MOCPy, a Python Library to Manipulate Spatial Coverage Maps

We present MOCPy, a Python library to handle and manipulate MOC (Multi Order Coverage maps). MOC is a Virtual Observatory standard (Fernique et al. 2014) - based on the HEALPix tessellation - that has proven to be quite useful to describe and compare spatial coverage of datasets. Our library allows for easy creation of MOC objects from a list of sources or for a given VizieR table with positions. Intersections of coverages can be computed and VizieR tables data can be efficiently queried to retrieve only rows inside a given MOC coverage. We will also discuss how we use Jupyter notebooks running on mybinder.org service to provide with interactive examples of MOCPy usage.