OrcaSong: Preprocessing KM3NeT data for DL ========================================== .. image:: https://badge.fury.io/py/orcasong.svg :target: https://badge.fury.io/py/orcasong .. image:: https://git.km3net.de/ml/OrcaSong/badges/master/pipeline.svg :target: https://git.km3net.de/ml/OrcaSong/pipelines .. image:: https://examples.pages.km3net.de/km3badges/docs-latest-brightgreen.svg :target: https://ml.pages.km3net.de/OrcaSong .. image:: https://git.km3net.de/ml/OrcaSong/badges/master/coverage.svg :target: https://ml.pages.km3net.de/OrcaSong/coverage .. image:: https://api.codacy.com/project/badge/Grade/1591b2d2d20e4c06a66cad99dc6aebe3 :alt: Codacy Badge :target: https://www.codacy.com/app/sreck/OrcaSong?utm_source=github.com&utm_medium=referral&utm_content=StefReck/OrcaSong&utm_campaign=Badge_Grade The documentation for OrcaSong can be found at https://ml.pages.km3net.de/OrcaSong! OrcaSong is a part of the Deep Learning efforts of the neutrino telescope KM3NeT. Find more information about KM3NeT on http://www.km3net.org. In this regard, OrcaSong is a project that preprocesses raw KM3NeT detector data for the use with deep neural networks, making use of km3nets data processing pipline km3pipe. Two different modes are available: - For convolutional networks: produce n-dimensional 'images' (histograms) - For graph networks: produce a list of nodes, each node representing infos about a hit in the detector Currently, only simulations with a hdf5 data format are supported as an input. OrcaSong can be installed via pip by running:: pip install orcasong You can get a list of all the bash commands in orcasong by typing:: orcasong --help A Singularity image of the latest stable version of OrcaSong is also provided. You can download it from the km3net sftp server ``pi1139.physik.uni-erlangen.de`` in ``singularity/orcasong.sif``.