\HeaderA{hilbertImage}{Produce a matrix that visualizes a long data vector along a Hilbert curve}{hilbertImage}
\begin{Description}\relax
Calculate a Hilbert curve visualization of a long data vector and return it as a square matrix.
\end{Description}
\begin{Usage}
\begin{verbatim}
hilbertImage(data, level = 9, mode = "absmax")
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{data}] A (potentially very long) vector of numerical data. 
\item[\code{level}] The level of the Hilbert curve, determining the size of the returned matrix 
\item[\code{mode}] The binning mode. See \code{\LinkA{shrinkVector}{shrinkVector}} for details.  
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
See the package vignette for an explanation of this visualization technique.
\end{Details}
\begin{Value}
A matrix of dimension \code{2\textasciicircum{}level} x \code{2\textasciicircum{}level}. Each matrix element corresponds to a bin of consecutive elements
of the data vector, the bins arranged to follow the Hilbert curve of the given level. By default, the value of a 
matrix element is either the largest or smallest element in the bin, whichever is larger by absolute value. (See 
\code{\LinkA{shrinkVector}{shrinkVector}} for other possible binning modes.)

To display such a matrix graphically, you can use the standard functions \code{image} or \code{levelplot}
but the function \code{\LinkA{showHilbertImage}{showHilbertImage}} may be more convenient.
\end{Value}
\begin{Note}\relax
For an interactive GUI to explore a Hilbert curve visualisation, use the function
\code{\LinkA{hilbertDisplay}{hilbertDisplay}} in the \code{HilbertVisGUI} package.
\end{Note}
\begin{Author}\relax
Simon Anders, EMBL-EBI, sanders@fs.tum.de
\end{Author}
\begin{Examples}
\begin{ExampleCode}
   # Get a vector with example data
   dataVec <- makeRandomTestData( )
   
   # Plot it in conventional (linear) fashion
   plotLongVector( dataVec )
   
   # Note how the peaks look quite uniform

   # Get the Hilbert curve matrix
   hMat <- hilbertImage( dataVec )
   
   # Plot it with the 'showHilbertImage' function
   showHilbertImage( hMat )
   
   # Note how you can now see the non-uniformity hidden in the previous plot.
   # Note also the ugly aliasing when you change the size of the plot window.
   # Using EBImage allows to display in each matrix element as one pixel:
   
   # if( require ( EBImage ) )
   #    showHilbertImage( hMat, mode="EBImage" )
\end{ExampleCode}
\end{Examples}

