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#!N
#!CSeaGreen #!N #!Rplohis Plots and Histograms #!N #!EC #!N #!N
Data Explorer provides a Plot module that will give you a
simple 2-D graphics plot of your data. This can be convenient
for showing one parameter plotted "traditionally" while you show a colored
3-D height Field illustrating the same or other parameters, in the
same scene. #!N #!N Histogram regroups your data into a specified
number of bins (it acts like a form of filter on
your data). The output of Histogram is a new Field with
connection-dependent data. The connections are the bars on the histogram (which
can be plotted). The height of each histogram bar is proportional
to the number of samples of original data that occur in
the range covered by that bar. You can feed the output
of Histogram through AutoColor then Plot to get a colored plot
of the data distribution. #!N #!N If the aspect ratio of
the Plot is distorted, you can correct it in the Plot
module. This will stretch the Plot out in either the X
or the Y direction until you achieve the look you want.
Visual designers recommend an aspect ratio of approximately 4 units wide
to 3 units high; since this is also the aspect ratio
of television, your image will be ready both for video and
for print. #!N #!N Be aware that "binning" your data with
Histogram can sometimes create rather arbitrary distributions. It is important to
make this clear to the viewer of your visualization. For example,
by carefully selecting bin size, you may turn a unimodal distribution
into a bimodal one. Which distribution is correct for the phenomenon
under study must be determined by the underlying science, not by
the arbitrary picture you create. #!N #!N On the other hand,
if you wish to actually redistribute your data rather than just
show a histogram of its distribution, you can use the Equalize
module. The output of this module is essentially the same scalar
Field you fed into it, but the data values have been
changed to fit the specified distribution. By default, the data values
are changed to approximate a uniform distribution, but you can create
your own custom distribution, like a normal Gaussian curve. Equalize is
useful to reduce extreme values back to a range similar to
the majority of data values. You may also wish to experiment
with other data "compression" and "expansion" techniques by connecting your data
Field to Compute and applying a function like "ln(a)" or "a^2,"
where "a" is the input Field. #!N #!N #!N #!F-adobe-times-medium-i-normal--18* Next
Topic #!EF #!N #!N #!Lrubsht,dxall605 h Rubbersheet #!EL #!N #!F-adobe-times-medium-i-normal--18* #!N
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