Save and display

The anatplot plot holds a 3D scene while you add data and choose a camera. plot.to_figure() captures it in a Matplotlib fig that you can save or annotate. Use plot.view() to explore the live scene, or plot.export_html() to share an independent browser scene.

Save a figure

Capture the current scene and save the result as a PNG:

fig = plot.to_figure()
fig.savefig("brain.png", dpi=300)

For PDF or SVG, use fig.savefig("brain.pdf") or fig.savefig("brain.svg"). Matplotlib titles and colorbars remain vectors; the brain image is embedded as captured pixels. These formats do not need an extra savefig DPI setting.

Call to_figure() after scene changes. It captures a snapshot: later calls such as plot.add_contacts(...) do not change an existing fig; call to_figure() again. The figure remains usable after plot.close() or after leaving a with block.

The default render_dpi=600 controls detail captured in the brain image, independently of Matplotlib's figure DPI. For ordinary PNG output, keep that default and use fig.savefig("brain.png", dpi=300). This savefig DPI sets the PNG pixel dimensions: a 10 × 7 inch figure at 300 DPI is 3000 × 2100 pixels.

Raising only savefig DPI cannot recover missing brain detail. If you enlarge the figure substantially, make a new snapshot at its intended size.

For titles and layouts with other charts, create a Matplotlib Axes and pass it to to_figure(). The default Matplotlib DPI can still produce a sharp brain snapshot:

import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(10, 7))
ax.set_title("My cortical map")
plot.to_figure(ax=ax)

Set the title before capturing the scene so the brain is sized for the available space.

For two separate brain plots in one figure, give each plot its own Matplotlib Axes; the advanced surface tutorial has a complete example.

Snapshots need an OpenGL context. For remote or headless use, see PyVista installation and Jupyter setup.

Display a static figure

to_figure() creates a Figure without displaying or saving it. The images in the tutorials are previews of those Figures.

In a Python script, show the figure with Matplotlib:

import matplotlib.pyplot as plt

plt.show()

In a notebook, use display(fig) to show a particular snapshot. Both work after the anatplot scene has closed.

Explore and choose a view

Call plot.view() while the plot is open to rotate the 3D scene. This view does not include Matplotlib titles or colorbars. A live notebook viewer using Trame server rendering can synchronize camera changes with Python. For a repeatable static figure, set the camera in Python before to_figure().

On the desktop, plot.view() opens a window and waits. Press Q in that window to return to Python and continue saving or editing the scene. Closing the window with its operating-system close button can destroy the graphics context.

Named SurfacePlot views cover common cortical orientations. For camera customization, plot.plotter is the built-in PyVista plotting object; see the advanced surface tutorial or ROI camera example. The initial content fits the camera. Later additions preserve its framing. For ROIPlot or VolumePlot, call plot.plotter.reset_camera() if new objects need to fit within the view. SurfacePlot can have multiple views; PyVista camera changes apply to the selected view.

Share an interactive scene

Call plot.export_html("brain.html") while the plot is open. The standalone HTML has its own camera: rotating it does not update the Python scene. It also omits Matplotlib titles and colorbars.

Detailed parameters are in the plotting API.