X‑LAB Plotting Manager

The X-LAB Plotting Manager is a modular framework for turning raw scientific measurements into clean, reusable, publication-ready plots. It was created to address a common research problem: as experiments pile up, so do ad-hoc scripts, commented-out file lists, duplicated plotting code and “just tweak this one” workflows that quickly become unmaintainable and impossible to reproduce.

This framework offers a structured, scalable alternative. By enforcing modular components with clear responsibilities and a configurable UI, it replaces tangled plotting scripts with a robust and reproducible analysis pipeline. It supports both quick exploratory work and long-term dataset management, making it a practical tool for anyone dealing with experimental data.

What this framework provides

The system is built around clear contracts that define how devices, processors, and plotters behave.
This yields maximum modularity and reusability.

At a high level, the framework consists of:

• Dataset creation

Define what to load, how to interpret it, and which processor and plotter to apply. Datasets are stored as JSON files that act as portable analysis definitions.

• Analysis/Plotting execution

The main window lets you loads a dataset (by creation or by reading from the disk), spawns worker threads, processes the data, and renders the plots.

• Contract‑based extensibility

You can implement new:

  • Data types (for giving meaning to raw data)
  • DataProcessor classes (for computing derived quantities)
  • Plotter classes (for new visualisations)
  • DeviceWorker classes (for experiment‑specific orchestration)

Modules stay clean because each class only does one thing, and all pieces work together through stable interfaces.

• Robust UI and threading

Long operations (like reading hundreds of files) run in background threads so the UI stays responsive.

Structure of these documentation pages

The documentation is organised to follow the natural workflow of using — and eventually extending — the framework. Each section builds on the previous one: 1. 2. Introduction (this page)
Overview of the project, its motivation, and the philosophy behind the framework.

  1. Installation
    Instructions for installing the application, its dependencies, and a baseline implementations module.

  2. Getting Started
    A hands-on walkthrough showing how to load existing implementations and generate your first plot.

  3. How to think about this application? The architectural mindset: modularity, reuse, and the reasoning behind the framework’s design.

  4. How to implement the contracts?
    Step-by-step guidance on creating your own Data, DataProcessor, Plotter, and DeviceWorker classes.

  5. What are datasets? Explanation of dataset files, their structure, how they define analyses, and why they enable reproducibility.

  6. Logging and debugging Tools and patterns for monitoring background workers, diagnosing issues, and keeping the UI stable.

  7. API Reference Class and module documentation for deeper integration or custom development.

The goal of this structure is to help you understand the framework quickly, use it effectively, and extend it confidently — giving you a robust foundation for clean, reproducible, and scalable scientific data analysis.