figgyplot
Plot NumPy data on the GPU. Edit directly. Reuse the settings in Python.
Explore signals, scatter populations and heatmaps in native windows or Jupyter. Adjust the axes, text, colors and layout, then copy executable Python to reuse the result across datasets. Export the edited chart directly to PNG.
Examples · Documentation · Measured performance
100,000 samples per series. Edit the native plot, copy Python, paste into VS Code and apply the same settings to three independent datasets. Demo recipe and recording.
Scientific plots
Every image below is actual Figgy output from synthetic data. Click a chart to open its runnable Python example.
Spectrogram |
Correlation matrix |
Scatter populations |
Interpolated heatmap |
Logarithmic axes |
Lines and measurements |
Draw styles
Sketch / handwriting |
Constellation |
Milkyway |
More chart families
Parametric curves |
Pie |
Ceramic donut |
Procedural materials: paper, wood, hatch and pearl · Gallery rendering provenance.
Performance measurements
An earlier Windows 11 / RTX 4070 Ti campaign measured completed 960×600 RGBA rendering at five million samples. Medians of five repeated renders, in ms:
| Workload | Figgy | fastplotlib | VisPy | pyqtgraph OpenGL | matplotlib default |
|---|---|---|---|---|---|
| Smooth line | 9.07 | 12.41 | 4.77 | 4.09 | 99.87 |
| Noisy line | 18.52 | 20.49 | 10.96 | 8.71 | 211.00 |
| Scatter | 9.11 | 10.37 | 12.24 | — | 1357.62 |
This historical campaign used Figgy 0.1.0. Precision and antialiasing differed between libraries; first-frame, upload, edits and export costs are reported separately. These measurements include RGBA readback and do not measure UI FPS. Full conditions, results and the comparison chart rendered with Figgy.
Install
To try the preview from TestPyPI, install dependencies from PyPI first:
python -m pip install numpy anywidget
python -m pip install --index-url https://test.pypi.org/simple/ --no-deps --only-binary=:all: figgyplot
For releases published to the regular PyPI index:
pip install figgyplot
# Optional Jupyter editor:
pip install 'figgyplot[notebook]'
Quick start
import numpy as np
import figgyplot as plt
x = np.linspace(0, 20, 100_000, dtype=np.float32)
fig, ax = plt.subplots(backend="native", figsize=(8, 5), dpi=120)
ax.plot(x, np.sin(x), id="signal")
ax.set_title("Edit, copy Python, and reuse")
try:
fig.show() # Edit; close the window to continue.
fig.savefig("edited.png") # Save the edited chart without the UI.
finally:
fig.close()
In Jupyter, use plt.subplots(backend="webgpu") and display fig.widget().
Selection and editing run in the notebook's browser.
Compatibility
CPython 3.8–3.15. A compatible wheel needs no Rust compiler; source builds need Rust 1.99+ and a C/C++ toolchain. Native rendering needs a supported GPU driver. Jupyter needs a WebGPU-capable browser over HTTPS or localhost. Native window creation and event pumping must run on the main thread. Native windows use the Rust winit/wgpu host; Jupyter uses packaged WASM and browser WebGPU. Normal wheel installation requires no separate WASM build or CDN.
Documentation · Examples · Platform compatibility and verification
The distribution and import name are both figgyplot. This is independent of
the unrelated figgy distribution on PyPI. MIT OR Apache-2.0.
1 maintainer
Metadata
1 maintainer
Release files for figgyplot 0.2.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| figgyplot-0.2.1.tar.gz | 6.8 MB | Details |
Built distributions (wheels)
Total release size: 877.6 MB
Release files / figgyplot-0.2.1.tar.gz
| Download URL | figgyplot-0.2.1.tar.gz |
|---|---|
| Size | 6.8 MB |
| Tags | Source |
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