Notebook for Tunable Narrowband Band-Pass Filters
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Updated
Oct 2, 2018 - Jupyter Notebook
Robot Framework is an open source, easy to learn, and easy to use, yet powerful, and extendable, generic automation framework for software testing and RPA (robotic process automation). While it's original purpose was to support acceptance testing and acceptance test driven development
(ATDD) of embedded software at Nokia Networks back in 2005, nowadays it is also widely used for automation of integration tests
and end-to-end tests
in desktop, web, and mobile development. You can use it to automate GUI based applications as well as REST, RPC, SOAP and other API, and protocol based testing. Due to it's text based nature and cli tools RF is a perfect match for agile and DevOps, and DevSecOps driven projects striving to succeed in continuous testing. It integrates very well with open source and commercial CI/CD solutions like Jenkins, CircleCI, GitHub Actions, and Azure Pipelines - just to name a few. It is also developer friendly and can easily be versioned with Git, and used with Docker and other container and virtualisation technologies to support building and maintaining of scalable (test) environments following the configuration as code
or infrastructure as code
approach. RF can be used in projects of any technology stack, and runs on any infrastructure (Windows, Mac, Linux, cloud) - it is stack and infrastructure agnostic.
Notebook for Tunable Narrowband Band-Pass Filters
This repository consists of notebook, backtesting logs and dataset along with the Problem Statement. This is was our approach to KDSH 2024 by Zelts Labs
Created by Pekka Klärck
Released 06 2008
Latest release 17 days ago