77925b218e
In this change, the RamanSolver is completely restructured in order to obtain a simplified and faster solution of the Raman equation. Additionally, the inter-channel Raman effect can be evaluated also in the standard fiber, when no Raman pumping is present. The same is true for the GGN model. The Raman pump parameter pumps_loss_coef has been removed as it was not used. The loss coefficient value evaluated at the pump frequency can be included within the fiber loss_coef parameter. This change induces variations in some expected test results as the Raman profile solution is calculated by a completely distinct algorithm. Nevertheless, these variations are negligible being lower than 0.1dB. Change-Id: Iaa40fbb23c555571497e1ff3bf19dbcbfcadf96b
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.