Table of Contents

1. Introduction
2. Plotting Terminology
3. Plot Trace Creator - Basic Usage
4. Plot Trace Creator - Bar Charts
5. Plot Trace Creator - Bar Charts (Categorical Data)
6. Plot Trace Creator - Box Plots
7. Plot Trace Creator - Contour Plots
8. Plot Trace Creator - Heat Maps
9. Plot Trace Creator - Heat Maps (Categorical Axes)
10. Plot Trace Creator - Histograms
11. Plot Trace Creator - Parallel Coordinates Plots
12. Plot Trace Creator - Scatter Plots
13. Plot Trace Creator - Scatter Plots (Categorical Axes)
14. Plot Trace Creator - Scatter 3D Plot
15. Plot Trace Creator - Surface 3D Plot
16. Editing Plot Files
17. Using Color Scales in Plot Traces
18. Plot Manager - Managing Multiple Traces and Canvases
19. Plot Templates - Basic Usage
20. Plot Templates - Bar Chart Variable Comparison
21. Plot Templates - Centered Parameter Study
22. Plot Templates - Correlation Coefficient Table
23. Plot Templates - Iteration History
24. Plot Templates - Scatter Plot Matrix
25. Plot Templates - Sobol Indices

Introduction

Chartreuse is a plotting library provided with the Dakota GUI that allows you to explore the data in Dakota output files by creating graphical plots. A number of different 2D and 3D plot types are supported.

Due to the scope of Chartreuse functionality, this manual page is not comprehensive and only covers the classic Chartreuse dialogs and editors. Other more specialized Chartreuse topics can be found on these pages:

• Chartreuse in Next-Gen Workflow
• Chartreuse Sandbox View

Note: Currently, the Dakota GUI fully supports Chartreuse plotting on Windows and Mac. For Linux, only RHEL7 and newer are supported (other comparably new Fedora-based distributions would be supported as well). In addition, Chartreuse plotting is limited to 2D on Linux.

Plotting Terminology

Before you begin plotting, there are three major terms used in Chartreuse that you should learn. Understanding these will give you a lot of flexibility in the types of plots you can create.

• Plot Trace A plot trace is the most basic grouping of information that can be visualized in the Dakota GUI. A trace represents a single Dakota variable (either parameter or response) visually rendered in some way. A trace could be a series of points plotted on a Cartesian plane (i.e. a scatter plot). A trace can also be represented in other ways that we don’t naturally think of as “tracing” (such as a histogram)
• Plot Canvas A plot canvas can be thought of a single set of axes. A canvas can be either 2D or 3D. A plot canvas can display one or more plot traces by grouping them onto the same axes.
• Plot Window A plot window is the top-level container for Chartreuse plot data. A plot window can contain one or more plot canvases. Only one plot window can be rendered at a time, but you can have multiple plot windows open at once by making use of editor tabs. Plot windows are saved as .plot files in the Project Explorer view.

Here is an illustration of these plotting terms:

In this image, we see two canvases - the one on the left and the one on the right. Each of these canvases has one trace on it - the left canvas has a red trace and the right canvas has a green trace. All of this is rendered onto one plot window. The plot window is named "Cantilever Example Plot."

Plot Dataset Terminology

In Chartreuse, plot datasets can either be interpreted as one-dimensional or two-dimensional.

• One-dimensional dataset A plot dataset is said to be one-dimensional if it only contains column-style data. That is, it must only have data labels along the top of the dataset, and the column below that label represents a one-dimensional data series for that label. For example, a Dakota tabular data file can be described in Chartreuse terminology as a one-dimensional dataset.
• Two-dimensional dataset A plot dataset is said to be two-dimensional if it contains grid-style data. That is, data labels exist along two axes, and a data point in the grid is described by the intersection of two data labels. For example, a Dakota correlation matrix can be described in Chartreuse terminology as a two-dimensional dataset.

Plot Trace Creator - Basic Usage

If you want to plot a single trace quickly and easily, use the Plot Trace Creator. You can access it by selecting a file with plottable data. Chartreuse recognizes the following file formats as sources of plottable data:

• CSV files
• Dakota tabular data files (with some caveats)
• HDF5 files

Right-click the chosen file in the Project Explorer view and select New > Plot trace from this file to access the Plot Trace Creator dialog.

Let’s talk about the Plot Window Settings options group (the bottom half of this dialog). These are options global to your entire plot window:

• Plot Title The title displayed at the top of your plot.
• Font The font used by your graph.
• Plot Size The size of your plot in pixels. This is useful to customize if you are interested in exporting your plot at a large size (for instance, for a publication), or if the default view of your data is too crowded. By default, the plotting library will scale your plot to fit within the size of the editor in your perspective.

Along the top of this dialog is the Plot Data group. There is a single button in this group (the file-and-folder icon button, hereafter referred to as the "Get Data" button) that allows you to select different data sets from the Dakota GUI for the purpose of plotting.

Getting Plot Data from a CSV File

Chartreuse supports CSV files as a possible data source. When you select the Get Data button from the Plot Trace Creator dialog, you will be presented with the following dialog:

This dialog allows you to choose a different CSV file if you wish (it will be default populated with the CSV file you used earlier when you selected "Plot trace from this file" from the Chartreuse context menu). You can also choose whether to interpret the first row of the CSV file as a header row or not.

Getting Plot Data from a Dakota Tabular Data File

Dakota's tabular data output file is another possible provider of plottable data. However, it is not sufficient to create a Chartreuse plot, because Dakota tabular data does not designate which columns belong to variables, and which columns belong to responses. Therefore, when creating a Chartreuse plot from Dakota tabular data, we also need to provide the original Dakota input file, which does have variable and response information in it.

When you select the Get Data button from the Plot Trace Creator dialog, the following dialog will display:

You must satisfy this two-file requirement by selecting the original Dakota input file in the left tree view, and the tabular data file in the right tree view.

In addition, tabular data files can contain more than one set of tabular data, so you will need to expand the tabular data file by clicking on the little arrow to its left, and choosing the proper tabular data set contained within.

Note: If you accidentally select the wrong Dakota input file or the wrong tabular data file, this dialog will warn you of a mismatch between the variable and response lists.

Pro Tip: Before you opened the Plot Trace Creator dialog, if you selected a Dakota tabular data file, right-clicked, and chose "Chartreuse > New plot trace from this file," AND you only have one Dakota study in your project, the Select Plot Data dialog will automatically deduce the connection between the tabular data file and your original Dakota study for you.

Click OK to return to the Plot Trace Creator. Note that the tabular data set we selected in the previous dialog, "Tabular Data Set 1", is now in the Plot Data group.

Getting Plot Data from a Dakota HDF5 Database File

This section discusses how to plot data from a Dakota-generated HDF5 file. For plotting data out of a general HDF5 file, see the next section.

Selecting a plottable dataset from a Dakota HDF5 database file requires some rudimentary knowledge of Dakota's HDF5 layout. It is recommended that you refer to these reference manual sections first:

• General information about Dakota's HDF5 Output
• Accessing HDF5 Data in the Dakota GUI

Suppose you have right-clicked a .h5 file created by Dakota, and then selected "Chartreuse > New plot trace from this file." If you click on the Get Data button from the Plot Trace Creator dialog, you will be presented with the following data selection dialog:

• H5 File This file selection control allows you to choose the .h5 file you want to search. If you right-clicked on an .h5 file before entering the Plot Trace Creator dialog, this field will already be populated for you.
• HDF5 Dataset Tree The tree on the right displays the hierarchical data contained within your selected HDF5 file. You can use this tree to manually locate the dataset you want to plot.
• HDF5 Search Group If you don't want to manually locate the dataset, use the controls in this group to automatically locate specific datasets.
  • Method This dropdown provides a list of method IDs contained in the .h5 file. If your Dakota study only had one method block / one method ID, you can ignore this dropdown.
  • Model This dropdown provides a list of model IDs contained in the .h5 file. If your Dakota study only had one model block / one model ID, you can ignore this dropdown.
  • HDF5 Target Object Perhaps the most useful dropdown in the search group, this dropdown provides you with a list of known, plottable Dakota HDF5 datasets. There are only a handful of specific HDF5 datasets that are recognized by Dakota GUI today. But with each release, we are working to support more and more of these datasets.
  • Discrete State Set Variable The only time you would fill this field out is if you are creating a plot with categorical axes. Categorical axes on a scatter plot imply data that comes from a Dakota discrete state set. Therefore, you would put the name of the discrete state set containing the categorical axes in this field.

Note: If you select a dataset that doesn't exist, or is not plottable, this dialog will provide a warning.

Getting Plot Data from a General HDF5 Database File

The previous section describes a plot data dialog that is highly tailored to Dakota-specific concepts and datasets. If you have imported a non-Dakota .h5 file into your workspace, you can still create plots from it using Chartreuse.

First, go to the Chartreuse section of the Preferences window and change the Default Plot Data Provider to "HDF5 Plot Data Provider", not "Dakota/HDF5 Plot Data Provider."

Right-click your .h5 file and choose "Chartreuse > New plot trace from this file."

Click on the Get Data button, and you'll be presented with this dialog.

You are responsible for traversing the HDF5 database and locating the dataset you want to plot.

You also must tell Chartreuse how you want the dataset to be interpreted.

Click OK when you are done.

Note: HDF5 dimension scales are not currently supported for plain, non-Dakota HDF5 database files. Therefore, if your database contains column labels, they will not be displayed - instead, you must refer to your data in the Plot Trace Creator dialog by index (i.e. "Column 0", "Column 1", etc.)

Plot Trace Creator - Bar Charts

Note: Bar charts require two-dimensional plot datasets.

Use this option to create standard bar charts.

• Data The data element to use as the dependent variable for your bar chart. Independent variables are gathered from the opposite side of the two-dimensional dataset.
• Orientation Determines whether the bars are oriented vertically or horizontally.
• Bar Color The color of each bar.

Plot Trace Creator - Bar Charts (Categorical Data)

Note: Bar charts require two-dimensional plot datasets.

Use this option if you want the independent axis of your bar chart to come from a categorical dataset with discrete state entries. This type of plot is best used in conjunction with Dakota-generated HDF5 files, with the source of the discrete state set provided.

• Data The data element to use as the dependent variable for your bar chart. Independent variables are gathered from the specified discrete data set.
• Orientation Determines whether the bars are oriented vertically or horizontally.
• Bar Color The color of each bar.

Plot Trace Creator - Box Plots

Use this option to create a standard box-and-whisker plot.

• Data The data element to use for your box plot. Box plots are one-dimensional, so only one Dakota variable or response needs to be provided.
• Trace Name A custom label for your data trace. The trace label does not get displayed as part of the final plot, but it's a good idea to give your traces memorable names in case you need to find them again later.
• Orientation Determines whether the box plot is oriented vertically or horizontally.
• Trace Color The color of the box plot.

Plot Trace Creator - Contour Plots

Use this option to create a standard contour plot.

• X/Y/Z Data for the X, Y, and Z dimensions of the contour plot. You can choose any combination of Dakota parameters and responses.
• Trace Name A custom label for your data trace. The trace label does not get displayed as part of the final plot, but it's a good idea to give your traces memorable names in case you need to find them again later.
• Contour Lower Edge The starting contour value. No contours will be drawn below this value.
• Contour Upper Edge The ending contour value. No contours will be drawn above this value.
• Contour Division Range The step size between contours.
• Contour Coloring Style The style of coloring for the contour plot.
  • Fill Coloring is done evenly between each contour level.
  • Heatmap A heatmap gradient coloring is applied between each contour level.
  • Lines Coloring is done on the contour lines.
  • None No coloring is applied on this trace.
• Color Scale Settings Specify a color scale for your contour plot. Use the wizard icon to choose from a library of recommended color scales.
• Show Color Scale Legend Determines whether or not a color scale legend is displayed on the right-hand side of your plot.

Plot Trace Creator - Heat Maps

Use this option to create a standard heatmap.

• X/Y/Z Data for the X, Y, and Z dimensions of the contour plot. You can choose any combination of Dakota parameters and responses.
• Trace Name A custom label for your data trace. The trace label does not get displayed as part of the final plot, but it's a good idea to give your traces memorable names in case you need to find them again later.
• Color Scale Settings Specify a color scale for your heatmap. Use the wizard icon to choose from a library of recommended color scales.
• Show Color Scale Legend Determines whether or not a color scale legend is displayed on the right-hand side of your plot.
• Show Value Labels in Table If selected, the value corresponding to each square of the heatmap will be displayed in the center of that square.
• Displayed Decimal Precision If values are being displayed in each square of the heatmap, you can also specify an integer value that will determine the number of decimal places to display for each value.

Plot Trace Creator - Heat Maps (Categorical Axes)

Note: Heatmaps with categorical axes require plot datasets that have two data label axes - one for each side of the heatmap.

Use this option to create a heatmap that has categorical axes rather than numerical axes.

• Orientation Choose the orientation for your heat map. You can place variables on the horizontal axis and responses on the vertical axis ("Variable-Response"), or vice versa ("Response-Variable"). You can also mirror variables against themselves ("Variable-Variable"), responses against themselves ("Response-Response"), or all variables and responses against themselves ("All-All").
• Color Scale Settings Specify a color scale for your heatmap. Use the wizard icon to choose from a library of recommended color scales.
• Show Color Scale Legend Determines whether or not a color scale legend is displayed on the right-hand side of your plot.
• Show Value Labels in Table If selected, the value corresponding to each square of the heatmap will be displayed in the center of that square.
• Displayed Decimal Precision If values are being displayed in each square of the heatmap, you can also specify an integer value that will determine the number of decimal places to display for each value.
• Parameter and Response Filters Filter which parameters and responses will be shown on the final heatmap.

Plot Trace Creator - Histograms

Use this option to create a histogram.

• Data The data element to use for your histogram. Histograms are one-dimensional, so only one Dakota variable or response needs to be provided.
• Trace Name A custom label for your data trace. The trace label does not get displayed as part of the final plot, but it's a good idea to give your traces memorable names in case you need to find them again later.
• Number of Bins Specify a recommended number of bins for your histogram. Most underlying plotting libraries use a smart binning algorithm based on the data, so this value is just a guideline.
• Histogram Type The type of your histogram.
  • Count The span of each bar corresponds to the number of occurrences (i.e. the number of data points lying inside the bins).
  • Percent The span of each bar corresponds to the percentage / fraction of occurrences with respect to the total number of sample points (here, the sum of all bin HEIGHTS equals 100% / 1).
  • Probability The span of each bar corresponds to the percentage / fraction of occurrences with respect to the total number of sample points (here, the sum of all bin HEIGHTS equals 100% / 1).
  • Density The span of each bar corresponds to the number of occurrences in a bin divided by the size of the bin interval (here, the sum of all bin AREAS equals the total number of sample points).
  • Probability Density The area of each bar corresponds to the probability that an event will fall into the corresponding bin (here, the sum of all bin AREAS equals 1).
• Display as Cumulative This option will enable a cumulative histogram, where values are added as the histogram proceeds.
• Orientation Whether the histogram is oriented vertically (histogram bars are pointing up and down) or horizontally (histogram bars are pointing left and right)
• Bar Color The color of the histogram bars.

Plot Trace Creator - Parallel Coordinates Plots

Use this option to create a parallel coordinates plot.

• Trace Name A custom label for your data trace. The trace label does not get displayed as part of the final plot, but it's a good idea to give your traces memorable names in case you need to find them again later.
• Parameter and Response Filters By default, all data in your dataset is used in the parallel coordinates plot, but you can manually filter which parameters and responses will be shown on the final plot.
• Color Scale Settings As the dialog text says, use the color scale to specify colors for different ranges of iterations. For example, in a Dakota centered parameter study, you may specify a different color for each variable during the range of iterations in which it was being individually varied.
• Show Color Scale Legend Determines whether or not a color scale legend is displayed on the right-hand side of your plot.

Plot Trace Creator - Scatter Plots

Use this option to create a standard 2D scatter plot.

• X/Y Data for the X and Y dimensions of your scatter plot. You can choose any combination of Dakota parameters and responses.
  • Note: "Time [Time]" is a special option in the X/Y dropdowns which allows you to plot using monotonically increasing timesteps.
• Trace Name A custom label for your data trace. The trace label does not get displayed as part of the final plot, but it's a good idea to give your traces memorable names in case you need to find them again later.
• Trace Color The color of your plot points.
• Dot Type The shape of your plot points. The default plotting library (plotly.js) provides dozens of shapes you can use.
• Connect Dots Select this checkbox to connect the points of your trace with a line.
• Linear Regression Select this checkbox to draw a linear regression line through your data set.
• Trim No Change Omits areas of your data where nothing changed (either along the X or Y axis). This is a useful feature if you want your trace to only show areas of change.
• Normalize Data Scale your data to fit between 0 and 1 (either along the X or Y axis)

Plot Trace Creator - Scatter Plots (Categorical Axes)

Note: Scatter plots with categorical data require plot datasets that have two data label axes.

Use this option if you want the independent axis of your scatter plot to come from a categorical dataset with discrete state entries. This type of plot is best used in conjunction with Dakota-generated HDF5 files, with the source of the discrete state set provided.

• Data The data element to use as the dependent variable for your bar chart. Independent variables are gathered from the specified discrete data set.
• Orientation Determines whether the discrete state set is oriented along the vertical or horizontal axis.
• Connect Dots Whether or not the discrete states of your scatter plot should appear as a connected line. Depending on what you are trying to convey with the scatter plot, a connecting line could be misleading, since there may be no interpolated data between the discrete states.
• Trace Color The color of your scatter plot.

Plot Trace Creator - Scatter 3D Plot

Use this option to create a standard 3D scatter plot.

• X/Y/Z Data for the X, Y, and Z dimensions of your scatter plot. You can choose any combination of Dakota parameters and responses.
  • Note: "Time Step [Time Step]" is a special option in the X/Y dropdowns which allows you to plot using monotonically increasing timesteps.
• Trace Name A custom label for your data trace. The trace label does not get displayed as part of the final plot, but it's a good idea to give your traces memorable names in case you need to find them again later.
• Dot Type The shape of your plot points. The default plotting library (plotly.js) provides dozens of shapes you can use.
• Connect Dots Select this checkbox to connect the points of your trace with a line.
• Color Scale Settings Specify a color scale for your surface plot. Use the wizard icon to choose from a library of recommended color scales.
  • 3D scatter plots have an extra "Color Axis" field that dictates which dimension of data the color scale should be applied to. Typically, the color scale is associated with the Z axis for 3D plots (in order to better indicate a sense of depth), but you may reassign the color scale to a different axis. For example, assigning Color Axis to "Time Step [Time Step]" will present a plot where the color scale indicates the temporal order in which the points were evaluated.

Plot Trace Creator - Surface 3D Plot

Use this option to create a 3D surface plot.

• X/Y/Z Data for the X, Y, and Z dimensions of your surface curve. You can choose any combination of Dakota parameters and responses, but there must be enough data to create the curve. For instance, Dakota multidimensional parameter studies are ideal for creating surface curves, but not all Dakota studies provide enough data to determine the shape of the curve.
• Trace Name A custom label for your data trace. The trace label does not get displayed as part of the final plot, but it's a good idea to give your traces memorable names in case you need to find them again later.
• Color Scale Settings Specify a color scale for your surface plot. Use the wizard icon to choose from a library of recommended color scales.
• Show Color Scale Legend Determines whether or not a color scale legend is displayed on the right-hand side of your plot.

Editing Plot Files

From the editor area

When you are viewing a Chartreuse plot in the editor area, an action bar will be displayed along the top of the plot.

• Screenshot (the camera icon) Save your plot to an image file on disk.
• Edit plot (the pencil icon) Go back and make modifications to this plot. Clicking this will open the more sophisticated Plot Manager dialog.

From the context menu

Right-clicking a created .plot file gives you some context menu options:

• Chartreuse > Show Plot Re-render the plot in the editor area of your current perspective.
• Chartreuse > Edit Plot Go back and make modifications to this plot. Clicking this will open the more sophisticated Plot Manager dialog.

Using Color Scales in Plot Traces

Color scales allow you to customize a gradient of colors for your plot. Here is an example color scale you could specify in the Plot Trace Creator dialog:

Color scales are made up of one or more rows of colors. Each row has its own button bar for individual customization:

• Set Color (the color wheel icon) Choose a color for this row.
• Move Up Move the row upwards in the list of colors.
• Move Down Move the row downwards in the list of colors.
• Remove Delete this row.

For each row, the chosen color and the value on which the color transition resides is displayed in the text field to the left of the button bar. Note that color scales do not permit out-of-order color transition values. The values must be organized from smallest to largest to make sense for a color scale.

• Color Axis The "Color Axis" dropdown allows you to define which axis the color gradient applies to. By default, the color gradient will apply to the Z axis to accentuate depth for 3D (or implied 3D) plots. For certain types of plots (such as 3D scatter plots), you can change which axis the color scale applies to. For example, you could set this dropdown to a set of timestep values, which would create the effect of using color to represent change over time.
• Color Value Type You can specify the meaning of the color scale values using the "Color Value Type" dropdown. If "Data Values" is selected, then the value in each color scale row corresponds to a specific point in your plotted dataset. If "Relative Percentages" is selected, then the value in each color scale is normalized on a 0-100 percentage scale, where 0 is the smallest value in your dataset, and 100 is the largest value in your dataset.
• Color Scale Type You can specify a continuous color scale or a color scale with discrete transitions using the "Color Scale Type" dropdown.

Finally, there are two more buttons to explain:

• Add (the blue plus icon) Adds another row to the color scale.
• Use Color Scale Template (the wizard icon) Choose from a library of color scale templates.
  • See "Color Map Advice for Scientific Visualization" by Kenneth Moreland.

Plot Manager: Managing Multiple Traces and Canvases

The Plot Manager dialog is the big brother to the Plot Trace Creator covered in the previous section. The Plot Manager allows you to specify multiple canvases with multiple traces. You can get extremely sophisticated with this dialog, so there are a lot of options to cover.

Note: This section uses a centered parameter study of the classic Dakota "cantilever beam" example to demonstrate usage of the Plot Manager Dialog.

To open the Plot Manager dialog, either...

• Right-click on a file with plottable data and select "Chartreuse > New plot from this file."
• Right-click on an existing Chartreuse .plot file and select "Chartreuse > Edit plot."
• Click on the Edit button (i.e. the pencil icon) from a Chartreuse plot already open in the editor area.

Upon launching, this is what the Plot Manager looks like by default:

• Choose a plotting template This option allows you to populate the entire Plot Manager dialog with a chosen plotting template.
• Title The title for your plot.
• Plot Size The plot size in pixels. Leave this blank to let the plot fill the available space in the editor area.
• Font The font for your plot.
• Show Legend Display a legend for every trace plotted.

Let's say we're studying the cantilever beam example, and we want to try plotting L/mass and w/mass on two separate canvases oriented horizontally. Note the two highlighted buttons in the Canvas Viewer section - "Add Row" and "Add Column."

Click "Add Column" twice.

In the middle of this dialog, we now have a rough preview of how our canvases will look once rendered – i.e. we have two boxes that are displayed side-by-side. We can edit what will go onto each canvas by pushing the respective “Edit this canvas” button.

Click on the "Edit this canvas" button inside of the left canvas. This puts the leftmost canvas into "focus" and activates several canvas-specific controls, as you can see here:

Along the top of the Canvas Viewer, some of the buttons have been activated...

• Delete Row This deletes the row that the current canvas is in. At the moment, this would have the effect of deleting both canvases, because we only have one row.
• Delete Column This deletes the column that the current canvas is in. At the moment, this would have the effect of only deleting the leftmost canvas.
• Move Right The blue arrow button will cause the selected canvas to shift to the right. Since there are only two canvases, this has the effect of causing our two canvases to swap places. The other blue arrow buttons perform similar functionality, depending on which canvas is in focus and the overall layout of your canvases.
• Overlay Canvas Overlaying canvases is a feature that is generally not required except in very specific cases. In some situations, a user may want to have several canvases with differing axis scales placed on top of each other, rather than placing a number of traces on the same canvas, which forces all those traces to adhere to one canvas scale. A good example of this can be seen in the Iteration History plotting template.
• Clear Canvas This button will clear any data off the selected canvas, including traces and canvas properties.

In the Canvas group on the right, we have...

• X Axis Label for the canvas' X axis. Note that we can also configure the color of the axis, and specify whether or not this axis uses a log scale.
• Y Axis Label for the canvas' Y axis. Note that we can also configure the color of the axis, and specify whether or not this axis uses a log scale.
• Z Axis Label for the canvas' Z axis. Note that we can also configure the color of the axis, and specify whether or not this axis uses a log scale. (Z axis configuration is disabled for 2D plots)
• # of Axis Significant Digits The number of displayed significant digits on the canvas axes of your plot.
• Autoscale Autoscale is an option that is used with canvases that are overlaid on top of each other. When checked, an overlaid canvas will disregard the data on other stacked canvases and determine its own scale based on its own data. When unchecked, an overlaid canvas will observe data on other canvases and attempt to scale itself relative to other canvases in the canvas stack.
• Axis Lines When checked, black lines will be drawn for the axes of the plot.
• Grid Lines When checked, gray grid lines will be drawn behind the plot.
• List of Traces This area will display a list of all trace data on the canvas.
• Add/Edit/Remove These buttons pertain to traces on the canvas.

We are interested in “L/mass” and “w/mass” for this example, so we can label the axes accordingly:

• For the currently-selected canvas, put "L" in the X Axis field and "mass" in the Y Axis field.
• Next, click on the "Edit this canvas" button of the rightmost canvas to put the right canvas into focus.
• Put "w" in the X Axis field and "mass" in the Y Axis field.

We still need to add our traces to the canvas. Re-select the leftmost canvas by clicking "Edit this canvas", then click Add" in the bottom-right corner to add a trace to your canvas. This will open the Plot Trace Creator dialog, from which you can specify the details of your scatter plot.

You should now have enough information to proceed on your own with populating a trace on each canvas. Put an “L/mass” scatter plot trace onto the left canvas, then and a “w/mass” scatter plot trace onto the right canvas.

When you’re done, your Plot Manager dialog should look something like this:

Note that the canvas previews in the center of the dialog give us summary information about how many traces are on each canvas.

When you’re satisfied that you’re done, hit the Plot button to see your plot in the Plot View.

If you did something wrong, or if you’re not satisfied with the way your plot looks, you can easily go back to edit your plot.

Plot Templates - Basic Usage

The Plotting Templates dialog fills a usage gap between the Plot Trace Creator dialog and the Plot Window Manager dialog...

• The Plot Trace Creator dialog is limited to create single-trace, single-canvas plots.
• The Plot Window Manager dialog becomes increasingly time-consuming to use as you manually add more canvases and traces. For instance, creating a comprehensive view of the Dakota centered parameter study for the cantilever beam model would require 21 traces (7 variables x 3 responses), which would take a long time to manually input, not to mention the fact that doing so is error-prone, requiring lots of trial and error to get the plot exactly the way you want it.

The Dakota GUI includes templates that facilitate easy access to the most common types of plots that you would need as part of a study performed by Dakota. To access the Plotting Templates dialog, either...

• From the Plot Window Manager dialog, click on the "Choose a plotting template" button in the top-left corner.
• Right-click on a file containing plottable data and select Chartreuse > New plot template from this file.

Each available plotting template is described in detail in the following sections.

Plot Templates - Bar Chart Variable Comparison

• Plot Data Select a plottable data source, as in the Plot Trace Creator dialog
• Choose canvas orientation Determine whether generated canvases will be added horizontally or vertically.
• Choose bar orientation Determine whether bars in the bar charts will be oriented vertically or horizontally.
• Create canvas by Create one canvas per response or one canvas per variable. If "Response" is chosen, variables will act as the independent variables of each canvas. If "Variable" is chosen, responses will act as the independent variables of each canvas. If "All" is chosen, a canvas will be created for each variable and each response, and each variable/response relationship will be represented on each canvas. "All" is not recommended unless you have a small number of variables and responses.
• Autoscale individual canvases If selected, "autoscale" rules are applied to each canvas, so that each canvas ignores the scale of every other canvas.
• Sort bars by size (tornado) Organizes the bars of each bar chart by absolute value magnitude, to create the visual image of a tornado.
• Filter Parameters and Responses Use these options to filter parameters and responses from being included in the final plot.

Plot Templates - Centered Parameter Study

• Plot Data Select a plottable data source, as in the Plot Trace Creator dialog
• Choose an orientation Determine whether generated canvases will be added horizontally or vertically.
• Filter Parameters and Responses Use these options to filter parameters and responses from being included in the final plot.

Plot Templates - Correlation Coefficient Table

Note: The color scale for your correlation coefficients table is not controlled by this template. Instead, this is controlled by the global Chartreuse preference for default color scale.

• Plot Data Select a plottable data source, as in the Plot Trace Creator dialog
• Choose an orientation Determine whether the correlation coefficients table should put responses on the vertical axis and variables on the horizontal axis, or vice versa. You can also choose "Mirror All Variables" to put both variables and responses on both the horizontal and vertical axes.
• Filter Parameters and Responses Use these options to filter parameters and responses from being included in the final plot.

Plot Templates - Iteration History

• Plot Data Select a plottable data source, as in the Plot Trace Creator dialog
• Choose an orientation Determine whether generated canvases will be added horizontally or vertically.
• Overlay response canvases Group responses onto a single stack of overlaid canvases.
• Ignore unchanging data If a variable's value does not change over the course of the study, it does not get its own canvas in the final plot.
• Filter Parameters and Responses Use these options to filter parameters and responses from being included in the final plot.

Plot Templates - Scatter Plot Matrix

• Plot Data Select a plottable data source, as in the Plot Trace Creator dialog
• Choose a type of scatter plot
  • Subset A to Subset B Allows you to put variables along one axis and responses along the other axis.
  • All to All Allows you to put all variables and all responses on each axis.

• X Axis Specify the data for the X axis (only available for the "Subset A to Subset B" option)
• Y Axis Specify the data for the Y axis (only available for the "Subset A to Subset B" option)
• Axis Specify the data for both axes (only available for the "All to All" option)
• Draw Linear Regression Draw a linear regression line on each generated canvas.
• Blank Diagonal Do not put data along the diagonal, where a variable or response will be mapped against itself (only available for the "All to All" option)
• Draw Lower Half Only Do not put mirrored data on the upper triangle of the grid of canvases (only available for the "All to All" option)
• Text on Outer Axes Only Do not repeat canvas text for canvases that are "inside" the grid. If unchecked, this will lead to redundant canvas labels throughout your final plot.

Plot Templates - Sobol Indices

Pro Tip: If you select a plottable data file that contains Sobol index data, the Plotting Templates dialog will automatically retrieve this data for you.

• Plot Data Select a plottable data source, as in the Plot Trace Creator dialog
• Effects Type For Sobol indices, we can plot the main effects dataset, the total effects dataset, or both.
• One canvas per effects type Split main effects and total effects onto separate canvases.
• One canvas per response type Create a canvas for each response.
• Sort Sort the bars by absolute value magnitude (i.e. a tornado plot).
• Filter Parameters and Responses Use these options to filter parameters and responses from being included in the final plot.