Data Item: Table

This data item contains a 2D array of elements arranged as rows and columns.  These elements can be single or double precision floating point values or fixed length strings.

Display options

The table item can be displayed as a physical table as text with optional column sorting or as an interactive plot.  In plot mode, line, bar, scatter and pie charts are all supported. The value of the 'plot' property controls the specific type of display that will be generated. There are a number of properties supported for all tables: labels, titles, formatting, etc, documented in the table at the end of this section.

A table data item displayed as a table might look like this:

Tables have optional row and column headers that are presented as bold-face labels along the top and left sides of the table. Each table has an optional title that is displayed, centered, over the top of the table. Columns support sorting and clicking on individual columns will sort the rows according to the order of the values in that column. Values in the table can be strings or floating point numbers.  The 'format' property sets the default formatting for the entire table or for individual columns. Of special note, a table value can be interpreted as a time value (the number of seconds since 1970-01-01T00.00:00.00000).  In conjunction with the 'date_XY' formatting option, these can be displayed in proper date/time formats. 

The various chart formats (for the same data) look like the following:

In line plot mode, rows in the table are mapped to lines in the line plot. A specific row or the column labels can be used as the common X axis for the data in each row. NaN values in the row are not included in any plots (they are simply skipped). Line colors, widths, styles, thicknesses and symbols can all be controlled via properties.  A line plot with symbols but with the 'line_style' of none results in a scatter plot.

In bar plot mode, rows in the table are mapped to sets of bars in the bar plot. A specific row or the column labels can be used as the common X axis for the data in each row. NaN values in the row are not included in any plots (they are simply skipped).  Bar plots map the 'line_color' property to the color of each bar set.  

In pie chart mode, each row is displayed as a separate pie graph with a common legend. The colors of the pie wedges can be set using the 'line_color' property. The row label and the specific wedge value can be seen when the mouse points at an individual wedge.

LaTeX Support in Plots

Most table properties that support text for display (e.g. plot_title, ytitle, etc) also support the use of LaTeX styling for the creation of math formulas.  If the table item is displayed as a table, use the general LaTeX syntax. If the LaTeX is part of a chart (pie, line, bar, etc), the syntax is a little different. The plotter LaTeX syntax is fairly simple, wrap the Latex text with '$' characters.  For example, a plot_title property with the value:

$\alpha_{1c} = 352 \pm 11 \text{ km s}^{-1}$

will be displayed in the plot like this:

Properties

Properties can be scalars or lists. Multiple rows can be used as y values for plot lines in the same plot, selected by the yaxis property. The value of that property can be a row name or the row index into the table (if a number). The xaxis property provides matching row names providing x values for each yaxis row. Other properties can provide values for each y row as well. These can be scalars (used for all nodes of all row lines) or arrays.  If arrays, individual values in the array are used for each y axis line. If the value is simple value, it is used for all nodes in the line (e.g. #ff0000 used as a color). If the value is prefixed with an '@' character, it is interpreted as a row reference. A row reference can be either the index number of a row or the name of a row.  For example when used with line_marker_size: '@0' selects the first row (row at index 0),  '@Temperature' selects the row with the name 'Temperature', while '0' specifies the value 0.0.

Note: for items listed as arrays or scalars, an array may be used to specify a specific value for each instance in a plot or if a single value is specified, it will be used for all instances.  Most such properties specify things like the color of a trace on a chart or the name of the marker to be used with each trace.

Table Conditional Formatting

The property is a conditional formatting string. The target portion of a rule is based on row and column labels. If those labels are not defined for a table, the row/column index (zero-based) can be used to select the portion of the table the rule applies to. 

Gallery Examples

This section will show in detail how to reproduce the plot and table example from the Nexus gallery.

Plot 1: how to create a plot that shows the air flow profile on top of the Force pressure profile

The first step is to isolate the data items that contain the data point. In the template item filter, enter the conditions to isolate those items. For example:

Once you have isolated the tables, you will need to merge them into a single item. Select in the Template type: Table Merge Generator. Enter in the Configure data item merge... menu to make sure the merging configurations are set to the default ones. Make sure also that in the Generated items you have Replace instead of the default Append. If you now generate your report, you should see something like this:

In this particular example, we are merging three tables. See how we have three times a "Distance" row: this is because each table had "Distance" as the x axis.

Now, it's time to set the properties so that the data will be displayed as plots. As the first thing, we need to isolate which rows to use as x ans y axis. This is done by setting xaxis and yaxis to the corresponding row indices - in this case, [0,4] and [1,5]. In this way, the first plot will be the air flow profile, the second the Force pressure profile. As a next step, we need to improve the visualization of the plots. We want the air flow profile to have only line and no dots in between the data points, and to be thicker than the normal plot. To achieve this, set the following properties:

line_color = [black, red]

plot = line

line_marker = [none, circle]

line_width = [3,1]

Now the plots are displayed as expected. You can still improve the visualization by setting the position of the legend so that it doesn't overlap your plots, setting the axis labels:

legend_position = [0.1, 0.95]

show_legend = 1

show_legend_border = 1

xtitle = Distance [m]

ytitle = Force Pressure [N]

This is the complete set of properties used to generate the gallery plot:

Plot 2: how to use size and color of points to display multiple variables

The goal of plot 2 is to display multiple variables in one single 2D visualization. The plot shows Work vs. Inflow mass, with each data point sized by the ratio of chemicals and colored according to a Temperature palette. Therefore, a total of three variables are displayed against the Inflow mass.

To obtain such visualization, the first step is to isolate the table that contains all these four parameters. Let's assume your table looks like this:

The first thing that we need to do is transpose the table so that a row corresponds to each variable (instead of a column). So, set the template type to be Table Merge Generator. In the configuration dialog, set the Merging to be Columns instead of the default Rows, and the toggle on the Transpose results:

Now that we have prepared the data in the correct way, it's time to set the plot attributes. Let's open the properties editor. As a first step, the basic: set the plot, the X and Y values, and make all the data into single dots:

plot = line

xaxis = 0

xaxis_format = floatdot4

yaxis = Work [Nm]

yaxis_format = floatdot0

line_style = [none]

Next, let's use the information from Temperature to color the dots by:

line_color = @3

palette = -hot

palette_range = [2500, 3200]

palette_show = 1

palette_title = Temp [K]

And now let's use the information from Ratio to set the size of the data points:

line_marker_size = @Ratio

line_marker_scale = [450, -90.0]

We can set a text to appear when we hover over each data point, to show its values:

line_marker_text = Ratio: {{v/size}} Temperature: {{v/color}} [K]

The complete list of properties set for generating this plot is the following:

Plot 3: how to display multiple queries with error bars on the same plot

Plot 3 shows how to display multiple queries on the same plot, and also how to add error bar visualization on a query. As always, the first step is to isolate the item(s) that contain the information needed for the plot. In this case, it is a single table that looks like this:

We therefore need to transpose it, so that each variable corresponds to a row. To do so, set the template type to be Table Merge Generator. In the configuration dialog, set the Merging to be Columns instead of the default Rows, and the toggle on the Transpose results:

Next step, let's set the three queries with different visualization property for each line, so to distinguish them better:

plot = line

xaxis = [0]

yaxis = [5,6,1]

line_marker = [circle, square, diamond]

line_size = [10, 10, 10]

line_style = [solid, dot, dash]

Let's add the error bar on each of the query. This is done by simply setting one property:

line_error_bars = [@2, @3, @4]

The gallery plot is generated with the following properties:

Plot 4: how to display two bar plots at the same time

Plot 4 displays two bar plots with the same X axis. First step, as always, is to isolate the item(s) that contain the data. In this particular case, these is how the items appear:

We have three sets of queries in three different items. We need to merge them into a single item. To do so, set the template type to be Table Merge Generator, and let the configuration be the default. Then, let's set the plot properties:

plot = bar

xaxis = 0

yaxis = [1,3]

Because of the particular values of the y axis in this case, we also want to turn on the log scale on the y direction only:

plot_yaxis_type = log

This will set the two bar plots to be against the same x axis. The complete list of properties to reproduce the example of the gallery is here:

Plot 5: how to display a pie plot

The first step to display a pie plot is to isolate the table element that contains the data. Here is how it looks like:

Note how the values are all in one single row, and how there is only one value for each column. Now, let's plot this as a pie chart by setting the following property:

plot = pie

That is all that is needed. If you want to reproduce exactly the chart in the gallery, these are the complete settings: