Example gallery =============== Each of the following ships under `pyccolo/examples/ `_ as a self-contained, tested tracer — great starting points to adapt. .. list-table:: :header-rows: 1 :widths: 30 70 * - Example - Demonstrates * - `coverage.py `_ - statement-level code coverage (``before_stmt``, ``should_instrument_file``) * - `optional_chaining.py `_ - ``?.``, ``.?``, ``??`` optional chaining / nullish coalescing via :class:`~pyccolo.AugmentationSpec` * - `pipeline_tracer.py `_ - ``|>`` / ``|>>`` pipeline operators (binop augmentation) * - `quick_lambda.py `_ - MacroPy-style ``f[_ + _]`` quick lambdas * - `quasiquote.py `_ - MacroPy-style ``q[...]`` / ``u[...]`` quasiquotes * - `block_lambda.py `_, `func_block.py `_, `brace_subscript.py `_ - statement-bodied ``name{ ... }`` blocks (paired-delimiter augmentation) * - `lazy_imports.py `_ - make (most) imports lazy, resolving on first use * - `future_tracer.py `_ - implicit async: run assignments on a thread pool, unwrap futures on use * - `concolic.py `_ - concolic (concrete + symbolic) execution with a Z3 / brute-force solver In the wild ----------- Beyond the bundled examples, Pyccolo is the instrumentation engine behind several larger projects — good places to see what it can do at scale: - `ipyflow `_ — a reactive Python kernel for Jupyter that tracks dataflow between cells using Pyccolo's dynamic analysis. - `pipescript `_ — a pipe operator (``|>``), placeholder (``$``), and macro syntax for IPython/Jupyter, built entirely on Pyccolo's syntax augmentation and composable event handlers. - `pycograd `_ — a small reverse-mode automatic-differentiation library that differentiates *ordinary* ``numpy`` code (no special "autodiff namespace"), using Pyccolo to trace the computation. People have also built statement-level coverage, syntactic macros (quasiquotes, quick lambdas), syntax-augmented Python (optional chaining, pipeline operators), lazy imports, concolic execution, and tools to uncover semantic memory leaks.