recursive-dep-trace-parallel

To trace all code and data dependencies from a Python entry script, you need a recursive closure analysis tool that resolves imports, path literals, and globs to absolute paths. This technique discovers every reachable file without making ad hoc assumptions.

Yes, you can extract path-like strings from binary data files like .npz and .pkl during dependency tracing. The process involves opening and inspecting data artifacts to find path-shaped references and push them into the closure.

The best way to speed up line-by-line repository analysis for large codebases is parallel worker dispatch. This approach dispatches batches of workers to read files concurrently, report external references, and push new items back to a central queue.

To resume an interrupted recursive dependency closure scan, you need a tool that maintains resumable state. It saves progress in state files and worker reports, allowing you to resume the scan and append incremental closure entries from where it stopped.

Yes, exhaustive dependency tracing works with Jupyter notebooks and bundled data artifacts. The recursive tracing process applies to software repositories, notebooks, and data artifacts when you need a complete code and data closure from one entry point.

Files are recorded as holes during a repository dependency closure when they are unreadable or cannot be resolved. The tracing process explicitly records these gaps alongside third-party leaves to produce a complete, auditable summary of missing dependencies.