Anaconda Enterprise: Documentation

# Exporting Plots

## PNG Generation

Bokeh can generate RGBA-format Portable Network Graphics (PNG) images from layouts using the `export()` function. This functionality uses a headless browser called WebKit to render the layout in memory and then capture a screenshot. The generated image will be of the same dimensions as the source layout.

In order to create a PNG with a transparent background, users should set the `Plot.background_fill_color` and `Plot.border_fill_color` properties to `None`.

### Limitations

Responsive sizing_modes may generate layouts with unexpected size and aspect ratios. It is recommended to use the default `fixed` sizing mode. Also, glyphs that are rendered via webgl won’t be included in the generated PNG, so it’s suggested to use the default `Plot.webgl=True` attribute.

### Additional dependencies

In order to use the `export()` function, users have to install some additional dependencies. These dependencies can be installed via conda:

    conda install selenium phantomjs pillow

Alternatively, you can install phantomjs from npm via

    npm install -g phantomjs-prebuilt

### Example usage

Usage is similar to the `save()` and `show()` functions.

    from bokeh.io import export_png

    export_png(plot, filename="plot.png")

## SVG Generation

Bokeh also supports replacing the HTML5 Canvas plot output with an SVG element that can be edited in image editing programs such as Adobe Illustrator and/or converted to PDFs. This functionality uses a JavaScript library called canvas2svg to mock the normal Canvas element and its methods with an SVG element.

The SVG output isn’t as performant as the default Canvas backend when it comes to rendering large number of glyphs or handling lots of user interactions like panning.

Like PNGs, in order to create a SVG with a transparent background, users should set the `Plot.background_fill_color` and `Plot.border_fill_color` properties to `None`.

### Limitations

It’s not possible to create a single SVG for a layout of plots, as each plot is it’s own distinct SVG element. Alternatively, it’s possible to download a SVG plot using a SaveTool from the toolbar. However, currently an SVG export of a plot with a toolbar will have a blank area where the toolbar was rendered in the browser.

### Example usage

The SVG backend is activated by setting the `Plot.output_backend` attribute to `"svg"`.

    # option one
    plot = Plot(output_backend="svg")
    # option two
    plot.output_backend = "svg"

### Exporting a SVG Image

The simplest method to manually export a SVG plot is to install a browser bookmarklet from the New York Times called SVG-Crowbar. When clicked, it runs a snippet of JavaScript and adds a prompt on the page to download the plot. It’s written to work with Chrome and should work with Firefox in most cases.

Alternatively, it’s possible to download a SVG plot using the `SaveTool`, but the toolbar isn’t captured though it figures into the plot layout solver calculations. It’s not great, but a workable option.

For headless export, there’s a utility function, `export_svgs()`, which similar usage to the `save()` and `show()` functions. This function will download all of SVG-enabled plots within a layout as distinct SVG files.

    from bokeh.io import export_svgs

    plot.output_backend = "svg"
    export_svgs(plot, filename="plot.svg")