OmicVerse-inspired DataFrame and AnnData previews for JupyterLab
Project description
omicverse-notebook
JupyterLab plugin that brings the omicverse-web DataFrame and AnnData preview ideas into a standalone package.
It provides:
- Rich output renderers for
pandas.DataFrame,pandas.Series, andanndata.AnnData - A JupyterLab command that opens an
OmicVerse Notebookpanel for inspecting kernel variables by name - Automatic formatter activation for the active notebook or console kernel when the JupyterLab frontend extension is loaded
- Color-coded DataFrame columns, sticky headers, compact shape cards, and AnnData slot summaries
Scope
This package intentionally focuses on the display layer:
- kernel-side Python helpers turn variables into JSON preview payloads
- JupyterLab frontend plugins render those payloads and provide a lightweight inspector UI
It does not depend on the omicverse-web Flask APIs.
Install
Development install:
cd omicverse-notebook
pip install -e .
For an end-user install, build a wheel first so the prebuilt labextension is bundled:
cd omicverse-notebook
jlpm install
jlpm build:prod
pip install .
Enable rich output
If the frontend extension is loaded correctly, it will try to enable formatters automatically for the active notebook or console kernel.
Manual fallback:
%load_ext omicverse_notebook
Or:
from omicverse_notebook import enable_formatters
enable_formatters()
After that, displaying a DataFrame or AnnData object in a notebook cell will use the OmicVerse renderers.
Inspector usage
Open Command Palette and run:
OmicVerse Notebook: Open
If rich output still does not appear in an already-running kernel, run:
OmicVerse Notebook: Enable Kernel Formatters
Then inspect variables like:
df
adata
adata.obs
adata.layers["counts"]
adata.obsm["X_umap"]
Notes
- The inspector executes a small helper snippet in the current kernel and expects the Python package to be installed in that kernel environment.
- AnnData is optional on install, but AnnData previews only activate if
anndatais available in the kernel.
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