            "region": "",
+                            "latitude": 0,
+                            "longitude": 0
+                            }
+                },
+    "type": "Transceiver"
+    }
+
+
+
+2.2.2. ROADM element
+*********************
+
+ROADM element with its parameters. **“params”** is optional, if not used
+default loss value of 20dB is used.
+
+.. code-block:: json
+
+    {"uid": "roadm Site_A",
+    "metadata": {
+                "location": {
+                            "city": "Site_A",
+                            "region": "",
+                            "latitude": 0,
+                            "longitude": 0
+                            }
+                },
+    "type": "Roadm",
+    "params": {
+                "loss": 17
+            }
+    }
+
+
+2.2.3. Fused element
+*********************
+
+Fused element with its parameters. **“params”** is optional, if not used
+default loss value of 1dB is used.
+
+.. code-block:: json
+
+    {"uid": "ingress fused spans in Site_B",
+    "metadata": {
+                "location": {
+                            "city": "Site_B",
+                            "region": "",
+                            "latitude": 0,
+                            "longitude": 0
+                            }
+                },
+    "type": "Fused",
+    "params": {
+                "loss": 0.5
+        }
+    }
+
+
+2.2.4. Fiber element
+*********************
+
+Fiber element with its parameters.
+
+.. code-block:: json
+
+    {"uid": "fiber (Site_A \\u2192 Site_B)",
+    "metadata": {
+                "location": {
+                            "city": "",
+                            "region": "",
+                            "latitude": 0.0,
+                            "longitude": 0.0
+                            }
+                },
+    "type": "Fiber",
+    "type_variety": "SSMF",
+    "params": {
+                "length": 40.0,
+                "length_units": "km",
+                "loss_coef": 0.2
+                }
+    }
+
+2.2.5. RamanFiber element
+*************************
+
+.. code-block:: json
+
+    {
+      "uid": "Span1",
+      "type": "RamanFiber",
+      "type_variety": "SSMF",
+      "operational": {
+        "temperature": 283,
+        "raman_pumps": [
+          {
+            "power": 200e-3,
+            "frequency": 205e12,
+            "propagation_direction": "counterprop"
+          },
+          {
+            "power": 206e-3,
+            "frequency": 201e12,
+            "propagation_direction": "counterprop"
+          }
+        ]
+      },
+      "params": {
+        "type_variety": "SSMF",
+        "length": 80.0,
+        "loss_coef": 0.2,
+        "length_units": "km",
+        "att_in": 0,
+        "con_in": 0.5,
+        "con_out": 0.5
+      },
+      "metadata": {
+        "location": {
+          "latitude": 1,
+          "longitude": 0,
+          "city": null,
+          "region": ""
+        }
+      }
+    }
+
+
+2.2.6. EDFA element
+********************
+
+EDFA element with its parameters.
+
+.. code-block:: json
+
+    {"uid": "Edfa1",
+    "type": "Edfa",
+    "type_variety": "std_low_gain",
+    "operational": {
+                    "gain_target": 16,
+                    "tilt_target": 0
+                    },
+    "metadata": {
+                "location": {
+                            "city": "Site_A",
+                            "region": "",
+                            "latitude": 2,
+                            "longitude": 0
+                            }
+                }
+    }
+
+2.3. Connections objects
+*************************
+
+Each unidirectional connection object in connections array consist of
+two unordered **”from_node”** and **”to_node”** name pair with values
+corresponding to element **”uid”**
+
+.. code-block:: json
+
+    {"from_node": "roadm Site_C",
+    "to_node": "trx Site_C"
+    }
+
+************************
+3. Simulation Parameters
+************************
+
+Additional details of the simulation are controlled via ``sim_params.json``:
+
+.. code-block:: json
+
+  {
+    "raman_computed_channels": [1, 18, 37, 56, 75],
+    "raman_parameters": {
+      "flag_raman": true,
+      "space_resolution": 10e3,
+      "tolerance": 1e-8
+    },
+    "nli_parameters": {
+      "nli_method_name": "ggn_spectrally_separated",
+      "wdm_grid_size": 50e9,
+      "dispersion_tolerance": 1,
+      "phase_shift_tollerance": 0.1
+    }
+  }

path_result_template.json

@@ -0,0 +1,40 @@
+{
+  "paths": [
+    {
+      "path": {
+        "path-id": null,
+        "path-properties": {
+          "path-metric": [
+            {
+              "metric-type": null,
+              "accumulative-value": null
+            }
+          ],
+          "path-srlgs": {
+            "usage": "not used yet",
+            "values": ["not used yet"]
+          },
+          "path-route-objects": [
+            {
+              "path-route-object": {
+                "index": null,
+                "unnumbered-hop": {
+                  "node-id": null,
+                  "link-tp-id": null,
+                  "hop-type": null,
+                  "direction": "not used"
+                },
+                "label-hop": {
+                  "te-label": {
+                    "generic": "not used yet",
+                    "direction": "not used yet"
+                  }
+                }
+              }
+            }
+          ]
+        }
+      }
+    }
+  ]
+}

pytest.ini

@@ -0,0 +1,2 @@
+[pytest]
+addopts = --doctest-modules

requirements.txt

@@ -1,36 +1,10 @@
-alabaster==0.7.10
-attrs==17.4.0
-Babel==2.5.3
-certifi==2017.11.5
-chardet==3.0.4
-cycler==0.10.0
-decorator==4.1.2
-docutils==0.14
-idna==2.6
-imagesize==0.7.1
-Jinja2==2.10
-latexcodec==1.0.5
-MarkupSafe==1.0
-matplotlib==2.1.0
-networkx==2.0
-numpy==1.13.3
-oset==0.1.3
-pluggy==0.6.0
-py==1.5.2
-pybtex==0.21
-pybtex-docutils==0.2.1
-Pygments==2.2.0
-pyparsing==2.2.0
-pytest==3.3.2
-python-dateutil==2.6.1
-pytz==2017.3
-PyYAML==3.12
-requests==2.18.4
-scipy==1.0.0
-six==1.11.0
-snowballstemmer==1.2.1
-Sphinx==1.6.6
-sphinxcontrib-bibtex==0.3.6
-sphinxcontrib-websupport==1.0.1
-urllib3==1.22
-xlrd==1.1.0
+alabaster>=0.7.12,<1
+matplotlib>=3.1.0,<4
+networkx>=2.3,<3
+numpy>=1.16.1,<2
+Pygments>=2.4.2,<3
+pytest>=4.0.0,<5
+scipy>=1.3.0,<2
+Sphinx>=2.1.1,<3
+sphinxcontrib-bibtex>=0.4.2,<1
+xlrd>=1.2.0,<2

service-template.json

@@ -0,0 +1,60 @@
+{
+  "path-request": [
+    {
+      "request-id": null,
+      "source": null,
+      "destination": null,
+      "src-tp-id": null,
+      "dst-tp-id": null,
+      "path-constraints": {
+        "te-bandwidth": {
+          "technology": "flexi-grid",
+          "trx_type": null,
+          "trx_mode": null,
+          "effective-freq-slot": [
+            {
+              "n": "null",
+              "m": "null"
+            }
+          ],
+          "spacing": null,
+          "max-nb-of-channel": null,
+          "output-power": null,
+          "path_bandwidth": null
+          }
+      },
+      "optimizations": {
+        "explicit-route-include-objects": {
+          "route-object-include-object": [
+          {
+            "index": null,
+            "unnumbered-hop": {
+              "node-id": null,
+              "link-tp-id": "link-tp-id is not used",
+              "hop-type": null,
+              "direction": "direction is not used"
+            },
+            "label-hop": {
+              "te-label": {
+                "generic": "generic is not used",
+                "direction": "direction is not used"
+              }
+            }
+          }
+          ]
+        }
+      }
+    }],
+  "synchronization": [
+    {
+      "synchronization-id": null,
+      "svec": {
+        "relaxable": "True",
+        "link-diverse": "False",
+        "node-diverse": "False",
+        "request-id-number": [
+         null ]
+        },
+    }
+    ]
+}

setup.py

@@ -11,29 +11,29 @@ with open(path.join(here, 'README.rst'), encoding='utf-8') as f:
 
 setup(
     name='gnpy',
-    version='0.0.1',
-    description='TIP optical network modeling library',
+    version='1.2.0',
+    description='route planning and optimization tool for mesh optical networks',
     long_description=long_description,
+    long_description_content_type='text/x-rst; charset=UTF-8',
     url='https://github.com/Telecominfraproject/gnpy',
     author='Telecom Infra Project',
+    author_email='jan.kundrat@telecominfraproject.com',
     classifiers=[
         'Development Status :: 3 - Alpha',
+        'Intended Audience :: Developers',
+        'Intended Audience :: Science/Research',
+        'Intended Audience :: Telecommunications Industry',
+        'License :: OSI Approved :: BSD License',
+        'Natural Language :: English',
+        'Programming Language :: Python',
+        'Programming Language :: Python :: 3 :: Only',
+        'Programming Language :: Python :: 3.6',
+        'Programming Language :: Python :: Implementation :: CPython',
         'Topic :: Scientific/Engineering',
         'Topic :: Scientific/Engineering :: Physics',
-        'License :: OSI Approved :: BSD License',
-        'Programming Language :: Python :: 3.6',
     ],
-    keywords='optics network fiber communication',
-    packages=find_packages(exclude=['examples', 'docs', 'tests']),  # Required
-    install_requires=['cycler',
-                      'decorator',
-                      'matplotlib',
-                      'networkx',
-                      'numpy',
-                      'scipy',
-                      'pyparsing',
-                      'python-dateutil',
-                      'pytz',
-                      'six',
-                      'xlrd']
+    keywords='optics network fiber communication route planning optimization',
+    #packages=find_packages(exclude=['examples', 'docs', 'tests']),  # Required
+    packages=find_packages(exclude=['docs', 'tests']),  # Required
+    install_requires=list(open('requirements.txt'))
 )

tests/LinkforTest.json

@@ -0,0 +1,299 @@
+{
+  "elements": [
+    {
+      "uid": "trx A",
+      "metadata": {
+        "location": {
+          "city": "A",
+          "region": "",
+          "latitude": 0,
+          "longitude": 0
+        }
+      },
+      "type": "Transceiver"
+    },
+    {
+      "uid": "trx B",
+      "metadata": {
+        "location": {
+          "city": "B",
+          "region": "",
+          "latitude": 0,
+          "longitude": 0
+        }
+      },
+      "type": "Transceiver"
+    },
+    {
+      "uid": "trx F",
+      "metadata": {
+        "location": {
+          "city": "F",
+          "region": "",
+          "latitude": 0,
+          "longitude": 0
+        }
+      },
+      "type": "Transceiver"
+    },
+    {
+      "uid": "fiber (A \u2192 B)-",
+      "metadata": {
+        "location": {
+          "latitude": 0.0,
+          "longitude": 0.0
+        }
+      },
+      "type": "Fiber",
+      "type_variety": "SSMF",
+      "params": {
+        "length": 80.0,
+        "length_units": "km",
+        "loss_coef": 0.2,
+        "con_in": 1.00,
+        "con_out": 1.00
+      }
+    },
+    {
+      "uid": "fiber (B \u2192 C)-",
+      "metadata": {
+        "location": {
+          "latitude": 0.0,
+          "longitude": 0.0
+        }
+      },
+      "type": "Fiber",
+      "type_variety": "SSMF",
+      "params": {
+        "length": 80.0,
+        "length_units": "km",
+        "loss_coef": 0.2,
+        "con_in": 1.00,
+        "con_out": 1.00
+      }
+    },
+    {
+      "uid": "fiber (C \u2192 D)-",
+      "metadata": {
+        "location": {
+          "latitude": 0.0,
+          "longitude": 0.0
+        }
+      },
+      "type": "Fiber",
+      "type_variety": "SSMF",
+      "params": {
+        "length": 80.0,
+        "length_units": "km",
+        "loss_coef": 0.2,
+        "con_in": 1.00,
+        "con_out": 1.00
+      }
+    },
+    {
+      "uid": "fiber (D \u2192 E)-",
+      "metadata": {
+        "location": {
+          "latitude": 0.0,
+          "longitude": 0.0
+        }
+      },
+      "type": "Fiber",
+      "type_variety": "SSMF",
+      "params": {
+        "length": 80.0,
+        "length_units": "km",
+        "loss_coef": 0.2,
+        "con_in": 1.00,
+        "con_out": 1.00
+      }
+    },
+    {
+      "uid": "fiber (E \u2192 F)-",
+      "metadata": {
+        "location": {
+          "latitude": 0.0,
+          "longitude": 0.0
+        }
+      },
+      "type": "Fiber",
+      "type_variety": "SSMF",
+      "params": {
+        "length": 80.0,
+        "length_units": "km",
+        "loss_coef": 0.2,
+        "con_in": 1.00,
+        "con_out": 1.00
+      }
+    },
+        {
+            "uid": "Edfa1",
+            "type": "Edfa",
+            "type_variety": "std_medium_gain",            
+            "operational": {
+                "gain_target": 18,
+                "tilt_target": 0
+            },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 2,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Edfa2",
+            "type": "Edfa",
+            "type_variety": "std_medium_gain",            
+            "operational": {
+                "gain_target": 18,
+                "tilt_target": 0
+            },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 2,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Edfa3",
+            "type": "Edfa",
+            "type_variety": "std_medium_gain",            
+            "operational": {
+                "gain_target": 18,
+                "tilt_target": 0
+            },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 2,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Edfa4",
+            "type": "Edfa",
+            "type_variety": "std_medium_gain",            
+            "operational": {
+                "gain_target": 18,
+                "tilt_target": 0
+            },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 2,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+            "uid": "Edfa5",
+            "type": "Edfa",
+            "type_variety": "std_medium_gain",            
+            "operational": {
+                "gain_target": 18,
+                "tilt_target": 0
+            },
+            "metadata": {
+                "location": {
+                    "region": "",
+                    "latitude": 2,
+                    "longitude": 0
+                }
+            }
+        },
+        {
+        "uid": "Att_B",
+        "type": "Fused",
+        "params":{
+            "loss":16
+        },
+        "metadata": {
+            "location": {
+              "latitude": 2.0,
+              "longitude": 1.0,
+              "city": "Corlay",
+              "region": "RLD"
+                }
+            }
+        },
+        {
+        "uid": "Att_F",
+        "type": "Fused",
+        "params":{
+            "loss":16
+        },
+        "metadata": {
+            "location": {
+              "latitude": 2.0,
+              "longitude": 1.0,
+              "city": "Corlay",
+              "region": "RLD"
+                }
+            }
+        }
+
+  ],
+  "connections": [
+    {
+      "from_node": "fiber (A \u2192 B)-",
+      "to_node": "Edfa1"
+    },
+    {
+      "from_node": "Edfa1",
+      "to_node": "fiber (B \u2192 C)-"
+    },
+    {
+      "from_node": "fiber (B \u2192 C)-",
+      "to_node": "Edfa2"
+    },
+    {
+      "from_node": "Edfa2",
+      "to_node": "fiber (C \u2192 D)-"
+    },
+    {
+      "from_node": "fiber (C \u2192 D)-",
+      "to_node": "Edfa3"
+    },
+    {
+      "from_node": "Edfa3",
+      "to_node": "fiber (D \u2192 E)-"
+    },
+    {
+      "from_node": "fiber (D \u2192 E)-",
+      "to_node": "Edfa4"
+    },
+    {
+      "from_node": "Edfa4",
+      "to_node": "fiber (E \u2192 F)-"
+    },
+    {
+      "from_node": "fiber (E \u2192 F)-",
+      "to_node": "Edfa5"
+    },
+    {
+      "from_node": "Edfa5",
+      "to_node": "Att_F"
+    },
+    {
+      "from_node": "Att_F",
+      "to_node": "trx F"
+    },
+    {
+      "from_node": "trx A",
+      "to_node": "fiber (A \u2192 B)-"
+    },
+    {
+      "from_node": "Edfa1",
+      "to_node": "Att_B"
+    },
+    {
+      "from_node": "Att_B",
+      "to_node": "trx B"
+    }
+  ]
+}

tests/__init__.py

@@ -1,3 +1 @@
 #!/usr/bin/env python
-
-from .test_utilities import *

tests/amplifier_test.py

@@ -1,139 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-# @Author: Jean-Luc Auge
-# @Date:   2018-02-02 14:06:55
-
-from gnpy.core.elements import Edfa
-import numpy as np
-from json import load
-import pytest
-from gnpy.core import network_from_json
-from gnpy.core.elements import Transceiver, Fiber, Edfa
-from gnpy.core.utils import lin2db, db2lin
-from gnpy.core.info import SpectralInformation, Channel, Power
-
-network_file_name = 'tests/test_network.json'
-
-@pytest.fixture(params=[(96, 0.05e12), (60, 0.075e12), (45, 0.1e12), (2, 0.1e12)], 
-    ids=['50GHz spacing', '75GHz spacing', '100GHz spacing', '2 channels'])
-# TODO in elements.py code: pytests doesn't pass with 1 channel: interpolate fail
-def nch_and_spacing(request):
-    """parametrize channel count vs channel spacing (Hz)"""
-    yield request.param
-
-@pytest.fixture()
-def bw():
-    """parametrize signal bandwidth (Hz)"""
-    return 45e9
-
-@pytest.fixture()
-def setup_edfa():
-    """init edfa class by reading test_network.json file
-    remove all gain and nf ripple"""
-    with open(network_file_name) as network_file:
-        network_json = load(network_file)
-    network = network_from_json(network_json)
-    edfa = [n for n in network.nodes() if isinstance(n, Edfa)][0]
-
-    #edfa.params.dgt = np.zeros(96)
-    edfa.params.gain_ripple = np.zeros(96)
-    edfa.params.nf_ripple = np.zeros(96)
-    yield edfa
-
-@pytest.fixture()
-def setup_trx():
-    """init transceiver class to access snr and osnr calculations"""
-    with open(network_file_name) as network_file:
-        network_json = load(network_file)
-    network = network_from_json(network_json)
-    trx = [n for n in network.nodes() if isinstance(n, Transceiver)][0]
-    return trx
-
-@pytest.fixture()
-def si(nch_and_spacing, bw):
-    """parametrize a channel comb with nch, spacing and signal bw"""
-    nch, spacing = nch_and_spacing
-    si = SpectralInformation()
-    si = si.update(carriers=tuple(Channel(f, 191.3e12+spacing*f, 
-            bw, 0.15, Power(1e-6, 0, 0)) for f in range(1,nch+1)))
-    return si
-
-@pytest.mark.parametrize("enabled", [True, False])
-@pytest.mark.parametrize("gain, nf_expected", [(10, 15), (15, 10), (25, 5.8)])              
-def test_nf_calc(gain, nf_expected, enabled, setup_edfa, si):
-    """ compare the 2 amplifier models (polynomial and estimated from nf_min and max)
-     => nf_model vs nf_poly_fit for boundary gain values: gain_min (and below) & gain_flatmax
-    same values are expected between the 2 models
-    => unitary test for Edfa._calc_nf() (and Edfa.interpol_params)"""
-    edfa = setup_edfa
-    frequencies = np.array([c.frequency for c in si.carriers])
-    pin = np.array([c.power.signal+c.power.nli+c.power.ase for c in si.carriers])
-    baud_rates = np.array([c.baud_rate for c in si.carriers])
-    edfa.operational.gain_target = gain
-    edfa.params.nf_model_enabled = enabled
-    edfa.interpol_params(frequencies, pin, baud_rates)
-
-    nf = edfa.nf
-    dif = abs(nf[0] - nf_expected)
-    assert dif < 0.01
-
-@pytest.mark.parametrize("gain", [17, 19, 21, 23])
-def test_compare_nf_models(gain, setup_edfa, si):
-    """ compare the 2 amplifier models (polynomial and estimated from nf_min and max)
-     => nf_model vs nf_poly_fit for intermediate gain values:
-     between gain_min and gain_flatmax some discrepancy is expected but target < 0.5dB
-     => unitary test for Edfa._calc_nf (and Edfa.interpol_params)"""
-    edfa = setup_edfa
-    frequencies = np.array([c.frequency for c in si.carriers])
-    pin = np.array([c.power.signal+c.power.nli+c.power.ase for c in si.carriers])
-    baud_rates = np.array([c.baud_rate for c in si.carriers])
-    edfa.operational.gain_target = gain
-    edfa.params.nf_model_enabled = True
-    edfa.interpol_params(frequencies, pin, baud_rates)
-    nf_model = edfa.nf[0]
-
-    edfa.params.nf_model_enabled = False
-    edfa.interpol_params(frequencies, pin, baud_rates)
-    nf_poly = edfa.nf[0]
-    dif = abs(nf_model - nf_poly)
-    assert dif < 0.5
-
-def test_si(si, nch_and_spacing):
-    """basic total power check of the channel comb generation"""
-    nch = nch_and_spacing[0]
-    pin = np.array([c.power.signal+c.power.nli+c.power.ase for c in si.carriers])
-    p_tot = np.sum(pin)
-    expected_p_tot = si.carriers[0].power.signal * nch
-    dif = abs(lin2db(p_tot/expected_p_tot))
-    assert dif < 0.01
-
-@pytest.mark.parametrize("gain", [13, 15, 17, 19, 21, 23, 25, 27])
-def test_ase_noise(gain, si, setup_edfa, setup_trx, bw):
-    """testing 3 different ways of calculating osnr:
-    1-pin-edfa.nf+58 vs 
-    2-pout/pase afet propagate
-    3-Transceiver osnr_ase_01nm
-    => unitary test for Edfa.noise_profile (Edfa.interpol_params, Edfa.propagate)"""
-    edfa = setup_edfa
-    frequencies = np.array([c.frequency for c in si.carriers])
-    pin = np.array([c.power.signal+c.power.nli+c.power.ase for c in si.carriers])
-    baud_rates = np.array([c.baud_rate for c in si.carriers])
-    edfa.operational.gain_target = gain
-    edfa.params.nf_model_enabled = False
-    edfa.interpol_params(frequencies, pin, baud_rates)
-    nf = edfa.nf
-    pin = lin2db(pin[0]*1e3)
-    osnr_expected = pin - nf[0] + 58
-
-    si = edfa(si)
-    pout = np.array([c.power.signal for c in si.carriers])
-    pase = np.array([c.power.ase for c in si.carriers])
-    osnr = lin2db(pout[0] / pase[0]) - lin2db(12.5e9/bw)
-    dif = abs(osnr - osnr_expected)
-
-    trx = setup_trx
-    si = trx(si)
-    osnr = trx.osnr_ase_01nm[0]
-    dif = dif + abs(osnr - osnr_expected)
-
-    assert dif < 0.01

tests/compare.py

@@ -0,0 +1,120 @@
+#!/usr/bin/env python3
+from json import load, dump
+from pathlib import Path
+from argparse import ArgumentParser
+from collections import namedtuple
+
+class Results(namedtuple('Results', 'missing extra different expected actual')):
+    def _asdict(self):
+        return {'missing':   self.missing,
+                'extra':     self.extra,
+                'different': self.different}
+    def __str__(self):
+        rv = []
+        if self.missing:
+            rv.append('Missing: {len(self.missing)}/{len(self.expected)}')
+            rv.extend(f'\t{x}' for x in sorted(self.missing))
+        if self.extra:
+            rv.append('Extra: {len(self.extra)}/{len(self.expected)}')
+            rv.extend(f'\t{x}' for x in sorted(self.extra))
+        if self.different:
+            rv.append('Different: {len(self.different)}/{len(self.expected)}')
+            rv.extend(f'\tExpected: {x}\n\tActual:   {y}' for x, y in self.different)
+        if not self.missing and not self.extra and not self.different:
+            rv.append('All match!')
+        return '\n'.join(rv)
+
+class NetworksResults(namedtuple('NetworksResult', 'elements connections')):
+    def _asdict(self):
+        return {'elements':    self.elements._asdict(),
+                'connections': self.connections._asdict()}
+    def __str__(self):
+        return '\n'.join([
+            'Elements'.center(40, '='),
+            str(self.elements),
+            'Connections'.center(40, '='),
+            str(self.connections),
+        ])
+
+class ServicesResults(namedtuple('ServicesResult', 'requests synchronizations')):
+    def _asdict(self):
+        return {'requests':         self.requests.asdict(),
+                'synchronizations': self.synchronizations.asdict()}
+    def __str__(self):
+        return '\n'.join([
+            'Requests'.center(40, '='),
+            str(self.requests),
+            'Synchronizations'.center(40, '='),
+            str(self.synchronizations),
+        ])
+
+class PathsResults(namedtuple('PathsResults', 'paths')):
+    def _asdict(self):
+        return {'paths': self.paths.asdict()}
+    def __str__(self):
+        return '\n'.join([
+            'Paths'.center(40, '='),
+            str(self.paths),
+        ])
+
+def compare(expected, actual, key=lambda x: x):
+    expected  = {key(el): el for el in expected}
+    actual    = {key(el): el for el in actual}
+    missing   = set(expected) - set(actual)
+    extra     = set(actual) - set(expected)
+    different = [(expected[x], actual[x]) for
+                 x in set(expected) & set(actual)
+                 if expected[x] != actual[x]]
+    return Results(missing, extra, different, expected, actual)
+
+def compare_networks(expected, actual):
+    elements = compare(expected['elements'], actual['elements'],
+                       key=lambda el: el['uid'])
+    connections = compare(expected['connections'], actual['connections'],
+                          key=lambda el: (el['from_node'], el['to_node']))
+    return NetworksResults(elements, connections)
+
+def compare_services(expected, actual):
+    requests = compare(expected['path-request'], actual['path-request'],
+                       key=lambda el: el['request-id'])
+    synchronizations = compare(expected['synchronization'], actual['synchronization'],
+                               key=lambda el: el['synchronization-id'])
+    return ServicesResults(requests, synchronizations)
+
+def compare_paths(expected_output, actual_output):
+    paths = compare(expected['path'], actual['path'], key=lambda el: el['path-id'])
+    return PathsResults(paths)
+
+COMPARISONS = {
+    'networks': compare_networks,
+    'services': compare_services,
+    'paths':    compare_paths,
+}
+
+parser = ArgumentParser()
+parser.add_argument('expected_output', type=Path, metavar='FILE')
+parser.add_argument('actual_output',   type=Path, metavar='FILE')
+parser.add_argument('-o', '--output',  default=None)
+parser.add_argument('-c', '--comparison', choices=COMPARISONS, default='networks')
+
+def encode_sets(obj):
+    if isinstance(obj, set):
+        return list(obj)
+    raise TypeError(f'{obj!r} is not JSON serializable!')
+
+if __name__ == '__main__':
+    args = parser.parse_args()
+
+    with open(args.expected_output, encoding='utf-8') as f:
+        expected = load(f)
+
+    with open(args.actual_output, encoding='utf-8') as f:
+        actual = load(f)
+
+    result = COMPARISONS[args.comparison](expected, actual)
+
+    if args.output:
+        with open(args.output, 'w', encoding='utf-8') as f:
+            dump(result, f, default=encode_sets, indent=2, ensure_ascii=False)
+    else:
+        print(str(result))

tests/data/default_edfa_config.json

@@ -0,0 +1,296 @@
+{
+    "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,
+        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": [
+        0.0,
+        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,
+        0.0,
+        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,
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+        0.0,
+        0.0,
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+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        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,
+        1.017807767853702,
+        1.0
+    ]
+}

tests/data/eqpt_config.json

@@ -0,0 +1,232 @@
+{     "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,
+            "gamma": 0.00127
+            }
+      ],
+      "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,
+            "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":{
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+                        "max": 196.1e12
+                        },
+            "mode":[
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+                       "cost":1
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+                       "cost":1
+                       },
+                       {
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+                       "cost":1
+                       },
+                       {
+                       "format": "mode 2 - fake",
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+                       "bit_rate": 400e9,
+                       "roll_off": 0.15,
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+                       "cost":1
+                       },                       
+                       {
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+                       "cost":1
+                       }
+                   ]
+            }
+      ]
+
+}

tests/data/std_medium_gain_advanced_config.json

@@ -0,0 +1,301 @@
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