reading "lists" of events into numpy arrays

Disclaimer: I am not sure if the following is compatible with the way yardl is designed.

Another way to potentially improve the efficiency of the python-based reader for "lists" of events with fixed (uncompressed) structure would be the following:

• assume we have an event with a fixed structure (e.g. 1 float, 2 ints, or e.g. a 32bit word that encodes 4x 6bit unints, and 2x 4bit ints, ...)
• assume that we stream and store many of those event (1e6) into a "TimeBlock"

If the structure of the events is fixed, numpy's memmap together with a custom dtype could be used to read all events efficiently in the TimeBlock into a "2D" array avoiding more expensive loops over events.

Obviously, we don't want to loose the compression feature, but instead of compressing individual events, the whole TimeBlock could be compressed / decompressed.

Start by locating the Python-based reader and the TimeBlock handling mentioned in the issue; no specific files or tests are named. Determine how fixed-structure event lists and compression are currently handled, then define measurable completion criteria around efficient array reads while preserving the compression feature.