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@@ -2782,6 +2782,234 @@ You can plot plot objects of any class which has either `AsDouble` or
`AsString` can be returning either a `char*`, or a **`TString`** or an
`std::string`.
+### Multiple variables visualisation
+
+
+This section presents the visualization technique available in ROOT
+to represent multiple variables (>4) data sets.
+
+#### Spider (Radar) Plots
+
+Spider plots (sometimes called “web-plots” or “radar plots”) are used
+to compare series of data points (events). They use the human ability
+to spot un-symmetry.
+
+
+
+Variables are represented on individual axes displayed along a circle.
+For each variable the minimum value sits on the circle’s center, and
+the maximum on the circle’s radius. Spider plots are not suitable for
+an accurate graph reading since, by their nature, it can be difficult
+to read out very detailed values, but they give quickly a global view
+of an event in order to compare it with the others. In ROOT the spider
+plot facility is accessed from the tree viewer GUI. The variables to
+be visualized are selected in the tree viewer and can be scanned using
+the spider plot button.
+
+
+
+The spider plot graphics editor provides two tabs to interact with
+the spider plots’ output: the tab “Style” defining the spider layout
+and the tab “Browse” to navigate in the tree.
+
+#### Parallel Coordinates Plots
+
+The Parallel Coordinates Plots are a common way of studying and
+visualizing multiple variables data sets. They were proposed by in
+A.Inselberg in 1981 as a new way to represent multi-dimensional
+information. In traditional Cartesian coordinates, axes are mutually
+perpendicular. In Parallel coordinates, all axes are parallel which
+allows representing data in much more than three dimensions. To show
+a set of points in Parallel Coordinates, a set of parallel lines is
+drawn, typically vertical and equally spaced. A point in n-dimensional
+space is represented as a polyline with vertices on the parallel axes.
+The position of the vertex on the i-th axis corresponds to the i-th
+coordinate of the point. The three following figures show some very
+simple examples:
+
+
+
+
+
+The Parallel Coordinates technique is good at: spotting irregular
+events, seeing the data trend, finding correlations and clusters. Its
+main weakness is the cluttering of the output. Because each “point” in
+the multidimensional space is represented as a line, the output is very
+quickly opaque and therefore it is difficult to see the data clusters.
+Most of the work done about Parallel Coordinates is to find techniques
+to reduce the output’s cluttering. The Parallel Coordinates plots in
+ROOT have been implemented as a new plotting option “PARA” in the
+`TTree::Draw()method`. To demonstrate how the Parallel Coordinates
+works in ROOT we will use the tree produced by the following
+“pseudo C++” code:
+
+``` {.cpp}
+void parallel_example() {
+ TNtuple *nt = new TNtuple("nt","Demo ntuple","x:y:z:u:v:w:a:b:c");
+ for (Int_t i=0; i<3000; i++) {
+ nt->Fill( rnd, rnd, rnd, rnd, rnd, rnd, rnd, rnd, rnd );
+ nt->Fill( s1x, s1y, s1z, s2x, s2y, s2z, rnd, rnd, rnd );
+ nt->Fill( rnd, rnd, rnd, rnd, rnd, rnd, rnd, s3y, rnd );
+ nt->Fill( s2x-1, s2y-1, s2z, s1x+.5, s1y+.5, s1z+.5, rnd, rnd, rnd );
+ nt->Fill( rnd, rnd, rnd, rnd, rnd, rnd, rnd, rnd, rnd );
+ nt->Fill( s1x+1, s1y+1, s1z+1, s3x-2, s3y-2, s3z-2, rnd, rnd, rnd );
+ nt->Fill( rnd, rnd, rnd, rnd, rnd, rnd, s3x, rnd, s3z );
+ nt->Fill( rnd, rnd, rnd, rnd, rnd, rnd, rnd, rnd, rnd );
+ }
+```
+
+The data set generated has:
+
+- 9 variables: x, y, z, u, v, w, a, b, c.
+- 3000*8 = 24000 events.
+- 3 sets of random points distributed on spheres: s1, s2, s3
+- Random values (noise): rnd
+- The variables a,b,c are almost completely random. The variables a
+and c are correlated via the 1st and 3rd coordinates of the 3rd “sphere” s3.
+
+The command used to produce the Parallel Coordinates plot is:
+
+``` {.cpp}
+ nt->Draw("x:a:y:b:z:u:c:v:w","","PARA");
+```
+
+
+
+If the 24000 events are plotted as solid lines and no special techniques
+are used to clarify the picture, the result is the previous picture
+which is very cluttered and useless. To improve the readability of the
+Parallel Coordinates output and to explore interactively the data set,
+many techniques are available. We have implemented a few in ROOT. First
+of all, in order to show better where the clusters on the various axes
+are, a 1D histogram is associated to each axis. These histograms
+(one per axis) are filled according to the number of lines passing
+through the bins.
+
+
+
+These histograms can be represented which colors (get from a palette
+according to the bin contents) or as bar charts. Both representations
+can be cumulated on the same plot. This technique allows seeing clearly
+where the clusters are on an individual axis but it does not give any
+hints about the correlations between the axes.
+
+Avery simple technique allows to make the clusters appearing:
+Instead of painting solid lines we paint dotted lines. The cluttering of
+each individual line is reduced and the clusters show clearly as we can
+see on the next figure. The spacing between the dots is a parameter which
+can be adjusted in order to get the best results.
+
+
+
+Interactivity is a very important aspect of the Parallel Coordinates plots.
+To really explore the data set it is essential to act directly with the
+events and the axes. For instance, changing the axes order may show clusters
+which were not visible in a different order. On the next figure the axes
+order has been changed interactively. We can see that many more clusters
+appear and all the “random spheres” we put in the data set are now
+clearly visible. Having moved the variables `u,v,w` after the variables
+`x,y,z` the correlation between these two sets of variables is clear also.
+
+
+
+To pursue further data sets exploration we have implemented the possibility
+to define selections interactively. A selection is a set of ranges combined
+together. Within a selection, ranges along the same axis are combined with
+logical OR, and ranges on different axes with logical AND. A selection is
+displayed on top of the complete data set using its own color. Only the
+events fulfilling the selection criteria (ranges) are displayed. Ranges
+are defined interactively using cursors, like on the first axis on the
+figure. Several selections can be defined at the same time,
+each selection having its own color.
+
+
+
+Several selections can been defined. Each cluster is now clearly visible
+and the zone with crossing clusters is now understandable whereas,
+without any selection or with only a single one, it was not easy to
+understand.
+
+
+
+Interactive selections on Parallel Coordinates are a powerful tool because
+they can be defined graphically on many variables (graphical cuts in ROOT can
+be defined on two variables only) which allow a very accurate events
+filtering. Selections allow making precise events choices: a single outlying
+event is clearly visible when the lines are displayed as “solid” therefore
+it is easy to make cuts in order to eliminate one single event from a
+selection. Such selection (to filter one single event) on a scatter plot
+would be much more difficult.
+
+
+
+Once a selection has been defined, it is possible to use it to generate a
+`TEntryList` which is applied on the tree and used at drawing time. In our
+example the selection we defined allows to select exactly the two
+correlated “random spheres”.
+
+
+
+Another technique has been implemented in order to show clusters when
+the picture is cluttered. A weight is assigned to each event. The weight
+value is computed as:
+$$
+weight = \sum_{i=1}^{n} b_i
+$$
+
+Where:
+
+- bi is the content of bin crossed by the event on the i-th axis.
+- n is the number of axis.
+
+The events having the bigger weights are those belonging to clusters.
+It is possible to paint only the events having a weight above a given
+value and the clusters appear. The next example “weight cut” applied on
+the right plot is 50. Only the events with a weight greater than 50 are displayed.
+
+
+
+In case only a few events are displayed, drawing them as smooth curves
+instead of straight lines helps to differentiate them.
+
+
+
+Interactivity and therefore the Graphical User Interface are very important
+to manipulate the Parallel Coordinates plots. The ROOT framework allows
+to easily implement the direct interactions on the graphical area and the
+graphical editor facility provides dedicated GUI.
+
+
+
+Tranparency is very useful with parallel coordinates plots. It alows to
+show cleraly the clusters.
+
+
+
+
+
+#### Box (Candle) Plots
+
+A Box Plot (also known as a “box-and whisker” plot or “candle stick” plot)
+is a convenient way to describe graphically a data distribution (D) with only
+the five numbers. It was invented in 1977 by John Tukey. The five numbers are:
+
+1. The minimum value of the distribution D (Min).
+2. The lower quartile (Q1): 25% of the data points in D are less than Q1.
+3. The median (M): 50% of the data points in D are less than M.
+4. The upper quartile (Q3): 75% of the data points in D are less than Q3.
+5. The maximum value of the distribution D (Max).
+
+
+
+In ROOT Box Plots (Candle Plots) can be produced from a TTree using the
+“candle” option in TTree::Draw().
+
+``` {.cpp}
+ tree->Draw(“px:cos(py):sin(pz)”,””,”candle”);
+```
+
+
+
### Using TTree::Scan
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