2.42. Wrap up

You have walked through the parts of Python that come up constantly when writing scripts for the OpenMV Cam:

  • The language fundamentals – values and variables, arithmetic and comparison, strings and bytes, the four core collections (list, tuple, dict, set), conditionals and loops, defining functions and managing their arguments and scope, defining classes and using inheritance, raising and handling exceptions. These are the bricks every other piece of Python is built from.

  • The structural pieces – splitting code across modules and packages, importing what you need, reading and writing files with with, exchanging structured data through json, and packing binary records through struct with endianness and fixed-width integer fields. These show up the moment a script grows past a single file or needs to talk to something outside itself.

  • The parts that change shape on a constrained runtime – why MicroPython floats are 32-bit and how to compare them reliably, how the garbage collector hands out and reclaims blocks, why fragmentation matters on a small heap, and how pre-allocation keeps long-running scripts well behaved. Desktop habits sometimes mislead here; this material gives you the right mental model for code that lives on the device.

  • The introspection and dynamic-code tools – id(), hash(), isinstance(), issubclass(), callable(), globals(), locals() for looking at values and the environment; eval() / exec() / compile() for the rare cases where producing code at run time is the right answer. Most scripts never touch these, but knowing they exist (and when not to reach for them) is part of reading other people’s Python.

  • Pattern matching on text – the re module for strings whose form you can describe but cannot enumerate. Character classes, quantifiers, capturing groups, anchors, and the greedy-versus-lazy trade-off, together with the specific places MicroPython’s subset stops short of CPython. Reach for it when str.find or str.split runs out of road.

  • Containers past the built-ins – collections.namedtuple() for typed records, collections.deque for bounded ring buffers and rolling windows, collections.OrderedDict when insertion order is part of equality, and heapq for “what’s the smallest” or “what’s the most urgent” questions built on a plain list. Each one fills a gap the four core container types leave behind.

  • The everyday workflow tools – comprehensions for building collections from existing ones, generators and the iterator protocol for processing data lazily, decorators and context managers for wrapping common acquire/release and before/after patterns, and the debugging habits (reading tracebacks, repr(), dir(), help(), the logging module) that turn a broken script into a fixed one.

2.42.1. Using this primer later

Treat the primer as reference material, not a one-pass read. The chapters are short on purpose; coming back to refresh on slicing or context managers or comprehensions is the intended use. Bookmark the section you reach for most.

If something in the camera’s documentation later references a Python concept you do not recognise – say, “this returns a context manager” or “iterate the result” – the corresponding primer page is the place to start.

2.42.2. Where to go from here

Basic hardware control builds directly on the Python you now know. Where the primer lived in memory, hardware control lives in the physical world – voltages on pins, pulses on wires, bytes clocked over buses to other chips. The toolkit shifts to the machine module and a thin layer of electronics. Everything from the primer carries forward; you will not relearn def or with or how bytearray differs from bytes.

When you hit a Python feature that feels unfamiliar in the hardware material, this primer is where to come back to.