Example O2: Add a data plot to rendered images

Tip

An extended version of the Python-based viewport overlay described on this page is available as a ready-to-use installable extension for OVITO Pro. You can find its source code online at https://github.com/ovito-org/DataTablePlotOverlay.

This user-defined viewport overlay demonstrates to use of the Matplotlib Python package to render a plot of a distribution function, dynamically computed by OVITO’s CoordinationAnalysisModifier and HistogramModifier, on top of the three-dimensional visualization.

../../../_images/python_script_plot_example.png

from ovito.vis import ViewportOverlayInterface
from ovito.data import DataCollection

class CoordinationPlotOverlay(ViewportOverlayInterface):
    def render(self, canvas: ViewportOverlayInterface.Canvas, data: DataCollection, **kwargs):
        with canvas.mpl_figure(pos=(0.02,0.98), size=(0.5,0.5), anchor="north west", alpha=0.5, tight_layout=True) as fig:
            ax = fig.subplots()
            ax.set_title('Coordination number histogram')
            plot_data = data.tables['histogram[Coordination]'].xy()
            ax.bar(plot_data[:,0], plot_data[:,1], width=0.8)

This implementation makes use of the mpl_figure() function, which makes drawing a Matplotlib figure easy.

Note

It may be necessary to first install the matplotlib module in the embedded Python interpreter of OVITO Pro. See Installing third-party Python modules.

The overlay implementation defined above can be used out of the box with the manual:viewport_layers.python_script feature available in OVITO Pro. If you want to use it in a standalone Python program to render an image programatically, you can do the following:

from ovito.vis import PythonViewportOverlay, Viewport, TachyonRenderer
from ovito.io import import_file
from ovito.modifiers import CoordinationAnalysisModifier, HistogramModifier

# Pipeline setup:
pipeline = import_file("input/simulation.dump")
pipeline.add_to_scene()
pipeline.modifiers.append(CoordinationAnalysisModifier(cutoff=3.0))
pipeline.modifiers.append(HistogramModifier(property='Coordination', bin_count=13, fix_xrange=True, xrange_start=0.5, xrange_end=13.5))

# Viewport setup and rendering:
viewport = Viewport(type = Viewport.Type.Perspective)
viewport.overlays.append(PythonViewportOverlay(delegate=CoordinationPlotOverlay()))
viewport.zoom_all(size=(900, 600))
viewport.render_image(filename='output/coordination_plot.png', size=(900, 600), renderer=TachyonRenderer())