  With the next-generation Timepix3 hybrid pixel detector, new possibilities
and challenges have arisen. The Timepix3 segments active sensor area of 1.98
$cm^2$ into a square matrix of 256 x 256 pixels. In each pixel, the Time of
Arrival (ToA, with a time binning of 1.56 $ns$) and Time over Threshold (ToT,
energy) are measured simultaneously in a data-driven, i.e. self-triggered,
read-out scheme. This contribution presents a framework for data acquisition,
real-time clustering, visualization, classification and data saving. All of
these tasks can be performed online, directly from multiple readouts through
UDP protocol. Clusters are reconstructed on a pixel-by-pixel decision from the
stream of not-necessarily chronologically sorted pixel data. To achieve quick
spatial pixel-to-cluster matching, non-trivial data structures (quadtree) are
utilized. Furthermore, parallelism (i.e multi-threaded architecture) is used to
further improve the performance of the framework. Such real-time clustering
offers the advantages of online filtering and classification of events.
Versatility of the software is ensured by supporting all major operating
systems (macOS, Windows and Linux) with both graphical and command-line
interfaces. The performance of the real-time clustering and applied filtration
methods are demonstrated using data from the Timepix3 network installed in the
ATLAS and MoEDAL experiments at CERN.
