FETCH
fetch is Fast Extragalactic Transient Candidate Hunter. It has been detailed in the paper Towards deeper neural networks for Fast Radio Burst detection.
This is the tensorflow>=2 version of the fetch, if you are looking for the older tensorflow version click here.
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 for this purpose, this notebook explains the whole process. Your also comes with a command line script your_candmaker.py 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. 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.
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}
}