ClassiPyGRB - Updated JOSS version

The final version of ClassiPyGRB for the Journal of Open Source Software, satisfying all of its requirements. Major upgrades include:

• Added automatic testing using GitHub actions.
• Removed bugs and feature upgrading in GUI code.
• Improved compatibility with Linux, Windows, and MacOS systems.

Other minor changes include adding an explicit statement of need to the README file and deprecating the requirements file.

Scientific Software - Peer-reviewed - Python
Published by KenethGarcia over 1 year ago

ClassiPyGRB - First JOSS Version

First Journal of Open Source Software version of ClassiPyGRB

Scientific Software - Peer-reviewed - Python
Published by KenethGarcia over 2 years ago

ClassiPyGRB - Version 1.0.0

First version for JOSS preparation. Updated:

• Improved version of paper.md
• Added a missing file of testing

Scientific Software - Peer-reviewed - Python
Published by KenethGarcia over 2 years ago

ClassiPyGRB - Version 0.1.2

Main Changes

• Added flux_calculator instance. It calculates the total flux in any band for the entire or a part of the light curve.
• Added hardness_proxy instance. It calculates the flux ratio of the 50–100 and 25–50 keV bands (a proxy for hardness) for any GRB in Swift Data.
• Added nearest_neighbors instance. It calculates the n nearest neighbors for any GRB in the resulting t-SNE visualization map. ### Minor Changes
• Change the Interpolation Settings Path from "/" to "\"; following Linux indexing.

Scientific Software - Peer-reviewed - Python
Published by KenethGarcia over 3 years ago

ClassiPyGRB - Version 0.1.1

Welcome to version 0.1.1

In this version, we made minor changes to the repository:

• Removed old README.md
• Updated Documentation section
• Added more animations and images

Scientific Software - Peer-reviewed - Python
Published by KenethGarcia over 3 years ago

ClassiPyGRB - Holiday Season

Main changes

1. Added Documentation section for more flexibility
2. Added Non-parametric noise reduction to data
3. Added interpolation to data
4. Improved parallelization using repeat itertools package
5. Removed Pre-processing bugs when changing resolution in normalize function
6. Separate concatenate from DFT (useful when you don't need DFT in pre-processing)
7. Added background GRB plotting (used when interpolating)

Minor changes

• Updated docstrings
• Updated animations
• Refined DFT plot by using fftshift from scipy

Thanks to Carlos del Burgo from INAOE for the FABADA noise filtering recommendation.

Scientific Software - Peer-reviewed - Python
Published by KenethGarcia over 3 years ago