Description Usage Arguments Details Value Note Author(s) Examples
Calculate a Hilbert curve visualization of a long data vector and return it as a square matrix.
1 | hilbertImage(data, level = 9, mode = "absmax")
|
data |
A (potentially very long) vector of numerical data. |
level |
The level of the Hilbert curve, determining the size of the returned matrix |
mode |
The binning mode. See |
See the package vignette for an explanation of this visualization technique.
A matrix of dimension 2^level
x 2^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
shrinkVector
for other possible binning modes.)
To display such a matrix graphically, you can use the standard functions image
or levelplot
but the function showHilbertImage
may be more convenient.
For an interactive GUI to explore a Hilbert curve visualisation,
use the function
hilbertDisplay
in the
HilbertVisGUI
package.
Simon Anders, EMBL-EBI, sanders@fs.tum.de
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | # 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" )
|
Loading required package: grid
Loading required package: lattice
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