Explore projects

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.

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

DEPRECATED. Please use the latest version from: https://gitlab-p4n.aip.de/punch-dev/punch-docs

First demo for NFDI-PUNCH4 TA4

Excel Table

orchest repo from ml_solar_plates

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

The docker related project for the pipeline

Notebooks and related for the ML workshop at DESY

The test instance of daiquiri

try to build my own image

Lithium abundance fitting functions

Notebooks and related for the ML workshop at AIP

This is the example how to deploy OKD python applications

PLATYPOS - PLAneTarY PhOtoevaporation Simulator