From c8ce640a2aa212bbee7a05ba425dc7644e2a1b79 Mon Sep 17 00:00:00 2001 From: James Powell Date: Tue, 31 Oct 2017 19:35:45 -0400 Subject: [PATCH] start fresh --- .editorconfig | 21 - .github/ISSUE_TEMPLATE.md | 15 - .travis.yml | 29 - AUTHORS.rst | 13 - CONTRIBUTING.rst | 114 --- HISTORY.rst | 8 - LICENSE | 31 - MANIFEST.in | 11 - Makefile | 82 -- README.rst | 32 - docs/.gitignore | 3 - docs/Makefile | 177 ---- docs/authors.rst | 1 - docs/conf.py | 275 ------ docs/contributing.rst | 1 - docs/history.rst | 1 - docs/index.rst | 22 - docs/installation.rst | 51 - docs/make.bat | 242 ----- docs/readme.rst | 1 - docs/usage.rst | 7 - gnpy/__init__.py | 10 - gnpy/configuration/__init__.py | 1 - gnpy/configuration/fiber_parameters.py | 32 - gnpy/configuration/general_parameters.py | 40 - gnpy/configuration/link_description.py | 59 -- gnpy/constants.py | 7 - gnpy/examples/__main__.py | 75 -- gnpy/examples/architecture.py | 314 ------ gnpy/examples/config/config_ex1.json | 160 ---- gnpy/examples/sim_ex.py | 36 - gnpy/gnpy.py | 904 ------------------ gnpy/input/spectrum_in.py | 29 - gnpy/network_elements.py | 150 --- .../Output from component ID #000 | 21 - .../Output from component ID #001 | 27 - .../Output from component ID #002 | 27 - .../Output from component ID #003 | 27 - .../Output from component ID #004 | 27 - .../Output from component ID #005 | 27 - .../Output from component ID #006 | 27 - .../Output from component ID #007 | 27 - .../Output from component ID #008 | 27 - .../Output from component ID #009 | 27 - .../Output from component ID #010 | 27 - .../Output from component ID #011 | 27 - .../Output from component ID #012 | 27 - .../Output from component ID #013 | 27 - .../Output from component ID #014 | 27 - .../Output from component ID #015 | 27 - .../Output from component ID #016 | 27 - .../Output from component ID #017 | 27 - .../Output from component ID #018 | 27 - .../Output from component ID #019 | 27 - .../Output from component ID #020 | 27 - .../Output from component ID #021 | 27 - .../Output from component ID #022 | 27 - .../Output from component ID #023 | 27 - .../Output from component ID #024 | 27 - .../Output from component ID #025 | 27 - .../Output from component ID #026 | 27 - .../Output from component ID #027 | 26 - .../Output from component ID #028 | 26 - .../Output from component ID #029 | 27 - .../Output from component ID #030 | 27 - .../Output from component ID #031 | 27 - .../Output from component ID #032 | 27 - .../Output from component ID #033 | 27 - .../Output from component ID #034 | 27 - .../Output from component ID #035 | 26 - .../Output from component ID #036 | 27 - .../Output from component ID #037 | 27 - .../Output from component ID #038 | 27 - .../Output from component ID #039 | 27 - .../Output from component ID #040 | 27 - gnpy/output/2017-08-04_17-49-28/link_output | 27 - gnpy/sandbox/incoherent_gn.py | 123 --- gnpy/sandbox/network_element.py | 48 - gnpy/sandbox/optical_elements.py | 159 --- gnpy/sandbox/sandbox.py | 26 - gnpy/sandbox/span.py | 94 -- gnpy/sandbox/transmit_psd.py | 35 - gnpy/utils.py | 43 - requirements_dev.txt | 12 - setup.cfg | 22 - setup.py | 66 -- tests/__init__.py | 3 - tests/test_gnpy.py | 38 - 88 files changed, 4748 deletions(-) delete mode 100644 .editorconfig delete mode 100644 .github/ISSUE_TEMPLATE.md delete mode 100644 .travis.yml delete mode 100644 AUTHORS.rst delete mode 100644 CONTRIBUTING.rst delete mode 100644 HISTORY.rst delete mode 100644 LICENSE delete mode 100644 MANIFEST.in delete mode 100644 Makefile delete mode 100644 README.rst delete mode 100644 docs/.gitignore delete mode 100644 docs/Makefile delete mode 100644 docs/authors.rst delete mode 100755 docs/conf.py delete mode 100644 docs/contributing.rst delete mode 100644 docs/history.rst delete mode 100644 docs/index.rst delete mode 100644 docs/installation.rst delete mode 100644 docs/make.bat delete mode 100644 docs/readme.rst delete mode 100644 docs/usage.rst delete mode 100644 gnpy/__init__.py delete mode 100644 gnpy/configuration/__init__.py delete mode 100644 gnpy/configuration/fiber_parameters.py delete mode 100644 gnpy/configuration/general_parameters.py delete mode 100644 gnpy/configuration/link_description.py delete mode 100644 gnpy/constants.py delete mode 100644 gnpy/examples/__main__.py delete mode 100644 gnpy/examples/architecture.py delete mode 100644 gnpy/examples/config/config_ex1.json delete mode 100644 gnpy/examples/sim_ex.py delete mode 100644 gnpy/gnpy.py delete mode 100644 gnpy/input/spectrum_in.py delete mode 100644 gnpy/network_elements.py delete mode 100644 gnpy/output/2017-08-04_17-49-28/Output from component ID #000 delete mode 100644 gnpy/output/2017-08-04_17-49-28/Output from component ID #001 delete mode 100644 gnpy/output/2017-08-04_17-49-28/Output from component ID #002 delete mode 100644 gnpy/output/2017-08-04_17-49-28/Output from component ID #003 delete mode 100644 gnpy/output/2017-08-04_17-49-28/Output from component ID #004 delete mode 100644 gnpy/output/2017-08-04_17-49-28/Output from component ID #005 delete mode 100644 gnpy/output/2017-08-04_17-49-28/Output from component ID #006 delete mode 100644 gnpy/output/2017-08-04_17-49-28/Output from component ID #007 delete mode 100644 gnpy/output/2017-08-04_17-49-28/Output from component ID #008 delete mode 100644 gnpy/output/2017-08-04_17-49-28/Output from component ID #009 delete mode 100644 gnpy/output/2017-08-04_17-49-28/Output from component ID #010 delete mode 100644 gnpy/output/2017-08-04_17-49-28/Output from component ID #011 delete mode 100644 gnpy/output/2017-08-04_17-49-28/Output from component ID #012 delete mode 100644 gnpy/output/2017-08-04_17-49-28/Output from component ID #013 delete mode 100644 gnpy/output/2017-08-04_17-49-28/Output from component ID #014 delete mode 100644 gnpy/output/2017-08-04_17-49-28/Output from component ID #015 delete mode 100644 gnpy/output/2017-08-04_17-49-28/Output from component ID #016 delete mode 100644 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gnpy/output/2017-08-04_17-49-28/link_output delete mode 100644 gnpy/sandbox/incoherent_gn.py delete mode 100644 gnpy/sandbox/network_element.py delete mode 100644 gnpy/sandbox/optical_elements.py delete mode 100644 gnpy/sandbox/sandbox.py delete mode 100644 gnpy/sandbox/span.py delete mode 100644 gnpy/sandbox/transmit_psd.py delete mode 100644 gnpy/utils.py delete mode 100644 requirements_dev.txt delete mode 100644 setup.cfg delete mode 100644 setup.py delete mode 100644 tests/__init__.py delete mode 100644 tests/test_gnpy.py diff --git a/.editorconfig b/.editorconfig deleted file mode 100644 index d4a2c440..00000000 --- a/.editorconfig +++ /dev/null @@ -1,21 +0,0 @@ -# http://editorconfig.org - -root = true - -[*] -indent_style = space -indent_size = 4 -trim_trailing_whitespace = true -insert_final_newline = true -charset = utf-8 -end_of_line = lf - -[*.bat] -indent_style = tab -end_of_line = crlf - -[LICENSE] -insert_final_newline = false - -[Makefile] -indent_style = tab diff --git a/.github/ISSUE_TEMPLATE.md b/.github/ISSUE_TEMPLATE.md deleted file mode 100644 index a49ff6ea..00000000 --- a/.github/ISSUE_TEMPLATE.md +++ /dev/null @@ -1,15 +0,0 @@ -* gnpy version: -* Python version: -* Operating System: - -### Description - -Describe what you were trying to get done. -Tell us what happened, what went wrong, and what you expected to happen. - -### What I Did - -``` -Paste the command(s) you ran and the output. -If there was a crash, please include the traceback here. -``` diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index 4e554271..00000000 --- a/.travis.yml +++ /dev/null @@ -1,29 +0,0 @@ -# Config file for automatic testing at travis-ci.org -# This file will be regenerated if you run travis_pypi_setup.py - -language: python -python: - - 3.5 - - 3.4 - - 3.3 - - 2.7 - - 2.6 - -# command to install dependencies, e.g. pip install -r requirements.txt --use-mirrors -install: pip install -U tox-travis - -# command to run tests, e.g. python setup.py test -script: tox - -# After you create the Github repo and add it to Travis, run the -# travis_pypi_setup.py script to finish PyPI deployment setup -deploy: - provider: pypi - distributions: sdist bdist_wheel - user: - password: - secure: PLEASE_REPLACE_ME - on: - tags: true - repo: /gnpy - python: 2.7 diff --git a/AUTHORS.rst b/AUTHORS.rst deleted file mode 100644 index 0722bad8..00000000 --- a/AUTHORS.rst +++ /dev/null @@ -1,13 +0,0 @@ -======= -Credits -======= - -Development Lead ----------------- - -* <@.com> - -Contributors ------------- - -None yet. Why not be the first? diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst deleted file mode 100644 index a0a71f0f..00000000 --- a/CONTRIBUTING.rst +++ /dev/null @@ -1,114 +0,0 @@ -.. highlight:: shell - -============ -Contributing -============ - -Contributions are welcome, and they are greatly appreciated! Every -little bit helps, and credit will always be given. - -You can contribute in many ways: - -Types of Contributions ----------------------- - -Report Bugs -~~~~~~~~~~~ - -Report bugs at https://github.com//gnpy/issues. - -If you are reporting a bug, please include: - -* Your operating system name and version. -* Any details about your local setup that might be helpful in troubleshooting. -* Detailed steps to reproduce the bug. - -Fix Bugs -~~~~~~~~ - -Look through the GitHub issues for bugs. Anything tagged with "bug" -and "help wanted" is open to whoever wants to implement it. - -Implement Features -~~~~~~~~~~~~~~~~~~ - -Look through the GitHub issues for features. Anything tagged with "enhancement" -and "help wanted" is open to whoever wants to implement it. - -Write Documentation -~~~~~~~~~~~~~~~~~~~ - -gnpy could always use more documentation, whether as part of the -official gnpy docs, in docstrings, or even on the web in blog posts, -articles, and such. - -Submit Feedback -~~~~~~~~~~~~~~~ - -The best way to send feedback is to file an issue at https://github.com//gnpy/issues. - -If you are proposing a feature: - -* Explain in detail how it would work. -* Keep the scope as narrow as possible, to make it easier to implement. -* Remember that this is a volunteer-driven project, and that contributions - are welcome :) - -Get Started! ------------- - -Ready to contribute? Here's how to set up `gnpy` for local development. - -1. Fork the `gnpy` repo on GitHub. -2. Clone your fork locally:: - - $ git clone git@github.com:your_name_here/gnpy.git - -3. Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development:: - - $ mkvirtualenv gnpy - $ cd gnpy/ - $ python setup.py develop - -4. Create a branch for local development:: - - $ git checkout -b name-of-your-bugfix-or-feature - - Now you can make your changes locally. - -5. When you're done making changes, check that your changes pass flake8 and the tests, including testing other Python versions with tox:: - - $ flake8 gnpy tests - $ python setup.py test or py.test - $ tox - - To get flake8 and tox, just pip install them into your virtualenv. - -6. Commit your changes and push your branch to GitHub:: - - $ git add . - $ git commit -m "Your detailed description of your changes." - $ git push origin name-of-your-bugfix-or-feature - -7. Submit a pull request through the GitHub website. - -Pull Request Guidelines ------------------------ - -Before you submit a pull request, check that it meets these guidelines: - -1. The pull request should include tests. -2. If the pull request adds functionality, the docs should be updated. Put - your new functionality into a function with a docstring, and add the - feature to the list in README.rst. -3. The pull request should work for Python 2.6, 2.7, 3.3, 3.4 and 3.5, and for PyPy. Check - https://travis-ci.org//gnpy/pull_requests - and make sure that the tests pass for all supported Python versions. - -Tips ----- - -To run a subset of tests:: - -$ py.test tests.test_gnpy - diff --git a/HISTORY.rst b/HISTORY.rst deleted file mode 100644 index b2828bbe..00000000 --- a/HISTORY.rst +++ /dev/null @@ -1,8 +0,0 @@ -======= -History -======= - -0.1.0 (2017-06-29) ------------------- - -* First release on PyPI. diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 7932b642..00000000 --- a/LICENSE +++ /dev/null @@ -1,31 +0,0 @@ - -BSD License - -Copyright (c) 2017, -All rights reserved. - -Redistribution and use in source and binary forms, with or without modification, -are permitted provided that the following conditions are met: - -* Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - -* Redistributions in binary form must reproduce the above copyright notice, this - list of conditions and the following disclaimer in the documentation and/or - other materials provided with the distribution. - -* Neither the name of the copyright holder nor the names of its - contributors may be used to endorse or promote products derived from this - software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND -ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED -WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. -IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, -INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, -BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY -OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE -OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED -OF THE POSSIBILITY OF SUCH DAMAGE. - diff --git a/MANIFEST.in b/MANIFEST.in deleted file mode 100644 index 965b2dda..00000000 --- a/MANIFEST.in +++ /dev/null @@ -1,11 +0,0 @@ -include AUTHORS.rst -include CONTRIBUTING.rst -include HISTORY.rst -include LICENSE -include README.rst - -recursive-include tests * -recursive-exclude * __pycache__ -recursive-exclude * *.py[co] - -recursive-include docs *.rst conf.py Makefile make.bat *.jpg *.png *.gif diff --git a/Makefile b/Makefile deleted file mode 100644 index f360f13f..00000000 --- a/Makefile +++ /dev/null @@ -1,82 +0,0 @@ -.PHONY: clean clean-test clean-pyc clean-build docs help -.DEFAULT_GOAL := help -define BROWSER_PYSCRIPT -import os, webbrowser, sys -try: - from urllib import pathname2url -except: - from urllib.request import pathname2url - -webbrowser.open("file://" + pathname2url(os.path.abspath(sys.argv[1]))) -endef -export BROWSER_PYSCRIPT - -define PRINT_HELP_PYSCRIPT -import re, sys - -for line in sys.stdin: - match = re.match(r'^([a-zA-Z_-]+):.*?## (.*)$$', line) - if match: - target, help = match.groups() - print("%-20s %s" % (target, help)) -endef -export PRINT_HELP_PYSCRIPT -BROWSER := python -c "$$BROWSER_PYSCRIPT" - -help: - @python -c "$$PRINT_HELP_PYSCRIPT" < $(MAKEFILE_LIST) - -clean: clean-build clean-pyc clean-test ## remove all build, test, coverage and Python artifacts - - -clean-build: ## remove build artifacts - rm -fr build/ - rm -fr dist/ - rm -fr .eggs/ - find . -name '*.egg-info' -exec rm -fr {} + - find . -name '*.egg' -exec rm -f {} + - -clean-pyc: ## remove Python file artifacts - find . -name '*.pyc' -exec rm -f {} + - find . -name '*.pyo' -exec rm -f {} + - find . -name '*~' -exec rm -f {} + - find . -name '__pycache__' -exec rm -fr {} + - -clean-test: ## remove test and coverage artifacts - rm -f .coverage - rm -fr htmlcov/ - -lint: ## check style with flake8 - flake8 gnpy tests - -test: ## run tests quickly with the default Python - py.test - -coverage: ## check code coverage quickly with the default Python - coverage run --source gnpy -m pytest - coverage report -m - coverage html - $(BROWSER) htmlcov/index.html - -docs: ## generate Sphinx HTML documentation, including API docs - rm -f docs/gnpy.rst - rm -f docs/modules.rst - sphinx-apidoc -o docs/ gnpy - $(MAKE) -C docs clean - $(MAKE) -C docs html - $(BROWSER) docs/_build/html/index.html - -servedocs: docs ## compile the docs watching for changes - watchmedo shell-command -p '*.rst' -c '$(MAKE) -C docs html' -R -D . - -release: clean ## package and upload a release - python setup.py sdist upload - python setup.py bdist_wheel upload - -dist: clean ## builds source and wheel package - python setup.py sdist - python setup.py bdist_wheel - ls -l dist - -install: clean ## install the package to the active Python's site-packages - python setup.py install diff --git a/README.rst b/README.rst deleted file mode 100644 index bb7e726b..00000000 --- a/README.rst +++ /dev/null @@ -1,32 +0,0 @@ -==== -gnpy -==== - - -.. image:: https://img.shields.io/pypi/v/gnpy.svg - :target: https://pypi.python.org/pypi/gnpy - -.. image:: https://img.shields.io/travis//gnpy.svg - :target: https://travis-ci.org//gnpy - -.. image:: https://readthedocs.org/projects/gnpy/badge/?version=latest - :target: https://gnpy.readthedocs.io/en/latest/?badge=latest - :alt: Documentation Status - -.. image:: https://pyup.io/repos/github//gnpy/shield.svg - :target: https://pyup.io/repos/github//gnpy/ - :alt: Updates - - -Gaussian Noise (GN) modeling library - - -* Free software: BSD license -* Documentation: https://gnpy.readthedocs.io. - - -Features --------- - -* TODO - diff --git a/docs/.gitignore b/docs/.gitignore deleted file mode 100644 index 92c2ad47..00000000 --- a/docs/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -/gnpy.rst -/gnpy.*.rst -/modules.rst diff --git a/docs/Makefile b/docs/Makefile deleted file mode 100644 index 1b888815..00000000 --- a/docs/Makefile +++ /dev/null @@ -1,177 +0,0 @@ -# Makefile for Sphinx documentation -# - -# You can set these variables from the command line. -SPHINXOPTS = -SPHINXBUILD = sphinx-build -PAPER = -BUILDDIR = _build - -# User-friendly check for sphinx-build -ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1) -$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/) -endif - -# Internal variables. -PAPEROPT_a4 = -D latex_paper_size=a4 -PAPEROPT_letter = -D latex_paper_size=letter -ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . -# the i18n builder cannot share the environment and doctrees with the others -I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . - -.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest gettext - -help: - @echo "Please use \`make ' where is one of" - @echo " html to make standalone HTML files" - @echo " dirhtml to make HTML files named index.html in directories" - @echo " singlehtml to make a single large HTML file" - @echo " pickle to make pickle files" - @echo " json to make JSON files" - @echo " htmlhelp to make HTML files and a HTML help project" - @echo " qthelp to make HTML files and a qthelp project" - @echo " devhelp to make HTML files and a Devhelp project" - @echo " epub to make an epub" - @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" - @echo " latexpdf to make LaTeX files and run them through pdflatex" - @echo " latexpdfja to make LaTeX files and run them through platex/dvipdfmx" - @echo " text to make text files" - @echo " man to make manual pages" - @echo " texinfo to make Texinfo files" - @echo " info to make Texinfo files and run them through makeinfo" - @echo " gettext to make PO message catalogs" - @echo " changes to make an overview of all changed/added/deprecated items" - @echo " xml to make Docutils-native XML files" - @echo " pseudoxml to make pseudoxml-XML files for display purposes" - @echo " linkcheck to check all external links for integrity" - @echo " doctest to run all doctests embedded in the documentation (if enabled)" - -clean: - rm -rf $(BUILDDIR)/* - -html: - $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html - @echo - @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." - -dirhtml: - $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml - @echo - @echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml." - -singlehtml: - $(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml - @echo - @echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml." - -pickle: - $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle - @echo - @echo "Build finished; now you can process the pickle files." - -json: - $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json - @echo - @echo "Build finished; now you can process the JSON files." - -htmlhelp: - $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp - @echo - @echo "Build finished; now you can run HTML Help Workshop with the" \ - ".hhp project file in $(BUILDDIR)/htmlhelp." - -qthelp: - $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp - @echo - @echo "Build finished; now you can run "qcollectiongenerator" with the" \ - ".qhcp project file in $(BUILDDIR)/qthelp, like this:" - @echo "# qcollectiongenerator $(BUILDDIR)/qthelp/gnpy.qhcp" - @echo "To view the help file:" - @echo "# assistant -collectionFile $(BUILDDIR)/qthelp/gnpy.qhc" - -devhelp: - $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp - @echo - @echo "Build finished." - @echo "To view the help file:" - @echo "# mkdir -p $$HOME/.local/share/devhelp/gnpy" - @echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/gnpy" - @echo "# devhelp" - -epub: - $(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub - @echo - @echo "Build finished. The epub file is in $(BUILDDIR)/epub." - -latex: - $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex - @echo - @echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex." - @echo "Run \`make' in that directory to run these through (pdf)latex" \ - "(use \`make latexpdf' here to do that automatically)." - -latexpdf: - $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex - @echo "Running LaTeX files through pdflatex..." - $(MAKE) -C $(BUILDDIR)/latex all-pdf - @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." - -latexpdfja: - $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex - @echo "Running LaTeX files through platex and dvipdfmx..." - $(MAKE) -C $(BUILDDIR)/latex all-pdf-ja - @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." - -text: - $(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text - @echo - @echo "Build finished. The text files are in $(BUILDDIR)/text." - -man: - $(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man - @echo - @echo "Build finished. The manual pages are in $(BUILDDIR)/man." - -texinfo: - $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo - @echo - @echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo." - @echo "Run \`make' in that directory to run these through makeinfo" \ - "(use \`make info' here to do that automatically)." - -info: - $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo - @echo "Running Texinfo files through makeinfo..." - make -C $(BUILDDIR)/texinfo info - @echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo." - -gettext: - $(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale - @echo - @echo "Build finished. The message catalogs are in $(BUILDDIR)/locale." - -changes: - $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes - @echo - @echo "The overview file is in $(BUILDDIR)/changes." - -linkcheck: - $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck - @echo - @echo "Link check complete; look for any errors in the above output " \ - "or in $(BUILDDIR)/linkcheck/output.txt." - -doctest: - $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest - @echo "Testing of doctests in the sources finished, look at the " \ - "results in $(BUILDDIR)/doctest/output.txt." - -xml: - $(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml - @echo - @echo "Build finished. The XML files are in $(BUILDDIR)/xml." - -pseudoxml: - $(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml - @echo - @echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml." diff --git a/docs/authors.rst b/docs/authors.rst deleted file mode 100644 index e122f914..00000000 --- a/docs/authors.rst +++ /dev/null @@ -1 +0,0 @@ -.. include:: ../AUTHORS.rst diff --git a/docs/conf.py b/docs/conf.py deleted file mode 100755 index c83fc762..00000000 --- a/docs/conf.py +++ /dev/null @@ -1,275 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -# -# gnpy documentation build configuration file, created by -# sphinx-quickstart on Tue Jul 9 22:26:36 2013. -# -# This file is execfile()d with the current directory set to its -# containing dir. -# -# Note that not all possible configuration values are present in this -# autogenerated file. -# -# All configuration values have a default; values that are commented out -# serve to show the default. - -import sys -import os - -# If extensions (or modules to document with autodoc) are in another -# directory, add these directories to sys.path here. If the directory is -# relative to the documentation root, use os.path.abspath to make it -# absolute, like shown here. -#sys.path.insert(0, os.path.abspath('.')) - -# Get the project root dir, which is the parent dir of this -cwd = os.getcwd() -project_root = os.path.dirname(cwd) - -# Insert the project root dir as the first element in the PYTHONPATH. -# This lets us ensure that the source package is imported, and that its -# version is used. -sys.path.insert(0, project_root) - -import gnpy - -# -- General configuration --------------------------------------------- - -# If your documentation needs a minimal Sphinx version, state it here. -#needs_sphinx = '1.0' - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones. -extensions = ['sphinx.ext.autodoc', 'sphinx.ext.viewcode'] - -# Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] - -# The suffix of source filenames. -source_suffix = '.rst' - -# The encoding of source files. -#source_encoding = 'utf-8-sig' - -# The master toctree document. -master_doc = 'index' - -# General information about the project. -project = u'gnpy' -copyright = u"2017, " - -# The version info for the project you're documenting, acts as replacement -# for |version| and |release|, also used in various other places throughout -# the built documents. -# -# The short X.Y version. -version = gnpy.__version__ -# The full version, including alpha/beta/rc tags. -release = gnpy.__version__ - -# The language for content autogenerated by Sphinx. Refer to documentation -# for a list of supported languages. -#language = None - -# There are two options for replacing |today|: either, you set today to -# some non-false value, then it is used: -#today = '' -# Else, today_fmt is used as the format for a strftime call. -#today_fmt = '%B %d, %Y' - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -exclude_patterns = ['_build'] - -# The reST default role (used for this markup: `text`) to use for all -# documents. -#default_role = None - -# If true, '()' will be appended to :func: etc. cross-reference text. -#add_function_parentheses = True - -# If true, the current module name will be prepended to all description -# unit titles (such as .. function::). -#add_module_names = True - -# If true, sectionauthor and moduleauthor directives will be shown in the -# output. They are ignored by default. -#show_authors = False - -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' - -# A list of ignored prefixes for module index sorting. -#modindex_common_prefix = [] - -# If true, keep warnings as "system message" paragraphs in the built -# documents. -#keep_warnings = False - - -# -- Options for HTML output ------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -html_theme = 'default' - -# Theme options are theme-specific and customize the look and feel of a -# theme further. For a list of options available for each theme, see the -# documentation. -#html_theme_options = {} - -# Add any paths that contain custom themes here, relative to this directory. -#html_theme_path = [] - -# The name for this set of Sphinx documents. If None, it defaults to -# " v documentation". -#html_title = None - -# A shorter title for the navigation bar. Default is the same as -# html_title. -#html_short_title = None - -# The name of an image file (relative to this directory) to place at the -# top of the sidebar. -#html_logo = None - -# The name of an image file (within the static path) to use as favicon -# of the docs. This file should be a Windows icon file (.ico) being -# 16x16 or 32x32 pixels large. -#html_favicon = None - -# Add any paths that contain custom static files (such as style sheets) -# here, relative to this directory. They are copied after the builtin -# static files, so a file named "default.css" will overwrite the builtin -# "default.css". -html_static_path = ['_static'] - -# If not '', a 'Last updated on:' timestamp is inserted at every page -# bottom, using the given strftime format. -#html_last_updated_fmt = '%b %d, %Y' - -# If true, SmartyPants will be used to convert quotes and dashes to -# typographically correct entities. -#html_use_smartypants = True - -# Custom sidebar templates, maps document names to template names. -#html_sidebars = {} - -# Additional templates that should be rendered to pages, maps page names -# to template names. -#html_additional_pages = {} - -# If false, no module index is generated. -#html_domain_indices = True - -# If false, no index is generated. -#html_use_index = True - -# If true, the index is split into individual pages for each letter. -#html_split_index = False - -# If true, links to the reST sources are added to the pages. -#html_show_sourcelink = True - -# If true, "Created using Sphinx" is shown in the HTML footer. -# Default is True. -#html_show_sphinx = True - -# If true, "(C) Copyright ..." is shown in the HTML footer. -# Default is True. -#html_show_copyright = True - -# If true, an OpenSearch description file will be output, and all pages -# will contain a tag referring to it. The value of this option -# must be the base URL from which the finished HTML is served. -#html_use_opensearch = '' - -# This is the file name suffix for HTML files (e.g. ".xhtml"). -#html_file_suffix = None - -# Output file base name for HTML help builder. -htmlhelp_basename = 'gnpydoc' - - -# -- Options for LaTeX output ------------------------------------------ - -latex_elements = { - # The paper size ('letterpaper' or 'a4paper'). - #'papersize': 'letterpaper', - - # The font size ('10pt', '11pt' or '12pt'). - #'pointsize': '10pt', - - # Additional stuff for the LaTeX preamble. - #'preamble': '', -} - -# Grouping the document tree into LaTeX files. List of tuples -# (source start file, target name, title, author, documentclass -# [howto/manual]). -latex_documents = [ - ('index', 'gnpy.tex', - u'gnpy Documentation', - u'', 'manual'), -] - -# The name of an image file (relative to this directory) to place at -# the top of the title page. -#latex_logo = None - -# For "manual" documents, if this is true, then toplevel headings -# are parts, not chapters. -#latex_use_parts = False - -# If true, show page references after internal links. -#latex_show_pagerefs = False - -# If true, show URL addresses after external links. -#latex_show_urls = False - -# Documents to append as an appendix to all manuals. -#latex_appendices = [] - -# If false, no module index is generated. -#latex_domain_indices = True - - -# -- Options for manual page output ------------------------------------ - -# One entry per manual page. List of tuples -# (source start file, name, description, authors, manual section). -man_pages = [ - ('index', 'gnpy', - u'gnpy Documentation', - [u''], 1) -] - -# If true, show URL addresses after external links. -#man_show_urls = False - - -# -- Options for Texinfo output ---------------------------------------- - -# Grouping the document tree into Texinfo files. List of tuples -# (source start file, target name, title, author, -# dir menu entry, description, category) -texinfo_documents = [ - ('index', 'gnpy', - u'gnpy Documentation', - u'', - 'gnpy', - 'One line description of project.', - 'Miscellaneous'), -] - -# Documents to append as an appendix to all manuals. -#texinfo_appendices = [] - -# If false, no module index is generated. -#texinfo_domain_indices = True - -# How to display URL addresses: 'footnote', 'no', or 'inline'. -#texinfo_show_urls = 'footnote' - -# If true, do not generate a @detailmenu in the "Top" node's menu. -#texinfo_no_detailmenu = False diff --git a/docs/contributing.rst b/docs/contributing.rst deleted file mode 100644 index e582053e..00000000 --- a/docs/contributing.rst +++ /dev/null @@ -1 +0,0 @@ -.. include:: ../CONTRIBUTING.rst diff --git a/docs/history.rst b/docs/history.rst deleted file mode 100644 index 25064996..00000000 --- a/docs/history.rst +++ /dev/null @@ -1 +0,0 @@ -.. include:: ../HISTORY.rst diff --git a/docs/index.rst b/docs/index.rst deleted file mode 100644 index 3619ab56..00000000 --- a/docs/index.rst +++ /dev/null @@ -1,22 +0,0 @@ -Welcome to gnpy's documentation! -====================================== - -Contents: - -.. toctree:: - :maxdepth: 2 - - readme - installation - usage - modules - contributing - authors - history - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/docs/installation.rst b/docs/installation.rst deleted file mode 100644 index 10135822..00000000 --- a/docs/installation.rst +++ /dev/null @@ -1,51 +0,0 @@ -.. highlight:: shell - -============ -Installation -============ - - -Stable release --------------- - -To install gnpy, run this command in your terminal: - -.. code-block:: console - - $ pip install gnpy - -This is the preferred method to install gnpy, as it will always install the most recent stable release. - -If you don't have `pip`_ installed, this `Python installation guide`_ can guide -you through the process. - -.. _pip: https://pip.pypa.io -.. _Python installation guide: http://docs.python-guide.org/en/latest/starting/installation/ - - -From sources ------------- - -The sources for gnpy can be downloaded from the `Github repo`_. - -You can either clone the public repository: - -.. code-block:: console - - $ git clone git://github.com//gnpy - -Or download the `tarball`_: - -.. code-block:: console - - $ curl -OL https://github.com//gnpy/tarball/master - -Once you have a copy of the source, you can install it with: - -.. code-block:: console - - $ python setup.py install - - -.. _Github repo: https://github.com//gnpy -.. _tarball: https://github.com//gnpy/tarball/master diff --git a/docs/make.bat b/docs/make.bat deleted file mode 100644 index 605cfc30..00000000 --- a/docs/make.bat +++ /dev/null @@ -1,242 +0,0 @@ -@ECHO OFF - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set BUILDDIR=_build -set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% . -set I18NSPHINXOPTS=%SPHINXOPTS% . -if NOT "%PAPER%" == "" ( - set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS% - set I18NSPHINXOPTS=-D latex_paper_size=%PAPER% %I18NSPHINXOPTS% -) - -if "%1" == "" goto help - -if "%1" == "help" ( - :help - echo.Please use `make ^` where ^ is one of - echo. html to make standalone HTML files - echo. dirhtml to make HTML files named index.html in directories - echo. singlehtml to make a single large HTML file - echo. pickle to make pickle files - echo. json to make JSON files - echo. htmlhelp to make HTML files and a HTML help project - echo. qthelp to make HTML files and a qthelp project - echo. devhelp to make HTML files and a Devhelp project - echo. epub to make an epub - echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter - echo. text to make text files - echo. man to make manual pages - echo. texinfo to make Texinfo files - echo. gettext to make PO message catalogs - echo. changes to make an overview over all changed/added/deprecated items - echo. xml to make Docutils-native XML files - echo. pseudoxml to make pseudoxml-XML files for display purposes - echo. linkcheck to check all external links for integrity - echo. doctest to run all doctests embedded in the documentation if enabled - goto end -) - -if "%1" == "clean" ( - for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i - del /q /s %BUILDDIR%\* - goto end -) - - -%SPHINXBUILD% 2> nul -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. Make sure you have Sphinx - echo.installed, then set the SPHINXBUILD environment variable to point - echo.to the full path of the 'sphinx-build' executable. Alternatively you - echo.may add the Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.http://sphinx-doc.org/ - exit /b 1 -) - -if "%1" == "html" ( - %SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The HTML pages are in %BUILDDIR%/html. - goto end -) - -if "%1" == "dirhtml" ( - %SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml. - goto end -) - -if "%1" == "singlehtml" ( - %SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml. - goto end -) - -if "%1" == "pickle" ( - %SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle - if errorlevel 1 exit /b 1 - echo. - echo.Build finished; now you can process the pickle files. - goto end -) - -if "%1" == "json" ( - %SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json - if errorlevel 1 exit /b 1 - echo. - echo.Build finished; now you can process the JSON files. - goto end -) - -if "%1" == "htmlhelp" ( - %SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp - if errorlevel 1 exit /b 1 - echo. - echo.Build finished; now you can run HTML Help Workshop with the ^ -.hhp project file in %BUILDDIR%/htmlhelp. - goto end -) - -if "%1" == "qthelp" ( - %SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp - if errorlevel 1 exit /b 1 - echo. - echo.Build finished; now you can run "qcollectiongenerator" with the ^ -.qhcp project file in %BUILDDIR%/qthelp, like this: - echo.^> qcollectiongenerator %BUILDDIR%\qthelp\gnpy.qhcp - echo.To view the help file: - echo.^> assistant -collectionFile %BUILDDIR%\qthelp\gnpy.ghc - goto end -) - -if "%1" == "devhelp" ( - %SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. - goto end -) - -if "%1" == "epub" ( - %SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The epub file is in %BUILDDIR%/epub. - goto end -) - -if "%1" == "latex" ( - %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex - if errorlevel 1 exit /b 1 - echo. - echo.Build finished; the LaTeX files are in %BUILDDIR%/latex. - goto end -) - -if "%1" == "latexpdf" ( - %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex - cd %BUILDDIR%/latex - make all-pdf - cd %BUILDDIR%/.. - echo. - echo.Build finished; the PDF files are in %BUILDDIR%/latex. - goto end -) - -if "%1" == "latexpdfja" ( - %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex - cd %BUILDDIR%/latex - make all-pdf-ja - cd %BUILDDIR%/.. - echo. - echo.Build finished; the PDF files are in %BUILDDIR%/latex. - goto end -) - -if "%1" == "text" ( - %SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The text files are in %BUILDDIR%/text. - goto end -) - -if "%1" == "man" ( - %SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The manual pages are in %BUILDDIR%/man. - goto end -) - -if "%1" == "texinfo" ( - %SPHINXBUILD% -b texinfo %ALLSPHINXOPTS% %BUILDDIR%/texinfo - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The Texinfo files are in %BUILDDIR%/texinfo. - goto end -) - -if "%1" == "gettext" ( - %SPHINXBUILD% -b gettext %I18NSPHINXOPTS% %BUILDDIR%/locale - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The message catalogs are in %BUILDDIR%/locale. - goto end -) - -if "%1" == "changes" ( - %SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes - if errorlevel 1 exit /b 1 - echo. - echo.The overview file is in %BUILDDIR%/changes. - goto end -) - -if "%1" == "linkcheck" ( - %SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck - if errorlevel 1 exit /b 1 - echo. - echo.Link check complete; look for any errors in the above output ^ -or in %BUILDDIR%/linkcheck/output.txt. - goto end -) - -if "%1" == "doctest" ( - %SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest - if errorlevel 1 exit /b 1 - echo. - echo.Testing of doctests in the sources finished, look at the ^ -results in %BUILDDIR%/doctest/output.txt. - goto end -) - -if "%1" == "xml" ( - %SPHINXBUILD% -b xml %ALLSPHINXOPTS% %BUILDDIR%/xml - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The XML files are in %BUILDDIR%/xml. - goto end -) - -if "%1" == "pseudoxml" ( - %SPHINXBUILD% -b pseudoxml %ALLSPHINXOPTS% %BUILDDIR%/pseudoxml - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The pseudo-XML files are in %BUILDDIR%/pseudoxml. - goto end -) - -:end diff --git a/docs/readme.rst b/docs/readme.rst deleted file mode 100644 index 72a33558..00000000 --- a/docs/readme.rst +++ /dev/null @@ -1 +0,0 @@ -.. include:: ../README.rst diff --git a/docs/usage.rst b/docs/usage.rst deleted file mode 100644 index 82e17a3b..00000000 --- a/docs/usage.rst +++ /dev/null @@ -1,7 +0,0 @@ -===== -Usage -===== - -To use gnpy in a project:: - - import gnpy diff --git a/gnpy/__init__.py b/gnpy/__init__.py deleted file mode 100644 index cefdc8fd..00000000 --- a/gnpy/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -# -*- coding: utf-8 -*- - -from .gnpy import (raised_cosine_comb, analytic_formula, compute_psi, fwm_eff, - get_f_computed_interp, get_freqarray, gn_analytic, gn_model, - interpolate_in_range, GN_integral) - -from .constants import (pi, c, h) -from .network_elements import (Network, Tx, Rx, Fiber, Edfa) - -__all__ = ['gnpy', 'constants', 'network_elements'] diff --git a/gnpy/configuration/__init__.py b/gnpy/configuration/__init__.py deleted file mode 100644 index 8d1c8b69..00000000 --- a/gnpy/configuration/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/gnpy/configuration/fiber_parameters.py b/gnpy/configuration/fiber_parameters.py deleted file mode 100644 index 22810010..00000000 --- a/gnpy/configuration/fiber_parameters.py +++ /dev/null @@ -1,32 +0,0 @@ -# coding=utf-8 -""" fiber_parameters.py describes the fiber parameters. - fibers is a dictionary containing a dictionary for each kind of fiber - each dictionary has to report: - reference_frequency: the frequency at which the parameters are evaluated [THz] - alpha: the attenuation coefficient [dB/km] - alpha_1st: the first derivative of alpha indicating the alpha slope [dB/km/THz] - if you assume a flat attenuation with respect to the frequency you put it as zero - beta_2: the dispersion coefficient [ps^2/km] - n_2: second-order nonlinear refractive index [m^2/W] - a typical value is 2.5E-20 m^2/W - a_eff: the effective area of the fiber [um^2] -""" - -fibers = { - 'SMF': { - 'reference_frequency': 193.5, - 'alpha': 0.2, - 'alpha_1st': 0, - 'beta_2': 21.27, - 'n_2': 2.5E-20, - 'a_eff': 77.77, - }, - 'NZDF': { - 'reference_frequency': 193.5, - 'alpha': 0.22, - 'alpha_1st': 0, - 'beta_2': 21, - 'n_2': 2.5E-20, - 'a_eff': 70, - } -} diff --git a/gnpy/configuration/general_parameters.py b/gnpy/configuration/general_parameters.py deleted file mode 100644 index d0b12922..00000000 --- a/gnpy/configuration/general_parameters.py +++ /dev/null @@ -1,40 +0,0 @@ -# -*- coding: utf-8 -* -"""general_parameters.py contains the general configuration settings - - The sectings are subdivided in two dictionaries: - sys_param: a dictionary containing the general system parameters: - f0: the starting frequency of the laser grid used to describe the WDM system [THz] - ns: the number of 6.25 GHz slots in the grid - - control_param: - save_each_comp: a boolean flag. If true, it saves in output folder one spectrum file at the output of each - component, otherwise it saves just the last spectrum - is_linear: a bool flag. If true, is doesn't compute NLI, if false, OLE will consider NLI - is_analytic: a boolean flag. If true, the NLI is computed through the analytic formula, otherwise it uses - the double integral. Warning: the double integral is very slow. - points_not_interp: if the double integral is used, it indicates how much points are calculated, others will - be interpolated - kind_interp: a string indicating the interpolation method for the double integral - th_fwm: the threshold of the four wave mixing efficiency for the double integral - n_points: number of points in which the double integral is computed in the high FWM efficiency region - n_points_min: number of points in which the double integral is computed in the low FWM efficiency region - n_cores: number of cores for parallel computation [not yet implemented] -""" -# System parameters -sys_param = { - 'f0': 192.075, - 'ns': 328 -} - -# control parameters -control_param = { - 'save_each_comp': True, - 'is_linear': False, - 'is_analytic': True, - 'points_not_interp': 2, - 'kind_interp': 'linear', - 'th_fwm': 50, - 'n_points': 500, - 'n_points_min': 4, - 'n_cores': 1 -} diff --git a/gnpy/configuration/link_description.py b/gnpy/configuration/link_description.py deleted file mode 100644 index 519568ba..00000000 --- a/gnpy/configuration/link_description.py +++ /dev/null @@ -1,59 +0,0 @@ -# coding=utf-8 -""" link_description.py contains the full description of that OLE has to emulate. - It contains a list of dictionaries, following the structure of the link and each element of the list describes one - component. - - 'comp_cat': the kind of link component: - PC: a passive component defined by a loss at a certain frequency and a loss tilt - OA: an optical amplifier defined by a gain at a certain frequency, a gain tilt and a noise figure - fiber: a span of fiber described by the type and the length - 'comp_id': is an id identifying the component. It has to be unique for each component! - - extra fields for PC: - 'ref_freq': the frequency at which the 'loss' parameter is evaluated [THz] - 'loss': the loss at the frequency 'ref_freq' [dB] - 'loss_tlt': the frequency dependent loss [dB/THz] - extra fields for OA: - 'ref_freq': the frequency at which the 'gain' parameter is evaluated [THz] - 'gain': the gain at the frequency 'ref_freq' [dB] - 'gain_tlt': the frequency dependent gain [dB/THz] - 'noise_figure': the noise figure of the optical amplifier [dB] - extra fields for fiber: - 'fiber_type': a string calling the type of fiber described in the file fiber_parameters.py - 'length': the fiber length [km] - -""" -smf = { - 'comp_cat': 'fiber', - 'comp_id': '', - 'fiber_type': 'SMF', - 'length': 100 - } - -oa = { - 'comp_cat': 'OA', - 'comp_id': '', - 'ref_freq': 193.5, - 'gain': 20, - 'gain_tlt': 0.0, - 'noise_figure': 5 - } - -pc = { - 'comp_cat': 'PC', - 'comp_id': '04', - 'ref_freq': 193., - 'loss': 2.0, - 'loss_tlt': 0.0 - } - -link = [] - -for index in range(20): - smf['comp_id'] = '%03d' % (2 * index) - oa['comp_id'] = '%03d' % (2 * index + 1) - link += [dict(smf)] - link += [dict(oa)] - -pc['comp_id'] = '%03d' % 40 -link += [dict(pc)] diff --git a/gnpy/constants.py b/gnpy/constants.py deleted file mode 100644 index 74a525b9..00000000 --- a/gnpy/constants.py +++ /dev/null @@ -1,7 +0,0 @@ -# -*- coding: utf-8 -*- - -from math import pi - -pi = pi -c = 299792458 # Speed of light -h = 6.62606896e-34 # Planck's constant diff --git a/gnpy/examples/__main__.py b/gnpy/examples/__main__.py deleted file mode 100644 index 4c2d8088..00000000 --- a/gnpy/examples/__main__.py +++ /dev/null @@ -1,75 +0,0 @@ -import gnpy as gn -import numpy as np -import matplotlib.pyplot as plt -import time - - -def main(): - - # Accuracy parameters - flag_analytic = True - num_computed_values = 2 - interp_method = 'linear' - threshold_fwm = 50 - n_points = 500 - n_points_min = 4 - accuracy_param = {'is_analytic': flag_analytic, 'points_not_interp': num_computed_values, 'kind_interp': interp_method, - 'th_fwm': threshold_fwm, 'n_points': n_points, 'n_points_min': n_points_min} - - # Parallelization Parameters - n_cores = 1 - - # Spectrum parameters - num_ch = 95 - rs = np.ones(num_ch) * 0.032 - b_ch = rs # For root raised cosine shapes, the -3 dB band is equal to the symbol rate - roll_off = np.ones(num_ch) * 0.05 - power = np.ones(num_ch) * 0.001 - central_freq = 193.5 - if num_ch % 2 == 1: # odd number of channels - fch = np.arange(-(num_ch // 2), (num_ch // 2) + 1, 1) * 0.05 # noqa: E501 - else: - fch = (np.arange(0, num_ch) - (num_ch / 2.0) + 0.5) * 0.05 - spectrum_param = {'num_ch': num_ch, 'f_ch': fch, 'b_ch': b_ch, 'roll_off': roll_off, 'power': power} - - # Fiber Parameters - beta2 = 21.27 - l_span = 100.0 - loss = 0.2 - gam = 1.27 - fiber_param = {'alpha': loss, 'span_length': l_span, 'beta_2': beta2, 'gamma': gam} - - # EDFA Parameters - noise_fig = 5.5 - gain_zero = 25.0 - gain_tilting = 0.5 - - # Compute the GN model - t = time.time() - nli_cmp, f_nli_cmp, nli_int, f_nli_int = gn.gn_model(spectrum_param, fiber_param, accuracy_param, n_cores) # noqa: E501 - print('Elapsed: %s' % (time.time() - t)) - - # Compute the EDFA profile - gain, g_ase = gn.compute_edfa_profile(gain_zero, gain_tilting, noise_fig, central_freq, fch) - - # Compute the raised cosine comb - f1_array = np.linspace(np.amin(fch), np.amax(fch), 1e3) - gtx = gn.raised_cosine_comb(f1_array, rs, roll_off, fch, power) - gtx = gtx + 10 ** -6 # To avoid log10 issues. - - # Plot the results - plt.figure(1) - plt.plot(f1_array, 10 * np.log10(gtx), '-b', label='WDM comb') - plt.plot(f_nli_cmp, 10 * np.log10(nli_cmp), 'ro', label='GNLI computed') - plt.plot(f_nli_int, 10 * np.log10(nli_int), 'g+', label='GNLI interpolated') - plt.plot(fch, 10 * np.log10(g_ase), 'yo', label='GASE') - plt.ylabel('PSD [dB(W/THz)]') - plt.xlabel('f [THz]') - plt.legend(loc='upper left') - plt.grid() - plt.draw() - plt.show() - - -if __name__ == '__main__': - main() diff --git a/gnpy/examples/architecture.py b/gnpy/examples/architecture.py deleted file mode 100644 index 711baad9..00000000 --- a/gnpy/examples/architecture.py +++ /dev/null @@ -1,314 +0,0 @@ -from networkx import DiGraph -from networkx.algorithms import all_simple_paths -from collections import namedtuple -from scipy.spatial.distance import cdist -from numpy import array -from itertools import product, islice, tee, count -from networkx import (draw_networkx_nodes, - draw_networkx_edges, - draw_networkx_labels, - draw_networkx_edge_labels, - spring_layout) -from matplotlib.pyplot import show, figure -from warnings import catch_warnings, simplefilter -from argparse import ArgumentParser - -from logging import getLogger -logger = getLogger(__name__) - -# remember me? -nwise = lambda g, n=2: zip(*(islice(g, i, None) - for i, g in enumerate(tee(g, n)))) - -# here's an example that includes a little -# bit of complexity to help suss out details -# of the proposed architecture - -# let's pretend there's a working group whose -# job is to minimise latency of a network -# that working group gets the namespace LATENCY - -# they interact with a working group whose -# job is to capture physical details of a network -# such as the geographic location of each node -# and whether nodes are mobile or fixed -# that working group gets the namespace PHYSICAL - -# each working group can put any arbitrary Python -# object as the data for their given namespace - -# the PHYSICAL group captures the following details -# of a NODE: - whether it is mobile or fixed -# - its (x, y) position -# of an EDGE: - the physical length of this connection -# - the speed of transmission over this link - -# NOTE: for this example, we will consider network -# objects to be MUTABLE just to simplify -# the code -# if the graph object is immutable, then -# computations/transformations would return copies -# of the original graph. This can be done easily via -# `G.copy()`, but you'll have to account for the -# semantic difference between shallow-vs-deep copy - -# NOTE: we'll put the Node & Edge information for these -# two working groups inside classes just for the -# purpose of namespacing & just so that we can -# write all the code for this example -# in a single .py file: normally these pieces -# would be in separate modules so that you can -# `from tpe.physical import Node, Edge` - - -class Physical: - # for Node: neither fixed nor position are computed fields - # - fixed cannot be changed (immutable) - # - position can be changed (mutable) - class Node: - def __init__(self, fixed, position): - self._fixed = fixed - self.position = position - - @property - def fixed(self): - return self._fixed - - @property - def position(self): - return self._position - - @position.setter - def position(self, value): - if len(value) != 2: - raise ValueError('position must be (x, y) value') - self._position = value - - def __repr__(self): - return f'Node({self.fixed}, {self.position})' - - # for Edge: - # - speed (m/s) cannot be changed (immutable) - # - distance is COMPUTED - class Edge(namedtuple('Edge', 'speed endpoints')): - def distance(self, graph): - from_node, to_node = self.endpoints - positions = [graph.node[from_node]['physical'].position], \ - [graph.node[to_node]['physical'].position] - return cdist(*positions)[0][0] - - # NOTE: in this above, the computed edge data - # is computed on a per-edge basis - # which forces loops into Python - # however, the PHYSICAL working group has the - # power to decide what their API looks like - # and they could just as easily have provided - # some top-level function as part of their API - # to compute this "update" graph-wise - @staticmethod - def compute_distances(graph): - # XXX: here you can see the general clumsiness of moving - # in and out of the `numpy`/`scipy` computation "domain" - # which exposes another potential flaw in our model - # our model is very "naturalistic": we have Python objects - # that match to real-world objects like routers (nodes) - # and links (edges) - # however, from a computational perspective, we may find - # it more efficient to work within a mathematical - # domain where are objects are simply (sparse) matrices of - # graph data - # moving between these "naturalistic" and "mathematical" - # domains can be very clumsy - # note that there's also clumsiness in that the "naturalistic" - # modelling pushes data storage onto individual Python objects - # such as the edge data dicts whereas the mathematical - # modelling keeps the data in a single place (probably in - # the graph data dict); moving data between the two is also clumsy - data = {k: v['physical'].position for k, v in graph.node.items()} - positions = array(list(data.values())) - distances = cdist(positions, positions) - - # we can either store the above information back onto the graph itself: - ## graph['physical'].distances = distances - - # or back onto the edge data itself: - # for (i, u), (j, v) in product(enumerate(data), enumerate(data)): - # if (u, v) not in graph.edge: - # continue - ## edge, redge = graph.edge[u][v], graph.edge[v][u] - ## dist = distances[i, j] - ## edge['physical'].computed_distance = dist - -# as part of the latency group's API, they specify that: -# - they consume PHYSICAL data -# - they modify PHYSICAl data -# - they do not add their own data - - -class Latency: - @staticmethod - def latency(graph, u, v): - paths = list(all_simple_paths(graph, u, v)) - data = [(graph.get_edge_data(a, b)['physical'].speed, - graph.get_edge_data(a, b)['physical'].distance(graph)) - for path in paths - for a, b in nwise(path)] - return min(distance / speed for speed, distance in data) - - @staticmethod - def total_latency(graph): - return sum(Latency.latency(graph, u, v) for u, v in graph.edges()) - - @staticmethod - def nudge(u, v, precision=4): - (ux, uy), (vx, vy) = u, v - return (round(ux + (vx - ux) / 2, precision), - round(uy + (vy - uy) / 2, precision),) - - @staticmethod - def gradient(graph, nodes): - # independently move each mobile node in the direction of one - # of its neighbors and compute the change in total_latency - for u in nodes: - for v in nodes[u]: - upos, vpos = graph.node[u]['physical'].position, \ - graph.node[v]['physical'].position - new_upos = Latency.nudge(upos, vpos) - before = Latency.total_latency(graph) - graph.node[u]['physical'].position = new_upos - after = Latency.total_latency(graph) - graph.node[u]['physical'].position = upos - logger.info( - f'Gradient {u} ⇋ {v}; u to {new_upos}; grad {after-before}') - yield u, v, new_upos, after - before - - # their public API may include only the following - # function for minimizing latency over a network - @staticmethod - def minimize(graph, *, n=5, threshold=1e-5 * 1e-9, d=None): - mobile = {k: list(graph.edge[k]) for k, v in graph.node.items() - if not v['physical'].fixed} - # XXX: VERY sloppy optimization repeatedly compute gradients - # nudging nodes in the direction of the best latency improvement - for it in count(): - gradients = u, v, pos, grad = min(Latency.gradient(graph, mobile), - key=lambda rv: rv[-1]) - logger.info(f'Best gradient {u} ⇋ {v}; u to {pos}; grad {grad}') - logger.info( - f'Moving {u} in dir of {v} for {grad/1e-12:.2f} ps gain') - graph.node[u]['physical'].position = pos - if d: - d.send((f'step #{it}', graph)) - if it > n or abs(grad) < threshold: # stop after N iterations - break # or if improvement < threshold - -# our Network object is just a networkx.DiGraph -# with some additional storage for graph-level -# data -# NOTE: this may actually need to be a networkx.MultiDiGraph? -# in the event that graphs may have multiple links -# with distance edge data connecting them - - -def Network(*args, data=None, **kwargs): - n = DiGraph() - n.data = {} if data is None else data - return n - - -def draw_changes(): - ''' simple helper to draw changes to the network ''' - fig = figure() - for n in count(): - data = yield - if not data: - break - for i, ax in enumerate(fig.axes): - ax.change_geometry(n + 1, 1, i + 1) - ax = fig.add_subplot(n + 1, 1, n + 1) - title, network, *edge_labels = data - node_data = {u: (u, network.node[u]['physical'].position) - for u in network.nodes()} - edge_data = {(u, v): (network.get_edge_data(u, v)['physical'].distance(network), - network.get_edge_data(u, v)['physical'].speed,) - for u, v in network.edges()} - labels = {u: f'{n}' for u, (n, p) in node_data.items()} - distances = {(u, v): f'dist = {d:.2f} m\nspeed = {s/1e6:.2f}e6 m/s' - for (u, v), (d, s) in edge_data.items()} - - pos = {u: p for u, (_, p) in node_data.items()} - label_pos = pos - - draw_networkx_edges(network, alpha=.25, width=.5, pos=pos, ax=ax) - draw_networkx_nodes(network, node_size=600, alpha=.5, pos=pos, ax=ax) - draw_networkx_labels(network, labels=labels, - pos=pos, label_pos=.3, ax=ax) - if edge_labels: - draw_networkx_edge_labels( - network, edge_labels=distances, pos=pos, font_size=8, ax=ax) - - ax.set_title(title) - ax.set_axis_off() - - with catch_warnings(): - simplefilter('ignore') - show() - yield - - -parser = ArgumentParser() -parser.add_argument('-v', action='count') - -if __name__ == '__main__': - from logging import basicConfig, INFO - args = parser.parse_args() - if args.v: - basicConfig(level=INFO) - - print(''' - Sample network has nodes: - a ⇋ b ⇋ c ⇋ d - - signals a ⇋ b travel at speed of light through copper - signals b ⇋ c travel at speed of light through water - signals c ⇋ d travel at speed of light through water - - all connections are bidirectional - - a, c, d are fixed position - b is mobile - - How can we move b to maximise speed of transmission a ⇋ d? - ''') - - # create network - n = Network() - for name, fixed, (x, y) in [('a', True, (0, 0)), - ('b', False, (5, 5)), - ('c', True, (10, 10)), - ('d', True, (20, 20)), ]: - n.add_node(name, physical=Physical.Node(fixed=fixed, position=(x, y))) - for u, v, speed in [('a', 'b', 299790000), - ('b', 'c', 225000000), - ('c', 'd', 225000000), ]: - n.add_edge(u, v, physical=Physical.Edge(speed=speed, endpoints=(u, v))) - n.add_edge(v, u, physical=Physical.Edge(speed=speed, endpoints=(v, u))) - - d = draw_changes() - next(d) - d.send(('initial', n, True)) - - # core computation - latency = Latency.latency(n, 'a', 'd') - total_latency = Latency.total_latency(n) - Latency.minimize(n, d=d) - total_latency = Latency.total_latency(n) - - print('Before:') - print(f' Current latency from a ⇋ d: {latency/1e-9:.2f} ns') - print(f' Total latency on n: {total_latency/1e-9:.2f} ns') - - print('After:') - print(f' Total latency on n: {total_latency/1e-9:.2f} ns') - - next(d) diff --git a/gnpy/examples/config/config_ex1.json b/gnpy/examples/config/config_ex1.json deleted file mode 100644 index 0fbb83f9..00000000 --- a/gnpy/examples/config/config_ex1.json +++ /dev/null @@ -1,160 +0,0 @@ -{ - "elements": [{ - "id": "span0", - "type": "fiber", - "name": "ggg", - "description": "Booster_Connection ffff", - "parameters": { - "length": 80.0, - "dispersion": null, - "dispersion_slope": 16.7, - "pmd": 0.0, - "loss": 0.2, - "fiber_type": "SMF-28e", - "nonlinear_coef": 0.0 - } - }, - { - "id": "span1", - "type": "fiber", - "name": "", - "description": "Booster_Connection", - "parameters": { - "length": 100.0, - "dispersion": null, - "dispersion_slope": 16.7, - "pmd": 0.0, - "loss": 0.2, - "fiber_type": "SMF-28e", - "nonlinear_coef": 0.0 - } - }, - { - "id": "span2", - "type": "fiber", - "name": "", - "description": "Booster_Connection", - "parameters": { - "length": 80.0, - "dispersion": null, - "dispersion_slope": 16.7, - "pmd": 0.0, - "loss": 0.2, - "fiber_type": "SMF-28e", - "nonlinear_coef": 0.0 - } - }, - { - "id": "amp0", - "name": "Booster", - "description": "This is the booster amp", - "type": "edfa", - "parameters": { - "manufacturer": "acme corp", - "manufacturer_pn": "acme model1", - "manufacturer_gain_profile": "?", - "frequencies": [193.95], - "gain": [15.0], - "nf": [8], - "input_voa": 0.0, - "output_voa": 14.0, - "pin": [-10], - "ptarget": [0], - "pmax": 23.0 - } - }, - { - "id": "amp1", - "name": "line", - "description": "This is the line amp", - "type": "edfa", - "parameters": { - "manufacturer": "acme corp", - "manufacturer_pn": "acme model2", - "manufacturer_gain_profile": null, - "frequencies": [193.95], - "gain": [24.0], - "nf": [5.5], - "input_voa": 0.0, - "output_voa": 4.0, - "pin": [-20], - "ptarget": [0], - "pmax": 23.0 - } - }, - { - "id": "amp2", - "name": "PreAmp", - "description": "This is the preamp", - "type": "edfa", - "parameters": { - "manufacturer": "acme corp", - "manufacturer_pn": "acme model2", - "manufacturer_gain_profile": null, - "frequencies": [193.95], - "gain": [24.0], - "nf": [5.5], - "nf_vs_gain": [[24, 5.5], [25, 5.6]], - "input_voa": 0.0, - "output_voa": 4.0, - "pin": [-20], - "ptarget": [0], - "pmax": 23.0 - } - }, - { - "type": "tx", - "name": "tx1", - "id": "tx1", - "description": "transmitter 1", - "parameters": { - "channels": [{ - "manufacturer": "acme corp", - "manufacturer_pn": "acme model1", - "frequency": 193.95, - "modulation": "QPSK", - "baud_rate": 32.0, - "capacity": 100, - "psd": "acme_model1_mode_1", - "dispersion_precomp": 0, - "fecOH": 25.0, - "filter": "rrc", - "filter_params": [0.4], - "polarization_mux": "interleaved", - "osnr": 40.0, - "power": -10.0 - }, - { - "manufacturer": "acme corp", - "manufacturer_pn": "acme model1", - "frequency": 194.15, - "modulation": "QPSK", - "baud_rate": 32.0, - "capacity": 100, - "psd": "acme_model1_mode_1", - "dispersion_precomp": 0, - "fecOH": 25.0, - "filter": "rrc", - "filter_params": [0.4], - "polarization_mux": "interleaved", - "osnr": 40.0, - "power": -5.0 - } - ] - } - }, - { - "type": "rx", - "name": "rx1", - "id": "rx1", - "description": "receiver 1", - "parameters":{ - "sensitivity": -7 - } - } - ], - "topology": [ - ["tx1", "amp0", "span0", "amp1", "span1", "amp2", "rx1"], - ["tx1", "span2", "rx1"] - ] -} diff --git a/gnpy/examples/sim_ex.py b/gnpy/examples/sim_ex.py deleted file mode 100644 index 4981a96f..00000000 --- a/gnpy/examples/sim_ex.py +++ /dev/null @@ -1,36 +0,0 @@ -from gnpy import Network -from gnpy.utils import read_config -from os.path import realpath, join, dirname - -if __name__ == '__main__': - basedir = dirname(realpath(__file__)) - filename = join(basedir, 'config/config_ex1.json') - config = read_config(filename) - nw = Network(config) - nw.propagate_all_paths() - - # output OSNR propagation - for path in nw.tr_paths: - print(' → '.join(x.id for x in path.path)) - for u, v in path.edge_list: - channels = nw.g[u][v]['channels'] - channel_info = ('\n' + ' ' * 24).join( - ' '.join([f'freq: {x["frequency"]:7.2f}', - f'osnr: {x["osnr"]:7.2f}', - f'power: {x["power"]:7.2f}']) - for x in channels) - print(f'{u.id:^10s} → {v.id:^10s} {channel_info}') - - if 1: # plot network graph - import networkx as nx - import matplotlib.pyplot as plt - layout = nx.spring_layout(nw.g) - nx.draw_networkx_nodes(nw.g, layout, node_size=1000, - node_color='b', alpha=0.2, node_shape='s') - nx.draw_networkx_labels(nw.g, layout) - nx.draw_networkx_edges(nw.g, layout, width=2, - alpha=0.3, edge_color='green') - nx.draw_networkx_edge_labels(nw.g, layout, font_size=10) - plt.rcdefaults() - plt.axis('off') - plt.show() diff --git a/gnpy/gnpy.py b/gnpy/gnpy.py deleted file mode 100644 index de2e46ab..00000000 --- a/gnpy/gnpy.py +++ /dev/null @@ -1,904 +0,0 @@ -# -*- coding: utf-8 -*- - -"""Top-level package for gnpy.""" - -__author__ = """""" -__email__ = '@.com' -__version__ = '0.1.0' - -import numpy as np -import multiprocessing as mp -import scipy.interpolate as interp - -""" -GNPy: a Python 3 implementation of the Gaussian Noise (GN) Model of nonlinear -propagation, developed by the OptCom group, Department of Electronics and -Telecommunications, Politecnico di Torino, Italy -""" - -__credits__ = ["Mattia Cantono", "Vittorio Curri", "Alessio Ferrari"] - - -def raised_cosine_comb(f, rs, roll_off, center_freq, power): - """ Returns an array storing the PSD of a WDM comb of raised cosine shaped - channels at the input frequencies defined in array f - - :param f: Array of frequencies in THz - :param rs: Array of Symbol Rates in TBaud. One Symbol rate for each channel - :param roll_off: Array of roll-off factors [0,1). One per channel - :param center_freq: Array of channels central frequencies in THz. One per channel - :param power: Array of channel powers in W. One per channel - :return: PSD of the WDM comb evaluated over f - """ - ts_arr = 1.0 / rs - passband_arr = (1.0 - roll_off) / (2.0 * ts_arr) - stopband_arr = (1.0 + roll_off) / (2.0 * ts_arr) - g = power / rs - psd = np.zeros(np.shape(f)) - for ind in range(np.size(center_freq)): - f_nch = center_freq[ind] - g_ch = g[ind] - ts = ts_arr[ind] - passband = passband_arr[ind] - stopband = stopband_arr[ind] - ff = np.abs(f - f_nch) - tf = ff - passband - if roll_off[ind] == 0: - psd = np.where(tf <= 0, g_ch, 0.) + psd - else: - psd = g_ch * (np.where(tf <= 0, 1., 0.) + 1.0 / 2.0 * (1 + np.cos(np.pi * ts / roll_off[ind] * - tf)) * np.where(tf > 0, 1., 0.) * - np.where(np.abs(ff) <= stopband, 1., 0.)) + psd - - return psd - - -def fwm_eff(a, Lspan, b2, ff): - """ Computes the four-wave mixing efficiency given the fiber characteristics - over a given frequency set ff - :param a: Fiber loss coefficient in 1/km - :param Lspan: Fiber length in km - :param b2: Fiber Dispersion coefficient in ps/THz/km - :param ff: Array of Frequency points in THz - :return: FWM efficiency rho - """ - rho = np.power(np.abs((1.0 - np.exp(-2.0 * a * Lspan + 1j * 4.0 * np.pi * np.pi * b2 * Lspan * ff)) / ( - 2.0 * a - 1j * 4.0 * np.pi * np.pi * b2 * ff)), 2) - return rho - - -def get_freqarray(f, Bopt, fmax, max_step, f_dense_low, f_dense_up, df_dense): - """ Returns a non-uniformly spaced frequency array useful for fast GN-model. - integration. The frequency array is made of a denser area, sided by two - log-spaced arrays - :param f: Central frequency at which NLI is evaluated in THz - :param Bopt: Total optical bandwidth of the system in THz - :param fmax: Upper limit of the integration domain in THz - :param max_step: Maximum step size for frequency array definition in THz - :param f_dense_low: Lower limit of denser frequency region in THz - :param f_dense_up: Upper limit of denser frequency region in THz - :param df_dense: Step size to be used in the denser frequency region in THz - :return: Non uniformly defined frequency array - """ - f_dense = np.arange(f_dense_low, f_dense_up, df_dense) - k = Bopt / 2.0 / (Bopt / 2.0 - max_step) # Compute Step ratio for log-spaced array definition - if f < 0: - Nlog_short = np.ceil(np.log(fmax / np.abs(f_dense_low)) / np.log(k) + 1.0) - f1_short = -(np.abs(f_dense_low) * np.power(k, np.arange(Nlog_short, 0.0, -1.0) - 1.0)) - k = (Bopt / 2 + (np.abs(f_dense_up) - f_dense_low)) / (Bopt / 2.0 - max_step + (np.abs(f_dense_up) - f_dense_up)) - Nlog_long = np.ceil(np.log((fmax + (np.abs(f_dense_up) - f_dense_up)) / abs(f_dense_up)) * 1.0 / np.log(k) + 1.0) - f1_long = np.abs(f_dense_up) * np.power(k, (np.arange(1, Nlog_long + 1) - 1.0)) - ( - np.abs(f_dense_up) - f_dense_up) - f1_array = np.concatenate([f1_short, f_dense[1:], f1_long]) - else: - Nlog_short = np.ceil(np.log(fmax / np.abs(f_dense_up)) / np.log(k) + 1.0) - f1_short = f_dense_up * np.power(k, np.arange(1, Nlog_short + 1.0) - 1.0) - k = (Bopt / 2.0 + (abs(f_dense_low) + f_dense_low)) / (Bopt / 2.0 - max_step + (abs(f_dense_low) + f_dense_low)) - Nlog_long = np.ceil(np.log((fmax + (np.abs(f_dense_low) + f_dense_low)) / np.abs(f_dense_low)) / np.log(k) + 1) - f1_long = -(np.abs(f_dense_low) * np.power(k, np.arange(Nlog_long, 0, -1) - 1.0)) + ( - abs(f_dense_low) + f_dense_low) - f1_array = np.concatenate([f1_long, f_dense[1:], f1_short]) - return f1_array - - -def GN_integral(b2, Lspan, a_db, gam, f_ch, b_ch, roll_off, power, Nch, model_param): - """ GN_integral computes the GN reference formula via smart brute force integration. The Gaussian Noise model is - applied in its incoherent form (phased-array factor =1). The function computes the integral by columns: for each f1, - a non-uniformly spaced f2 array is generated, and the integrand function is computed there. At the end of the loop - on f1, the overall GNLI is computed. Accuracy can be tuned by operating on model_param argument. - - :param b2: Fiber dispersion coefficient in ps/THz/km. Scalar - :param Lspan: Fiber Span length in km. Scalar - :param a_db: Fiber loss coeffiecient in dB/km. Scalar - :param gam: Fiber nonlinear coefficient in 1/W/km. Scalar - :param f_ch: Baseband channels center frequencies in THz. Array of size 1xNch - :param b_ch: Channels' -3 dB bandwidth. Array of size 1xNch - :param roll_off: Channels' Roll-off factors [0,1). Array of size 1xNch - :param power: Channels' power values in W. Array of size 1xNch - :param Nch: Number of channels. Scalar - :param model_param: Dictionary with model parameters for accuracy tuning - model_param['min_FWM_inv']: Minimum FWM efficiency value to be considered for high density - integration in dB - model_param['n_grid']: Maximum Number of integration points to be used in each frequency slot of - the spectrum - model_param['n_grid_min']: Minimum Number of integration points to be used in each frequency - slot of the spectrum - model_param['f_array']: Frequencies at which evaluate GNLI, expressed in THz - :return: GNLI: power spectral density in W/THz of the nonlinear interference at frequencies model_param['f_array'] - """ - alpha_lin = a_db / 20.0 / np.log10(np.e) # Conversion in linear units 1/km - min_FWM_inv = np.power(10, model_param['min_FWM_inv'] / 10) # Conversion in linear units - n_grid = model_param['n_grid'] - n_grid_min = model_param['n_grid_min'] - f_array = model_param['f_array'] - fmax = (f_ch[-1] - (b_ch[-1] / 2.0)) - (f_ch[0] - (b_ch[0] / 2.0)) # Get frequency limit - f2eval = np.max(np.diff(f_ch)) - Bopt = f2eval * Nch # Overall optical bandwidth [THz] - min_step = f2eval / n_grid # Minimum integration step - max_step = f2eval / n_grid_min # Maximum integration step - f_dense_start = np.abs( - np.sqrt(np.power(alpha_lin, 2) / (4.0 * np.power(np.pi, 4) * b2 * b2) * (min_FWM_inv - 1.0)) / f2eval) - f_ind_eval = 0 - GNLI = np.full(f_array.size, np.nan) # Pre-allocate results - for f in f_array: # Loop over f - f_dense_low = f - f_dense_start - f_dense_up = f + f_dense_start - if f_dense_low < -fmax: - f_dense_low = -fmax - if f_dense_low == 0.0: - f_dense_low = -min_step - if f_dense_up == 0.0: - f_dense_up = min_step - if f_dense_up > fmax: - f_dense_up = fmax - f_dense_width = np.abs(f_dense_up - f_dense_low) - n_grid_dense = np.ceil(f_dense_width / min_step) - df = f_dense_width / n_grid_dense - # Get non-uniformly spaced f1 array - f1_array = get_freqarray(f, Bopt, fmax, max_step, f_dense_low, f_dense_up, df) - G1 = raised_cosine_comb(f1_array, b_ch, roll_off, f_ch, power) # Get corresponding spectrum - Gpart = np.zeros(f1_array.size) # Pre-allocate partial result for inner integral - f_ind = 0 - for f1 in f1_array: # Loop over f1 - if f1 != f: - f_lim = np.sqrt(np.power(alpha_lin, 2) / (4.0 * np.power(np.pi, 4) * b2 * b2) * (min_FWM_inv - 1.0)) / ( - f1 - f) + f - f2_dense_up = np.maximum(f_lim, -f_lim) - f2_dense_low = np.minimum(f_lim, -f_lim) - if f2_dense_low == 0: - f2_dense_low = -min_step - if f2_dense_up == 0: - f2_dense_up = min_step - if f2_dense_low < -fmax: - f2_dense_low = -fmax - if f2_dense_up > fmax: - f2_dense_up = fmax - else: - f2_dense_up = fmax - f2_dense_low = -fmax - f2_dense_width = np.abs(f2_dense_up - f2_dense_low) - n2_grid_dense = np.ceil(f2_dense_width / min_step) - df2 = f2_dense_width / n2_grid_dense - # Get non-uniformly spaced f2 array - f2_array = get_freqarray(f, Bopt, fmax, max_step, f2_dense_low, f2_dense_up, df2) - f2_array = f2_array[f2_array >= f1] # Do not consider points below the bisector of quadrants I and III - if f2_array.size > 0: - G2 = raised_cosine_comb(f2_array, b_ch, roll_off, f_ch, power) # Get spectrum there - f3_array = f1 + f2_array - f # Compute f3 - G3 = raised_cosine_comb(f3_array, b_ch, roll_off, f_ch, power) # Get spectrum over f3 - G = G2 * G3 * G1[f_ind] - if np.count_nonzero(G): - FWM_eff = fwm_eff(alpha_lin, Lspan, b2, (f1 - f) * (f2_array - f)) # Compute FWM efficiency - Gpart[f_ind] = 2.0 * np.trapz(FWM_eff * G, f2_array) # Compute inner integral - f_ind += 1 - # Compute outer integral. Nominal span loss already compensated - GNLI[f_ind_eval] = 16.0 / 27.0 * gam * gam * np.trapz(Gpart, f1_array) - f_ind_eval += 1 # Next frequency - return GNLI # Return GNLI array in W/THz and the array of the corresponding frequencies - - -def compute_psi(b2, l_eff_a, f_ch, channel_index, interfering_index, b_ch): - """ compute_psi computes the psi coefficient of the analytical formula. - - :param b2: Fiber dispersion coefficient in ps/THz/km. Scalar - :param l_eff_a: Asymptotic effective length in km. Scalar - :param f_ch: Baseband channels center frequencies in THz. Array of size 1xNch - :param channel_index: Index of the channel. Scalar - :param interfering_index: Index of the interfering signal. Scalar - :param b_ch: Channels' -3 dB bandwidth [THz]. Array of size 1xNch - :return: psi: the coefficient - """ - b2 = np.abs(b2) - - if channel_index == interfering_index: # The signal interferes with itself - b_ch_sig = b_ch[channel_index] - psi = np.arcsinh(0.5 * np.pi ** 2.0 * l_eff_a * b2 * b_ch_sig ** 2.0) - else: - f_sig = f_ch[channel_index] - b_ch_sig = b_ch[channel_index] - f_int = f_ch[interfering_index] - b_ch_int = b_ch[interfering_index] - del_f = f_sig - f_int - psi = np.arcsinh(np.pi ** 2.0 * l_eff_a * b2 * b_ch_sig * (del_f + 0.5 * b_ch_int)) - psi -= np.arcsinh(np.pi ** 2.0 * l_eff_a * b2 * b_ch_sig * (del_f - 0.5 * b_ch_int)) - - return psi - - -def analytic_formula(ind, b2, l_eff, l_eff_a, gam, f_ch, g_ch, b_ch, n_ch): - """ analytic_formula computes the analytical formula. - - :param ind: index of the channel at which g_nli is computed. Scalar - :param b2: Fiber dispersion coefficient in ps/THz/km. Scalar - :param l_eff: Effective length in km. Scalar - :param l_eff_a: Asymptotic effective length in km. Scalar - :param gam: Fiber nonlinear coefficient in 1/W/km. Scalar - :param f_ch: Baseband channels center frequencies in THz. Array of size 1xNch - :param g_ch: Power spectral density W/THz. Array of size 1xNch - :param b_ch: Channels' -3 dB bandwidth [THz]. Array of size 1xNch - :param n_ch: Number of channels. Scalar - :return: g_nli: power spectral density in W/THz of the nonlinear interference - """ - ch_psd = g_ch[ind] - b2 = abs(b2) - - g_nli = 0.0 - for n in np.arange(0, n_ch): - psi = compute_psi(b2, l_eff_a, f_ch, ind, n, b_ch) - g_nli += g_ch[n] * ch_psd ** 2.0 * psi - - g_nli *= (16.0 / 27.0) * (gam * l_eff) ** 2.0 / (2.0 * np.pi * b2 * l_eff_a) - - return g_nli - - -def gn_analytic(b2, l_span, a_db, gam, f_ch, b_ch, power, n_ch): - """ gn_analytic computes the GN reference formula via analytical solution. - - :param b2: Fiber dispersion coefficient in ps/THz/km. Scalar - :param l_span: Fiber Span length in km. Scalar - :param a_db: Fiber loss coefficient in dB/km. Scalar - :param gam: Fiber nonlinear coefficient in 1/W/km. Scalar - :param f_ch: Baseband channels center frequencies in THz. Array of size 1xNch - :param b_ch: Channels' -3 dB bandwidth [THz]. Array of size 1xNch - :param power: Channels' power values in W. Array of size 1xNch - :param n_ch: Number of channels. Scalar - :return: g_nli: power spectral density in W/THz of the nonlinear interference at frequencies model_param['f_array'] - """ - g_ch = power / b_ch - alpha_lin = a_db / 20.0 / np.log10(np.e) # Conversion in linear units 1/km - l_eff = (1.0 - np.exp(-2.0 * alpha_lin * l_span)) / (2.0 * alpha_lin) # Effective length - l_eff_a = 1.0 / (2.0 * alpha_lin) # Asymptotic effective length - g_nli = np.zeros(f_ch.size) - for ind in np.arange(0, f_ch.size): - g_nli[ind] = analytic_formula(ind, b2, l_eff, l_eff_a, gam, f_ch, g_ch, b_ch, n_ch) - - return g_nli - - -def get_f_computed_interp(f_ch, n_not_interp): - """ get_f_computed_array returns the arrays containing the frequencies at which g_nli is computed and interpolated. - - :param f_ch: the overall frequency array. Array of size 1xnum_ch - :param n_not_interp: the number of points at which g_nli has to be computed - :return: f_nli_comp: the array containing the frequencies at which g_nli is computed - :return: f_nli_interp: the array containing the frequencies at which g_nli is interpolated - """ - num_ch = len(f_ch) - if num_ch < n_not_interp: # It's useless to compute g_nli in a number of points larger than num_ch - n_not_interp = num_ch - - # Compute f_nli_comp - n_not_interp_left = np.ceil((n_not_interp - 1.0) / 2.0) - n_not_interp_right = np.floor((n_not_interp - 1.0) / 2.0) - central_index = len(f_ch) // 2 - print(central_index) - - f_nli_central = np.array([f_ch[central_index]], copy=True) - - if n_not_interp_left > 0: - index = np.linspace(0, central_index - 1, n_not_interp_left, dtype='int') - f_nli_left = np.array(f_ch[index], copy=True) - else: - f_nli_left = np.array([]) - - if n_not_interp_right > 0: - index = np.linspace(-1, -central_index, n_not_interp_right, dtype='int') - f_nli_right = np.array(f_ch[index], copy=True) - f_nli_right = f_nli_right[::-1] # Reverse the order of the array - else: - f_nli_right = np.array([]) - - f_nli_comp = np.concatenate([f_nli_left, f_nli_central, f_nli_right]) - - # Compute f_nli_interp - f_ch_sorted = np.sort(f_ch) - index = np.searchsorted(f_ch_sorted, f_nli_comp) - - f_nli_interp = np.array(f_ch, copy=True) - f_nli_interp = np.delete(f_nli_interp, index) - return f_nli_comp, f_nli_interp - - -def interpolate_in_range(x, y, x_new, kind_interp): - """ Given some samples y of the function y(x), interpolate_in_range returns the interpolation of values y(x_new) - - :param x: The points at which y(x) is evaluated. Array - :param y: The values of y(x). Array - :param x_new: The values at which y(x) has to be interpolated. Array - :param kind_interp: The interpolation method of the function scipy.interpolate.interp1d. String - :return: y_new: the new interpolates samples - """ - if x.size == 1: - y_new = y * np.ones(x_new.size) - elif x.size == 2: - x = np.append(x, x_new[-1]) - y = np.append(y, y[-1]) - func = interp.interp1d(x, y, kind=kind_interp, bounds_error=False) - y_new = func(x_new) - else: - func = interp.interp1d(x, y, kind=kind_interp, bounds_error=False) - y_new = func(x_new) - - return y_new - - -def gn_model(spectrum_param, fiber_param, accuracy_param, n_cores): - """ gn_model can compute the gn model both analytically or through the smart brute force - integral. - - :param spectrum_param: Dictionary with spectrum parameters - spectrum_param['num_ch']: Number of channels. Scalar - spectrum_param['f_ch']: Baseband channels center frequencies in THz. Array of size 1xnum_ch - spectrum_param['b_ch']: Channels' -3 dB band [THz]. Array of size 1xnum_ch - spectrum_param['roll_off']: Channels' Roll-off factors [0,1). Array of size 1xnum_ch - spectrum_param['power']: Channels' power values in W. Array of size 1xnum_ch - :param fiber_param: Dictionary with the parameters of the fiber - fiber_param['alpha']: Fiber loss coefficient in dB/km. Scalar - fiber_param['span_length']: Fiber Span length in km. Scalar - fiber_param['beta_2']: Fiber dispersion coefficient in ps/THz/km. Scalar - fiber_param['gamma']: Fiber nonlinear coefficient in 1/W/km. Scalar - :param accuracy_param: Dictionary with model parameters for accuracy tuning - accuracy_param['is_analytic']: A boolean indicating if you want to compute the NLI through - the analytic formula (is_analytic = True) of the smart brute force integration (is_analytic = - False). Boolean - accuracy_param['points_not_interp']: The number of NLI which will be calculated. Others are - interpolated - accuracy_param['kind_interp']: The kind of interpolation using the function - scipy.interpolate.interp1d - accuracy_param['th_fwm']: Minimum FWM efficiency value to be considered for high density - integration in dB - accuracy_param['n_points']: Maximum Number of integration points to be used in each frequency - slot of the spectrum - accuracy_param['n_points_min']: Minimum Number of integration points to be used in each - frequency - slot of the spectrum - :return: g_nli_comp: the NLI power spectral density in W/THz computed through GN model - :return: f_nli_comp: the frequencies at which g_nli_comp is evaluated - :return: g_nli_interp: the NLI power spectral density in W/THz computed through interpolation of g_nli_comp - :return: f_nli_interp: the frequencies at which g_nli_interp is estimated - """ - # Take signal parameters - num_ch = spectrum_param['num_ch'] - f_ch = spectrum_param['f_ch'] - b_ch = spectrum_param['b_ch'] - roll_off = spectrum_param['roll_off'] - power = spectrum_param['power'] - - # Take fiber parameters - a_db = fiber_param['alpha'] - l_span = fiber_param['span_length'] - beta2 = fiber_param['beta_2'] - gam = fiber_param['gamma'] - - # Take accuracy parameters - is_analytic = accuracy_param['is_analytic'] - n_not_interp = accuracy_param['points_not_interp'] - kind_interp = accuracy_param['kind_interp'] - th_fwm = accuracy_param['th_fwm'] - n_points = accuracy_param['n_points'] - n_points_min = accuracy_param['n_points_min'] - - # Computing NLI - if is_analytic: # Analytic solution - g_nli_comp = gn_analytic(beta2, l_span, a_db, gam, f_ch, b_ch, power, num_ch) - f_nli_comp = np.copy(f_ch) - g_nli_interp = [] - f_nli_interp = [] - else: # Smart brute force integration - f_nli_comp, f_nli_interp = get_f_computed_interp(f_ch, n_not_interp) - - model_param = {'min_FWM_inv': th_fwm, 'n_grid': n_points, 'n_grid_min': n_points_min, - 'f_array': np.array(f_nli_comp, copy=True)} - - g_nli_comp = GN_integral(beta2, l_span, a_db, gam, f_ch, b_ch, roll_off, power, num_ch, model_param) - - # Interpolation - g_nli_interp = interpolate_in_range(f_nli_comp, g_nli_comp, f_nli_interp, kind_interp) - - a_zero = fiber_param['alpha'] * fiber_param['span_length'] - a_tilting = fiber_param['alpha_1st'] * fiber_param['span_length'] - - attenuation_db_comp = compute_attenuation_profile(a_zero, a_tilting, f_nli_comp) - attenuation_lin_comp = 10 ** (-abs(attenuation_db_comp) / 10) - - g_nli_comp *= attenuation_lin_comp - - attenuation_db_interp = compute_attenuation_profile(a_zero, a_tilting, f_nli_interp) - attenuation_lin_interp = 10 ** (-np.abs(attenuation_db_interp) / 10) - - g_nli_interp *= attenuation_lin_interp - - return g_nli_comp, f_nli_comp, g_nli_interp, f_nli_interp - - -def compute_gain_profile(gain_zero, gain_tilting, freq): - """ compute_gain_profile evaluates the gain at the frequencies freq. - - :param gain_zero: the gain at f=0 in dB. Scalar - :param gain_tilting: the gain tilt in dB/THz. Scalar - :param freq: the baseband frequencies at which the gain profile is computed in THz. Array - :return: gain: the gain profile in dB - """ - gain = gain_zero + gain_tilting * freq - return gain - - -def compute_ase_noise(noise_fig, gain, central_freq, freq): - """ compute_ase_noise evaluates the ASE spectral density at the frequencies freq. - - :param noise_fig: the amplifier noise figure in dB. Scalar - :param gain: the gain profile in dB at the frequencies contained in freq array. Array - :param central_freq: the central frequency of the WDM comb. Scalar - :param freq: the baseband frequencies at which the ASE noise is computed in THz. Array - :return: g_ase: the ase noise profile - """ - # the Planck constant in W/THz^2 - planck = (6.62607004 * 1e-34) * 1e24 - - # Conversion from dB to linear - gain_lin = np.power(10, gain / 10.0) - noise_fig_lin = np.power(10, noise_fig / 10.0) - - g_ase = (gain_lin - 1) * noise_fig_lin * planck * (central_freq + freq) - return g_ase - - -def compute_edfa_profile(gain_zero, gain_tilting, noise_fig, central_freq, freq): - """ compute_edfa_profile evaluates the gain profile and the ASE spectral density at the frequencies freq. - - :param gain_zero: the gain at f=0 in dB. Scalar - :param gain_tilting: the gain tilt in dB/THz. Scalar - :param noise_fig: the amplifier noise figure in dB. Scalar - :param central_freq: the central frequency of the WDM comb. Scalar - :param freq: the baseband frequencies at which the ASE noise is computed in THz. Array - :return: gain: the gain profile in dB - :return: g_ase: the ase noise profile in W/THz - """ - gain = compute_gain_profile(gain_zero, gain_tilting, freq) - g_ase = compute_ase_noise(noise_fig, gain, central_freq, freq) - - return gain, g_ase - - -def compute_attenuation_profile(a_zero, a_tilting, freq): - """compute_attenuation_profile returns the attenuation profile at the frequencies freq - - :param a_zero: the attenuation [dB] @ the baseband central frequency. Scalar - :param a_tilting: the attenuation tilt in dB/THz. Scalar - :param freq: the baseband frequencies at which attenuation is computed [THz]. Array - :return: attenuation: the attenuation profile in dB - """ - - if len(freq): - attenuation = a_zero + a_tilting * freq - - # abs in order to avoid ambiguity due to the sign convention - attenuation = abs(attenuation) - else: - attenuation = [] - - return attenuation - - -def passive_component(spectrum, a_zero, a_tilting, freq): - """passive_component updates the input spectrum with the attenuation described by a_zero and a_tilting - - :param spectrum: the WDM spectrum to be attenuated. List of dictionaries - :param a_zero: attenuation at the central frequency [dB]. Scalar - :param a_tilting: attenuation tilting [dB/THz]. Scalar - :param freq: the baseband frequency of each WDM channel [THz]. Array - :return: None - """ - attenuation_db = compute_attenuation_profile(a_zero, a_tilting, freq) - attenuation_lin = 10 ** np.divide(-abs(attenuation_db), 10.0) - - for index, s in enumerate(spectrum['signals']): - spectrum['signals'][index]['p_ch'] *= attenuation_lin[index] - spectrum['signals'][index]['p_nli'] *= attenuation_lin[index] - spectrum['signals'][index]['p_ase'] *= attenuation_lin[index] - - return None - - -def optical_amplifier(spectrum, gain_zero, gain_tilting, noise_fig, central_freq, freq, b_eq): - """optical_amplifier updates the input spectrum with the gain described by gain_zero and gain_tilting plus ASE noise - - :param spectrum: the WDM spectrum to be attenuated. List of dictionaries - :param gain_zero: gain at the central frequency [dB]. Scalar - :param gain_tilting: gain tilting [dB/THz]. Scalar - :param noise_fig: the noise figure of the amplifier [dB]. Scalar - :param central_freq: the central frequency of the optical band [THz]. Scalar - :param freq: the central frequency of each WDM channel [THz]. Array - :param b_eq: the equivalent -3 dB bandwidth of each WDM channel [THZ]. Array - :return: None - """ - - gain_db, g_ase = compute_edfa_profile(gain_zero, gain_tilting, noise_fig, central_freq, freq) - - p_ase = np.multiply(g_ase, b_eq) - - gain_lin = 10 ** np.divide(gain_db, 10.0) - - for index, s in enumerate(spectrum['signals']): - spectrum['signals'][index]['p_ch'] *= gain_lin[index] - spectrum['signals'][index]['p_nli'] *= gain_lin[index] - spectrum['signals'][index]['p_ase'] *= gain_lin[index] - spectrum['signals'][index]['p_ase'] += p_ase[index] - - return None - - -def fiber(spectrum, fiber_param, fiber_length, f_ch, b_ch, roll_off, control_param): - """ fiber updates spectrum with the effects of the fiber - - :param spectrum: the WDM spectrum to be attenuated. List of dictionaries - :param fiber_param: Dictionary with the parameters of the fiber - fiber_param['alpha']: Fiber loss coeffiecient in dB/km. Scalar - fiber_param['beta_2']: Fiber dispersion coefficient in ps/THz/km. Scalar - fiber_param['n_2']: second-order nonlinear refractive index [m^2/W]. Scalar - fiber_param['a_eff']: the effective area of the fiber [um^2]. Scalar - :param fiber_length: the span length [km]. Scalar - :param f_ch: the baseband frequencies of the WDM channels [THz]. Scalar - :param b_ch: the -3 dB bandwidth of each WDM channel [THz]. Array - :param roll_off: the roll off of each WDM channel. Array - :param control_param: Dictionary with the control parameters - control_param['save_each_comp']: a boolean flag. If true, it saves in output folder one spectrum file at - the output of each component, otherwise it saves just the last spectrum. Boolean - control_param['is_linear']: a bool flag. If true, is doesn't compute NLI, if false, OLE will consider - NLI. Boolean - control_param['is_analytic']: a boolean flag. If true, the NLI is computed through the analytic - formula, otherwise it uses the double integral. Warning: the double integral is very slow. Boolean - control_param['points_not_interp']: if the double integral is used, it indicates how much points are - calculated, others will be interpolated. Scalar - control_param['kind_interp']: the interpolation method when double integral is used. String - control_param['th_fwm']: he threshold of the four wave mixing efficiency for the double integral. Scalar - control_param['n_points']: number of points in the high FWM efficiency region in which the double - integral is computed. Scalar - control_param['n_points_min']: number of points in which the double integral is computed in the low FWM - efficiency region. Scalar - control_param['n_cores']: number of cores for parallel computation [not yet implemented]. Scalar - :return: None - """ - - n_cores = control_param['n_cores'] - - # Evaluation of NLI - if not control_param['is_linear']: - num_ch = len(spectrum['signals']) - spectrum_param = { - 'num_ch': num_ch, - 'f_ch': f_ch, - 'b_ch': b_ch, - 'roll_off': roll_off - } - - p_ch = np.zeros(num_ch) - for index, signal in enumerate(spectrum['signals']): - p_ch[index] = signal['p_ch'] - - spectrum_param['power'] = p_ch - fiber_param['span_length'] = fiber_length - - nli_cmp, f_nli_cmp, nli_int, f_nli_int = gn_model(spectrum_param, fiber_param, control_param, n_cores) - f_nli = np.concatenate((f_nli_cmp, f_nli_int)) - order = np.argsort(f_nli) - g_nli = np.concatenate((nli_cmp, nli_int)) - g_nli = np.array(g_nli)[order] - - p_nli = np.multiply(g_nli, b_ch) - - a_zero = fiber_param['alpha'] * fiber_length - a_tilting = fiber_param['alpha_1st'] * fiber_length - - # Apply attenuation - passive_component(spectrum, a_zero, a_tilting, f_ch) - - # Apply NLI - if not control_param['is_linear']: - for index, s in enumerate(spectrum['signals']): - spectrum['signals'][index]['p_nli'] += p_nli[index] - - return None - - -def get_frequencies_wdm(spectrum, sys_param): - """ the function computes the central frequency of the WDM comb and the frequency of each channel. - - :param spectrum: the WDM spectrum to be attenuated. List of dictionaries - :param sys_param: a dictionary containing the system parameters: - 'f0': the starting frequency, i.e the frequency of the first spectral slot [THz] - 'ns': the number of spectral slots. The space between two slots is 6.25 GHz - :return: f_cent: the central frequency of the WDM comb [THz] - :return: f_ch: the baseband frequency of each WDM channel [THz] - """ - - delta_f = 6.25E-3 - # Evaluate the central frequency - f0 = sys_param['f0'] - ns = sys_param['ns'] - - f_cent = f0 + ((ns // 2.0) * delta_f) - - # Evaluate the baseband frequencies - n_ch = spectrum['laser_position'].count(1) - f_ch = np.zeros(n_ch) - count = 0 - for index, bool_laser in enumerate(spectrum['laser_position']): - if bool_laser: - f_ch[count] = (f0 - f_cent) + delta_f * index - count += 1 - - return f_cent, f_ch - - -def get_spectrum_param(spectrum): - """ the function returns the number of WDM channels and 3 arrays containing the power, the equivalent bandwidth - and the roll off of each WDM channel. - - :param spectrum: the WDM spectrum to be attenuated. List of dictionaries - :return: power: the power of each WDM channel [W] - :return: b_eq: the equivalent bandwidth of each WDM channel [THz] - :return: roll_off: the roll off of each WDM channel - :return: p_ase: the power of the ASE noise [W] - :return: p_nli: the power of NLI [W] - :return: n_ch: the number of WDM channels - """ - - n_ch = spectrum['laser_position'].count(1) - roll_off = np.zeros(n_ch) - b_eq = np.zeros(n_ch) - power = np.zeros(n_ch) - p_ase = np.zeros(n_ch) - p_nli = np.zeros(n_ch) - for index, signal in enumerate(spectrum['signals']): - b_eq[index] = signal['b_ch'] - roll_off[index] = signal['roll_off'] - power[index] = signal['p_ch'] - p_ase[index] = signal['p_ase'] - p_nli[index] = signal['p_nli'] - - return power, b_eq, roll_off, p_ase, p_nli, n_ch - - -def change_component_ref(f_ref, link, fibers): - """ it updates the reference frequency of OA gain, PC attenuation and fiber attenuation coefficient - - :param f_ref: the new reference frequency [THz]. Scalar - :param link: the link structure. A list in which each element indicates one link component (PC, OA or fiber). List - :param fibers: a dictionary containing the description of each fiber type. Dictionary - :return: None - """ - - light_speed = 3e8 # [m/s] - - # Change reference to the central frequency f_cent for OA and PC - for index, component in enumerate(link): - if component['comp_cat'] is 'PC': - - old_loss = component['loss'] - delta_loss = component['loss_tlt'] - old_ref = component['ref_freq'] - new_loss = old_loss + delta_loss * (f_ref - old_ref) - - link[index]['ref_freq'] = f_ref - link[index]['loss'] = new_loss - - elif component['comp_cat'] is 'OA': - - old_gain = component['gain'] - delta_gain = component['gain_tlt'] - old_ref = component['ref_freq'] - new_gain = old_gain + delta_gain * (f_ref - old_ref) - - link[index]['ref_freq'] = f_ref - link[index]['gain'] = new_gain - - elif not component['comp_cat'] is 'fiber': - - error_string = 'Error in link structure: the ' + str(index+1) + '-th component have unknown category \n'\ - + 'allowed values are (case sensitive): PC, OA and fiber' - print(error_string) - - # Change reference to the central frequency f_cent for fiber - for fib_type in fibers: - old_ref = fibers[fib_type]['reference_frequency'] - old_alpha = fibers[fib_type]['alpha'] - alpha_1st = fibers[fib_type]['alpha_1st'] - new_alpha = old_alpha + alpha_1st * (f_ref - old_ref) - - fibers[fib_type]['reference_frequency'] = f_ref - fibers[fib_type]['alpha'] = new_alpha - - fibers[fib_type]['gamma'] = (2 * np.pi) * (f_ref / light_speed) * \ - (fibers[fib_type]['n_2'] / fibers[fib_type]['a_eff']) * 1e27 - - return None - - -def compute_and_save_osnr(spectrum, flag_save=False, file_name='00', output_path='./output/'): - """ Given the spectrum structure, the function returns the linear and non linear OSNR. If the boolean variable - flag_save is true, the function also saves the osnr values for the central channel, the osnr for each channel and - spectrum in a file with the name file_name, in the folder indicated by output_path - - :param spectrum: the spectrum dictionary containing the laser position (a list of boolean) and the list signals, - which is a list of dictionaries (one for each channel) containing: - 'b_ch': the -3 dB bandwidth of the signal [THz] - 'roll_off': the roll off of the signal - 'p_ch': the signal power [W] - 'p_nli': the equivalent nli power [W] - 'p_ase': the ASE noise [W] - :param flag_save: if True it saves all the data, otherwise it doesn't - :param file_name: the name of the file in which the variables are saved - :param output_path: the path in which you want to save the file - :return: osnr_lin_db: the linear OSNR [dB] - :return: osnr_nli_db: the non-linear equivalent OSNR (in linear units, NOT in [dB] - """ - - # Get the parameters from spectrum - p_ch, b_eq, roll_off, p_ase, p_nli, n_ch = get_spectrum_param(spectrum) - - # Compute the linear OSNR - if (p_ase == 0).any(): - osnr_lin = np.zeros(n_ch) - for index, p_noise in enumerate(p_ase): - if p_noise == 0: - osnr_lin[index] = float('inf') - else: - osnr_lin[index] = p_ch[index] / p_noise - - else: - osnr_lin = np.divide(p_ch, p_ase) - - # Compute the non-linear OSNR - if ((p_ase + p_nli) == 0).any(): - osnr_nli = np.zeros(n_ch) - for index, p_noise in enumerate(p_ase + p_nli): - - if p_noise == 0: - osnr_nli[index] = float('inf') - else: - osnr_nli[index] = p_ch[index] / p_noise - else: - osnr_nli = np.divide(p_ch, p_ase + p_nli) - - # Compute linear and non linear OSNR for the central channel - ind_c = n_ch // 2 - osnr_lin_central_channel_db = 10 * np.log10(osnr_lin[ind_c]) - osnr_nl_central_channel_db = 10 * np.log10(osnr_nli[ind_c]) - - # Conversion in dB - osnr_lin_db = 10 * np.log10(osnr_lin) - osnr_nli_db = 10 * np.log10(osnr_nli) - - # Save spectrum, the non linear OSNR and the linear OSNR - out_fle_name = output_path + file_name - - if flag_save: - - f = open(out_fle_name, 'w') - f.write(''.join(('# Output parameters. The values of OSNR are evaluated in the -3 dB channel band', '\n\n'))) - f.write(''.join(('osnr_lin_central_channel_db = ', str(osnr_lin_central_channel_db), '\n\n'))) - f.write(''.join(('osnr_nl_central_channel_db = ', str(osnr_nl_central_channel_db), '\n\n'))) - f.write(''.join(('osnr_lin_db = ', str(osnr_lin_db), '\n\n'))) - f.write(''.join(('osnr_nl_db = ', str(osnr_nli_db), '\n\n'))) - f.write(''.join(('spectrum = ', str(spectrum), '\n'))) - - f.close() - - return osnr_nli_db, osnr_lin_db - - -def ole(spectrum, link, fibers, sys_param, control_param, output_path='./output/'): - """ The function takes the input spectrum, the link description, the fiber description, the system parameters, - the control parameters and a string describing the destination folder of the output files. After the function is - executed the spectrum is updated with all the impairments of the link. The function also returns the linear and - non linear OSNR, computed in the equivalent bandwidth. - - :param spectrum: the spectrum dictionary containing the laser position (a list of boolean) and the list signals, - which is a list of dictionaries (one for each channel) containing: - 'b_ch': the -3 dB bandwidth of the signal [THz] - 'roll_off': the roll off of the signal - 'p_ch': the signal power [W] - 'p_nli': the equivalent nli power [W] - 'p_ase': the ASE noise [W] - :param link: the link structure. A list in which each element is a dictionary and it indicates one link component - (PC, OA or fiber). List - :param fibers: fibers is a dictionary containing a dictionary for each kind of fiber. Each dictionary has to report: - reference_frequency: the frequency at which the parameters are evaluated [THz] - alpha: the attenuation coefficient [dB/km] - alpha_1st: the first derivative of alpha indicating the alpha slope [dB/km/THz] - if you assume a flat attenuation with respect to the frequency you put it as zero - beta_2: the dispersion coefficient [ps^2/km] - n_2: second-order nonlinear refractive index [m^2/W] - a typical value is 2.5E-20 m^2/W - a_eff: the effective area of the fiber [um^2] - :param sys_param: a dictionary containing the general system parameters: - f0: the starting frequency of the laser grid used to describe the WDM system - ns: the number of 6.25 GHz slots in the grid - :param control_param: a dictionary containing the following parameters: - save_each_comp: a boolean flag. If true, it saves in output folder one spectrum file at the output of each - component, otherwise it saves just the last spectrum - is_linear: a bool flag. If true, is doesn't compute NLI, if false, OLE will consider NLI - is_analytic: a boolean flag. If true, the NLI is computed through the analytic formula, otherwise it uses - the double integral. Warning: the double integral is very slow. - points_not_interp: if the double integral is used, it indicates how much points are calculated, others will - be interpolated - kind_interp: a string indicating the interpolation method for the double integral - th_fwm: the threshold of the four wave mixing efficiency for the double integral - n_points: number of points in which the double integral is computed in the high FWM efficiency region - n_points_min: number of points in which the double integral is computed in the low FWM efficiency region - n_cores: number of cores for parallel computation [not yet implemented] - :param output_path: the path in which the output files are saved. String - :return: osnr_nli_db: an array containing the non-linear OSNR [dB], one value for each WDM channel. Array - :return: osnr_lin_db: an array containing the linear OSNR [dB], one value for each WDM channel. Array - """ - - # Take control parameters - flag_save_each_comp = control_param['save_each_comp'] - - # Evaluate frequency parameters - f_cent, f_ch = get_frequencies_wdm(spectrum, sys_param) - - # Evaluate spectrum parameters - power, b_eq, roll_off, p_ase, p_nli, n_ch = get_spectrum_param(spectrum) - - # Change reference to the central frequency f_cent for OA, PC and fibers - change_component_ref(f_cent, link, fibers) - - # Emulate the link - for component in link: - if component['comp_cat'] is 'PC': - a_zero = component['loss'] - a_tilting = component['loss_tlt'] - - passive_component(spectrum, a_zero, a_tilting, f_ch) - - elif component['comp_cat'] is 'OA': - gain_zero = component['gain'] - gain_tilting = component['gain_tlt'] - noise_fig = component['noise_figure'] - - optical_amplifier(spectrum, gain_zero, gain_tilting, noise_fig, f_cent, f_ch, b_eq) - - elif component['comp_cat'] is 'fiber': - fiber_type = component['fiber_type'] - fiber_param = fibers[fiber_type] - fiber_length = component['length'] - - fiber(spectrum, fiber_param, fiber_length, f_ch, b_eq, roll_off, control_param) - - else: - error_string = 'Error in link structure: the ' + component['comp_cat'] + ' category is unknown \n' \ - + 'allowed values are (case sensitive): PC, OA and fiber' - print(error_string) - - if flag_save_each_comp: - f_name = 'Output from component ID #' + component['comp_id'] - osnr_nli_db, osnr_lin_db = \ - compute_and_save_osnr(spectrum, flag_save=True, file_name=f_name, output_path=output_path) - - osnr_nli_db, osnr_lin_db = \ - compute_and_save_osnr(spectrum, flag_save=True, file_name='link_output', output_path=output_path) - - return osnr_nli_db, osnr_lin_db diff --git a/gnpy/input/spectrum_in.py b/gnpy/input/spectrum_in.py deleted file mode 100644 index f43778e0..00000000 --- a/gnpy/input/spectrum_in.py +++ /dev/null @@ -1,29 +0,0 @@ -# coding=utf-8 -""" spectrum_in.py describes the input spectrum of OLE, i.e. spectrum. - spectrum is a dictionary containing two fields: - laser_position: a list of bool indicating if a laser is turned on or not - signals: a list of dictionaries each of them, describing one channel in the WDM comb - - The laser_position is defined respect to a frequency grid of 6.25 GHz space and the first slot is at the - frequency described by the variable f0 in the dictionary sys_param in the file "general_parameters.py" - - Each dictionary element of the list 'signals' describes the profile of a WDM channel: - b_ch: the -3 dB channel bandwidth (for a root raised cosine, it is equal to the symbol rate) - roll_off: the roll off parameter of the root raised cosine shape - p_ch: the channel power [W] - p_nli: power of accumulated NLI in b_ch [W] - p_ase: power of accumulated ASE noise in b_ch [W] -""" - -n_ch = 41 - -spectrum = { - 'laser_position': [0, 0, 0, 1, 0, 0, 0, 0] * n_ch, - 'signals': [{ - 'b_ch': 0.032, - 'roll_off': 0.15, - 'p_ch': 1E-3, - 'p_nli': 0, - 'p_ase': 0 - } for _ in range(n_ch)] -} diff --git a/gnpy/network_elements.py b/gnpy/network_elements.py deleted file mode 100644 index 77df8103..00000000 --- a/gnpy/network_elements.py +++ /dev/null @@ -1,150 +0,0 @@ -from networkx import DiGraph, all_simple_paths -from collections import defaultdict -from itertools import product - -import gnpy -from . import utils - - -class Opath: - - def __init__(self, nw, path): - self.nw, self.path = nw, path - - self.edge_list = {(elem, path[en + 1]) - for en, elem in enumerate(path[:-1])} - self.elem_dict = {elem: self.find_io_edges(elem) - for elem in self.path} - - def find_io_edges(self, elem): - iedges = set(self.nw.g.in_edges(elem) ) & self.edge_list - oedges = set(self.nw.g.out_edges(elem)) & self.edge_list - return {'in': iedges, 'out': oedges} - - def propagate(self): - for elem in self.path: - elem.propagate(path=self) - - -class Network: - - def __init__(self, config): - self.config = config - self.nw_elems = defaultdict(list) - self.g = DiGraph() - - for elem in self.config['elements']: - ne_type = TYPE_MAP[elem['type']] - params = elem.pop('parameters') - ne = ne_type(self, **elem, **params) - self.nw_elems[ne_type].append(ne) - self.g.add_node(ne) - - for gpath in self.config['topology']: - for u, v in utils.nwise(gpath): - n0 = utils.find_by_node_id(self.g, u) - n1 = utils.find_by_node_id(self.g, v) - self.g.add_edge(n0, n1, channels=[]) - - # define all possible paths between tx's and rx's - self.tr_paths = [] - for tx, rx in product(self.nw_elems[Tx], self.nw_elems[Rx]): - for spath in all_simple_paths(self.g, tx, rx): - self.tr_paths.append(Opath(self, spath)) - - def propagate_all_paths(self): - for opath in self.tr_paths: - opath.propagate() - - -class NetworkElement: - - def __init__(self, nw, *, id, type, name, description, **kwargs): - self.nw = nw - self.id, self.type = id, type - self.name, self.description = name, description - - def fetch_edge(self, edge): - u, v = edge - return self.nw.g[u][v] - - def edge_dict(self, chan, osnr, d_power): - return {'frequency': chan['frequency'], - 'osnr': osnr if osnr else chan['osnr'], - 'power': chan['power'] + d_power} - - def __repr__(self): - return f'NetworkElement(id={self.id}, type={self.type})' - - -class Fiber(NetworkElement): - - def __init__(self, *args, length, loss, **kwargs): - super().__init__(*args, **kwargs) - self.length = length - self.loss = loss - - def propagate(self, path): - attn = self.length * self.loss - - for oedge in path.elem_dict[self]['out']: - edge = self.fetch_edge(oedge) - for pedge in (self.fetch_edge(x) - for x in path.elem_dict[self]['in']): - for chan in pedge['channels']: - dct = self.edge_dict(chan, None, -attn) - edge['channels'].append(dct) - - -class Edfa(NetworkElement): - - def __init__(self, *args, gain, nf, **kwargs): - super().__init__(*args, **kwargs) - self.gain = gain - self.nf = nf - - def propagate(self, path): - gain = self.gain[0] - for inedge in path.elem_dict[self]['in']: - - in_channels = self.fetch_edge(inedge)['channels'] - for chan in in_channels: - osnr = utils.chan_osnr(chan, self) - for edge in (self.fetch_edge(x) - for x in path.elem_dict[self]['out']): - dct = self.edge_dict(chan, osnr, gain) - edge['channels'].append(dct) - - -class Tx(NetworkElement): - - def __init__(self, *args, channels, **kwargs): - super().__init__(*args, **kwargs) - self.channels = channels - - def propagate(self, path): - for edge in (self.fetch_edge(x) for x in path.elem_dict[self]['out']): - for chan in self.channels: - dct = self.edge_dict(chan, None, 0) - edge['channels'].append(dct) - - -class Rx(NetworkElement): - - def __init__(self, *args, sensitivity, **kwargs): - super().__init__(*args, **kwargs) - self.sensitivity = sensitivity - - def propagate(self, path): - self.channels = {} - for iedge in path.elem_dict[self]['in']: - edge = self.fetch_edge(iedge) - self.channels[path] = edge['channels'] - - -TYPE_MAP = { - 'fiber': Fiber, - 'tx': Tx, - 'rx': Rx, - 'edfa': Edfa, -} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #000 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #000 deleted file mode 100644 index 08d95209..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #000 +++ /dev/null @@ -1,21 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = inf - -osnr_nl_central_channel_db = 30.1323954499 - -osnr_lin_db = [ inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf - inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf - inf inf inf inf inf inf inf inf inf inf inf] - -osnr_nl_db = [ 31.69922151 31.10200314 30.84343237 30.6844807 30.57272041 - 30.48837332 30.42192513 30.36809885 30.32367901 30.28657145 - 30.25534367 30.2289789 30.20673512 30.18805965 30.17253526 - 30.15984491 30.14974812 30.14206485 30.13666451 30.1334585 - 30.13239545 30.1334585 30.13666451 30.14206485 30.14974812 - 30.15984491 30.17253526 30.18805965 30.20673512 30.2289789 - 30.25534367 30.28657145 30.32367901 30.36809885 30.42192513 - 30.48837332 30.57272041 30.6844807 30.84343237 31.10200314 - 31.69922151] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.7620417736498842e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.7588916339732908e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.2348702894102491e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.541849790170285e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.7645164112162291e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.9364014066621709e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.0741820354655847e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.1873469021830433e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.2817977024377939e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.3614442436256126e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.4289999467616479e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.4864147782585101e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.5351271287615462e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.5762182338732439e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.610510855193152e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.6386344237500019e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.6610690941725243e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.678175989461703e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.6902180344027836e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.69737409146712e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.6997480803028857e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.69737409146712e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.6902180344027819e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.678175989461703e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.6610690941725243e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.6386344237499986e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.610510855193152e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.5762182338732406e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.5351271287615462e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.4864147782585084e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.4289999467616462e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.3614442436256093e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.2817977024377906e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.18734690218304e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.0741820354655863e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.9364014066621709e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.7645164112162258e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.5418497901702784e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.2348702894102491e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.7588916339732809e-09, 'p_ase': 0.0}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.7620417736498776e-09, 'p_ase': 0.0}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #001 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #001 deleted file mode 100644 index cd8d6667..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #001 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 28.9219058797 - -osnr_nl_central_channel_db = 26.4748122308 - -osnr_lin_db = [ 28.9444557 28.94332542 28.94219544 28.94106575 28.93993636 - 28.93880726 28.93767845 28.93654994 28.93542172 28.93429379 - 28.93316615 28.93203881 28.93091176 28.92978501 28.92865854 - 28.92753237 28.92640649 28.9252809 28.9241556 28.92303059 - 28.92190588 28.92078146 28.91965732 28.91853348 28.91740993 - 28.91628667 28.9151637 28.91404102 28.91291863 28.91179653 - 28.91067472 28.9095532 28.90843197 28.90731103 28.90619038 - 28.90507002 28.90394995 28.90283016 28.90171067 28.90059146 - 28.89947254] - -osnr_nl_db = [ 27.09668371 26.87960029 26.77929479 26.71557074 26.66974296 - 26.63454305 26.60640094 26.58330503 26.56401403 26.547711 - 26.53383218 26.52197434 26.51184129 26.50321113 26.49591549 - 26.48982588 26.48484441 26.48089754 26.4779317 26.47591038 - 26.47481223 26.47462996 26.47537004 26.477053 26.47971446 - 26.48340703 26.48820322 26.49419969 26.50152357 26.51034156 - 26.5208735 26.53341304 26.54836003 26.56627374 26.58796473 - 26.61466486 26.64837068 26.69262597 26.75464607 26.85298033 - 27.06723606] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.7620417736498839e-07, 'p_ase': 1.2751299014133424e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.758891633973291e-07, 'p_ase': 1.2754618044298155e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.234870289410249e-07, 'p_ase': 1.2757937074462888e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.5418497901702848e-07, 'p_ase': 1.2761256104627619e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.7645164112162293e-07, 'p_ase': 1.2764575134792353e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.9364014066621707e-07, 'p_ase': 1.2767894164957086e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.0741820354655844e-07, 'p_ase': 1.2771213195121819e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.1873469021830436e-07, 'p_ase': 1.2774532225286553e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.2817977024377942e-07, 'p_ase': 1.2777851255451284e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.3614442436256125e-07, 'p_ase': 1.2781170285616017e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.4289999467616479e-07, 'p_ase': 1.2784489315780751e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.48641477825851e-07, 'p_ase': 1.2787808345945484e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.5351271287615459e-07, 'p_ase': 1.2791127376110217e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.5762182338732447e-07, 'p_ase': 1.2794446406274948e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.6105108551931517e-07, 'p_ase': 1.2797765436439682e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.6386344237500027e-07, 'p_ase': 1.2801084466604415e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.6610690941725237e-07, 'p_ase': 1.2804403496769146e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.6781759894617028e-07, 'p_ase': 1.2807722526933882e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.6902180344027836e-07, 'p_ase': 1.2811041557098615e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.697374091467119e-07, 'p_ase': 1.2814360587263348e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.6997480803028854e-07, 'p_ase': 1.2817679617428082e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.697374091467119e-07, 'p_ase': 1.2820998647592813e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.6902180344027815e-07, 'p_ase': 1.2824317677757546e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.6781759894617028e-07, 'p_ase': 1.282763670792228e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.6610690941725237e-07, 'p_ase': 1.2830955738087013e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.6386344237499985e-07, 'p_ase': 1.2834274768251746e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.6105108551931517e-07, 'p_ase': 1.2837593798416477e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.5762182338732404e-07, 'p_ase': 1.2840912828581211e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.5351271287615459e-07, 'p_ase': 1.2844231858745942e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.4864147782585089e-07, 'p_ase': 1.2847550888910677e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.4289999467616457e-07, 'p_ase': 1.2850869919075411e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.3614442436256094e-07, 'p_ase': 1.2854188949240142e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.281797702437791e-07, 'p_ase': 1.2857507979404875e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.1873469021830404e-07, 'p_ase': 1.2860827009569607e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.0741820354655865e-07, 'p_ase': 1.286414603973434e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.9364014066621707e-07, 'p_ase': 1.2867465069899075e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.7645164112162262e-07, 'p_ase': 1.2870784100063807e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.5418497901702785e-07, 'p_ase': 1.287410313022854e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.234870289410249e-07, 'p_ase': 1.2877422160393273e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.7588916339732804e-07, 'p_ase': 1.2880741190558007e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.7620417736498776e-07, 'p_ase': 1.288406022072274e-06}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #002 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #002 deleted file mode 100644 index 64f5b949..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #002 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 28.9219058797 - -osnr_nl_central_channel_db = 24.9191253337 - -osnr_lin_db = [ 28.9444557 28.94332542 28.94219544 28.94106575 28.93993636 - 28.93880726 28.93767845 28.93654994 28.93542172 28.93429379 - 28.93316615 28.93203881 28.93091176 28.92978501 28.92865854 - 28.92753237 28.92640649 28.9252809 28.9241556 28.92303059 - 28.92190588 28.92078146 28.91965732 28.91853348 28.91740993 - 28.91628667 28.9151637 28.91404102 28.91291863 28.91179653 - 28.91067472 28.9095532 28.90843197 28.90731103 28.90619038 - 28.90507002 28.90394995 28.90283016 28.90171067 28.90059146 - 28.89947254] - -osnr_nl_db = [ 25.80450952 25.48637303 25.34205691 25.25129062 25.18648999 - 25.1370136 25.09766452 25.0655274 25.03880961 25.01633491 - 24.99729359 24.98110783 24.96735395 24.9557151 24.94595123 - 24.93787934 24.93136022 24.92628941 24.92259088 24.92021292 - 24.91912533 24.91931796 24.92080017 24.92360133 24.92777231 - 24.93338817 24.94055237 24.94940292 24.96012144 24.97294632 - 24.98819237 25.00628068 25.02778568 25.05351248 25.08463081 - 25.12292352 25.17129032 25.23490044 25.3243388 25.46704225 - 25.78262124] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3524083547299768e-08, 'p_ase': 1.2751299014133424e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5517783267946582e-08, 'p_ase': 1.2754618044298155e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6469740578820498e-08, 'p_ase': 1.2757937074462889e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.708369958034057e-08, 'p_ase': 1.276125610462762e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7529032822432458e-08, 'p_ase': 1.2764575134792352e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7872802813324342e-08, 'p_ase': 1.2767894164957086e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8148364070931169e-08, 'p_ase': 1.2771213195121819e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8374693804366087e-08, 'p_ase': 1.2774532225286552e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8563595404875588e-08, 'p_ase': 1.2777851255451284e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8722888487251225e-08, 'p_ase': 1.2781170285616017e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8857999893523296e-08, 'p_ase': 1.2784489315780751e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.897282955651702e-08, 'p_ase': 1.2787808345945484e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9070254257523092e-08, 'p_ase': 1.2791127376110218e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9152436467746488e-08, 'p_ase': 1.2794446406274949e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9221021710386304e-08, 'p_ase': 1.2797765436439683e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9277268847500007e-08, 'p_ase': 1.2801084466604415e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9322138188345049e-08, 'p_ase': 1.2804403496769146e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9356351978923406e-08, 'p_ase': 1.2807722526933881e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9380436068805567e-08, 'p_ase': 1.2811041557098615e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.939474818293424e-08, 'p_ase': 1.2814360587263348e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9399496160605771e-08, 'p_ase': 1.2817679617428082e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.939474818293424e-08, 'p_ase': 1.2820998647592813e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9380436068805564e-08, 'p_ase': 1.2824317677757547e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9356351978923406e-08, 'p_ase': 1.282763670792228e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9322138188345049e-08, 'p_ase': 1.2830955738087013e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9277268847499997e-08, 'p_ase': 1.2834274768251747e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9221021710386304e-08, 'p_ase': 1.2837593798416477e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9152436467746481e-08, 'p_ase': 1.284091282858121e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9070254257523092e-08, 'p_ase': 1.2844231858745942e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8972829556517017e-08, 'p_ase': 1.2847550888910677e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8857999893523292e-08, 'p_ase': 1.2850869919075411e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8722888487251219e-08, 'p_ase': 1.2854188949240142e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8563595404875581e-08, 'p_ase': 1.2857507979404876e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.837469380436608e-08, 'p_ase': 1.2860827009569607e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8148364070931173e-08, 'p_ase': 1.2864146039734341e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7872802813324342e-08, 'p_ase': 1.2867465069899076e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7529032822432452e-08, 'p_ase': 1.2870784100063808e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7083699580340557e-08, 'p_ase': 1.2874103130228539e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6469740578820498e-08, 'p_ase': 1.2877422160393273e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5517783267946562e-08, 'p_ase': 1.2880741190558006e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3524083547299755e-08, 'p_ase': 1.288406022072274e-08}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #003 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #003 deleted file mode 100644 index d9d9448c..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #003 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 25.911605923 - -osnr_nl_central_channel_db = 23.4645122741 - -osnr_lin_db = [ 25.93415574 25.93302547 25.93189549 25.9307658 25.9296364 - 25.9285073 25.9273785 25.92624998 25.92512176 25.92399383 - 25.9228662 25.92173886 25.92061181 25.91948505 25.91835858 - 25.91723241 25.91610653 25.91498094 25.91385564 25.91273064 - 25.91160592 25.9104815 25.90935737 25.90823353 25.90710998 - 25.90598671 25.90486375 25.90374107 25.90261868 25.90149658 - 25.90037477 25.89925325 25.89813202 25.89701108 25.89589043 - 25.89477006 25.89364999 25.89253021 25.89141071 25.8902915 - 25.88917258] - -osnr_nl_db = [ 24.08638375 23.86930034 23.76899484 23.70527078 23.659443 - 23.62424309 23.59610098 23.57300507 23.55371407 23.53741105 - 23.52353222 23.51167439 23.50154133 23.49291117 23.48561554 - 23.47952592 23.47454445 23.47059758 23.46763174 23.46561043 - 23.46451227 23.46433001 23.46507009 23.46675304 23.4694145 - 23.47310707 23.47790326 23.48389973 23.49122361 23.5000416 - 23.51057354 23.52311308 23.53806008 23.55597378 23.57766478 - 23.60436491 23.63807072 23.68232601 23.74434611 23.84268037 - 24.0569361 ] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3524083547299768e-06, 'p_ase': 2.5502598028266847e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5517783267946582e-06, 'p_ase': 2.550923608859631e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6469740578820498e-06, 'p_ase': 2.5515874148925776e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.708369958034057e-06, 'p_ase': 2.5522512209255239e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7529032822432459e-06, 'p_ase': 2.5529150269584705e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7872802813324341e-06, 'p_ase': 2.5535788329914172e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8148364070931169e-06, 'p_ase': 2.5542426390243639e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8374693804366087e-06, 'p_ase': 2.5549064450573105e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8563595404875588e-06, 'p_ase': 2.5555702510902568e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8722888487251225e-06, 'p_ase': 2.5562340571232034e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8857999893523296e-06, 'p_ase': 2.5568978631561501e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.897282955651702e-06, 'p_ase': 2.5575616691890968e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9070254257523092e-06, 'p_ase': 2.5582254752220434e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9152436467746489e-06, 'p_ase': 2.5588892812549897e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9221021710386303e-06, 'p_ase': 2.5595530872879368e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9277268847500005e-06, 'p_ase': 2.560216893320883e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9322138188345047e-06, 'p_ase': 2.5608806993538293e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9356351978923406e-06, 'p_ase': 2.5615445053867763e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9380436068805567e-06, 'p_ase': 2.562208311419723e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9394748182934238e-06, 'p_ase': 2.5628721174526697e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9399496160605771e-06, 'p_ase': 2.5635359234856163e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9394748182934238e-06, 'p_ase': 2.5641997295185626e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9380436068805563e-06, 'p_ase': 2.5648635355515093e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9356351978923406e-06, 'p_ase': 2.5655273415844559e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9322138188345047e-06, 'p_ase': 2.5661911476174026e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9277268847499997e-06, 'p_ase': 2.5668549536503493e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9221021710386303e-06, 'p_ase': 2.5675187596832955e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9152436467746481e-06, 'p_ase': 2.5681825657162422e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9070254257523092e-06, 'p_ase': 2.5688463717491884e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8972829556517018e-06, 'p_ase': 2.5695101777821355e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8857999893523291e-06, 'p_ase': 2.5701739838150822e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8722888487251219e-06, 'p_ase': 2.5708377898480284e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8563595404875582e-06, 'p_ase': 2.5715015958809751e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8374693804366081e-06, 'p_ase': 2.5721654019139213e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8148364070931173e-06, 'p_ase': 2.572829207946868e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7872802813324341e-06, 'p_ase': 2.5734930139798151e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7529032822432452e-06, 'p_ase': 2.5741568200127617e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7083699580340557e-06, 'p_ase': 2.574820626045708e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6469740578820498e-06, 'p_ase': 2.5754844320786546e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5517783267946561e-06, 'p_ase': 2.5761482381116013e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3524083547299755e-06, 'p_ase': 2.576812044144548e-06}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #004 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #004 deleted file mode 100644 index f827ac08..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #004 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 25.911605923 - -osnr_nl_central_channel_db = 22.6173802381 - -osnr_lin_db = [ 25.93415574 25.93302547 25.93189549 25.9307658 25.9296364 - 25.9285073 25.9273785 25.92624998 25.92512176 25.92399383 - 25.9228662 25.92173886 25.92061181 25.91948505 25.91835858 - 25.91723241 25.91610653 25.91498094 25.91385564 25.91273064 - 25.91160592 25.9104815 25.90935737 25.90823353 25.90710998 - 25.90598671 25.90486375 25.90374107 25.90261868 25.90149658 - 25.90037477 25.89925325 25.89813202 25.89701108 25.89589043 - 25.89477006 25.89364999 25.89253021 25.89141071 25.8902915 - 25.88917258] - -osnr_nl_db = [ 23.39241465 23.11705581 22.99119097 22.91170821 22.85479968 - 22.81124858 22.77654284 22.74814662 22.72449833 22.70457204 - 22.68766079 22.67325961 22.66099794 22.65059847 22.64185097 - 22.6345951 22.62870881 22.62410048 22.62070341 22.61847217 - 22.61738024 22.61741863 22.61859551 22.62093654 22.62448626 - 22.62931039 22.6354995 22.6431744 22.652494 22.66366684 - 22.67696811 22.69276571 22.71156115 22.73405669 22.76127161 - 22.79475758 22.83703461 22.89258684 22.97057443 23.09465858 - 23.36730326] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.0286125320949652e-08, 'p_ase': 2.5502598028266847e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.3276674901919874e-08, 'p_ase': 2.5509236088596311e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.4704610868230747e-08, 'p_ase': 2.5515874148925777e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.5625549370510855e-08, 'p_ase': 2.5522512209255241e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.6293549233648687e-08, 'p_ase': 2.5529150269584704e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.6809204219986511e-08, 'p_ase': 2.5535788329914171e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.7222546106396754e-08, 'p_ase': 2.5542426390243638e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.756204070654913e-08, 'p_ase': 2.5549064450573105e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.7845393107313382e-08, 'p_ase': 2.5555702510902568e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.8084332730876839e-08, 'p_ase': 2.5562340571232035e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.8286999840284944e-08, 'p_ase': 2.5568978631561502e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.845924433477553e-08, 'p_ase': 2.5575616691890969e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.860538138628464e-08, 'p_ase': 2.5582254752220435e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.8728654701619735e-08, 'p_ase': 2.5588892812549899e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.8831532565579456e-08, 'p_ase': 2.5595530872879369e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.8915903271250011e-08, 'p_ase': 2.5602168933208829e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.8983207282517575e-08, 'p_ase': 2.5608806993538293e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.9034527968385107e-08, 'p_ase': 2.5615445053867763e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.9070654103208349e-08, 'p_ase': 2.562208311419723e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.9092122274401358e-08, 'p_ase': 2.5628721174526696e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.9099244240908657e-08, 'p_ase': 2.5635359234856163e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.9092122274401358e-08, 'p_ase': 2.5641997295185627e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.9070654103208346e-08, 'p_ase': 2.5648635355515093e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.9034527968385107e-08, 'p_ase': 2.565527341584456e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.8983207282517575e-08, 'p_ase': 2.5661911476174027e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.8915903271249997e-08, 'p_ase': 2.5668549536503494e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.8831532565579456e-08, 'p_ase': 2.5675187596832954e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.8728654701619722e-08, 'p_ase': 2.5681825657162421e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.860538138628464e-08, 'p_ase': 2.5688463717491884e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.8459244334775527e-08, 'p_ase': 2.5695101777821354e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.8286999840284937e-08, 'p_ase': 2.5701739838150821e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.8084332730876826e-08, 'p_ase': 2.5708377898480285e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.7845393107313372e-08, 'p_ase': 2.5715015958809751e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.756204070654912e-08, 'p_ase': 2.5721654019139215e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.7222546106396761e-08, 'p_ase': 2.5728292079468682e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.6809204219986511e-08, 'p_ase': 2.5734930139798152e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.6293549233648677e-08, 'p_ase': 2.5741568200127619e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.5625549370510835e-08, 'p_ase': 2.5748206260457079e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.4704610868230747e-08, 'p_ase': 2.5754844320786546e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.3276674901919841e-08, 'p_ase': 2.5761482381116012e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.0286125320949632e-08, 'p_ase': 2.5768120441445479e-08}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #005 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #005 deleted file mode 100644 index 17bdf72f..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #005 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 24.1506933325 - -osnr_nl_central_channel_db = 21.7035996836 - -osnr_lin_db = [ 24.17324315 24.17211288 24.1709829 24.16985321 24.16872381 - 24.16759471 24.1664659 24.16533739 24.16420917 24.16308124 - 24.16195361 24.16082627 24.15969922 24.15857246 24.15744599 - 24.15631982 24.15519394 24.15406835 24.15294305 24.15181805 - 24.15069333 24.14956891 24.14844478 24.14732094 24.14619738 - 24.14507412 24.14395115 24.14282848 24.14170609 24.14058399 - 24.13946218 24.13834066 24.13721943 24.13609849 24.13497784 - 24.13385747 24.1327374 24.13161761 24.13049812 24.12937891 - 24.12825999] - -osnr_nl_db = [ 22.32547116 22.10838775 22.00808225 21.94435819 21.89853041 - 21.8633305 21.83518839 21.81209248 21.79280148 21.77649846 - 21.76261963 21.7507618 21.74062874 21.73199858 21.72470295 - 21.71861333 21.71363186 21.70968499 21.70671915 21.70469784 - 21.70359968 21.70341742 21.7041575 21.70584045 21.70850191 - 21.71219448 21.71699067 21.72298714 21.73031102 21.73912901 - 21.74966095 21.76220049 21.77714749 21.79506119 21.81675218 - 21.84345231 21.87715813 21.92141342 21.98343352 22.08176778 - 22.29602351] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.0286125320949652e-06, 'p_ase': 3.8253897042400269e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.3276674901919874e-06, 'p_ase': 3.8263854132894462e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.4704610868230746e-06, 'p_ase': 3.8273811223388664e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.5625549370510856e-06, 'p_ase': 3.8283768313882858e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.6293549233648686e-06, 'p_ase': 3.829372540437706e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.6809204219986513e-06, 'p_ase': 3.8303682494871262e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.7222546106396755e-06, 'p_ase': 3.8313639585365456e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.7562040706549129e-06, 'p_ase': 3.8323596675859658e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.7845393107313383e-06, 'p_ase': 3.8333553766353852e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.808433273087684e-06, 'p_ase': 3.8343510856848054e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.8286999840284943e-06, 'p_ase': 3.8353467947342256e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.8459244334775531e-06, 'p_ase': 3.836342503783645e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.8605381386284642e-06, 'p_ase': 3.8373382128330652e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.8728654701619734e-06, 'p_ase': 3.8383339218824845e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.8831532565579457e-06, 'p_ase': 3.8393296309319047e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.891590327125001e-06, 'p_ase': 3.8403253399813249e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.8983207282517573e-06, 'p_ase': 3.8413210490307435e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.9034527968385106e-06, 'p_ase': 3.8423167580801645e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.9070654103208351e-06, 'p_ase': 3.8433124671295847e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.9092122274401357e-06, 'p_ase': 3.8443081761790041e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.9099244240908658e-06, 'p_ase': 3.8453038852284243e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.9092122274401357e-06, 'p_ase': 3.8462995942778437e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.9070654103208347e-06, 'p_ase': 3.8472953033272639e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.9034527968385106e-06, 'p_ase': 3.8482910123766841e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.8983207282517573e-06, 'p_ase': 3.8492867214261035e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.8915903271249998e-06, 'p_ase': 3.8502824304755237e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.8831532565579457e-06, 'p_ase': 3.851278139524943e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.8728654701619721e-06, 'p_ase': 3.8522738485743632e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.8605381386284642e-06, 'p_ase': 3.8532695576237826e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.8459244334775527e-06, 'p_ase': 3.8542652666732028e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.8286999840284938e-06, 'p_ase': 3.855260975722623e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.8084332730876827e-06, 'p_ase': 3.8562566847720424e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.784539310731337e-06, 'p_ase': 3.8572523938214626e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.756204070654912e-06, 'p_ase': 3.858248102870882e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.722254610639676e-06, 'p_ase': 3.8592438119203022e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.6809204219986513e-06, 'p_ase': 3.8602395209697224e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.6293549233648677e-06, 'p_ase': 3.8612352300191426e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.5625549370510834e-06, 'p_ase': 3.862230939068562e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.4704610868230746e-06, 'p_ase': 3.8632266481179822e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.327667490191984e-06, 'p_ase': 3.8642223571674015e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.0286125320949631e-06, 'p_ase': 3.8652180662168218e-06}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #006 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #006 deleted file mode 100644 index 0e84b7f1..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #006 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 24.1506933325 - -osnr_nl_central_channel_db = 21.1209009293 - -osnr_lin_db = [ 24.17324315 24.17211288 24.1709829 24.16985321 24.16872381 - 24.16759471 24.1664659 24.16533739 24.16420917 24.16308124 - 24.16195361 24.16082627 24.15969922 24.15857246 24.15744599 - 24.15631982 24.15519394 24.15406835 24.15294305 24.15181805 - 24.15069333 24.14956891 24.14844478 24.14732094 24.14619738 - 24.14507412 24.14395115 24.14282848 24.14170609 24.14058399 - 24.13946218 24.13834066 24.13721943 24.13609849 24.13497784 - 24.13385747 24.1327374 24.13161761 24.13049812 24.12937891 - 24.12825999] - -osnr_nl_db = [ 21.85073091 21.59270396 21.47438935 21.39954612 21.34589277 - 21.30479112 21.2720082 21.2451632 21.22278914 21.20392174 - 21.18789623 21.17423759 21.16259714 21.15271387 21.14438984 - 21.13747406 21.13185159 21.12743609 21.12416472 21.12199468 - 21.12090093 21.12087501 21.12192455 21.12407374 21.12736447 - 21.13185863 21.13764146 21.14482669 21.15356388 21.16404925 - 21.17654171 21.1913873 21.20905757 21.2302126 21.25581004 - 21.28730705 21.32706866 21.3793002 21.45258378 21.56905632 - 21.82432332] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.7048167094599537e-08, 'p_ase': 3.8253897042400269e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.1035566535893163e-08, 'p_ase': 3.8263854132894464e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.2939481157640997e-08, 'p_ase': 3.8273811223388666e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.416739916068114e-08, 'p_ase': 3.8283768313882861e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.5058065644864916e-08, 'p_ase': 3.8293725404377063e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.5745605626648683e-08, 'p_ase': 3.8303682494871265e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.6296728141862339e-08, 'p_ase': 3.8313639585365454e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.6749387608732173e-08, 'p_ase': 3.8323596675859662e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.7127190809751176e-08, 'p_ase': 3.8333553766353851e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.744577697450245e-08, 'p_ase': 3.8343510856848052e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.7715999787046591e-08, 'p_ase': 3.8353467947342254e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.794565911303404e-08, 'p_ase': 3.836342503783645e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.8140508515046191e-08, 'p_ase': 3.8373382128330651e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.8304872935492975e-08, 'p_ase': 3.8383339218824847e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.8442043420772608e-08, 'p_ase': 3.8393296309319048e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.8554537695000014e-08, 'p_ase': 3.840325339981325e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.8644276376690097e-08, 'p_ase': 3.8413210490307432e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.8712703957846812e-08, 'p_ase': 3.8423167580801647e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.8760872137611134e-08, 'p_ase': 3.8433124671295849e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.878949636586848e-08, 'p_ase': 3.8443081761790044e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.8798992321211543e-08, 'p_ase': 3.8453038852284246e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.878949636586848e-08, 'p_ase': 3.8462995942778435e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.8760872137611128e-08, 'p_ase': 3.8472953033272637e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.8712703957846812e-08, 'p_ase': 3.8482910123766839e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.8644276376690097e-08, 'p_ase': 3.8492867214261034e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.8554537694999994e-08, 'p_ase': 3.8502824304755236e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.8442043420772608e-08, 'p_ase': 3.8512781395249431e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.8304872935492962e-08, 'p_ase': 3.8522738485743633e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.8140508515046191e-08, 'p_ase': 3.8532695576237828e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.7945659113034034e-08, 'p_ase': 3.854265266673203e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.7715999787046585e-08, 'p_ase': 3.8552609757226232e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.7445776974502437e-08, 'p_ase': 3.8562566847720427e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.7127190809751163e-08, 'p_ase': 3.8572523938214629e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.674938760873216e-08, 'p_ase': 3.8582481028708817e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.6296728141862345e-08, 'p_ase': 3.8592438119203026e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.5745605626648683e-08, 'p_ase': 3.8602395209697228e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.5058065644864903e-08, 'p_ase': 3.861235230019143e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.4167399160681113e-08, 'p_ase': 3.8622309390685618e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.2939481157640997e-08, 'p_ase': 3.863226648117982e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.1035566535893124e-08, 'p_ase': 3.8642223571674015e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 2.704816709459951e-08, 'p_ase': 3.8652180662168217e-08}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #007 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #007 deleted file mode 100644 index d8fcfa8d..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #007 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 22.9013059664 - -osnr_nl_central_channel_db = 20.4542123175 - -osnr_lin_db = [ 22.92385579 22.92272551 22.92159553 22.92046584 22.91933645 - 22.91820735 22.91707854 22.91595002 22.9148218 22.91369388 - 22.91256624 22.9114389 22.91031185 22.90918509 22.90805863 - 22.90693245 22.90580657 22.90468098 22.90355569 22.90243068 - 22.90130597 22.90018154 22.89905741 22.89793357 22.89681002 - 22.89568676 22.89456379 22.89344111 22.89231872 22.89119662 - 22.89007481 22.88895329 22.88783206 22.88671112 22.88559047 - 22.88447011 22.88335003 22.88223025 22.88111075 22.87999155 - 22.87887263] - -osnr_nl_db = [ 21.07608379 20.85900038 20.75869488 20.69497082 20.64914304 - 20.61394314 20.58580102 20.56270512 20.54341412 20.52711109 - 20.51323226 20.50137443 20.49124138 20.48261121 20.47531558 - 20.46922597 20.4642445 20.46029762 20.45733179 20.45531047 - 20.45421232 20.45403005 20.45477013 20.45645308 20.45911454 - 20.46280712 20.4676033 20.47359978 20.48092366 20.48974164 - 20.50027359 20.51281313 20.52776012 20.54567383 20.56736482 - 20.59406495 20.62777077 20.67202605 20.73404616 20.83238042 - 21.04663614] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.7048167094599536e-06, 'p_ase': 5.1005196056533694e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.1035566535893164e-06, 'p_ase': 5.1018472177192619e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.2939481157640996e-06, 'p_ase': 5.1031748297851553e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.4167399160681139e-06, 'p_ase': 5.1045024418510477e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.5058065644864917e-06, 'p_ase': 5.1058300539169411e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.5745605626648683e-06, 'p_ase': 5.1071576659828344e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.6296728141862338e-06, 'p_ase': 5.1084852780487277e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.6749387608732174e-06, 'p_ase': 5.1098128901146211e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.7127190809751177e-06, 'p_ase': 5.1111405021805136e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.744577697450245e-06, 'p_ase': 5.1124681142464069e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.7715999787046591e-06, 'p_ase': 5.1137957263123002e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.794565911303404e-06, 'p_ase': 5.1151233383781936e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.8140508515046192e-06, 'p_ase': 5.1164509504440869e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.8304872935492979e-06, 'p_ase': 5.1177785625099794e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.8442043420772607e-06, 'p_ase': 5.1191061745758727e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.8554537695000011e-06, 'p_ase': 5.120433786641766e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.8644276376690095e-06, 'p_ase': 5.1217613987076585e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.8712703957846811e-06, 'p_ase': 5.1230890107735527e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.8760872137611134e-06, 'p_ase': 5.124416622839446e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.8789496365868476e-06, 'p_ase': 5.1257442349053394e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.8798992321211542e-06, 'p_ase': 5.1270718469712327e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.8789496365868476e-06, 'p_ase': 5.1283994590371252e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.8760872137611126e-06, 'p_ase': 5.1297270711030185e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.8712703957846811e-06, 'p_ase': 5.1310546831689118e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.8644276376690095e-06, 'p_ase': 5.1323822952348052e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.8554537694999994e-06, 'p_ase': 5.1337099073006985e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.8442043420772607e-06, 'p_ase': 5.135037519366591e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.8304872935492962e-06, 'p_ase': 5.1363651314324843e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.8140508515046192e-06, 'p_ase': 5.1376927434983768e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.7945659113034036e-06, 'p_ase': 5.139020355564271e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.7715999787046583e-06, 'p_ase': 5.1403479676301643e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.7445776974502437e-06, 'p_ase': 5.1416755796960568e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.7127190809751164e-06, 'p_ase': 5.1430031917619501e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.6749387608732162e-06, 'p_ase': 5.1443308038278426e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.6296728141862346e-06, 'p_ase': 5.145658415893736e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.5745605626648683e-06, 'p_ase': 5.1469860279596301e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.5058065644864905e-06, 'p_ase': 5.1483136400255235e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.4167399160681114e-06, 'p_ase': 5.149641252091416e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.2939481157640996e-06, 'p_ase': 5.1509688641573093e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.1035566535893122e-06, 'p_ase': 5.1522964762232026e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 2.704816709459951e-06, 'p_ase': 5.153624088289096e-06}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #008 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #008 deleted file mode 100644 index f10e021d..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #008 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 22.9013059664 - -osnr_nl_central_channel_db = 20.010023833 - -osnr_lin_db = [ 22.92385579 22.92272551 22.92159553 22.92046584 22.91933645 - 22.91820735 22.91707854 22.91595002 22.9148218 22.91369388 - 22.91256624 22.9114389 22.91031185 22.90918509 22.90805863 - 22.90693245 22.90580657 22.90468098 22.90355569 22.90243068 - 22.90130597 22.90018154 22.89905741 22.89793357 22.89681002 - 22.89568676 22.89456379 22.89344111 22.89231872 22.89119662 - 22.89007481 22.88895329 22.88783206 22.88671112 22.88559047 - 22.88447011 22.88335003 22.88223025 22.88111075 22.87999155 - 22.87887263] - -osnr_nl_db = [ 20.7152526 20.46661134 20.35240348 20.28008928 20.22821284 - 20.18844963 20.15671819 20.13072201 20.10904555 20.09075809 - 20.07521786 20.06196616 20.05066626 20.04106601 20.0329742 - 20.02624495 20.02076718 20.01645746 20.01325505 20.01111857 - 20.01002383 20.00996262 20.01094232 20.01298627 20.01613495 - 20.02044813 20.02600816 20.03292491 20.04134282 20.05145142 - 20.06350072 20.07782486 20.09487904 20.1153004 20.14001307 - 20.17042277 20.2088104 20.25922997 20.32995044 20.44228463 - 20.6881454 ] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.3810208868249418e-08, 'p_ase': 5.1005196056533694e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.8794458169866452e-08, 'p_ase': 5.1018472177192621e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.1174351447051242e-08, 'p_ase': 5.1031748297851555e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.2709248950851422e-08, 'p_ase': 5.1045024418510482e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.3822582056081145e-08, 'p_ase': 5.1058300539169409e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.4682007033310856e-08, 'p_ase': 5.1071576659828342e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.5370910177327923e-08, 'p_ase': 5.1084852780487276e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.5936734510915217e-08, 'p_ase': 5.1098128901146209e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.640898851218897e-08, 'p_ase': 5.1111405021805136e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.6807221218128061e-08, 'p_ase': 5.112468114246407e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.7144999733808239e-08, 'p_ase': 5.1137957263123004e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.7432073891292547e-08, 'p_ase': 5.1151233383781937e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.7675635643807736e-08, 'p_ase': 5.1164509504440871e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.7881091169366226e-08, 'p_ase': 5.1177785625099798e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.805255427596576e-08, 'p_ase': 5.1191061745758731e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.8193172118750018e-08, 'p_ase': 5.1204337866417658e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.830534547086262e-08, 'p_ase': 5.1217613987076585e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.8390879947308517e-08, 'p_ase': 5.1230890107735525e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.8451090172013919e-08, 'p_ase': 5.1244166228394459e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.8486870457335602e-08, 'p_ase': 5.1257442349053393e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.8498740401514428e-08, 'p_ase': 5.1270718469712326e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.8486870457335602e-08, 'p_ase': 5.1283994590371253e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.8451090172013913e-08, 'p_ase': 5.1297270711030187e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.8390879947308517e-08, 'p_ase': 5.131054683168912e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.830534547086262e-08, 'p_ase': 5.1323822952348054e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.8193172118749991e-08, 'p_ase': 5.1337099073006988e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.805255427596576e-08, 'p_ase': 5.1350375193665908e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.78810911693662e-08, 'p_ase': 5.1363651314324841e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.7675635643807736e-08, 'p_ase': 5.1376927434983768e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.7432073891292541e-08, 'p_ase': 5.1390203555642709e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.7144999733808233e-08, 'p_ase': 5.1403479676301642e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.6807221218128048e-08, 'p_ase': 5.1416755796960569e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.640898851218895e-08, 'p_ase': 5.1430031917619503e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.5936734510915197e-08, 'p_ase': 5.144330803827843e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.537091017732793e-08, 'p_ase': 5.1456584158937363e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.4682007033310856e-08, 'p_ase': 5.1469860279596304e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.3822582056081125e-08, 'p_ase': 5.1483136400255237e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.2709248950851395e-08, 'p_ase': 5.1496412520914157e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.1174351447051242e-08, 'p_ase': 5.1509688641573091e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.8794458169866406e-08, 'p_ase': 5.1522964762232025e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 3.3810208868249385e-08, 'p_ase': 5.1536240882890958e-08}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #009 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #009 deleted file mode 100644 index 9cebf1f4..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #009 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 21.9322058363 - -osnr_nl_central_channel_db = 19.4851121874 - -osnr_lin_db = [ 21.95475566 21.95362538 21.9524954 21.95136571 21.95023632 - 21.94910722 21.94797841 21.94684989 21.94572167 21.94459375 - 21.94346611 21.94233877 21.94121172 21.94008496 21.9389585 - 21.93783232 21.93670644 21.93558085 21.93445556 21.93333055 - 21.93220584 21.93108141 21.92995728 21.92883344 21.92770989 - 21.92658663 21.92546366 21.92434098 21.92321859 21.92209649 - 21.92097468 21.91985316 21.91873193 21.91761099 21.91649034 - 21.91536998 21.9142499 21.91313012 21.91201062 21.91089142 - 21.9097725 ] - -osnr_nl_db = [ 20.10698366 19.88990025 19.78959475 19.72587069 19.68004291 - 19.64484301 19.61670089 19.59360499 19.57431399 19.55801096 - 19.54413213 19.5322743 19.52214125 19.51351108 19.50621545 - 19.50012584 19.49514437 19.49119749 19.48823166 19.48621034 - 19.48511219 19.48492992 19.48567 19.48735295 19.49001441 - 19.49370699 19.49850317 19.50449965 19.51182353 19.52064151 - 19.53117346 19.543713 19.55865999 19.5765737 19.59826469 - 19.62496482 19.65867064 19.70292592 19.76494603 19.86328029 - 20.07753601] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.381020886824942e-06, 'p_ase': 6.375649507066712e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.879445816986645e-06, 'p_ase': 6.3773090221490776e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.1174351447051246e-06, 'p_ase': 6.3789685372314441e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.2709248950851423e-06, 'p_ase': 6.3806280523138097e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.3822582056081149e-06, 'p_ase': 6.3822875673961761e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.4682007033310852e-06, 'p_ase': 6.3839470824785426e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.537091017732792e-06, 'p_ase': 6.3856065975609099e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.593673451091522e-06, 'p_ase': 6.3872661126432763e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.6408988512188971e-06, 'p_ase': 6.3889256277256419e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.6807221218128061e-06, 'p_ase': 6.3905851428080084e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.7144999733808236e-06, 'p_ase': 6.3922446578903749e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.7432073891292549e-06, 'p_ase': 6.3939041729727422e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.7675635643807738e-06, 'p_ase': 6.3955636880551086e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.7881091169366223e-06, 'p_ase': 6.3972232031374742e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.8052554275965761e-06, 'p_ase': 6.3988827182198415e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.8193172118750016e-06, 'p_ase': 6.4005422333022071e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.8305345470862616e-06, 'p_ase': 6.4022017483845736e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.8390879947308516e-06, 'p_ase': 6.4038612634669409e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.8451090172013918e-06, 'p_ase': 6.4055207785493073e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.8486870457335604e-06, 'p_ase': 6.4071802936316746e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.8498740401514425e-06, 'p_ase': 6.4088398087140411e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.8486870457335604e-06, 'p_ase': 6.4104993237964067e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.845109017201391e-06, 'p_ase': 6.4121588388787731e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.8390879947308516e-06, 'p_ase': 6.4138183539611396e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.8305345470862616e-06, 'p_ase': 6.4154778690435069e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.819317211874999e-06, 'p_ase': 6.4171373841258734e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.8052554275965761e-06, 'p_ase': 6.418796899208239e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.7881091169366198e-06, 'p_ase': 6.4204564142906054e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.7675635643807738e-06, 'p_ase': 6.422115929372971e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.743207389129254e-06, 'p_ase': 6.4237754444553392e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.7144999733808236e-06, 'p_ase': 6.4254349595377056e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.6807221218128052e-06, 'p_ase': 6.4270944746200712e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.6408988512188954e-06, 'p_ase': 6.4287539897024377e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.5936734510915195e-06, 'p_ase': 6.4304135047848033e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.5370910177327928e-06, 'p_ase': 6.4320730198671697e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.4682007033310852e-06, 'p_ase': 6.4337325349495379e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.3822582056081123e-06, 'p_ase': 6.4353920500319043e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.2709248950851398e-06, 'p_ase': 6.43705156511427e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.1174351447051246e-06, 'p_ase': 6.4387110801966364e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.8794458169866407e-06, 'p_ase': 6.4403705952790037e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 3.3810208868249386e-06, 'p_ase': 6.4420301103613702e-06}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #010 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #010 deleted file mode 100644 index cf5e2196..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #010 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 21.9322058363 - -osnr_nl_central_channel_db = 19.1262011202 - -osnr_lin_db = [ 21.95475566 21.95362538 21.9524954 21.95136571 21.95023632 - 21.94910722 21.94797841 21.94684989 21.94572167 21.94459375 - 21.94346611 21.94233877 21.94121172 21.94008496 21.9389585 - 21.93783232 21.93670644 21.93558085 21.93445556 21.93333055 - 21.93220584 21.93108141 21.92995728 21.92883344 21.92770989 - 21.92658663 21.92546366 21.92434098 21.92321859 21.92209649 - 21.92097468 21.91985316 21.91873193 21.91761099 21.91649034 - 21.91536998 21.9142499 21.91313012 21.91201062 21.91089142 - 21.9097725 ] - -osnr_nl_db = [ 19.81596014 19.57320395 19.46157766 19.39085522 19.34009807 - 19.30117845 19.27011004 19.24464927 19.22341297 19.20549152 - 19.19025761 19.17726284 19.16617797 19.15675645 19.14881131 - 19.14219987 19.13681351 19.13257061 19.12941176 19.12729649 - 19.12620112 19.12611762 19.12705319 19.12903066 19.13208961 - 19.13628848 19.14170781 19.148455 19.15667131 19.16654202 - 19.17831158 19.19230659 19.20897201 19.22893053 19.2530852 - 19.28280943 19.32033126 19.36960971 19.43871734 19.54845076 - 19.78841509] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.0572250641899303e-08, 'p_ase': 6.3756495070667126e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.6553349803839742e-08, 'p_ase': 6.3773090221490771e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.9409221736461501e-08, 'p_ase': 6.3789685372314443e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.1251098741021703e-08, 'p_ase': 6.3806280523138102e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.2587098467297381e-08, 'p_ase': 6.3822875673961761e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.3618408439973029e-08, 'p_ase': 6.3839470824785433e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.4445092212793508e-08, 'p_ase': 6.3856065975609105e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.5124081413098267e-08, 'p_ase': 6.3872661126432763e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.5690786214626764e-08, 'p_ase': 6.3889256277256422e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.6168665461753672e-08, 'p_ase': 6.3905851428080081e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.6573999680569887e-08, 'p_ase': 6.3922446578903753e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.6918488669551054e-08, 'p_ase': 6.3939041729727425e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.721076277256928e-08, 'p_ase': 6.3955636880551083e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.745730940323947e-08, 'p_ase': 6.3972232031374742e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.7663065131158912e-08, 'p_ase': 6.3988827182198414e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.7831806542500021e-08, 'p_ase': 6.4005422333022073e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.7966414565035143e-08, 'p_ase': 6.4022017483845732e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.8069055936770221e-08, 'p_ase': 6.4038612634669417e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.8141308206416705e-08, 'p_ase': 6.4055207785493075e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.8184244548802723e-08, 'p_ase': 6.4071802936316747e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.8198488481817314e-08, 'p_ase': 6.4088398087140406e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.8184244548802723e-08, 'p_ase': 6.4104993237964065e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.8141308206416698e-08, 'p_ase': 6.4121588388787737e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.8069055936770221e-08, 'p_ase': 6.4138183539611395e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.7966414565035143e-08, 'p_ase': 6.4154778690435067e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.7831806542499988e-08, 'p_ase': 6.4171373841258739e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.7663065131158912e-08, 'p_ase': 6.4187968992082385e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.7457309403239437e-08, 'p_ase': 6.4204564142906057e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.721076277256928e-08, 'p_ase': 6.4221159293729715e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.6918488669551047e-08, 'p_ase': 6.4237754444553387e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.6573999680569887e-08, 'p_ase': 6.4254349595377059e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.6168665461753666e-08, 'p_ase': 6.4270944746200718e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.569078621462675e-08, 'p_ase': 6.4287539897024377e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.5124081413098234e-08, 'p_ase': 6.4304135047848036e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.4445092212793515e-08, 'p_ase': 6.4320730198671694e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.3618408439973029e-08, 'p_ase': 6.4337325349495379e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.2587098467297348e-08, 'p_ase': 6.4353920500319051e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.1251098741021677e-08, 'p_ase': 6.4370515651142697e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.9409221736461501e-08, 'p_ase': 6.4387110801966369e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.6553349803839689e-08, 'p_ase': 6.4403705952790041e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.0572250641899264e-08, 'p_ase': 6.4420301103613699e-08}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #011 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #011 deleted file mode 100644 index 8e2a99f7..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #011 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 21.1403933758 - -osnr_nl_central_channel_db = 18.6932997269 - -osnr_lin_db = [ 21.1629432 21.16181292 21.16068294 21.15955325 21.15842386 - 21.15729476 21.15616595 21.15503743 21.15390921 21.15278129 - 21.15165365 21.15052631 21.14939926 21.1482725 21.14714604 - 21.14601986 21.14489398 21.14376839 21.1426431 21.14151809 - 21.14039338 21.13926895 21.13814482 21.13702098 21.13589743 - 21.13477417 21.1336512 21.13252852 21.13140613 21.13028403 - 21.12916222 21.1280407 21.12691947 21.12579853 21.12467788 - 21.12355752 21.12243744 21.12131766 21.12019816 21.11907895 - 21.11796004] - -osnr_nl_db = [ 19.3151712 19.09808779 18.99778229 18.93405823 18.88823045 - 18.85303055 18.82488843 18.80179253 18.78250153 18.7661985 - 18.75231967 18.74046184 18.73032879 18.72169862 18.71440299 - 18.70831338 18.70333191 18.69938503 18.6964192 18.69439788 - 18.69329973 18.69311746 18.69385754 18.69554049 18.69820195 - 18.70189453 18.70669071 18.71268719 18.72001107 18.72882905 - 18.739361 18.75190054 18.76684753 18.78476124 18.80645223 - 18.83315236 18.86685818 18.91111346 18.97313356 19.07146783 - 19.28572355] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.0572250641899304e-06, 'p_ase': 7.6507794084800554e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.655334980383974e-06, 'p_ase': 7.6527708265788925e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.94092217364615e-06, 'p_ase': 7.6547622446777329e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.1251098741021703e-06, 'p_ase': 7.6567536627765716e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.258709846729738e-06, 'p_ase': 7.658745080875412e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.3618408439973026e-06, 'p_ase': 7.6607364989742525e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.4445092212793511e-06, 'p_ase': 7.6627279170730929e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.5124081413098266e-06, 'p_ase': 7.6647193351719316e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.5690786214626765e-06, 'p_ase': 7.6667107532707703e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.6168665461753671e-06, 'p_ase': 7.6687021713696108e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.6573999680569885e-06, 'p_ase': 7.6706935894684495e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.6918488669551053e-06, 'p_ase': 7.6726850075672899e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.7210762772569284e-06, 'p_ase': 7.6746764256661303e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.7457309403239468e-06, 'p_ase': 7.6766678437649691e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.7663065131158914e-06, 'p_ase': 7.6786592618638095e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.7831806542500021e-06, 'p_ase': 7.6806506799626482e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.7966414565035147e-06, 'p_ase': 7.6826420980614886e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.8069055936770221e-06, 'p_ase': 7.6846335161603307e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.8141308206416702e-06, 'p_ase': 7.6866249342591695e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.8184244548802723e-06, 'p_ase': 7.6886163523580099e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.8198488481817317e-06, 'p_ase': 7.6906077704568486e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.8184244548802723e-06, 'p_ase': 7.6925991885556874e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.8141308206416702e-06, 'p_ase': 7.6945906066545295e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.8069055936770221e-06, 'p_ase': 7.6965820247533682e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.7966414565035147e-06, 'p_ase': 7.6985734428522086e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.7831806542499987e-06, 'p_ase': 7.700564860951049e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.7663065131158914e-06, 'p_ase': 7.7025562790498861e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.7457309403239434e-06, 'p_ase': 7.7045476971487265e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.7210762772569284e-06, 'p_ase': 7.7065391152475652e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.6918488669551045e-06, 'p_ase': 7.7085305333464073e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.6573999680569885e-06, 'p_ase': 7.7105219514452461e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.6168665461753663e-06, 'p_ase': 7.7125133695440865e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.5690786214626748e-06, 'p_ase': 7.7145047876429252e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.5124081413098232e-06, 'p_ase': 7.7164962057417639e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.4445092212793511e-06, 'p_ase': 7.7184876238406044e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.3618408439973026e-06, 'p_ase': 7.7204790419394448e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.2587098467297346e-06, 'p_ase': 7.7224704600382852e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.1251098741021677e-06, 'p_ase': 7.7244618781371239e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.94092217364615e-06, 'p_ase': 7.7264532962359644e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.6553349803839689e-06, 'p_ase': 7.7284447143348048e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.0572250641899261e-06, 'p_ase': 7.7304361324336435e-06}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #012 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #012 deleted file mode 100644 index 62a7f159..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #012 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 21.1403933758 - -osnr_nl_central_channel_db = 18.3921849868 - -osnr_lin_db = [ 21.1629432 21.16181292 21.16068294 21.15955325 21.15842386 - 21.15729476 21.15616595 21.15503743 21.15390921 21.15278129 - 21.15165365 21.15052631 21.14939926 21.1482725 21.14714604 - 21.14601986 21.14489398 21.14376839 21.1426431 21.14151809 - 21.14039338 21.13926895 21.13814482 21.13702098 21.13589743 - 21.13477417 21.1336512 21.13252852 21.13140613 21.13028403 - 21.12916222 21.1280407 21.12691947 21.12579853 21.12467788 - 21.12355752 21.12243744 21.12131766 21.12019816 21.11907895 - 21.11796004] - -osnr_nl_db = [ 19.0713174 18.83259631 18.72274298 18.65311462 18.6031272 - 18.56478793 18.53417578 18.50908356 18.48815028 18.47048086 - 18.4554579 18.44264008 18.43170331 18.42240492 18.41456081 - 18.40803054 18.40270715 18.39851028 18.39538145 18.39328079 - 18.39218499 18.39208613 18.3929913 18.39492297 18.39792009 - 18.40204022 18.40736261 18.41399301 18.42207045 18.43177733 - 18.44335423 18.45712261 18.47352038 18.49316029 18.51693092 - 18.5461835 18.58310978 18.6316036 18.69960284 18.80755039 - 19.04347251] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.7334292415549188e-08, 'p_ase': 7.6507794084800551e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.4312241437813031e-08, 'p_ase': 7.6527708265788928e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.7644092025871747e-08, 'p_ase': 7.6547622446777332e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.9792948531191985e-08, 'p_ase': 7.6567536627765722e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.135161487851361e-08, 'p_ase': 7.6587450808754126e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.2554809846635201e-08, 'p_ase': 7.660736498974253e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.3519274248259099e-08, 'p_ase': 7.6627279170730934e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.431142831528131e-08, 'p_ase': 7.6647193351719324e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.4972583917064551e-08, 'p_ase': 7.6667107532707701e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.553010970537929e-08, 'p_ase': 7.6687021713696105e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.6002999627331535e-08, 'p_ase': 7.6706935894684495e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.6404903447809561e-08, 'p_ase': 7.6726850075672899e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.6745889901330832e-08, 'p_ase': 7.6746764256661303e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.7033527637112714e-08, 'p_ase': 7.6766678437649693e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.727357598635207e-08, 'p_ase': 7.6786592618638097e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.7470440966250018e-08, 'p_ase': 7.6806506799626487e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.7627483659207672e-08, 'p_ase': 7.6826420980614891e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.7747231926231926e-08, 'p_ase': 7.6846335161603308e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.7831526240819483e-08, 'p_ase': 7.6866249342591698e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.7881618640269838e-08, 'p_ase': 7.6886163523580102e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.7898236562120206e-08, 'p_ase': 7.6906077704568493e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.7881618640269838e-08, 'p_ase': 7.692599188555687e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.7831526240819483e-08, 'p_ase': 7.69459060665453e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.7747231926231926e-08, 'p_ase': 7.6965820247533677e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.7627483659207672e-08, 'p_ase': 7.6985734428522094e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.7470440966249992e-08, 'p_ase': 7.7005648609510498e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.727357598635207e-08, 'p_ase': 7.7025562790498862e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.7033527637112674e-08, 'p_ase': 7.7045476971487265e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.6745889901330832e-08, 'p_ase': 7.7065391152475656e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.6404903447809561e-08, 'p_ase': 7.7085305333464073e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.6002999627331535e-08, 'p_ase': 7.7105219514452463e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.5530109705379277e-08, 'p_ase': 7.7125133695440867e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.4972583917064538e-08, 'p_ase': 7.7145047876429257e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.431142831528127e-08, 'p_ase': 7.7164962057417635e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.3519274248259099e-08, 'p_ase': 7.7184876238406052e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.2554809846635201e-08, 'p_ase': 7.7204790419394455e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.135161487851357e-08, 'p_ase': 7.7224704600382859e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.9792948531191958e-08, 'p_ase': 7.7244618781371236e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.7644092025871747e-08, 'p_ase': 7.726453296235964e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.4312241437812971e-08, 'p_ase': 7.7284447143348044e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 4.7334292415549142e-08, 'p_ase': 7.7304361324336434e-08}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #013 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #013 deleted file mode 100644 index dce50ba9..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #013 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 20.4709254795 - -osnr_nl_central_channel_db = 18.0238318306 - -osnr_lin_db = [ 20.4934753 20.49234502 20.49121504 20.49008535 20.48895596 - 20.48782686 20.48669805 20.48556954 20.48444132 20.48331339 - 20.48218575 20.48105841 20.47993136 20.47880461 20.47767814 - 20.47655197 20.47542609 20.4743005 20.4731752 20.47205019 - 20.47092548 20.46980106 20.46867692 20.46755308 20.46642953 - 20.46530627 20.4641833 20.46306062 20.46193823 20.46081613 - 20.45969432 20.4585728 20.45745157 20.45633063 20.45520998 - 20.45408962 20.45296955 20.45184976 20.45073027 20.44961106 - 20.44849214] - -osnr_nl_db = [ 18.64570331 18.42861989 18.32831439 18.26459034 18.21876256 - 18.18356265 18.15542054 18.13232463 18.11303363 18.0967306 - 18.08285178 18.07099394 18.06086089 18.05223073 18.04493509 - 18.03884548 18.03386401 18.02991714 18.0269513 18.02492998 - 18.02383183 18.02364956 18.02438964 18.0260726 18.02873406 - 18.03242663 18.03722282 18.04321929 18.05054317 18.05936116 - 18.0698931 18.08243264 18.09737963 18.11529334 18.13698433 - 18.16368446 18.19739028 18.24164557 18.30366567 18.40199993 - 18.61625566] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.7334292415549192e-06, 'p_ase': 8.9259093098933971e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.431224143781303e-06, 'p_ase': 8.9282326310087082e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.7644092025871746e-06, 'p_ase': 8.9305559521240225e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.9792948531191982e-06, 'p_ase': 8.9328792732393335e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.1351614878513612e-06, 'p_ase': 8.9352025943546479e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.25548098466352e-06, 'p_ase': 8.9375259154699606e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.3519274248259101e-06, 'p_ase': 8.939849236585275e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.4311428315281312e-06, 'p_ase': 8.9421725577005877e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.4972583917064551e-06, 'p_ase': 8.9444958788158987e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.553010970537929e-06, 'p_ase': 8.9468191999312131e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.6002999627331534e-06, 'p_ase': 8.9491425210465241e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.6404903447809558e-06, 'p_ase': 8.9514658421618385e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.6745889901330829e-06, 'p_ase': 8.9537891632771529e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.7033527637112713e-06, 'p_ase': 8.9561124843924639e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.7273575986352068e-06, 'p_ase': 8.9584358055077783e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.7470440966250017e-06, 'p_ase': 8.9607591266230893e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.7627483659207668e-06, 'p_ase': 8.9630824477384037e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.7747231926231926e-06, 'p_ase': 8.9654057688537181e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.7831526240819485e-06, 'p_ase': 8.9677290899690308e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.7881618640269842e-06, 'p_ase': 8.9700524110843452e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.7898236562120209e-06, 'p_ase': 8.9723757321996562e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.7881618640269842e-06, 'p_ase': 8.9746990533149689e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.7831526240819485e-06, 'p_ase': 8.9770223744302832e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.7747231926231926e-06, 'p_ase': 8.9793456955455959e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.7627483659207668e-06, 'p_ase': 8.9816690166609103e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.7470440966249992e-06, 'p_ase': 8.983992337776223e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.7273575986352068e-06, 'p_ase': 8.986315658891534e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.703352763711267e-06, 'p_ase': 8.9886389800068484e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.6745889901330829e-06, 'p_ase': 8.9909623011221594e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.6404903447809558e-06, 'p_ase': 8.9932856222374755e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.6002999627331534e-06, 'p_ase': 8.9956089433527865e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.5530109705379273e-06, 'p_ase': 8.9979322644681009e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.4972583917064534e-06, 'p_ase': 9.0002555855834136e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.4311428315281269e-06, 'p_ase': 9.0025789066987246e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.3519274248259101e-06, 'p_ase': 9.004902227814039e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.25548098466352e-06, 'p_ase': 9.0072255489293517e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.1351614878513569e-06, 'p_ase': 9.0095488700446661e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.9792948531191957e-06, 'p_ase': 9.0118721911599788e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.7644092025871746e-06, 'p_ase': 9.0141955122752915e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.431224143781297e-06, 'p_ase': 9.0165188333906059e-06}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 4.7334292415549141e-06, 'p_ase': 9.0188421545059169e-06}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #014 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #014 deleted file mode 100644 index 02379d3f..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #014 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 20.4709254795 - -osnr_nl_central_channel_db = 17.7644762269 - -osnr_lin_db = [ 20.4934753 20.49234502 20.49121504 20.49008535 20.48895596 - 20.48782686 20.48669805 20.48556954 20.48444132 20.48331339 - 20.48218575 20.48105841 20.47993136 20.47880461 20.47767814 - 20.47655197 20.47542609 20.4743005 20.4731752 20.47205019 - 20.47092548 20.46980106 20.46867692 20.46755308 20.46642953 - 20.46530627 20.4641833 20.46306062 20.46193823 20.46081613 - 20.45969432 20.4585728 20.45745157 20.45633063 20.45520998 - 20.45408962 20.45296955 20.45184976 20.45073027 20.44961106 - 20.44849214] - -osnr_nl_db = [ 18.4358586 18.20007648 18.09151598 18.02268586 17.97326024 - 17.93534467 17.9050657 17.88024264 17.8595307 17.84204539 - 17.82717655 17.81448802 17.80365947 17.79445104 17.78668078 - 17.78020982 17.77493244 17.77076922 17.76766233 17.76557235 - 17.76447623 17.76436614 17.7652491 17.76714729 17.77009924 - 17.77416181 17.77941337 17.78595842 17.7939344 17.80352156 - 17.81495771 17.82856053 17.84476279 17.86416999 17.88766016 - 17.91656837 17.95305988 18.00098108 18.06817173 18.17481719 - 18.40779551] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.409633418919908e-08, 'p_ase': 8.9259093098933976e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.2071133071786327e-08, 'p_ase': 8.9282326310087085e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.5878962315281993e-08, 'p_ase': 8.9305559521240234e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.8334798321362267e-08, 'p_ase': 8.9328792732393343e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.0116131289729833e-08, 'p_ase': 8.9352025943546478e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.1491211253297367e-08, 'p_ase': 8.9375259154699614e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.2593456283724677e-08, 'p_ase': 8.9398492365852749e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.3498775217464347e-08, 'p_ase': 8.9421725577005885e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.4254381619502352e-08, 'p_ase': 8.9444958788158994e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.4891553949004901e-08, 'p_ase': 8.9468191999312129e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.5431999574093183e-08, 'p_ase': 8.9491425210465238e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.5891318226068068e-08, 'p_ase': 8.9514658421618387e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.6281017030092383e-08, 'p_ase': 8.9537891632771535e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.6609745870985951e-08, 'p_ase': 8.9561124843924644e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.6884086841545216e-08, 'p_ase': 8.958435805507778e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.7109075390000015e-08, 'p_ase': 8.9607591266230889e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.7288552753380194e-08, 'p_ase': 8.9630824477384037e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.7425407915693624e-08, 'p_ase': 8.9654057688537186e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.7521744275222268e-08, 'p_ase': 8.9677290899690308e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.757899273173696e-08, 'p_ase': 8.9700524110843457e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.7597984642423085e-08, 'p_ase': 8.9723757321996566e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.757899273173696e-08, 'p_ase': 8.9746990533149688e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.7521744275222268e-08, 'p_ase': 8.9770223744302837e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.7425407915693624e-08, 'p_ase': 8.9793456955455959e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.7288552753380194e-08, 'p_ase': 8.9816690166609108e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.7109075389999989e-08, 'p_ase': 8.983992337776223e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.6884086841545216e-08, 'p_ase': 8.9863156588915339e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.6609745870985911e-08, 'p_ase': 8.9886389800068487e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.6281017030092383e-08, 'p_ase': 8.9909623011221596e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.5891318226068068e-08, 'p_ase': 8.9932856222374758e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.5431999574093183e-08, 'p_ase': 8.9956089433527867e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.4891553949004888e-08, 'p_ase': 8.9979322644681016e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.4254381619502325e-08, 'p_ase': 9.0002555855834138e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.3498775217464307e-08, 'p_ase': 9.0025789066987247e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.2593456283724691e-08, 'p_ase': 9.0049022278140396e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.1491211253297367e-08, 'p_ase': 9.0072255489293518e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.0116131289729793e-08, 'p_ase': 9.0095488700446667e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.833479832136224e-08, 'p_ase': 9.0118721911599789e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.5878962315281993e-08, 'p_ase': 9.0141955122752911e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.2071133071786247e-08, 'p_ase': 9.016518833390606e-08}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 5.409633418919902e-08, 'p_ase': 9.0188421545059169e-08}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #015 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #015 deleted file mode 100644 index 45f0032a..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #015 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 19.8910060097 - -osnr_nl_central_channel_db = 17.4439123609 - -osnr_lin_db = [ 19.91355583 19.91242555 19.91129557 19.91016588 19.90903649 - 19.90790739 19.90677858 19.90565007 19.90452185 19.90339392 - 19.90226628 19.90113894 19.90001189 19.89888514 19.89775867 - 19.8966325 19.89550662 19.89438103 19.89325573 19.89213072 - 19.89100601 19.88988159 19.88875745 19.88763361 19.88651006 - 19.8853868 19.88426383 19.88314115 19.88201876 19.88089666 - 19.87977485 19.87865334 19.8775321 19.87641116 19.87529051 - 19.87417015 19.87305008 19.87193029 19.8708108 19.86969159 - 19.86857267] - -osnr_nl_db = [ 18.06578384 17.84870042 17.74839492 17.68467087 17.63884309 - 17.60364318 17.57550107 17.55240516 17.53311416 17.51681113 - 17.50293231 17.49107447 17.48094142 17.47231126 17.46501562 - 17.45892601 17.45394454 17.44999767 17.44703183 17.44501051 - 17.44391236 17.44373009 17.44447018 17.44615313 17.44881459 - 17.45250716 17.45730335 17.46329982 17.4706237 17.47944169 - 17.48997363 17.50251317 17.51746016 17.53537387 17.55706486 - 17.58376499 17.61747081 17.6617261 17.7237462 17.82208046 - 18.03633619] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.409633418919908e-06, 'p_ase': 1.0201039211306739e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.2071133071786328e-06, 'p_ase': 1.0203694435438524e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.5878962315281992e-06, 'p_ase': 1.0206349659570311e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.833479832136227e-06, 'p_ase': 1.0209004883702095e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.0116131289729835e-06, 'p_ase': 1.0211660107833884e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.1491211253297365e-06, 'p_ase': 1.0214315331965669e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.2593456283724675e-06, 'p_ase': 1.0216970556097457e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.3498775217464349e-06, 'p_ase': 1.0219625780229242e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.4254381619502354e-06, 'p_ase': 1.0222281004361027e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.48915539490049e-06, 'p_ase': 1.0224936228492815e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.5431999574093183e-06, 'p_ase': 1.0227591452624599e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.5891318226068071e-06, 'p_ase': 1.0230246676756387e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.6281017030092384e-06, 'p_ase': 1.0232901900888174e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.6609745870985957e-06, 'p_ase': 1.0235557125019959e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.6884086841545214e-06, 'p_ase': 1.0238212349151747e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.7109075390000022e-06, 'p_ase': 1.024086757328353e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.728855275338019e-06, 'p_ase': 1.0243522797415319e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.7425407915693622e-06, 'p_ase': 1.0246178021547105e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.7521744275222269e-06, 'p_ase': 1.0248833245678892e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.7578992731736952e-06, 'p_ase': 1.025148846981068e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.7597984642423083e-06, 'p_ase': 1.0254143693942464e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.7578992731736952e-06, 'p_ase': 1.025679891807425e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.7521744275222269e-06, 'p_ase': 1.0259454142206037e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.7425407915693622e-06, 'p_ase': 1.0262109366337824e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.728855275338019e-06, 'p_ase': 1.0264764590469612e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.7109075389999988e-06, 'p_ase': 1.0267419814601397e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.6884086841545214e-06, 'p_ase': 1.0270075038733182e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.6609745870985907e-06, 'p_ase': 1.0272730262864969e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.6281017030092384e-06, 'p_ase': 1.0275385486996754e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.5891318226068071e-06, 'p_ase': 1.0278040711128544e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.5431999574093183e-06, 'p_ase': 1.0280695935260327e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.4891553949004883e-06, 'p_ase': 1.0283351159392115e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.4254381619502328e-06, 'p_ase': 1.02860063835239e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.3498775217464306e-06, 'p_ase': 1.0288661607655685e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.2593456283724692e-06, 'p_ase': 1.0291316831787474e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.1491211253297365e-06, 'p_ase': 1.0293972055919259e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.0116131289729792e-06, 'p_ase': 1.0296627280051047e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.8334798321362236e-06, 'p_ase': 1.0299282504182832e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.5878962315281992e-06, 'p_ase': 1.0301937728314619e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.2071133071786244e-06, 'p_ase': 1.0304592952446407e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 5.4096334189199021e-06, 'p_ase': 1.030724817657819e-05}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #016 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #016 deleted file mode 100644 index 34cfee2a..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #016 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 19.8910060097 - -osnr_nl_central_channel_db = 17.2161417435 - -osnr_lin_db = [ 19.91355583 19.91242555 19.91129557 19.91016588 19.90903649 - 19.90790739 19.90677858 19.90565007 19.90452185 19.90339392 - 19.90226628 19.90113894 19.90001189 19.89888514 19.89775867 - 19.8966325 19.89550662 19.89438103 19.89325573 19.89213072 - 19.89100601 19.88988159 19.88875745 19.88763361 19.88651006 - 19.8853868 19.88426383 19.88314115 19.88201876 19.88089666 - 19.87977485 19.87865334 19.8775321 19.87641116 19.87529051 - 19.87417015 19.87305008 19.87193029 19.8708108 19.86969159 - 19.86857267] - -osnr_nl_db = [ 17.88162189 17.64807582 17.54049978 17.47227777 17.42328027 - 17.38568762 17.35566263 17.33104477 17.3105016 17.29315667 - 17.27840536 17.26581543 17.25506942 17.24592961 17.23821565 - 17.23178993 17.22654766 17.22241011 17.21931994 17.2172381 - 17.21614174 17.2160231 17.21688911 17.21876177 17.22167925 - 17.22569793 17.23089546 17.23737542 17.24527401 17.25476988 - 17.26609869 17.27957527 17.29562845 17.31485824 17.3381346 - 17.36678031 17.40294049 17.45042537 17.51700007 17.6226541 - 17.85339289] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.0858375962848965e-08, 'p_ase': 1.0201039211306739e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.9830024705759622e-08, 'p_ase': 1.0203694435438524e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.4113832604692239e-08, 'p_ase': 1.0206349659570311e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.6876648111532548e-08, 'p_ase': 1.0209004883702096e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.8880647700946068e-08, 'p_ase': 1.0211660107833884e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.0427612659959533e-08, 'p_ase': 1.0214315331965668e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.1667638319190255e-08, 'p_ase': 1.0216970556097458e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.2686122119647397e-08, 'p_ase': 1.0219625780229242e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.3536179321940152e-08, 'p_ase': 1.0222281004361027e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.4252998192630512e-08, 'p_ase': 1.0224936228492815e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.4860999520854831e-08, 'p_ase': 1.0227591452624599e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.5377733004326574e-08, 'p_ase': 1.0230246676756387e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.5816144158853934e-08, 'p_ase': 1.0232901900888174e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.6185964104859215e-08, 'p_ase': 1.023555712501996e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.6494597696738361e-08, 'p_ase': 1.0238212349151748e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.6747709813750025e-08, 'p_ase': 1.024086757328353e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.6949621847552717e-08, 'p_ase': 1.0243522797415318e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.7103583905155322e-08, 'p_ase': 1.0246178021547105e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.7211962309625054e-08, 'p_ase': 1.0248833245678892e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.7276366823204082e-08, 'p_ase': 1.0251488469810681e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.7297732722725965e-08, 'p_ase': 1.0254143693942464e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.7276366823204082e-08, 'p_ase': 1.0256798918074251e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.7211962309625054e-08, 'p_ase': 1.0259454142206037e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.7103583905155322e-08, 'p_ase': 1.0262109366337824e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.6949621847552717e-08, 'p_ase': 1.0264764590469612e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.6747709813749986e-08, 'p_ase': 1.0267419814601398e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.6494597696738361e-08, 'p_ase': 1.0270075038733182e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.6185964104859149e-08, 'p_ase': 1.0272730262864968e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.5816144158853934e-08, 'p_ase': 1.0275385486996754e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.5377733004326574e-08, 'p_ase': 1.0278040711128544e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.4860999520854831e-08, 'p_ase': 1.0280695935260327e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.4252998192630499e-08, 'p_ase': 1.0283351159392115e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.3536179321940112e-08, 'p_ase': 1.0286006383523901e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.2686122119647344e-08, 'p_ase': 1.0288661607655686e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.1667638319190282e-08, 'p_ase': 1.0291316831787474e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.0427612659959533e-08, 'p_ase': 1.0293972055919259e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.8880647700946015e-08, 'p_ase': 1.0296627280051047e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.6876648111532522e-08, 'p_ase': 1.0299282504182831e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.4113832604692239e-08, 'p_ase': 1.0301937728314618e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.983002470575953e-08, 'p_ase': 1.0304592952446408e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.0858375962848899e-08, 'p_ase': 1.030724817657819e-07}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #017 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #017 deleted file mode 100644 index 235dd987..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #017 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 19.3794807853 - -osnr_nl_central_channel_db = 16.9323871364 - -osnr_lin_db = [ 19.40203061 19.40090033 19.39977035 19.39864066 19.39751127 - 19.39638216 19.39525336 19.39412484 19.39299662 19.39186869 - 19.39074106 19.38961372 19.38848667 19.38735991 19.38623345 - 19.38510727 19.38398139 19.3828558 19.38173051 19.3806055 - 19.37948079 19.37835636 19.37723223 19.37610839 19.37498484 - 19.37386158 19.37273861 19.37161593 19.37049354 19.36937144 - 19.36824963 19.36712811 19.36600688 19.36488594 19.36376529 - 19.36264493 19.36152485 19.36040507 19.35928557 19.35816636 - 19.35704744] - -osnr_nl_db = [ 17.55425861 17.3371752 17.2368697 17.17314564 17.12731786 - 17.09211795 17.06397584 17.04087994 17.02158893 17.00528591 - 16.99140708 16.97954925 16.96941619 16.96078603 16.9534904 - 16.94740078 16.94241932 16.93847244 16.93550661 16.93348529 - 16.93238714 16.93220487 16.93294495 16.9346279 16.93728936 - 16.94098194 16.94577812 16.9517746 16.95909848 16.96791646 - 16.97844841 16.99098794 17.00593494 17.02384865 17.04553964 - 17.07223977 17.10594559 17.15020087 17.21222097 17.31055524 - 17.52481096] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.0858375962848968e-06, 'p_ase': 1.1476169112720081e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.9830024705759618e-06, 'p_ase': 1.147915623986834e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.4113832604692238e-06, 'p_ase': 1.1482143367016598e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.6876648111532541e-06, 'p_ase': 1.1485130494164857e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.8880647700946066e-06, 'p_ase': 1.148811762131312e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.0427612659959539e-06, 'p_ase': 1.1491104748461377e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.1667638319190249e-06, 'p_ase': 1.1494091875609639e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.2686122119647403e-06, 'p_ase': 1.1497079002757897e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.3536179321940148e-06, 'p_ase': 1.1500066129906155e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.4252998192630511e-06, 'p_ase': 1.1503053257054418e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.4860999520854823e-06, 'p_ase': 1.1506040384202673e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.5377733004326567e-06, 'p_ase': 1.1509027511350936e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.5816144158853938e-06, 'p_ase': 1.1512014638499195e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.6185964104859219e-06, 'p_ase': 1.1515001765647454e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.6494597696738367e-06, 'p_ase': 1.1517988892795716e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.6747709813750018e-06, 'p_ase': 1.1520976019943971e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.694962184755272e-06, 'p_ase': 1.1523963147092234e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.7103583905155319e-06, 'p_ase': 1.1526950274240493e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.7211962309625061e-06, 'p_ase': 1.1529937401388753e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.7276366823204088e-06, 'p_ase': 1.1532924528537016e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.7297732722725967e-06, 'p_ase': 1.1535911655685271e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.7276366823204088e-06, 'p_ase': 1.1538898782833532e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.7211962309625061e-06, 'p_ase': 1.1541885909981791e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.7103583905155319e-06, 'p_ase': 1.1544873037130051e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.694962184755272e-06, 'p_ase': 1.1547860164278314e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.6747709813749984e-06, 'p_ase': 1.1550847291426571e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.6494597696738367e-06, 'p_ase': 1.155383441857483e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.6185964104859151e-06, 'p_ase': 1.1556821545723089e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.5816144158853938e-06, 'p_ase': 1.1559808672871348e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.5377733004326567e-06, 'p_ase': 1.1562795800019612e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.4860999520854823e-06, 'p_ase': 1.1565782927167867e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.4252998192630494e-06, 'p_ase': 1.156877005431613e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.3536179321940114e-06, 'p_ase': 1.1571757181464387e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.2686122119647335e-06, 'p_ase': 1.1574744308612646e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.1667638319190283e-06, 'p_ase': 1.1577731435760908e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.0427612659959539e-06, 'p_ase': 1.1580718562909165e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.8880647700946015e-06, 'p_ase': 1.1583705690057428e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.6876648111532524e-06, 'p_ase': 1.1586692817205685e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.4113832604692238e-06, 'p_ase': 1.1589679944353946e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.9830024705759533e-06, 'p_ase': 1.1592667071502208e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.08583759628489e-06, 'p_ase': 1.1595654198650464e-05}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #018 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #018 deleted file mode 100644 index cbe85a80..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #018 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 19.3794807853 - -osnr_nl_central_channel_db = 16.7293424208 - -osnr_lin_db = [ 19.40203061 19.40090033 19.39977035 19.39864066 19.39751127 - 19.39638216 19.39525336 19.39412484 19.39299662 19.39186869 - 19.39074106 19.38961372 19.38848667 19.38735991 19.38623345 - 19.38510727 19.38398139 19.3828558 19.38173051 19.3806055 - 19.37948079 19.37835636 19.37723223 19.37610839 19.37498484 - 19.37386158 19.37273861 19.37161593 19.37049354 19.36937144 - 19.36824963 19.36712811 19.36600688 19.36488594 19.36376529 - 19.36264493 19.36152485 19.36040507 19.35928557 19.35816636 - 19.35704744] - -osnr_nl_db = [ 17.39017767 17.15838999 17.05158863 16.9838453 16.93518485 - 16.8978465 16.86802153 16.84356528 16.82315505 16.8059207 - 16.79126198 16.77874972 16.76806874 16.75898298 16.75131339 - 16.74492332 16.73970871 16.73559137 16.73251439 16.73043897 - 16.72934242 16.72921703 16.73006968 16.73192223 16.73481256 - 16.73879666 16.74395162 16.75038029 16.75821792 16.76764187 - 16.77888612 16.79226325 16.80819901 16.82728905 16.85039703 - 16.87883602 16.91473531 16.96187669 17.02796657 17.13284048 - 17.36181827] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.762041773649885e-08, 'p_ase': 1.1476169112720081e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.7588916339732918e-08, 'p_ase': 1.147915623986834e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.2348702894102485e-08, 'p_ase': 1.1482143367016598e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.541849790170283e-08, 'p_ase': 1.1485130494164857e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.7645164112162304e-08, 'p_ase': 1.148811762131312e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.9364014066621712e-08, 'p_ase': 1.1491104748461377e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.0741820354655833e-08, 'p_ase': 1.1494091875609639e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.1873469021830447e-08, 'p_ase': 1.1497079002757897e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.2817977024377953e-08, 'p_ase': 1.1500066129906155e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.3614442436256123e-08, 'p_ase': 1.1503053257054418e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.4289999467616479e-08, 'p_ase': 1.1506040384202674e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.4864147782585081e-08, 'p_ase': 1.1509027511350936e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.5351271287615485e-08, 'p_ase': 1.1512014638499195e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.5762182338732452e-08, 'p_ase': 1.1515001765647453e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.6105108551931533e-08, 'p_ase': 1.1517988892795716e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.6386344237500022e-08, 'p_ase': 1.1520976019943972e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.661069094172524e-08, 'p_ase': 1.1523963147092234e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.678175989461702e-08, 'p_ase': 1.1526950274240493e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.6902180344027852e-08, 'p_ase': 1.1529937401388754e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.6973740914671217e-08, 'p_ase': 1.1532924528537015e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.6997480803028844e-08, 'p_ase': 1.1535911655685271e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.6973740914671217e-08, 'p_ase': 1.1538898782833532e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.6902180344027852e-08, 'p_ase': 1.1541885909981791e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.678175989461702e-08, 'p_ase': 1.1544873037130052e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.661069094172524e-08, 'p_ase': 1.1547860164278313e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.6386344237499982e-08, 'p_ase': 1.1550847291426571e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.6105108551931533e-08, 'p_ase': 1.1553834418574831e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.5762182338732386e-08, 'p_ase': 1.1556821545723089e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.5351271287615485e-08, 'p_ase': 1.1559808672871348e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.4864147782585081e-08, 'p_ase': 1.1562795800019612e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.4289999467616479e-08, 'p_ase': 1.1565782927167868e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.361444243625611e-08, 'p_ase': 1.156877005431613e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.28179770243779e-08, 'p_ase': 1.1571757181464387e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.1873469021830367e-08, 'p_ase': 1.1574744308612646e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.0741820354655873e-08, 'p_ase': 1.1577731435760908e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.9364014066621712e-08, 'p_ase': 1.1580718562909166e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.7645164112162238e-08, 'p_ase': 1.1583705690057428e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.5418497901702803e-08, 'p_ase': 1.1586692817205685e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.2348702894102485e-08, 'p_ase': 1.1589679944353945e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.7588916339732812e-08, 'p_ase': 1.1592667071502208e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 6.7620417736498771e-08, 'p_ase': 1.1595654198650464e-07}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #019 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #019 deleted file mode 100644 index 7b0611f2..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #019 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 18.9219058797 - -osnr_nl_central_channel_db = 16.4748122308 - -osnr_lin_db = [ 18.9444557 18.94332542 18.94219544 18.94106575 18.93993636 - 18.93880726 18.93767845 18.93654994 18.93542172 18.93429379 - 18.93316615 18.93203881 18.93091176 18.92978501 18.92865854 - 18.92753237 18.92640649 18.9252809 18.9241556 18.92303059 - 18.92190588 18.92078146 18.91965732 18.91853348 18.91740993 - 18.91628667 18.9151637 18.91404102 18.91291863 18.91179653 - 18.91067472 18.9095532 18.90843197 18.90731103 18.90619038 - 18.90507002 18.90394995 18.90283016 18.90171067 18.90059146 - 18.89947254] - -osnr_nl_db = [ 17.09668371 16.87960029 16.77929479 16.71557074 16.66974296 - 16.63454305 16.60640094 16.58330503 16.56401403 16.547711 - 16.53383218 16.52197434 16.51184129 16.50321113 16.49591549 - 16.48982588 16.48484441 16.48089754 16.4779317 16.47591038 - 16.47481223 16.47462996 16.47537004 16.477053 16.47971446 - 16.48340703 16.48820322 16.49419969 16.50152357 16.51034156 - 16.5208735 16.53341304 16.54836003 16.56627374 16.58796473 - 16.61466486 16.64837068 16.69262597 16.75464607 16.85298033 - 17.06723606] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.7620417736498848e-06, 'p_ase': 1.2751299014133422e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.7588916339732917e-06, 'p_ase': 1.2754618044298155e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.2348702894102492e-06, 'p_ase': 1.2757937074462886e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.5418497901702829e-06, 'p_ase': 1.2761256104627619e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.7645164112162298e-06, 'p_ase': 1.2764575134792356e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.9364014066621705e-06, 'p_ase': 1.2767894164957085e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.074182035465584e-06, 'p_ase': 1.2771213195121821e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.187346902183044e-06, 'p_ase': 1.2774532225286551e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.2817977024377959e-06, 'p_ase': 1.2777851255451284e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.3614442436256121e-06, 'p_ase': 1.278117028561602e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.4289999467616472e-06, 'p_ase': 1.2784489315780748e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.4864147782585081e-06, 'p_ase': 1.2787808345945484e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.5351271287615492e-06, 'p_ase': 1.2791127376110216e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.5762182338732447e-06, 'p_ase': 1.2794446406274948e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.6105108551931538e-06, 'p_ase': 1.2797765436439685e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.6386344237500015e-06, 'p_ase': 1.2801084466604413e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.6610690941725233e-06, 'p_ase': 1.2804403496769149e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.6781759894617015e-06, 'p_ase': 1.280772252693388e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.6902180344027853e-06, 'p_ase': 1.2811041557098615e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.6973740914671224e-06, 'p_ase': 1.2814360587263351e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.699748080302885e-06, 'p_ase': 1.2817679617428079e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.6973740914671224e-06, 'p_ase': 1.2820998647592813e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.6902180344027853e-06, 'p_ase': 1.2824317677757545e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.6781759894617015e-06, 'p_ase': 1.2827636707922279e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.6610690941725233e-06, 'p_ase': 1.2830955738087015e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.6386344237499981e-06, 'p_ase': 1.2834274768251745e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.6105108551931538e-06, 'p_ase': 1.2837593798416478e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.5762182338732379e-06, 'p_ase': 1.2840912828581209e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.5351271287615492e-06, 'p_ase': 1.2844231858745942e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.4864147782585081e-06, 'p_ase': 1.284755088891068e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.4289999467616472e-06, 'p_ase': 1.2850869919075408e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.3614442436256104e-06, 'p_ase': 1.2854188949240144e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.2817977024377908e-06, 'p_ase': 1.2857507979404874e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.1873469021830373e-06, 'p_ase': 1.2860827009569607e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.0741820354655874e-06, 'p_ase': 1.2864146039734343e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.9364014066621705e-06, 'p_ase': 1.2867465069899072e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.764516411216223e-06, 'p_ase': 1.2870784100063809e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.5418497901702795e-06, 'p_ase': 1.2874103130228538e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.2348702894102492e-06, 'p_ase': 1.2877422160393273e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.7588916339732815e-06, 'p_ase': 1.2880741190558009e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 6.7620417736498772e-06, 'p_ase': 1.2884060220722737e-05}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #020 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #020 deleted file mode 100644 index 0df35908..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #020 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 18.9219058797 - -osnr_nl_central_channel_db = 16.2916500961 - -osnr_lin_db = [ 18.9444557 18.94332542 18.94219544 18.94106575 18.93993636 - 18.93880726 18.93767845 18.93654994 18.93542172 18.93429379 - 18.93316615 18.93203881 18.93091176 18.92978501 18.92865854 - 18.92753237 18.92640649 18.9252809 18.9241556 18.92303059 - 18.92190588 18.92078146 18.91965732 18.91853348 18.91740993 - 18.91628667 18.9151637 18.91404102 18.91291863 18.91179653 - 18.91067472 18.9095532 18.90843197 18.90731103 18.90619038 - 18.90507002 18.90394995 18.90283016 18.90171067 18.90059146 - 18.89947254] - -osnr_nl_db = [ 16.94873469 16.71836602 16.61219015 16.54483341 16.49644523 - 16.45931231 16.42964894 16.40532329 16.38502047 16.36787549 - 16.35329159 16.3408421 16.33021367 16.3211716 16.31353787 - 16.30717661 16.30198435 16.29788336 16.29481703 16.2927468 - 16.2916501 16.29151925 16.29236111 16.2941974 16.29706579 - 16.30102193 16.30614249 16.31252972 16.32031807 16.3296839 - 16.34085982 16.35415658 16.36999745 16.38897458 16.41194651 - 16.44021849 16.47590701 16.52277094 16.58846929 16.69271336 - 16.9202701 ] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.4382459510148735e-08, 'p_ase': 1.2751299014133423e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.5347807973706214e-08, 'p_ase': 1.2754618044298154e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.0583573183512744e-08, 'p_ase': 1.2757937074462886e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.3960347691873112e-08, 'p_ase': 1.276125610462762e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.640968052337854e-08, 'p_ase': 1.2764575134792355e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.8300415473283878e-08, 'p_ase': 1.2767894164957087e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.9816002390121438e-08, 'p_ase': 1.2771213195121821e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.010608159240135e-07, 'p_ase': 1.277453222528655e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0209977472681575e-07, 'p_ase': 1.2777851255451284e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0297588667988173e-07, 'p_ase': 1.2781170285616021e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0371899941437813e-07, 'p_ase': 1.2784489315780748e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0435056256084359e-07, 'p_ase': 1.2787808345945485e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0488639841637705e-07, 'p_ase': 1.2791127376110217e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0533840057260569e-07, 'p_ase': 1.2794446406274948e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.057156194071247e-07, 'p_ase': 1.2797765436439685e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0602497866125002e-07, 'p_ase': 1.2801084466604412e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0627176003589776e-07, 'p_ase': 1.2804403496769149e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0645993588407872e-07, 'p_ase': 1.2807722526933881e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0659239837843064e-07, 'p_ase': 1.2811041557098615e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0667111500613835e-07, 'p_ase': 1.2814360587263352e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0669722888333175e-07, 'p_ase': 1.2817679617428079e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0667111500613835e-07, 'p_ase': 1.2820998647592813e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0659239837843064e-07, 'p_ase': 1.2824317677757545e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0645993588407872e-07, 'p_ase': 1.2827636707922279e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0627176003589776e-07, 'p_ase': 1.2830955738087016e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0602497866124998e-07, 'p_ase': 1.2834274768251745e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.057156194071247e-07, 'p_ase': 1.2837593798416477e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0533840057260562e-07, 'p_ase': 1.2840912828581209e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0488639841637705e-07, 'p_ase': 1.2844231858745943e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0435056256084359e-07, 'p_ase': 1.284755088891068e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0371899941437813e-07, 'p_ase': 1.2850869919075409e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0297588667988172e-07, 'p_ase': 1.2854188949240144e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.020997747268157e-07, 'p_ase': 1.2857507979404873e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0106081592401342e-07, 'p_ase': 1.2860827009569607e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.9816002390121464e-08, 'p_ase': 1.2864146039734344e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.8300415473283878e-08, 'p_ase': 1.2867465069899073e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.640968052337846e-08, 'p_ase': 1.287078410006381e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.3960347691873085e-08, 'p_ase': 1.2874103130228539e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.0583573183512744e-08, 'p_ase': 1.2877422160393274e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.5347807973706095e-08, 'p_ase': 1.2880741190558008e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 7.4382459510148642e-08, 'p_ase': 1.2884060220722737e-07}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #021 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #021 deleted file mode 100644 index 81cb8758..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #021 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 18.5079790281 - -osnr_nl_central_channel_db = 16.0608853792 - -osnr_lin_db = [ 18.53052885 18.52939857 18.52826859 18.5271389 18.52600951 - 18.52488041 18.5237516 18.52262309 18.52149487 18.52036694 - 18.5192393 18.51811196 18.51698491 18.51585815 18.51473169 - 18.51360552 18.51247964 18.51135405 18.51022875 18.50910374 - 18.50797903 18.5068546 18.50573047 18.50460663 18.50348308 - 18.50235982 18.50123685 18.50011417 18.49899178 18.49786968 - 18.49674787 18.49562635 18.49450512 18.49338418 18.49226353 - 18.49114317 18.4900231 18.48890331 18.48778381 18.48666461 - 18.48554569] - -osnr_nl_db = [ 16.68275686 16.46567344 16.36536794 16.30164389 16.2558161 - 16.2206162 16.19247408 16.16937818 16.15008718 16.13378415 - 16.11990533 16.10804749 16.09791444 16.08928428 16.08198864 - 16.07589903 16.07091756 16.06697069 16.06400485 16.06198353 - 16.06088538 16.06070311 16.06144319 16.06312615 16.06578761 - 16.06948018 16.07427636 16.08027284 16.08759672 16.09641471 - 16.10694665 16.11948619 16.13443318 16.15234689 16.17403788 - 16.20073801 16.23444383 16.27869911 16.34071922 16.43905348 - 16.6533092 ] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.4382459510148736e-06, 'p_ase': 1.4026428915546764e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.5347807973706215e-06, 'p_ase': 1.4030079848727969e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.0583573183512738e-06, 'p_ase': 1.4033730781909174e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.3960347691873117e-06, 'p_ase': 1.4037381715090383e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.6409680523378546e-06, 'p_ase': 1.4041032648271592e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.830041547328387e-06, 'p_ase': 1.4044683581452795e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.981600239012143e-06, 'p_ase': 1.4048334514634004e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0106081592401349e-05, 'p_ase': 1.4051985447815205e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0209977472681575e-05, 'p_ase': 1.4055636380996412e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0297588667988173e-05, 'p_ase': 1.4059287314177624e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0371899941437812e-05, 'p_ase': 1.4062938247358823e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0435056256084359e-05, 'p_ase': 1.4066589180540033e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0488639841637705e-05, 'p_ase': 1.407024011372124e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0533840057260569e-05, 'p_ase': 1.4073891046902443e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0571561940712471e-05, 'p_ase': 1.4077541980083654e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0602497866125001e-05, 'p_ase': 1.4081192913264854e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0627176003589776e-05, 'p_ase': 1.4084843846446064e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0645993588407871e-05, 'p_ase': 1.4088494779627267e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0659239837843065e-05, 'p_ase': 1.4092145712808476e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0667111500613834e-05, 'p_ase': 1.4095796645989688e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0669722888333175e-05, 'p_ase': 1.4099447579170886e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0667111500613834e-05, 'p_ase': 1.4103098512352095e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0659239837843065e-05, 'p_ase': 1.4106749445533298e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0645993588407871e-05, 'p_ase': 1.4110400378714507e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0627176003589776e-05, 'p_ase': 1.4114051311895717e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0602497866124998e-05, 'p_ase': 1.4117702245076919e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0571561940712471e-05, 'p_ase': 1.4121353178258124e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0533840057260562e-05, 'p_ase': 1.4125004111439329e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0488639841637705e-05, 'p_ase': 1.4128655044620538e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0435056256084359e-05, 'p_ase': 1.4132305977801748e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0371899941437812e-05, 'p_ase': 1.413595691098295e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0297588667988171e-05, 'p_ase': 1.4139607844164159e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.020997747268157e-05, 'p_ase': 1.414325877734536e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0106081592401341e-05, 'p_ase': 1.4146909710526567e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.9816002390121464e-06, 'p_ase': 1.4150560643707779e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.830041547328387e-06, 'p_ase': 1.4154211576888979e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.6409680523378461e-06, 'p_ase': 1.4157862510070191e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.3960347691873084e-06, 'p_ase': 1.4161513443251395e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.0583573183512738e-06, 'p_ase': 1.4165164376432602e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.5347807973706096e-06, 'p_ase': 1.4168815309613808e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 7.4382459510148643e-06, 'p_ase': 1.417246624279501e-05}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #022 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #022 deleted file mode 100644 index 6ddd1380..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #022 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 18.5079790281 - -osnr_nl_central_channel_db = 15.8940587889 - -osnr_lin_db = [ 18.53052885 18.52939857 18.52826859 18.5271389 18.52600951 - 18.52488041 18.5237516 18.52262309 18.52149487 18.52036694 - 18.5192393 18.51811196 18.51698491 18.51585815 18.51473169 - 18.51360552 18.51247964 18.51135405 18.51022875 18.50910374 - 18.50797903 18.5068546 18.50573047 18.50460663 18.50348308 - 18.50235982 18.50123685 18.50011417 18.49899178 18.49786968 - 18.49674787 18.49562635 18.49450512 18.49338418 18.49226353 - 18.49114317 18.4900231 18.48890331 18.48778381 18.48666461 - 18.48554569] - -osnr_nl_db = [ 16.54805141 16.31885197 16.21319171 16.14615372 16.09799006 - 16.06102656 16.03149648 16.00727854 15.98706433 15.96999306 - 15.95547087 15.94307317 15.93248809 15.92348207 15.91587793 - 15.90954043 15.90436662 15.90027911 15.89722157 15.89515562 - 15.89405879 15.89392345 15.8947564 15.89657927 15.89942956 - 15.90336264 15.90845482 15.91480785 15.92255555 15.93187343 - 15.94299298 15.95622345 15.97198605 15.99087004 16.01372976 - 16.041864 16.07737872 16.12401385 16.18938939 16.29311428 - 16.51950017] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.114450128379862e-08, 'p_ase': 1.4026428915546764e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.310669960767951e-08, 'p_ase': 1.403007984872797e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.881844347292299e-08, 'p_ase': 1.4033730781909174e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0250219748204341e-07, 'p_ase': 1.4037381715090382e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0517419693459478e-07, 'p_ase': 1.4041032648271591e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0723681687994604e-07, 'p_ase': 1.4044683581452795e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0889018442558702e-07, 'p_ase': 1.4048334514634004e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1024816282619655e-07, 'p_ase': 1.4051985447815205e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1138157242925355e-07, 'p_ase': 1.4055636380996414e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1233733092350734e-07, 'p_ase': 1.4059287314177625e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1314799936113977e-07, 'p_ase': 1.4062938247358823e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1383697733910211e-07, 'p_ase': 1.4066589180540032e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.144215255451386e-07, 'p_ase': 1.4070240113721241e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1491461880647893e-07, 'p_ase': 1.4073891046902445e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1532613026231785e-07, 'p_ase': 1.4077541980083654e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1566361308500002e-07, 'p_ase': 1.4081192913264855e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1593282913007029e-07, 'p_ase': 1.4084843846446064e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1613811187354042e-07, 'p_ase': 1.4088494779627267e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1628261641283344e-07, 'p_ase': 1.4092145712808476e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1636848909760547e-07, 'p_ase': 1.4095796645989688e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1639697696363463e-07, 'p_ase': 1.4099447579170886e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1636848909760547e-07, 'p_ase': 1.4103098512352095e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1628261641283344e-07, 'p_ase': 1.4106749445533298e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1613811187354042e-07, 'p_ase': 1.4110400378714507e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1593282913007029e-07, 'p_ase': 1.4114051311895719e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1566361308499998e-07, 'p_ase': 1.411770224507692e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1532613026231785e-07, 'p_ase': 1.4121353178258123e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1491461880647886e-07, 'p_ase': 1.412500411143933e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.144215255451386e-07, 'p_ase': 1.4128655044620538e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1383697733910211e-07, 'p_ase': 1.4132305977801747e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1314799936113977e-07, 'p_ase': 1.4135956910982951e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1233733092350733e-07, 'p_ase': 1.413960784416416e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1138157242925349e-07, 'p_ase': 1.4143258777345361e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1024816282619645e-07, 'p_ase': 1.4146909710526567e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0889018442558706e-07, 'p_ase': 1.4150560643707779e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0723681687994604e-07, 'p_ase': 1.415421157688898e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0517419693459468e-07, 'p_ase': 1.4157862510070191e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0250219748204337e-07, 'p_ase': 1.4161513443251395e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.881844347292299e-08, 'p_ase': 1.4165164376432601e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.3106699607679377e-08, 'p_ase': 1.416881530961381e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.1144501283798514e-08, 'p_ase': 1.4172466242795011e-07}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #023 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #023 deleted file mode 100644 index ffff7482..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #023 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 18.1300934192 - -osnr_nl_central_channel_db = 15.6829997703 - -osnr_lin_db = [ 18.15264324 18.15151296 18.15038298 18.14925329 18.1481239 - 18.1469948 18.14586599 18.14473748 18.14360926 18.14248133 - 18.14135369 18.14022635 18.1390993 18.13797254 18.13684608 - 18.13571991 18.13459403 18.13346844 18.13234314 18.13121813 - 18.13009342 18.128969 18.12784486 18.12672102 18.12559747 - 18.12447421 18.12335124 18.12222856 18.12110617 18.11998407 - 18.11886226 18.11774074 18.11661951 18.11549857 18.11437792 - 18.11325756 18.11213749 18.1110177 18.10989821 18.108779 - 18.10766008] - -osnr_nl_db = [ 16.30487125 16.08778783 15.98748233 15.92375828 15.87793049 - 15.84273059 15.81458848 15.79149257 15.77220157 15.75589854 - 15.74201972 15.73016188 15.72002883 15.71139867 15.70410303 - 15.69801342 15.69303195 15.68908508 15.68611924 15.68409792 - 15.68299977 15.6828175 15.68355758 15.68524054 15.687902 - 15.69159457 15.69639076 15.70238723 15.70971111 15.7185291 - 15.72906104 15.74160058 15.75654757 15.77446128 15.79615227 - 15.8228524 15.85655822 15.90081351 15.96283361 16.06116787 - 16.2754236 ] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.1144501283798624e-06, 'p_ase': 1.5301558816960107e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.3106699607679513e-06, 'p_ase': 1.5305541653157785e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.8818443472922984e-06, 'p_ase': 1.5309524489355462e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0250219748204341e-05, 'p_ase': 1.5313507325553143e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0517419693459478e-05, 'p_ase': 1.5317490161750827e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0723681687994605e-05, 'p_ase': 1.5321472997948505e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0889018442558702e-05, 'p_ase': 1.5325455834146186e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1024816282619655e-05, 'p_ase': 1.532943867034386e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1138157242925355e-05, 'p_ase': 1.5333421506541541e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1233733092350734e-05, 'p_ase': 1.5337404342739228e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1314799936113977e-05, 'p_ase': 1.5341387178936899e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1383697733910211e-05, 'p_ase': 1.534537001513458e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.144215255451386e-05, 'p_ase': 1.5349352851332264e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1491461880647892e-05, 'p_ase': 1.5353335687529938e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1532613026231785e-05, 'p_ase': 1.5357318523727622e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1566361308500002e-05, 'p_ase': 1.5361301359925296e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1593282913007029e-05, 'p_ase': 1.5365284196122977e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1613811187354041e-05, 'p_ase': 1.5369267032320655e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1628261641283344e-05, 'p_ase': 1.5373249868518339e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1636848909760548e-05, 'p_ase': 1.5377232704716023e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1639697696363463e-05, 'p_ase': 1.5381215540913694e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1636848909760548e-05, 'p_ase': 1.5385198377111375e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1628261641283344e-05, 'p_ase': 1.5389181213309052e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1613811187354041e-05, 'p_ase': 1.5393164049506736e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1593282913007029e-05, 'p_ase': 1.5397146885704421e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1566361308499997e-05, 'p_ase': 1.5401129721902095e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1532613026231785e-05, 'p_ase': 1.5405112558099769e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1491461880647887e-05, 'p_ase': 1.540909539429745e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.144215255451386e-05, 'p_ase': 1.541307823049513e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1383697733910211e-05, 'p_ase': 1.5417061066692815e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1314799936113977e-05, 'p_ase': 1.5421043902890492e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1233733092350733e-05, 'p_ase': 1.5425026739088173e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1138157242925348e-05, 'p_ase': 1.5429009575285847e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1024816282619645e-05, 'p_ase': 1.5432992411483528e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0889018442558706e-05, 'p_ase': 1.5436975247681212e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0723681687994605e-05, 'p_ase': 1.5440958083878886e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0517419693459468e-05, 'p_ase': 1.544494092007657e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0250219748204337e-05, 'p_ase': 1.5448923756274248e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.8818443472922984e-06, 'p_ase': 1.5452906592471929e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.3106699607679378e-06, 'p_ase': 1.545688942866961e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.1144501283798523e-06, 'p_ase': 1.5460872264867284e-05}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #024 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #024 deleted file mode 100644 index fdcf0744..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #024 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 18.1300934192 - -osnr_nl_central_channel_db = 15.5298332279 - -osnr_lin_db = [ 18.15264324 18.15151296 18.15038298 18.14925329 18.1481239 - 18.1469948 18.14586599 18.14473748 18.14360926 18.14248133 - 18.14135369 18.14022635 18.1390993 18.13797254 18.13684608 - 18.13571991 18.13459403 18.13346844 18.13234314 18.13121813 - 18.13009342 18.128969 18.12784486 18.12672102 18.12559747 - 18.12447421 18.12335124 18.12222856 18.12110617 18.11998407 - 18.11886226 18.11774074 18.11661951 18.11549857 18.11437792 - 18.11325756 18.11213749 18.1110177 18.10989821 18.108779 - 18.10766008] - -osnr_nl_db = [ 16.18123304 15.95301367 15.84778576 15.7810151 15.73303975 - 15.69621837 15.6668001 15.64267252 15.62253265 15.60552322 - 15.59105283 15.57869858 15.56814987 15.5591741 15.55159477 - 15.54527722 15.54011888 15.53604269 15.53299253 15.53093017 - 15.52983323 15.52969411 15.5305196 15.53233121 15.53516631 - 15.53908004 15.5441484 15.55047274 15.55818634 15.56746398 - 15.57853624 15.59171108 15.60740802 15.62621388 15.64897945 - 15.67699815 15.71236708 15.75881032 15.82391514 15.9272047 - 16.15260918] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.7906543057448505e-08, 'p_ase': 1.5301558816960108e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0086559124165281e-07, 'p_ase': 1.5305541653157786e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0705331376233324e-07, 'p_ase': 1.5309524489355464e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1104404727221369e-07, 'p_ase': 1.5313507325553144e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1393871334581101e-07, 'p_ase': 1.5317490161750828e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1617321828660822e-07, 'p_ase': 1.5321472997948506e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1796436646105262e-07, 'p_ase': 1.5325455834146187e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.194355097283796e-07, 'p_ase': 1.532943867034386e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2066337013169135e-07, 'p_ase': 1.533342150654154e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2169877516713296e-07, 'p_ase': 1.5337404342739229e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2257699930790142e-07, 'p_ase': 1.5341387178936899e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2332339211736061e-07, 'p_ase': 1.534537001513458e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2395665267390014e-07, 'p_ase': 1.5349352851332263e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2449083704035216e-07, 'p_ase': 1.5353335687529939e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.24936641117511e-07, 'p_ase': 1.5357318523727622e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2530224750875003e-07, 'p_ase': 1.5361301359925297e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2559389822424282e-07, 'p_ase': 1.5365284196122978e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2581628786300211e-07, 'p_ase': 1.5369267032320656e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2597283444723622e-07, 'p_ase': 1.537324986851834e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2606586318907259e-07, 'p_ase': 1.5377232704716023e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2609672504393753e-07, 'p_ase': 1.5381215540913693e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2606586318907259e-07, 'p_ase': 1.5385198377111374e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2597283444723622e-07, 'p_ase': 1.5389181213309052e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2581628786300211e-07, 'p_ase': 1.5393164049506735e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2559389822424282e-07, 'p_ase': 1.5397146885704421e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2530224750874997e-07, 'p_ase': 1.5401129721902094e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.24936641117511e-07, 'p_ase': 1.540511255809977e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2449083704035211e-07, 'p_ase': 1.540909539429745e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2395665267390014e-07, 'p_ase': 1.5413078230495131e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2332339211736061e-07, 'p_ase': 1.5417061066692815e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2257699930790142e-07, 'p_ase': 1.5421043902890493e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2169877516713293e-07, 'p_ase': 1.5425026739088173e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2066337013169127e-07, 'p_ase': 1.5429009575285846e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1943550972837949e-07, 'p_ase': 1.5432992411483527e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1796436646105265e-07, 'p_ase': 1.5436975247681213e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1617321828660822e-07, 'p_ase': 1.5440958083878886e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1393871334581091e-07, 'p_ase': 1.5444940920076572e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1104404727221366e-07, 'p_ase': 1.5448923756274247e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0705331376233324e-07, 'p_ase': 1.5452906592471928e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0086559124165266e-07, 'p_ase': 1.5456889428669609e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 8.7906543057448399e-08, 'p_ase': 1.5460872264867284e-07}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #025 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #025 deleted file mode 100644 index 05a9ace4..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #025 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 17.7824723566 - -osnr_nl_central_channel_db = 15.3353787077 - -osnr_lin_db = [ 17.80502218 17.8038919 17.80276192 17.80163223 17.80050284 - 17.79937374 17.79824493 17.79711641 17.79598819 17.79486027 - 17.79373263 17.79260529 17.79147824 17.79035148 17.78922502 - 17.78809884 17.78697296 17.78584737 17.78472208 17.78359707 - 17.78247236 17.78134793 17.7802238 17.77909996 17.77797641 - 17.77685315 17.77573018 17.7746075 17.77348511 17.77236301 - 17.7712412 17.77011968 17.76899845 17.76787751 17.76675686 - 17.7656365 17.76451642 17.76339664 17.76227714 17.76115794 - 17.76003902] - -osnr_nl_db = [ 15.95725018 15.74016677 15.63986127 15.57613722 15.53030943 - 15.49510953 15.46696741 15.44387151 15.42458051 15.40827748 - 15.39439865 15.38254082 15.37240777 15.3637776 15.35648197 - 15.35039236 15.34541089 15.34146401 15.33849818 15.33647686 - 15.33537871 15.33519644 15.33593652 15.33761948 15.34028093 - 15.34397351 15.34876969 15.35476617 15.36209005 15.37090804 - 15.38143998 15.39397952 15.40892651 15.42684022 15.44853121 - 15.47523134 15.50893716 15.55319244 15.61521255 15.71354681 - 15.92780253] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.7906543057448512e-06, 'p_ase': 1.6576688718373451e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0086559124165281e-05, 'p_ase': 1.6581003457587599e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0705331376233323e-05, 'p_ase': 1.658531819680175e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1104404727221369e-05, 'p_ase': 1.6589632936015905e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1393871334581101e-05, 'p_ase': 1.6593947675230063e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1617321828660822e-05, 'p_ase': 1.6598262414444215e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1796436646105263e-05, 'p_ase': 1.6602577153658366e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.194355097283796e-05, 'p_ase': 1.6606891892872514e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2066337013169136e-05, 'p_ase': 1.6611206632086669e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2169877516713295e-05, 'p_ase': 1.6615521371300831e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2257699930790142e-05, 'p_ase': 1.6619836110514975e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2332339211736062e-05, 'p_ase': 1.6624150849729127e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2395665267390014e-05, 'p_ase': 1.6628465588943285e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2449083704035216e-05, 'p_ase': 1.6632780328157433e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.24936641117511e-05, 'p_ase': 1.6637095067371591e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2530224750875002e-05, 'p_ase': 1.6641409806585739e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2559389822424282e-05, 'p_ase': 1.6645724545799891e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2581628786300212e-05, 'p_ase': 1.6650039285014042e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2597283444723623e-05, 'p_ase': 1.66543540242282e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.260658631890726e-05, 'p_ase': 1.6658668763442358e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2609672504393753e-05, 'p_ase': 1.6662983502656503e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.260658631890726e-05, 'p_ase': 1.6667298241870655e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2597283444723623e-05, 'p_ase': 1.6671612981084806e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2581628786300212e-05, 'p_ase': 1.6675927720298964e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2559389822424282e-05, 'p_ase': 1.6680242459513122e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2530224750874997e-05, 'p_ase': 1.668455719872727e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.24936641117511e-05, 'p_ase': 1.6688871937941415e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2449083704035211e-05, 'p_ase': 1.669318667715557e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2395665267390014e-05, 'p_ase': 1.6697501416369725e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2332339211736062e-05, 'p_ase': 1.6701816155583883e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2257699930790142e-05, 'p_ase': 1.6706130894798034e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2169877516713294e-05, 'p_ase': 1.6710445634012186e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2066337013169127e-05, 'p_ase': 1.6714760373226334e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1943550972837948e-05, 'p_ase': 1.6719075112440489e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1796436646105265e-05, 'p_ase': 1.6723389851654647e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1617321828660822e-05, 'p_ase': 1.6727704590868795e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1393871334581091e-05, 'p_ase': 1.673201933008295e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1104404727221366e-05, 'p_ase': 1.6736334069297101e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0705331376233323e-05, 'p_ase': 1.6740648808511256e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0086559124165266e-05, 'p_ase': 1.6744963547725411e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 8.7906543057448394e-06, 'p_ase': 1.6749278286939559e-05}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #026 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #026 deleted file mode 100644 index 8b5e856d..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #026 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 17.7824723566 - -osnr_nl_central_channel_db = 15.1938043213 - -osnr_lin_db = [ 17.80502218 17.8038919 17.80276192 17.80163223 17.80050284 - 17.79937374 17.79824493 17.79711641 17.79598819 17.79486027 - 17.79373263 17.79260529 17.79147824 17.79035148 17.78922502 - 17.78809884 17.78697296 17.78584737 17.78472208 17.78359707 - 17.78247236 17.78134793 17.7802238 17.77909996 17.77797641 - 17.77685315 17.77573018 17.7746075 17.77348511 17.77236301 - 17.7712412 17.77011968 17.76899845 17.76787751 17.76675686 - 17.7656365 17.76451642 17.76339664 17.76227714 17.76115794 - 17.76003902] - -osnr_nl_db = [ 15.84299864 15.61561265 15.51075249 15.44420925 15.39639412 - 15.35969366 15.33037053 15.30631985 15.28624322 15.26928643 - 15.25486011 15.24254283 15.23202508 15.22307505 15.21551685 - 15.20921627 15.2040711 15.20000453 15.19696066 15.19490135 - 15.19380432 15.19366199 15.19448112 15.19628315 15.19910532 - 15.20300258 15.20805067 15.2143506 15.22203517 15.23127858 - 15.24231058 15.2554381 15.27107915 15.28981852 15.31250399 - 15.34042437 15.37566926 15.42194925 15.48682376 15.58974306 - 15.81431306] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.4668584831098403e-08, 'p_ase': 1.6576688718373451e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0862448287562611e-07, 'p_ase': 1.6581003457587599e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1528818405174348e-07, 'p_ase': 1.6585318196801751e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.19585897062384e-07, 'p_ase': 1.6589632936015907e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2270322975702725e-07, 'p_ase': 1.6593947675230064e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.251096196932704e-07, 'p_ase': 1.6598262414444214e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2703854849651822e-07, 'p_ase': 1.6602577153658367e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2862285663056265e-07, 'p_ase': 1.6606891892872514e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2994516783412915e-07, 'p_ase': 1.6611206632086669e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3106021941075858e-07, 'p_ase': 1.661552137130083e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3200599925466307e-07, 'p_ase': 1.6619836110514975e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3280980689561912e-07, 'p_ase': 1.6624150849729127e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3349177980266169e-07, 'p_ase': 1.6628465588943285e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.340670552742254e-07, 'p_ase': 1.6632780328157432e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3454715197270414e-07, 'p_ase': 1.663709506737159e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3494088193250004e-07, 'p_ase': 1.664140980658574e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3525496731841534e-07, 'p_ase': 1.664572454579989e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3549446385246383e-07, 'p_ase': 1.6650039285014043e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3566305248163899e-07, 'p_ase': 1.6654354024228201e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.357632372805397e-07, 'p_ase': 1.6658668763442359e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3579647312424041e-07, 'p_ase': 1.6662983502656503e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.357632372805397e-07, 'p_ase': 1.6667298241870656e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3566305248163899e-07, 'p_ase': 1.6671612981084806e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3549446385246383e-07, 'p_ase': 1.6675927720298964e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3525496731841534e-07, 'p_ase': 1.6680242459513121e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3494088193249998e-07, 'p_ase': 1.6684557198727271e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3454715197270414e-07, 'p_ase': 1.6688871937941416e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3406705527422535e-07, 'p_ase': 1.6693186677155571e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3349177980266169e-07, 'p_ase': 1.6697501416369724e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3280980689561912e-07, 'p_ase': 1.6701816155583884e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3200599925466307e-07, 'p_ase': 1.6706130894798034e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3106021941075853e-07, 'p_ase': 1.6710445634012187e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2994516783412908e-07, 'p_ase': 1.6714760373226334e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2862285663056254e-07, 'p_ase': 1.671907511244049e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2703854849651822e-07, 'p_ase': 1.6723389851654647e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.251096196932704e-07, 'p_ase': 1.6727704590868795e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2270322975702714e-07, 'p_ase': 1.673201933008295e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1958589706238394e-07, 'p_ase': 1.6736334069297103e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1528818405174348e-07, 'p_ase': 1.6740648808511255e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0862448287562594e-07, 'p_ase': 1.674496354772541e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 9.4668584831098271e-08, 'p_ase': 1.674927828693956e-07}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #027 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #027 deleted file mode 100644 index 79e61a4c..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #027 +++ /dev/null @@ -1,26 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 17.4606255229 - -osnr_nl_central_channel_db = 15.013531874 - -osnr_lin_db = [ 17.48317534 17.48204507 17.48091509 17.4797854 17.478656 17.4775269 - 17.4763981 17.47526958 17.47414136 17.47301343 17.4718858 - 17.47075846 17.46963141 17.46850465 17.46737818 17.46625201 - 17.46512613 17.46400054 17.46287524 17.46175024 17.46062552 - 17.4595011 17.45837697 17.45725313 17.45612957 17.45500631 - 17.45388335 17.45276067 17.45163828 17.45051618 17.44939437 - 17.44827285 17.44715162 17.44603068 17.44491003 17.44378966 - 17.44266959 17.44154981 17.44043031 17.4393111 17.43819218] - -osnr_nl_db = [ 15.63540335 15.41831994 15.31801444 15.25429038 15.2084626 - 15.17326269 15.14512058 15.12202467 15.10273367 15.08643065 - 15.07255182 15.06069399 15.05056093 15.04193077 15.03463514 - 15.02854552 15.02356405 15.01961718 15.01665134 15.01463003 - 15.01353187 15.01334961 15.01408969 15.01577264 15.0184341 - 15.02212667 15.02692286 15.03291933 15.04024321 15.0490612 - 15.05959314 15.07213268 15.08707968 15.10499338 15.12668437 - 15.15338451 15.18709032 15.23134561 15.29336571 15.39169997 - 15.6059557 ] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.4668584831098401e-06, 'p_ase': 1.7851818619786794e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0862448287562611e-05, 'p_ase': 1.7856465262017413e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1528818405174348e-05, 'p_ase': 1.7861111904248038e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.19585897062384e-05, 'p_ase': 1.7865758546478667e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2270322975702724e-05, 'p_ase': 1.7870405188709299e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.251096196932704e-05, 'p_ase': 1.7875051830939925e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2703854849651822e-05, 'p_ase': 1.7879698473170547e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2862285663056264e-05, 'p_ase': 1.7884345115401169e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2994516783412915e-05, 'p_ase': 1.7888991757631797e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3106021941075858e-05, 'p_ase': 1.7893638399862433e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3200599925466307e-05, 'p_ase': 1.7898285042093052e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3280980689561912e-05, 'p_ase': 1.7902931684323674e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3349177980266169e-05, 'p_ase': 1.7907578326554306e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3406705527422541e-05, 'p_ase': 1.7912224968784928e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3454715197270414e-05, 'p_ase': 1.791687161101556e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3494088193250003e-05, 'p_ase': 1.7921518253246182e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3525496731841534e-05, 'p_ase': 1.7926164895476804e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3549446385246382e-05, 'p_ase': 1.7930811537707429e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3566305248163899e-05, 'p_ase': 1.7935458179938062e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.357632372805397e-05, 'p_ase': 1.7940104822168694e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3579647312424042e-05, 'p_ase': 1.7944751464399312e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.357632372805397e-05, 'p_ase': 1.7949398106629934e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3566305248163899e-05, 'p_ase': 1.795404474886056e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3549446385246382e-05, 'p_ase': 1.7958691391091192e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3525496731841534e-05, 'p_ase': 1.7963338033321824e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3494088193249998e-05, 'p_ase': 1.7967984675552446e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3454715197270414e-05, 'p_ase': 1.7972631317783061e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3406705527422534e-05, 'p_ase': 1.797727796001369e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3349177980266169e-05, 'p_ase': 1.7981924602244319e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3280980689561912e-05, 'p_ase': 1.7986571244474951e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3200599925466307e-05, 'p_ase': 1.7991217886705576e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3106021941075853e-05, 'p_ase': 1.7995864528936198e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2994516783412907e-05, 'p_ase': 1.800051117116682e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2862285663056254e-05, 'p_ase': 1.8005157813397449e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2703854849651822e-05, 'p_ase': 1.8009804455628081e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.251096196932704e-05, 'p_ase': 1.8014451097858703e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2270322975702714e-05, 'p_ase': 1.8019097740089329e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1958589706238395e-05, 'p_ase': 1.8023744382319954e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1528818405174348e-05, 'p_ase': 1.8028391024550583e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0862448287562594e-05, 'p_ase': 1.8033037666781212e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 9.4668584831098265e-06, 'p_ase': 1.8037684309011834e-05}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #028 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #028 deleted file mode 100644 index 4b16c136..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #028 +++ /dev/null @@ -1,26 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 17.4606255229 - -osnr_nl_central_channel_db = 14.8819183079 - -osnr_lin_db = [ 17.48317534 17.48204507 17.48091509 17.4797854 17.478656 17.4775269 - 17.4763981 17.47526958 17.47414136 17.47301343 17.4718858 - 17.47075846 17.46963141 17.46850465 17.46737818 17.46625201 - 17.46512613 17.46400054 17.46287524 17.46175024 17.46062552 - 17.4595011 17.45837697 17.45725313 17.45612957 17.45500631 - 17.45388335 17.45276067 17.45163828 17.45051618 17.44939437 - 17.44827285 17.44715162 17.44603068 17.44491003 17.44378966 - 17.44266959 17.44154981 17.44043031 17.4393111 17.43819218] - -osnr_nl_db = [ 15.5292137 15.30254504 15.1980015 15.13165408 15.08397692 - 15.0473806 15.01813941 14.99415495 14.97413281 14.95722135 - 14.94283299 14.93054756 14.92005648 14.91112861 14.90358862 - 14.89730266 14.89216883 14.88811056 14.88507209 14.88301541 - 14.88191831 14.88177321 14.88258686 14.88438064 14.88719167 - 14.89107475 14.89610538 14.90238427 14.91004384 14.91925776 - 14.9302551 14.94334184 14.95893476 14.97761687 15.00023335 - 15.02806906 15.06320712 15.10934653 15.17402274 15.27662326 - 15.500475 ] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0143062660474829e-07, 'p_ase': 1.7851818619786795e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1638337450959941e-07, 'p_ase': 1.7856465262017414e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2352305434115374e-07, 'p_ase': 1.7861111904248039e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2812774685255428e-07, 'p_ase': 1.7865758546478669e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3146774616824348e-07, 'p_ase': 1.7870405188709301e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3404602109993258e-07, 'p_ase': 1.7875051830939925e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.361127305319838e-07, 'p_ase': 1.7879698473170547e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.378102035327457e-07, 'p_ase': 1.7884345115401169e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3922696553656696e-07, 'p_ase': 1.7888991757631799e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.404216636543842e-07, 'p_ase': 1.7893638399862434e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4143499920142472e-07, 'p_ase': 1.7898285042093053e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4229622167387763e-07, 'p_ase': 1.7902931684323675e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4302690693142324e-07, 'p_ase': 1.7907578326554307e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4364327350809864e-07, 'p_ase': 1.7912224968784929e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4415766282789729e-07, 'p_ase': 1.7916871611015561e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4457951635625005e-07, 'p_ase': 1.7921518253246183e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4491603641258787e-07, 'p_ase': 1.7926164895476805e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4517263984192554e-07, 'p_ase': 1.7930811537707429e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4535327051604176e-07, 'p_ase': 1.7935458179938062e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4546061137200681e-07, 'p_ase': 1.7940104822168694e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4549622120454329e-07, 'p_ase': 1.7944751464399313e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4546061137200681e-07, 'p_ase': 1.7949398106629935e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4535327051604176e-07, 'p_ase': 1.7954044748860559e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4517263984192554e-07, 'p_ase': 1.7958691391091192e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4491603641258787e-07, 'p_ase': 1.7963338033321824e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4457951635624999e-07, 'p_ase': 1.7967984675552446e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4415766282789729e-07, 'p_ase': 1.7972631317783062e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4364327350809859e-07, 'p_ase': 1.797727796001369e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4302690693142324e-07, 'p_ase': 1.7981924602244319e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4229622167387763e-07, 'p_ase': 1.7986571244474952e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4143499920142472e-07, 'p_ase': 1.7991217886705576e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4042166365438412e-07, 'p_ase': 1.7995864528936198e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3922696553656688e-07, 'p_ase': 1.800051117116682e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3781020353274559e-07, 'p_ase': 1.8005157813397449e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.361127305319838e-07, 'p_ase': 1.8009804455628082e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3404602109993258e-07, 'p_ase': 1.8014451097858704e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3146774616824338e-07, 'p_ase': 1.8019097740089328e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2812774685255422e-07, 'p_ase': 1.8023744382319955e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2352305434115374e-07, 'p_ase': 1.8028391024550582e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1638337450959923e-07, 'p_ase': 1.8033037666781212e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0143062660474814e-07, 'p_ase': 1.8037684309011834e-07}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #029 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #029 deleted file mode 100644 index f2c4da54..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #029 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 17.1609932891 - -osnr_nl_central_channel_db = 14.7138996402 - -osnr_lin_db = [ 17.18354311 17.18241283 17.18128285 17.18015316 17.17902377 - 17.17789467 17.17676586 17.17563735 17.17450913 17.1733812 - 17.17225356 17.17112622 17.16999917 17.16887241 17.16774595 - 17.16661978 17.1654939 17.16436831 17.16324301 17.162118 - 17.16099329 17.15986887 17.15874473 17.15762089 17.15649734 - 17.15537408 17.15425111 17.15312843 17.15200604 17.15088394 - 17.14976213 17.14864061 17.14751938 17.14639844 17.14527779 - 17.14415743 17.14303736 17.14191757 17.14079808 17.13967887 - 17.13855995] - -osnr_nl_db = [ 15.33577112 15.1186877 15.0183822 14.95465815 14.90883036 - 14.87363046 14.84548834 14.82239244 14.80310144 14.78679841 - 14.77291959 14.76106175 14.7509287 14.74229854 14.7350029 - 14.72891329 14.72393182 14.71998495 14.71701911 14.71499779 - 14.71389964 14.71371737 14.71445745 14.71614041 14.71880187 - 14.72249444 14.72729063 14.7332871 14.74061098 14.74942897 - 14.75996091 14.77250045 14.78744744 14.80536115 14.82705214 - 14.85375227 14.88745809 14.93171337 14.99373348 15.09206774 - 15.30632347] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0143062660474829e-05, 'p_ase': 1.9126948521200138e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1638337450959941e-05, 'p_ase': 1.9131927066447227e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2352305434115374e-05, 'p_ase': 1.9136905611694326e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2812774685255427e-05, 'p_ase': 1.9141884156941429e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3146774616824349e-05, 'p_ase': 1.9146862702188539e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3404602109993258e-05, 'p_ase': 1.9151841247435635e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3611273053198381e-05, 'p_ase': 1.9156819792682727e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3781020353274569e-05, 'p_ase': 1.9161798337929823e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3922696553656696e-05, 'p_ase': 1.9166776883176926e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4042166365438421e-05, 'p_ase': 1.9171755428424035e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4143499920142472e-05, 'p_ase': 1.9176733973671128e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4229622167387763e-05, 'p_ase': 1.9181712518918221e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4302690693142325e-05, 'p_ase': 1.9186691064165327e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4364327350809864e-05, 'p_ase': 1.9191669609412423e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4415766282789729e-05, 'p_ase': 1.9196648154659529e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4457951635625005e-05, 'p_ase': 1.9201626699906625e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4491603641258787e-05, 'p_ase': 1.9206605245153717e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4517263984192553e-05, 'p_ase': 1.9211583790400817e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4535327051604176e-05, 'p_ase': 1.9216562335647923e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4546061137200682e-05, 'p_ase': 1.9221540880895029e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.454962212045433e-05, 'p_ase': 1.9226519426142122e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4546061137200682e-05, 'p_ase': 1.9231497971389214e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4535327051604176e-05, 'p_ase': 1.9236476516636314e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4517263984192553e-05, 'p_ase': 1.924145506188342e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4491603641258787e-05, 'p_ase': 1.9246433607130526e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4457951635625e-05, 'p_ase': 1.9251412152377622e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4415766282789729e-05, 'p_ase': 1.9256390697624708e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4364327350809859e-05, 'p_ase': 1.926136924287181e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4302690693142325e-05, 'p_ase': 1.9266347788118913e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4229622167387763e-05, 'p_ase': 1.9271326333366019e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4143499920142472e-05, 'p_ase': 1.9276304878613119e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4042166365438412e-05, 'p_ase': 1.9281283423860211e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3922696553656688e-05, 'p_ase': 1.9286261969107307e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3781020353274559e-05, 'p_ase': 1.929124051435441e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3611273053198381e-05, 'p_ase': 1.9296219059601516e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3404602109993258e-05, 'p_ase': 1.9301197604848612e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3146774616824337e-05, 'p_ase': 1.9306176150095708e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2812774685255422e-05, 'p_ase': 1.9311154695342807e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2352305434115374e-05, 'p_ase': 1.931613324058991e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1638337450959922e-05, 'p_ase': 1.9321111785837013e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0143062660474814e-05, 'p_ase': 1.9326090331084109e-05}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #030 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #030 deleted file mode 100644 index 2105fced..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #030 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 17.1609932891 - -osnr_nl_central_channel_db = 14.5909373035 - -osnr_lin_db = [ 17.18354311 17.18241283 17.18128285 17.18015316 17.17902377 - 17.17789467 17.17676586 17.17563735 17.17450913 17.1733812 - 17.17225356 17.17112622 17.16999917 17.16887241 17.16774595 - 17.16661978 17.1654939 17.16436831 17.16324301 17.162118 - 17.16099329 17.15986887 17.15874473 17.15762089 17.15649734 - 17.15537408 17.15425111 17.15312843 17.15200604 17.15088394 - 17.14976213 17.14864061 17.14751938 17.14639844 17.14527779 - 17.14415743 17.14303736 17.14191757 17.14079808 17.13967887 - 17.13855995] - -osnr_nl_db = [ 15.23658056 15.01053585 14.90626777 14.84009074 14.79253364 - 14.75602794 14.72685805 14.70293123 14.68295651 14.6660845 - 14.65172917 14.63947146 14.62900359 14.62009502 14.61257086 - 14.60629763 14.60117367 14.59712262 14.59408886 14.59203448 - 14.5909373 14.5907898 14.59159868 14.59338528 14.59618661 - 14.60005734 14.60507278 14.61133337 14.61897118 14.62815943 - 14.63912659 14.65217785 14.66772888 14.68636115 14.7089176 - 14.73667963 14.77172473 14.8177418 14.88224548 14.98456866 - 15.20779566] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0819266837839817e-07, 'p_ase': 1.9126948521200139e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2414226614357271e-07, 'p_ase': 1.9131927066447227e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3175792463056399e-07, 'p_ase': 1.9136905611694326e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3666959664272456e-07, 'p_ase': 1.9141884156941431e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4023226257945972e-07, 'p_ase': 1.914686270218854e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4298242250659476e-07, 'p_ase': 1.9151841247435634e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4518691256744938e-07, 'p_ase': 1.9156819792682727e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4699755043492875e-07, 'p_ase': 1.9161798337929824e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4850876323900476e-07, 'p_ase': 1.9166776883176925e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4978310789800983e-07, 'p_ase': 1.9171755428424035e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5086399914818637e-07, 'p_ase': 1.9176733973671128e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5178263645213614e-07, 'p_ase': 1.9181712518918222e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5256203406018479e-07, 'p_ase': 1.9186691064165326e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5321949174197188e-07, 'p_ase': 1.9191669609412423e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5376817368309043e-07, 'p_ase': 1.919664815465953e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5421815078000006e-07, 'p_ase': 1.9201626699906626e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5457710550676039e-07, 'p_ase': 1.9206605245153717e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5485081583138725e-07, 'p_ase': 1.9211583790400818e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5504348855044454e-07, 'p_ase': 1.9216562335647923e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5515798546347392e-07, 'p_ase': 1.922154088089503e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5519596928484617e-07, 'p_ase': 1.9226519426142123e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5515798546347392e-07, 'p_ase': 1.9231497971389214e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5504348855044454e-07, 'p_ase': 1.9236476516636313e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5485081583138725e-07, 'p_ase': 1.924145506188342e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5457710550676039e-07, 'p_ase': 1.9246433607130527e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5421815078e-07, 'p_ase': 1.9251412152377623e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5376817368309043e-07, 'p_ase': 1.9256390697624709e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5321949174197182e-07, 'p_ase': 1.926136924287181e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5256203406018479e-07, 'p_ase': 1.9266347788118915e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5178263645213614e-07, 'p_ase': 1.9271326333366019e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5086399914818637e-07, 'p_ase': 1.9276304878613118e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4978310789800972e-07, 'p_ase': 1.9281283423860211e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4850876323900468e-07, 'p_ase': 1.9286261969107308e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4699755043492864e-07, 'p_ase': 1.9291240514354409e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4518691256744938e-07, 'p_ase': 1.9296219059601516e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4298242250659476e-07, 'p_ase': 1.9301197604848613e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4023226257945961e-07, 'p_ase': 1.9306176150095709e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3666959664272451e-07, 'p_ase': 1.9311154695342808e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3175792463056399e-07, 'p_ase': 1.9316133240589909e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2414226614357249e-07, 'p_ase': 1.9321111785837014e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.0819266837839801e-07, 'p_ase': 1.932609033108411e-07}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #031 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #031 deleted file mode 100644 index b4d852a0..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #031 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 16.8807060531 - -osnr_nl_central_channel_db = 14.4336124042 - -osnr_lin_db = [ 16.90325587 16.9021256 16.90099562 16.89986593 16.89873653 - 16.89760743 16.89647863 16.89535011 16.89422189 16.89309396 - 16.89196633 16.89083899 16.88971194 16.88858518 16.88745871 - 16.88633254 16.88520666 16.88408107 16.88295577 16.88183077 - 16.88070605 16.87958163 16.8784575 16.87733366 16.87621011 - 16.87508685 16.87396388 16.8728412 16.87171881 16.87059671 - 16.8694749 16.86835338 16.86723215 16.86611121 16.86499056 - 16.86387019 16.86275012 16.86163034 16.86051084 16.85939163 - 16.85827271] - -osnr_nl_db = [ 15.05548388 14.83840047 14.73809497 14.67437091 14.62854313 - 14.59334322 14.56520111 14.5421052 14.5228142 14.50651118 - 14.49263235 14.48077452 14.47064146 14.4620113 14.45471567 - 14.44862605 14.44364458 14.43969771 14.43673187 14.43471056 - 14.4336124 14.43343014 14.43417022 14.43585317 14.43851463 - 14.4422072 14.44700339 14.45299986 14.46032374 14.46914173 - 14.47967367 14.49221321 14.50716021 14.52507391 14.54676491 - 14.57346504 14.60717085 14.65142614 14.71344624 14.8117805 - 15.02603623] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0819266837839818e-05, 'p_ase': 2.0402078422613481e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2414226614357271e-05, 'p_ase': 2.0407388870877041e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3175792463056398e-05, 'p_ase': 2.0412699319140614e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3666959664272456e-05, 'p_ase': 2.0418009767404191e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4023226257945972e-05, 'p_ase': 2.0423320215667774e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4298242250659476e-05, 'p_ase': 2.0428630663931344e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4518691256744938e-05, 'p_ase': 2.0433941112194908e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4699755043492875e-05, 'p_ase': 2.0439251560458478e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4850876323900476e-05, 'p_ase': 2.0444562008722054e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4978310789800983e-05, 'p_ase': 2.0449872456985638e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5086399914818637e-05, 'p_ase': 2.0455182905249204e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5178263645213614e-05, 'p_ase': 2.0460493353512767e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5256203406018478e-05, 'p_ase': 2.0465803801776348e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5321949174197188e-05, 'p_ase': 2.0471114250039917e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5376817368309043e-05, 'p_ase': 2.0476424698303498e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5421815078000004e-05, 'p_ase': 2.0481735146567068e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5457710550676038e-05, 'p_ase': 2.0487045594830631e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5485081583138724e-05, 'p_ase': 2.0492356043094204e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5504348855044454e-05, 'p_ase': 2.0497666491357784e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.551579854634739e-05, 'p_ase': 2.0502976939621364e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5519596928484617e-05, 'p_ase': 2.0508287387884931e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.551579854634739e-05, 'p_ase': 2.0513597836148494e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5504348855044454e-05, 'p_ase': 2.0518908284412067e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5485081583138724e-05, 'p_ase': 2.0524218732675647e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5457710550676038e-05, 'p_ase': 2.0529529180939227e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5421815078000001e-05, 'p_ase': 2.0534839629202797e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5376817368309043e-05, 'p_ase': 2.0540150077466354e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5321949174197181e-05, 'p_ase': 2.0545460525729931e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5256203406018478e-05, 'p_ase': 2.0550770973993507e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5178263645213614e-05, 'p_ase': 2.0556081422257087e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5086399914818637e-05, 'p_ase': 2.0561391870520661e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4978310789800972e-05, 'p_ase': 2.0566702318784224e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4850876323900467e-05, 'p_ase': 2.0572012767047794e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4699755043492865e-05, 'p_ase': 2.0577323215311371e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4518691256744938e-05, 'p_ase': 2.0582633663574951e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4298242250659476e-05, 'p_ase': 2.0587944111838521e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4023226257945962e-05, 'p_ase': 2.0593254560102087e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3666959664272451e-05, 'p_ase': 2.059856500836566e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3175792463056398e-05, 'p_ase': 2.0603875456629237e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2414226614357249e-05, 'p_ase': 2.0609185904892814e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.0819266837839801e-05, 'p_ase': 2.0614496353156384e-05}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #032 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #032 deleted file mode 100644 index 4e601c7e..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #032 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 16.8807060531 - -osnr_nl_central_channel_db = 14.3182340559 - -osnr_lin_db = [ 16.90325587 16.9021256 16.90099562 16.89986593 16.89873653 - 16.89760743 16.89647863 16.89535011 16.89422189 16.89309396 - 16.89196633 16.89083899 16.88971194 16.88858518 16.88745871 - 16.88633254 16.88520666 16.88408107 16.88295577 16.88183077 - 16.88070605 16.87958163 16.8784575 16.87733366 16.87621011 - 16.87508685 16.87396388 16.8728412 16.87171881 16.87059671 - 16.8694749 16.86835338 16.86723215 16.86611121 16.86499056 - 16.86387019 16.86275012 16.86163034 16.86051084 16.85939163 - 16.85827271] - -osnr_nl_db = [ 14.96242681 14.73692977 14.63290353 14.5668761 14.51942442 - 14.4829983 14.45389102 14.43001482 14.41008173 14.39324438 - 14.37891806 14.3666847 14.35623721 14.34734559 14.33983534 - 14.33357328 14.328458 14.32441329 14.32138366 14.31933129 - 14.31823406 14.31808443 14.31888913 14.32066942 14.32346224 - 14.32732213 14.33232422 14.33856873 14.34618743 14.35535313 - 14.3662938 14.3793139 14.39482815 14.41341665 14.43592037 - 14.4636177 14.49858117 14.54449083 14.60884303 14.71092272 - 14.93360136] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1495471015204806e-07, 'p_ase': 2.040207842261348e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3190115777754599e-07, 'p_ase': 2.040738887087704e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3999279491997423e-07, 'p_ase': 2.0412699319140614e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4521144643289484e-07, 'p_ase': 2.0418009767404193e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4899677899067595e-07, 'p_ase': 2.0423320215667774e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5191882391325694e-07, 'p_ase': 2.0428630663931345e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5426109460291496e-07, 'p_ase': 2.0433941112194908e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.561848973371118e-07, 'p_ase': 2.0439251560458478e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5779056094144256e-07, 'p_ase': 2.0444562008722055e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5914455214163545e-07, 'p_ase': 2.0449872456985639e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6029299909494801e-07, 'p_ase': 2.0455182905249204e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6126905123039464e-07, 'p_ase': 2.0460493353512767e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6209716118894634e-07, 'p_ase': 2.0465803801776348e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6279570997584511e-07, 'p_ase': 2.0471114250039919e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6337868453828358e-07, 'p_ase': 2.0476424698303498e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6385678520375007e-07, 'p_ase': 2.0481735146567069e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6423817460093291e-07, 'p_ase': 2.0487045594830631e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6452899182084896e-07, 'p_ase': 2.0492356043094205e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6473370658484731e-07, 'p_ase': 2.0497666491357784e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6485535955494103e-07, 'p_ase': 2.0502976939621365e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6489571736514905e-07, 'p_ase': 2.050828738788493e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6485535955494103e-07, 'p_ase': 2.0513597836148493e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6473370658484731e-07, 'p_ase': 2.0518908284412067e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6452899182084896e-07, 'p_ase': 2.0524218732675648e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6423817460093291e-07, 'p_ase': 2.0529529180939227e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6385678520375001e-07, 'p_ase': 2.0534839629202798e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6337868453828358e-07, 'p_ase': 2.0540150077466355e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6279570997584506e-07, 'p_ase': 2.0545460525729931e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6209716118894634e-07, 'p_ase': 2.0550770973993507e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6126905123039464e-07, 'p_ase': 2.0556081422257089e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6029299909494801e-07, 'p_ase': 2.0561391870520662e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5914455214163532e-07, 'p_ase': 2.0566702318784225e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5779056094144248e-07, 'p_ase': 2.0572012767047793e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5618489733711169e-07, 'p_ase': 2.0577323215311372e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5426109460291496e-07, 'p_ase': 2.0582633663574951e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5191882391325694e-07, 'p_ase': 2.0587944111838521e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4899677899067585e-07, 'p_ase': 2.0593254560102087e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4521144643289479e-07, 'p_ase': 2.059856500836566e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3999279491997423e-07, 'p_ase': 2.0603875456629236e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3190115777754578e-07, 'p_ase': 2.0609185904892815e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.1495471015204789e-07, 'p_ase': 2.0614496353156383e-07}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #033 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #033 deleted file mode 100644 index b4050965..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #033 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 16.6174166659 - -osnr_nl_central_channel_db = 14.170323017 - -osnr_lin_db = [ 16.63996649 16.63883621 16.63770623 16.63657654 16.63544715 - 16.63431805 16.63318924 16.63206072 16.6309325 16.62980458 - 16.62867694 16.6275496 16.62642255 16.62529579 16.62416933 - 16.62304315 16.62191727 16.62079168 16.61966639 16.61854138 - 16.61741667 16.61629224 16.61516811 16.61404427 16.61292072 - 16.61179746 16.61067449 16.60955181 16.60842942 16.60730732 - 16.60618551 16.60506399 16.60394276 16.60282182 16.60170117 - 16.60058081 16.59946073 16.59834095 16.59722145 16.59610224 - 16.59498333] - -osnr_nl_db = [ 14.79219449 14.57511108 14.47480558 14.41108152 14.36525374 - 14.33005384 14.30191172 14.27881582 14.25952482 14.24322179 - 14.22934296 14.21748513 14.20735208 14.19872191 14.19142628 - 14.18533667 14.1803552 14.17640832 14.17344249 14.17142117 - 14.17032302 14.17014075 14.17088083 14.17256378 14.17522524 - 14.17891782 14.183714 14.18971048 14.19703436 14.20585234 - 14.21638429 14.22892383 14.24387082 14.26178453 14.28347552 - 14.31017565 14.34388147 14.38813675 14.45015685 14.54849112 - 14.76274684] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1495471015204807e-05, 'p_ase': 2.1677208324026825e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3190115777754599e-05, 'p_ase': 2.1682850675306855e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3999279491997423e-05, 'p_ase': 2.1688493026586902e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4521144643289485e-05, 'p_ase': 2.1694135377866953e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4899677899067595e-05, 'p_ase': 2.169977772914701e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5191882391325695e-05, 'p_ase': 2.1705420080427054e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5426109460291496e-05, 'p_ase': 2.1711062431707088e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.561848973371118e-05, 'p_ase': 2.1716704782987132e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5779056094144255e-05, 'p_ase': 2.1722347134267183e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5914455214163544e-05, 'p_ase': 2.172798948554724e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.60292999094948e-05, 'p_ase': 2.1733631836827281e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6126905123039464e-05, 'p_ase': 2.1739274188107314e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6209716118894636e-05, 'p_ase': 2.1744916539387368e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6279570997584513e-05, 'p_ase': 2.1750558890667412e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6337868453828358e-05, 'p_ase': 2.1756201241947466e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6385678520375007e-05, 'p_ase': 2.176184359322751e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6423817460093291e-05, 'p_ase': 2.1767485944507544e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6452899182084898e-05, 'p_ase': 2.1773128295787591e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.647337065848473e-05, 'p_ase': 2.1778770647067645e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6485535955494102e-05, 'p_ase': 2.17844129983477e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6489571736514907e-05, 'p_ase': 2.179005534962774e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6485535955494102e-05, 'p_ase': 2.1795697700907774e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.647337065848473e-05, 'p_ase': 2.1801340052187821e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6452899182084898e-05, 'p_ase': 2.1806982403467875e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6423817460093291e-05, 'p_ase': 2.1812624754747929e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6385678520375001e-05, 'p_ase': 2.1818267106027973e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6337868453828358e-05, 'p_ase': 2.1823909457308e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6279570997584506e-05, 'p_ase': 2.1829551808588051e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6209716118894636e-05, 'p_ase': 2.1835194159868101e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6126905123039464e-05, 'p_ase': 2.1840836511148156e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.60292999094948e-05, 'p_ase': 2.1846478862428203e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5914455214163531e-05, 'p_ase': 2.1852121213708237e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5779056094144248e-05, 'p_ase': 2.185776356498828e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.561848973371117e-05, 'p_ase': 2.1863405916268331e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5426109460291496e-05, 'p_ase': 2.1869048267548385e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5191882391325695e-05, 'p_ase': 2.1874690618828429e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4899677899067585e-05, 'p_ase': 2.1880332970108466e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4521144643289479e-05, 'p_ase': 2.1885975321388514e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3999279491997423e-05, 'p_ase': 2.1891617672668564e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3190115777754579e-05, 'p_ase': 2.1897260023948615e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.1495471015204788e-05, 'p_ase': 2.1902902375228659e-05}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #034 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #034 deleted file mode 100644 index 5aae6d2d..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #034 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 16.6174166659 - -osnr_nl_central_channel_db = 14.0616474391 - -osnr_lin_db = [ 16.63996649 16.63883621 16.63770623 16.63657654 16.63544715 - 16.63431805 16.63318924 16.63206072 16.6309325 16.62980458 - 16.62867694 16.6275496 16.62642255 16.62529579 16.62416933 - 16.62304315 16.62191727 16.62079168 16.61966639 16.61854138 - 16.61741667 16.61629224 16.61516811 16.61404427 16.61292072 - 16.61179746 16.61067449 16.60955181 16.60842942 16.60730732 - 16.60618551 16.60506399 16.60394276 16.60282182 16.60170117 - 16.60058081 16.59946073 16.59834095 16.59722145 16.59610224 - 16.59498333] - -osnr_nl_db = [ 14.70455652 14.47954407 14.37573185 14.30983682 14.26247844 - 14.22612275 14.19707091 14.1732395 14.15334328 14.13653659 - 14.12223596 14.11002415 14.0995947 14.09071808 14.08322015 - 14.07696799 14.07186038 14.06782128 14.06479531 14.06274473 - 14.06164744 14.06149594 14.06229693 14.06407164 14.06685692 - 14.07070721 14.07569749 14.08192776 14.08952955 14.0986753 - 14.1095925 14.12258502 14.13806671 14.15661647 14.17907352 - 14.20671358 14.24160481 14.2874194 14.35163752 14.45350175 - 14.6756952 ] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2171675192569796e-07, 'p_ase': 2.1677208324026824e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3966004941151927e-07, 'p_ase': 2.1682850675306856e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4822766520938448e-07, 'p_ase': 2.1688493026586901e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5375329622306512e-07, 'p_ase': 2.1694135377866952e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5776129540189219e-07, 'p_ase': 2.1699777729147011e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6085522531991912e-07, 'p_ase': 2.1705420080427056e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6333527663838054e-07, 'p_ase': 2.1711062431707088e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6537224423929485e-07, 'p_ase': 2.1716704782987133e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6707235864388036e-07, 'p_ase': 2.1722347134267184e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6850599638526108e-07, 'p_ase': 2.172798948554724e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6972199904170966e-07, 'p_ase': 2.1733631836827282e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7075546600865315e-07, 'p_ase': 2.1739274188107314e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7163228831770792e-07, 'p_ase': 2.1744916539387368e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7237192820971838e-07, 'p_ase': 2.1750558890667413e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7298919539347672e-07, 'p_ase': 2.1756201241947466e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7349541962750008e-07, 'p_ase': 2.1761843593227511e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7389924369510543e-07, 'p_ase': 2.1767485944507543e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.742071678103107e-07, 'p_ase': 2.1773128295787591e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7442392461925008e-07, 'p_ase': 2.1778770647067645e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7455273364640814e-07, 'p_ase': 2.17844129983477e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7459546544545196e-07, 'p_ase': 2.179005534962774e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7455273364640814e-07, 'p_ase': 2.1795697700907775e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7442392461925008e-07, 'p_ase': 2.180134005218782e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.742071678103107e-07, 'p_ase': 2.1806982403467876e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7389924369510543e-07, 'p_ase': 2.181262475474793e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7349541962750002e-07, 'p_ase': 2.1818267106027975e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7298919539347672e-07, 'p_ase': 2.1823909457308002e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.723719282097183e-07, 'p_ase': 2.1829551808588052e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7163228831770792e-07, 'p_ase': 2.1835194159868103e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7075546600865315e-07, 'p_ase': 2.1840836511148156e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6972199904170966e-07, 'p_ase': 2.1846478862428204e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6850599638526092e-07, 'p_ase': 2.1852121213708236e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6707235864388028e-07, 'p_ase': 2.1857763564988281e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6537224423929477e-07, 'p_ase': 2.1863405916268332e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6333527663838054e-07, 'p_ase': 2.1869048267548385e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6085522531991912e-07, 'p_ase': 2.187469061882843e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5776129540189208e-07, 'p_ase': 2.1880332970108468e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5375329622306507e-07, 'p_ase': 2.1885975321388513e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4822766520938448e-07, 'p_ase': 2.1891617672668564e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3966004941151906e-07, 'p_ase': 2.1897260023948614e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2171675192569777e-07, 'p_ase': 2.1902902375228659e-07}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #035 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #035 deleted file mode 100644 index 797dbe7c..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #035 +++ /dev/null @@ -1,26 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 16.3691808286 - -osnr_nl_central_channel_db = 13.9220871798 - -osnr_lin_db = [ 16.39173065 16.39060037 16.38947039 16.3883407 16.38721131 - 16.38608221 16.3849534 16.38382489 16.38269667 16.38156874 - 16.3804411 16.37931376 16.37818671 16.37705995 16.37593349 - 16.37480732 16.37368144 16.37255585 16.37143055 16.37030554 - 16.36918083 16.36805641 16.36693227 16.36580843 16.36468488 - 16.36356162 16.36243865 16.36131597 16.36019358 16.35907148 - 16.35794967 16.35682815 16.35570692 16.35458598 16.35346533 - 16.35234497 16.3512249 16.35010511 16.34898561 16.34786641 - 16.34674749] - -osnr_nl_db = [ 14.54395866 14.32687524 14.22656974 14.16284569 14.1170179 14.081818 - 14.05367588 14.03057998 14.01128898 13.99498595 13.98110713 - 13.96924929 13.95911624 13.95048608 13.94319044 13.93710083 - 13.93211936 13.92817249 13.92520665 13.92318533 13.92208718 - 13.92190491 13.92264499 13.92432795 13.92698941 13.93068198 - 13.93547817 13.94147464 13.94879852 13.95761651 13.96814845 - 13.98068799 13.99563498 14.01354869 14.03523968 14.06193981 - 14.09564563 14.13990091 14.20192102 14.30025528 14.51451101] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2171675192569795e-05, 'p_ase': 2.2952338225440168e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3966004941151927e-05, 'p_ase': 2.2958312479736669e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4822766520938448e-05, 'p_ase': 2.296428673403319e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5375329622306512e-05, 'p_ase': 2.2970260988329715e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.577612954018922e-05, 'p_ase': 2.2976235242626246e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6085522531991911e-05, 'p_ase': 2.2982209496922764e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6333527663838053e-05, 'p_ase': 2.2988183751219268e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6537224423929484e-05, 'p_ase': 2.2994158005515786e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6707235864388036e-05, 'p_ase': 2.3000132259812311e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6850599638526109e-05, 'p_ase': 2.3006106514108842e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6972199904170965e-05, 'p_ase': 2.3012080768405357e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7075546600865313e-05, 'p_ase': 2.3018055022701861e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7163228831770791e-05, 'p_ase': 2.3024029276998389e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7237192820971837e-05, 'p_ase': 2.3030003531294907e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7298919539347673e-05, 'p_ase': 2.3035977785591435e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7349541962750007e-05, 'p_ase': 2.3041952039887953e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7389924369510544e-05, 'p_ase': 2.3047926294184457e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7420716781031071e-05, 'p_ase': 2.3053900548480979e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7442392461925009e-05, 'p_ase': 2.3059874802777507e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7455273364640814e-05, 'p_ase': 2.3065849057074035e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7459546544545197e-05, 'p_ase': 2.3071823311370549e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7455273364640814e-05, 'p_ase': 2.3077797565667054e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7442392461925009e-05, 'p_ase': 2.3083771819963575e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7420716781031071e-05, 'p_ase': 2.3089746074260103e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7389924369510544e-05, 'p_ase': 2.3095720328556631e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7349541962750004e-05, 'p_ase': 2.3101694582853152e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7298919539347673e-05, 'p_ase': 2.3107668837149646e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.723719282097183e-05, 'p_ase': 2.3113643091446171e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7163228831770791e-05, 'p_ase': 2.3119617345742696e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7075546600865313e-05, 'p_ase': 2.3125591600039224e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6972199904170965e-05, 'p_ase': 2.3131565854335745e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6850599638526092e-05, 'p_ase': 2.3137540108632249e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6707235864388026e-05, 'p_ase': 2.3143514362928767e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6537224423929477e-05, 'p_ase': 2.3149488617225292e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6333527663838053e-05, 'p_ase': 2.315546287152182e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6085522531991911e-05, 'p_ase': 2.3161437125818338e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.577612954018921e-05, 'p_ase': 2.3167411380114845e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5375329622306508e-05, 'p_ase': 2.3173385634411367e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4822766520938448e-05, 'p_ase': 2.3179359888707891e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3966004941151907e-05, 'p_ase': 2.3185334143004416e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2171675192569777e-05, 'p_ase': 2.3191308397300934e-05}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #036 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #036 deleted file mode 100644 index 8f9f0d9e..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #036 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 16.3691808286 - -osnr_nl_central_channel_db = 13.8193783171 - -osnr_lin_db = [ 16.39173065 16.39060037 16.38947039 16.3883407 16.38721131 - 16.38608221 16.3849534 16.38382489 16.38269667 16.38156874 - 16.3804411 16.37931376 16.37818671 16.37705995 16.37593349 - 16.37480732 16.37368144 16.37255585 16.37143055 16.37030554 - 16.36918083 16.36805641 16.36693227 16.36580843 16.36468488 - 16.36356162 16.36243865 16.36131597 16.36019358 16.35907148 - 16.35794967 16.35682815 16.35570692 16.35458598 16.35346533 - 16.35234497 16.3512249 16.35010511 16.34898561 16.34786641 - 16.34674749] - -osnr_nl_db = [ 14.46114334 14.2365627 14.13294121 14.06716419 14.01988898 - 13.98359608 13.95459364 13.93080217 13.9109388 13.89415945 - 13.87988171 13.86768912 13.85727576 13.84841251 13.84092555 - 13.83468221 13.82958145 13.82554736 13.82252465 13.82047565 - 13.81937832 13.81922515 13.82002283 13.82179256 13.82457113 - 13.82841286 13.8333926 13.83961019 13.84719689 13.85632486 - 13.86722115 13.88018908 13.89564174 13.91415696 13.93657242 - 13.96416142 13.99898825 14.04471811 14.10881674 14.21048895 - 14.43225005] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2847879369934784e-07, 'p_ase': 2.2952338225440168e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4741894104549255e-07, 'p_ase': 2.2958312479736669e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5646253549879472e-07, 'p_ase': 2.2964286734033192e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.622951460132354e-07, 'p_ase': 2.2970260988329714e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6652581181310845e-07, 'p_ase': 2.2976235242626247e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.697916267265813e-07, 'p_ase': 2.2982209496922764e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7240945867384612e-07, 'p_ase': 2.2988183751219268e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.745595911414779e-07, 'p_ase': 2.2994158005515788e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7635415634631816e-07, 'p_ase': 2.300013225981231e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7786744062888673e-07, 'p_ase': 2.3006106514108843e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7915099898847131e-07, 'p_ase': 2.3012080768405358e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8024188078691166e-07, 'p_ase': 2.3018055022701862e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8116741544646947e-07, 'p_ase': 2.3024029276998389e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8194814644359161e-07, 'p_ase': 2.3030003531294907e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8259970624866989e-07, 'p_ase': 2.3035977785591437e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8313405405125009e-07, 'p_ase': 2.3041952039887954e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8356031278927796e-07, 'p_ase': 2.3047926294184458e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8388534379977241e-07, 'p_ase': 2.305390054848098e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8411414265365285e-07, 'p_ase': 2.3059874802777508e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8425010773787525e-07, 'p_ase': 2.3065849057074036e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8429521352575486e-07, 'p_ase': 2.307182331137055e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8425010773787525e-07, 'p_ase': 2.3077797565667054e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8411414265365285e-07, 'p_ase': 2.3083771819963574e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8388534379977241e-07, 'p_ase': 2.3089746074260104e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8356031278927796e-07, 'p_ase': 2.3095720328556632e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8313405405125006e-07, 'p_ase': 2.3101694582853152e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8259970624866989e-07, 'p_ase': 2.3107668837149648e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8194814644359153e-07, 'p_ase': 2.311364309144617e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8116741544646947e-07, 'p_ase': 2.3119617345742695e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8024188078691166e-07, 'p_ase': 2.3125591600039223e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7915099898847131e-07, 'p_ase': 2.3131565854335746e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7786744062888652e-07, 'p_ase': 2.313754010863225e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7635415634631808e-07, 'p_ase': 2.3143514362928767e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7455959114147782e-07, 'p_ase': 2.3149488617225292e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7240945867384612e-07, 'p_ase': 2.3155462871521819e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.697916267265813e-07, 'p_ase': 2.3161437125818339e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6652581181310835e-07, 'p_ase': 2.3167411380114846e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6229514601323538e-07, 'p_ase': 2.3173385634411368e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5646253549879472e-07, 'p_ase': 2.3179359888707891e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.4741894104549234e-07, 'p_ase': 2.3185334143004416e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.2847879369934766e-07, 'p_ase': 2.3191308397300936e-07}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #037 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #037 deleted file mode 100644 index 70412d0e..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #037 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 16.1343698701 - -osnr_nl_central_channel_db = 13.6872762213 - -osnr_lin_db = [ 16.15691969 16.15578941 16.15465943 16.15352974 16.15240035 - 16.15127125 16.15014244 16.14901393 16.14788571 16.14675778 - 16.14563014 16.1445028 16.14337575 16.142249 16.14112253 - 16.13999636 16.13887048 16.13774489 16.13661959 16.13549458 - 16.13436987 16.13324545 16.13212131 16.13099747 16.12987392 - 16.12875066 16.12762769 16.12650501 16.12538262 16.12426052 - 16.12313872 16.1220172 16.12089597 16.11977502 16.11865437 - 16.11753401 16.11641394 16.11529415 16.11417466 16.11305545 - 16.11193653] - -osnr_nl_db = [ 14.3091477 14.09206428 13.99175878 13.92803473 13.88220695 - 13.84700704 13.81886493 13.79576902 13.77647802 13.76017499 - 13.74629617 13.73443833 13.72430528 13.71567512 13.70837948 - 13.70228987 13.6973084 13.69336153 13.69039569 13.68837437 - 13.68727622 13.68709395 13.68783404 13.68951699 13.69217845 - 13.69587102 13.70066721 13.70666368 13.71398756 13.72280555 - 13.73333749 13.74587703 13.76082402 13.77873773 13.80042872 - 13.82712885 13.86083467 13.90508996 13.96711006 14.06544432 - 14.27970005] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2847879369934784e-05, 'p_ase': 2.4227468126853511e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4741894104549255e-05, 'p_ase': 2.4233774284166483e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5646253549879472e-05, 'p_ase': 2.4240080441479478e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6229514601323539e-05, 'p_ase': 2.4246386598792477e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6652581181310847e-05, 'p_ase': 2.4252692756105482e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6979162672658131e-05, 'p_ase': 2.4258998913418474e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7240945867384611e-05, 'p_ase': 2.4265305070731449e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7455959114147791e-05, 'p_ase': 2.4271611228044441e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7635415634631817e-05, 'p_ase': 2.4277917385357439e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7786744062888673e-05, 'p_ase': 2.4284223542670445e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.791509989884713e-05, 'p_ase': 2.4290529699983433e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8024188078691166e-05, 'p_ase': 2.4296835857296408e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8116741544646946e-05, 'p_ase': 2.430314201460941e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8194814644359161e-05, 'p_ase': 2.4309448171922402e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8259970624866989e-05, 'p_ase': 2.4315754329235407e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.831340540512501e-05, 'p_ase': 2.4322060486548396e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8356031278927797e-05, 'p_ase': 2.4328366643861371e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.838853437997724e-05, 'p_ase': 2.4334672801174369e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8411414265365285e-05, 'p_ase': 2.4340978958487368e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8425010773787526e-05, 'p_ase': 2.434728511580037e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8429521352575487e-05, 'p_ase': 2.4353591273113358e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8425010773787526e-05, 'p_ase': 2.4359897430426333e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8411414265365285e-05, 'p_ase': 2.4366203587739329e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.838853437997724e-05, 'p_ase': 2.4372509745052331e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8356031278927797e-05, 'p_ase': 2.4378815902365333e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8313405405125007e-05, 'p_ase': 2.4385122059678328e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8259970624866989e-05, 'p_ase': 2.4391428216991293e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8194814644359155e-05, 'p_ase': 2.4397734374304291e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8116741544646946e-05, 'p_ase': 2.440404053161729e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8024188078691166e-05, 'p_ase': 2.4410346688930292e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.791509989884713e-05, 'p_ase': 2.4416652846243287e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7786744062888653e-05, 'p_ase': 2.4422959003556262e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7635415634631807e-05, 'p_ase': 2.4429265160869254e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7455959114147781e-05, 'p_ase': 2.4435571318182252e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7240945867384611e-05, 'p_ase': 2.4441877475495255e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6979162672658131e-05, 'p_ase': 2.4448183632808246e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6652581181310833e-05, 'p_ase': 2.4454489790121225e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6229514601323539e-05, 'p_ase': 2.446079594743422e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5646253549879472e-05, 'p_ase': 2.4467102104747219e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.4741894104549235e-05, 'p_ase': 2.4473408262060217e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.2847879369934766e-05, 'p_ase': 2.4479714419373209e-05}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #038 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #038 deleted file mode 100644 index 32010e77..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #038 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 16.1343698701 - -osnr_nl_central_channel_db = 13.589912956 - -osnr_lin_db = [ 16.15691969 16.15578941 16.15465943 16.15352974 16.15240035 - 16.15127125 16.15014244 16.14901393 16.14788571 16.14675778 - 16.14563014 16.1445028 16.14337575 16.142249 16.14112253 - 16.13999636 16.13887048 16.13774489 16.13661959 16.13549458 - 16.13436987 16.13324545 16.13212131 16.13099747 16.12987392 - 16.12875066 16.12762769 16.12650501 16.12538262 16.12426052 - 16.12313872 16.1220172 16.12089597 16.11977502 16.11865437 - 16.11753401 16.11641394 16.11529415 16.11417466 16.11305545 - 16.11193653] - -osnr_nl_db = [ 14.23065193 14.0064585 13.90300807 13.83733689 13.79013628 - 13.75389968 13.72494156 13.70118591 13.68135202 13.6645972 - 13.65033999 13.63816463 13.6277657 13.61891445 13.61143734 - 13.60520191 13.60010729 13.59607769 13.59305791 13.59101033 - 13.58991296 13.58975829 13.59055301 13.59231827 13.59509081 - 13.59892486 13.60389516 13.61010137 13.61767454 13.62678655 - 13.63766408 13.65060996 13.66603658 13.68452083 13.70689897 - 13.73444218 13.76921125 13.8148651 13.87885656 13.98035656 - 14.20172999] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3524083547299773e-07, 'p_ase': 2.4227468126853512e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5517783267946584e-07, 'p_ase': 2.4233774284166482e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6469740578820497e-07, 'p_ase': 2.4240080441479479e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7083699580340566e-07, 'p_ase': 2.4246386598792476e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7529032822432471e-07, 'p_ase': 2.4252692756105484e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.787280281332435e-07, 'p_ase': 2.4258998913418475e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8148364070931169e-07, 'p_ase': 2.4265305070731451e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8374693804366097e-07, 'p_ase': 2.4271611228044443e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8563595404875598e-07, 'p_ase': 2.427791738535744e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8722888487251235e-07, 'p_ase': 2.4284223542670447e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8857999893523296e-07, 'p_ase': 2.4290529699983433e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8972829556517019e-07, 'p_ase': 2.4296835857296409e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9070254257523102e-07, 'p_ase': 2.4303142014609411e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9152436467746485e-07, 'p_ase': 2.4309448171922403e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9221021710386304e-07, 'p_ase': 2.4315754329235405e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9277268847500012e-07, 'p_ase': 2.4322060486548397e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9322138188345051e-07, 'p_ase': 2.4328366643861373e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9356351978923412e-07, 'p_ase': 2.433467280117437e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9380436068805562e-07, 'p_ase': 2.4340978958487367e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9394748182934238e-07, 'p_ase': 2.4347285115800369e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9399496160605774e-07, 'p_ase': 2.435359127311336e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9394748182934238e-07, 'p_ase': 2.4359897430426336e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9380436068805562e-07, 'p_ase': 2.4366203587739328e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9356351978923412e-07, 'p_ase': 2.437250974505233e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9322138188345051e-07, 'p_ase': 2.4378815902365332e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9277268847500007e-07, 'p_ase': 2.4385122059678329e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9221021710386304e-07, 'p_ase': 2.4391428216991294e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.915243646774648e-07, 'p_ase': 2.4397734374304291e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.9070254257523102e-07, 'p_ase': 2.4404040531617288e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8972829556517019e-07, 'p_ase': 2.441034668893029e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8857999893523296e-07, 'p_ase': 2.4416652846243287e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8722888487251214e-07, 'p_ase': 2.4422959003556263e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8563595404875588e-07, 'p_ase': 2.4429265160869255e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8374693804366087e-07, 'p_ase': 2.4435571318182252e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.8148364070931169e-07, 'p_ase': 2.4441877475495254e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.787280281332435e-07, 'p_ase': 2.4448183632808246e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7529032822432458e-07, 'p_ase': 2.4454489790121227e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.7083699580340566e-07, 'p_ase': 2.4460795947434218e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.6469740578820497e-07, 'p_ase': 2.446710210474722e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.5517783267946562e-07, 'p_ase': 2.4473408262060217e-07}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 1.0000000000000001e-05, 'p_nli': 1.3524083547299754e-07, 'p_ase': 2.4479714419373209e-07}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #039 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #039 deleted file mode 100644 index b1263df3..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #039 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 15.911605923 - -osnr_nl_central_channel_db = 13.4645122741 - -osnr_lin_db = [ 15.93415574 15.93302547 15.93189549 15.9307658 15.9296364 - 15.9285073 15.9273785 15.92624998 15.92512176 15.92399383 - 15.9228662 15.92173886 15.92061181 15.91948505 15.91835858 - 15.91723241 15.91610653 15.91498094 15.91385564 15.91273064 - 15.91160592 15.9104815 15.90935737 15.90823353 15.90710998 - 15.90598671 15.90486375 15.90374107 15.90261868 15.90149658 - 15.90037477 15.89925325 15.89813202 15.89701108 15.89589043 - 15.89477006 15.89364999 15.89253021 15.89141071 15.8902915 - 15.88917258] - -osnr_nl_db = [ 14.08638375 13.86930034 13.76899484 13.70527078 13.659443 - 13.62424309 13.59610098 13.57300507 13.55371407 13.53741105 - 13.52353222 13.51167439 13.50154133 13.49291117 13.48561554 - 13.47952592 13.47454445 13.47059758 13.46763174 13.46561043 - 13.46451227 13.46433001 13.46507009 13.46675304 13.4694145 - 13.47310707 13.47790326 13.48389973 13.49122361 13.5000416 - 13.51057354 13.52311308 13.53806008 13.55597378 13.57766478 - 13.60436491 13.63807072 13.68232601 13.74434611 13.84268037 - 14.0569361 ] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3524083547299773e-05, 'p_ase': 2.5502598028266855e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5517783267946583e-05, 'p_ase': 2.5509236088596297e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6469740578820498e-05, 'p_ase': 2.5515874148925766e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7083699580340566e-05, 'p_ase': 2.5522512209255239e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.752903282243247e-05, 'p_ase': 2.5529150269584718e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7872802813324351e-05, 'p_ase': 2.5535788329914184e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8148364070931168e-05, 'p_ase': 2.5542426390243633e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8374693804366098e-05, 'p_ase': 2.5549064450573099e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8563595404875599e-05, 'p_ase': 2.5555702510902568e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8722888487251234e-05, 'p_ase': 2.5562340571232051e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8857999893523294e-05, 'p_ase': 2.556897863156151e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.897282955651702e-05, 'p_ase': 2.5575616691890955e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9070254257523102e-05, 'p_ase': 2.5582254752220431e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9152436467746486e-05, 'p_ase': 2.5588892812549897e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9221021710386304e-05, 'p_ase': 2.5595530872879373e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9277268847500013e-05, 'p_ase': 2.5602168933208839e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.932213818834505e-05, 'p_ase': 2.5608806993538288e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9356351978923413e-05, 'p_ase': 2.5615445053867757e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9380436068805564e-05, 'p_ase': 2.5622083114197229e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9394748182934238e-05, 'p_ase': 2.5628721174526705e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9399496160605773e-05, 'p_ase': 2.5635359234856171e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9394748182934238e-05, 'p_ase': 2.5641997295185617e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9380436068805564e-05, 'p_ase': 2.5648635355515082e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9356351978923413e-05, 'p_ase': 2.5655273415844558e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.932213818834505e-05, 'p_ase': 2.5661911476174034e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9277268847500006e-05, 'p_ase': 2.5668549536503504e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9221021710386304e-05, 'p_ase': 2.5675187596832942e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9152436467746479e-05, 'p_ase': 2.5681825657162411e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.9070254257523102e-05, 'p_ase': 2.5688463717491881e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.897282955651702e-05, 'p_ase': 2.569510177782136e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8857999893523294e-05, 'p_ase': 2.5701739838150829e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8722888487251214e-05, 'p_ase': 2.5708377898480275e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8563595404875588e-05, 'p_ase': 2.5715015958809741e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8374693804366088e-05, 'p_ase': 2.5721654019139213e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.8148364070931168e-05, 'p_ase': 2.5728292079468689e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7872802813324351e-05, 'p_ase': 2.5734930139798155e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.752903282243246e-05, 'p_ase': 2.5741568200127607e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.7083699580340566e-05, 'p_ase': 2.574820626045707e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.6469740578820498e-05, 'p_ase': 2.5754844320786549e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.5517783267946563e-05, 'p_ase': 2.5761482381116018e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.001, 'p_nli': 1.3524083547299754e-05, 'p_ase': 2.5768120441445484e-05}]} diff --git a/gnpy/output/2017-08-04_17-49-28/Output from component ID #040 b/gnpy/output/2017-08-04_17-49-28/Output from component ID #040 deleted file mode 100644 index 47b0ac86..00000000 --- a/gnpy/output/2017-08-04_17-49-28/Output from component ID #040 +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 15.911605923 - -osnr_nl_central_channel_db = 13.4645122741 - -osnr_lin_db = [ 15.93415574 15.93302547 15.93189549 15.9307658 15.9296364 - 15.9285073 15.9273785 15.92624998 15.92512176 15.92399383 - 15.9228662 15.92173886 15.92061181 15.91948505 15.91835858 - 15.91723241 15.91610653 15.91498094 15.91385564 15.91273064 - 15.91160592 15.9104815 15.90935737 15.90823353 15.90710998 - 15.90598671 15.90486375 15.90374107 15.90261868 15.90149658 - 15.90037477 15.89925325 15.89813202 15.89701108 15.89589043 - 15.89477006 15.89364999 15.89253021 15.89141071 15.8902915 - 15.88917258] - -osnr_nl_db = [ 14.08638375 13.86930034 13.76899484 13.70527078 13.659443 - 13.62424309 13.59610098 13.57300507 13.55371407 13.53741105 - 13.52353222 13.51167439 13.50154133 13.49291117 13.48561554 - 13.47952592 13.47454445 13.47059758 13.46763174 13.46561043 - 13.46451227 13.46433001 13.46507009 13.46675304 13.4694145 - 13.47310707 13.47790326 13.48389973 13.49122361 13.5000416 - 13.51057354 13.52311308 13.53806008 13.55597378 13.57766478 - 13.60436491 13.63807072 13.68232601 13.74434611 13.84268037 - 14.0569361 ] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 8.5331198415325372e-06, 'p_ase': 1.6091051529261068e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 9.791059322962751e-06, 'p_ase': 1.6095239862179033e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.0391703779890262e-05, 'p_ase': 1.6099428195097011e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.0779085721109076e-05, 'p_ase': 1.6103616528014996e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.1060072000948138e-05, 'p_ase': 1.6107804860932984e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.127697620151326e-05, 'p_ase': 1.6111993193850963e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.145084360085448e-05, 'p_ase': 1.6116181526768931e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.1593648008439492e-05, 'p_ase': 1.6120369859686909e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.1712836860665025e-05, 'p_ase': 1.612455819260489e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.1813344000914821e-05, 'p_ase': 1.6128746525522882e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.1898593535025224e-05, 'p_ase': 1.6132934858440857e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.1971046154255301e-05, 'p_ase': 1.6137123191358822e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.2032516984888877e-05, 'p_ase': 1.6141311524276807e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.2084370454014935e-05, 'p_ase': 1.6145499857194785e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.2127644816581485e-05, 'p_ase': 1.6149688190112773e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.2163134360849363e-05, 'p_ase': 1.6153876523030752e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.2191445000997525e-05, 'p_ase': 1.615806485594872e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.221303244344545e-05, 'p_ase': 1.6162253188866701e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.2228228476841715e-05, 'p_ase': 1.6166441521784683e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.223725881036624e-05, 'p_ase': 1.6170629854702668e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.2240254581749524e-05, 'p_ase': 1.617481818762065e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.223725881036624e-05, 'p_ase': 1.6179006520538614e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.2228228476841715e-05, 'p_ase': 1.6183194853456592e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.221303244344545e-05, 'p_ase': 1.6187383186374581e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.2191445000997525e-05, 'p_ase': 1.6191571519292566e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.216313436084936e-05, 'p_ase': 1.6195759852210547e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.2127644816581485e-05, 'p_ase': 1.6199948185128509e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.208437045401493e-05, 'p_ase': 1.620413651804649e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.2032516984888877e-05, 'p_ase': 1.6208324850964469e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.1971046154255301e-05, 'p_ase': 1.6212513183882457e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.1898593535025224e-05, 'p_ase': 1.6216701516800439e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.1813344000914809e-05, 'p_ase': 1.6220889849718407e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.1712836860665018e-05, 'p_ase': 1.6225078182636385e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.1593648008439487e-05, 'p_ase': 1.6229266515554367e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.145084360085448e-05, 'p_ase': 1.6233454848472352e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.127697620151326e-05, 'p_ase': 1.6237643181390333e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.1060072000948131e-05, 'p_ase': 1.6241831514308301e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.0779085721109076e-05, 'p_ase': 1.6246019847226279e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.0391703779890262e-05, 'p_ase': 1.6250208180144268e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 9.7910593229627391e-06, 'p_ase': 1.6254396513062249e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 8.5331198415325253e-06, 'p_ase': 1.6258584845980228e-05}]} diff --git a/gnpy/output/2017-08-04_17-49-28/link_output b/gnpy/output/2017-08-04_17-49-28/link_output deleted file mode 100644 index 47b0ac86..00000000 --- a/gnpy/output/2017-08-04_17-49-28/link_output +++ /dev/null @@ -1,27 +0,0 @@ -# Output parameters. The values of OSNR are evaluated in the -3 dB channel band - -osnr_lin_central_channel_db = 15.911605923 - -osnr_nl_central_channel_db = 13.4645122741 - -osnr_lin_db = [ 15.93415574 15.93302547 15.93189549 15.9307658 15.9296364 - 15.9285073 15.9273785 15.92624998 15.92512176 15.92399383 - 15.9228662 15.92173886 15.92061181 15.91948505 15.91835858 - 15.91723241 15.91610653 15.91498094 15.91385564 15.91273064 - 15.91160592 15.9104815 15.90935737 15.90823353 15.90710998 - 15.90598671 15.90486375 15.90374107 15.90261868 15.90149658 - 15.90037477 15.89925325 15.89813202 15.89701108 15.89589043 - 15.89477006 15.89364999 15.89253021 15.89141071 15.8902915 - 15.88917258] - -osnr_nl_db = [ 14.08638375 13.86930034 13.76899484 13.70527078 13.659443 - 13.62424309 13.59610098 13.57300507 13.55371407 13.53741105 - 13.52353222 13.51167439 13.50154133 13.49291117 13.48561554 - 13.47952592 13.47454445 13.47059758 13.46763174 13.46561043 - 13.46451227 13.46433001 13.46507009 13.46675304 13.4694145 - 13.47310707 13.47790326 13.48389973 13.49122361 13.5000416 - 13.51057354 13.52311308 13.53806008 13.55597378 13.57766478 - 13.60436491 13.63807072 13.68232601 13.74434611 13.84268037 - 14.0569361 ] - -spectrum = {'laser_position': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 'signals': [{'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 8.5331198415325372e-06, 'p_ase': 1.6091051529261068e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 9.791059322962751e-06, 'p_ase': 1.6095239862179033e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.0391703779890262e-05, 'p_ase': 1.6099428195097011e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.0779085721109076e-05, 'p_ase': 1.6103616528014996e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.1060072000948138e-05, 'p_ase': 1.6107804860932984e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.127697620151326e-05, 'p_ase': 1.6111993193850963e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.145084360085448e-05, 'p_ase': 1.6116181526768931e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.1593648008439492e-05, 'p_ase': 1.6120369859686909e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.1712836860665025e-05, 'p_ase': 1.612455819260489e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.1813344000914821e-05, 'p_ase': 1.6128746525522882e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.1898593535025224e-05, 'p_ase': 1.6132934858440857e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.1971046154255301e-05, 'p_ase': 1.6137123191358822e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.2032516984888877e-05, 'p_ase': 1.6141311524276807e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.2084370454014935e-05, 'p_ase': 1.6145499857194785e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.2127644816581485e-05, 'p_ase': 1.6149688190112773e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.2163134360849363e-05, 'p_ase': 1.6153876523030752e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.2191445000997525e-05, 'p_ase': 1.615806485594872e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.221303244344545e-05, 'p_ase': 1.6162253188866701e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.2228228476841715e-05, 'p_ase': 1.6166441521784683e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.223725881036624e-05, 'p_ase': 1.6170629854702668e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.2240254581749524e-05, 'p_ase': 1.617481818762065e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.223725881036624e-05, 'p_ase': 1.6179006520538614e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.2228228476841715e-05, 'p_ase': 1.6183194853456592e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.221303244344545e-05, 'p_ase': 1.6187383186374581e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.2191445000997525e-05, 'p_ase': 1.6191571519292566e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.216313436084936e-05, 'p_ase': 1.6195759852210547e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.2127644816581485e-05, 'p_ase': 1.6199948185128509e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.208437045401493e-05, 'p_ase': 1.620413651804649e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.2032516984888877e-05, 'p_ase': 1.6208324850964469e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.1971046154255301e-05, 'p_ase': 1.6212513183882457e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.1898593535025224e-05, 'p_ase': 1.6216701516800439e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.1813344000914809e-05, 'p_ase': 1.6220889849718407e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.1712836860665018e-05, 'p_ase': 1.6225078182636385e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.1593648008439487e-05, 'p_ase': 1.6229266515554367e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.145084360085448e-05, 'p_ase': 1.6233454848472352e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.127697620151326e-05, 'p_ase': 1.6237643181390333e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.1060072000948131e-05, 'p_ase': 1.6241831514308301e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.0779085721109076e-05, 'p_ase': 1.6246019847226279e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 1.0391703779890262e-05, 'p_ase': 1.6250208180144268e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 9.7910593229627391e-06, 'p_ase': 1.6254396513062249e-05}, {'b_ch': 0.032, 'roll_off': 0.15, 'p_ch': 0.00063095734448019331, 'p_nli': 8.5331198415325253e-06, 'p_ase': 1.6258584845980228e-05}]} diff --git a/gnpy/sandbox/incoherent_gn.py b/gnpy/sandbox/incoherent_gn.py deleted file mode 100644 index b6e3707a..00000000 --- a/gnpy/sandbox/incoherent_gn.py +++ /dev/null @@ -1,123 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Tue Dec 27 16:16:49 2016 - -@author: briantaylor -""" -from numpy import exp, pi -import numpy as np -from scipy.integrate import dblquad - - -def ign_rho(f, span, f1, f2): - """ - This form or \\rho assumes lumped EDFA-like amplifcation. This function is - also known as the link function. - - Inputs: - f = frequency array - f1, f2 frequency bounds used to create a grid. - span = the span object as defined by the Span class. - ign_rho expects several parameters from Span in order to calculate the - \\rho function. - - This form currently sets \\beta_3 in the denominator to zero. This - equation is taken from EQ[6], page 103 of: - - The GN-Model of Fiber Non-Linear Propagation and its Applications - P. Poggiolini;G. Bosco;A. Carena;V. Curri;Y. Jiang;F. Forghieri (2014) - - Version used for this code came from: - http://porto.polito.it/2542088/ - - TODO: Update the docu string with the IGN rho in Latex form - TODO: Fix num length - - """ - num = 1 - exp(-2 * span.alpha * span.length) * \ - exp((1j * 4 * pi**2) * (f1 - f) * (f2 - f) * span.beta2 * span.length) - den = 2 * span.alpha - (1j * 4 * pi**2) * (f1 - f) * (f2 - f) * span.beta2 - rho = np.abs(num / den) * span.leff**-2 - return rho - - -def ign_function(f, span, f1, f2): - """ - This creates the integrand for the incoherenet gaussian noise reference - function (IGNRF). It assumes \\rho for lumped EDFA-like amplifcation. - - Inputs: - f = frequency array - f1, f2 frequency bounds used to create a grid. - span = the span object as defined by the Span class. - - This - equation is taken from EQ[11], page 104 of: - The GN-Model of Fiber Non-Linear Propagation and its Applications - P. Poggiolini;G. Bosco;A. Carena;V. Curri;Y. Jiang;F. Forghieri (2014) - - Version used for this code came from: - http://porto.polito.it/2542088/ - - TODO: Update the docu string with the IGN rho in Latex form - """ - mult_coefs = 16 / 27 * (span.gamma ** 2) * span.nchan - ign = mult_coefs * span.psd(f1) * span.psd(2)*span.psd(f1 + f2 - f) * \ - ign_rho(f, span, f1, f2) - return ign - - -def integrate_ign(span, f1, f2, f, options=None): - """ - integrate_ign integrates the ign function defined in ign_function. - It uses scipy.integrate.dblquad to perform the double integral. - - The GN model is integrated over 3 distinct regions and the result is then - summed. - """ - - """ - func : callable - A Python function or method of at least two variables: y must be the first - argument and x the second argument. - a, b : float - The limits of integration in x: a < b - gfun : callable - The lower boundary curve in y which is a function taking a single floating - point argument (x) and returning a floating point result: a lambda function - can be useful here. - hfun : callable - The upper boundary curve in y (same requirements as gfun). - args : sequence, optional - Extra arguments to pass to func. - epsabs : float, optional - Absolute tolerance passed directly to the inner 1-D quadrature integration. - Default is 1.49e-8. - epsrel : float, optional - Relative tolerance of the inner 1-D integrals. Default is 1.49e-8. - - dblquad(func, a, b, gfun, hfun, args=(), epsabs=1.49e-08, epsrel=1.49e-08) - - - - Definition : quad(func, a, b, args=(), full_output=0, epsabs=1.49e-08, - epsrel=1.49e-08, limit=50, points=None, weight=None, - wvar=None, wopts=None, maxp1=50, limlst=50) - - - r1 = integral2(@(f1,f2) incoherent_inner(f, link, f1, f2),... - max_lower_bound, max_upper_bound, mid_lower_bound, mid_upper_bound, ... - 'RelTol', model.RelTol, 'AbsTol', model.AbsTol_incoherent); - - """ - - max_lower_bound = np.min(span.psd) - max_upper_bound = np.max(span.psd) - mid_lower_bound = f - span.model.bound - mid_upper_bound = f + span.model.bound - - return [max_lower_bound, max_upper_bound, mid_lower_bound, mid_upper_bound] - - -def integrate_hyperbolic(span, f1, f2, f, options=None): - return None diff --git a/gnpy/sandbox/network_element.py b/gnpy/sandbox/network_element.py deleted file mode 100644 index 7e5bceb2..00000000 --- a/gnpy/sandbox/network_element.py +++ /dev/null @@ -1,48 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Mon Jun 19 19:08:37 2017 - -@author: briantaylor -""" -from uuid import uuid4 - - -class NetworkElement: - - def __init__(self, **kwargs): - """ - self.direction = [("E", "Z"), ("E", "Z"), ("E", "Z"), ("W", "Z")] - self.port_mapping = [(1, 5), (2, 5), (3, 5), (4, 5)] - self.uid = uuid4() - self.coordinates = (29.9792, 31.1342) - """ - try: - for key in ('port_mapping', 'direction', 'coordinates', 'name', - 'description', 'manufacturer', 'model', 'sn', 'id'): - if key in kwargs: - setattr(self, key, kwargs[key]) - else: - setattr(self, key, None) - # print('No Value defined for :', key) - # TODO: add logging functionality - except KeyError as e: - if 'name' in kwargs: - s = kwargs['name'] - print('Missing Required Network Element Key!', 'name:=', s) -# TODO Put log here instead of print - print(e) - raise - - def get_output_ports(self): - """Translate the port mapping into list of output ports - """ - return None - - def get_input_ports(self): - """Translate the port mapping into list of output ports - """ - return None - - def __repr__(self): - return self.__class__.__name__ diff --git a/gnpy/sandbox/optical_elements.py b/gnpy/sandbox/optical_elements.py deleted file mode 100644 index 2838c563..00000000 --- a/gnpy/sandbox/optical_elements.py +++ /dev/null @@ -1,159 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Wed Dec 21 15:09:47 2016 - -@author: briantaylor -""" - -import numpy as np -from gnpy.constants import c, h - - -class NetworkElement: - - def __init__(self, **kwargs): - """ - self.direction = [("E", "Z"), ("E", "Z"), ("E", "Z"), ("W", "Z")] - self.port_mapping = [(1, 5), (2, 5), (3, 5), (4, 5)] - self.uid = uuid4() - self.coordinates = (29.9792, 31.1342) - """ - try: - for key in ('port_mapping', 'direction', 'coordinates', 'name', - 'description', 'manufacturer', 'model', 'sn', 'id'): - if key in kwargs: - setattr(self, key, kwargs[key]) - else: - setattr(self, key, None) - # print('No Value defined for :', key) - # TODO: add logging functionality - except KeyError as e: - if 'name' in kwargs: - s = kwargs['name'] - print('Missing Required Network Element Key!', 'name:=', s) -# TODO Put log here instead of print - print(e) - raise - - def get_output_ports(self): - """Translate the port mapping into list of output ports - """ - return None - - def get_input_ports(self): - """Translate the port mapping into list of output ports - """ - return None - - def __repr__(self): - return self.__class__.__name__ - - -class Edfa(NetworkElement): - - def __init__(self, **kwargs): - '''Reads in configuration data checking for keys. Sets those attributes - for each element that exists. - conventions: - units are SI except where noted below (meters, seconds, Hz) - rbw=12.5 GHz today. - TODO add unit checking so inputs can be added in conventional - nm units. - nfdB = noise figure in dB units - psatdB = saturation power in dB units - gaindB = gain in dB units - pdgdB = polarization dependent gain in dB - rippledB = gain ripple in dB - ''' - try: - for key in ('gaindB', 'nfdB', 'psatdB', 'rbw', 'wavelengths', - 'pdgdB', 'rippledB', 'id', 'node', 'location'): - if key in kwargs: - setattr(self, key, kwargs[key]) - elif 'id' in kwargs is None: - setattr(self, 'id', Edfa.class_counter) - else: - setattr(self, key, None) - print('No Value defined for :', key) - self.pas = [(h*c/ll)*self.rbw*1e9 for ll in self.wavelengths] - - except KeyError as e: - if 'name' in kwargs: - s = kwargs['name'] - print('Missing Edfa Input Key!', 'name:=', s) - print(e) - raise - - -class Fiber(NetworkElement): - class_counter = 0 - - def __init__(self, **kwargs): - """ Reads in configuration data checking for keys. Sets those - attributes for each element that exists. - conventions: - units are SI (meters, seconds, Hz) except where noted below - rbw=12.5 GHz today. TODO add unit checking so inputs can be added - in conventional nm units. - nf_db = noise figure in dB units - psat_db = saturation power in dB units - gain_db = gain in dB units - pdg_db = polarization dependent gain in dB - ripple_db = gain ripple in dB - """ - try: - for key in ('fiber_type', 'attenuationdB', 'span_length', - 'dispersion', 'wavelengths', 'id', 'name', 'location', - 'direction', 'launch_power', 'rbw'): - if key in kwargs: - setattr(self, key, kwargs[key]) - elif 'id' in kwargs is None: - setattr(self, 'id', Span.class_counter) - Span.class_counter += 1 - else: - setattr(self, key, None) - print('No Value defined for :', key) - except KeyError as e: - if 'name' in kwargs: - s = kwargs['name'] - print('Missing Span Input Key!', 'name:=', s) - print(e) - raise - - def effective_length(self, loss_coef): - alpha_dict = self.dbkm_2_lin(loss_coef) - alpha = alpha_dict['alpha_acoef'] - leff = 1 - np.exp(-2 * alpha * self.span_length) - return leff - - def asymptotic_length(self, loss_coef): - alpha_dict = self.dbkm_2_lin(loss_coef) - alpha = alpha_dict['alpha_acoef'] - aleff = 1/(2 * alpha) - return aleff - - - - def beta2(self, dispersion, ref_wavelength=None): - """ Returns beta2 from dispersion parameter. Dispersion is entered in - ps/nm/km. Disperion can be a numpy array or a single value. If a - value ref_wavelength is not entered 1550e-9m will be assumed. - ref_wavelength can be a numpy array. - """ - if ref_wavelength is None: - ref_wavelength = 1550e-9 - wl = ref_wavelength - D = np.abs(dispersion) - b2 = (10**21) * (wl**2) * D / (2 * np.pi * c) -# 10^21 scales to ps^2/km - return b2 - -# TODO - def generic_span(self): - """ calculates a generic version that shows how all the functions of - the class are used. It makes the following assumptions about the span: - """ - return - - def __repr__(self): - return f'{self.__class__.__name__}({self.span_length}km)' diff --git a/gnpy/sandbox/sandbox.py b/gnpy/sandbox/sandbox.py deleted file mode 100644 index be1375ff..00000000 --- a/gnpy/sandbox/sandbox.py +++ /dev/null @@ -1,26 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Wed Jun 28 14:29:12 2017 - -@author: briantaylor -""" - -import networkx as nx -import matplotlib.pyplot as plt - - -G = nx.Graph() - -G.add_node(1) - -G.add_nodes_from([2, 3]) -H = nx.path_graph(10) -G.add_nodes_from(H) -G = nx.path_graph(8) -nx.draw_spring(G) -plt.show() - - - -class NetworkElement(nx.node) \ No newline at end of file diff --git a/gnpy/sandbox/span.py b/gnpy/sandbox/span.py deleted file mode 100644 index 8eaf8e41..00000000 --- a/gnpy/sandbox/span.py +++ /dev/null @@ -1,94 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Tue Dec 27 11:58:40 2016 - -@author: briantaylor -""" - -import numpy as np -from scipy.constants import h, c -from numpy import array -from network_element import NetworkElement - - -class Span(NetworkElement): - class_counter = 0 - - def __init__(self, **kwargs): - """ Reads in configuration data checking for keys. Sets those - attributes for each element that exists. - conventions: - units are SI (meters, seconds, Hz) except where noted below - rbw=12.5 GHz today. TODO add unit checking so inputs can be added - in conventional nm units. - nf_db = noise figure in dB units - psat_db = saturation power in dB units - gain_db = gain in dB units - pdg_db = polarization dependent gain in dB - ripple_db = gain ripple in dB - """ - try: - for key in ('fiber_type', 'attenuationdB', 'span_length', - 'dispersion', 'wavelengths', 'id', 'name', 'location', - 'direction', 'launch_power', 'rbw'): - if key in kwargs: - setattr(self, key, kwargs[key]) - elif 'id' in kwargs is None: - setattr(self, 'id', Span.class_counter) - Span.class_counter += 1 - else: - setattr(self, key, None) - print('No Value defined for :', key) - except KeyError as e: - if 'name' in kwargs: - s = kwargs['name'] - print('Missing Span Input Key!', 'name:=', s) - print(e) - raise - - def effective_length(self, loss_coef): - alpha_dict = self.dbkm_2_lin(loss_coef) - alpha = alpha_dict['alpha_acoef'] - leff = 1 - np.exp(-2 * alpha * self.span_length) - return leff - - def asymptotic_length(self, loss_coef): - alpha_dict = self.dbkm_2_lin(loss_coef) - alpha = alpha_dict['alpha_acoef'] - aleff = 1/(2 * alpha) - return aleff - - def dbkm_2_lin(self, loss_coef): - """ calculates the linear loss coefficient - """ - alpha_pcoef = loss_coef - alpha_acoef = alpha_pcoef/(2*4.3429448190325184) - s = 'alpha_pcoef is linear loss coefficient in [dB/km^-1] units' - s = ''.join([s, "alpha_acoef is linear loss field amplitude \ - coefficient in [km^-1] units"]) - d = {'alpha_pcoef': alpha_pcoef, 'alpha_acoef': alpha_acoef, - 'description:': s} - return d - - def beta2(self, dispersion, ref_wavelength=None): - """ Returns beta2 from dispersion parameter. Dispersion is entered in - ps/nm/km. Disperion can be a numpy array or a single value. If a - value ref_wavelength is not entered 1550e-9m will be assumed. - ref_wavelength can be a numpy array. - """ - if ref_wavelength is None: - ref_wavelength = 1550e-9 - wl = ref_wavelength - D = np.abs(dispersion) - b2 = (10**21) * (wl**2) * D / (2 * np.pi * c) -# 10^21 scales to ps^2/km - return b2 - -# TODO - def generic_span(self): - """ calculates a generic version that shows how all the functions of - the class are used. It makes the following assumptions about the span: - - """ - - return diff --git a/gnpy/sandbox/transmit_psd.py b/gnpy/sandbox/transmit_psd.py deleted file mode 100644 index dc53c248..00000000 --- a/gnpy/sandbox/transmit_psd.py +++ /dev/null @@ -1,35 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Sat Mar 25 18:46:19 2017 - -@author: briantaylor -""" -import numpy as np - - -def generic_box_psd(): - """ - creates a generic rectangular PSD at the channel spacing and baud rate - TODO: convert input to kwargs - Input is in THz (for now). Also normalizes the total power to 1 over the - band of interest. - """ - baud = 0.034 - ffs = np.arange(193.95, 194.5, 0.05) - zffs = 1e-6 - grid = [] - power = [] - """ - TODO: The implementation below is awful. Please fix. - """ - for ea in ffs: - fl1 = ea - baud/2 - zffs - fl = ea - baud/2 - fr = ea + baud/2 - fr1 = ea + baud/2 + zffs - grid = grid + [fl1, fl, ea, fr, fr1] - power = power + [0, 1, 1, 1, 0] - grid = np.array(grid) - power = np.power(power)/np.sum(power) - data = np.hstack(grid, power) - return data diff --git a/gnpy/utils.py b/gnpy/utils.py deleted file mode 100644 index b6e7772d..00000000 --- a/gnpy/utils.py +++ /dev/null @@ -1,43 +0,0 @@ -import json -from gnpy.constants import c, h -import numpy as np -from itertools import tee, islice - -nwise = lambda g, n=2: zip(*(islice(g, i, None) - for i, g in enumerate(tee(g, n)))) - - -def read_config(filepath): - with open(filepath, 'r') as f: - return json.load(f) - - -def find_by_node_id(g, nid): - # TODO: What if nid is not found in graph (g)? - return next(n for n in g.nodes() if n.id == nid) - - -def dbkm_2_lin(loss_coef): - """ calculates the linear loss coefficient - """ - alpha_pcoef = loss_coef - alpha_acoef = alpha_pcoef/(2*4.3429448190325184) - s = 'alpha_pcoef is linear loss coefficient in [dB/km^-1] units' - s = ''.join([s, "alpha_acoef is linear loss field amplitude \ - coefficient in [km^-1] units"]) - d = {'alpha_pcoef': alpha_pcoef, 'alpha_acoef': alpha_acoef, - 'description:': s} - return d - - -def db_to_lin(val): - return 10 ** (val / 10) - - -def chan_osnr(chan_params, amp_params): - in_osnr = db_to_lin(chan_params['osnr']) - pin = db_to_lin(chan_params['power']) / 1e3 - nf = db_to_lin(amp_params.nf[0]) - ase_cont = nf * h * chan_params['frequency'] * 12.5 * 1e21 - ret = -10 * np.log10(1 / in_osnr + ase_cont / pin) - return ret diff --git a/requirements_dev.txt b/requirements_dev.txt deleted file mode 100644 index 014b44a0..00000000 --- a/requirements_dev.txt +++ /dev/null @@ -1,12 +0,0 @@ -pip==8.1.2 -bumpversion==0.5.3 -wheel==0.29.0 -watchdog==0.8.3 -flake8==2.6.0 -tox==2.3.1 -coverage==4.1 -Sphinx==1.4.8 -cryptography==1.7 -PyYAML==3.11 -pytest==2.9.2 -pytest-runner==2.11.1 diff --git a/setup.cfg b/setup.cfg deleted file mode 100644 index 1a42e2cd..00000000 --- a/setup.cfg +++ /dev/null @@ -1,22 +0,0 @@ -[bumpversion] -current_version = 0.1.0 -commit = True -tag = True - -[bumpversion:file:setup.py] -search = version='{current_version}' -replace = version='{new_version}' - -[bumpversion:file:gnpy/__init__.py] -search = __version__ = '{current_version}' -replace = __version__ = '{new_version}' - -[bdist_wheel] -universal = 1 - -[flake8] -exclude = docs - -[aliases] -test = pytest -# Define setup.py command aliases here diff --git a/setup.py b/setup.py deleted file mode 100644 index 326df1e6..00000000 --- a/setup.py +++ /dev/null @@ -1,66 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -"""The setup script.""" - -from setuptools import setup, find_packages - -with open('README.rst') as readme_file: - readme = readme_file.read() - -with open('HISTORY.rst') as history_file: - history = history_file.read() - -requirements = [ - 'Click>=6.0', - 'numpy', - 'scipy' - # TODO: put package requirements here -] - -setup_requirements = [ - 'pytest-runner', - # TODO(): put setup requirements (distutils extensions, etc.) here -] - -test_requirements = [ - 'pytest', - # TODO: put package test requirements here -] - -setup( - name='gnpy', - version='0.1.0', - description="Gaussian Noise (GN) modeling library", - long_description=readme + '\n\n' + history, - author="", - author_email='@.com', - url='https://github.com/Telecominfraproject/gnpy', - packages=find_packages(include=['gnpy']), - entry_points={ - 'console_scripts': [ - 'gnpy=gnpy.cli:main' - ] - }, - include_package_data=True, - install_requires=requirements, - license="BSD license", - zip_safe=False, - keywords='gnpy', - classifiers=[ - 'Development Status :: 2 - Pre-Alpha', - 'Intended Audience :: Developers', - 'License :: OSI Approved :: BSD License', - 'Natural Language :: English', - "Programming Language :: Python :: 2", - 'Programming Language :: Python :: 2.6', - 'Programming Language :: Python :: 2.7', - 'Programming Language :: Python :: 3', - 'Programming Language :: Python :: 3.3', - 'Programming Language :: Python :: 3.4', - 'Programming Language :: Python :: 3.5', - ], - test_suite='tests', - tests_require=test_requirements, - setup_requires=setup_requirements, -) diff --git a/tests/__init__.py b/tests/__init__.py deleted file mode 100644 index b0aaf4a8..00000000 --- a/tests/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -# -*- coding: utf-8 -*- - -"""Unit test package for gnpy.""" diff --git a/tests/test_gnpy.py b/tests/test_gnpy.py deleted file mode 100644 index 44145d93..00000000 --- a/tests/test_gnpy.py +++ /dev/null @@ -1,38 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -"""Tests for `gnpy` package.""" - -import pytest - -from click.testing import CliRunner - -from gnpy import gnpy -from gnpy import cli - - -@pytest.fixture -def response(): - """Sample pytest fixture. - - See more at: http://doc.pytest.org/en/latest/fixture.html - """ - # import requests - # return requests.get('https://github.com/audreyr/cookiecutter-pypackage') - - -def test_content(response): - """Sample pytest test function with the pytest fixture as an argument.""" - # from bs4 import BeautifulSoup - # assert 'GitHub' in BeautifulSoup(response.content).title.string - - -def test_command_line_interface(): - """Test the CLI.""" - runner = CliRunner() - result = runner.invoke(cli.main) - assert result.exit_code == 0 - assert 'gnpy.cli.main' in result.output - help_result = runner.invoke(cli.main, ['--help']) - assert help_result.exit_code == 0 - assert '--help Show this message and exit.' in help_result.output