python utilities

A personal Python utility library collecting helpers I reach for repeatedly across projects. Rather than copying the same code every time, it's packaged for installation directly from GitHub via pip. Covers structured logging, typed null/error handling, environment variable access, and HTTP response validation. MIT licensed.

Clogger is the library's main component. It is a static logger that produces structured, colored output with timestamps, log levels, and automatic source file and line number tracking. Source tracking uses Python's inspect module to walk the call stack at log time and extract the caller's filename and line number, so Clogger.info("message") prints the file and line that called it, not the logger internals.

Twelve config settings control behavior: minimum log level, color toggle, timestamp format, source file display, file write mode, and more. A CloggerConfig object is swapped on Clogger.config to change global behavior. CloggerOverrideFactory generates per-call override dicts that layer on top of the global config for a single log statement. verbose(), file_only("path"), pretty(), and combine() for merging multiple overrides.

Clogobj provides the same capabilities but as instances rather than a global. ClogobjFactory has presets: verbose(), silent() (useful in tests), file_only(), for_module() (prepends a name to every line), for_file(), and custom(). Instance loggers have a log_errors decorator that automatically routes exceptions through the logger when applied to a function.

Option and Result bring Rust-style explicit null and error handling to Python. The core challenge these types solve is distinguishing "no value was provided" from "the value is None": Option.some(None) is a valid present value, distinct from Option.none(). Result uses an explicit _is_ok boolean rather than checking the type of the wrapped value, so Result.ok(None) and Result.err(None) are both valid and unambiguous.

Building a library you actually use in other projects gives immediate feedback on API design quality. Some key takeaways:

• Stack frame inspection with the inspect module for automatic source attribution in logging
• Sentinel object pattern to distinguish Some(None) from None in an Option type
• Python package structure with pyproject.toml and direct GitHub pip installation
• API design for layered configuration, global defaults, instance overrides, and per-call overrides
• log_errors decorator pattern for automatic exception routing through a logger instance