At first blush, it may seem odd that we are contemplating the distributed use of a library that has historically been focused on desktop-type environments. However, if we pause to ponder over this, we will see its value. You will have probably noticed that with large data sets or complex plots, matplotlib runs more slowly than we might like. What should we do when we need to generate a handful of plots for very large data sets or thousands of plots from diverse sources? If this sounds far-fetched, keep in mind that there are companies that have massive PDF-generating farms of servers for such activities.
This chapter will deal with a similar use case. You are a researcher working for a small company, tracking climactic patterns and possible changes at both the poles. Your team is focused on the Arctic and your assignment is to process the satellite imagery for the east coast of Greenland, which includes not only the new images as they come (every...
