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Code for paper entitled "Learning to detect RFI in radio astronomy without seeing it"

Home Page: https://academic.oup.com/mnras/advance-article/doi/10.1093/mnras/stac2503/6692884

License: MIT License

Python 52.28% Shell 0.58% Lua 47.14%
anomaly-detection novelty-detection radio-astronomy rfi unsupervised-learning weak-supervision

rfi-nln's Introduction

RFI-NLN: Learning to detect RFI in radio astronomy without seeing it

A repository containing the implementation of the paper entitled Learning to detect RFI in radio astronomy without seeing it

Installation

Install conda environment by:

    conda create --name rfi python=3.9.7

Run conda environment by:

    conda activate rfi

Install dependancies by running:

    pip install -r requirements

Replication of results in paper

Run the following to replicate the results for HERA or LOFAR

    ./experiments/run_lofar.sh
    ./experiments/run_hera.sh

Dataset

You will need to download the LOFAR and HERA datasets and specify the its path using -data_path command line option.

Dependencies

This project makes use of the python wrapping for AOFlagger, for detailed instructions on the installation and usage see the AOFlagger documentation

Licensing

Source code of RFI-AE is licensed under the MIT License.

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