![]() ![]() At any time, you can select a figure from this list to make it the active plot for the session.Įxample of multiple plots automatically numberedĪll Plot windows, regardless of the plot that they contain, have the same main menu and the same toolbars. The Figure Selector dropdown list in the Plot Controls window contains a list all of the plots created during an analysis session and that are under control of the window. Most of the plots that you create during an analysis session are under the control of a single Plot Controls window. The number is displayed in the Title bar of the Plot window. You can create multiple plots during an analysis session, and the plots are automatically numbered as they are created. ![]() ![]() For example, the figure below shows an Eigenvalues plot in a Plot window. Most plots created in Solo are contained in a Plot window. As you can see in the figure on the right, a selected sample has a value of approximately 660 for variable 9, but it has a value in the 1100s for variable 15. The figure shown below on the right is the plot of also the plot of the response of variable 9 for all of the data samples however, the color of the data is points is based on the response of variable 15 for all of the data samples. For example, the figure shown below on the left is the plot of the response of variable 9 for all of the data samples. You use the Color By option to implicitly superimpose the response of one variable onto the plot of another. ![]() If you clear this option, you must click Plot to manually update a plot after you make a change to it. With this option selected, a plot in a Plot window is automatically updated after you make a change to a plot. Regardless of how you open the Plot Controls window, two options are common to the window- auto-update and Color. Analysis-specific plot options are not discussed in this chapter. Note: If you open the Plot Controls window by clicking on an Analysis window toolbar button, then the options that are available on the Plot Controls window are specific to the analysis method and the plot that is generated. You can click on an active Analysis window toolbar button that is specific for a plot (for example, an active Plot Eigenvalues button ).(See Data plotting options, Data selection and editing options, Other options, and Search bar.) You can right-click on any set of data (for example, on a set of data in the Workspace Browser window or on the X calibration control after you have loaded data into the control in an Analysis window) and on the context menu that opens, click Plot to open the Plot Controls window.You can open the Plot Controls window in one of two ways: It contains an extensive number of tools for labeling, manipulating, and publishing plots that are contained in a Plot window. The Plot Controls window is the principal data visualization tool for Solo. Well, the difference I am talking/writing about is nothing very deep and complicated, it is just that while in Julia you are computing eigenvalues for a sequence/array of matrices, and you obtain a sequence of vectors of eigenvalues, in Matlab you are computing eigenvalues for a function of a single parameter, at which, lucky you, Symbolic toolbox succeeded, and you get a vector of five functions.Table of Contents | Previous | Next Plot Controls Window I may have a look at it, but I am not quite fluent with symbolics in Julia unfortunately. Therefore, rather than investigating the issue of possibly different ordering of eigenvalues in Julia compared to Matlab, you may have to think about how to compute eigenvalues of a matrix parameterized by a single (symbolic) parameter eps in Julia, which is what you did in Matlab. Strict Matlab equivalent of your Julia code is mu = 57.88e-3 Į(:,i)=eig(A) %energy levels (eigenvalues) It is rather that in Matlab you solve the problem much differently than in Julia. Strictly speaking, this is not much about ordering of eigenvalues in Matlab vs ordering of eigenvalues in Julia. I will be tweaking the parameters so ideally don’t want to have to go back in and adjust it every time the points of intersection change. So essentially I want to replicate the ordering Matlab’s eig function uses without switching the eigenvalues after the fact. The points of intersection are important and unfortunately, I lose them in the Julia code because of the ascending order. ![]()
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