The KST Handbook

The simplest input file format is the ASCII text file. These files are usually human-readable and can be created by hand if desired. The following is an example of an ASCII input file.

112.5 3776 428
187.5 5380 429
262.5 5245 345
337.5 2942 184
412.5 1861 119
487.5 2424 138
567.5 2520 162
637.5 1868 144
712.5 1736 211
787.5 1736 211
862.5 2172 292
937.5 1174 377
1000.5 499 623

Each column of this file represents a vector in KST. Columns are simply separated by spaces, and rows are separated by carriage returns.

To create a new data object from the Data Manager, click on one of the buttons listed under New:. A new window will be displayed, allowing options and settings for the data object to be set.

Since you are creating a new data object, you may enter a unique name to identify the object. The default name can be used as well (if it is unique).

The settings for all plottable data objects have two common sections—Curve Appearance and Curve Placement. These sections are described below. For data-specific settings, see Data Types.

To edit an existing data object, simply highlight it in the Data Manager window and click the Edit button. The settings window for the selected object will be displayed. This window is identical to the one displayed when creating new data objects, so refer to Creating New Data Objects for more information on the settings and options.

To delete a data object, highlight it in the Data Manager window and click the Delete button. Note that the entry in the # Used column for an object must be 0 before the object can be deleted. The # Used column indicates the number of times that a particular data object and its children (if any) are used by either other data object or by plots. Listed below are some consequences of this restriction to keep in mind when attempting to delete data objects.

After a sequence of deletions and removals of curves from plots, you may find that there are numerous unused data objects displayed in the Data Manager. To quickly remove these objects, you can click the Purge button.

Vectors are one of the most often-used data objects in KST. As their name implies, vectors are simply ordered lists of numbers. Most often they contain contain the x or y coordinates of a set of data points.

The following is a screenshot of the window displayed when creating or editing vectors. Explanations of the settings are listed below.

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Curves are primarily used to create plottable objects from vectors. Curves are created from two vectors - an “X axis vector” and a “Y axis vector”, that presumably provide a set of data points. Thus, a curve can be thought of as a set of data points and the lines that connect them.

The following is a screenshot of the window displayed when creating or editing curves. Explanations of the settings are listed below.

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An equation data object consists of a mathematical expression and an independent variable. The expression is built using a combination of scalars, vectors, and operators, and usually represents the values of the dependent variable. The independent variable can be an existing vector or it can be generated when the equation object is created or edited. Since an equation ultimately consists of a set of data points, an equation object is plottable.

The following is a screenshot of the window displayed when creating or editing equations. Explanations of the settings are listed below.

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A histogram data object simply represents a histogram of a particular vector. Histogram objects are plottable.

The following is a screenshot of the window displayed when creating or editing histograms. Explanations of the settings are listed below.

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A power spectrum data object represents the power spectrum of a vector.

The following is a screenshot of the window displayed when creating or editing power spectra. Explanations of the settings are listed below.

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A plugin data object represents a KST plugin. All plugins have a common format, and show up as type “Plugin” in the Data Manager. For more information about plugins, please see Plugins and Filters

An event data object represents an even monitor in KST. An event monitor essentially keeps track of one or more vectors or scalars, and performs a specified action when a specified condition involving the vectors or scalars is true. Event monitors are usually used in conjunction with “live”, or streaming data. For example, an event monitor could be created to monitor whether or not elements of a data vector representing temperature exceed a predefined value.

The following is a screenshot of the window displayed when creating or editing events. Explanations of the settings are listed below.

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Below is a screenshot of the Content tab.

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As the name implies, the settings on this tab specify the content of the plot. The following are brief explanations of the settings.

Below is a screenshot of the Limits tab.

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The settings for the plot axes are specified on this tab. The settings are split into two sections—an x axis section and a y axis section. The settings are identical between the two sections.

Below is a screenshot of the Legend tab.

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Legend settings for the plot can be specified on this tab; most of these settings concern the appearance of the legend. The legend itself is optional.

Below is a screenshot of the Labels tab.

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Settings for the plot labels can be specified on this tab. Brief descriptions of the settings are provided below.

To create a new label using the label editor, click anywhere within the x and y axes of a plot where there is not already an existing label. The label editor dialog should appear:

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The following are explanations of the dialog box elements.

Once you are satisfied with the label settings, click Apply to apply the label settings without closing the label editor dialog. Click OK to apply the label settings and close the dialog. Alternatively, you can click Close to close the label editor dialog without applying any label settings.

To edit an existing label, click on the label in the plot. The label editor dialog should be displayed. You can delete the label from the label editor dialog by clicking the Delete button.

To move an existing label in label editor mode, simply drag the label to the desired location, anywhere within the plot.

To access the different zoom modes, choose one of XY Mouse Zoom, X Mouse Zoom, or Y Mouse Zoom from the Plots menu. The different modes are described below.

The Zoom menu can be accessed by right-clicking on a plot and selecting the Zoom submenu from the context menu. A list of zoom actions and their corresponding keyboard shortcuts will be displayed. These actions are described below.

Many of the zoom actions are best used in conjunction with the various mouse zoom modes.

To quickly change the axes of a plot to match those of another plot, right-click on the plot and select a different plot name from the Match Axis... menu. Both the x and y axes scales of the current plot will change to match those of the selected plot. Note that this does not permanently tie the axes scales together; changing the zoom on either plot will unmatch the axes scales again. To tie the axes scales of two or more plots together, use the Tied Zoom feature.

Functions for scrolling a plot are available by right-clicking a plot and selecting the Scroll submenu from the context menu. The scrolling functions and their keyboard shortcuts should be self-explanatory. Assuming non-logarithmic axis scales are used, each function scrolls the plot in the indicated direction by 0.1 of the current length of the x axis (when scrolling left or right), or 0.25 of the current length of the y axis (when scrolling up or down).

When looking at two or more related plots (for example, two curves on separate plots both dependent on the same time vector), it can sometimes be useful to zoom or scroll the plots simultaneously. This is possible using KST's tied zoom feature. To activate tied zoom, click the small square at the top-right corner of the plots you wish to tie together. The squares will turn black in colour to indicate the plots are tied, as shown below.

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Zooming and scrolling actions performed on one plot in a group of tied plots will be performed on on all the plots in the group. To remove a plot from the tied group, simply click on the small square at the top-right corner of the plot again. The square will turn white in colour to indicate the plot is not tied.

KST features special zooming and scrolling functions designed to make it easy to work with “live” data, or data that is being updated while KST is running. These features are designed to be used in conjunction with the regular zooming and scrolling functions.

While layout mode is activated, right-clicking on any plot displays a modified context menu. The selections available in this menu are listed below.

Moving and resizing plots in layout mode is analogous to moving and resizing regular windows. To move a plot, simply click anywhere on the desired plot and drag the plot. An outline of the plot will appear, indicating where the plot will be placed. You can drag plots anywhere within the current window. To resize a plot, move the mouse cursor to the any edge or corner of the plot. The cursor will change to indicate that the plot can be resized. Click and drag the outline of the plot until the plot is the desired shape and size.

There is a choice of four different MDI (Multi-Document Interface) modes available. To select a mode, choose one of the menu items from the MDI Mode... submenu of the Window menu. The selected MDI mode will take effect immediately. The sections below provide more information on each mode.

Top Level Mode

Top level mode is the mode traditionally used by other KDE applications. In this mode, the parent window (the window containing all the main menus, toolbars, and status bars) is a separate window from the plot windows. The plot windows, while still functionally tied to the main window, appear as separate windows on the desktop.

To quickly switch between plot windows in top level mode, you can click the buttons on the parent window, as shown below.

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Childframe Mode

In childframe mode, all the plot windows appear inside the main KST window and can be individually resized. Each plot window contains buttons for minimizing, maximizing, closing, and docking themselves. The main KST window also contains functions for arranging the plot windows. The following lists the functionality of the buttons located in the top right corner of each plot window:

Undocks the plot window. An undocked window is not contained within the main KST window, but is located directly on the desktop.

Tip

You can also undock or dock a window by selecting its name from the Dock/Undock submenu of the Window menu. This is also the only way to dock an undocked window.

Minimizes the plot window. Since the plot window is contained within the main KST window, clicking this button will minimize the window to a titlebar within the KST window.

Maximizes the plot window. The plot window will take up the entirety of the main KST window.

Closes the plot window.

To quickly switch between plot windows in childframe mode, you can click the buttons on the parent window, as shown below.

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To easily organize the plot windows within the main KST window, you can choose one of the tiling functions from the Tile... submenu of the Window menu. These functions are described below.

Cascade Windows

Cascades the windows starting from the top left corner, maintaining the original window sizes.

Cascade Windows

Cascades the windows starting from the top left corner, but resizes each window so that the bottom right corner of each window reaches the bottom right of the main KST window.

Expand Vertically

Resizes each window to its maximum height. The widths of the windows are not changed.

Expand Horizontally

Resizes each window to its maximum width. The heights of the windows are not changed.

Tile Non-overlapped

Arranges the windows in a tile pattern, with no windows overlapping and each window being as close to a square as possible.

Tile Overlapped

Arranges the windows in three or four stacks, depending on the number of windows and the main KST window size.

Tile Vertically

Arranges the windows in a tile pattern, with each window having maximum height and no windows overlapping.

Tab Page Mode

Tab page mode arranges each plot window on a separate tab within the main KST window. In this mode, the plot windows are not independently resizeable or moveable—they conform to the size and shape of the main KST window.

To switch between plot windows in tab page mode, you can click on the tabs corresponding to each window, as shown below.

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New windows in KST can be created in a number of ways. To create a new, empty, window, select New Window from the File menu. You will be prompted for a unique name for the new window. You can also create a new window while creating a new plottable data object. See Curve Placement for the specific details.

To delete an existing window, simply select Close from the Window menu. To delete all current windows in KST, select Close All from the Window menu.

Currently there is no facility for moving plots from one window to another.

To export or save a vector to a file, select Save Vectors to Disk... from the File menu. The vector will be saved as an ASCII data file. The first column of the file will contain an automatically generated index vector, starting a 0, while the second column of the file will contain the elements of the selected vector. See below for an example of an exported vector.

0 3776
1 5380
2 5245
3 2942
4 1861
5 2424
6 2520
7 1868
8 1736
9 1736
10 2172
11 1174
12 499

To export a KST plot to a graphics file, select Export to Graphics File... from the File menu. The following dialog box will appear.

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The following settings and options are available when saving:

Click Save Once to immediately perform a save with the specified options and settings. Click Close to close the dialog box (autosave will still be performed, if specified).

The autocorrelation plugin calculates correlation values between a series (vector) and a lagged version of itself, using lag values from floor(-(N-1)/2) to floor((N-1)/2), where N is the number of points in the data set. The time vector is not an input as it is assumed that the data is sampled at equal time intervals. The correlation value r at lag k is:

The bin plugin bins elements of a single data vector into bins of a specified size. The value of each bin is the mean of the elements belonging to the bin. For example, if the bin size is 3, and the input vector is [9,2,7,3,4,74,5,322,444,2,1], then the outputted bins would be [6,27,257]. Note that any elements remaining at the end of the input vector that do not form a complete bin (in this case, elements 2 and 1), are simply discarded.

The convolution plugin generates the convolution of one vector with another. The convolution of two functions f and g is given by:

The order of the vectors does not matter, since f*g=g*f. In addition, the vectors do not need to be of the same size, as the plugin will automatically extrapolate the smaller vector.

The deconvolution plugin generates the deconvolution of one vector with another. Deconvolution is the inverse of convolution. Given the convolved vector h and another vector g, the deconvolution f is given by:

The vectors do not need to be of the same size, as the plugin will automatically extrapolate the shorter vector. The shorter vector is assumed to be the response function g.

The crosscorrelation plugin calculates correlation values between two series (vectors) x and y, using lag values from floor(-(N-1)/2) to floor((N-1)/2), where N is the number of elements in the longer vector. The shorter vector is padded to the length of the longer vector using 0s. The time vector is not an input as it is assumed that the data is sampled at equal time intervals. The correlation value r at lag k is:

The chop plugin takes an input vector and divides it into two vectors. Every second element in the input vector is placed in one output vector, while all other elements from the input vector are placed in another output vector.

The kstfit_linear_weighted plugin performs a weighted least-squares fit to a straight line model:

The best-fit is found by minimizing the weighted sum of squared residuals:

for a and b, where w_i is the weight at index i.

The kstfit_linear_unweighted plugin is identical in function to the kstfit_linear_weighted plugin with the exception that the weight value w_i is equal to 1 for all index values i. As a result, the Weights (vector) input does not exist.

The kstfit_gradient_weighted plugin performs a weighted least-squares fit to a straight line model without a constant term:

The best-fit is found by minimizing the weighted sum of squared residuals:

for b, where w_i is the weight at index i.

The kstfit_linear_unweighted plugin is identical in function to the kstfit_gradient_weighted plugin with the exception that the weight value w_i is equal to 1 for all index values i. As a result, the Weights (vector) input does not exist.

The statistics plugin calculates statistics for a given data set. Most of the output scalars are named such that the values they represent should be apparent. Standard formulas are used to calculate the statistical values.

The kstfit_polynomial_weighted plugin performs a weighted least-squares fit to a polynomial model:

where n is the degree of the polynomial model.

The kstfit_polynomial_unweighted plugin is identical in function to the kstfit_polynomial_weighted plugin with the exception that the weight value w_i is equal to 1 for all index values i. As a result, the Weights (vector) input does not exist.

The kstfit_sinusoid_weighted plugin performs a least-squares fit to a sinusoid model:

where T is the specified period, and n=2+2H, where H is the specified number of harmonics.

The kstfit_sinusoid_unweighted plugin is identical in function to the kstfit_sinusoid_weighted plugin with the exception that the weight value w_i is equal to 1 for all index values i. As a result, the Weights (vector) input does not exist.

The kstfit_exponential_weighted plugin performs a weighted non-linear least-squares fit to an exponential model:

An initial estimate of a=1.0, =0, and b=0 is used. The plugin subsequently iterates to the solution until a precision of 1.0e-4 is reached or 500 iterations have been performed.

The kstfit_exponential_unweighted plugin is identical in function to the kstfit_exponential_weighted plugin with the exception that the weight value w_i is equal to 1 for all index values i. As a result, the Weights (vector) input does not exist.

The kstfit_gaussian_weighted plugin performs a weighted non-linear least-squares fit to a Gaussian model:

An initial estimate of a=(maximum of the y values), =(mean of the x values), and =(the midpoint of the x values) is used. The plugin subsequently iterates to the solution until a precision of 1.0e-4 is reached or 500 iterations have been performed.

The kstfit_gaussian_unweighted plugin is identical in function to the kstfit_gaussian_weighted plugin with the exception that the weight value w_i is equal to 1 for all index values i. As a result, the Weights (vector) input does not exist.

The kstfit_lorentzian_weighted plugin performs a weighted non-linear least-squares fit to a Lorentzian model:

An initial estimate of a=(maximum of the y values), x₀=(mean of the x values), and =(the midpoint of the x values) is used. The plugin subsequently iterates to the solution until a precision of 1.0e-4 is reached or 500 iterations have been performed.

The kstfit_lorentzian_unweighted plugin is identical in function to the kstfit_lorentzian_weighted plugin with the exception that the weight value w_i is equal to 1 for all index values i. As a result, the Weights (vector) input does not exist.

The kstinterp_akima plugin generates a non-rounded Akima spline interpolation for the supplied data set, using natural boundary conditions.

The kstinterp_akima_periodic plugin generates a non-rounded Akima spline interpolation for the supplied data set, using periodic boundary conditions.

The kstinterp_cspline plugin generates a cubic spline interpolation for the supplied data set, using natural boundary conditions.

The kstinterp_cspline_periodic plugin generates a cubic spline interpolation for the supplied data set, using periodic boundary conditions.

The kstinterp_linear plugin generates a linear interpolation for the supplied data set.

The kstinterp_polynomial plugin generates a polynomial interpolation for the supplied data set. The number of terms in the polynomial used is equal to the number of points in the supplied data set.

The periodogram plugin produces the periodogram of a given data set. One of two algorithms is used depending on the size of the data set—a fast algorithm is used if there are greater than 100 data points, while a slower algorithm is used if there are less than or equal to 100 data points.

The butterworth_bandpass plugin filters a set of data with a Butterworth band pass filter. The filtering is performed by calculating the Fourier transform of the data and recalculating the the frequency responses using the following formula

where f is the frequency, f_c is the low frequency cutoff, b is the bandwidth of the band to pass, and n is the order of the Butterworth filter. The inverse Fourier transform is then calculated using the new filtered frequency responses.

The butterworth_bandstop plugin filters a set of data with a Butterworth band stop filter. The filtering is performed by calculating the Fourier transform of the data and recalculating the the frequency responses using the following formula

where f is the frequency, f_c is the low frequency cutoff, b is the bandwidth of the band to stop, and n is the order of the Butterworth filter. The inverse Fourier transform is then calculated using the new filtered frequency responses.

The butterworth_highpass plugin filters a set of data with a Butterworth high pass filter. The filtering is performed by calculating the Fourier transform of the data and recalculating the the frequency responses using the following formula

where f is the frequency, f_c is the high frequency cutoff, and n is the order of the Butterworth filter. The inverse Fourier transform is then calculated using the new filtered frequency responses.

The butterworth_lowpass plugin filters a set of data with a Butterworth low pass filter. The filtering is performed by calculating the Fourier transform of the data and recalculating the the frequency responses using the following formula

where f is the frequency, f_c is the low frequency cutoff, and n is the order of the Butterworth filter. The inverse Fourier transform is then calculated using the new filtered frequency responses.

The noise addition plugin adds a Gaussian random variable to each element of the input vector. The Gaussian distribution used has a mean of 0 and the specified standard deviation. The probability density function of a Gaussian random variable is:

The toolbar menu, accessible by right-clicking anywhere on the toolbar, contains functions for modifying the layout, appearance, and behaviour of the toolbar. The three submenus available are described below.