# nbimplot

> ImPlot-powered WASM plotting for Jupyter notebooks and browser web apps, engineered for interactive million-point time-series visualization.

nbimplot is a strict WASM + ImGui + ImPlot plotting toolkit. It renders inside notebook output cells or normal browser DOM containers, moves numeric data through binary buffers, and uses screen-resolution min/max LOD so large line plots remain interactive.

Primary use cases:
- Fast plotting in Jupyter notebooks without native windows or side processes.
- Interactive million-point and multi-million-point line charts.
- Browser/webapp plotting with the same WASM core through `@nbimplot/web`.
- Scientific and engineering dashboards using ImPlot interactions, subplots, heatmaps, images, annotations, drag primitives, and streaming updates.

Install:
```bash
python -m pip install -U nbimplot
npm install @nbimplot/web
```

Notebook example:
```python
import numpy as np
import nbimplot as ip

x = np.linspace(0, 100, 1_000_000, dtype=np.float32)
y = np.sin(x)

p = ip.Plot(width=900, height=450, title="Signal")
h = p.line("mid", y, x=x)
p.show()

h.set_data((0.8 * y).astype(np.float32), x=x)
p.render()
```

Web example:
```js
import { createPlot } from "@nbimplot/web";

const plot = await createPlot("#plot", { width: 900, height: 450, title: "Signal" });
const x = new Float32Array(1_000_000);
const y = new Float32Array(x.length);
for (let i = 0; i < y.length; i += 1) {
  x[i] = i * 0.001;
  y[i] = Math.sin(x[i]);
}
const h = plot.line("mid", y, { x });
plot.render();
```

Important constraints:
- Requires WebGL2.
- Strict WASM/ImPlot runtime, no JavaScript renderer fallback.
- Line x-data must be finite, equal length with y, and sorted non-decreasing for LOD.
- Streaming append uses implicit sample indices; custom x streaming should use set_data/setData.

Key links:
- Homepage and demo: https://prinkesh.github.io/nbimplot/
- GitHub: https://github.com/Prinkesh/nbimplot
- PyPI: https://pypi.org/project/nbimplot/
- npm: https://www.npmjs.com/package/@nbimplot/web
- Full LLM documentation: https://prinkesh.github.io/nbimplot/llms-full.txt
- Notebook examples: https://github.com/Prinkesh/nbimplot/tree/main/notebooks
- Web docs: https://github.com/Prinkesh/nbimplot/blob/main/docs/WEB.md
- Examples cookbook: https://github.com/Prinkesh/nbimplot/blob/main/docs/EXAMPLES.md
- Fast Jupyter plotting guide: https://github.com/Prinkesh/nbimplot/blob/main/docs/FAST_JUPYTER_PLOTTING.md
- Million-point plotting guide: https://github.com/Prinkesh/nbimplot/blob/main/docs/MILLION_POINT_NOTEBOOK_PLOTTING.md
- Webapp integration guide: https://github.com/Prinkesh/nbimplot/blob/main/docs/WEBAPP_INTEGRATION.md