GX-TXT Script Debugger – Scientific Debugging with GNU Octave and Python

A hands-on learning project focused on scientific debugging with GNU Octave and Python in GX-TXT. Through reproducible exercises, examples tested in real sessions and illustrated tutorials, readers will learn to follow program execution, inspect variables, identify errors and understand how scripts interact with the framework’s public APIs.

The project grew out of practical work with the Octave Script Workspace and GX-TXT’s managed script execution tools. Rather than merely explaining where debugger controls are located, it offers a progressive series of experiments: from the first breakpoint to investigating scientific workflows involving Datasets, metadata and CORE services.

An evolving learning laboratory

The GX-TXT debugger is actively evolving. The articles therefore have two complementary goals: to teach debugging techniques applicable to GNU Octave and Python, and to verify the capabilities actually available in GX-TXT through practical examples. Later tutorials will address new interface features, richer inspection tools and improved error diagnostics as these capabilities are implemented and tested.

Every exercise will be checked against the GX-TXT version used for the test. Each article will identify the framework version, the official GitHub source commit and any limitations observed, allowing readers to distinguish proven functionality from features introduced in later revisions.

The learning path: six practical tutorials

1. Getting started with the GNU Octave debugger: breakpoints and variables. Creating and registering a script, running it through Script Jobs, using breakpoints, Continue, Step Into, Step Over and Step Out, and examining the Call Stack and local variables. Two working examples introduce nested function calls and temperature conversion through GX-TXT’s public Numeric API.
2. Python debugging: controlled execution and state inspection. Debugging Python scripts, navigating functions and stack frames, monitoring values while the program runs, and comparing the Python and GNU Octave debugging workflows.
3. What happens when a GX-TXT API is unavailable? Discovering registered APIs, checking requirements and public operations, and reading diagnostic messages. Exercises with nonexistent APIs, missing providers and incompatible requests will distinguish mistakes in the script from problems in the framework.
4. Finding and fixing runtime errors. Out-of-bounds vector access, exceptions, diagnostic messages and locating the failing source line. This chapter will be completed after improvements to the debugger’s exception handling have been verified.
5. Debugging scientific data: vectors, structures and Datasets. Inspecting arrays and structures, reading Datasets through canonical identifiers, handling Variable IDs and verifying results returned by public GX-TXT APIs.
6. Debugging a complete scientific workflow. From reading data and performing numerical computations to publishing results: an end-to-end workflow covering the Script Registry, immutable execution snapshots, Script Jobs, logs and reproducibility checks.

Beyond the first six tutorials

The project will remain open to new articles about debugger features as they are developed: deeper inspection of structures and arrays, exception presentation, advanced analysis tools, Call Stack navigation and comparisons between runs. New functionality will be documented only after it has been implemented and verified with reproducible examples.

Working method and materials

• Runnable examples: every tutorial includes test scripts and expected results.
• Authentic illustrations: screenshots captured during real debugging sessions, accompanied by explanations of the controls and displayed information.
• Public APIs only: GX-TXT integration uses documented public contracts rather than private CORE implementation details.
• Progressive explanations: each API introduced is explained in terms of its purpose, syntax, parameters, results and possible errors.
• Traceability: articles identify the GX-TXT version and the relevant GitHub commit, clearly separating completed tests from future developments.
• Downloadable resources: GNU Octave .m scripts, Python .py scripts and supporting documentation will accompany their respective articles. All source-code comments are in English.

Who is this project for?

The tutorials are intended for students, researchers, laboratory technicians and software developers who use programming for numerical computing, experimental data analysis and scientific automation. No prior debugging experience is required: the first exercises introduce the fundamentals, while later tutorials address increasingly complex situations.

Source code and references

The framework’s source code is available in the official GX-TXT GitHub repository. Every tutorial will reference the public API contracts and documentation associated with its tested version. Educational scripts will include the appropriate license notices.

Explore the articles associated with this project: the learning path will grow alongside GX-TXT’s debugging tools, with verified examples and documented improvements.