caloutils 0.0.18

4D Point Clouds

The 4D point cloud data handled by caloutils consists of three spatial coordinates and a fourth dimension representing the energy deposited at each point in the calorimeter. This multidimensional dataset captures a comprehensive view of particle showers, serving as a valuable resource in experimental physics.

Key Features

caloutils offers a comprehensive suite of functions and methods to analyze these 4D point clouds:

• Voxelization: The package provides functionalities to convert raw, continuous point clouds into a to a voxel representation. This regular structure can simplifie subsequent analysis or machine learning tasks.

• Energy Response Calculation: Calculate the detector response for a calorimeter shower by summing the hit energies and normalizing by the incoming energy of the particle.

• Geometric Feature Extraction: The package offers tools to calculate geometric features such as the first principal component, spherical ratios, and more.

• Data Transformation: caloutils can transform data from cylindrical to Cartesian coordinates, calculate pseudorapidity and azimuthal angle, and efficiently handle batch data operations.

1. Convert Voxelized Data to Point Cloud

import caloutils
# Convert the point cloud data into a voxel representation.
batch = caloutils.processing.voxel_to_pc(shower, energies)

batch is an instance of a PyTorch Geometric Batch object, storing the point cloud data

2. Data Transformation

Transform the cylindrical coordinates to Cartesian coordinates and add pseudorapidity and azimuthal angle:

batch_transformed = caloutils.batch_to_Exyz(batch)

These examples are meant to be illustrative and provide a quick understanding of the package usage. For a more comprehensive understanding of each function’s intricacies, users are recommended to refer to the full function documentation in the package.

3. Calculate High Level Variables

# Calculate the energy response of a batch of showers.
energy_response = caloutils.variables.energy_response(batch)
# Calculate the principal component of a batch of showers.
first_principal_component = caloutils.variables.fpc_from_batch(batch)
# Or, all at once, stored as attributes of the batch:
batch=caloutils.variables.calc_vars(batch)
print(batch.cyratio.mean())