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. 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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