https://github.com/deeplearnphysics/flow2supera
https://github.com/deeplearnphysics/flow2supera
Last synced: 12 months ago
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- Host: GitHub
- URL: https://github.com/deeplearnphysics/flow2supera
- Owner: DeepLearnPhysics
- License: mit
- Created: 2024-03-11T21:44:50.000Z (about 2 years ago)
- Default Branch: develop
- Last Pushed: 2025-03-31T17:01:47.000Z (about 1 year ago)
- Last Synced: 2025-04-13T00:34:49.138Z (12 months ago)
- Language: Python
- Size: 1.52 MB
- Stars: 0
- Watchers: 5
- Forks: 4
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Contributing: contributing.md
- License: LICENSE
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README
# flow2supera
This repository contains code to translate the HDF5 files output by [ndlar_flow](https://github.com/DUNE/ndlar_flow) to [Supera](https://github.com/DeepLearnPhysics/SuperaAtomic) format for use by the DUNE machine learning reconstruction chain, [lartpc_mlreco3d](https://github.com/DeepLearnPhysics/lartpc_mlreco3d).
# Prerequisites
`flow2supera` depends on [edep2supera](https://github.com/DeepLearnPhysics/edep2supera), [SuperaAtomic](https://github.com/DeepLearnPhysics/SuperaAtomic), [larcv](https://github.com/DeepLearnPhysics/larcv2) and [h5flow](https://github.com/peter-madigan/h5flow). Install each of those repositories using the instructions on their respective READMEs and ensure that you can import them in python. Make sure the installation follows this order: `larcv` -> `SuperaAtomic` -> `edep2supera` -> `flow2supera`.
# Installation
Once the prerequisites are met, simply run this command from the top directory:
```
python3 -m pip install .
```
# Usage
The main executable script is located at `bin/run_flow2supera.py` relative to the top directory. The _required_ arguments are the input and output file names and the configuration:
```
python3 bin/run_flow2supera.py -o -c 2x2
```
Configuration keyword or a file path (full or relative including the file name). Supported configurations: `2x2`, `2x2_data`, `mod1_data`, `2x2_mpvmpr`.
You can also specify the following _optional_ arguments:
- `-n` or `--num_events`: Number of events to process.
- `-s` or `--skip`: Number of first events to skip.
- `-l` or `--log`: Name of a log file to be created.
Upon successful completion, this will produce an output larcv-format file that can be used as input to the machine learning reconstruction.
# Contributing
Please read the contributing.md file for information on how you can contribute.
# License
Distributed under the MIT License. See LICENSE for more information.