Giter VIP home page Giter VIP logo

scipy2016_material's Introduction

SciPy 2016 Material

This repository contains material related to the two contributions

  • Storing Reproducible Results from Computational Experiments using Scientific Python Packages
  • Validating Function Arguments in Python Signal Processing Applications

presented at the 15th annual Scientific Computing with Python (SciPy 2016) conference held in Austin, Texas, USA, July 11-17, 2016. The two contributions were part of the poster session.

Two papers that further detail the above contributions have been accepted for publication in the conference proceedings. The open peer review process is documented on GitHub: scipy-conference/scipy_proceedings#191 and scipy-conference/scipy_proceedings#215

Files Overview

Files related to the contribution Storing Reproducible Results from Computational Experiments using Scientific Python Packages

  • scipy16_reproducibility_poster.pdf - The poster.
  • scipy16_reproducibility_paper.pdf - The accompanying paper (publised version)
  • magni_reproduciblity_example.ipynb - A Jupyter IPython Notebook detailing an extensive example of the use of the 'magni.reproduciblity' Python package.
  • magni_reproducibility_example.py - The script used to generate the results in the extensive example.
  • mandlebrot.hdf5 - The results database that is part of the extensive example.

The extensive example is also available at: http://dx.doi.org/10.5278/VBN/MISC/MagniRE

Files related to the contribution Validating Function Arguments in Python Signal Processing Applications

  • scipy16_validation_poster.pdf - The poster.
  • scipy16_validation_paper.pdf - The accompanying paper (published version).

Magni

Reference implementations of the proposed reproducibility and validation packages are part of the open source 'magni' Python package. Further information about 'magni' is available at

Licenses

The scipy16_reproducibility_poster.pdf poster is subject to the following copyright notice

Copyright (C) Christian Schou Oxvig, Thomas Arildsen, and Torben Larsen. Aalborg University, Department of Electronic Systems, Signal and Information Processing, Fredrik Bajers Vej 7, DK-9220 Aalborg, Denmark. This work is licensed under a Creative Common Attribution 4.0 International License (CC-BY 4.0).

The scipy16_validation_poster.pdf poster is subject to the following copyright notice

Copyright (C) Patrick Steffen Pedersen, Christian Schou Oxvig, Jan Østergaard, and Torben Larsen. Aalborg University, Department of Electronic Systems, Signal and Information Processing, Fredrik Bajers Vej 7, DK-9220 Aalborg, Denmark. This work is licensed under a Creative Common Attribution 4.0 International License (CC-BY 4.0).

See the individual file for the license for the remaining files.

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.