Python Package Guide#
This guide documents the cellucid Python package: how to prepare datasets, run servers, embed the viewer in notebooks, and integrate Python with the web UI (hooks/events).
If you’re looking for the end-user web UI docs (loading data, filtering, analysis, annotation), start with Web App Guide.
Where to start (pick one)#
“I have an AnnData and want to view it” → Viewing APIs (serve / serve_anndata / show / show_anndata + loading options) (and Jupyter Integration (Notebook Embedding))
“I want the fastest web viewing” → Data Preparation API (prepare/export) — The Big One (exports via
prepare)“I want notebook embedding + programmatic control” → Viewing APIs (serve / serve_anndata / show / show_anndata + loading options) + Jupyter Hooks System (Python ↔ Frontend)
“I’m new and want a guided path” → Landing Pages
“Something failed” → Global Troubleshooting Index (Python)
Chapters (what each folder contains)#
Quickstarts and entry points: installation, first run, and “what should I do next?” pages.
How the Python package maps data → exports/servers → the web viewer, plus core terminology you’ll see across docs.
prepare(...) and export-time options (fields, gene IDs, embeddings, vector fields) for reproducible, high-performance viewing.
How to view data: serve(...), serve_anndata(...), show(...), and show_anndata(...) (Jupyter embedding and server mode).
React to selection/hover/click events and send commands back to the viewer (highlight, color-by, visibility).
Long-form notebook-style tutorials and “copy/paste” workflows for common analysis and visualization patterns.
A map of what parts of the Python API are documented where (useful for maintainers and contributors).
Implementation notes, architectural decisions, and debugging patterns for the Python package.
Symptom → diagnosis → fix for installation, exports, servers, Jupyter embedding, and performance issues.
Complete reference for all functions and classes in the Cellucid Python package.