  Fast pixelated detectors incorporating direct electron detection (DED)
technology are increasingly being regarded as universal detectors for scanning
transmission electron microscopy (STEM), capable of imaging under multiple
modes of operation. However, several issues remain around the post acquisition
processing and visualisation of the often very large multidimensional STEM
datasets produced by them. We discuss these issues and present open source
software libraries to enable efficient processing and visualisation of such
datasets. Throughout, we provide examples of the analysis methodologies
presented, utilising data from a 256$\times$256 pixel Medipix3 hybrid DED
detector, with a particular focus on the STEM characterisation of the
structural properties of materials. These include the techniques of virtual
detector imaging; higher order Laue zone analysis; nanobeam electron
diffraction; and scanning precession electron diffraction. In the latter, we
demonstrate nanoscale lattice parameter mapping with a fractional precision
$\le 6\times10^{-4}$ (0.06%).
