2. Concepts

Data Source

Ezel's APIs expect polars::frame::DataFrame as a data source.

If you have other data types, they should be converted to DataFrame first.

use ezel::prelude::*;
use polars::prelude::*;

let mut x = vec![..];
let mut y = vec![..];

let df = df!(
    "x" => &x,
    "y" => &y
)
.unwrap();

let mut plot = Cartesian2::default();
plot.scatter(df, Column("x".to_string()), Column("y".to_string()));

More ergonomic interfaces are planned to be added.

let x: Vec<f64>;
let y: Vec<f64>;

ezel::quick::scatter_xy(x, y); // Cartesian2 + x + y

let mut plot = Cartesian2::default();
plot.scatter_xy(x, y); // two Vec<f64>

Data Types (i64, f64, ..)

f64 are expected in most places. If you have f32 data, simply convert it to f64.

Composition

Ezel borrows the idea of protrusion from Makie.jl. The main areas of items in the grid are aligned by rows and columns.

Composition

TODO

Protrusion

TODO

Attribute

Attributes are the properties of plots such as marker size, color, etc.

There are 3 types of attribute value: a const, categorical column, or scalar column.

marker_size: ConstOrScalar<f64>, // const or scalar
marker_shape: ConstOrCategorical<MarkerShape>, // const or categorical
marker_color: ConstOrColumn<Color>, // const or categorical or scalar

• scatter.marker.size = Const(10.0) means all markers have the same size 10.0.

• scatter.marker.shape = Column("species".to_string())assigns to each species a marker shape from the theme's shape cycle (e.g. 🟢->🟩 ->💚-> ..).
• scatter.marker.color = Column("column_name".to_string())uses a color from the current color cycle (🟢->🟡->🟤->🔴->..) if the column is categorical or string. Otherwise it uses a color from the current color map ()

If you want to use a f64 column in polars' DataFrame as categorical data, the easiest way is to cast it to the string dtype.