Add ocular health visualization suite

[zanax1990]

Summary

• add a curated ocular health dataset, shared utilities, and a scripted analysis pipeline that generates correlation, box/violin, and clustering visuals
• introduce a Plotly Dash dashboard so the demographic, disease, and Schwann cell metrics can be explored interactively
• document the workflow with a focused README and dependency list tailored for the new project

Testing

• ⚠️ pip install -r requirements.txt (fails: ProxyError – Tunnel connection failed: 403 Forbidden)

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Codex Task

[chatgpt-codex-connector]

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[chatgpt-codex-connector]

Prevent callback errors when filters yield no rows

The Dash callback builds the violin plot from filtered_long[filtered_long['Disease'] == disease] without checking whether that slice contains data. If the age slider is narrowed to a range that has no patients with the selected disease (which is easy to hit with this 30‑row sample set), plotly.express.violin raises ValueError: DataFrame is empty, causing the callback to fail and the dashboard graphs to stop updating. Guard for empty frames or return a placeholder figure before calling Plotly.

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