histplot

The histplot function computes the histogram of an array and graphs it as a stepped line plot. The histogram of n values is automatically quantized into the square root of n bins. This function is an efficient method for plotting the histogram of a data sample without writing code to set binning parameters, compute the hstogram, and then graph the result.

This function creates a new plot window and returns a CPlotView object for the window and a CPlot object to access the plot window and plot series for further processing.

Syntax

CPlotView, CPlot = histplot( t )

CPlotView, CPlot = histplot( t, sLabelX )

CPlotView, CPlot = histplot( t, sLabelX, sLabelY )

CPlotView, CPlot = histplot( t, sLabelX, sLabelY, sCaption )

CPlotView, CPlot = histplot( t, sLabelX, sLabelY, sCaption, sWindowTitle )

t is a 1-dimensional array containing the values to plot as a histogram.

sLabelX is an optional label for the x axis.

sLabelY is an optional label for the y axis.

sCaption is an optional plot caption.

sWindowTitle is an optional plot window title.

Parameters that are nil or missing to the right adopt default values.

Return values:

CPlotView is returned as a new CPlotView object attached to the plot window. It is nil on failure.

CPlot is returned as a new CPlot object attached to the current plot series. It is nil on failure.

Remarks

This function creates a quick plot using just one line of code. The returned values of V (CPlotView object) and P (CPlot object) provide access to the plot window and plotted data. After the plot is created, the Series Attributes and Plot Attributes can be changed using methods of the V and P objects.

Only the first argument, the source array, is required. To specify a later argument, such as sWindowTitle, without specifying all intervening arguments, use nil as a placeholder for the missing arguments.

Examples

The script below plots the histogram of random Guassian deviates. The plot will show a Gaussian distribution which includes statistical noise; the greater the number of deviates, the smaller the statistical noise will appear.

x = gaussdev( 10000, 1000, 33 )	-- 10,000 deviates with mean = 1000 and sdev = 33
histplot( x )	-- plot the histogram using default labels

This script could be reduced to one line, like this:

histplot( gaussdev( 10000, 1000, 33 ) )

-- plot the histogram using default labels

Syntax

Remarks

Examples

Related Topics