render.plot

render.plot(_fn=None, *, alt=None, width=MISSING, height=MISSING, **kwargs)

Reactively render a plot object as an HTML image.

Parameters

alt : Optional[str] = None

Alternative text for the image if it cannot be displayed or viewed (i.e., the user uses a screen reader).

width : float | None | MISSING_TYPE = MISSING

Width of the plot in pixels. If None or MISSING, the width will be determined by the size of the corresponding output_plot. (You should not need to use this argument in most Shiny apps–set the desired width on output_plot instead.)

height : float | None | MISSING_TYPE = MISSING

Height of the plot in pixels. If None or MISSING, the height will be determined by the size of the corresponding output_plot. (You should not need to use this argument in most Shiny apps–set the desired height on output_plot instead.)

**kwargs : object = {}

Additional keyword arguments passed to the relevant method for saving the image (e.g., for matplotlib, arguments to savefig(); for PIL and plotnine, arguments to save()).

Returns

A decorator for a function that returns any of the following:

  1. A matplotlib.figure.Figure instance.
  2. An matplotlib.artist.Artist instance.
  3. A list/tuple of Figure/Artist instances.
  4. An object with a ‘figure’ attribute pointing to a matplotlib.figure.Figure instance.
  5. A PIL.Image.Image instance.

It’s also possible to use the matplotlib.pyplot interface; in that case, your function should just call pyplot functions and not return anything. (Note that if the decorated function is async, then it’s not safe to use pyplot. Shiny will detect this case and throw an error asking you to use matplotlib’s object-oriented interface instead.)

Tip

The name of the decorated function (or @output(id=...)) should match the id of a output_plot container (see output_plot for example usage).

See Also

Examples

#| standalone: true
#| components: [editor, viewer]
#| layout: vertical
#| viewerHeight: 400

## file: app.py
import matplotlib.pyplot as plt
import numpy as np

from shiny import App, Inputs, Outputs, Session, render, ui

app_ui = ui.page_fluid(
    ui.input_slider(
        "n", "input_slider()", min=10, max=100, value=50, step=5, animate=True
    ),
    ui.output_plot("p"),
)


def server(input: Inputs, output: Outputs, session: Session):
    @render.plot
    def p():
        np.random.seed(19680801)
        x_rand = 100 + 15 * np.random.randn(437)
        fig, ax = plt.subplots()
        ax.hist(x_rand, int(input.n()), density=True)
        return fig


app = App(app_ui, server)