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Frequently Asked Questions
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Where does the name "Chaco" come from?
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======================================
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It is named after `Chaco Canyon <http://www.nps.gov/chcu/>`_, which had
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astronomical markings that served as an observatory for Native Americans. The
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original version of Chaco was built as part of a project for the `Space
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Telescope Science Institute <http://www.stsci.edu/resources/>`_. This is also
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the origin of the name "Kiva" for our vector graphics layer that Chaco uses for
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Why was Chaco named "Chaco2" for a while?
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=========================================
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Starting in January of 2006, we refactored and reimplemented much of the core
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Chaco API. The effort has been named "chaco2", and lives in the
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:mod:`chaco2` namespace. During that time, the original Chaco package
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("Chaco Classic") was in maintenance-only mode, but there was still code that
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needed features from both Chaco Classic and Chaco2. That code has finally been
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either shelved or refactored, and the latest versions of Chaco (3.0 and up) are
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back to residing in the :mod:`chaco` namespace. We still have
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compatibility modules in :mod:`chaco2`, but they just proxy for the
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real code in :mod:`chaco`.
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The same applies to the :mod:`enable` and :mod:`enable2`
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What are the pros and cons of Chaco vs. matplotlib?
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===================================================
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This question comes up quite a bit. The bottom line is that the two projects
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initially set out to do different things, and although each project has grown a
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lot of overlapping features, the different original charters are reflected in
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the capabilities and feature sets of the two projects.
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Here is an `excerpt from a thread about this question
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<https://mail.enthought.com/pipermail/enthought-dev/2007-May/005363.html>`_ on
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the enthought-dev mailing list.
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Gael Varoquaux's response::
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On Fri, May 11, 2007 at 10:03:21PM +0900, Bill Baxter wrote:
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> Just curious. What are the pros and cons of chaco vs matplotlib?
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To me it seem the big pro of chaco is that it is much easier to use in a
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"programatic way" (I have no clue this means something in English). It is
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fully traited and rely quite a lot on inversion of control (sorry, I love
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this concept, so it has become my new buzz-word). You can make very nice
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object oriented interactive code.
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Another nice aspect is that it is much faster than MPL.
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The cons are that it is not as fully featured as MPL, that it does not
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has an as nice interactively useable functional interface (ie chaco.shell
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vs pylab) and that it is not as well documented and does not have the
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I would say that the codebase of chaco is nicer, but than if you are not
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developping interactive application, it is MPL is currently an option
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that is lickely to get you where you want to go quicker. Not that I
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wouldn't like to see chaco building up a bit more and becoming **the** reference.
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Developers, if you want chaco to pick up momentum, give it a pylab-like
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interface (as close as you can to pylab) !
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Peter Wang's response (excerpt)::
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On May 11, 2007, at 8:03 AM, Bill Baxter wrote:
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> Just curious. What are the pros and cons of chaco vs matplotlib?
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You had to go and ask, didn't you? :) There are many more folks here
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who have used MPL more extensively than myself, so I'll defer the
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comparisons to them. (Gael, as always, thanks for your comments and
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feedback!) I can comment, however, on the key goals of Chaco.
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Chaco is a plotting toolkit targeted towards developers for building
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interactive visualizations. You hook up pieces to build a plot that
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is then easy to inspect, interact with, add configuration UIs for
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(using Traits UI), etc. The layout of plot areas, the multiplicity
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and types of renderers within those windows, the appearance and
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locations of axes, etc. are all completely configurable since these
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are all first-class objects participating in a visual canvas. They
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can all receive mouse and keyboard events, and it's easy to subclass
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them (or attach tools to them) to achieve new kinds of behavior.
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We've tried to make all the plot renderers adhere to a standard
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interface, so that tools and interactors can easily inspect data and
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map between screen space and data space. Once these are all hooked
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up, you can swap out or update the data independently of the plots.
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One of the downsides we had a for a while was that this rich set of
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objects required the programmer to put several different classes
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together just to make a basic plot. To solve this problem, we've
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assembled some higher-level classes that have the most common
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behaviors built-in by default, but which can still be easily
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customized or extended. It's clear to me that this is a good general
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approach to preserving flexibility while reducing verbosity.
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At this point, Chaco is definitely capable of handling a large number
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of different plotting tasks, and a lot of them don't require too much
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typing or hacking skills. (Folks will probably require more
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documentation, however, but I'm working on that. :) I linked to the
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source for all of the screenshots in the gallery to demonstrate that
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you can do a lot of things with Chaco in a few dozen lines of code.
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(For instance, the audio spectrogram at the bottom of the gallery is
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just a little over 100 lines.)
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Fundamentally, I like the Chaco model of plots as compositions of
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interactive components. This really helps me think about
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visualization apps in a modular way, and it "fits my head". (Of
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course, the fact that I wrote much of it might have something to do
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with that as well. ;) The goal is to have data-related operations
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clearly happen in one set of objects, the view layout and
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configuration happen in another, and the interaction controls fit
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neatly into a third. IMHO a good toolkit should help me design/
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architect my application better, and we definitely aspire to make
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Chaco meet that criterion.
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Finally, one major perk is that since Chaco is built completely on
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top of traits and its event-based component model, you can call
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edit_traits() on any visual component from within your app (or
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ipython) and get a live GUI that lets you tweak all of its various
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parameters in realtime. This applies to the axis, grid, renderers,
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etc. This seems so natural to me that I sometimes forget what an
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awesome feature it is. :)
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