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the extract features steps: this step uses the threshold method to identify pixels that may be part of a sunspots

First demo for NFDI-PUNCH4 TA4

[python] a module based on myplotlib to plot results from the mean-field dynamo code of R. Hollerbach (see Hollerback R. 2000)

A REST API to submit ArchDescr and Generate Archives

A test to explore data with a web terminal

Moving files aroung grafana and draio and cloud.aip.de

Post-commit hook for SVN to post a comment to the proper Jira.

A machine learning library to detect solar spots on APPLAUSE solar plates: https://www.plate-archive.org/applause/

the pipeline repo for the ml_solar_plate project

K2SC de-trended light curves of stars in Open Clusters in K2. Used the re-processed, final version of the data products provided by MAST.

The extract-features-properties step: in this step we compute the geometrical/morphological properties of each meaningful features

The select-meaningful-features step: in this step we set the meaningfulness of each plates, and of each feature

the label-features step: in this step the pixels that may be part of a sunspots are grouped in regions and each region is labeled as a feature, the number of feature per plate is computed on the fly.

python script to check Wordpress instances and to inform about new users, posts and pages.

A java implementation of the FITS 3.0 standard

Code for plots used in "Focused Product Release: Sources from Service Interface Function Image Analysis" paper: https://ui.adsabs.harvard.edu/abs/2023A%26A...680A..35G/abstract

Notebooks and related for the ML workshop at AIP