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.

• You will need the following:
  • numpy
  • scipy
  • h5py
  • matplotlib
  • tensorflow
  • keras

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.