aquarel 0.0.6

Lightweight templating engine for matplotlib

Aquarel 🎨

Aquarel is a lightweight templating engine and wrapper around Matplotlibs' rcparams to make styling plots simple. Aquarel templates can be defined programmatically and be serialized and shared in a JSON format.

Full documentation is available at aquarel.readthedocs.io.

Installation

Install via pip:

python -m pip install aquarel

Usage

Applying a style

Styles can be either applied globally

from aquarel import load_theme

theme = load_theme("arctic_light")
theme.apply()
# ... plotting code here
theme.apply_transforms()

...or with a context manager:

from aquarel import load_theme

with load_theme("arctic_light"):
    figure = # ... plotting code here

Transforms

Themes may specify transforms. Transforms are functions applied on the finished plot to achieve aesthetics that are not possibly by means of rcparams only. For example, to trim the axes, one could apply the trim transform:

from aquarel import load_theme

with load_theme("arctic_light").set_transforms(trim=True):
    figure = # ... plotting code here

# plt.show() or savefig() have to be called outside the context manager to have the transforms correctly applied.
figure.savefig()

However, there is one important thing to keep in mind: since transforms require the matplotlib figure/axes object to be present and finished, they have to be applied after the plotting code. When using a theme with a context manager, this is automatically done in the __exit__ call. If global usage is desired, Theme.apply_transforms() has to be called after every figure. This also means that calls that make use of the finished figure, i.e. plt.show or plt.savefig have to commence after transform application, so outside the context manager.

Customization & Theme Creation

Besides loading a predefined theme, you can create a new theme

from aquarel import Theme

theme = (
    Theme(name="demo", description="A demo theme.")
    .set_grid(draw=True, width=0.5)
    .set_font(family="monospace")
    .set_color(grid_color="blue")
)

...modify an existing one

from aquarel import load_theme

theme = (
    load_theme("arctic_light")
    .set_grid(width=2)
)

...and write and load your custom styles to and from disk:

from aquarel import Theme

theme = Theme.from_file("custom.json")
theme.save("custom.json")

If the simplified API of aquarel is not sufficient for your use-case, you can also directly modify the underlying rcparams with overrides:

from aquarel import load_theme

theme = load_theme("arctic_light").set_overrides({
    "ytick.minor.visible": False,
    "xtick.minor.visible": True
})

Themes

aquarel ships with several pre-defined themes that are designed to showcase its templating capabilities. Add your own with a pull request!

Name	Description	Preview
arctic_dark	Frosty dark theme based on the nord color scheme
arctic_light	Frosty dark theme based on the nord color scheme
boxy_dark	Dark theme with enclosing box and grid
boxy_light	Light theme with enclosing box and grid
gruvbox_dark	Dark theme with pastel retro groove colors
gruvbox_light	Light theme with pastel retro groove colors
minimal_dark	Dark theme with minimal visual elements
minimal_light	Light theme with minimal visual elements
scientific	Space-efficient and color-blind friendly theme for printing on paper
umbra_dark	Balanced dark theme based on the penumbra color scheme
umbra_light	Balanced light theme based on the penumbra color scheme

FAQ

How is this different from matplotlib style sheets?

aquarel is a wrapper around the stylesheets, so everything you can do with stylesheets can be achieved with aquarel. However there are some notable shortcomings of stylesheets that aquarel adresses:

1. On-the-fly templating – the stylesheets are applied once and are then used for every plot in the current plotting context (py-file, notebook, ipython session, ...). aquarel takes a different approach here and aims to provide per-plot styling with optional temporary changes. The style aquarel applies lasts throughout the context manager (with aquarel.Theme:), and switches back to whatever is the global default style outside of it. This allows you to do plot-level temporary changes. You have one plot in your notebook that needs no minor ticks? just with theme.set_ticks(): for this plot only.
2. Simplified templating: matplotlib stylesheets have a lot of redundant keys for most applications. For example, you rarely want to have different colors for both axes; while possible with a stylefile, its cumbersome to change all the different keys to achieve a uniform look. aquarel simplifies this with e.x. a single set_color(ticks="#eee") call, which changes all related and relevant keys for ticks. Note that this simplifies the API, but does not restrict capabilities: the set_overrides method accepts every possible stylefile key if you want to access low-level styling.
3. Transforms: some style elements, like trimmed axes, are not achievable with stylesheets alone (see README for more informations). aquarel defines a few of these transforms (and hopefully many more in the future), and makes them persistable and shareable through aquarel themes. Instead of having to apply a seaborn despine after every plot, you can have a global style that specifies a trim, and have consistent styling throughout with minimal code repetition.