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PLATYPOS - PLAneTarY PhOtoevaporation Simulator
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This is the example how to deploy OKD python applications
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Notebooks and related for the ML workshop at AIP
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Lithium abundance fitting functions
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try to build my own image
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The test instance of daiquiri
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Notebooks and related for the ML workshop at DESY
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The docker related project for the pipeline
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the pipeline repo for the ml_solar_plate project
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A machine learning library to detect solar spots on APPLAUSE solar plates: https://www.plate-archive.org/applause/
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orchest repo from ml_solar_plates
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First demo for NFDI-PUNCH4 TA4
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the extract features steps: this step uses the threshold method to identify pixels that may be part of a sunspots
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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.
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The select-meaningful-features step: in this step we set the meaningfulness of each plates, and of each feature
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The extract-features-properties step: in this step we compute the geometrical/morphological properties of each meaningful features
