Generates point cloud showers in batches (one file for each batch) and optionally converts them to grid representation(images).

Requirements

• torch
• networkx
• numpy
• tqdm
• h5py

Usage

usage: main_discrete.py [-h] [-N NUM_SHOWERS] [-E INITIAL_ENERGY] [-grids] [-l] [-p SAVE_PATH] [-s SEED] [-bs BATCH_SIZE]

Sythnetic Calorimeter Simulator with particle propagation and scattering

optional arguments:
  -h, --help            show this help message and exit
  -N NUM_SHOWERS, --num-showers NUM_SHOWERS
                        Number of showers to generate. (default: 50)
  -E INITIAL_ENERGY, --initial-energy INITIAL_ENERGY
                        Initial energy of incoming particle (in GeV) (default: 65.0)
  -grids, --save-grids  Whether to convert and save at grid granularity (default: False)
  -l, --load            No generation. Retrieve showers from save_path (default: False)
  -p SAVE_PATH, --save-path SAVE_PATH
                        Path where to save the generated showers (default: None)
  -s SEED, --seed SEED  Numpy Random Seed (default: 1234)
  -bs BATCH_SIZE, --batch-size BATCH_SIZE
                        Number of showers to store in a single file (default: 10000)

Example

python main_discrete.py -p config_v3 -g -N 100 -bs 50 -E 65 -s 1122

Cell Grid Conversion

Need to specify the bin width and number of bins (currently hardcoded)

Canonical Datasets

The datasets can be downloaded from the following link.

They can be loaded with torch.load() utility. Each dataset is a python list of data points (or showers). Each shower is represented as a list of layers, and each layer is a list of points. Each point is a list of three features (x and y coordinates and the energy E). The script SimulatorData.py provides pytorch dataloaders for reading the SUPA datasets.