https://github.com/liamconnor/single_pulse_ml
https://github.com/liamconnor/single_pulse_ml
Last synced: 2 months ago
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- Host: GitHub
- URL: https://github.com/liamconnor/single_pulse_ml
- Owner: liamconnor
- License: gpl-2.0
- Created: 2017-05-02T12:17:52.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2020-01-13T18:40:36.000Z (over 5 years ago)
- Last Synced: 2024-11-02T05:32:39.463Z (7 months ago)
- Language: Python
- Size: 76.8 MB
- Stars: 15
- Watchers: 3
- Forks: 9
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- frbsoft - GitHub
README
### single_pulse_ml
Build, train, and apply deep neural networks to single pulse candidates.
run_frb_simulation.py constructs a training set that includes simulated FRBs
run_single_pulse_DL.py allows for training of deep neural networks for several
input data products, including:
-- dedispersed dynamic spectra (2D CNN)
-- DM/time intensity array (2D CNN)
-- frequency-collapsed pulse profile (1D CNN)
-- Multi-beam S/N information (1D feed forward DNN)
run_single_pulse_DL.py can also be used when a trained model already exists and candidates are to be classifiedThis code has been used on CHIME Pathfinder incoherent data as well as commissioning data on Apertif.
### Requirements
- You will need the following:
- numpy
- scipy
- h5py
- matplotlib
- tensorflow
- keras### Tests
In the single_pulse_ml/tests/ directory,
"test_run_frb_simulation.py" can be run to generate 100 simulated FRBs
to ensure the simulation backend works."test_frbkeras.py" will generate 1000 gaussian-noise
dynamic spectrum candidates of dimension 32x64, then
build, train, and test a CNN using the tools in frbkeras.
This allows a test of the keras/tensorflow code.