Introduction¶
This guide is designed to act as a conceptual guide to Chaco, an open-source data visualization library built and maintained by Enthought, Inc. Chaco is a set of interactive visualization tools built on top of the Enable and Kiva 2D drawing libraries and designed to complement other Enthought rapid application development tools including Traits and TraitsUI. This guide discusses many, but not all of the features of Chaco. For complete details of the Chaco API, refer to the Chaco API documentation.
What is Chaco?¶
Chaco is a 2D plotting library that is part of and integrates with the Enthought Tools Suite.
The strong points of Chaco are
it can be embedded in any Wx, Qt, or TraitsUI application
it is designed for building interactive plotting applications, rather than static 2D plots
Chaco classes can be easily extended to create new plot types, interactive tools, and plot containers
At the lowest level, Chaco is a hierarchy of classes that defines 2D plotting elements: plots, containers, interactive tools, color bars, etc. In principle, applications can create instances of these elements and lay them out in a container to define components that can be embedded in one of several of graphical back ends. Working at this level allows the maximum flexibility, but requires understanding Chaco’s basic elements.
Chaco defines an abstraction layer that allows a more high-level (albeit
less flexible) plotting experience. Namely, Chaco contains a
Plot class that defines several methods that create a
complete plot given one or more data sets. In other words,
Plot knows how to package data for the most common kinds
of plots.
Core Ideas¶
To venture deeper in Chaco’s architecture it is useful to understand a few basic ideas on which Chaco is based:
Plots are compositions of visual components
Each plot is composed by a number of graphical widgets: the plot graphics, axes, labels, legend, colorbar, etc. Everything you see in a plot is an individual component with position, shape, and appearance attributes, and with an opportunity to respond to events.
Data and screen space are separated
Although everything in a plot eventually ends up rendering into a common visual area, there are aspects of the plot which are intrinsically screen-space, and some which are fundamentally data-space. For example, data about the height of college students lives in data space (meters), but needs to be rendered in screen space (pixels). Chaco uses the concept of mapper to translate one into the other. Preserving the distinction between these two domains allows us to think about visualizations in a structured way.
Layers
Plot components are split into several layers, which are usually plotted in sequence. For example, axes and labels are usually plotted on the “underlay” layer, plot data on the “plot” layer, and legends and other plot annotations on the “overlay” layer. In this way one can define interactive tools that add graphical elements to a plot without having to modify the drawing logic.
Modular design and extensible classes
Chaco is meant to be used for writing tools and applications, and code reuse and good class design are important. We use the math behind the data and visualizations to give us architectural direction and conceptual modularity. The Traits framework allows us to use events to couple disjoint components at another level of modularity.
Also, rather than building super-flexible core objects with myriad configuration attributes, Chaco’s classes are written with subclassing in mind. While they are certainly configurable, the classes themselves are written in a modular way so that subclasses can easily customize particular aspects of a visual component’s appearance or a tool’s behavior.
These pages describe in detail the basic building blocks of Chaco plots, and the classes that implement them:
