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A clusterizer to cluster hits of a pixel detector with Python. The clustering happens with numba on numpy arrays to increase the speed.

Project description

# Pixel Clusterizer [![Build Status](https://travis-ci.org/SiLab-Bonn/pixel_clusterizer.svg?branch=master)](https://travis-ci.org/SiLab-Bonn/pixel_clusterizer) [![Build Status](https://ci.appveyor.com/api/projects/status/github/SiLab-Bonn/pixel_clusterizer)](https://ci.appveyor.com/project/SiLab-Bonn/pixel_clusterizer)

Pixel_clusterizer is an easy to use pixel hit-clusterizer for Python. It clusters hits on an event basis in space and time.

The hits have to be defined as a numpy recarray. The array has to have the following fields: - event_number - frame - column - row - charge

or a mapping of the names has to be provided. The data type does not matter.

The result of the clustering is the hit array extended by the following fields: - cluster_ID - is_seed - cluster_size - n_cluster

A new array with cluster information is also created created and has the following fields: - event_number - ID - size - charge - seed_column - seed_row - mean_column - mean_row

# Installation

The stable code is hosted on PyPI and can be installed by typing:

pip install pixel_clusterizer

# Usage

import numpy as np

from pixel_clusterizer.clusterizer import HitClusterizer

from pixel_clusterizer import data_struct

hits = np.ones(shape=(3, ), dtype=data_struct.HitInfo) # Create some data

clusterizer = HitClusterizer() # Initialize clusterizer

hits_clustered, cluster = clusterizer.cluster_hits(hits) # Cluster hits # add hits to clusterizer

print (cluster) # Print cluster

print (hits_clustered) # Print hits + cluster info

Also take a look at the example folder!

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