Code and documentation for the first machine learning focused mock data challenge hosted by the Albert-Einstein-Institut Hannover and the Friedrich-Schiller Universität Jena.
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Added a slice-generator that utilizes multiprocessing. #19
Multiprocessing is used to parallelize the work of post-processing
individual slices. The generator is supposed to work effortlessly
with the output data-files from generate_data.py of the MLGWSC-1.
To implement post-processing, sub-class the Slicer class and
overwrite the process_slice method. Lighter formatting of the
data from all detectors can be performed by overwriting the
format_returns method.
Multiprocessing is used to parallelize the work of post-processing individual slices. The generator is supposed to work effortlessly with the output data-files from generate_data.py of the MLGWSC-1.
To implement post-processing, sub-class the Slicer class and overwrite the
process_slicemethod. Lighter formatting of the data from all detectors can be performed by overwriting theformat_returnsmethod.