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https://github.com/liamconnor/single_pulse_ml


https://github.com/liamconnor/single_pulse_ml

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### 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 classified

This 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.