Numerical simulations often generate vast amounts of structured data; yet, extracting insights from these outputs remains a major challenge. Analysis is frequently performed through fragmented, ad-hoc scripts that are difficult to maintain, reuse, or reproduce. PyPLUTO is a Python package designed to address this gap by providing a clear and flexible interface for post-processing, analyzing, and visualizing simulation data produced by the PLUTO code for computational astrophysics.

This talk presents PyPLUTO as a case study in building lightweight, domain-specific scientific tools on top of the Python scientific ecosystem. The emphasis is on offline analysis and visualisation workflows that operate on completed simulation outputs, enabling efficient exploration, comparison, and communication of results. Rather than coupling visualisation to simulation runtime, PyPLUTO focuses on clarity, composability, and integration with established PyData libraries.

Through concrete examples, the session demonstrates how structured simulation data can be processed and visualised using tools such as NumPy and Matplotlib. Attendees will learn how Python-based workflows can replace scattered analysis scripts, how visualization supports rapid scientific insight, and how a clean separation between simulation and analysis enhances reproducibility and productivity.

Outline

1. From Simulation Output to Insight

• Common challenges in post-processing large numerical simulations
• The gap between raw data and scientific interpretation
• Why offline analysis and visualisation remain essential

2. PyPLUTO: Scope and Design

• What PyPLUTO does and the problems it targets
• Design goals: simplicity, flexibility, and interoperability
• Clear separation between simulation execution and analysis

3. Working with Simulation Data

• Loading and organising structured simulation outputs
• Handling scalar and vector fields across space and time
• Typical post-processing tasks and analysis patterns

4. Visualisation Workflows

• Exploratory plots and diagnostic views
• Time evolution and comparison between simulations
• Producing publication-quality figures with Matplotlib

5. Interactive GUI for Post-Processing

• Lightweight graphical interfaces for exploring simulation data
• Interactive selection of fields, slices, and time steps
• GUI as a complement to scripting, not a replacement

6. Integration with the Python Ecosystem

• Efficient data handling with NumPy
• Interoperability with existing scientific Python tools
• Benefits of building on established libraries

7. Software Design Lessons

• Building user-friendly scientific APIs
• Balancing usability, transparency, and performance

8. Broader Applicability and Outlook

• Relevance to other simulation-heavy fields
• Reusable patterns for Python-based post-processing
• Future directions and potential extensions

The talk is aimed at scientists, data practitioners, and Python developers interested in scientific visualisation and simulation data analysis. No background in astrophysics or PLUTO is required; the focus is on workflows, tools, and design principles applicable across the PyData community.