ChenLiu-1996/figures4papers

My Python scripts to make high-quality figures for publications in top AI conferences and journals.

What it solves

This project provides a collection of Python scripts and design principles for creating high-quality, publication-ready scientific figures. It addresses the common challenge of producing professional visualisations for top-tier academic venues (such as Nature Machine Intelligence, ICML, and NeurIPS) using a consistent and reproducible style.

How it works

The repository is structured as a library of reference scripts and a "skill" framework for AI coding agents. It includes:

  • Reference Scripts: Python code for various plot types, including bar plots (quantitative comparison and composition breakdown), 3D spheres, radar plots, line plots, concept plots, and trend plots.
  • AI Skill Integration: A dedicated scientific-figure-making/ folder containing metadata, design theory, and API conventions. This allows AI coding agents (like Cursor or Claude Code) to ingest these guidelines to generate new, publication-quality figures based on the user's data.
  • Design Guidelines: Documentation on style rationale, common patterns, and step-by-step tutorials.

Who it’s for

Researchers and academics who need to create professional figures for scientific papers, as well as developers using AI coding agents to automate the plotting process.

Highlights

  • Publication-Proven: Scripts used in papers published at Nature Machine Intelligence, ICML, NeurIPS, and ECCV.
  • Agent-Ready: Specifically designed to be integrated as a "skill" for AI coding agents via path-based references or symlinks.
  • Diverse Plot Types: Covers a wide range of scientific visualisations from simple bar charts to complex 3D and radar plots.
  • Consistent Styling: Provides a unified API for palettes, helpers, and export settings to ensure visual consistency across a figure set.

Related

  • Project
    ogx-ai/ogx

    OGX is an open‑source, model‑agnostic API server that mimics OpenAI’s REST endpoints. It lets you run any LLM (Llama, GPT, Gemini, etc.) locally or in the cloud, while keeping the same client code. In addition to standard chat/completion/embedding calls, OGX offers a “Responses” API for tool‑calling and RAG, versioned skill bundles, and native routes for Anthropic and Google GenAI SDKs. Install with a one‑line script, start the server, and point any OpenAI‑compatible client at it.

  • Project
    qualcomm/GenieX

    An on-device Gen AI inference runtime for Qualcomm Snapdragon devices that enables running LLMs and VLMs locally using the NPU, GPU, or CPU.

  • Project
    idosal/git-mcp

    A remote Model Context Protocol (MCP) server that transforms any public GitHub repository into a documentation hub for AI assistants to reduce hallucinations.

  • Project
    OptimalScale/LMFlow

    LMFlow is an extensible Python toolbox for fine‑tuning large language models. It supports full‑parameter training, LoRA, the memory‑efficient LISA algorithm, QLoRA quantization, and a suite of speed‑up tricks (FlashAttention‑2, DeepSpeed, vLLM, SGLang). After training, it provides simple scripts for chat‑style inference and optional Gradio/Flask deployment. The library is designed for Linux/Colab environments and offers optional extras for multimodal models, distributed reward‑model inference, and more.