Pixel Clusterizer

Intended Use

Pixel_clusterizer is an easy to use pixel hit clusterizer for Python. It clusters hits connected to unique event numbers in space and time.

The hits must be provided in a numpy recarray. The array must contain the following columns ("fields"):

• event_number
• frame
• column
• row
• charge

If the column names are different, a mapping of the names to the default names can be provided. The data type of each column can vary and is not fixed. The column/row values can be either indices (integer, default) or positions (float). Charge can be either integer or float (default).

After clustering, two new arrays are returned:

1. The cluster hits array is the hits array extended by the following columns:
   • cluster_ID
   • is_seed
   • cluster_size
   • n_cluster
2. The cluster array contains in each row the information about a single cluster. It has the following columns:
   • event_number
   • ID
   • n_hits
   • charge
   • seed_column
   • seed_row
   • mean_column
   • mean_row

Installation

Python 2.7 or Python 3 or higher must be used. There are many ways to install Python, though we recommend using Anaconda Python or Miniconda.

Prerequisites

The following packages are required:

numpy numba>=0.24.0

Installation of pixel_clusterizer

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

pip install pixel_clusterizer

For developer, clone the pixel_clusterizer git repository and use the following command to install pixel_clusterizer:

pip install -e .

For testing the basic functionality of pixel_clusterizer, execute the following command:

nosetests pixel_clusterizer

Usage

import numpy as np

from pixel_clusterizer import clusterizer

hits = np.ones(shape=(3, ), dtype=clusterizer.default_hits_dtype)  # Create some data with std. hit data type

cr = clusterizer.HitClusterizer()  # Initialize clusterizer

cluster_hits, clusters = cr.cluster_hits(hits)  # Cluster hits

Also please have a look at the examples folder!

Support

Please use GitHub's issue tracker for bug reports/feature requests/questions.