nbimplot 0.1.6

ImPlot-powered Jupyter plotting widget for interactive, million-point visualizations.

nbimplot

Jupyter-native, ImPlot-powered plotting for very large arrays.

nbimplot is built around three constraints:

• notebook cell rendering only (no native windows, no side process)
• binary transfer (numpy -> bytes -> wasm heap)
• WASM-owned interaction, state, and LOD

The runtime is strict: ImPlot + WASM + WebGL2 are required.

Install

python -m pip install -U nbimplot

Minimum recommended widget/runtime stack:

python -m pip install -U "nbimplot>=0.1.6" "anywidget>=0.9.21" ipywidgets jupyterlab_widgets

Quick Start

import numpy as np
import nbimplot as ip

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

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

# Update in place, then redraw
h.set_data((0.8 * y).astype(np.float32))
p.render()

Interaction Defaults

• initial X/Y view auto-fits to available data
• double-click inside plot area resets view (autoscale)
• right-drag box zoom, wheel zoom, drag pan, legend toggle

Core API

import numpy as np
import nbimplot as ip

y = np.random.randn(200_000).astype(np.float32).cumsum()

p = ip.Plot(width=1000, height=420, title="Core API")
h = p.line("price", y, color="#22c55e", line_weight=2.0, marker="none")
p.set_plot_flags(no_legend=False, no_menus=False, no_box_select=False)
p.set_colormap("Viridis")
p.show()

# Later update
h.set_data((y * 1.01).astype(np.float32))
p.render()

Common Examples

1) Line + Streaming

import numpy as np
import nbimplot as ip

p = ip.Plot(width=1000, height=380, title="Streaming")
h = p.stream_line("ticks", capacity=200_000, initial=np.zeros(1000, dtype=np.float32))
p.show()

chunk = np.random.randn(20_000).astype(np.float32)
h.append(chunk)
p.render()

2) Scatter / Bars / Histogram

import numpy as np
import nbimplot as ip

rng = np.random.default_rng(7)
x = rng.normal(0, 1, 4000).astype(np.float32)
y = (0.5 * x + 0.2 * rng.normal(size=x.size)).astype(np.float32)

p = ip.Plot(width=1100, height=420, title="Stat Plots")
p.scatter("cloud", y, x=x, size=2.0)
p.vlines("cuts", np.array([-1.0, 1.0], dtype=np.float32))
p.hlines("zero", np.array([0.0], dtype=np.float32))
p.show()

p2 = ip.Plot(width=1100, height=360, title="Histogram")
p2.histogram("x-dist", x, bins=60)
p2.show()

3) Heatmap / Histogram2D / Image

import numpy as np
import nbimplot as ip

rng = np.random.default_rng(0)
z = rng.normal(size=(50, 80)).astype(np.float32)

p = ip.Plot(width=1100, height=420, title="Heatmap")
p.set_colormap("Plasma")
p.heatmap(
    "z",
    z,
    label_fmt="",  # empty format disables cell text
    show_colorbar=True,
    colorbar_label="Intensity",
    colorbar_format="%.3f",
)
p.show()

x = rng.normal(size=200_000).astype(np.float32)
y = (0.3 * x + rng.normal(size=x.size)).astype(np.float32)
p2 = ip.Plot(width=1100, height=420, title="Histogram2D")
p2.histogram2d(
    "h2d",
    x,
    y,
    x_bins=100,
    y_bins=80,
    label_fmt="",
    show_colorbar=True,
    colorbar_label="Count",
)
p2.show()

4) Subplots

import numpy as np
import nbimplot as ip

sp = ip.Subplots(
    2,
    2,
    title="Dashboard",
    width=1100,
    height=760,
    link_rows=True,
    link_cols=True,
    share_items=True,
)

t = np.linspace(0, 30, 4000, dtype=np.float32)
sp.subplot(0, 0).line("sin", np.sin(t))
sp.subplot(0, 1).scatter("noise", np.random.randn(3000).astype(np.float32))
sp.subplot(1, 0).bars("bars", np.abs(np.random.randn(120)).astype(np.float32))
sp.subplot(1, 1).histogram("hist", np.random.randn(20_000).astype(np.float32), bins=50)
sp.show()

Plot and Primitive Coverage

Implemented plot/primitive APIs include:

• line, stream_line
• scatter, bubbles, stairs, stems, digital
• bars, bar_groups, bars_h, shaded
• error_bars, error_bars_h
• inf_lines, vlines, hlines
• histogram, histogram2d, heatmap, image, pie_chart
• text, annotation, dummy
• tag_x, tag_y, colormap_slider, colormap_button, colormap_selector
• drag_line_x, drag_line_y, drag_point, drag_rect
• drag_drop_plot, drag_drop_axis, drag_drop_legend

For a broader cookbook, see docs/EXAMPLES.md.

View, Axes, and Performance Controls

• p.set_view(x_min, x_max, y_min, y_max)
• p.autoscale()
• p.set_axis_scale(x="linear|log", y="linear|log")
• p.set_axis_state("x2|x3|y2|y3", enabled=True|False, scale="linear|log|time")
• p.set_secondary_axes(x2=..., x3=..., y2=..., y3=...)
• p.set_time_axis("x1|x2|x3|y1|y2|y3")
• p.set_axis_label(...), p.set_axis_format(...)
• p.set_axis_ticks(...), p.clear_axis_ticks(...)
• p.set_axis_limits_constraints(...), p.set_axis_zoom_constraints(...), p.set_axis_link(...)
• p.hide_next_item()
• p.on_perf_stats(callback, interval_ms=500)
• p.on_tool_change(callback)
• p.on_selection_change(callback)

Example Notebooks

• notebooks/nbimplot_examples.ipynb
• notebooks/nbimplot_api_gallery.ipynb
• notebooks/nbimplot_benchmarks.ipynb

Troubleshooting

Failed to load model class 'AnyModel' from module 'anywidget'

This is usually a server-kernel env mismatch or stale lab assets.

python -m pip install -U "nbimplot>=0.1.6" "anywidget>=0.9.21" ipywidgets jupyterlab_widgets
jupyter lab clean

Then restart the full JupyterLab server.

Quick verification:

import nbimplot as ip, anywidget, sys
print("python:", sys.executable)
print("anywidget:", anywidget.__version__)
print("has Plot:", hasattr(ip, "Plot"))

Unable to enable ImPlot in the WASM core or WebGL context errors

Strict mode requires WebGL2.

!!document.createElement("canvas").getContext("webgl2")

If this is false, run from a local desktop browser session with GPU acceleration enabled.

Build WASM Core

Prerequisites:

• Emscripten SDK (emcmake, emcc)
• CMake >= 3.20

Build:

scripts/build_wasm.sh

Expected outputs in nbimplot/wasm/:

• nbimplot_wasm.js
• nbimplot_wasm.wasm

Build explicitly with local ImGui/ImPlot sources:

NBIMPLOT_WITH_IMPLOT=ON \
NBIMPLOT_IMGUI_DIR=/path/to/imgui \
NBIMPLOT_IMPLOT_DIR=/path/to/implot \
scripts/build_wasm.sh

If you use vendored deps:

NBIMPLOT_WITH_IMPLOT=ON scripts/build_wasm.sh

Performance Model

• raw path when visible points <= 3 * pixel_width
• min/max LOD when visible points > 3 * pixel_width
• LOD computed in WASM, complexity scales with screen pixels