cddebd55a1
I'm using the yagnson library for this, and that library needs two
pieces of data as its inputs:
- a "YANG Module Library", which is usually a JSON description of all
available and activated YANG modules along its enabled features, etc,
- actual YANG models, typically specified as a list of filesystem paths
which hold them.
I generated that ietf-yanglib file via something like:
$ python path/to/yangson/tools/python/mkylib.py \
gnpy/yang/ext \
gnpy/yang/tip \
> gnpy/yang/yanglib.json`
When this adds support for `ietf-geo-location` in future, make sure to
edit the output so that it does not accidentally enable all of the
geolocation features (but that's for later, anyway). And we might
actually not end up doing that.
Change-Id: I51e342cd556ecc381ff0bf35df2bfa70f5f83ba8
GNPy: Optical Route Planning and DWDM Network Optimization
GNPy is an open-source, community-developed library for building route planning and optimization tools in real-world mesh optical networks. We are a consortium of operators, vendors, and academic researchers sponsored via the Telecom Infra Project's OOPT/PSE working group. Together, we are building this tool for rapid development of production-grade route planning tools which is easily extensible to include custom network elements and performant to the scale of real-world mesh optical networks.
Quick Start
Install either via Docker, or as a Python package. Read our documentation, learn from the demos, and get in touch with us.
This example demonstrates how GNPy can be used to check the expected SNR at the end of the line by varying the channel input power:
GNPy can do much more, including acting as a Path Computation Engine, tracking bandwidth requests, or advising the SDN controller about a best possible path through a large DWDM network. Learn more about this in the documentation.