% Copyright 2010 by Till Tantau
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\section{Visualizers}
\label{section-dv-visualizers}

\subsection{Overview}

In a data visualization a long stream of data points is
\emph{visualized} using \emph{visualizers}. Recall that it is the job
of the axis systems as described in Section~\ref{section-dv-axes} to
determine \emph{where} data points are visualized. It is the job of
the visualizers to determine \emph{how} they are visualized.

The most basic and common visualizer is the \emph{line visualizer}. It
simply connects subsequent data points by straight lines to indicate
either that the points on these lines interpolate between the real
data points or the straight lines are used to indicate the order in
which the data points appear. A different, more ``conservative''
visualizer is the \emph{scatter visualizer} or \emph{mark visualizer},
which just places a small mark at each data point. Such a visualizer
does not imply any interpolation or ordering between the data points.

Visualizers may, however, also be more complicated. For instance, a
visualizer used for a box plot could visualize a data point as a box
with a median value, standard deviation, outliers, and other
information; a rectangle visualizer might visualize data points as
larger areas; a projection visualizer might visualize the projection
of data points onto different axes; and so.

Creating a new visualizer is not quite trivial since a new \pgfname\
class needs to be implemented. Fortunately, using visualizers is much
simpler: For each kind of visualizer there is a key that allows you to
create such a visualizer. You can then use further keys to configure
the visualizer and to connect it to the data.

In a data visualization multiple visualizers may exist at the same
time. This happens in different situations:
\begin{itemize}
\item A data visualization may contain several independent data sets
  that are to be visualized. There might be a line plot, for which a
  line visualizer is used, and also a scatter plot, for which a
  scatter visualizer would be used.

  In this case, for each data point only one visualizer will do
  anything. To achieve this, each data point has an attribute called
  |visualizer| which tells the visualizer objects whether they should
  ``react'' to the data point or not.
\item A single data point might be visualized several times. For
  instance, a scatter visualizer might draw a mark at the data point's
  position on the page and a projection visualizer might draw,
  additionally, a mark at the projected position.
\end{itemize}


\subsection{Usage}

\subsubsection{Using a Single Visualizer}

The simplest scenario for using visualizers are data visualizations in
which there is only a single data set that is visualized in one
style. In this case, all that needs to be done in order to choose a
visualizer is use one of the options starting with |visualize as ...|
together with the |\datavisualization| command:

\begin{codeexample}[]
% Define a data set:  
\tikz \datavisualization data group {example} = {
data {
  x, y
  0, 0
  0.5, 2
  1, 2
  1.5, 1.5
  2, 0.5
}};
\tikz \datavisualization [school book axes, visualize as line]        data group {example};
\qquad 
\tikz \datavisualization [school book axes, visualize as smooth line] data group {example};
\qquad 
\tikz \datavisualization [school book axes, visualize as scatter]     data group {example};
\end{codeexample}

Methods for styling visualizers are discussed in Section~\ref{section-dv-visualizer-styling}.


\subsubsection{Using Multiple Visualizers}

A data visualization may contain multiple data groups and for each data
set we might wish to use a different visualizer. In this case, we need
some way of telling the data visualization engine to which visualizer
should be used with the different data points.

To solve this problem, you can \emph{name} a visualizer. The
visualizer's name can then both be used to configure the visualizer
and also to indicate that data points ``belong'' to the visualizer.

Naming a visualizer is quite simple: The |visualize as ...| keys
actually take a single parameter, which is the name of the
visualizer. For instance, the following code creates three
visualizers, named |sin|, |cos|, and |tan|:

\begin{codeexample}[code only]
visualize as line=sin,
visualize as line=cos,
visualize as scatter=tan
\end{codeexample}

(When you just say |visualize as line| without providing a name, the
name |line| is chosen as a default, for |visualize as scatter| the
name |scatter| is the default and so.)

In order to indicate which data points should be visualized by which
of these visualizers, the following key is important:

\begin{key}{/data point/set}
  A visualizer will only act on a data point when its name matches the
  value of this key. Initially, this key is set to the last visualizer
  created, so if there is only one, there is no need to set or worry
  about this key.
\end{key}

Since the |set| key has the path prefix |/data point|, it can
be set like any other attribute of a data key:

\begin{codeexample}[width=7cm]
\tikz \datavisualization
 [scientific axes=clean,
  visualize as line=sin,
  visualize as line=cos,
  visualize as scatter=tan]
data {
  x, y, set
  0, 0, sin
  1, 1, sin
  2, 0, sin
  3, -1, sin
  4, 0, sin
  0, 1, cos
  1, 0, cos
  0, 0, tan
  1, 1, tan
  2, 2, tan
  3, 4, tan
  2, -1, cos
  3, 0, cos
  4, 1, cos
};
\end{codeexample}

As can be seen, the data points with the same |set| attribute
do not need to be consecutive.

The above method of specifying the visualizer works nicely, but in
most cases it would be more natural to keep the |set| attribute
out of the table. This is easy to achieve by using multiple |data| and
using the following key:

\begin{key}{/pgf/data/set=\meta{name}}
  Shorthand for |/data point/set=|\meta{name}.
\begin{codeexample}[width=7cm]
\tikz \datavisualization
 [scientific axes=clean,
  visualize as line=sin,
  visualize as line=cos]
data [set=sin] {
  x, y
  0, 0
  1, 1
  2, 0
  3, -1
  4, 0
}
data [set=cos] {
  x, y
  0, 1
  1, 0
  2, -1
  3, 0
  4, 1
};
\end{codeexample}
\end{key}

When you need to visualize several similar things in a single plot
(like ten lines that all get visualized by |visualize as line|), it is
somewhat cumbersome having to write this ten times. In this case you
can shorten your code by making use of the |.list| key handler: When
you add it to a key, the ``value'' passed to the key is parsed as a
list of values. The key is then executed once for each of these
values:

\begin{codeexample}[width=7cm]
\tikz \datavisualization
 [scientific axes=clean,
  visualize as line/.list={sin, cos, tan}]
data [set=sin, format=function] {
  var x : interval[0:3*pi];
  func y = sin(\value x r);
}
data [set=cos, format=function] {
  var x : interval[0:3*pi];
  func y = cos(\value x r);
}
data [set=tan, format=function] {
  var x : interval[0:pi/2.2];
  func y = tan(\value x r);
};
\end{codeexample}



\subsubsection{Styling a Visualizer}
\label{section-dv-visualizer-styling}

In order to style a visualizer that has been created using for
instance |visualize as line=|\meta{visualizer name}, you can use the
following key: 

\begin{key}{/tikz/data visualization/\meta{visualizer
      name}=\meta{options}}
  For each visualizer, a key of the same name is created with the path
  prefix |/tikz/data visualization|. This key takes the \meta{options}
  and executes them with the path prefix
\begin{codeexample}[code only]
/tikz/data visualization/visualizer options/  
\end{codeexample}
  These options are then used to configure the appearance of the
  current visualizer. (This is quite similar to the way options are
  passed to an axis in order to configure the axis.)
  Possible options include |style|, but also |label in legend| and
  |label in data|. The latter two options are discussed in
  Section~\ref{section-dv-labels-in}, the first option below.

\begin{codeexample}[width=7cm]
\tikz \datavisualization
 [scientific axes=clean,
  visualize as smooth line/.list={sin, cos},
  sin={style=red},
  cos={style=blue}]
data [set=sin, format=function] {
  var x : interval[0:3*pi];
  func y = sin(\value x r);
}
data [set=cos, format=function] {
  var x : interval[0:3*pi];
  func y = cos(\value x r);
};
\end{codeexample}
\end{key}

\begin{key}{/tikz/data visualization/visualizer
    options/style=\meta{options}}
  The \meta{options} given to this key should be normal \tikzname\
  options. They will be executed when the visualizer is used.

\begin{codeexample}[width=7cm]
\tikz \datavisualization
 [scientific axes=clean,
  visualize as smooth line=sin,
  sin={style={red, densely dotted}},
  visualize as smooth line=cos,
  cos={style={mark=x}},
]
data [set=sin, format=function] {
  var x : interval[0:3*pi];
  func y = sin(\value x r);
}
data [set=cos, format=function] {
  var x : interval[0:3*pi];
  func y = cos(\value x r);
};
\end{codeexample}

  When you have multiple visualizers in a single data visualization,
  you can use the |style| option with each visualizer to configure
  their different appearances as in the above example. However, it is
  usually much better (and easier) to use a style sheet, see
  Section~\ref{section-dv-style-sheets}. 
  
  
\begin{codeexample}[width=7cm]
\tikz \datavisualization
 [scientific axes={clean, end labels},
  x axis={label=$x$}, y axis={grid={major also at=0}},
  visualize as smooth line/.list={sin,cos,sin 2,cos 2},
  legend={below, rows=2},
  sin={label in legend={text=$\sin x$}},
  cos={label in legend={text=$\cos x$}},
  sin 2={label in legend={text=$\sin 2x$}},
  cos 2={label in legend={text=$\cos 2x$}},
  style sheet=strong colors]
data [set=sin, format=function] {
  var x : interval[0:3*pi];
  func y = sin(\value x r);
}
data [set=cos, format=function] {
  var x : interval[0:3*pi];
  func y = cos(\value x r);
}
data [set=sin 2, format=function] {
  var x : interval[0:3*pi];
  func y = sin(2*\value x r);
}
data [set=cos 2, format=function] {
  var x : interval[0:3*pi];
  func y = cos(2*\value x r);
};
\end{codeexample}
\end{key}

\begin{key}{/tikz/data visualization/visualizer options/ignore style
    sheets}
  This option, which should be passed to a visualizer after its
  creation before another visualizer is created, causes style sheets
  \emph{not} to apply to the visualizer (but the |style| option will
  still have an effect). This allows you to create visualizers that
  are used for special purposes and that do not ``take part'' in the
  usual styling. For instance, a visualizer might be used internally
  to depict a regression line, even though the regression line itself
  should not participate in the usual styling by, say, dashing or
  different coloring.
\end{key}

In addition to the options passed to a visualizer via |style|, the
following also gets executed when a visualizer is used:

\begin{stylekey}{/tikz/data visualization/every visualizer}
  This style is used with every visualizer. Note that it should
  contain normal \tikzname\ keys.

\begin{codeexample}[width=7cm]
\tikz \datavisualization
 [scientific axes=clean,
  every visualizer/.style={dashed},
  visualize as smooth line]
data [format=function] {
  var x : interval[0:3*pi];
  func y = sin(\value x r);
};
\end{codeexample}
\end{stylekey}


\subsection{Reference: Basic Visualizers}

\subsubsection{Visualizing Data Points Using Lines}

\begin{key}{/tikz/data visualizers/visualize as line=\meta{visualizer
      name} (default line)}
  Creates a new visualizer named \meta{visualizer name}. Basically, 
  this visualizer connects all data points for which the
  |/data point/set| attribute equals \meta{visualizer name} by
  a line that is styled by the visualizer's style.

  In more detail, the following happens:
  \begin{enumerate}
  \item A new object is created (of class |plot handler visualizer|)
    that is configured to collect the canvas positions of all data
    points whose |set| attribute equals \meta{visualizer name}.
  \item During the end of the data visualization, \pgfname's plotting
    mechanism (see Section~\ref{section-plots}) is used to plot the
    stream of recorded data points.

    This means that, in principle, all of the plot handlers available
    in \tikzname\ could be used for the visualization (such as the
    |smooth| handler). However, some plot handlers such as, say, the
    |xcomb| are unsuitable as plot handlers since they do not support
    the advanced axis handling done by the data visualization
    engine. Because of this (and also for other reasons), you cannot
    set the plot handler directly, but must use one of the options
    like |straight line|, |smooth line| and others, documented in a
    moment.
  \item Additionally, plot marks can be drawn at the collected data
    points. Here, all of the options available to \tikzname\ for
    drawing plot marks are available. To configure them, all options
    offered by \tikzname\ for  configuring marks are available such as
    |mark repeat|:  
\begin{codeexample}[width=7cm]
\tikz \datavisualization
 [scientific axes=clean, 
  visualize as line=my data,
  my data={style={mark=x, mark repeat=3}}]
data [format=function] {
  var x : interval [0:pi] samples 10;
  func y = sin(\value x r);
};
\end{codeexample}
  \end{enumerate}
  
  The line visualizer also provides a method of dealing with gaps in a
  line. Take for instance the function $f(x) = \tan x$. When this
  function is plotted over the interval $[0,\pi]$, then the function
  will go to $\pm \infty$ at $\pi/2$. When we plot this, we might plot
  the function in the interval $[0,\frac{\pi}{2}-\epsilon]$ and then
  continue in the interval $[\frac{\pi}{2}+\epsilon,\pi]$. However, we
  do not want the point at coordinate $\bigl(\frac{\pi}{2}- \epsilon,
  \tan(\frac{\pi}{2}- \epsilon)\bigr)$ to be connected to the
  coordinate $\bigl(\frac{\pi}{2}+ \epsilon, \tan(\frac{\pi}{2}+
  \epsilon)\bigr)$ by a line. Rather, there should be a ``gap'' or a
  ``jump'' between these coordinates. To achieve this, the following
  key can be used:
  \begin{key}{/data point/outlier=\meta{value} (default true, initially \normalfont empty)}
    When this key is set to anything non-empty value, a visualizer
    will consider this data point to be an ``outlier.'' For a line
    visualizer this means that the point is not shown and that the
    current line ends at the previous data point and a new line starts
    at the next data point.
\begin{codeexample}[width=7cm]
\tikz \datavisualization
 [scientific axes=clean, x axis={grid={major at=(pi/2)}},
  visualize as smooth line]
data [format=function] {
  var x : interval[0:pi/2-0.1];
  func y = tan(\value x r);
}
data point [outlier]
data [format=function] {
  var x : interval[pi/2+0.1:pi];
  func y = tan(\value x r);
};
\end{codeexample}
  \end{key}
\end{key}


\begin{key}{/tikz/data visualizers/visualize as smooth line=\meta{visualizer
      name} (default line)}
  A shorthand |visualize as line=|\meta{visualizer name}
  followed \meta{visualizer name}|=smooth line|.
\end{key}


\begin{key}{/tikz/data visualization/visualizer options/straight line}
  Causes the data points to be connected by straight lines.
\begin{codeexample}[]
\tikz [scale=.55] \datavisualization
 [scientific axes=clean, all axes={ticks=few},
  visualize as smooth line=my data,  my data={straight line}]
data [format=function] {
  var t : interval [0:4] samples 5;
  func x = cos(\value t r);
  func y = sin(\value t r);
};
\end{codeexample}
\end{key}

\begin{key}{/tikz/data visualization/visualizer options/straight cycle}
  Causes the data points to be connected by a polygon.
\begin{codeexample}[]
\tikz [scale=.55] \datavisualization
 [scientific axes=clean, all axes={ticks=few},
  visualize as smooth line=my data,  my data={straight cycle}]
data [format=function] {
  var t : interval [0:4] samples 5;
  func x = cos(\value t r);
  func y = sin(\value t r);
};
\end{codeexample}
\end{key}
 
\begin{key}{/tikz/data visualization/visualizer options/polygon}
  This is an alias for |straight cycle|.
\end{key}

\begin{key}{/tikz/data visualization/visualizer options/smooth line}
  Causes the data points to be connected by a line that is smoothed
  at the joins:
\begin{codeexample}[]
\tikz [scale=.55] \datavisualization
 [scientific axes=clean, all axes={ticks=few},
  visualize as smooth line=my data,  my data={smooth line}]
data [format=function] {
  var t : interval [0:4] samples 5;
  func x = cos(\value t r);
  func y = sin(\value t r);
};
\end{codeexample}
\end{key}
 
\begin{key}{/tikz/data visualization/visualizer options/smooth cycle}
  Causes the data points to be connected by a circular line that is
  smoothed at the joins:
\begin{codeexample}[]
\tikz [scale=.55] \datavisualization
 [scientific axes=clean, all axes={ticks=few},
  visualize as smooth line=my data,  my data={smooth cycle}]
data [format=function] {
  var t : interval [0:4] samples 5;
  func x = cos(\value t r);
  func y = sin(\value t r);
};
\end{codeexample}
\end{key}
 
\begin{key}{/tikz/data visualization/visualizer options/gap line}
  This key causes the data points to be connected by lines that ``do
  not quite touch'' the data points. This is implemented by using the
  |\pgfplothandlergaplineto|, see Section~\ref{section-plot-gapped}. 
\begin{codeexample}[]
\tikz [scale=.55] \datavisualization
 [scientific axes=clean, all axes={ticks=few},
  visualize as smooth line=my data,  my data={gap line}]
data [format=function] {
  var t : interval [0:4] samples 5;
  func x = cos(\value t r);
  func y = sin(\value t r);
};
\end{codeexample}
\end{key}
 
\begin{key}{/tikz/data visualization/visualizer options/gap cycle}
  Like |gapped line|, only with a cycle:
\begin{codeexample}[]
\tikz [scale=.55] \datavisualization
 [scientific axes=clean, all axes={ticks=few},
  visualize as smooth line=my data,  my data={gap cycle}]
data [format=function] {
  var t : interval [0:4] samples 5;
  func x = cos(\value t r);
  func y = sin(\value t r);
};
\end{codeexample}
\end{key}
 
\begin{key}{/tikz/data visualization/visualizer options/no lines}
  Suppresses the line. This option only makes sense when the |mark|
  option is used.
\begin{codeexample}[]
\tikz [scale=.55] \datavisualization
 [scientific axes=clean, all axes={ticks=few},
  visualize as smooth line=my data,  my data={no lines, style={mark=x}}]
data [format=function] {
  var t : interval [0:4] samples 5;
  func x = cos(\value t r);
  func y = sin(\value t r);
};
\end{codeexample}
\end{key}



\subsubsection{Visualizing Data Points Using Marks}

\begin{key}{/tikz/data visualizers/visualize as scatter=\meta{visualizer
      name} (default scatter)}
  A shorthand  |visualize as line=|\meta{visualizer name}
  followed  \meta{visualizer name}|=no lines| and setting
  the |style| of the visualizer so that is will use |mark=x| (plus
  some size adjustments) to draw marks at the data points. 
\begin{codeexample}[width=7cm]
\tikz \datavisualization
 [scientific axes=clean, 
  visualize as scatter]
data [format=function] {
  var x : interval [0:pi] samples 10;
  func y = sin(\value x r);
};
\end{codeexample} 
\end{key}


\subsection{Advanced: Creating New Visualizers}

Creating a new visualizer is a two-stage process that does,
unfortunately, require in-depth knowledge of the data
visualization backend:
\begin{enumerate}
\item First, you need to create a new class using |\pgfooclass| whose
  instances react to the signal |visualize datapoint signal|. This requires
  detailed knowledge of the data visualization engine, see
  Section~\ref{section-dv-backend}.
\item Second, you should provide keys on the \tikzname\ level for
  creating the necessary objects. These keys invoke the key
  |new visualizer| internally.
\end{enumerate}

\begin{key}{/tikz/data visualization/new
    visualizer=\marg{name}\marg{options}\marg{legend entry options}}
  This key configures a new visualizer named \meta{name}. This entails
  the following actions:
  \begin{itemize}
  \item The key |/tikz/data visualization/|\meta{name} is
    created. As described earlier, this key can be used to pass
    for instance |style| options to the visualizer.
  \item The style key |/tikz/data visualization/visualizers/|\meta{name}|/styling|
    is created and made empty. This is the key in which the |style|
    key will store the options passed to the visualizer.
  \item The style key |/tikz/data visualization/visualizers/|\meta{name}|/label in legend options|
    is set to \meta{legend entry options}. These options are used to
    configure how the visualizer should be rendered in a legend, see
    Section~\ref{section-dv-legend-entries} for details.
  \item The key |/data point/set/|\meta{name} is set to a
    number that is increased for each visualizer in the current data
    visualization. This number is important for style sheets, see
    Section~\ref{section-dv-style-sheets}.
  \item The key |/data point/|\meta{name}|/execute at begin| is set to
    code that creates a |{scope}| that executes the following styles
    as options:
    \begin{enumerate}
    \item The \meta{options} passed to the |new visualizer| key.
    \item The |every visualizer| style.
    \item The styling from the currently active style sheets, see
      Section~\ref{section-dv-style-sheets}. 
    \item The styling stored in the |styling| key mentioned above.
    \end{enumerate}
  \item The key |/data point/|\meta{name}|/execute at end| is set to
    code that will finish all paths that may have been created by the
    visualizer and closes the scope.
  \end{itemize}
  
  All of the above mean the following in practice:
  \begin{itemize}
  \item Inside a new |visualize as ...| key, you pass the name of
    the to-be-created to |new visualizer| as the first parameter and
    any special default styling setup of the visualizer as the second
    parameter.
  \item The new |visualize as ...| key should also create a visualizer
    object using |new object|.
  \item When this object finally is about to create the actual
    visualization, it should surround the code by invoking the code
    stored in the |execute at begin| and the |execute at end| keys of
    the visualizer.
  \end{itemize}

  Everything else is usually taken care of by the |new visualizer| key
  automatically. 
\end{key}


As an example, let us create a simple visualizer that creates a
circle whose radius is dictated by the |radius| attribute. To keep
things simple in this example, this attribute cannot be configured.

First, we need the visualizer class. For this example I have boiled it
down to a minimum:

\begin{codeexample}[code only]
\pgfooclass{circle visualizer}
{
  % Stores the name of the visualizer. This is needed for filtering and configuration
  \attribute name;
  
  % The constructor. Just setup the attribute.
  \method circle visualizer(#1) { \pgfooset{name}{#1} }
  
  % Connect to visualize signal.
  \method default connects() {
    \pgfoothis.get handle(\me)
    \pgfkeysvalueof{/pgf/data visualization/obj}.connect(\me,visualize,visualize datapoint signal)
  }
  
  % This method is invoked for each data point. It checks whether the data point belongs to the correct
  % visualizer and, if so, calls the macro \dovisualization to do the actual visualization. 
  \method visualize() {
    \pgfdvfilterpassedtrue
    \pgfdvnamedvisualizerfilter
    \ifpgfdvfilterpassed
      \dovisualization
    \fi
  }
}
\end{codeexample}

The |\dovisualization| method must now do the correct
visualization.

\begin{codeexample}[code only]
\def\dovisualization{
  \pgfkeysvalueof{/data point/\pgfoovalueof{name}/execute at begin}
    \pgfpathcircle{\pgfpointdvdatapoint}{\pgfkeysvalueof{/data point/radius}}
    % \pgfusepath is done by |execute at end|
  \pgfkeysvalueof{/data point/\pgfoovalueof{name}/execute at end}
}
\end{codeexample}

Finally, we create a |visualize as| key:

\begin{codeexample}[code only]
\tikzdatavisualizationset{
  visualize as circle/.style={
    new object={
      when=after survey,
      store=/tikz/data visualization/visualizers/#1,
      class=circle visualizer,
      arg1=#1
    },
    new visualizer={#1}{%
      color=visualizer color,        % a color setup by the style sheet
      every path/.style={fill,draw}, % fill and draw the circle by default,
    }{}, % let's ignore legends in this example
    /data point/set=#1
  },
  visualize as circle/.default=circle
}
\end{codeexample}

Now, let's see how this works:


\pgfooclass{circle visualizer}
{
  % Stores the name of the visualizer. This is needed for filtering
  % and configuration
  \attribute name;
  
  % The constructor. Just setup the attribute.
  \method circle visualizer(#1) { \pgfooset{name}{#1} }
  
  % Connect to visualize signal.
  \method default connects() {
    \pgfoothis.get handle(\me)
    \pgfkeysvalueof{/pgf/data visualization/obj}.connect(\me,visualize,visualize datapoint signal)
  }
  
  % This method is invoked for each data point. It checks whether the
  % data point belongs to the correct visualizer and, if so, calls the
  % macro \dovisualization to do the actual visualization. 
  \method visualize() {
    \pgfdvfilterpassedtrue
    \pgfdvnamedvisualizerfilter
    \ifpgfdvfilterpassed
      \dovisualization
    \fi
  }
}

\def\dovisualization{
  \pgfkeysvalueof{/data point/\pgfoovalueof{name}/execute at begin}
    \pgfpathcircle{\pgfpointdvdatapoint}{\pgfkeysvalueof{/data point/radius}}
    % \pgfusepath is done by |execute at end|
  \pgfkeysvalueof{/data point/\pgfoovalueof{name}/execute at end}
}

\tikzdatavisualizationset{
  visualize as circle/.style={
    new object={
      when=after survey,
      store=/tikz/data visualization/visualizers/#1,
      class=circle visualizer,
      arg1=#1
    },
    new visualizer={#1}{%
      color=visualizer color,        % a color setup by the style sheet
      every path/.style={fill,draw}, % fill and draw the circle by default,
    }{},
    /data point/set=#1
  },
  visualize as circle/.default=circle
}


\begin{codeexample}[width=7cm]
\tikz \datavisualization [
  scientific axes=clean,
  visualize as circle/.list={a, b, c},
  style sheet=strong colors]
data [set=a] {
  x, y, radius
  0, 0, 2pt
  1, 1, 3pt
  1, 2, 3pt
  2, 0, 1pt
}
data [set=b] {
  x, y, radius
  0.5, 0.5, 5pt
  1, 1.5, 2pt
  1, 2.5, 3pt
  0, 2, 4pt
}
data [set=c] {
  x, y, radius
  3, 2, 3pt
  2.5, 0.5, 4pt
};  
\end{codeexample}
