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https://github.com/devanshkv/fetch

A set of deep learning models for FRB/RFI binary classification.
https://github.com/devanshkv/fetch

binary-classification deep-learning fast-radio-bursts transfer-learning

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A set of deep learning models for FRB/RFI binary classification.

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README 

        
# FETCH



[![DOI](https://zenodo.org/badge/165734093.svg?style=flat-square)](https://zenodo.org/badge/latestdoi/165734093)

[![issues](https://img.shields.io/github/issues/devanshkv/fetch)](https://github.com/devanshkv/fetch/issues)

[![forks](https://img.shields.io/github/forks/devanshkv/fetch)](https://github.com/devanshkv/fetch/network/members)

[![stars](https://img.shields.io/github/stars/devanshkv/fetch)](https://github.com/devanshkv/fetch/stargazers)

[![GitHub license](https://img.shields.io/github/license/devanshkv/fetch)](https://github.com/devanshkv/fetch/blob/master/LICENSE)

[![HitCount](http://hits.dwyl.com/devanshkv/fetch.svg)](http://hits.dwyl.com/devanshkv/fetch)

[![arXiv](https://img.shields.io/badge/arXiv-1902.06343-brightgreen.svg)](https://arxiv.org/abs/1902.06343)

[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)



fetch is Fast Extragalactic Transient Candidate Hunter. It has been detailed in the paper [Towards deeper neural networks for Fast Radio Burst detection](https://arxiv.org/abs/1902.06343).



This is the `tensorflow>=2` version of the fetch, if you are looking for the older tensorflow version click [here](https://github.com/devanshkv/fetch/archive/0.1.8.tar.gz).



Install 

---

    git clone https://github.com/devanshkv/fetch.git

    cd fetch

    pip install -r requirements.txt

    python setup.py install



The installation will put `predict.py` and `train.py` in your `PYTHONPATH`.



Usage

---

To use fetch, you would first have to create candidates. Use [`your`](https://thepetabyteproject.github.io/your/) for this purpose, [this notebook](https://thepetabyteproject.github.io/your/ipynb/Candidate/) explains the whole process. Your also comes with a command line script [`your_candmaker.py`](https://thepetabyteproject.github.io/your/bin/your_candmaker/) which allows you to use CPU or single/multiple GPUs. 



To predict a candidate h5 files living in the directory `/data/candidates/` use `predict.py` for model `a` as follows:



    predict.py --data_dir /data/candidates/ --model a

        

To fine-tune the model `a`, with a bunch of candidates, put them in a pandas readable csv, `candidate.csv` with headers 'h5' and 'label'. Use



    train.py --data_csv candidates.csv --model a --output_path ./

        

This would train the model `a` and save the training log, and model weights in the output path.



Example

---



Test filterbank data can be downloaded from [here](http://astro.phys.wvu.edu/files/askap_frb_180417.tgz). The folder contains three filterbanks: 28.fil  29.fil  34.fil.

Heimdall results for each of the files are as follows:



for 28.fil



    16.8128	1602	2.02888	1	127	475.284	22	1601	1604

for 29.fil



    18.6647	1602	2.02888	1	127	475.284	16	1601	1604

for 34.fil



    13.9271	1602	2.02888	1	127	475.284	12	1602	1604 



The `cand.csv` would look like the following:



    file,snr,stime,width,dm,label,chan_mask_path,num_files

    28.fil,16.8128,2.02888,1,475.284,1,,1

    29.fil,18.6647,2.02888,1,475.284,1,,1

    34.fil,13.9271,2.02888,1,475.284,1,,1



Running `your_candmaker.py` will create three files:



    cand_tstart_58682.620316710374_tcand_2.0288800_dm_475.28400_snr_13.92710.h5

    cand_tstart_58682.620316710374_tcand_2.0288800_dm_475.28400_snr_16.81280.h5

    cand_tstart_58682.620316710374_tcand_2.0288800_dm_475.28400_snr_18.66470.h5



Running `predict.py` with model `a` will give `results_a.csv`:



    ,candidate,probability,label

    0,cand_tstart_58682.620316710374_tcand_2.0288800_dm_475.28400_snr_18.66470.h5,1.0,1.0

    1,cand_tstart_58682.620316710374_tcand_2.0288800_dm_475.28400_snr_16.81280.h5,1.0,1.0

    2,cand_tstart_58682.620316710374_tcand_2.0288800_dm_475.28400_snr_13.92710.h5,1.0,1.0

    

Training Data

---



The training data is available at [astro.phys.wvu.edu/fetch](http://astro.phys.wvu.edu/fetch/).



## Citating this work

___



If you use this work please cite:



    @article{Agarwal2020,

      doi = {10.1093/mnras/staa1856},

      url = {https://doi.org/10.1093/mnras/staa1856},

      year = {2020},

      month = jun,

      publisher = {Oxford University Press ({OUP})},

      author = {Devansh Agarwal and Kshitij Aggarwal and Sarah Burke-Spolaor and Duncan R Lorimer and Nathaniel Garver-Daniels},

      title = {{FETCH}: A deep-learning based classifier for fast transient classification},

      journal = {Monthly Notices of the Royal Astronomical Society}

    }

    @software{agarwal_aggarwal_2020,

      author       = {Devansh Agarwal and

                      Kshitij Aggarwal},

      title        = {{devanshkv/fetch: Software release with the 

                       manuscript}},

      month        = jun,

      year         = 2020,

      publisher    = {Zenodo},

      version      = {0.1.8},

      doi          = {10.5281/zenodo.3905437},

      url          = {https://doi.org/10.5281/zenodo.3905437}

    }