Analyzing maritime traffic supports understanding activities that take place at sea, for purposes ranging from maritime spatial planning, improving safety of operations and environmental protection, to studying the biodiversity and sustainability of ecosystems. Often studying maritime traffic requires processing very large datasets of vessel movement, such as those produced by the Automatic Identification System (AIS). Originally developed for the purpose of vessel collision avoidance, the AIS allows for constant monitoring of vessel activity through spatiotemporal messages transmitted by the vessels themselves. The surplus of positional data, originating from the large amounts of messages received daily, and the noise on AIS, coming from all sorts of interference, render further analysis a challenging task. In order to overcome these issues we released an open source toolbox that provides a number of modules to support easy handling of AIS data, while improving their transformation into actionable visualizations, such as traffic density maps. More specifically, the toolbox provides scalable mechanisms for processing AIS datasets, like the removal of spoofing or erroneous messages, while the density map extraction can be easily configured to fit the user needs. The implementation is written in python for simplicity, readability and overall ease of use.