Neural TRex v0.9.5 1. converter.py *Complete* Used for convert from ADST to numpy binary format. arguments --signal: signal file --noise: noised signal file output: 2 numpy files 2. creator.py *In progress* Used for creation of training or test sets or change vectors dimension for neural network. Use "upsampling" for create dataset with augmentation. arguments --signal: signals --noise: noised signals --center: approximate peak position --window: size of output numpy array output: 2 numpy files 3. trainer.py *In progress* Used for create and train CCN via uDocker container arguments --signal: signals --noise: noised signals --min: min amplitude --max: max amplitude --epochs: count of epochs --arch: structure of cnn in .json file output: model in h5 file 4. denoiser.py *In progress* Used for denoising input file. arguments --noise: noised signals --result: output file --model: model for denoising output: 1 numpy file 5. estimator.py *In progress* Creates .csv table which summarizing result. arguments --true: true signals --reco: reco signals --upsampling: upsampling for transfer count to ns --mode: snr or simple output: 1 csv file 6. plotter.py *In progress* Used for plot graphics based on result from csv table. arguments --input: csv files from estimator output: pdf files Simple create CNN pipeline: 1. Download prepared dataset from Google Drive: https://drive.google.com/open?id=1ESXEmZLb20R-d8ok8n8wczhGpWduhaFx 2. Run trainer.py python trainer.py --signal cuted_signal.npy --noise cuted_noise.npy --min 100 --max 200 --epochs 100 --arch model_baseline.json 3. Get the model in .h5 format
Dmitriy Kostunin
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