plotive-py documentation

Note

plotive-py are the Python bindings to plotive, a plotting library written in Rust.

plotive is a data plotting library. It stands for Declarative plotting. The basic concept is that the design of figures is separated from the data and from the style you apply to the figure.

Supported figure types

• XY line plots

• Scatter plots

• Histograms

• Bar plots

Modular architecture

• Declarative design

  • Design of figure is entirely declarative and decoupled from data and drawing primitives.

  • Sensible defaults.

  • Figure units are decorrelated from pixel size for easy scaling

• Data sources

  • Flexible, column-friendly data source system

  • Compatible with pandas, numpy arrays, lists…

• Rendering surfaces

  • Rendering backend is decoupled from drawing. You can render onto:

  • pixel grids, PNG

  • SVG

  • GUI window

Automatic Layout

• All the layout is done consistently and automatically.

  You can add multiple axes, multiple plots etc. Everything will be laid-out consistently, leaving enough space for axis ticks labels, legends etc. Your code never need to calculate size of anything.

Advanced typography

• Automatic font look-up and text shaping

• Rich text

• Automatic right to left layout using unicode-bidirectional algorithm

• vertical layout

Themes

• Change the theme of your figure with a single line of code.

• Buitin themes included

Annotations

• Annotate your figures, with labels, infinite lines, markers etc.

• Annotations are placed using data space coordinates

Code example for a simple figure

import plotive as pv
import numpy as np

# We first create the figure design
# A figure must have at least one plot

fig = pv.Figure(
    title="Sine Wave",
    # A figure must have at least one plot
    # Multiple plots can be provided with `plots` list property
    plot=pv.Plot(
        series=[
            pv.series.Line(
                # Data is referred to by column name in the data source
                # They can also be inline in the figure object
                x="x",
                y="y",
                name="sin(x)",
            )
        ],
        # By default, axis have no tick, no title.
        # Here is how to customize them.
        x_axis=pv.Axis(title="x", ticks="pimultiple", grid="auto"),
        y_axis=pv.Axis(title="sin(x)", ticks="auto", grid="auto"),
        # Legend can be at plot level or figure level.
        # They automatically gather all series that have a name property
        legend="in-top-right",
    ),
)

# Now we define the data source
x = np.linspace(0, 2 * np.pi, 500)
y = np.sin(x)
data_src = {"x": x, "y": y}

# and we can finally show the figure.
fig.show(data_source=data_src)

The following window should show