Merge changes from topic "mixed-rate"

* changes:
  Change saturation verification to total input power
  Prepare for Pref definition
  Add utilities

.docker-travis.sh

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

.github/workflows/main.yml

@@ -23,10 +23,12 @@ jobs:
         with:
           files: ${{ github.workspace }}/cover/coverage.xml
     strategy:
+      fail-fast: false
       matrix:
         tox_env:
           - py38
-          - py39-cover
+          - py39
+          - py310-cover
         include:
           - tox_env: docs
             dnf_install: graphviz
@@ -43,7 +45,7 @@ jobs:
       - uses: actions/setup-python@v2
         name: Install Python
         with:
-          python-version: '3.9'
+          python-version: '3.10'
       - uses: casperdcl/deploy-pypi@bb869aafd89f657ceaafe9561d3b5584766c0f95
         with:
           password: ${{ secrets.PYPI_API_TOKEN }}
@@ -91,9 +93,9 @@ jobs:
             telecominfraproject/oopt-gnpy:${{ steps.extract_tag_name.outputs.GIT_DESC }}
             telecominfraproject/oopt-gnpy:latest
 
-  windows:
+  other-platforms:
-    name: Tests on Windows
+    name: Tests on other platforms
-    runs-on: windows-2019
+    runs-on: ${{ matrix.os }}
     steps:
       - uses: actions/checkout@v2
         with:
@@ -102,10 +104,14 @@ jobs:
         with:
           python-version: ${{ matrix.python_version }}
       - run: |
+          pip install -r tests/requirements.txt
           pip install --editable .
-          pip install 'pytest>=5.0.0,<6'
           pytest -vv
     strategy:
+      fail-fast: false
       matrix:
-        python_version:
+        include:
-          - "3.9"
+          - os: windows-2019
+            python_version: "3.10"
+          - os: macos-12
+            python_version: "3.10"

.lgtm.yml

@@ -0,0 +1,3 @@
+queries:
+- exclude: py/clear-text-logging-sensitive-data
+- exclude: py/clear-text-storage-sensitive-data

.travis.yml

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

.zuul.yaml

@@ -2,25 +2,21 @@
 - project:
     check:
       jobs:
-        - tox-py38-f34
+        - tox-py38
-        - tox-py39-cover
+        - tox-py39
-        - tox-docs-f34
+        - tox-py310-cover
+        - tox-docs-f35
         - coverage-diff:
             voting: false
             dependencies:
-              - tox-py39-cover-previous
+              - tox-py310-cover-previous
-              - tox-py39-cover
+              - tox-py310-cover
             vars:
-              coverage_job_name_previous: tox-py39-cover-previous
+              coverage_job_name_previous: tox-py310-cover-previous
-              coverage_job_name_current: tox-py39-cover
+              coverage_job_name_current: tox-py310-cover
         - tox-linters-diff-n-report:
             voting: false
-        - tox-py39-cover-previous
+        - tox-py310-cover-previous
-    gate:
-      jobs:
-        - tox-py38-f34
-        - tox-py39-f34
-        - tox-docs-f34
     tag:
       jobs:
         - oopt-release-python:

README.md

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

docs/about-project.md

@@ -7,6 +7,7 @@ There are weekly calls 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/) and especially to [get in touch with us](https://github.com/Telecominfraproject/oopt-gnpy/discussions).
 
+(contributing)=
 ## Contributing
 
 `gnpy` is looking for additional contributors, especially those with experience planning and maintaining large-scale, real-world mesh optical networks.

docs/concepts.rst

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

docs/conf.py

@@ -190,3 +190,5 @@ autodoc_default_options = {
 }
 
 graphviz_output_format = 'svg'
+
+bibtex_bibfiles = ['biblio.bib']

docs/extending.rst

@@ -4,7 +4,7 @@ Extending GNPy with vendor-specific data
 ========================================
 
 GNPy ships with an :ref:`equipment library<concepts-equipment>` containing machine-readable datasheets of networking equipment.
-Vendors who are willing to contribute descriptions of their supported products are encouraged to `submit a patch <https://review.gerrithub.io/Documentation/intro-gerrit-walkthrough-github.html>`__.
+Vendors who are willing to contribute descriptions of their supported products are encouraged to `submit a patch <https://review.gerrithub.io/Documentation/intro-gerrit-walkthrough-github.html>`__ -- or just :ref:`get in touch with us directly<contributing>`.
 
 This chapter discusses option for modeling performance of :ref:`EDFA amplifiers<extending-edfa>`, :ref:`Raman amplifiers<extending-raman>`, :ref:`transponders<extending-transponder>` and :ref:`ROADMs<extending-roadm>`.
 
@@ -29,7 +29,7 @@ The NF is expressed as a third-degree polynomial:
 
        f(x) &= \text{a}x^3 + \text{b}x^2 + \text{c}x + \text{d}
 
-  \text{NF} &= f(G_\text{max} - G)
+  \text{NF} &= f(G - G_\text{max})
 
 This model can be also used for fixed-gain fixed-NF amplifiers.
 In that case, use:
@@ -100,10 +100,10 @@ Raman Amplifiers
 
 An accurate simulation of Raman amplification requires knowledge of:
 
-- the *power* and *wavelength* of all Raman pumping lasers,
+* the *power* and *wavelength* of all Raman pumping lasers,
-- the *direction*, whether it is co-propagating or counter-propagating,
+* the *direction*, whether it is co-propagating or counter-propagating,
-- the Raman efficiency of the fiber,
+* the Raman efficiency of the fiber,
-- the fiber temperature.
+* the fiber temperature.
 
 Under certain scenarios it is useful to be able to run a simulation without an accurate Raman description.
 For these purposes, it is possible to approximate a Raman amplifier via a fixed-gain EDFA with the :ref:`polynomial NF<ext-nf-model-polynomial-NF>` model using :math:`\text{a} = \text{b} = \text{c} = 0`, and a desired effective :math:`\text{d} = NF`.

docs/json.rst

@@ -1,4 +1,4 @@
-.. _json:
+.. _legacy-json:
 
 JSON Input Files
 ================
@@ -73,13 +73,57 @@ The fiber library currently describes SSMF and NZDF but additional fiber types c
 | ``dispersion_slope`` | (number)  | In :math:`s \times m^{-1} \times m^{-1}  |
 |                      |           | \times m^{-1}`                           |
 +----------------------+-----------+------------------------------------------+
-| ``gamma``            | (number)  | :math:`2\pi\times n^2/(\lambda*A_{eff})`,|
+| ``effective_area``   | (number)  | Effective area of the fiber (not just    |
-|                      |           | in :math:`w^{-1} \times m^{-1}`.         |
+|                      |           | the MFD circle). This is the             |
+|                      |           | :math:`A_{eff}`, see e.g., the           |
+|                      |           | `Corning whitepaper on MFD/EA`_.         |
+|                      |           | Specified in :math:`m^{2}`.              |
++----------------------+-----------+------------------------------------------+
+| ``gamma``            | (number)  | Coefficient :math:`\gamma = 2\pi\times   |
+|                      |           | n^2/(\lambda*A_{eff})`.                  |
+|                      |           | If not provided, this will be derived    |
+|                      |           | from the ``effective_area``              |
+|                      |           | :math:`A_{eff}`.                         |
+|                      |           | In :math:`w^{-1} \times m^{-1}`.         |
 +----------------------+-----------+------------------------------------------+
 | ``pmd_coef``         | (number)  | Polarization mode dispersion (PMD)       |
 |                      |           | coefficient. In                          |
 |                      |           | :math:`s\times\sqrt{m}^{-1}`.            |
 +----------------------+-----------+------------------------------------------+
+| ``lumped_losses``    | (array)   | Places along the fiber length with extra |
+|                      |           | losses. Specified as a loss in dB at     |
+|                      |           | each relevant position (in km):          |
+|                      |           | ``{"position": 10, "loss": 1.5}``)       |
++----------------------+-----------+------------------------------------------+
+
+.. _Corning whitepaper on MFD/EA: https://www.corning.com/microsites/coc/oem/documents/specialty-fiber/WP7071-Mode-Field-Diam-and-Eff-Area.pdf
+
+RamanFiber
+~~~~~~~~~~
+
+The RamanFiber can be used to simulate Raman amplification through dedicated Raman pumps. The Raman pumps must be listed
+in the key ``raman_pumps`` within the RamanFiber ``operational`` dictionary. The description of each Raman pump must
+contain the following:
+
++---------------------------+-----------+------------------------------------------------------------+
+| field                     | type      | description                                                |
++===========================+===========+============================================================+
+| ``power``                 | (number)  | Total pump power in :math:`W`                              |
+|                           |           | considering a depolarized pump                             |
++---------------------------+-----------+------------------------------------------------------------+
+| ``frequency``             | (number)  | Pump central frequency in :math:`Hz`                       |
++---------------------------+-----------+------------------------------------------------------------+
+| ``propagation_direction`` | (number)  | The pumps can propagate in the same or opposite direction  |
+|                           |           | with respect the signal. Valid choices are ``coprop`` and  |
+|                           |           | ``counterprop``, respectively                              |
++---------------------------+-----------+------------------------------------------------------------+
+
+Beside the list of Raman pumps, the RamanFiber ``operational`` dictionary must include the ``temperature`` that affects
+the amplified spontaneous emission noise generated by the Raman amplification.
+As the loss coefficient significantly varies outside the C-band, where the Raman pumps are usually placed,
+it is suggested to include an estimation of the loss coefficient for the Raman pump central frequencies within
+a dictionary-like definition of the ``RamanFiber.params.loss_coef``
+(e.g. ``loss_coef = {"value": [0.18, 0.18, 0.20, 0.20], "frequency": [191e12, 196e12, 200e12, 210e12]}``).
 
 Transceiver
 ~~~~~~~~~~~
@@ -176,6 +220,61 @@ 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.
 
+The file ``sim_params.json`` contains the tuning parameters used within both the ``gnpy.science_utils.RamanSolver`` and
+the ``gnpy.science_utils.NliSolver`` for the evaluation of the Raman profile and the NLI generation, respectively.
+
++---------------------------------------------+-----------+---------------------------------------------+
+| field                                       |   type    | description                                 |
++=============================================+===========+=============================================+
+| ``raman_params.flag``                       | (boolean) | Enable/Disable the Raman effect that        |
+|                                             |           | produces a power transfer from higher to    |
+|                                             |           | lower frequencies.                          |
+|                                             |           | In general, considering the Raman effect    |
+|                                             |           | provides more accurate results. It is       |
+|                                             |           | mandatory when Raman amplification is       |
+|                                             |           | included in the simulation                  |
++---------------------------------------------+-----------+---------------------------------------------+
+| ``raman_params.result_spatial_resolution``  | (number)  | Spatial resolution of the output            |
+|                                             |           | Raman profile along the entire fiber span.  |
+|                                             |           | This affects the accuracy and the           |
+|                                             |           | computational time of the NLI               |
+|                                             |           | calculation when the GGN method is used:    |
+|                                             |           | smaller the spatial resolution higher both  |
+|                                             |           | the accuracy and the computational time.    |
+|                                             |           | In C-band simulations, with input power per |
+|                                             |           | channel around 0 dBm, a suggested value of  |
+|                                             |           | spatial resolution is 10e3 m                |
++---------------------------------------------+-----------+---------------------------------------------+
+| ``raman_params.solver_spatial_resolution``  | (number)  | Spatial step for the iterative solution     |
+|                                             |           | of the first order differential equation    |
+|                                             |           | used to calculate the Raman profile         |
+|                                             |           | along the entire fiber span.                |
+|                                             |           | This affects the accuracy and the           |
+|                                             |           | computational time of the evaluated         |
+|                                             |           | Raman profile:                              |
+|                                             |           | smaller the spatial resolution higher both  |
+|                                             |           | the accuracy and the computational time.    |
+|                                             |           | In C-band simulations, with input power per |
+|                                             |           | channel around 0 dBm, a suggested value of  |
+|                                             |           | spatial resolution is 100 m                 |
++---------------------------------------------+-----------+---------------------------------------------+
+| ``nli_params.method``                       | (string)  | Model used for the NLI evaluation. Valid    |
+|                                             |           | choices are ``gn_model_analytic`` (see      |
+|                                             |           | eq. 120 from `arXiv:1209.0394               |
+|                                             |           | <https://arxiv.org/abs/1209.0394>`_) and    |
+|                                             |           | ``ggn_spectrally_separated`` (see eq. 21    |
+|                                             |           | from `arXiv:1710.02225                      |
+|                                             |           | <https://arxiv.org/abs/1710.02225>`_).      |
++---------------------------------------------+-----------+---------------------------------------------+
+| ``nli_params.computed_channels``            | (number)  | The channels on which the NLI is            |
+|                                             |           | explicitly evaluated.                       |
+|                                             |           | The NLI of the other channels is            |
+|                                             |           | interpolated using ``numpy.interp``.        |
+|                                             |           | In a C-band simulation with 96 channels in  |
+|                                             |           | a 50 GHz spacing fix-grid we recommend at   |
+|                                             |           | one computed channel every 20 channels.     |
++---------------------------------------------+-----------+---------------------------------------------+
+
 Span
 ~~~~
 

docs/model.rst

@@ -145,4 +145,4 @@ 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::

docs/requirements.txt

@@ -1,7 +1,7 @@
 alabaster>=0.7.12,<1
-docutils>=0.15.2,<1
+docutils>=0.17.1,<1
-myst-parser>=0.14.0,<1
+myst-parser>=0.16.1,<1
-Pygments>=2.7.4,<3
+Pygments>=2.11.2,<3
 rstcheck
-Sphinx>=3.5.0,<4
+Sphinx>=4.4.0,<5
-sphinxcontrib-bibtex>=0.4.2,<1
+sphinxcontrib-bibtex>=2.4.1,<3

gnpy/core/elements.py

@@ -20,13 +20,17 @@ unique identifier and a printable name, and provide the :py:meth:`__call__` meth
 instance as a result.
 """
 
-from numpy import abs, arange, array, divide, errstate, ones, interp, mean, pi, polyfit, polyval, sum, sqrt
+from numpy import abs, array, errstate, ones, interp, mean, pi, polyfit, polyval, sum, sqrt, log10, exp, asarray, full,\
+    squeeze, zeros, append, flip, outer
 from scipy.constants import h, c
+from scipy.interpolate import interp1d
 from collections import namedtuple
 
-from gnpy.core.utils import lin2db, db2lin, arrange_frequencies, snr_sum
+from gnpy.core.utils import lin2db, db2lin, arrange_frequencies, snr_sum, watt2dbm
-from gnpy.core.parameters import FiberParams, PumpParams
+from gnpy.core.parameters import RoadmParams, FusedParams, FiberParams, PumpParams, EdfaParams, EdfaOperational
-from gnpy.core.science_utils import NliSolver, RamanSolver, propagate_raman_fiber, _psi
+from gnpy.core.science_utils import NliSolver, RamanSolver
+from gnpy.core.info import SpectralInformation
+from gnpy.core.exceptions import NetworkTopologyError, SpectrumError
 
 
 class Location(namedtuple('Location', 'latitude longitude city region')):
@@ -79,32 +83,47 @@ class Transceiver(_Node):
         self.baud_rate = None
         self.chromatic_dispersion = None
         self.pmd = None
+        self.pdl = None
+        self.penalties = {}
+        self.total_penalty = 0
 
     def _calc_cd(self, spectral_info):
         """ Updates the Transceiver property with the CD of the received channels. CD in ps/nm.
         """
-        self.chromatic_dispersion = [carrier.chromatic_dispersion * 1e3 for carrier in spectral_info.carriers]
+        self.chromatic_dispersion = spectral_info.chromatic_dispersion * 1e3
 
     def _calc_pmd(self, spectral_info):
         """Updates the Transceiver property with the PMD of the received channels. PMD in ps.
         """
-        self.pmd = [carrier.pmd*1e12 for carrier in spectral_info.carriers]
+        self.pmd = spectral_info.pmd * 1e12
+
+    def _calc_pdl(self, spectral_info):
+        """Updates the Transceiver property with the PDL of the received channels. PDL in dB.
+        """
+        self.pdl = spectral_info.pdl
+
+    def _calc_penalty(self, impairment_value, boundary_list):
+        return interp(impairment_value, boundary_list['up_to_boundary'], boundary_list['penalty_value'],
+                      left=float('inf'), right=float('inf'))
+
+    def calc_penalties(self, penalties):
+        """Updates the Transceiver property with penalties (CD, PMD, etc.) of the received channels in dB.
+           Penalties are linearly interpolated between given points and set to 'inf' outside interval.
+        """
+        self.penalties = {impairment: self._calc_penalty(getattr(self, impairment), boundary_list)
+                          for impairment, boundary_list in penalties.items()}
+        self.total_penalty = sum(list(self.penalties.values()), axis=0)
 
     def _calc_snr(self, spectral_info):
         with errstate(divide='ignore'):
-            self.baud_rate = [c.baud_rate for c in spectral_info.carriers]
+            self.baud_rate = spectral_info.baud_rate
-            ratio_01nm = [lin2db(12.5e9 / b_rate) for b_rate in self.baud_rate]
+            ratio_01nm = lin2db(12.5e9 / self.baud_rate)
             # set raw values to record original calculation, before update_snr()
-            self.raw_osnr_ase = [lin2db(divide(c.power.signal, c.power.ase))
+            self.raw_osnr_ase = lin2db(spectral_info.signal / spectral_info.ase)
-                                 for c in spectral_info.carriers]
+            self.raw_osnr_ase_01nm = self.raw_osnr_ase - ratio_01nm
-            self.raw_osnr_ase_01nm = [ase - ratio for ase, ratio
+            self.raw_osnr_nli = lin2db(spectral_info.signal / spectral_info.nli)
-                                      in zip(self.raw_osnr_ase, ratio_01nm)]
+            self.raw_snr = lin2db(spectral_info.signal / (spectral_info.ase + spectral_info.nli))
-            self.raw_osnr_nli = [lin2db(divide(c.power.signal, c.power.nli))
+            self.raw_snr_01nm = self.raw_snr - ratio_01nm
-                                 for c in spectral_info.carriers]
-            self.raw_snr = [lin2db(divide(c.power.signal, c.power.nli + c.power.ase))
-                            for c in spectral_info.carriers]
-            self.raw_snr_01nm = [snr - ratio for snr, ratio
-                                 in zip(self.raw_snr, ratio_01nm)]
 
             self.osnr_ase = self.raw_osnr_ase
             self.osnr_ase_01nm = self.raw_osnr_ase_01nm
@@ -124,14 +143,10 @@ class Transceiver(_Node):
         for s in args:
             snr_added += db2lin(-s)
         snr_added = -lin2db(snr_added)
-        self.osnr_ase = list(map(lambda x, y: snr_sum(x, y, snr_added),
+        self.osnr_ase = snr_sum(self.raw_osnr_ase, self.baud_rate, snr_added)
-                                 self.raw_osnr_ase, self.baud_rate))
+        self.snr = snr_sum(self.raw_snr, self.baud_rate, snr_added)
-        self.snr = list(map(lambda x, y: snr_sum(x, y, snr_added),
+        self.osnr_ase_01nm = snr_sum(self.raw_osnr_ase_01nm, 12.5e9, snr_added)
-                            self.raw_snr, self.baud_rate))
+        self.snr_01nm = snr_sum(self.raw_snr_01nm, 12.5e9, snr_added)
-        self.osnr_ase_01nm = list(map(lambda x: snr_sum(x, 12.5e9, snr_added),
-                                      self.raw_osnr_ase_01nm))
-        self.snr_01nm = list(map(lambda x: snr_sum(x, 12.5e9, snr_added),
-                                 self.raw_snr_01nm))
 
     @property
     def to_json(self):
@@ -150,7 +165,9 @@ class Transceiver(_Node):
                 f'osnr_nli={self.osnr_nli!r}, '
                 f'snr={self.snr!r}, '
                 f'chromatic_dispersion={self.chromatic_dispersion!r}, '
-                f'pmd={self.pmd!r})')
+                f'pmd={self.pmd!r}, '
+                f'pdl={self.pdl!r}, '
+                f'penalties={self.penalties!r})')
 
     def __str__(self):
         if self.snr is None or self.osnr_ase is None:
@@ -162,46 +179,58 @@ class Transceiver(_Node):
         snr_01nm = round(mean(self.snr_01nm), 2)
         cd = mean(self.chromatic_dispersion)
         pmd = mean(self.pmd)
+        pdl = mean(self.pdl)
 
-        return '\n'.join([f'{type(self).__name__} {self.uid}',
+        result = '\n'.join([f'{type(self).__name__} {self.uid}',
 
                           f'  GSNR (0.1nm, dB):          {snr_01nm:.2f}',
                           f'  GSNR (signal bw, dB):      {snr:.2f}',
                           f'  OSNR ASE (0.1nm, dB):      {osnr_ase_01nm:.2f}',
                           f'  OSNR ASE (signal bw, dB):  {osnr_ase:.2f}',
                           f'  CD (ps/nm):                {cd:.2f}',
-                          f'  PMD (ps):                  {pmd:.2f}'])
+                          f'  PMD (ps):                  {pmd:.2f}',
+                          f'  PDL (dB):                  {pdl:.2f}'])
+
+        cd_penalty = self.penalties.get('chromatic_dispersion')
+        if cd_penalty is not None:
+            result += f'\n  CD penalty (dB):           {mean(cd_penalty):.2f}'
+        pmd_penalty = self.penalties.get('pmd')
+        if pmd_penalty is not None:
+            result += f'\n  PMD penalty (dB):          {mean(pmd_penalty):.2f}'
+        pdl_penalty = self.penalties.get('pdl')
+        if pdl_penalty is not None:
+            result += f'\n  PDL penalty (dB):          {mean(pdl_penalty):.2f}'
+
+        return result
 
     def __call__(self, spectral_info):
         self._calc_snr(spectral_info)
         self._calc_cd(spectral_info)
         self._calc_pmd(spectral_info)
+        self._calc_pdl(spectral_info)
         return spectral_info
 
 
-RoadmParams = namedtuple('RoadmParams', 'target_pch_out_db add_drop_osnr pmd restrictions per_degree_pch_out_db')
-
-
 class Roadm(_Node):
-    def __init__(self, *args, params, **kwargs):
+    def __init__(self, *args, params=None, **kwargs):
-        if 'per_degree_pch_out_db' not in params.keys():
+        if not params:
-            params['per_degree_pch_out_db'] = {}
+            params = {}
         super().__init__(*args, params=RoadmParams(**params), **kwargs)
+        self.ref_pch_out_dbm = self.params.target_pch_out_db
         self.loss = 0  # auto-design interest
         self.effective_loss = None
-        self.effective_pch_out_db = self.params.target_pch_out_db
         self.passive = True
         self.restrictions = self.params.restrictions
-        self.per_degree_pch_out_db = self.params.per_degree_pch_out_db
+        self.per_degree_pch_out_dbm = self.params.per_degree_pch_out_db
 
     @property
     def to_json(self):
         return {'uid': self.uid,
                 'type': type(self).__name__,
                 'params': {
-                    'target_pch_out_db': self.effective_pch_out_db,
+                    'target_pch_out_db': self.ref_pch_out_dbm,
                     'restrictions': self.restrictions,
-                    'per_degree_pch_out_db': self.per_degree_pch_out_db
+                    'per_degree_pch_out_db': self.per_degree_pch_out_dbm
                     },
                 'metadata': {
                     'location': self.metadata['location']._asdict()
@@ -217,9 +246,9 @@ class Roadm(_Node):
 
         return '\n'.join([f'{type(self).__name__} {self.uid}',
                           f'  effective loss (dB):  {self.effective_loss:.2f}',
-                          f'  pch out (dBm):        {self.effective_pch_out_db:.2f}'])
+                          f'  pch out (dBm):        {self.ref_pch_out_dbm:.2f}'])
 
-    def propagate(self, pref, *carriers, degree):
+    def propagate(self, spectral_info, degree):
         # pin_target and loss are read from eqpt_config.json['Roadm']
         # all ingress channels in xpress are set to this power level
         # but add channels are not, so we define an effective loss
@@ -229,38 +258,39 @@ class Roadm(_Node):
         # if the input power is lower than the target one, use the input power instead because
         # a ROADM doesn't amplify, it can only attenuate
         # TODO maybe add a minimum loss for the ROADM
-        per_degree_pch = self.per_degree_pch_out_db[degree] if degree in self.per_degree_pch_out_db.keys() else self.params.target_pch_out_db
+        per_degree_pch = self.per_degree_pch_out_dbm.get(degree, self.ref_pch_out_dbm)
-        self.effective_pch_out_db = min(pref.p_spani, per_degree_pch)
+        # Definition of ref_pch_out_dbm for the reference channel:
-        self.effective_loss = pref.p_spani - self.effective_pch_out_db
+        # Depending on propagation upstream from this ROADM, the input power (p_spani) might be smaller than
-        carriers_power = array([c.power.signal + c.power.nli + c.power.ase for c in carriers])
+        # the target power out configured for this ROADM degree's egress. Since ROADM does not amplify,
-        carriers_att = list(map(lambda x: lin2db(x * 1e3) - per_degree_pch, carriers_power))
+        # the power out of the ROADM for the ref channel is the min value between target power and input power.
-        exceeding_att = -min(list(filter(lambda x: x < 0, carriers_att)), default=0)
+        # (TODO add a minimum loss for the ROADM crossing)
-        carriers_att = list(map(lambda x: db2lin(x + exceeding_att), carriers_att))
+        self.ref_pch_out_dbm = min(spectral_info.pref.p_spani, per_degree_pch)
-        for carrier_att, carrier in zip(carriers_att, carriers):
+        # Definition of effective_loss:
-            pwr = carrier.power
+        # Optical power of carriers are equalized by the ROADM, so that the experienced loss is not the same for
-            pwr = pwr._replace(signal=pwr.signal / carrier_att,
+        # different carriers. effective_loss records the loss for a reference carrier.
-                               nli=pwr.nli / carrier_att,
+        self.effective_loss = spectral_info.pref.p_spani - self.ref_pch_out_dbm
-                               ase=pwr.ase / carrier_att)
+        input_power = spectral_info.signal + spectral_info.nli + spectral_info.ase
-            pmd = sqrt(carrier.pmd**2 + self.params.pmd**2)
+        min_power = min(lin2db(input_power * 1e3))
-            yield carrier._replace(power=pwr, pmd=pmd)
+        per_degree_pch = per_degree_pch if per_degree_pch < min_power else min_power
+        delta_power = lin2db(input_power * 1e3) - per_degree_pch
+        spectral_info.apply_attenuation_db(delta_power)
+        spectral_info.pmd = sqrt(spectral_info.pmd ** 2 + self.params.pmd ** 2)
+        spectral_info.pdl = sqrt(spectral_info.pdl ** 2 + self.params.pdl ** 2)
 
-    def update_pref(self, pref):
+    def update_pref(self, spectral_info):
-        return pref._replace(p_span0=pref.p_span0, p_spani=self.effective_pch_out_db)
+        spectral_info.pref = spectral_info.pref._replace(p_span0=spectral_info.pref.p_span0,
+                                                         p_spani=self.ref_pch_out_dbm)
 
     def __call__(self, spectral_info, degree):
-        carriers = tuple(self.propagate(spectral_info.pref, *spectral_info.carriers, degree=degree))
+        self.propagate(spectral_info, degree=degree)
-        pref = self.update_pref(spectral_info.pref)
+        self.update_pref(spectral_info)
-        return spectral_info._replace(carriers=carriers, pref=pref)
+        return spectral_info
-
-
-FusedParams = namedtuple('FusedParams', 'loss')
 
 
 class Fused(_Node):
     def __init__(self, *args, params=None, **kwargs):
-        if params is None:
+        if not params:
-            # default loss value if not mentioned in loaded network json
+            params = {}
-            params = {'loss': 1}
         super().__init__(*args, params=FusedParams(**params), **kwargs)
         self.loss = self.params.loss
         self.passive = True
@@ -284,23 +314,17 @@ class Fused(_Node):
         return '\n'.join([f'{type(self).__name__} {self.uid}',
                           f'  loss (dB): {self.loss:.2f}'])
 
-    def propagate(self, *carriers):
+    def propagate(self, spectral_info):
-        attenuation = db2lin(self.loss)
+        spectral_info.apply_attenuation_db(self.loss)
 
-        for carrier in carriers:
+    def update_pref(self, spectral_info):
-            pwr = carrier.power
+        spectral_info.pref = spectral_info.pref._replace(p_span0=spectral_info.pref.p_span0,
-            pwr = pwr._replace(signal=pwr.signal / attenuation,
+                                                         p_spani=spectral_info.pref.p_spani - self.loss)
-                               nli=pwr.nli / attenuation,
-                               ase=pwr.ase / attenuation)
-            yield carrier._replace(power=pwr)
-
-    def update_pref(self, pref):
-        return pref._replace(p_span0=pref.p_span0, p_spani=pref.p_spani - self.loss)
 
     def __call__(self, spectral_info):
-        carriers = tuple(self.propagate(*spectral_info.carriers))
+        self.propagate(spectral_info)
-        pref = self.update_pref(spectral_info.pref)
+        self.update_pref(spectral_info)
-        return spectral_info._replace(carriers=carriers, pref=pref)
+        return spectral_info
 
 
 class Fiber(_Node):
@@ -309,7 +333,28 @@ class Fiber(_Node):
             params = {}
         super().__init__(*args, params=FiberParams(**params), **kwargs)
         self.pch_out_db = None
-        self.nli_solver = NliSolver(self)
+        self.passive = True
+
+        # Raman efficiency matrix function of the delta frequency constructed such that each row is related to a
+        # fixed frequency: positive elements represent a gain (from higher frequency) and negative elements represent
+        # a loss (to lower frequency)
+        if self.params.raman_efficiency:
+            frequency_offset = self.params.raman_efficiency['frequency_offset']
+            frequency_offset = append(-flip(frequency_offset[1:]), frequency_offset)
+            cr = self.params.raman_efficiency['cr']
+            cr = append(- flip(cr[1:]), cr)
+            self._cr_function = lambda frequency: interp(frequency, frequency_offset, cr)
+        else:
+            self._cr_function = lambda frequency: zeros(squeeze(frequency).shape)
+
+        # Lumped losses
+        z_lumped_losses = array([lumped['position'] for lumped in self.params.lumped_losses])  # km
+        lumped_losses_power = array([lumped['loss'] for lumped in self.params.lumped_losses])  # dB
+        if not ((z_lumped_losses > 0) * (z_lumped_losses < 1e-3 * self.params.length)).all():
+            raise NetworkTopologyError("Lumped loss positions must be between 0 and the fiber length "
+                                       f"({1e-3 * self.params.length} km), boundaries excluded.")
+        self.lumped_losses = db2lin(- lumped_losses_power)  # [linear units]
+        self.z_lumped_losses = array(z_lumped_losses) * 1e3  # [m]
 
     @property
     def to_json(self):
@@ -319,7 +364,7 @@ class Fiber(_Node):
                 'params': {
                     # have to specify each because namedtupple cannot be updated :(
                     'length': round(self.params.length * 1e-3, 6),
-                    'loss_coef': self.params.loss_coef * 1e3,
+                    'loss_coef': round(self.params.loss_coef * 1e3, 6),
                     'length_units': 'km',
                     'att_in': self.params.att_in,
                     'con_in': self.params.con_in,
@@ -348,43 +393,56 @@ class Fiber(_Node):
                           f'  (conn loss out includes EOL margin defined in eqpt_config.json)',
                           f'  pch out (dBm): {self.pch_out_db:.2f}'])
 
+    def loss_coef_func(self, frequency):
+        frequency = asarray(frequency)
+        if self.params.loss_coef.size > 1:
+            try:
+                loss_coef = interp1d(self.params.f_loss_ref, self.params.loss_coef)(frequency)
+            except ValueError:
+                raise SpectrumError('The spectrum bandwidth exceeds the frequency interval used to define the fiber '
+                                    f'loss coefficient in "{type(self).__name__} {self.uid}".'
+                                    f'\nSpectrum f_min-f_max: {round(frequency[0]*1e-12,2)}-'
+                                    f'{round(frequency[-1]*1e-12,2)}'
+                                    f'\nLoss coefficient f_min-f_max: {round(self.params.f_loss_ref[0]*1e-12,2)}-'
+                                    f'{round(self.params.f_loss_ref[-1]*1e-12,2)}')
+        else:
+            loss_coef = full(frequency.size, self.params.loss_coef)
+        return squeeze(loss_coef)
+
+
     @property
     def loss(self):
         """total loss including padding att_in: useful for polymorphism with roadm loss"""
-        return self.params.loss_coef * self.params.length + self.params.con_in + self.params.con_out + self.params.att_in
+        return self.loss_coef_func(self.params.ref_frequency) * self.params.length + \
+            self.params.con_in + self.params.con_out + self.params.att_in
 
-    @property
+    def alpha(self, frequency):
-    def passive(self):
+        """Returns the linear exponent attenuation coefficient such that
-        return True
+        :math: `lin_attenuation = e^{- alpha length}`
 
-    def alpha(self, frequencies):
+        :param frequency: the frequency at which alpha is computed [Hz]
-        """It returns the values of the series expansion of attenuation coefficient alpha(f) for all f in frequencies
+        :return: alpha: power attenuation coefficient for f in frequency [Neper/m]
-
-        :param frequencies: frequencies of series expansion [Hz]
-        :return: alpha: power attenuation coefficient for f in frequencies [Neper/m]
         """
-        if type(self.params.loss_coef) == dict:
+        return self.loss_coef_func(frequency) / (10 * log10(exp(1)))
-            alpha = interp(frequencies, self.params.f_loss_ref, self.params.lin_loss_exp)
-        else:
-            alpha = self.params.lin_loss_exp * ones(frequencies.shape)
 
-        return alpha
+    def cr(self, frequency):
+        """Returns the raman efficiency matrix including the vibrational loss
 
-    def alpha0(self, f_ref=193.5e12):
+        :param frequency: the frequency at which cr is computed [Hz]
-        """It returns the zero element of the series expansion of attenuation coefficient alpha(f) in the
+        :return: cr: raman efficiency matrix [1 / (W m)]
-        reference frequency f_ref
-
-        :param f_ref: reference frequency of series expansion [Hz]
-        :return: alpha0: power attenuation coefficient in f_ref [Neper/m]
         """
-        return self.alpha(f_ref * ones(1))[0]
+        df = outer(ones(frequency.shape), frequency) - outer(frequency, ones(frequency.shape))
+        cr = self._cr_function(df)
+        vibrational_loss = outer(frequency, ones(frequency.shape)) / outer(ones(frequency.shape), frequency)
+        return cr * (cr >= 0) + cr * (cr < 0) * vibrational_loss  # Raman efficiency [1/(W m)]
 
-    def chromatic_dispersion(self, freq=193.5e12):
+    def chromatic_dispersion(self, freq=None):
         """Returns accumulated chromatic dispersion (CD).
 
         :param freq: the frequency at which the chromatic dispersion is computed
         :return: chromatic dispersion: the accumulated dispersion [s/m]
         """
+        freq = self.params.ref_frequency if freq is None else freq
         beta2 = self.params.beta2
         beta3 = self.params.beta3
         ref_f = self.params.ref_frequency
@@ -398,147 +456,103 @@ class Fiber(_Node):
         """differential group delay (PMD) [s]"""
         return self.params.pmd_coef * sqrt(self.params.length)
 
-    def _gn_analytic(self, carrier, *carriers):
+    def propagate(self, spectral_info: SpectralInformation):
-        r"""Computes the nonlinear interference power on a single carrier.
+        """Modifies the spectral information computing the attenuation, the non-linear interference generation,
-        The method uses eq. 120 from `arXiv:1209.0394 <https://arxiv.org/abs/1209.0394>`__.
+        the CD and PMD accumulation.
-
-        :param carrier: the signal under analysis
-        :param \*carriers: the full WDM comb
-        :return: carrier_nli: the amount of nonlinear interference in W on the under analysis
         """
+        # apply the attenuation due to the input connector loss
+        attenuation_in_db = self.params.con_in + self.params.att_in
+        spectral_info.apply_attenuation_db(attenuation_in_db)
 
-        g_nli = 0
+        # inter channels Raman effect
-        for interfering_carrier in carriers:
+        stimulated_raman_scattering = RamanSolver.calculate_stimulated_raman_scattering(spectral_info, self)
-            psi = _psi(carrier, interfering_carrier, beta2=self.params.beta2,
-                       asymptotic_length=self.params.asymptotic_length)
-            g_nli += (interfering_carrier.power.signal / interfering_carrier.baud_rate)**2 \
-                * (carrier.power.signal / carrier.baud_rate) * psi
 
-        g_nli *= (16 / 27) * (self.params.gamma * self.params.effective_length)**2 \
+        # NLI noise evaluated at the fiber input
-            / (2 * pi * abs(self.params.beta2) * self.params.asymptotic_length)
+        spectral_info.nli += NliSolver.compute_nli(spectral_info, stimulated_raman_scattering, self)
 
-        carrier_nli = carrier.baud_rate * g_nli
+        # chromatic dispersion and pmd variations
-        return carrier_nli
+        spectral_info.chromatic_dispersion += self.chromatic_dispersion(spectral_info.frequency)
+        spectral_info.pmd = sqrt(spectral_info.pmd ** 2 + self.pmd ** 2)
 
-    def propagate(self, *carriers):
+        # apply the attenuation due to the fiber losses
-        r"""Generator that computes the fiber propagation: attenuation, non-linear interference generation, CD
+        attenuation_fiber = stimulated_raman_scattering.loss_profile[:, -1]
-        accumulation and PMD accumulation.
+        spectral_info.apply_attenuation_lin(attenuation_fiber)
 
-        :param: \*carriers: the channels at the input of the fiber
+        # apply the attenuation due to the output connector loss
-        :yield: carrier: the next channel at the output of the fiber
+        attenuation_out_db = self.params.con_out
-        """
+        spectral_info.apply_attenuation_db(attenuation_out_db)
 
-        # apply connector_att_in on all carriers before computing gn analytics  premiere partie pas bonne
+    def update_pref(self, spectral_info):
-        attenuation = db2lin(self.params.con_in + self.params.att_in)
+        # in case of Raman, the resulting loss of the fiber is not equivalent to self.loss
-
+        # because of Raman gain. In order to correctly update pref, we need the resulting loss:
-        chan = []
+        # power_out - power_in. We use the total signal power (sum on all channels) to compute
-        for carrier in carriers:
+        # this loss, because pref is a noiseless reference.
-            pwr = carrier.power
+        loss = round(lin2db(self._psig_in / sum(spectral_info.signal)), 2)
-            pwr = pwr._replace(signal=pwr.signal / attenuation,
+        self.pch_out_db = spectral_info.pref.p_spani - loss
-                               nli=pwr.nli / attenuation,
+        spectral_info.pref = spectral_info.pref._replace(p_span0=spectral_info.pref.p_span0,
-                               ase=pwr.ase / attenuation)
+                                                         p_spani=self.pch_out_db)
-            carrier = carrier._replace(power=pwr)
-            chan.append(carrier)
-
-        carriers = tuple(f for f in chan)
-
-        # propagate in the fiber and apply attenuation out
-        attenuation = db2lin(self.params.con_out)
-        for carrier in carriers:
-            pwr = carrier.power
-            carrier_nli = self._gn_analytic(carrier, *carriers)
-            pwr = pwr._replace(signal=pwr.signal / self.params.lin_attenuation / attenuation,
-                               nli=(pwr.nli + carrier_nli) / self.params.lin_attenuation / attenuation,
-                               ase=pwr.ase / self.params.lin_attenuation / attenuation)
-            chromatic_dispersion = carrier.chromatic_dispersion + self.chromatic_dispersion(carrier.frequency)
-            pmd = sqrt(carrier.pmd**2 + self.pmd**2)
-            yield carrier._replace(power=pwr, chromatic_dispersion=chromatic_dispersion, pmd=pmd)
-
-    def update_pref(self, pref):
-        self.pch_out_db = round(pref.p_spani - self.loss, 2)
-        return pref._replace(p_span0=pref.p_span0, p_spani=self.pch_out_db)
 
     def __call__(self, spectral_info):
-        carriers = tuple(self.propagate(*spectral_info.carriers))
+        # _psig_in records the total signal power of the spectral information before propagation.
-        pref = self.update_pref(spectral_info.pref)
+        self._psig_in = sum(spectral_info.signal)
-        return spectral_info._replace(carriers=carriers, pref=pref)
+        self.propagate(spectral_info)
+        self.update_pref(spectral_info)
+        return spectral_info
 
 
 class RamanFiber(Fiber):
     def __init__(self, *args, params=None, **kwargs):
         super().__init__(*args, params=params, **kwargs)
-        if self.operational and 'raman_pumps' in self.operational:
+        if not self.operational:
-            self.raman_pumps = tuple(PumpParams(p['power'], p['frequency'], p['propagation_direction'])
+            raise NetworkTopologyError(f'Fiber element uid:{self.uid} '
-                                     for p in self.operational['raman_pumps'])
+                                       'defined as RamanFiber without operational parameters')
-        else:
+
-            self.raman_pumps = None
+        if 'raman_pumps' not in self.operational:
-        self.raman_solver = RamanSolver(self)
+            raise NetworkTopologyError(f'Fiber element uid:{self.uid} '
+                                       'defined as RamanFiber without raman pumps description in operational')
+
+        if 'temperature' not in self.operational:
+            raise NetworkTopologyError(f'Fiber element uid:{self.uid} '
+                                       'defined as RamanFiber without temperature in operational')
+
+        pump_loss = db2lin(self.params.con_out)
+        self.raman_pumps = tuple(PumpParams(p['power'] / pump_loss, p['frequency'], p['propagation_direction'])
+                                 for p in self.operational['raman_pumps'])
+        self.temperature = self.operational['temperature']
 
     @property
     def to_json(self):
         return dict(super().to_json, operational=self.operational)
 
-    def update_pref(self, pref, *carriers):
+    def propagate(self, spectral_info: SpectralInformation):
-        pch_out_db = lin2db(mean([carrier.power.signal for carrier in carriers])) + 30
+        """Modifies the spectral information computing the attenuation, the non-linear interference generation,
-        self.pch_out_db = round(pch_out_db, 2)
+        the CD and PMD accumulation.
-        return pref._replace(p_span0=pref.p_span0, p_spani=self.pch_out_db)
+        """
+        # apply the attenuation due to the input connector loss
+        attenuation_in_db = self.params.con_in + self.params.att_in
+        spectral_info.apply_attenuation_db(attenuation_in_db)
 
-    def __call__(self, spectral_info):
+        # Raman pumps and inter channel Raman effect
-        carriers = tuple(self.propagate(*spectral_info.carriers))
+        stimulated_raman_scattering = RamanSolver.calculate_stimulated_raman_scattering(spectral_info, self)
-        pref = self.update_pref(spectral_info.pref, *carriers)
+        spontaneous_raman_scattering = \
-        return spectral_info._replace(carriers=carriers, pref=pref)
+            RamanSolver.calculate_spontaneous_raman_scattering(spectral_info, stimulated_raman_scattering, self)
 
-    def propagate(self, *carriers):
+        # nli and ase noise evaluated at the fiber input
-        for propagated_carrier in propagate_raman_fiber(self, *carriers):
+        spectral_info.nli += NliSolver.compute_nli(spectral_info, stimulated_raman_scattering, self)
-            chromatic_dispersion = propagated_carrier.chromatic_dispersion + \
+        spectral_info.ase += spontaneous_raman_scattering
-                                   self.chromatic_dispersion(propagated_carrier.frequency)
-            pmd = sqrt(propagated_carrier.pmd**2 + self.pmd**2)
-            propagated_carrier = propagated_carrier._replace(chromatic_dispersion=chromatic_dispersion, pmd=pmd)
-            yield propagated_carrier
 
+        # chromatic dispersion and pmd variations
+        spectral_info.chromatic_dispersion += self.chromatic_dispersion(spectral_info.frequency)
+        spectral_info.pmd = sqrt(spectral_info.pmd ** 2 + self.pmd ** 2)
 
-class EdfaParams:
+        # apply the attenuation due to the fiber losses
-    def __init__(self, **params):
+        attenuation_fiber = stimulated_raman_scattering.loss_profile[:spectral_info.number_of_channels, -1]
-        self.update_params(params)
-        if params == {}:
-            self.type_variety = ''
-            self.type_def = ''
-            # self.gain_flatmax = 0
-            # self.gain_min = 0
-            # self.p_max = 0
-            # self.nf_model = None
-            # self.nf_fit_coeff = None
-            # self.nf_ripple = None
-            # self.dgt = None
-            # self.gain_ripple = None
-            # self.out_voa_auto = False
-            # self.allowed_for_design = None
 
-    def update_params(self, kwargs):
+        spectral_info.apply_attenuation_lin(attenuation_fiber)
-        for k, v in kwargs.items():
-            setattr(self, k, self.update_params(**v) if isinstance(v, dict) else v)
 
-
+        # apply the attenuation due to the output connector loss
-class EdfaOperational:
+        attenuation_out_db = self.params.con_out
-    default_values = {
+        spectral_info.apply_attenuation_db(attenuation_out_db)
-        'gain_target': None,
-        'delta_p': None,
-        'out_voa': None,
-        'tilt_target': 0
-    }
-
-    def __init__(self, **operational):
-        self.update_attr(operational)
-
-    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():
-            setattr(self, k, clean_kwargs.get(k, v))
-
-    def __repr__(self):
-        return (f'{type(self).__name__}('
-                f'gain_target={self.gain_target!r}, '
-                f'tilt_target={self.tilt_target!r})')
 
 
 class Edfa(_Node):
@@ -548,12 +562,7 @@ class Edfa(_Node):
         if operational is None:
             operational = {}
         self.variety_list = kwargs.pop('variety_list', None)
-        super().__init__(
+        super().__init__(*args, params=EdfaParams(**params), operational=EdfaOperational(**operational), **kwargs)
-            *args,
-            params=EdfaParams(**params),
-            operational=EdfaOperational(**operational),
-            **kwargs
-        )
         self.interpol_dgt = None  # interpolated dynamic gain tilt
         self.interpol_gain_ripple = None  # gain ripple
         self.interpol_nf_ripple = None  # nf_ripple
@@ -579,7 +588,7 @@ class Edfa(_Node):
                 'type': type(self).__name__,
                 'type_variety': self.params.type_variety,
                 'operational': {
-                    'gain_target': self.effective_gain,
+                    'gain_target': round(self.effective_gain, 6),
                     'delta_p': self.delta_p,
                     'tilt_target': self.tilt_target,
                     'out_voa': self.out_voa
@@ -619,44 +628,50 @@ class Edfa(_Node):
                           f'  effective pch (dBm):    {self.effective_pch_out_db:.2f}',
                           f'  output VOA (dB):        {self.out_voa:.2f}'])
 
-    def interpol_params(self, frequencies, pin, baud_rates, pref):
+    def interpol_params(self, spectral_info):
         """interpolate SI channel frequencies with the edfa dgt and gain_ripple frquencies from JSON
+        :param spectral_info: instance of gnpy.core.info.SpectralInformation
+        :return: None
         """
         # TODO|jla: read amplifier actual frequencies from additional params in json
-        self.channel_freq = frequencies
 
+        self.channel_freq = spectral_info.frequency
         amplifier_freq = arrange_frequencies(len(self.params.dgt), self.params.f_min, self.params.f_max)  # Hz
-        self.interpol_dgt = interp(self.channel_freq, amplifier_freq, self.params.dgt)
+        self.interpol_dgt = interp(spectral_info.frequency, amplifier_freq, self.params.dgt)
 
         amplifier_freq = arrange_frequencies(len(self.params.gain_ripple), self.params.f_min, self.params.f_max)  # Hz
-        self.interpol_gain_ripple = interp(self.channel_freq, amplifier_freq, self.params.gain_ripple)
+        self.interpol_gain_ripple = interp(spectral_info.frequency, amplifier_freq, self.params.gain_ripple)
 
         amplifier_freq = arrange_frequencies(len(self.params.nf_ripple), self.params.f_min, self.params.f_max)  # Hz
-        self.interpol_nf_ripple = interp(self.channel_freq, amplifier_freq, self.params.nf_ripple)
+        self.interpol_nf_ripple = interp(spectral_info.frequency, amplifier_freq, self.params.nf_ripple)
 
-        self.nch = frequencies.size
+        self.nch = spectral_info.number_of_channels
-        self.pin_db = lin2db(sum(pin * 1e3))
+        pin = spectral_info.signal + spectral_info.ase + spectral_info.nli
+        self.pin_db = watt2dbm(sum(pin))
+        # The following should be changed when we have the new spectral information including slot widths.
+        # For now, with homogeneous spectrum, we can calculate it as the difference between neighbouring channels.
+        self.slot_width = self.channel_freq[1] - self.channel_freq[0]
 
         """in power mode: delta_p is defined and can be used to calculate the power target
         This power target is used calculate the amplifier gain"""
+        pref = spectral_info.pref
         if self.delta_p is not None:
             self.target_pch_out_db = round(self.delta_p + pref.p_span0, 2)
             self.effective_gain = self.target_pch_out_db - pref.p_spani
 
         """check power saturation and correct effective gain & power accordingly:"""
+        # Compute the saturation accounting for actual power at the input of the amp
         self.effective_gain = min(
             self.effective_gain,
-            self.params.p_max - (pref.p_spani + pref.neq_ch)
+            self.params.p_max - self.pin_db
         )
-        #print(self.uid, self.effective_gain, self.operational.gain_target)
         self.effective_pch_out_db = round(pref.p_spani + self.effective_gain, 2)
 
         """check power saturation and correct target_gain accordingly:"""
-        #print(self.uid, self.effective_gain, self.pin_db, pref.p_spani)
         self.nf = self._calc_nf()
         self.gprofile = self._gain_profile(pin)
 
-        pout = (pin + self.noise_profile(baud_rates)) * db2lin(self.gprofile)
+        pout = (pin + self.noise_profile(spectral_info)) * db2lin(self.gprofile)
         self.pout_db = lin2db(sum(pout * 1e3))
         # ase & nli are only calculated in signal bandwidth
         #    pout_db is not the absolute full output power (negligible if sufficient channels)
@@ -673,13 +688,17 @@ class Edfa(_Node):
         elif type_def == 'fixed_gain':
             nf_avg = nf_model.nf0
         elif type_def == 'openroadm':
-            pin_ch = self.pin_db - lin2db(self.nch)
+            # OpenROADM specifies OSNR vs. input power per channel for 50 GHz slot width so we
-            # model OSNR = f(Pin)
+            # scale it to 50 GHz based on actual slot width.
-            nf_avg = pin_ch - polyval(nf_model.nf_coef, pin_ch) + 58
+            pin_ch_50GHz = self.pin_db - lin2db(self.nch) + lin2db(50e9 / self.slot_width)
+            # model OSNR = f(Pin per 50 GHz channel)
+            nf_avg = pin_ch_50GHz - polyval(nf_model.nf_coef, pin_ch_50GHz) + 58
         elif type_def == 'openroadm_preamp':
-            pin_ch = self.pin_db - lin2db(self.nch)
+            # OpenROADM specifies OSNR vs. input power per channel for 50 GHz slot width so we
-            # model OSNR = f(Pin)
+            # scale it to 50 GHz based on actual slot width.
-            nf_avg = pin_ch - min((4 * pin_ch + 275) / 7, 33) + 58
+            pin_ch_50GHz = self.pin_db - lin2db(self.nch) + lin2db(50e9 / self.slot_width)
+            # model OSNR = f(Pin per 50 GHz channel)
+            nf_avg = pin_ch_50GHz - min((4 * pin_ch_50GHz + 275) / 7, 33) + 58
         elif type_def == 'openroadm_booster':
             # model a zero-noise amp with "infinitely negative" (in dB) NF
             nf_avg = float('-inf')
@@ -725,13 +744,8 @@ class Edfa(_Node):
         else:
             return self.interpol_nf_ripple + nf_avg  # input VOA = 1 for 1 NF degradation
 
-    def noise_profile(self, df):
+    def noise_profile(self, spectral_info: SpectralInformation):
-        """noise_profile(bw) computes amplifier ASE (W) in signal bandwidth (Hz)
+        """Computes amplifier ASE noise integrated over the signal bandwidth. This is calculated at amplifier input.
-
-        Noise is calculated at amplifier input
-
-        :bw: signal bandwidth = baud rate in Hz
-        :type bw: float
 
         :return: the asepower in W in the signal bandwidth bw for 96 channels
         :return type: numpy array of float
@@ -767,7 +781,7 @@ class Edfa(_Node):
         quoting power spectral density in the same BW for both signal and ASE,
         e.g. 12.5GHz."""
 
-        ase = h * df * self.channel_freq * db2lin(self.nf)  # W
+        ase = h * spectral_info.baud_rate * spectral_info.frequency * db2lin(self.nf)  # W
         return ase  # in W at amplifier input
 
     def _gain_profile(self, pin, err_tolerance=1.0e-11, simple_opt=True):
@@ -873,30 +887,24 @@ class Edfa(_Node):
 
         return g1st - voa + array(self.interpol_dgt) * dgts3
 
-    def propagate(self, pref, *carriers):
+    def propagate(self, spectral_info):
         """add ASE noise to the propagating carriers of :class:`.info.SpectralInformation`"""
-        pin = array([c.power.signal + c.power.nli + c.power.ase for c in carriers])  # pin in W
-        freq = array([c.frequency for c in carriers])
-        brate = array([c.baud_rate for c in carriers])
         # interpolate the amplifier vectors with the carriers freq, calculate nf & gain profile
-        self.interpol_params(freq, pin, brate, pref)
+        self.interpol_params(spectral_info)
 
-        gains = db2lin(self.gprofile)
+        ase = self.noise_profile(spectral_info)
-        carrier_ases = self.noise_profile(brate)
+        spectral_info.ase += ase
-        att = db2lin(self.out_voa)
 
-        for gain, carrier_ase, carrier in zip(gains, carrier_ases, carriers):
+        spectral_info.apply_gain_db(self.gprofile - self.out_voa)
-            pwr = carrier.power
+        spectral_info.pmd = sqrt(spectral_info.pmd ** 2 + self.params.pmd ** 2)
-            pwr = pwr._replace(signal=pwr.signal * gain / att,
+        spectral_info.pdl = sqrt(spectral_info.pdl ** 2 + self.params.pdl ** 2)
-                               nli=pwr.nli * gain / att,
-                               ase=(pwr.ase + carrier_ase) * gain / att)
-            yield carrier._replace(power=pwr)
 
-    def update_pref(self, pref):
+    def update_pref(self, spectral_info):
-        return pref._replace(p_span0=pref.p_span0,
+        spectral_info.pref = \
-                             p_spani=pref.p_spani + self.effective_gain - self.out_voa)
+            spectral_info.pref._replace(p_span0=spectral_info.pref.p_span0,
+                                        p_spani=spectral_info.pref.p_spani + self.effective_gain - self.out_voa)
 
     def __call__(self, spectral_info):
-        carriers = tuple(self.propagate(spectral_info.pref, *spectral_info.carriers))
+        self.propagate(spectral_info)
-        pref = self.update_pref(spectral_info.pref)
+        self.update_pref(spectral_info)
-        return spectral_info._replace(carriers=carriers, pref=pref)
+        return spectral_info

gnpy/core/equipment.py

@@ -35,6 +35,7 @@ def trx_mode_params(equipment, trx_type_variety='', trx_mode='', error_message=F
             mode_params = {"format": "undetermined",
                            "baud_rate": None,
                            "OSNR": None,
+                           "penalties": None,
                            "bit_rate": None,
                            "roll_off": None,
                            "tx_osnr": None,
@@ -59,6 +60,7 @@ def trx_mode_params(equipment, trx_type_variety='', trx_mode='', error_message=F
             trx_params['baud_rate'] = default_si_data.baud_rate
             trx_params['spacing'] = default_si_data.spacing
             trx_params['OSNR'] = None
+            trx_params['penalties'] = {}
             trx_params['bit_rate'] = None
             trx_params['cost'] = None
             trx_params['roll_off'] = default_si_data.roll_off

gnpy/core/info.py

@@ -8,24 +8,36 @@ gnpy.core.info
 This module contains classes for modelling :class:`SpectralInformation`.
 """
 
+from __future__ import annotations
 from collections import namedtuple
-from gnpy.core.utils import automatic_nch, lin2db
+from collections.abc import Iterable
+from typing import Union
+from numpy import argsort, mean, array, append, ones, ceil, any, zeros, outer, full, ndarray, asarray
+
+from gnpy.core.utils import automatic_nch, lin2db, db2lin
+from gnpy.core.exceptions import SpectrumError
+
+DEFAULT_SLOT_WIDTH_STEP = 12.5e9  # Hz
+"""Channels with unspecified slot width will have their slot width evaluated as the baud rate rounded up to the minimum
+multiple of the DEFAULT_SLOT_WIDTH_STEP (the baud rate is extended including the roll off in this evaluation)"""
 
 
 class Power(namedtuple('Power', 'signal nli ase')):
     """carriers power in W"""
 
 
-class Channel(namedtuple('Channel', 'channel_number frequency baud_rate roll_off power chromatic_dispersion pmd')):
+class Channel(namedtuple('Channel',
+                         'channel_number frequency baud_rate slot_width roll_off power chromatic_dispersion pmd pdl')):
     """ Class containing the parameters of a WDM signal.
-
         :param channel_number: channel number in the WDM grid
         :param frequency: central frequency of the signal (Hz)
         :param baud_rate: the symbol rate of the signal (Baud)
+        :param slot_width: the slot width (Hz)
         :param roll_off: the roll off of the signal. It is a pure number between 0 and 1
         :param power (gnpy.core.info.Power): power of signal, ASE noise and NLI (W)
         :param chromatic_dispersion: chromatic dispersion (s/m)
         :param pmd: polarization mode dispersion (s)
+        :param pdl: polarization dependent loss (dB)
     """
 
 
@@ -36,21 +48,217 @@ class Pref(namedtuple('Pref', 'p_span0, p_spani, neq_ch ')):
     neq_ch: equivalent channel count in dB"""
 
 
-class SpectralInformation(namedtuple('SpectralInformation', 'pref carriers')):
+class SpectralInformation(object):
+    """ Class containing the parameters of the entire WDM comb."""
 
-    def __new__(cls, pref, carriers):
+    def __init__(self, frequency: array, baud_rate: array, slot_width: array, signal: array, nli: array, ase: array,
-        return super().__new__(cls, pref, carriers)
+                 roll_off: array, chromatic_dispersion: array, pmd: array, pdl: array):
+        indices = argsort(frequency)
+        self._frequency = frequency[indices]
+        self._df = outer(ones(frequency.shape), frequency) - outer(frequency, ones(frequency.shape))
+        self._number_of_channels = len(self._frequency)
+        self._channel_number = [*range(1, self._number_of_channels + 1)]
+        self._slot_width = slot_width[indices]
+        self._baud_rate = baud_rate[indices]
+        overlap = self._frequency[:-1] + self._slot_width[:-1] / 2 > self._frequency[1:] - self._slot_width[1:] / 2
+        if any(overlap):
+            overlap = [pair for pair in zip(overlap * self._channel_number[:-1], overlap * self._channel_number[1:])
+                       if pair != (0, 0)]
+            raise SpectrumError(f'Spectrum required slot widths larger than the frequency spectral distances '
+                                f'between channels: {overlap}.')
+        exceed = self._baud_rate > self._slot_width
+        if any(exceed):
+            raise SpectrumError(f'Spectrum baud rate, including the roll off, larger than the slot width for channels: '
+                                f'{[ch for ch in exceed * self._channel_number if ch]}.')
+        self._signal = signal[indices]
+        self._nli = nli[indices]
+        self._ase = ase[indices]
+        self._roll_off = roll_off[indices]
+        self._chromatic_dispersion = chromatic_dispersion[indices]
+        self._pmd = pmd[indices]
+        self._pdl = pdl[indices]
+        pref = lin2db(mean(signal) * 1e3)
+        self._pref = Pref(pref, pref, lin2db(self._number_of_channels))
+
+    @property
+    def pref(self):
+        """Instance of gnpy.info.Pref"""
+        return self._pref
+
+    @pref.setter
+    def pref(self, pref: Pref):
+        self._pref = pref
+
+    @property
+    def frequency(self):
+        return self._frequency
+
+    @property
+    def df(self):
+        """Matrix of relative frequency distances between all channels. Positive elements in the upper right side."""
+        return self._df
+
+    @property
+    def slot_width(self):
+        return self._slot_width
+
+    @property
+    def baud_rate(self):
+        return self._baud_rate
+
+    @property
+    def number_of_channels(self):
+        return self._number_of_channels
+
+    @property
+    def powers(self):
+        powers = zip(self.signal, self.nli, self.ase)
+        return [Power(*p) for p in powers]
+
+    @property
+    def signal(self):
+        return self._signal
+
+    @signal.setter
+    def signal(self, signal):
+        self._signal = signal
+
+    @property
+    def nli(self):
+        return self._nli
+
+    @nli.setter
+    def nli(self, nli):
+        self._nli = nli
+
+    @property
+    def ase(self):
+        return self._ase
+
+    @ase.setter
+    def ase(self, ase):
+        self._ase = ase
+
+    @property
+    def roll_off(self):
+        return self._roll_off
+
+    @property
+    def chromatic_dispersion(self):
+        return self._chromatic_dispersion
+
+    @chromatic_dispersion.setter
+    def chromatic_dispersion(self, chromatic_dispersion):
+        self._chromatic_dispersion = chromatic_dispersion
+
+    @property
+    def pmd(self):
+        return self._pmd
+
+    @pmd.setter
+    def pmd(self, pmd):
+        self._pmd = pmd
+
+    @property
+    def pdl(self):
+        return self._pdl
+
+    @pdl.setter
+    def pdl(self, pdl):
+        self._pdl = pdl
+
+    @property
+    def channel_number(self):
+        return self._channel_number
+
+    @property
+    def carriers(self):
+        entries = zip(self.channel_number, self.frequency, self.baud_rate, self.slot_width,
+                      self.roll_off, self.powers, self.chromatic_dispersion, self.pmd, self.pdl)
+        return [Channel(*entry) for entry in entries]
+
+    def apply_attenuation_lin(self, attenuation_lin):
+        self.signal *= attenuation_lin
+        self.nli *= attenuation_lin
+        self.ase *= attenuation_lin
+
+    def apply_attenuation_db(self, attenuation_db):
+        attenuation_lin = 1 / db2lin(attenuation_db)
+        self.apply_attenuation_lin(attenuation_lin)
+
+    def apply_gain_lin(self, gain_lin):
+        self.signal *= gain_lin
+        self.nli *= gain_lin
+        self.ase *= gain_lin
+
+    def apply_gain_db(self, gain_db):
+        gain_lin = db2lin(gain_db)
+        self.apply_gain_lin(gain_lin)
+
+    def __add__(self, other: SpectralInformation):
+        try:
+            return SpectralInformation(frequency=append(self.frequency, other.frequency),
+                                       slot_width=append(self.slot_width, other.slot_width),
+                                       signal=append(self.signal, other.signal), nli=append(self.nli, other.nli),
+                                       ase=append(self.ase, other.ase),
+                                       baud_rate=append(self.baud_rate, other.baud_rate),
+                                       roll_off=append(self.roll_off, other.roll_off),
+                                       chromatic_dispersion=append(self.chromatic_dispersion,
+                                                                   other.chromatic_dispersion),
+                                       pmd=append(self.pmd, other.pmd),
+                                       pdl=append(self.pdl, other.pdl))
+        except SpectrumError:
+            raise SpectrumError('Spectra cannot be summed: channels overlapping.')
+
+    def _replace(self, carriers, pref):
+        self.chromatic_dispersion = array([c.chromatic_dispersion for c in carriers])
+        self.pmd = array([c.pmd for c in carriers])
+        self.pdl = array([c.pdl for c in carriers])
+        self.signal = array([c.power.signal for c in carriers])
+        self.nli = array([c.power.nli for c in carriers])
+        self.ase = array([c.power.ase for c in carriers])
+        self.pref = pref
+        return self
+
+
+def create_arbitrary_spectral_information(frequency: Union[ndarray, Iterable, int, float],
+                                          signal: Union[int, float, ndarray, Iterable],
+                                          baud_rate: Union[int, float, ndarray, Iterable],
+                                          slot_width: Union[int, float, ndarray, Iterable] = None,
+                                          roll_off: Union[int, float, ndarray, Iterable] = 0.,
+                                          chromatic_dispersion: Union[int, float, ndarray, Iterable] = 0.,
+                                          pmd: Union[int, float, ndarray, Iterable] = 0.,
+                                          pdl: Union[int, float, ndarray, Iterable] = 0.):
+    """This is just a wrapper around the SpectralInformation.__init__() that simplifies the creation of
+    a non-uniform spectral information with NLI and ASE powers set to zero."""
+    frequency = asarray(frequency)
+    number_of_channels = frequency.size
+    try:
+        signal = full(number_of_channels, signal)
+        baud_rate = full(number_of_channels, baud_rate)
+        roll_off = full(number_of_channels, roll_off)
+        slot_width = full(number_of_channels, slot_width) if slot_width is not None else \
+            ceil((1 + roll_off) * baud_rate / DEFAULT_SLOT_WIDTH_STEP) * DEFAULT_SLOT_WIDTH_STEP
+        chromatic_dispersion = full(number_of_channels, chromatic_dispersion)
+        pmd = full(number_of_channels, pmd)
+        pdl = full(number_of_channels, pdl)
+        nli = zeros(number_of_channels)
+        ase = zeros(number_of_channels)
+        return SpectralInformation(frequency=frequency, slot_width=slot_width,
+                                   signal=signal, nli=nli, ase=ase,
+                                   baud_rate=baud_rate, roll_off=roll_off,
+                                   chromatic_dispersion=chromatic_dispersion,
+                                   pmd=pmd, pdl=pdl)
+    except ValueError as e:
+        if 'could not broadcast' in str(e):
+            raise SpectrumError('Dimension mismatch in input fields.')
+        else:
+            raise
 
 
 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
+    """ Creates a fixed slot width spectral information with flat power """
-    pref = lin2db(power * 1e3)
     nb_channel = automatic_nch(f_min, f_max, spacing)
-    si = SpectralInformation(
+    frequency = [(f_min + spacing * i) for i in range(1, nb_channel + 1)]
-        pref=Pref(pref, pref, lin2db(nb_channel)),
+    return create_arbitrary_spectral_information(frequency, slot_width=spacing, signal=power, baud_rate=baud_rate,
-        carriers=[
+                                                 roll_off=roll_off)
-            Channel(f, (f_min + spacing * f),
-                    baud_rate, roll_off, Power(power, 0, 0), 0, 0) for f in range(1, nb_channel + 1)
-        ]
-    )
-    return si

gnpy/core/network.py

@@ -27,6 +27,7 @@ def edfa_nf(gain_target, variety_type, equipment):
     )
     amp.pin_db = 0
     amp.nch = 88
+    amp.slot_width = 50e9
     return amp._calc_nf(True)
 
 
@@ -237,7 +238,7 @@ def set_egress_amplifier(network, this_node, equipment, pref_ch_db, pref_total_d
     """
     power_mode = equipment['Span']['default'].power_mode
     next_oms = (n for n in network.successors(this_node) if not isinstance(n, elements.Transceiver))
-    this_node_degree = {k: v for k, v in this_node.per_degree_pch_out_db.items()} if hasattr(this_node, 'per_degree_pch_out_db') else {}
+    this_node_degree = getattr(this_node, 'per_degree_pch_out_dbm', {})
     for oms in next_oms:
         # go through all the OMS departing from the ROADM
         prev_node = this_node
@@ -282,7 +283,7 @@ def set_egress_amplifier(network, this_node, equipment, pref_ch_db, pref_total_d
 
                 if isinstance(prev_node, elements.Fiber):
                     max_fiber_lineic_loss_for_raman = \
-                        equipment['Span']['default'].max_fiber_lineic_loss_for_raman
+                        equipment['Span']['default'].max_fiber_lineic_loss_for_raman * 1e-3  # dB/m
                     raman_allowed = prev_node.params.loss_coef < max_fiber_lineic_loss_for_raman
                 else:
                     raman_allowed = False
@@ -303,9 +304,14 @@ def set_egress_amplifier(network, this_node, equipment, pref_ch_db, pref_total_d
                     node.params.update_params(extra_params.__dict__)
                     dp += power_reduction
                     gain_target += power_reduction
-                elif node.params.raman and not raman_allowed:
-                    print(f'{ansi_escapes.red}WARNING{ansi_escapes.reset}: raman is used in node {node.uid}\n but fiber lineic loss is above threshold\n')
                 else:
+                    if node.params.raman and not raman_allowed:
+                        if isinstance(prev_node, elements.Fiber):
+                            print(f'{ansi_escapes.red}WARNING{ansi_escapes.reset}: raman is used in node {node.uid}\n '
+                                  'but fiber lineic loss is above threshold\n')
+                        else:
+                            print(f'{ansi_escapes.red}WARNING{ansi_escapes.reset}: raman is used in node {node.uid}\n '
+                                  'but previous node is not a fiber\n')
                     # if variety is imposed by user, and if the gain_target (computed or imposed) is also above
                     # variety max gain + extended range, then warn that gain > max_gain + extended range
                     if gain_target - equipment['Edfa'][node.params.type_variety].gain_flatmax - \
@@ -328,7 +334,7 @@ def set_egress_amplifier(network, this_node, equipment, pref_ch_db, pref_total_d
             # print(f'{node.uid}')
 
     if isinstance(this_node, elements.Roadm):
-        this_node.per_degree_pch_out_db = {k: v for k, v in this_node_degree.items()}
+        this_node.per_degree_pch_out_dbm = {k: v for k, v in this_node_degree.items()}
 
 
 def add_roadm_booster(network, roadm):
@@ -521,7 +527,6 @@ def build_network(network, equipment, pref_ch_db, pref_total_db, no_insert_edfas
     # set roadm loss for gain_mode before to build network
     fibers = [f for f in network.nodes() if isinstance(f, elements.Fiber)]
     add_connector_loss(network, fibers, default_span_data.con_in, default_span_data.con_out, default_span_data.EOL)
-    add_fiber_padding(network, fibers, default_span_data.padding)
     # don't group split fiber and add amp in the same loop
     # =>for code clarity (at the expense of speed):
 
@@ -539,6 +544,8 @@ def build_network(network, equipment, pref_ch_db, pref_total_db, no_insert_edfas
         for fiber in fibers:
             add_inline_amplifier(network, fiber)
 
+    add_fiber_padding(network, fibers, default_span_data.padding)
+
     for roadm in roadms:
         set_egress_amplifier(network, roadm, equipment, pref_ch_db, pref_total_db)
 

gnpy/core/parameters.py

@@ -9,9 +9,9 @@ This module contains all parameters to configure standard network elements.
 """
 
 from scipy.constants import c, pi
-from numpy import squeeze, log10, exp
+from numpy import asarray, array
 
-from gnpy.core.utils import db2lin, convert_length
+from gnpy.core.utils import convert_length
 from gnpy.core.exceptions import ParametersError
 
 
@@ -28,110 +28,102 @@ class Parameters:
 
 class PumpParams(Parameters):
     def __init__(self, power, frequency, propagation_direction):
-        self._power = power
+        self.power = power
-        self._frequency = frequency
+        self.frequency = frequency
-        self._propagation_direction = propagation_direction
+        self.propagation_direction = propagation_direction.lower()
-
-    @property
-    def power(self):
-        return self._power
-
-    @property
-    def frequency(self):
-        return self._frequency
-
-    @property
-    def propagation_direction(self):
-        return self._propagation_direction
 
 
 class RamanParams(Parameters):
-    def __init__(self, **kwargs):
+    def __init__(self, flag=False, result_spatial_resolution=10e3, solver_spatial_resolution=50):
-        self._flag_raman = kwargs['flag_raman']
+        """ Simulation parameters used within the Raman Solver
-        self._space_resolution = kwargs['space_resolution'] if 'space_resolution' in kwargs else None
+        :params flag: boolean for enabling/disable the evaluation of the Raman power profile in frequency and position
-        self._tolerance = kwargs['tolerance'] if 'tolerance' in kwargs else None
+        :params result_spatial_resolution: spatial resolution of the evaluated Raman power profile
-
+        :params solver_spatial_resolution: spatial step for the iterative solution of the first order ode
-    @property
+        """
-    def flag_raman(self):
+        self.flag = flag
-        return self._flag_raman
+        self.result_spatial_resolution = result_spatial_resolution  # [m]
-
+        self.solver_spatial_resolution = solver_spatial_resolution  # [m]
-    @property
-    def space_resolution(self):
-        return self._space_resolution
-
-    @property
-    def tolerance(self):
-        return self._tolerance
 
 
 class NLIParams(Parameters):
-    def __init__(self, **kwargs):
+    def __init__(self, method='gn_model_analytic', dispersion_tolerance=1, phase_shift_tolerance=0.1,
-        self._nli_method_name = kwargs['nli_method_name']
+                 computed_channels=None):
-        self._wdm_grid_size = kwargs['wdm_grid_size']
+        """ Simulation parameters used within the Nli Solver
-        self._dispersion_tolerance = kwargs['dispersion_tolerance']
+        :params method: formula for NLI calculation
-        self._phase_shift_tolerance = kwargs['phase_shift_tolerance']
+        :params dispersion_tolerance: tuning parameter for ggn model solution
-        self._f_cut_resolution = None
+        :params phase_shift_tolerance: tuning parameter for ggn model solution
-        self._f_pump_resolution = None
+        :params computed_channels: the NLI is evaluated for these channels and extrapolated for the others
-        self._computed_channels = kwargs['computed_channels'] if 'computed_channels' in kwargs else None
+        """
-
+        self.method = method.lower()
-    @property
+        self.dispersion_tolerance = dispersion_tolerance
-    def nli_method_name(self):
+        self.phase_shift_tolerance = phase_shift_tolerance
-        return self._nli_method_name
+        self.computed_channels = computed_channels
-
-    @property
-    def wdm_grid_size(self):
-        return self._wdm_grid_size
-
-    @property
-    def dispersion_tolerance(self):
-        return self._dispersion_tolerance
-
-    @property
-    def phase_shift_tolerance(self):
-        return self._phase_shift_tolerance
-
-    @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
-
-    @property
-    def computed_channels(self):
-        return self._computed_channels
 
 
 class SimParams(Parameters):
-    def __init__(self, **kwargs):
+    _shared_dict = {'nli_params': NLIParams(), 'raman_params': RamanParams()}
-        try:
+
-            if 'nli_parameters' in kwargs:
+    def __init__(self):
-                self._nli_params = NLIParams(**kwargs['nli_parameters'])
+        if type(self) == SimParams:
-            else:
+            raise NotImplementedError('Instances of SimParams cannot be generated')
-                self._nli_params = None
+
-            if 'raman_parameters' in kwargs:
+    @classmethod
-                self._raman_params = RamanParams(**kwargs['raman_parameters'])
+    def set_params(cls, sim_params):
-            else:
+        cls._shared_dict['nli_params'] = NLIParams(**sim_params.get('nli_params', {}))
-                self._raman_params = None
+        cls._shared_dict['raman_params'] = RamanParams(**sim_params.get('raman_params', {}))
-        except KeyError as e:
+
-            raise ParametersError(f'Simulation parameters must include {e}. Configuration: {kwargs}')
+    @classmethod
+    def get(cls):
+        self = cls.__new__(cls)
+        return self
 
     @property
     def nli_params(self):
-        return self._nli_params
+        return self._shared_dict['nli_params']
 
     @property
     def raman_params(self):
-        return self._raman_params
+        return self._shared_dict['raman_params']
+
+
+class RoadmParams(Parameters):
+    def __init__(self, **kwargs):
+        try:
+            self.target_pch_out_db = kwargs['target_pch_out_db']
+            self.add_drop_osnr = kwargs['add_drop_osnr']
+            self.pmd = kwargs['pmd']
+            self.pdl = kwargs['pdl']
+            self.restrictions = kwargs['restrictions']
+            self.per_degree_pch_out_db = kwargs['per_degree_pch_out_db'] if 'per_degree_pch_out_db' in kwargs else {}
+        except KeyError as e:
+            raise ParametersError(f'ROADM configurations must include {e}. Configuration: {kwargs}')
+
+
+class FusedParams(Parameters):
+    def __init__(self, **kwargs):
+        self.loss = kwargs['loss'] if 'loss' in kwargs else 1
+
+
+# SSMF Raman coefficient profile normalized with respect to the effective area (Cr * A_eff)
+CR_NORM = array([
+    0., 7.802e-16, 2.4236e-15, 4.0504e-15, 5.6606e-15, 6.8973e-15, 7.802e-15, 8.4162e-15, 8.8727e-15, 9.2877e-15,
+    1.01011e-14, 1.05244e-14, 1.13295e-14, 1.2367e-14, 1.3695e-14, 1.5023e-14, 1.64091e-14, 1.81936e-14, 2.04927e-14,
+    2.28167e-14, 2.48917e-14, 2.66098e-14, 2.82615e-14, 2.98136e-14, 3.1042e-14, 3.17558e-14, 3.18803e-14, 3.17558e-14,
+    3.15566e-14, 3.11748e-14, 2.94567e-14, 3.14985e-14, 2.8552e-14, 2.43439e-14, 1.67992e-14, 9.6114e-15, 7.02180e-15,
+    5.9262e-15, 5.6938e-15, 7.055e-15, 7.4119e-15, 7.4783e-15, 6.7645e-15, 5.5361e-15, 3.6271e-15, 2.7224e-15,
+    2.4568e-15, 2.1995e-15, 2.1331e-15, 2.3323e-15, 2.5564e-15, 3.0461e-15, 4.8555e-15, 5.5029e-15, 5.2788e-15,
+    4.565e-15, 3.3698e-15, 2.2991e-15, 2.0086e-15, 1.5521e-15, 1.328e-15, 1.162e-15, 9.379e-16, 8.715e-16, 8.134e-16,
+    8.134e-16, 9.379e-16, 1.3612e-15, 1.6185e-15, 1.9754e-15, 1.8758e-15, 1.6849e-15, 1.2284e-15, 9.047e-16, 8.134e-16,
+    8.715e-16, 9.711e-16, 1.0375e-15, 1.0043e-15, 9.047e-16, 8.134e-16, 6.806e-16, 5.478e-16, 3.901e-16, 2.241e-16,
+    1.577e-16, 9.96e-17, 3.32e-17, 1.66e-17, 8.3e-18])
+
+# Note the non-uniform spacing of this range; this is required for properly capturing the Raman peak shape.
+FREQ_OFFSET = array([
+    0., 0.5, 1., 1.5, 2., 2.5, 3., 3.5, 4., 4.5, 5., 5.5, 6., 6.5, 7., 7.5, 8., 8.5, 9., 9.5, 10., 10.5, 11., 11.5, 12.,
+    12.5, 12.75, 13., 13.25, 13.5, 14., 14.5, 14.75, 15., 15.5, 16., 16.5, 17., 17.5, 18., 18.25, 18.5, 18.75, 19.,
+    19.5, 20., 20.5, 21., 21.5, 22., 22.5, 23., 23.5, 24., 24.5, 25., 25.5, 26., 26.5, 27., 27.5, 28., 28.5, 29., 29.5,
+    30., 30.5, 31., 31.5, 32., 32.5, 33., 33.5, 34., 34.5, 35., 35.5, 36., 36.5, 37., 37.5, 38., 38.5, 39., 39.5, 40.,
+    40.5, 41., 41.5, 42.]) * 1e12
 
 
 class FiberParams(Parameters):
@@ -139,45 +131,50 @@ class FiberParams(Parameters):
         try:
             self._length = convert_length(kwargs['length'], kwargs['length_units'])
             # fixed attenuator for padding
-            self._att_in = kwargs['att_in'] if 'att_in' in kwargs else 0
+            self._att_in = kwargs.get('att_in', 0)
             # if not defined in the network json connector loss in/out
             # the None value will be updated in network.py[build_network]
             # with default values from eqpt_config.json[Spans]
-            self._con_in = kwargs['con_in'] if 'con_in' in kwargs else None
+            self._con_in = kwargs.get('con_in')
-            self._con_out = kwargs['con_out'] if 'con_out' in kwargs else None
+            self._con_out = kwargs.get('con_out')
             if 'ref_wavelength' in kwargs:
                 self._ref_wavelength = kwargs['ref_wavelength']
-                self._ref_frequency = c / self.ref_wavelength
+                self._ref_frequency = c / self._ref_wavelength
             elif 'ref_frequency' in kwargs:
                 self._ref_frequency = kwargs['ref_frequency']
-                self._ref_wavelength = c / self.ref_frequency
+                self._ref_wavelength = c / self._ref_frequency
             else:
-                self._ref_wavelength = 1550e-9
+                self._ref_wavelength = 1550e-9  # conventional central C band wavelength [m]
-                self._ref_frequency = c / self.ref_wavelength
+                self._ref_frequency = c / self._ref_wavelength
             self._dispersion = kwargs['dispersion']  # s/m/m
-            self._dispersion_slope = kwargs['dispersion_slope'] if 'dispersion_slope' in kwargs else \
+            self._dispersion_slope = \
-                -2 * self._dispersion/self.ref_wavelength  # s/m/m/m
+                kwargs.get('dispersion_slope', -2 * self._dispersion / self.ref_wavelength)  # s/m/m/m
             self._beta2 = -(self.ref_wavelength ** 2) * self.dispersion / (2 * pi * c)  # 1/(m * Hz^2)
             # Eq. (3.23) in  Abramczyk, Halina. "Dispersion phenomena in optical fibers." Virtual European University
             # on Lasers. Available online: http://mitr.p.lodz.pl/evu/lectures/Abramczyk3.pdf
             # (accessed on 25 March 2018) (2005).
             self._beta3 = ((self.dispersion_slope - (4*pi*c/self.ref_wavelength**3) * self.beta2) /
                            (2*pi*c/self.ref_wavelength**2)**2)
-            self._gamma = kwargs['gamma']  # 1/W/m
+            self._effective_area = kwargs.get('effective_area')  # m^2
+            n2 = 2.6e-20  # m^2/W
+            if self._effective_area:
+                self._gamma = kwargs.get('gamma', 2 * pi * n2 / (self.ref_wavelength * self._effective_area))  # 1/W/m
+            elif 'gamma' in kwargs:
+                self._gamma = kwargs['gamma']  # 1/W/m
+                self._effective_area = 2 * pi * n2 / (self.ref_wavelength * self._gamma)  # m^2
+            else:
+                self._gamma = 0  # 1/W/m
+                self._effective_area = 83e-12  # m^2
+            default_raman_efficiency = {'cr': CR_NORM / self._effective_area, 'frequency_offset': FREQ_OFFSET}
+            self._raman_efficiency = kwargs.get('raman_efficiency', default_raman_efficiency)
             self._pmd_coef = kwargs['pmd_coef']  # s/sqrt(m)
             if type(kwargs['loss_coef']) == dict:
-                self._loss_coef = squeeze(kwargs['loss_coef']['loss_coef_power']) * 1e-3  # lineic loss dB/m
+                self._loss_coef = asarray(kwargs['loss_coef']['value']) * 1e-3  # lineic loss dB/m
-                self._f_loss_ref = squeeze(kwargs['loss_coef']['frequency'])  # Hz
+                self._f_loss_ref = asarray(kwargs['loss_coef']['frequency'])  # Hz
             else:
-                self._loss_coef = kwargs['loss_coef'] * 1e-3  # lineic loss dB/m
+                self._loss_coef = asarray(kwargs['loss_coef']) * 1e-3  # lineic loss dB/m
-                self._f_loss_ref = 193.5e12  # Hz
+                self._f_loss_ref = asarray(self._ref_frequency)  # Hz
-            self._lin_attenuation = db2lin(self.length * self.loss_coef)
+            self._lumped_losses = kwargs['lumped_losses'] if 'lumped_losses' in kwargs else []
-            self._lin_loss_exp = self.loss_coef / (10 * log10(exp(1)))  # linear power exponent loss Neper/m
-            self._effective_length = (1 - exp(- self.lin_loss_exp * self.length)) / self.lin_loss_exp
-            self._asymptotic_length = 1 / self.lin_loss_exp
-            # raman parameters (not compulsory)
-            self._raman_efficiency = kwargs['raman_efficiency'] if 'raman_efficiency' in kwargs else None
-            self._pumps_loss_coef = kwargs['pumps_loss_coef'] if 'pumps_loss_coef' in kwargs else None
         except KeyError as e:
             raise ParametersError(f'Fiber configurations json must include {e}. Configuration: {kwargs}')
 
@@ -210,6 +207,10 @@ class FiberParams(Parameters):
     def con_out(self):
         return self._con_out
 
+    @property
+    def lumped_losses(self):
+        return self._lumped_losses
+
     @con_out.setter
     def con_out(self, con_out):
         self._con_out = con_out
@@ -254,32 +255,60 @@ class FiberParams(Parameters):
     def f_loss_ref(self):
         return self._f_loss_ref
 
-    @property
-    def lin_loss_exp(self):
-        return self._lin_loss_exp
-
-    @property
-    def lin_attenuation(self):
-        return self._lin_attenuation
-
-    @property
-    def effective_length(self):
-        return self._effective_length
-
-    @property
-    def asymptotic_length(self):
-        return self._asymptotic_length
-
     @property
     def raman_efficiency(self):
         return self._raman_efficiency
 
-    @property
-    def pumps_loss_coef(self):
-        return self._pumps_loss_coef
-
     def asdict(self):
         dictionary = super().asdict()
         dictionary['loss_coef'] = self.loss_coef * 1e3
         dictionary['length_units'] = 'm'
+        if not self.lumped_losses:
+            dictionary.pop('lumped_losses')
+        if not self.raman_efficiency:
+            dictionary.pop('raman_efficiency')
         return dictionary
+
+
+class EdfaParams:
+    def __init__(self, **params):
+        self.update_params(params)
+        if params == {}:
+            self.type_variety = ''
+            self.type_def = ''
+            # self.gain_flatmax = 0
+            # self.gain_min = 0
+            # self.p_max = 0
+            # self.nf_model = None
+            # self.nf_fit_coeff = None
+            # self.nf_ripple = None
+            # self.dgt = None
+            # self.gain_ripple = None
+            # self.out_voa_auto = False
+            # self.allowed_for_design = None
+
+    def update_params(self, kwargs):
+        for k, v in kwargs.items():
+            setattr(self, k, self.update_params(**v) if isinstance(v, dict) else v)
+
+
+class EdfaOperational:
+    default_values = {
+        'gain_target': None,
+        'delta_p': None,
+        'out_voa': None,
+        'tilt_target': 0
+    }
+
+    def __init__(self, **operational):
+        self.update_attr(operational)
+
+    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():
+            setattr(self, k, clean_kwargs.get(k, v))
+
+    def __repr__(self):
+        return (f'{type(self).__name__}('
+                f'gain_target={self.gain_target!r}, '
+                f'tilt_target={self.tilt_target!r})')

gnpy/core/utils.py

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

gnpy/example-data/eqpt_config.json

@@ -180,51 +180,27 @@
       "Fiber":[{
             "type_variety": "SSMF",
             "dispersion": 1.67e-05,
-            "gamma": 0.00127,
+            "effective_area": 83e-12,
             "pmd_coef": 1.265e-15
             },
             {
             "type_variety": "NZDF",
             "dispersion": 0.5e-05,
-            "gamma": 0.00146,
+            "effective_area": 72e-12,
             "pmd_coef": 1.265e-15
             },
             {
             "type_variety": "LOF",
             "dispersion": 2.2e-05,
-            "gamma": 0.000843,
+            "effective_area": 125e-12,
             "pmd_coef": 1.265e-15
             }
       ],
       "RamanFiber":[{
             "type_variety": "SSMF",
             "dispersion": 1.67e-05,
-            "gamma": 0.00127,
+            "effective_area": 83e-12,
-            "pmd_coef": 1.265e-15,
+            "pmd_coef": 1.265e-15
-            "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":[{
@@ -245,6 +221,7 @@
             "target_pch_out_db": -20,
             "add_drop_osnr": 38,
             "pmd": 0,
+            "pdl": 0,
             "restrictions": {
                             "preamp_variety_list":[],
                             "booster_variety_list":[]

gnpy/example-data/eqpt_config_openroadm_ver4.json

@@ -1,95 +1,80 @@
-{     "Edfa":[
+{
-            {
+    "Edfa": [
+        {
             "type_variety": "openroadm_ila_low_noise",
             "type_def": "openroadm",
             "gain_flatmax": 27,
             "gain_min": 0,
             "p_max": 22,
-            "nf_coef": [-8.104e-4,-6.221e-2,-5.889e-1,37.62],
+            "nf_coef": [-8.104e-4, -6.221e-2, -5.889e-1, 37.62],
+            "pmd": 3e-12,
+            "pdl": 0.7,
             "allowed_for_design": true
-            },
+        },
-            {
+        {
             "type_variety": "openroadm_ila_standard",
             "type_def": "openroadm",
             "gain_flatmax": 27,
             "gain_min": 0,
             "p_max": 22,
-            "nf_coef": [-5.952e-4,-6.250e-2,-1.071,28.99],
+            "nf_coef": [-5.952e-4, -6.250e-2, -1.071, 28.99],
+            "pmd": 3e-12,
+            "pdl": 0.7,
             "allowed_for_design": true
-            },
+        },
-		{
+        {
             "type_variety": "openroadm_mw_mw_preamp",
             "type_def": "openroadm_preamp",
             "gain_flatmax": 27,
             "gain_min": 0,
             "p_max": 22,
+            "pmd": 0,
+            "pdl": 0,
             "allowed_for_design": false
-            },
+        },
-		{
+        {
             "type_variety": "openroadm_mw_mw_booster",
             "type_def": "openroadm_booster",
             "gain_flatmax": 32,
             "gain_min": 0,
             "p_max": 22,
+            "pmd": 0,
+            "pdl": 0,
             "allowed_for_design": false
-            }
+        }
-      ],
+    ],
-      "Fiber":[
+    "Fiber": [
-            {
+        {
             "type_variety": "SSMF",
             "dispersion": 1.67e-05,
-            "gamma": 0.00127,
+            "effective_area": 83e-12,
             "pmd_coef": 1.265e-15
-            },
+        },
-            {
+        {
             "type_variety": "NZDF",
             "dispersion": 0.5e-05,
-            "gamma": 0.00146,
+            "effective_area": 72e-12,
             "pmd_coef": 1.265e-15
-            },
+        },
-            {
+        {
             "type_variety": "LOF",
             "dispersion": 2.2e-05,
-            "gamma": 0.000843,
+            "effective_area": 125e-12,
             "pmd_coef": 1.265e-15
-            }
+        }
-      ],
+    ],
-      "RamanFiber":[
+    "RamanFiber": [
-            {
+        {
             "type_variety": "SSMF",
             "dispersion": 1.67e-05,
-            "gamma": 0.00127,
+            "effective_area": 83e-12,
-            "pmd_coef": 1.265e-15,
+            "pmd_coef": 1.265e-15
-            "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,
+    "Span": [
-                  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,
+            "power_mode": true,
-                  0.0003802, 0.0003756, 0.0003549, 0.0003795, 0.000344, 0.0002933, 0.0002024, 0.0001158, 8.46E-05,
+            "delta_power_range_db": [0, 0, 0],
-                  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": [0,0,0],
             "max_fiber_lineic_loss_for_raman": 0.25,
             "target_extended_gain": 0,
             "max_length": 135,
@@ -99,92 +84,266 @@
             "EOL": 0,
             "con_in": 0,
             "con_out": 0
-            }
+        }
-      ],
+    ],
-      "Roadm":[
+    "Roadm": [
-            {
+        {
             "target_pch_out_db": -20,
             "add_drop_osnr": 30,
-            "pmd": 0,
+            "pmd": 3e-12,
+            "pdl": 1.5,
             "restrictions": {
-                            "preamp_variety_list":["openroadm_mw_mw_preamp"],
+                "preamp_variety_list": ["openroadm_mw_mw_preamp"],
-                            "booster_variety_list":["openroadm_mw_mw_booster"]
+                "booster_variety_list": ["openroadm_mw_mw_booster"]
-                            }            
             }
-      ],
+        }
-      "SI":[
+    ],
-            {
+    "SI": [
+        {
             "f_min": 191.3e12,
             "baud_rate": 31.57e9,
-            "f_max":196.1e12,
+            "f_max": 196.1e12,
             "spacing": 50e9,
             "power_dbm": 2,
-            "power_range_db": [0,0,1],
+            "power_range_db": [0, 0, 1],
             "roll_off": 0.15,
             "tx_osnr": 35,
             "sys_margins": 2
-            }
+        }
-      ],
+    ],
-      "Transceiver":[
+    "Transceiver": [
-		{
+        {
             "type_variety": "OpenROADM MSA ver. 4.0",
-            "frequency":{
+            "frequency": {
-                        "min": 191.35e12,
+                "min": 191.35e12,
-                        "max": 196.1e12
+                "max": 196.1e12
+            },
+            "mode": [
+                {
+                    "format": "100 Gbit/s, 27.95 Gbaud, DP-QPSK",
+                    "baud_rate": 27.95e9,
+                    "OSNR": 17,
+                    "bit_rate": 100e9,
+                    "roll_off": null,
+                    "tx_osnr": 33,
+                    "penalties": [
+                        {
+                            "chromatic_dispersion": 4e3,
+                            "penalty_value": 0
                         },
-            "mode":[
+                        {
-                       {
+                            "chromatic_dispersion": 18e3,
-                       "format": "100 Gbit/s, 27.95 Gbaud, DP-QPSK",
+                            "penalty_value": 0.5
-                       "baud_rate": 27.95e9,
+                        },
-                       "OSNR": 17,
+                        {
-                       "bit_rate": 100e9,
+                            "pmd": 10,
-                       "roll_off": null,
+                            "penalty_value": 0
-                       "tx_osnr": 33,
+                        },
-                       "min_spacing": 50e9,
+                        {
-                       "cost":1
+                            "pmd": 30,
-                       },
+                            "penalty_value": 0.5
-					   {
+                        },
-                       "format": "100 Gbit/s, 31.57 Gbaud, DP-QPSK",
+                        {
-                       "baud_rate": 31.57e9,
+                            "pdl": 1,
-                       "OSNR": 12,
+                            "penalty_value": 0.5
-                       "bit_rate": 100e9,
+                        },
-                       "roll_off": 0.15,
+                        {
-                       "tx_osnr": 35,
+                            "pdl": 2,
-                       "min_spacing": 50e9,
+                            "penalty_value": 1
-                       "cost":1
+                        },
-                       },
+                        {
-                       {
+                            "pdl": 4,
-                       "format": "200 Gbit/s, DP-QPSK",
+                            "penalty_value": 2.5
-                       "baud_rate": 63.1e9,
+                        },
-                       "OSNR": 17,
+                        {
-                       "bit_rate": 200e9,
+                            "pdl": 6,
-                       "roll_off": 0.15,
+                            "penalty_value": 4
-                       "tx_osnr": 36,
+                        }
-                       "min_spacing": 87.5e9,
+                    ],
-                       "cost":1
+                    "min_spacing": 50e9,
-                       },
+                    "cost": 1
-                       {
+                },
-                       "format": "300 Gbit/s, DP-8QAM",
+                {
-                       "baud_rate": 63.1e9,
+                    "format": "100 Gbit/s, 31.57 Gbaud, DP-QPSK",
-                       "OSNR": 21,
+                    "baud_rate": 31.57e9,
-                       "bit_rate": 300e9,
+                    "OSNR": 12,
-                       "roll_off": 0.15,
+                    "bit_rate": 100e9,
-                       "tx_osnr": 36,
+                    "roll_off": 0.15,
-                       "min_spacing": 87.5e9,
+                    "tx_osnr": 35,
-                       "cost":1
+                    "penalties": [
-                       },
+                        {
-					   {
+                            "chromatic_dispersion": -1e3,
-                       "format": "400 Gbit/s, DP-16QAM",
+                            "penalty_value": 0
-                       "baud_rate": 63.1e9,
+                        },
-                       "OSNR": 24,
+                        {
-                       "bit_rate": 400e9,
+                            "chromatic_dispersion": 4e3,
-                       "roll_off": 0.15,
+                            "penalty_value": 0
-                       "tx_osnr": 36,
+                        },
-                       "min_spacing": 87.5e9,
+                        {
-                       "cost":1
+                            "chromatic_dispersion": 40e3,
-                       }
+                            "penalty_value": 0.5
-                   ]
+                        },
-            }
+                        {
-      ]
+                            "pmd": 10,
-
+                            "penalty_value": 0
+                        },
+                        {
+                            "pmd": 30,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 1,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 2,
+                            "penalty_value": 1
+                        },
+                        {
+                            "pdl": 4,
+                            "penalty_value": 2.5
+                        },
+                        {
+                            "pdl": 6,
+                            "penalty_value": 4
+                        }
+                    ],
+                    "min_spacing": 50e9,
+                    "cost": 1
+                },
+                {
+                    "format": "200 Gbit/s, DP-QPSK",
+                    "baud_rate": 63.1e9,
+                    "OSNR": 17,
+                    "bit_rate": 200e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 36,
+                    "penalties": [
+                        {
+                            "chromatic_dispersion": -1e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 4e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 24e3,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pmd": 10,
+                            "penalty_value": 0
+                        },
+                        {
+                            "pmd": 25,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 1,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 2,
+                            "penalty_value": 1
+                        },
+                        {
+                            "pdl": 4,
+                            "penalty_value": 2.5
+                        }
+                    ],
+                    "min_spacing": 87.5e9,
+                    "cost": 1
+                },
+                {
+                    "format": "300 Gbit/s, DP-8QAM",
+                    "baud_rate": 63.1e9,
+                    "OSNR": 21,
+                    "bit_rate": 300e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 36,
+                    "penalties": [
+                        {
+                            "chromatic_dispersion": -1e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 4e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 18e3,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pmd": 10,
+                            "penalty_value": 0
+                        },
+                        {
+                            "pmd": 25,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 1,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 2,
+                            "penalty_value": 1
+                        },
+                        {
+                            "pdl": 4,
+                            "penalty_value": 2.5
+                        }
+                    ],
+                    "min_spacing": 87.5e9,
+                    "cost": 1
+                },
+                {
+                    "format": "400 Gbit/s, DP-16QAM",
+                    "baud_rate": 63.1e9,
+                    "OSNR": 24,
+                    "bit_rate": 400e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 36,
+                    "penalties": [
+                        {
+                            "chromatic_dispersion": -1e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 4e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 12e3,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pmd": 10,
+                            "penalty_value": 0
+                        },
+                        {
+                            "pmd": 20,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 1,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 2,
+                            "penalty_value": 1
+                        },
+                        {
+                            "pdl": 4,
+                            "penalty_value": 2.5
+                        }
+                    ],
+                    "min_spacing": 87.5e9,
+                    "cost": 1
+                }
+            ]
+        }
+    ]
 }

gnpy/example-data/eqpt_config_openroadm_ver5.json

@@ -1,105 +1,92 @@
-{     "Edfa":[
+{
-            {
+    "Edfa": [
+        {
             "type_variety": "openroadm_ila_low_noise",
             "type_def": "openroadm",
             "gain_flatmax": 27,
             "gain_min": 0,
             "p_max": 22,
-            "nf_coef": [-8.104e-4,-6.221e-2,-5.889e-1,37.62],
+            "nf_coef": [-8.104e-4, -6.221e-2, -5.889e-1, 37.62],
+            "pmd": 3e-12,
+            "pdl": 0.7,
             "allowed_for_design": true
-            },
+        },
-            {
+        {
             "type_variety": "openroadm_ila_standard",
             "type_def": "openroadm",
             "gain_flatmax": 27,
             "gain_min": 0,
             "p_max": 22,
-            "nf_coef": [-5.952e-4,-6.250e-2,-1.071,28.99],
+            "nf_coef": [-5.952e-4, -6.250e-2, -1.071, 28.99],
+            "pmd": 3e-12,
+            "pdl": 0.7,
             "allowed_for_design": true
-            },
+        },
-		{
+        {
             "type_variety": "openroadm_mw_mw_preamp_typical_ver5",
             "type_def": "openroadm",
             "gain_flatmax": 27,
             "gain_min": 0,
             "p_max": 22,
-            "nf_coef": [-5.952e-4,-6.250e-2,-1.071,28.99],
+            "nf_coef": [-5.952e-4, -6.250e-2, -1.071, 28.99],
+            "pmd": 0,
+            "pdl": 0,
             "allowed_for_design": false
-            },
+        },
-            {
+        {
             "type_variety": "openroadm_mw_mw_preamp_worstcase_ver5",
             "type_def": "openroadm",
             "gain_flatmax": 27,
             "gain_min": 0,
             "p_max": 22,
-            "nf_coef": [-5.952e-4,-6.250e-2,-1.071,27.99],
+            "nf_coef": [-5.952e-4, -6.250e-2, -1.071, 27.99],
+            "pmd": 0,
+            "pdl": 0,
             "allowed_for_design": false
-            },
+        },
-		{
+        {
             "type_variety": "openroadm_mw_mw_booster",
             "type_def": "openroadm_booster",
             "gain_flatmax": 32,
             "gain_min": 0,
             "p_max": 22,
+            "pmd": 0,
+            "pdl": 0,
             "allowed_for_design": false
-            }
+        }
-      ],
+    ],
-      "Fiber":[
+    "Fiber": [
-            {
+        {
             "type_variety": "SSMF",
             "dispersion": 1.67e-05,
-            "gamma": 0.00127,
+            "effective_area": 83e-12,
             "pmd_coef": 1.265e-15
-            },
+        },
-            {
+        {
             "type_variety": "NZDF",
             "dispersion": 0.5e-05,
-            "gamma": 0.00146,
+            "effective_area": 72e-12,
             "pmd_coef": 1.265e-15
-            },
+        },
-            {
+        {
             "type_variety": "LOF",
             "dispersion": 2.2e-05,
-            "gamma": 0.000843,
+            "effective_area": 125e-12,
             "pmd_coef": 1.265e-15
-            }
+        }
-      ],
+    ],
-      "RamanFiber":[
+    "RamanFiber": [
-            {
+        {
             "type_variety": "SSMF",
             "dispersion": 1.67e-05,
-            "gamma": 0.00127,
+            "effective_area": 83e-12,
-            "pmd_coef": 1.265e-15,
+            "pmd_coef": 1.265e-15
-            "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,
+    "Span": [
-                  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,
+            "power_mode": true,
-                  0.0003802, 0.0003756, 0.0003549, 0.0003795, 0.000344, 0.0002933, 0.0002024, 0.0001158, 8.46E-05,
+            "delta_power_range_db": [0, 0, 0],
-                  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": [0,0,0],
             "max_fiber_lineic_loss_for_raman": 0.25,
             "target_extended_gain": 0,
             "max_length": 135,
@@ -109,102 +96,314 @@
             "EOL": 0,
             "con_in": 0,
             "con_out": 0
-            }
+        }
-      ],
+    ],
-      "Roadm":[
+    "Roadm": [
-            {
+        {
             "target_pch_out_db": -20,
             "add_drop_osnr": 33,
-            "pmd": 0,
+            "pmd": 3e-12,
+            "pdl": 1.5,
             "restrictions": {
-                            "preamp_variety_list":["openroadm_mw_mw_preamp_worstcase_ver5"],
+                "preamp_variety_list": ["openroadm_mw_mw_preamp_worstcase_ver5"],
-                            "booster_variety_list":["openroadm_mw_mw_booster"]
+                "booster_variety_list": ["openroadm_mw_mw_booster"]
-                            }            
             }
-      ],
+        }
-      "SI":[
+    ],
-            {
+    "SI": [
+        {
             "f_min": 191.3e12,
             "baud_rate": 31.57e9,
-            "f_max":196.1e12,
+            "f_max": 196.1e12,
             "spacing": 50e9,
             "power_dbm": 2,
-            "power_range_db": [0,0,1],
+            "power_range_db": [0, 0, 1],
             "roll_off": 0.15,
             "tx_osnr": 35,
             "sys_margins": 2
-            }
+        }
-      ],
+    ],
-      "Transceiver":[
+    "Transceiver": [
-		{
+        {
             "type_variety": "OpenROADM MSA ver. 5.0",
-            "frequency":{
+            "frequency": {
-                        "min": 191.35e12,
+                "min": 191.35e12,
-                        "max": 196.1e12
+                "max": 196.1e12
-                        },
+            },
-            "mode":[
+            "mode": [
+                {
+                    "format": "100 Gbit/s, 27.95 Gbaud, DP-QPSK",
+                    "baud_rate": 27.95e9,
+                    "OSNR": 17,
+                    "bit_rate": 100e9,
+                    "roll_off": null,
+                    "tx_osnr": 33,
+                    "penalties": [
                         {
-                        "format": "100 Gbit/s, 27.95 Gbaud, DP-QPSK",
+                            "chromatic_dispersion": 4e3,
-                        "baud_rate": 27.95e9,
+                            "penalty_value": 0
-                        "OSNR": 17,
-                        "bit_rate": 100e9,
-                        "roll_off": null,
-                        "tx_osnr": 33,
-                        "min_spacing": 50e9,
-                        "cost":1
                         },
                         {
-                        "format": "100 Gbit/s, 31.57 Gbaud, DP-QPSK",
+                            "chromatic_dispersion": 18e3,
-                        "baud_rate": 31.57e9,
+                            "penalty_value": 0.5
-                        "OSNR": 12,
-                        "bit_rate": 100e9,
-                        "roll_off": 0.15,
-                        "tx_osnr": 36,
-                        "min_spacing": 50e9,
-                        "cost":1
                         },
                         {
-                        "format": "200 Gbit/s, 31.57 Gbaud, DP-16QAM",
+                            "pmd": 10,
-                        "baud_rate": 31.57e9,
+                            "penalty_value": 0
-                        "OSNR": 20.5,
-                        "bit_rate": 100e9,
-                        "roll_off": 0.15,
-                        "tx_osnr": 36,
-                        "min_spacing": 50e9,
-                        "cost":1
                         },
                         {
-                        "format": "200 Gbit/s, DP-QPSK",
+                            "pmd": 30,
-                        "baud_rate": 63.1e9,
+                            "penalty_value": 0.5
-                        "OSNR": 17,
-                        "bit_rate": 200e9,
-                        "roll_off": 0.15,
-                        "tx_osnr": 36,
-                        "min_spacing": 87.5e9,
-                        "cost":1
                         },
                         {
-                        "format": "300 Gbit/s, DP-8QAM",
+                            "pdl": 1,
-                        "baud_rate": 63.1e9,
+                            "penalty_value": 0.5
-                        "OSNR": 21,
-                        "bit_rate": 300e9,
-                        "roll_off": 0.15,
-                        "tx_osnr": 36,
-                        "min_spacing": 87.5e9,
-                        "cost":1
                         },
                         {
-                        "format": "400 Gbit/s, DP-16QAM",
+                            "pdl": 2,
-                        "baud_rate": 63.1e9,
+                            "penalty_value": 1
-                        "OSNR": 24,
+                        },
-                        "bit_rate": 400e9,
+                        {
-                        "roll_off": 0.15,
+                            "pdl": 4,
-                        "tx_osnr": 36,
+                            "penalty_value": 2.5
-                        "min_spacing": 87.5e9,
+                        },
-                        "cost":1
+                        {
+                            "pdl": 6,
+                            "penalty_value": 4
                         }
-                        ]
+                    ],
-            }
+                    "min_spacing": 50e9,
-      ]
+                    "cost": 1
-
+                },
+                {
+                    "format": "100 Gbit/s, 31.57 Gbaud, DP-QPSK",
+                    "baud_rate": 31.57e9,
+                    "OSNR": 12,
+                    "bit_rate": 100e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 36,
+                    "penalties": [
+                        {
+                            "chromatic_dispersion": -1e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 4e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 48e3,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pmd": 10,
+                            "penalty_value": 0
+                        },
+                        {
+                            "pmd": 30,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 1,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 2,
+                            "penalty_value": 1
+                        },
+                        {
+                            "pdl": 4,
+                            "penalty_value": 2.5
+                        },
+                        {
+                            "pdl": 6,
+                            "penalty_value": 4
+                        }
+                    ],
+                    "min_spacing": 50e9,
+                    "cost": 1
+                },
+                {
+                    "format": "200 Gbit/s, 31.57 Gbaud, DP-16QAM",
+                    "baud_rate": 31.57e9,
+                    "OSNR": 20.5,
+                    "bit_rate": 100e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 36,
+                    "penalties": [
+                        {
+                            "chromatic_dispersion": -1e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 4e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 24e3,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pmd": 10,
+                            "penalty_value": 0
+                        },
+                        {
+                            "pmd": 30,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 1,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 2,
+                            "penalty_value": 1
+                        },
+                        {
+                            "pdl": 4,
+                            "penalty_value": 2.5
+                        },
+                        {
+                            "pdl": 6,
+                            "penalty_value": 4
+                        }
+                    ],
+                    "min_spacing": 50e9,
+                    "cost": 1
+                },
+                {
+                    "format": "200 Gbit/s, DP-QPSK",
+                    "baud_rate": 63.1e9,
+                    "OSNR": 17,
+                    "bit_rate": 200e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 36,
+                    "penalties": [
+                        {
+                            "chromatic_dispersion": -1e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 4e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 24e3,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pmd": 10,
+                            "penalty_value": 0
+                        },
+                        {
+                            "pmd": 25,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 1,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 2,
+                            "penalty_value": 1
+                        },
+                        {
+                            "pdl": 4,
+                            "penalty_value": 2.5
+                        }
+                    ],
+                    "min_spacing": 87.5e9,
+                    "cost": 1
+                },
+                {
+                    "format": "300 Gbit/s, DP-8QAM",
+                    "baud_rate": 63.1e9,
+                    "OSNR": 21,
+                    "bit_rate": 300e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 36,
+                    "penalties": [
+                        {
+                            "chromatic_dispersion": -1e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 4e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 18e3,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pmd": 10,
+                            "penalty_value": 0
+                        },
+                        {
+                            "pmd": 25,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 1,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 2,
+                            "penalty_value": 1
+                        },
+                        {
+                            "pdl": 4,
+                            "penalty_value": 2.5
+                        }
+                    ],
+                    "min_spacing": 87.5e9,
+                    "cost": 1
+                },
+                {
+                    "format": "400 Gbit/s, DP-16QAM",
+                    "baud_rate": 63.1e9,
+                    "OSNR": 24,
+                    "bit_rate": 400e9,
+                    "roll_off": 0.15,
+                    "tx_osnr": 36,
+                    "penalties": [
+                        {
+                            "chromatic_dispersion": -1e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 4e3,
+                            "penalty_value": 0
+                        },
+                        {
+                            "chromatic_dispersion": 12e3,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pmd": 10,
+                            "penalty_value": 0
+                        },
+                        {
+                            "pmd": 20,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 1,
+                            "penalty_value": 0.5
+                        },
+                        {
+                            "pdl": 2,
+                            "penalty_value": 1
+                        },
+                        {
+                            "pdl": 4,
+                            "penalty_value": 2.5
+                        }
+                    ],
+                    "min_spacing": 87.5e9,
+                    "cost": 1
+                }
+            ]
+        }
+    ]
 }

gnpy/example-data/raman_edfa_example_network.json

@@ -20,12 +20,12 @@
         "temperature": 283,
         "raman_pumps": [
           {
-            "power": 200e-3,
+            "power": 224.403e-3,
             "frequency": 205e12,
             "propagation_direction": "counterprop"
           },
           {
-            "power": 206e-3,
+            "power": 231.135e-3,
             "frequency": 201e12,
             "propagation_direction": "counterprop"
           }
@@ -49,6 +49,21 @@
         }
       }
     },
+    {
+      "uid": "Fused1",
+      "type": "Fused",
+      "params": {
+        "loss": 0
+      },
+      "metadata": {
+        "location": {
+          "latitude": 1.5,
+          "longitude": 0,
+          "city": null,
+          "region": ""
+        }
+      }
+    },
     {
       "uid": "Edfa1",
       "type": "Edfa",
@@ -88,6 +103,10 @@
     },
     {
       "from_node": "Span1",
+      "to_node": "Fused1"
+    },
+    {
+      "from_node": "Fused1",
       "to_node": "Edfa1"
     },
     {

gnpy/example-data/sim_params.json

@@ -1,14 +1,13 @@
 {
-  "raman_parameters": {
+  "raman_params": {
-    "flag_raman": true,
+    "flag": true,
-    "space_resolution": 10e3,
+    "result_spatial_resolution": 10e3,
-    "tolerance": 1e-8
+    "solver_spatial_resolution": 50
   },
-  "nli_parameters": {
+  "nli_params": {
-  	"nli_method_name": "ggn_spectrally_separated",
+    "method": "ggn_spectrally_separated",
-  	"wdm_grid_size": 50e9,
+    "dispersion_tolerance": 1,
-  	"dispersion_tolerance": 1,
+    "phase_shift_tolerance": 0.1,
-  	"phase_shift_tolerance": 0.1,
+    "computed_channels": [1, 18, 37, 56, 75]
-	"computed_channels": [1, 18, 37, 56, 75] 
   }
-}
+}

gnpy/tools/cli_examples.py

@@ -10,7 +10,6 @@ Common code for CLI examples
 
 import argparse
 import logging
-import os.path
 import sys
 from math import ceil
 from numpy import linspace, mean
@@ -21,7 +20,6 @@ from gnpy.core.equipment import trx_mode_params
 import gnpy.core.exceptions as exceptions
 from gnpy.core.network import build_network
 from gnpy.core.parameters import SimParams
-from gnpy.core.science_utils import Simulation
 from gnpy.core.utils import db2lin, lin2db, automatic_nch
 from gnpy.topology.request import (ResultElement, jsontocsv, compute_path_dsjctn, requests_aggregation,
                                    BLOCKING_NOPATH, correct_json_route_list,
@@ -57,14 +55,15 @@ def load_common_data(equipment_filename, topology_filename, simulation_filename,
         if save_raw_network_filename is not None:
             save_network(network, save_raw_network_filename)
             print(f'{ansi_escapes.blue}Raw network (no optimizations) saved to {save_raw_network_filename}{ansi_escapes.reset}')
-        sim_params = SimParams(**load_json(simulation_filename)) if simulation_filename is not None else None
+        if not simulation_filename:
-        if not sim_params:
+            sim_params = {}
             if next((node for node in network if isinstance(node, RamanFiber)), None) is not None:
                 print(f'{ansi_escapes.red}Invocation error:{ansi_escapes.reset} '
                       f'RamanFiber requires passing simulation params via --sim-params')
                 sys.exit(1)
         else:
-            Simulation.set_params(sim_params)
+            sim_params = load_json(simulation_filename)
+        SimParams.set_params(sim_params)
     except exceptions.EquipmentConfigError as e:
         print(f'{ansi_escapes.red}Configuration error in the equipment library:{ansi_escapes.reset} {e}')
         sys.exit(1)

gnpy/tools/json_io.py

@@ -94,6 +94,7 @@ class Roadm(_JsonThing):
         'target_pch_out_db': -17,
         'add_drop_osnr': 100,
         'pmd': 0,
+        'pdl': 0,
         'restrictions': {
             'preamp_variety_list': [],
             'booster_variety_list': []
@@ -113,36 +114,45 @@ class Transceiver(_JsonThing):
 
     def __init__(self, **kwargs):
         self.update_attr(self.default_values, kwargs, 'Transceiver')
+        for mode_params in self.mode:
+            penalties = mode_params.get('penalties')
+            mode_params['penalties'] = {}
+            if not penalties:
+                continue
+            for impairment in ('chromatic_dispersion', 'pmd', 'pdl'):
+                imp_penalties = [p for p in penalties if impairment in p]
+                if not imp_penalties:
+                    continue
+                if all(p[impairment] > 0 for p in imp_penalties):
+                    # make sure the list of penalty values include a proper lower boundary
+                    # (we assume 0 penalty for 0 impairment)
+                    imp_penalties.insert(0, {impairment: 0, 'penalty_value': 0})
+                # make sure the list of penalty values are sorted by impairment value
+                imp_penalties.sort(key=lambda i: i[impairment])
+                # rearrange as dict of lists instead of list of dicts
+                mode_params['penalties'][impairment] = {
+                    'up_to_boundary': [p[impairment] for p in imp_penalties],
+                    'penalty_value': [p['penalty_value'] for p in imp_penalties]
+                }
 
 
 class Fiber(_JsonThing):
     default_values = {
         'type_variety': '',
         'dispersion': None,
-        'gamma': 0,
+        'effective_area': None,
         'pmd_coef': 0
     }
 
     def __init__(self, **kwargs):
-        self.update_attr(self.default_values, kwargs, 'Fiber')
+        self.update_attr(self.default_values, kwargs, self.__class__.__name__)
+        for optional in ['gamma', 'raman_efficiency']:
+            if optional in kwargs:
+                setattr(self, optional, kwargs[optional])
 
 
-class RamanFiber(_JsonThing):
+class RamanFiber(Fiber):
-    default_values = {
+    pass
-        'type_variety': '',
-        'dispersion': None,
-        'gamma': 0,
-        'pmd_coef': 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(_JsonThing):
@@ -162,7 +172,9 @@ class Amp(_JsonThing):
         'gain_ripple': None,
         'out_voa_auto': False,
         'allowed_for_design': False,
-        'raman': False
+        'raman': False,
+        'pmd': 0,
+        'pdl': 0
     }
 
     def __init__(self, **kwargs):
@@ -277,7 +289,7 @@ def _check_fiber_vs_raman_fiber(equipment):
     if 'RamanFiber' not in equipment:
         return
     for fiber_type in set(equipment['Fiber'].keys()) & set(equipment['RamanFiber'].keys()):
-        for attr in ('dispersion', 'dispersion-slope', 'gamma', 'pmd-coefficient'):
+        for attr in ('dispersion', 'dispersion-slope', 'effective_area', 'gamma', 'pmd-coefficient'):
             fiber = equipment['Fiber'][fiber_type]
             raman = equipment['RamanFiber'][fiber_type]
             a = getattr(fiber, attr, None)

gnpy/topology/request.py

@@ -20,7 +20,7 @@ from logging import getLogger
 from networkx import (dijkstra_path, NetworkXNoPath,
                       all_simple_paths, shortest_simple_paths)
 from networkx.utils import pairwise
-from numpy import mean
+from numpy import mean, argmin
 from gnpy.core.elements import Transceiver, Roadm
 from gnpy.core.utils import lin2db
 from gnpy.core.info import create_input_spectral_information
@@ -32,12 +32,12 @@ from math import ceil
 
 LOGGER = getLogger(__name__)
 
-RequestParams = namedtuple('RequestParams', 'request_id source destination bidir trx_type' +
+RequestParams = namedtuple('RequestParams', 'request_id source destination bidir trx_type'
-                           ' trx_mode nodes_list loose_list spacing power nb_channel f_min' +
+                           ' trx_mode nodes_list loose_list spacing power nb_channel f_min'
-                           ' f_max format baud_rate OSNR bit_rate roll_off tx_osnr' +
+                           ' f_max format baud_rate OSNR penalties bit_rate'
-                           ' min_spacing cost path_bandwidth effective_freq_slot')
+                           ' roll_off tx_osnr min_spacing cost path_bandwidth effective_freq_slot')
-DisjunctionParams = namedtuple('DisjunctionParams', 'disjunction_id relaxable link' +
+DisjunctionParams = namedtuple('DisjunctionParams', 'disjunction_id relaxable link_diverse'
-                               '_diverse node_diverse disjunctions_req')
+                               ' node_diverse disjunctions_req')
 
 
 class PathRequest:
@@ -62,6 +62,7 @@ class PathRequest:
         self.f_max = params.f_max
         self.format = params.format
         self.OSNR = params.OSNR
+        self.penalties = params.penalties
         self.bit_rate = params.bit_rate
         self.roll_off = params.roll_off
         self.tx_osnr = params.tx_osnr
@@ -348,10 +349,12 @@ def propagate(path, req, equipment):
         else:
             si = el(si)
     path[0].update_snr(req.tx_osnr)
+    path[0].calc_penalties(req.penalties)
     if any(isinstance(el, Roadm) for el in path):
         path[-1].update_snr(req.tx_osnr, equipment['Roadm']['default'].add_drop_osnr)
     else:
         path[-1].update_snr(req.tx_osnr)
+    path[-1].calc_penalties(req.penalties)
     return si
 
 
@@ -386,11 +389,13 @@ def propagate_and_optimize_mode(path, req, equipment):
             for this_mode in modes_to_explore:
                 if path[-1].snr is not None:
                     path[0].update_snr(this_mode['tx_osnr'])
+                    path[0].calc_penalties(this_mode['penalties'])
                     if any(isinstance(el, Roadm) for el in path):
                         path[-1].update_snr(this_mode['tx_osnr'], equipment['Roadm']['default'].add_drop_osnr)
                     else:
                         path[-1].update_snr(this_mode['tx_osnr'])
-                    if round(min(path[-1].snr + lin2db(this_br / (12.5e9))), 2) \
+                    path[-1].calc_penalties(this_mode['penalties'])
+                    if round(min(path[-1].snr_01nm - path[-1].total_penalty), 2) \
                             > this_mode['OSNR'] + equipment['SI']['default'].sys_margins:
                         return path, this_mode
                     else:
@@ -1107,12 +1112,16 @@ def compute_path_with_disjunction(network, equipment, pathreqlist, pathlist):
                 # means that at this point the mode was entered/forced by user and thus a
                 # baud_rate was defined
                 propagate(total_path, pathreq, equipment)
-                temp_snr01nm = round(mean(total_path[-1].snr+lin2db(pathreq.baud_rate/(12.5e9))), 2)
+                snr01nm_with_penalty = total_path[-1].snr_01nm - total_path[-1].total_penalty
-                if temp_snr01nm < pathreq.OSNR + equipment['SI']['default'].sys_margins:
+                min_ind = argmin(snr01nm_with_penalty)
+                if round(snr01nm_with_penalty[min_ind], 2) < pathreq.OSNR + equipment['SI']['default'].sys_margins:
                     msg = f'\tWarning! Request {pathreq.request_id} computed path from' +\
-                          f' {pathreq.source} to {pathreq.destination} does not pass with' +\
+                          f' {pathreq.source} to {pathreq.destination} does not pass with {pathreq.tsp_mode}' +\
-                          f' {pathreq.tsp_mode}\n\tcomputedSNR in 0.1nm = {temp_snr01nm} ' +\
+                          f'\n\tcomputed SNR in 0.1nm = {round(total_path[-1].snr_01nm[min_ind], 2)}' +\
-                          f'- required osnr {pathreq.OSNR} + {equipment["SI"]["default"].sys_margins} margin'
+                          f'\n\tCD penalty = {round(total_path[-1].penalties["chromatic_dispersion"][min_ind], 2)}' +\
+                          f'\n\tPMD penalty = {round(total_path[-1].penalties["pmd"][min_ind], 2)}' +\
+                          f'\n\trequired osnr = {pathreq.OSNR}' +\
+                          f'\n\tsystem margin = {equipment["SI"]["default"].sys_margins}'
                     print(msg)
                     LOGGER.warning(msg)
                     pathreq.blocking_reason = 'MODE_NOT_FEASIBLE'
@@ -1133,6 +1142,7 @@ def compute_path_with_disjunction(network, equipment, pathreqlist, pathlist):
                         pathreq.OSNR = mode['OSNR']
                         pathreq.tx_osnr = mode['tx_osnr']
                         pathreq.bit_rate = mode['bit_rate']
+                        pathreq.penalties = mode['penalties']
                     # other blocking reason should not appear at this point
                 except AttributeError:
                     pathreq.baud_rate = mode['baud_rate']
@@ -1141,6 +1151,7 @@ def compute_path_with_disjunction(network, equipment, pathreqlist, pathlist):
                     pathreq.OSNR = mode['OSNR']
                     pathreq.tx_osnr = mode['tx_osnr']
                     pathreq.bit_rate = mode['bit_rate']
+                    pathreq.penalties = mode['penalties']
 
             # reversed path is needed for correct spectrum assignment
             reversed_path = find_reversed_path(pathlist[i])
@@ -1152,14 +1163,16 @@ def compute_path_with_disjunction(network, equipment, pathreqlist, pathlist):
                 print(f'\tPath (roadsm) {[r.uid for r in rev_p if isinstance(r,Roadm)]}\n')
                 propagate(rev_p, pathreq, equipment)
                 propagated_reversed_path = rev_p
-                temp_snr01nm = round(mean(propagated_reversed_path[-1].snr +\
+                snr01nm_with_penalty = rev_p[-1].snr_01nm - rev_p[-1].total_penalty
-                                          lin2db(pathreq.baud_rate/(12.5e9))), 2)
+                min_ind = argmin(snr01nm_with_penalty)
-                if temp_snr01nm < pathreq.OSNR + equipment['SI']['default'].sys_margins:
+                if round(snr01nm_with_penalty[min_ind], 2) < pathreq.OSNR + equipment['SI']['default'].sys_margins:
                     msg = f'\tWarning! Request {pathreq.request_id} computed path from' +\
-                          f' {pathreq.source} to {pathreq.destination} does not pass with' +\
+                          f' {pathreq.source} to {pathreq.destination} does not pass with {pathreq.tsp_mode}' +\
-                          f' {pathreq.tsp_mode}\n' +\
+                          f'\n\tcomputed SNR in 0.1nm = {round(rev_p[-1].snr_01nm[min_ind], 2)}' +\
-                          f'\tcomputedSNR in 0.1nm = {temp_snr01nm} -' \
+                          f'\n\tCD penalty = {round(rev_p[-1].penalties["chromatic_dispersion"][min_ind], 2)}' +\
-                          f' required osnr {pathreq.OSNR} + {equipment["SI"]["default"].sys_margins} margin'
+                          f'\n\tPMD penalty = {round(rev_p[-1].penalties["pmd"][min_ind], 2)}' +\
+                          f'\n\trequired osnr = {pathreq.OSNR}' +\
+                          f'\n\tsystem margin = {equipment["SI"]["default"].sys_margins}'
                     print(msg)
                     LOGGER.warning(msg)
                     # TODO selection of mode should also be on reversed direction !!

gnpy/topology/spectrum_assignment.py

@@ -15,7 +15,6 @@ element/oms correspondace
 
 from collections import namedtuple
 from logging import getLogger
-from math import ceil
 from gnpy.core.elements import Roadm, Transceiver
 from gnpy.core.exceptions import ServiceError, SpectrumError
 from gnpy.topology.request import compute_spectrum_slot_vs_bandwidth

requirements.txt

@@ -1,7 +1,7 @@
-matplotlib>=3.3.3,<4
+matplotlib>=3.5.1,<4
-networkx>=2.5,<3
+networkx>=2.6,<3
-numpy>=1.19.4,<2
+numpy>=1.22.0,<2
-pandas>=1.1.5,<2
+pandas>=1.3.5,<2
-pbr>=5.5.1,<6
+pbr>=5.7.0,<6
-scipy>=1.5.4,<2
+scipy>=1.7.3,<2
 xlrd>=1.2.0,<2

setup.cfg

@@ -21,6 +21,7 @@ classifier =
     Programming Language :: Python :: 3 :: Only
     Programming Language :: Python :: 3.8
     Programming Language :: Python :: 3.9
+    Programming Language :: Python :: 3.10
     Programming Language :: Python :: Implementation :: CPython
     Topic :: Scientific/Engineering
     Topic :: Scientific/Engineering :: Physics
@@ -39,9 +40,6 @@ warnerrors = True
 
 [files]
 packages = gnpy
-data_files =
-	examples = examples/*
-# FIXME: solve example data files
 
 [options.entry_points]
 console_scripts =

tests/compare.py

@@ -1,135 +0,0 @@
-#!/usr/bin/env python3
-from json import dump
-from pathlib import Path
-from argparse import ArgumentParser
-from collections import namedtuple
-from gnpy.tools.json_io import load_json
-
-
-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['path-request'], expected['path-request'],
-                               key=lambda el: el['request-id'])
-    if 'synchronization' in expected.keys():
-        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()
-    expected = load_json(args.expected_output)
-    actual = load_json(args.actual_output)
-
-    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/conftest.py

@@ -0,0 +1,13 @@
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# Copyright (C) 2020 Telecom Infra Project and GNPy contributors
+# see LICENSE.md for a list of contributors
+#
+
+import pytest
+from gnpy.core.parameters import SimParams, NLIParams, RamanParams
+
+
+@pytest.fixture
+def set_sim_params(monkeypatch):
+    monkeypatch.setattr(SimParams, '_shared_dict', {'nli_params': NLIParams(), 'raman_params': RamanParams()})

tests/data/default_edfa_config.json

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

tests/data/eqpt_config.json

@@ -63,7 +63,7 @@
       "Fiber":[{
             "type_variety": "SSMF",
             "dispersion": 1.67e-05,
-            "gamma": 0.00127,
+            "effective_area": 83e-12,
             "pmd_coef": 1.265e-15
             }
       ],
@@ -85,6 +85,7 @@
             "target_pch_out_db": -20,
             "add_drop_osnr": 38,
             "pmd": 0,
+            "pdl": 0,
             "restrictions": {
                             "preamp_variety_list":[],
                             "booster_variety_list":[]

tests/data/raman_fiber_config.json

@@ -1,224 +0,0 @@
-{
-  "uid": "Span1",
-  "params": {
-    "length": 80,
-    "loss_coef": 0.2,
-    "length_units": "km",
-    "att_in": 0,
-    "con_in": 0.5,
-    "con_out": 0.5,
-    "type_variety": "SSMF",
-    "dispersion": 0.0000167,
-    "gamma": 0.00127,
-    "pmd_coef": 1.265e-15,
-    "raman_efficiency": {
-      "cr": [
-        0,
-        0.0000094,
-        0.0000292,
-        0.0000488,
-        0.0000682,
-        0.0000831,
-        0.000094,
-        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,
-        0.0000846,
-        0.0000714,
-        0.0000686,
-        0.000085,
-        0.0000893,
-        0.0000901,
-        0.0000815,
-        0.0000667,
-        0.0000437,
-        0.0000328,
-        0.0000296,
-        0.0000265,
-        0.0000257,
-        0.0000281,
-        0.0000308,
-        0.0000367,
-        0.0000585,
-        0.0000663,
-        0.0000636,
-        0.000055,
-        0.0000406,
-        0.0000277,
-        0.0000242,
-        0.0000187,
-        0.000016,
-        0.000014,
-        0.0000113,
-        0.0000105,
-        0.0000098,
-        0.0000098,
-        0.0000113,
-        0.0000164,
-        0.0000195,
-        0.0000238,
-        0.0000226,
-        0.0000203,
-        0.0000148,
-        0.0000109,
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tests/data/sim_params.json

@@ -1,14 +1,13 @@
 {
-  "raman_parameters": {
+  "raman_params": {
-    "flag_raman": true,
+    "flag": true,
-    "space_resolution": 10e3,
+    "result_spatial_resolution": 10e3,
-    "tolerance": 1e-8
+    "solver_spatial_resolution": 50
   },
-  "nli_parameters": {
+  "nli_params": {
-    "nli_method_name": "ggn_spectrally_separated",
+    "method": "ggn_spectrally_separated",
-    "wdm_grid_size": 50e9,
     "dispersion_tolerance": 1,
     "phase_shift_tolerance": 0.1,
     "computed_channels": [1, 18, 37, 56, 75]
   }
-}
+}

tests/data/testTopology_response.json

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

tests/data/testTopology_response_expected.csv

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

tests/data/test_fiber_fix_expected_results.csv

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tests/data/test_fiber_flex_expected_results.csv

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tests/data/test_lumped_losses_fiber_no_pumps.csv

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tests/data/test_lumped_losses_raman_fiber_config.json

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+}

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-67,9.772837346090753e-05,1.834034757300294e-08,8.512422533827403e-08
-68,9.62041343011343e-05,1.8145993316507615e-08,8.341482250640209e-08
-69,9.470627135912848e-05,1.7953733512786736e-08,8.174028142913557e-08
-70,9.32342835979764e-05,1.776354374489084e-08,8.009985766376519e-08
-71,9.178813743816069e-05,1.757538990695628e-08,7.849321446941075e-08
-72,9.036733009485282e-05,1.7389250225057777e-08,7.691961625609573e-08
-73,8.897136946428169e-05,1.7205104136353174e-08,7.537834446343352e-08
-74,8.760740745801088e-05,1.7025340034280735e-08,7.38751341742058e-08
-75,8.626710469266231e-05,1.6847609082084475e-08,7.274492099364066e-08
-76,8.495000573672366e-05,1.6671897815367364e-08,7.16342744751107e-08
-77,8.365569697520734e-05,1.6498202874185357e-08,7.054284583689086e-08
-78,8.238374036673638e-05,1.6326516066391613e-08,6.94702656996508e-08
-79,8.11337070649851e-05,1.615683240442047e-08,6.84161724378069e-08
-80,7.990517700271111e-05,1.5989150837085435e-08,6.738021182875641e-08
-81,7.869784230919362e-05,1.5823472723367315e-08,6.63621242598539e-08
-82,7.751129541079501e-05,1.5659808141896922e-08,6.536156604375558e-08
-83,7.634513730458697e-05,1.5498175122781168e-08,6.437820072038669e-08
-84,7.530262080974513e-05,1.5364277079429572e-08,6.349909645089698e-08
-85,7.427675504203511e-05,1.523236493234819e-08,6.263403294276124e-08
-86,7.326723873728716e-05,1.510251249079146e-08,6.178275615432246e-08
-87,7.227232864620995e-05,1.4974078108462424e-08,6.094379608687809e-08
-88,7.1291797553153e-05,1.4847055996011248e-08,6.011696114034367e-08
-89,7.032542203609039e-05,1.4721440784517874e-08,5.930206291361685e-08
-90,6.937298231673965e-05,1.4597227547292096e-08,5.849891607818969e-08
-91,6.843339696762385e-05,1.447443282270653e-08,5.7706608718023645e-08
-92,6.750649045006057e-05,1.4353051811356354e-08,5.6924992809748396e-08
-93,6.65920896785063e-05,1.4233080214004659e-08,5.615392239860827e-08
-94,6.554258932109667e-05,1.407504972937325e-08,5.5268928972034444e-08
-95,6.450957734109368e-05,1.3918655180382722e-08,5.439783940506079e-08

tests/data/test_science_utils_fiber_config.json

@@ -0,0 +1,38 @@
+{
+  "uid": "Span1",
+  "params": {
+    "length": 80,
+    "loss_coef": 0.2,
+    "length_units": "km",
+    "att_in": 0,
+    "con_in": 0.5,
+    "con_out": 0.5,
+    "type_variety": "SSMF",
+    "dispersion": 0.0000167,
+    "effective_area": 83e-12,
+    "pmd_coef": 1.265e-15
+  },
+  "operational": {
+        "temperature": 283,
+        "raman_pumps": [
+          {
+            "power": 224.403e-3,
+            "frequency": 205e12,
+            "propagation_direction": "counterprop"
+          },
+          {
+            "power": 231.135e-3,
+            "frequency": 201e12,
+            "propagation_direction": "counterprop"
+          }
+        ]
+      },
+  "metadata": {
+    "location": {
+      "latitude": 1,
+      "longitude": 0,
+      "city": null,
+      "region": ""
+    }
+  }
+}

tests/invocation/openroadm-v4-Stockholm-Gothenburg

@@ -14,6 +14,7 @@ Transceiver trx_Stockholm
   OSNR ASE (signal bw, dB):  30.98
   CD (ps/nm):                0.00
   PMD (ps):                  0.00
+  PDL (dB):                  0.00
 Roadm roadm_Stockholm
   effective loss (dB):  22.00
   pch out (dBm):        -20.00
@@ -229,7 +230,8 @@ Transceiver trx_Gothenburg
   OSNR ASE (0.1nm, dB):      21.20
   OSNR ASE (signal bw, dB):  17.18
   CD (ps/nm):                8350.42
-  PMD (ps):                  0.89
+  PMD (ps):                  7.99
+  PDL (dB):                  3.74
 
 Transmission result for input power = 2.00 dBm:
   Final GSNR (0.1 nm): 18.90 dB

tests/invocation/openroadm-v5-Stockholm-Gothenburg

@@ -14,6 +14,7 @@ Transceiver trx_Stockholm
   OSNR ASE (signal bw, dB):  30.98
   CD (ps/nm):                0.00
   PMD (ps):                  0.00
+  PDL (dB):                  0.00
 Roadm roadm_Stockholm
   effective loss (dB):  22.00
   pch out (dBm):        -20.00
@@ -229,7 +230,8 @@ Transceiver trx_Gothenburg
   OSNR ASE (0.1nm, dB):      21.84
   OSNR ASE (signal bw, dB):  17.82
   CD (ps/nm):                8350.42
-  PMD (ps):                  0.89
+  PMD (ps):                  7.99
+  PDL (dB):                  3.74
 
 Transmission result for input power = 2.00 dBm:
   Final GSNR (0.1 nm): 19.27 dB

tests/invocation/path_requests_run

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

tests/invocation/transmission_main_example

@@ -14,6 +14,7 @@ Transceiver Site_A
   OSNR ASE (signal bw, dB):  35.92
   CD (ps/nm):                0.00
   PMD (ps):                  0.00
+  PDL (dB):                  0.00
 Fiber          Span1
   type_variety:                SSMF
   length (km):                 80.00
@@ -42,6 +43,7 @@ Transceiver Site_B
   OSNR ASE (signal bw, dB):  29.21
   CD (ps/nm):                1336.00
   PMD (ps):                  0.36
+  PDL (dB):                  0.00
 
 Transmission result for input power = 0.00 dBm:
   Final GSNR (0.1 nm): 31.17 dB

tests/invocation/transmission_main_example__raman

@@ -14,6 +14,7 @@ Transceiver Site_A
   OSNR ASE (signal bw, dB):  35.92
   CD (ps/nm):                0.00
   PMD (ps):                  0.00
+  PDL (dB):                  0.00
 RamanFiber          Span1
   type_variety:                SSMF
   length (km):                 80.00
@@ -21,109 +22,112 @@ RamanFiber          Span1
   total loss (dB):             17.00
   (includes conn loss (dB) in: 0.50 out: 0.50)
   (conn loss out includes EOL margin defined in eqpt_config.json)
-  pch out (dBm): -7.74
+  pch out (dBm): -7.77
+Fused Fused1
+  loss (dB): 0.00
 Edfa Edfa1
   type_variety:           std_low_gain
-  effective gain(dB):     5.74
+  effective gain(dB):     5.77
   (before att_in and before output VOA)
-  noise figure (dB):      13.26
+  noise figure (dB):      13.23
   (including att_in)
-  pad att_in (dB):        2.26
+  pad att_in (dB):        2.23
-  Power In (dBm):         11.07
+  Power In (dBm):         11.04
-  Power Out (dBm):        16.82
+  Power Out (dBm):        16.81
   Delta_P (dB):           -2.00
   target pch (dBm):       -2.00
   effective pch (dBm):    -2.00
   output VOA (dB):        0.00
 Transceiver Site_B
-  GSNR (0.1nm, dB):          31.43
+  GSNR (0.1nm, dB):          31.44
-  GSNR (signal bw, dB):      27.35
+  GSNR (signal bw, dB):      27.36
-  OSNR ASE (0.1nm, dB):      34.18
+  OSNR ASE (0.1nm, dB):      34.21
-  OSNR ASE (signal bw, dB):  30.10
+  OSNR ASE (signal bw, dB):  30.13
   CD (ps/nm):                1336.00
   PMD (ps):                  0.36
+  PDL (dB):                  0.00
 
 Transmission result for input power = 0.00 dBm:
-  Final GSNR (0.1 nm): 31.43 dB
+  Final GSNR (0.1 nm): 31.44 dB
 
 The GSNR per channel at the end of the line is:
 Ch. #   Channel frequency (THz)       Channel power (dBm)    OSNR ASE (signal bw, dB)     SNR NLI (signal bw, dB)        GSNR (signal bw, dB)
-    1                    191.35                      0.21                       31.56                       31.47                       28.50
+    1                    191.35                      0.21                       31.62                       31.43                       28.52
-    2                    191.40                      0.17                       31.54                       31.38                       28.45
+    2                    191.40                      0.17                       31.60                       31.35                       28.46
-    3                    191.45                      0.14                       31.52                       31.30                       28.40
+    3                    191.45                      0.13                       31.58                       31.26                       28.41
-    4                    191.50                      0.10                       31.50                       31.22                       28.34
+    4                    191.50                      0.09                       31.56                       31.18                       28.36
-    5                    191.55                      0.04                       31.47                       31.14                       28.29
+    5                    191.55                      0.03                       31.53                       31.10                       28.30
-    6                    191.60                     -0.02                       31.44                       31.06                       28.23
+    6                    191.60                     -0.02                       31.50                       31.02                       28.24
-    7                    191.65                     -0.08                       31.41                       30.98                       28.18
+    7                    191.65                     -0.08                       31.46                       30.94                       28.19
-    8                    191.70                     -0.14                       31.37                       30.90                       28.12
+    8                    191.70                     -0.14                       31.43                       30.87                       28.13
-    9                    191.75                     -0.20                       31.34                       30.83                       28.07
+    9                    191.75                     -0.20                       31.40                       30.79                       28.08
-   10                    191.80                     -0.26                       31.31                       30.75                       28.01
+   10                    191.80                     -0.27                       31.37                       30.72                       28.02
-   11                    191.85                     -0.33                       31.27                       30.68                       27.96
+   11                    191.85                     -0.33                       31.33                       30.65                       27.97
-   12                    191.90                     -0.39                       31.24                       30.61                       27.90
+   12                    191.90                     -0.40                       31.29                       30.58                       27.91
-   13                    191.95                     -0.46                       31.20                       30.54                       27.85
+   13                    191.95                     -0.46                       31.26                       30.51                       27.86
-   14                    192.00                     -0.52                       31.17                       30.47                       27.79
+   14                    192.00                     -0.53                       31.22                       30.44                       27.80
-   15                    192.05                     -0.59                       31.13                       30.40                       27.74
+   15                    192.05                     -0.59                       31.18                       30.37                       27.75
-   16                    192.10                     -0.66                       31.10                       30.33                       27.69
+   16                    192.10                     -0.66                       31.15                       30.30                       27.69
-   17                    192.15                     -0.72                       31.06                       30.26                       27.63
+   17                    192.15                     -0.73                       31.11                       30.24                       27.64
-   18                    192.20                     -0.79                       31.02                       30.20                       27.58
+   18                    192.20                     -0.80                       31.07                       30.17                       27.59
-   19                    192.25                     -0.86                       30.98                       30.21                       27.57
+   19                    192.25                     -0.86                       31.03                       30.18                       27.57
-   20                    192.30                     -0.94                       30.94                       30.21                       27.55
+   20                    192.30                     -0.94                       30.99                       30.19                       27.56
-   21                    192.35                     -1.01                       30.90                       30.22                       27.54
+   21                    192.35                     -1.02                       30.94                       30.20                       27.54
-   22                    192.40                     -1.09                       30.86                       30.23                       27.52
+   22                    192.40                     -1.09                       30.90                       30.20                       27.53
-   23                    192.45                     -1.16                       30.81                       30.23                       27.50
+   23                    192.45                     -1.17                       30.86                       30.21                       27.51
-   24                    192.50                     -1.24                       30.77                       30.24                       27.49
+   24                    192.50                     -1.24                       30.81                       30.22                       27.50
-   25                    192.55                     -1.31                       30.73                       30.25                       27.47
+   25                    192.55                     -1.31                       30.77                       30.23                       27.48
-   26                    192.60                     -1.38                       30.69                       30.25                       27.46
+   26                    192.60                     -1.38                       30.73                       30.23                       27.46
-   27                    192.65                     -1.45                       30.65                       30.26                       27.44
+   27                    192.65                     -1.45                       30.69                       30.24                       27.45
-   28                    192.70                     -1.52                       30.61                       30.27                       27.42
+   28                    192.70                     -1.52                       30.65                       30.25                       27.43
-   29                    192.75                     -1.59                       30.56                       30.28                       27.41
+   29                    192.75                     -1.59                       30.60                       30.26                       27.42
-   30                    192.80                     -1.66                       30.52                       30.28                       27.39
+   30                    192.80                     -1.67                       30.56                       30.27                       27.40
-   31                    192.85                     -1.73                       30.48                       30.29                       27.37
+   31                    192.85                     -1.74                       30.52                       30.27                       27.38
-   32                    192.90                     -1.80                       30.44                       30.30                       27.36
+   32                    192.90                     -1.81                       30.47                       30.28                       27.37
-   33                    192.95                     -1.87                       30.39                       30.30                       27.34
+   33                    192.95                     -1.88                       30.43                       30.29                       27.35
-   34                    193.00                     -1.94                       30.35                       30.31                       27.32
+   34                    193.00                     -1.95                       30.39                       30.30                       27.33
-   35                    193.05                     -2.01                       30.31                       30.32                       27.30
+   35                    193.05                     -2.02                       30.34                       30.30                       27.31
-   36                    193.10                     -2.08                       30.27                       30.33                       27.29
+   36                    193.10                     -2.08                       30.30                       30.31                       27.30
-   37                    193.15                     -2.15                       30.22                       30.33                       27.27
+   37                    193.15                     -2.15                       30.26                       30.32                       27.28
-   38                    193.20                     -2.22                       30.18                       30.35                       27.25
+   38                    193.20                     -2.22                       30.21                       30.34                       27.26
-   39                    193.25                     -2.29                       30.14                       30.37                       27.24
+   39                    193.25                     -2.29                       30.17                       30.36                       27.25
-   40                    193.30                     -2.36                       30.09                       30.39                       27.23
+   40                    193.30                     -2.36                       30.13                       30.37                       27.24
-   41                    193.35                     -2.43                       30.05                       30.40                       27.21
+   41                    193.35                     -2.43                       30.08                       30.39                       27.22
-   42                    193.40                     -2.49                       30.01                       30.42                       27.20
+   42                    193.40                     -2.50                       30.04                       30.41                       27.21
-   43                    193.45                     -2.56                       29.96                       30.44                       27.18
+   43                    193.45                     -2.56                       29.99                       30.43                       27.19
-   44                    193.50                     -2.63                       29.92                       30.46                       27.17
+   44                    193.50                     -2.63                       29.95                       30.44                       27.18
-   45                    193.55                     -2.70                       29.87                       30.47                       27.15
+   45                    193.55                     -2.70                       29.90                       30.46                       27.16
-   46                    193.60                     -2.78                       29.83                       30.49                       27.13
+   46                    193.60                     -2.78                       29.85                       30.48                       27.15
-   47                    193.65                     -2.85                       29.78                       30.51                       27.12
+   47                    193.65                     -2.85                       29.80                       30.50                       27.13
-   48                    193.70                     -2.92                       29.73                       30.53                       27.10
+   48                    193.70                     -2.92                       29.76                       30.52                       27.11
-   49                    193.75                     -2.99                       29.68                       30.54                       27.08
+   49                    193.75                     -2.99                       29.71                       30.54                       27.09
-   50                    193.80                     -3.06                       29.64                       30.56                       27.06
+   50                    193.80                     -3.06                       29.66                       30.55                       27.07
-   51                    193.85                     -3.14                       29.59                       30.58                       27.05
+   51                    193.85                     -3.14                       29.61                       30.57                       27.06
-   52                    193.90                     -3.21                       29.54                       30.60                       27.03
+   52                    193.90                     -3.21                       29.56                       30.59                       27.04
-   53                    193.95                     -3.28                       29.49                       30.62                       27.01
+   53                    193.95                     -3.28                       29.52                       30.61                       27.02
-   54                    194.00                     -3.35                       29.44                       30.64                       26.99
+   54                    194.00                     -3.35                       29.47                       30.63                       27.00
-   55                    194.05                     -3.42                       29.39                       30.65                       26.97
+   55                    194.05                     -3.42                       29.42                       30.65                       26.98
-   56                    194.10                     -3.50                       29.34                       30.67                       26.95
+   56                    194.10                     -3.50                       29.37                       30.67                       26.96
-   57                    194.15                     -3.57                       29.29                       30.73                       26.94
+   57                    194.15                     -3.57                       29.32                       30.72                       26.95
-   58                    194.20                     -3.64                       29.24                       30.79                       26.94
+   58                    194.20                     -3.64                       29.26                       30.78                       26.95
-   59                    194.25                     -3.72                       29.19                       30.85                       26.93
+   59                    194.25                     -3.72                       29.21                       30.84                       26.94
-   60                    194.30                     -3.79                       29.14                       30.91                       26.93
+   60                    194.30                     -3.79                       29.16                       30.90                       26.94
-   61                    194.35                     -3.86                       29.09                       30.97                       26.92
+   61                    194.35                     -3.86                       29.11                       30.96                       26.93
-   62                    194.40                     -3.93                       29.04                       31.03                       26.91
+   62                    194.40                     -3.93                       29.06                       31.02                       26.92
-   63                    194.45                     -4.01                       28.99                       31.09                       26.90
+   63                    194.45                     -4.01                       29.01                       31.09                       26.91
-   64                    194.50                     -4.08                       28.94                       31.15                       26.90
+   64                    194.50                     -4.08                       28.96                       31.15                       26.91
-   65                    194.55                     -4.14                       28.89                       31.22                       26.89
+   65                    194.55                     -4.14                       28.91                       31.21                       26.90
-   66                    194.60                     -4.21                       28.85                       31.28                       26.88
+   66                    194.60                     -4.21                       28.86                       31.28                       26.90
-   67                    194.65                     -4.28                       28.80                       31.35                       26.88
+   67                    194.65                     -4.27                       28.82                       31.34                       26.89
-   68                    194.70                     -4.34                       28.75                       31.41                       26.87
+   68                    194.70                     -4.34                       28.77                       31.41                       26.88
-   69                    194.75                     -4.41                       28.70                       31.48                       26.86
+   69                    194.75                     -4.41                       28.72                       31.48                       26.88
-   70                    194.80                     -4.47                       28.66                       31.55                       26.86
+   70                    194.80                     -4.47                       28.67                       31.55                       26.87
-   71                    194.85                     -4.54                       28.61                       31.62                       26.85
+   71                    194.85                     -4.54                       28.63                       31.62                       26.86
-   72                    194.90                     -4.60                       28.56                       31.69                       26.84
+   72                    194.90                     -4.60                       28.58                       31.69                       26.85
-   73                    194.95                     -4.67                       28.51                       31.77                       26.83
+   73                    194.95                     -4.67                       28.53                       31.76                       26.84
-   74                    195.00                     -4.73                       28.47                       31.84                       26.82
+   74                    195.00                     -4.73                       28.48                       31.84                       26.83
-   75                    195.05                     -4.80                       28.42                       31.91                       26.81
+   75                    195.05                     -4.80                       28.43                       31.91                       26.82
-   76                    195.10                     -4.86                       28.37                       31.91                       26.78
+   76                    195.10                     -4.86                       28.38                       31.91                       26.79
 
 (No source node specified: picked Site_A)
 

tests/invocation/transmission_saturated

@@ -250,6 +250,7 @@ Transceiver trx Lannion_CAS
   OSNR ASE (signal bw, dB):  95.92
   CD (ps/nm):                0.00
   PMD (ps):                  0.00
+  PDL (dB):                  0.00
 Roadm roadm Lannion_CAS
   effective loss (dB):  23.00
   pch out (dBm):        -20.00
@@ -296,19 +297,19 @@ Fiber          fiber (Loudeac → Lorient_KMA)-F054
   pch out (dBm): -26.82
 Edfa west edfa in Lorient_KMA to Loudeac
   type_variety:           test
-  effective gain(dB):     28.00
+  effective gain(dB):     27.99
   (before att_in and before output VOA)
   noise figure (dB):      5.76
   (including att_in)
   pad att_in (dB):        0.00
   Power In (dBm):         -6.99
-  Power Out (dBm):        21.04
+  Power Out (dBm):        21.03
   Delta_P (dB):           -1.82
   target pch (dBm):       1.18
-  effective pch (dBm):    1.18
+  effective pch (dBm):    1.17
   output VOA (dB):        0.00
 Roadm roadm Lorient_KMA
-  effective loss (dB):  21.18
+  effective loss (dB):  21.17
   pch out (dBm):        -20.00
 Transceiver trx Lorient_KMA
   GSNR (0.1nm, dB):          23.94
@@ -317,6 +318,7 @@ Transceiver trx Lorient_KMA
   OSNR ASE (signal bw, dB):  20.20
   CD (ps/nm):                2171.00
   PMD (ps):                  0.46
+  PDL (dB):                  0.00
 
 Transmission result for input power = 3.00 dBm:
   Final GSNR (0.1 nm): 23.94 dB

tests/requirements.txt

@@ -0,0 +1 @@
+pytest>=6.2.5,<7

tests/test_amplifier.py

@@ -80,13 +80,12 @@ def si(nch_and_spacing, bw):
 def test_variable_gain_nf(gain, nf_expected, setup_edfa_variable_gain, si):
     """=> unitary test for variable gain model Edfa._calc_nf() (and Edfa.interpol_params)"""
     edfa = setup_edfa_variable_gain
-    frequencies = array([c.frequency for c in si.carriers])
+    si.signal /= db2lin(gain)
-    pin = array([c.power.signal + c.power.nli + c.power.ase for c in si.carriers])
+    si.nli /= db2lin(gain)
-    pin = pin / db2lin(gain)
+    si.ase /= db2lin(gain)
-    baud_rates = array([c.baud_rate for c in si.carriers])
     edfa.operational.gain_target = gain
-    pref = Pref(0, -gain, lin2db(len(frequencies)))
+    si.pref = si.pref._replace(p_span0=0, p_spani=-gain, neq_ch=lin2db(si.number_of_channels))
-    edfa.interpol_params(frequencies, pin, baud_rates, pref)
+    edfa.interpol_params(si)
     result = edfa.nf
     assert pytest.approx(nf_expected, abs=0.01) == result[0]
 
@@ -95,23 +94,20 @@ def test_variable_gain_nf(gain, nf_expected, setup_edfa_variable_gain, si):
 def test_fixed_gain_nf(gain, nf_expected, setup_edfa_fixed_gain, si):
     """=> unitary test for fixed gain model Edfa._calc_nf() (and Edfa.interpol_params)"""
     edfa = setup_edfa_fixed_gain
-    frequencies = array([c.frequency for c in si.carriers])
+    si.signal /= db2lin(gain)
-    pin = array([c.power.signal + c.power.nli + c.power.ase for c in si.carriers])
+    si.nli /= db2lin(gain)
-    pin = pin / db2lin(gain)
+    si.ase /= db2lin(gain)
-    baud_rates = array([c.baud_rate for c in si.carriers])
     edfa.operational.gain_target = gain
-    pref = Pref(0, -gain, lin2db(len(frequencies)))
+    si.pref = si.pref._replace(p_span0=0, p_spani=-gain, neq_ch=lin2db(si.number_of_channels))
-    edfa.interpol_params(frequencies, pin, baud_rates, pref)
+    edfa.interpol_params(si)
-
     assert pytest.approx(nf_expected, abs=0.01) == edfa.nf[0]
 
 
 def test_si(si, nch_and_spacing):
     """basic total power check of the channel comb generation"""
     nb_channel = nch_and_spacing[0]
-    pin = array([c.power.signal + c.power.nli + c.power.ase for c in si.carriers])
+    p_tot = sum(si.signal + si.ase + si.nli)
-    p_tot = pin.sum()
+    expected_p_tot = si.signal[0] * nb_channel
-    expected_p_tot = si.carriers[0].power.signal * nb_channel
     assert pytest.approx(expected_p_tot, abs=0.01) == p_tot
 
 
@@ -122,14 +118,13 @@ def test_compare_nf_models(gain, setup_edfa_variable_gain, si):
      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_variable_gain
-    frequencies = array([c.frequency for c in si.carriers])
+    si.signal /= db2lin(gain)
-    pin = array([c.power.signal + c.power.nli + c.power.ase for c in si.carriers])
+    si.nli /= db2lin(gain)
-    pin = pin / db2lin(gain)
+    si.ase /= db2lin(gain)
-    baud_rates = array([c.baud_rate for c in si.carriers])
     edfa.operational.gain_target = gain
     # edfa is variable gain type
-    pref = Pref(0, -gain, lin2db(len(frequencies)))
+    si.pref = si.pref._replace(p_span0=0, p_spani=-gain, neq_ch=lin2db(si.number_of_channels))
-    edfa.interpol_params(frequencies, pin, baud_rates, pref)
+    edfa.interpol_params(si)
     nf_model = edfa.nf[0]
 
     # change edfa type variety to a polynomial
@@ -155,7 +150,7 @@ def test_compare_nf_models(gain, setup_edfa_variable_gain, si):
     edfa = Edfa(**el_config)
 
     # edfa is variable gain type
-    edfa.interpol_params(frequencies, pin, baud_rates, pref)
+    edfa.interpol_params(si)
     nf_poly = edfa.nf[0]
     print(nf_poly, nf_model)
     assert pytest.approx(nf_model, abs=0.5) == nf_poly
@@ -183,21 +178,16 @@ def test_ase_noise(gain, si, setup_trx, bw):
     si = span(si)
     print(span)
 
-    frequencies = array([c.frequency for c in si.carriers])
+    si.pref = si.pref._replace(p_span0=0, p_spani=-gain, neq_ch=lin2db(si.number_of_channels))
-    pin = array([c.power.signal + c.power.nli + c.power.ase for c in si.carriers])
+    edfa.interpol_params(si)
-    baud_rates = array([c.baud_rate for c in si.carriers])
-    pref = Pref(0, -gain, lin2db(len(frequencies)))
-    edfa.interpol_params(frequencies, pin, baud_rates, pref)
     nf = edfa.nf
     print('nf', nf)
-    pin = lin2db(pin[0] * 1e3)
+    pin = lin2db((si.signal[0] + si.ase[0] + si.nli[0]) * 1e3)
     osnr_expected = pin - nf[0] + 58
 
     si = edfa(si)
     print(edfa)
-    pout = array([c.power.signal for c in si.carriers])
+    osnr = lin2db(si.signal[0] / si.ase[0]) - lin2db(12.5e9 / bw)
-    pase = array([c.power.ase for c in si.carriers])
-    osnr = lin2db(pout[0] / pase[0]) - lin2db(12.5e9 / bw)
     assert pytest.approx(osnr_expected, abs=0.01) == osnr
 
     trx = setup_trx

tests/test_info.py

@@ -0,0 +1,50 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import pytest
+from numpy import array, zeros, ones
+from numpy.testing import assert_array_equal
+
+from gnpy.core.info import create_arbitrary_spectral_information
+from gnpy.core.exceptions import SpectrumError
+
+
+def test_create_arbitrary_spectral_information():
+    si = create_arbitrary_spectral_information(frequency=[193.25e12, 193.3e12, 193.35e12],
+                                               baud_rate=32e9, signal=[1, 1, 1])
+    assert_array_equal(si.baud_rate, array([32e9, 32e9, 32e9]))
+    assert_array_equal(si.slot_width, array([37.5e9, 37.5e9, 37.5e9]))
+    assert_array_equal(si.signal, ones(3))
+    assert_array_equal(si.nli, zeros(3))
+    assert_array_equal(si.ase, zeros(3))
+    assert_array_equal(si.roll_off, zeros(3))
+    assert_array_equal(si.chromatic_dispersion, zeros(3))
+    assert_array_equal(si.pmd, zeros(3))
+    assert_array_equal(si.channel_number, array([1, 2, 3]))
+    assert_array_equal(si.number_of_channels, 3)
+    assert_array_equal(si.df, array([[0, 50e9, 100e9], [-50e9, 0, 50e9], [-100e9, -50e9, 0]]))
+
+    with pytest.raises(SpectrumError, match='Spectra cannot be summed: channels overlapping.'):
+        si += si
+
+    si = create_arbitrary_spectral_information(frequency=array([193.35e12, 193.3e12, 193.25e12]),
+                                               slot_width=array([50e9, 50e9, 50e9]),
+                                               baud_rate=32e9, signal=array([1, 2, 3]))
+    assert_array_equal(si.signal, array([3, 2, 1]))
+
+    with pytest.raises(SpectrumError, match='Spectrum baud rate, including the roll off, '
+                                            r'larger than the slot width for channels: \[1, 3\].'):
+        create_arbitrary_spectral_information(frequency=[193.25e12, 193.3e12, 193.35e12], signal=1,
+                                              baud_rate=[64e9, 32e9, 64e9], slot_width=50e9)
+    with pytest.raises(SpectrumError, match='Spectrum required slot widths larger than the frequency spectral '
+                                            r'distances between channels: \[\(1, 2\), \(3, 4\)\].'):
+        create_arbitrary_spectral_information(frequency=[193.26e12, 193.3e12, 193.35e12, 193.39e12], signal=1,
+                                              baud_rate=32e9, slot_width=50e9)
+    with pytest.raises(SpectrumError, match='Spectrum required slot widths larger than the frequency spectral '
+                                            r'distances between channels: \[\(1, 2\), \(2, 3\)\].'):
+        create_arbitrary_spectral_information(frequency=[193.25e12, 193.3e12, 193.35e12], signal=1, baud_rate=49e9,
+                                              roll_off=0.1)
+
+    with pytest.raises(SpectrumError,
+                       match='Dimension mismatch in input fields.'):
+        create_arbitrary_spectral_information(frequency=[193.25e12, 193.3e12, 193.35e12], signal=[1, 2], baud_rate=49e9)

tests/test_parameters.py

@@ -1,26 +1,23 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 
-from pathlib import Path
+"""
+Checks that the class SimParams behaves as a mutable Singleton.
+"""
 
+import pytest
 from gnpy.core.parameters import SimParams
-from gnpy.core.science_utils import Simulation
-from gnpy.tools.json_io import load_json
-
-TEST_DIR = Path(__file__).parent
-DATA_DIR = TEST_DIR / 'data'
 
 
+@pytest.mark.usefixtures('set_sim_params')
 def test_sim_parameters():
-    j = load_json(DATA_DIR / 'sim_params.json')
+    sim_params = {'nli_params': {}, 'raman_params': {}}
-    sim_params = SimParams(**j)
+    SimParams.set_params(sim_params)
-    Simulation.set_params(sim_params)
+    s1 = SimParams.get()
-    s1 = Simulation.get_simulation()
+    assert s1.nli_params.method == 'gn_model_analytic'
-    assert s1.sim_params.raman_params.flag_raman
+    s2 = SimParams.get()
-    s2 = Simulation.get_simulation()
+    assert not s1.raman_params.flag
-    assert s2.sim_params.raman_params.flag_raman
+    sim_params['raman_params']['flag'] = True
-    j['raman_parameters']['flag_raman'] = False
+    SimParams.set_params(sim_params)
-    sim_params = SimParams(**j)
+    assert s2.raman_params.flag
-    Simulation.set_params(sim_params)
+    assert s1.raman_params.flag
-    assert not s2.sim_params.raman_params.flag_raman
-    assert not s1.sim_params.raman_params.flag_raman

tests/test_parser.py

@@ -21,7 +21,6 @@ import shutil
 from pandas import read_csv
 from xlrd import open_workbook
 import pytest
-from tests.compare import compare_networks, compare_services
 from copy import deepcopy
 from gnpy.core.utils import automatic_nch, lin2db
 from gnpy.core.network import build_network
@@ -56,15 +55,7 @@ def test_excel_json_generation(tmpdir, xls_input, expected_json_output):
     actual_json_output = xls_copy.with_suffix('.json')
     actual = load_json(actual_json_output)
     unlink(actual_json_output)
-    expected = load_json(expected_json_output)
+    assert actual == load_json(expected_json_output)
-
-    results = compare_networks(expected, actual)
-    assert not results.elements.missing
-    assert not results.elements.extra
-    assert not results.elements.different
-    assert not results.connections.missing
-    assert not results.connections.extra
-    assert not results.connections.different
 
 # assume xls entries
 # test that the build network gives correct results in gain mode
@@ -95,15 +86,7 @@ def test_auto_design_generation_fromxlsgainmode(tmpdir, xls_input, expected_json
     save_network(network, actual_json_output)
     actual = load_json(actual_json_output)
     unlink(actual_json_output)
-    expected = load_json(expected_json_output)
+    assert actual == load_json(expected_json_output)
-
-    results = compare_networks(expected, actual)
-    assert not results.elements.missing
-    assert not results.elements.extra
-    assert not results.elements.different
-    assert not results.connections.missing
-    assert not results.connections.extra
-    assert not results.connections.different
 
 # test that autodesign creates same file as an input file already autodesigned
 
@@ -134,15 +117,7 @@ def test_auto_design_generation_fromjson(tmpdir, json_input, power_mode):
     save_network(network, actual_json_output)
     actual = load_json(actual_json_output)
     unlink(actual_json_output)
-    expected = load_json(json_input)
+    assert actual == load_json(json_input)
-
-    results = compare_networks(expected, actual)
-    assert not results.elements.missing
-    assert not results.elements.extra
-    assert not results.elements.different
-    assert not results.connections.missing
-    assert not results.connections.extra
-    assert not results.connections.different
 
 # test services creation
 
@@ -162,15 +137,7 @@ def test_excel_service_json_generation(xls_input, expected_json_output):
                                              equipment['SI']['default'].f_max, equipment['SI']['default'].spacing))
     build_network(network, equipment, p_db, p_total_db)
     from_xls = read_service_sheet(xls_input, equipment, network, network_filename=DATA_DIR / 'testTopology.xls')
-    expected = load_json(expected_json_output)
+    assert from_xls == load_json(expected_json_output)
-
-    results = compare_services(expected, from_xls)
-    assert not results.requests.missing
-    assert not results.requests.extra
-    assert not results.requests.different
-    assert not results.synchronizations.missing
-    assert not results.synchronizations.extra
-    assert not results.synchronizations.different
 
     # TODO verify that requested bandwidth is not zero !
 
@@ -391,6 +358,7 @@ def test_excel_ila_constraints(source, destination, route_list, hoptype, expecte
         'cost': None,
         'roll_off': 0,
         'tx_osnr': 0,
+        'penalties': None,
         'min_spacing': None,
         'nb_channel': 0,
         'power': 0,

tests/test_roadm_restrictions.py

@@ -12,6 +12,8 @@ checks that restrictions in roadms are correctly applied during autodesign
 
 from pathlib import Path
 import pytest
+from numpy.testing import assert_allclose
+
 from gnpy.core.utils import lin2db, automatic_nch
 from gnpy.core.elements import Fused, Roadm, Edfa
 from gnpy.core.network import build_network
@@ -254,10 +256,11 @@ def test_roadm_target_power(prev_node_type, effective_pch_out_db, power_dbm):
         req.power, req.spacing)
     for i, el in enumerate(path):
         if isinstance(el, Roadm):
-            carriers_power_in_roadm = min([c.power.signal + c.power.nli + c.power.ase for c in si.carriers])
+            min_power_in_roadm = min(si.signal + si.ase + si.nli)
             si = el(si, degree=path[i + 1].uid)
+            power_out_roadm = si.signal + si.ase + si.nli
             if el.uid == 'roadm node B':
-                print('input', carriers_power_in_roadm)
+                print('input', min_power_in_roadm)
                 # if previous was an EDFA, power level at ROADM input is enough for the ROADM to apply its
                 # target power (as specified in equipment ie -20 dBm)
                 # if it is a Fused, the input power to the ROADM is smaller than the target power, and the
@@ -266,23 +269,17 @@ def test_roadm_target_power(prev_node_type, effective_pch_out_db, power_dbm):
                 # corresponds to -22dBm + power_dbm
                 # next step (for ROADM modelling) will be to apply a minimum loss for ROADMs !
                 if prev_node_type == 'edfa':
-                    assert el.effective_pch_out_db == effective_pch_out_db
+                    # edfa prev_node sets input power to roadm to a high enough value:
-                if prev_node_type == 'fused':
+                    # check that target power is correctly set in the ROADM
-                    # then output power == input_power == effective_pch_out_db + power_dbm
+                    assert_allclose(el.ref_pch_out_dbm, effective_pch_out_db, rtol=1e-3)
-                    assert effective_pch_out_db + power_dbm == \
+                    # Check that egress power of roadm is equal to target power
-                        pytest.approx(lin2db(carriers_power_in_roadm * 1e3), rel=1e-3)
+                    assert_allclose(power_out_roadm, db2lin(effective_pch_out_db - 30), rtol=1e-3)
-                    assert el.effective_pch_out_db == effective_pch_out_db + power_dbm
+                elif prev_node_type == 'fused':
-                for carrier in si.carriers:
+                    # fused prev_node does reamplfy power after fiber propagation, so input power
-                    print(carrier.power.signal + carrier.power.nli + carrier.power.ase)
+                    # to roadm is low.
-                    power = carrier.power.signal + carrier.power.nli + carrier.power.ase
+                    # check that target power correctly reports power_dbm from previous propagation
-                    if prev_node_type == 'edfa':
+                    assert_allclose(el.ref_pch_out_dbm, effective_pch_out_db + power_dbm, rtol=1e-3)
-                        # edfa prev_node sets input power to roadm to a high enough value:
+                    # Check that egress power of roadm is equalized to the min carrier input power.
-                        # Check that egress power of roadm is equal to target power
+                    assert_allclose(power_out_roadm, min_power_in_roadm, rtol=1e-3)
-                        assert power == pytest.approx(db2lin(effective_pch_out_db - 30), rel=1e-3)
-                    elif prev_node_type == 'fused':
-                        # fused prev_node does reamplfy power after fiber propagation, so input power
-                        # to roadm is low.
-                        # Check that egress power of roadm is equalized to the min carrier input power.
-                        assert power == pytest.approx(carriers_power_in_roadm, rel=1e-3)
         else:
             si = el(si)

tests/test_science_utils.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
-# @Author: Alessio Ferrari
+
 """
 Checks that RamanFiber propagates properly the spectral information. In this way, also the RamanSolver and the NliSolver
 are tested.
@@ -9,40 +9,120 @@ are tested.
 from pathlib import Path
 from pandas import read_csv
 from numpy.testing import assert_allclose
+from numpy import array, genfromtxt
+import pytest
 
-from gnpy.core.info import create_input_spectral_information
+from gnpy.core.info import create_input_spectral_information, create_arbitrary_spectral_information
-from gnpy.core.elements import RamanFiber
+from gnpy.core.elements import Fiber, RamanFiber
 from gnpy.core.parameters import SimParams
-from gnpy.core.science_utils import Simulation
 from gnpy.tools.json_io import load_json
+from gnpy.core.exceptions import NetworkTopologyError
+from gnpy.core.science_utils import RamanSolver
 
 TEST_DIR = Path(__file__).parent
 
 
-def test_raman_fiber():
+def test_fiber():
-    """ Test the accuracy of propagating the RamanFiber."""
+    """ Test the accuracy of propagating the Fiber."""
-    # spectral information generation
+    fiber = Fiber(**load_json(TEST_DIR / 'data' / 'test_science_utils_fiber_config.json'))
-    power = 1e-3
-    eqpt_params = load_json(TEST_DIR / 'data' / 'eqpt_config.json')
-    spectral_info_params = eqpt_params['SI'][0]
-    spectral_info_params.pop('power_dbm')
-    spectral_info_params.pop('power_range_db')
-    spectral_info_params.pop('tx_osnr')
-    spectral_info_params.pop('sys_margins')
-    spectral_info_input = create_input_spectral_information(power=power, **spectral_info_params)
 
-    sim_params = SimParams(**load_json(TEST_DIR / 'data' / 'sim_params.json'))
+    # fix grid spectral information generation
-    Simulation.set_params(sim_params)
+    spectral_info_input = create_input_spectral_information(f_min=191.3e12, f_max=196.1e12, roll_off=0.15,
-    fiber = RamanFiber(**load_json(TEST_DIR / 'data' / 'raman_fiber_config.json'))
+                                                            baud_rate=32e9, power=1e-3, spacing=50e9)
+    # propagation
+    spectral_info_out = fiber(spectral_info_input)
+
+    p_signal = spectral_info_out.signal
+    p_nli = spectral_info_out.nli
+
+    expected_results = read_csv(TEST_DIR / 'data' / 'test_fiber_fix_expected_results.csv')
+    assert_allclose(p_signal, expected_results['signal'], rtol=1e-3)
+    assert_allclose(p_nli, expected_results['nli'], rtol=1e-3)
+
+    # flex grid spectral information generation
+    frequency = 191e12 + array([0, 50e9, 150e9, 225e9, 275e9])
+    slot_width = array([37.5e9, 50e9, 75e9, 50e9, 37.5e9])
+    baud_rate = array([32e9, 42e9, 64e9, 42e9, 32e9])
+    signal = 1e-3 + array([0, -1e-4, 3e-4, -2e-4, +2e-4])
+    spectral_info_input = create_arbitrary_spectral_information(frequency=frequency, slot_width=slot_width,
+                                                                signal=signal, baud_rate=baud_rate, roll_off=0.15)
 
     # propagation
     spectral_info_out = fiber(spectral_info_input)
 
-    p_signal = [carrier.power.signal for carrier in spectral_info_out.carriers]
+    p_signal = spectral_info_out.signal
-    p_ase = [carrier.power.ase for carrier in spectral_info_out.carriers]
+    p_nli = spectral_info_out.nli
-    p_nli = [carrier.power.nli for carrier in spectral_info_out.carriers]
 
-    expected_results = read_csv(TEST_DIR / 'data' / 'test_science_utils_expected_results.csv')
+    expected_results = read_csv(TEST_DIR / 'data' / 'test_fiber_flex_expected_results.csv')
+    assert_allclose(p_signal, expected_results['signal'], rtol=1e-3)
+    assert_allclose(p_nli, expected_results['nli'], rtol=1e-3)
+
+
+@pytest.mark.usefixtures('set_sim_params')
+def test_raman_fiber():
+    """ Test the accuracy of propagating the RamanFiber."""
+    # spectral information generation
+    spectral_info_input = create_input_spectral_information(f_min=191.3e12, f_max=196.1e12, roll_off=0.15,
+                                                            baud_rate=32e9, power=1e-3, spacing=50e9)
+    SimParams.set_params(load_json(TEST_DIR / 'data' / 'sim_params.json'))
+    fiber = RamanFiber(**load_json(TEST_DIR / 'data' / 'test_science_utils_fiber_config.json'))
+
+    # propagation
+    spectral_info_out = fiber(spectral_info_input)
+
+    p_signal = spectral_info_out.signal
+    p_ase = spectral_info_out.ase
+    p_nli = spectral_info_out.nli
+
+    expected_results = read_csv(TEST_DIR / 'data' / 'test_raman_fiber_expected_results.csv')
     assert_allclose(p_signal, expected_results['signal'], rtol=1e-3)
     assert_allclose(p_ase, expected_results['ase'], rtol=1e-3)
     assert_allclose(p_nli, expected_results['nli'], rtol=1e-3)
+
+
+@pytest.mark.parametrize(
+    "loss, position, errmsg",
+    ((0.5, -2, "Lumped loss positions must be between 0 and the fiber length (80.0 km), boundaries excluded."),
+     (0.5, 81, "Lumped loss positions must be between 0 and the fiber length (80.0 km), boundaries excluded.")))
+@pytest.mark.usefixtures('set_sim_params')
+def test_fiber_lumped_losses(loss, position, errmsg, set_sim_params):
+    """ Lumped losses length sanity checking."""
+    SimParams.set_params(load_json(TEST_DIR / 'data' / 'sim_params.json'))
+    fiber_dict = load_json(TEST_DIR / 'data' / 'test_lumped_losses_raman_fiber_config.json')
+    fiber_dict['params']['lumped_losses'] = [{'position': position, 'loss': loss}]
+    with pytest.raises(NetworkTopologyError) as e:
+        Fiber(**fiber_dict)
+    assert str(e.value) == errmsg
+
+
+@pytest.mark.usefixtures('set_sim_params')
+def test_fiber_lumped_losses_srs(set_sim_params):
+    """ Test the accuracy of Fiber with lumped losses propagation."""
+    # spectral information generation
+    spectral_info_input = create_input_spectral_information(f_min=191.3e12, f_max=196.1e12, roll_off=0.15,
+                                                            baud_rate=32e9, power=1e-3, spacing=50e9)
+
+    SimParams.set_params(load_json(TEST_DIR / 'data' / 'sim_params.json'))
+    fiber = Fiber(**load_json(TEST_DIR / 'data' / 'test_lumped_losses_raman_fiber_config.json'))
+    raman_fiber = RamanFiber(**load_json(TEST_DIR / 'data' / 'test_lumped_losses_raman_fiber_config.json'))
+
+    # propagation
+    # without Raman pumps
+    stimulated_raman_scattering = RamanSolver.calculate_stimulated_raman_scattering(
+        spectral_info_input, fiber)
+    power_profile = stimulated_raman_scattering.power_profile
+    expected_power_profile = genfromtxt(TEST_DIR / 'data' / 'test_lumped_losses_fiber_no_pumps.csv', delimiter=',')
+    assert_allclose(power_profile, expected_power_profile, rtol=1e-3)
+
+    # with Raman pumps
+    expected_power_profile = genfromtxt(TEST_DIR / 'data' / 'test_lumped_losses_raman_fiber.csv', delimiter=',')
+    stimulated_raman_scattering = RamanSolver.calculate_stimulated_raman_scattering(
+        spectral_info_input, raman_fiber)
+    power_profile = stimulated_raman_scattering.power_profile
+    assert_allclose(power_profile, expected_power_profile, rtol=1e-3)
+
+    # without Stimulated Raman Scattering
+    expected_power_profile = genfromtxt(TEST_DIR / 'data' / 'test_lumped_losses_fiber_no_raman.csv', delimiter=',')
+    stimulated_raman_scattering = RamanSolver.calculate_attenuation_profile(spectral_info_input, fiber)
+    power_profile = stimulated_raman_scattering.power_profile
+    assert_allclose(power_profile, expected_power_profile, rtol=1e-3)

tests/test_spectrum_assignment.py

@@ -291,6 +291,7 @@ def request_set():
         'cost': 1,
         'roll_off': 0.15,
         'tx_osnr': 38,
+        'penalties': {},
         'min_spacing': 37.5e9,
         'nb_channel': None,
         'power': 0,

tox.ini

@@ -4,7 +4,7 @@ skipsdist = True
 [testenv]
 deps =
 	-r{toxinidir}/requirements.txt
-	pytest>=5.0.0,<6
+	-r{toxinidir}/tests/requirements.txt
 	cover: pytest-cov
 	linters: flake8
 	linters: pep8-naming
@@ -43,3 +43,4 @@ commands =
 [flake8]
 max-line-length = 120
 max-complexity = 15
+ignore = N806