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SignalProcessing

 GenerateGaussian
 generate samples of a Gaussian random signal

 Calling Sequence GenerateGaussian(n, mean, stddev)

Parameters

 n - posint, number of samples mean - realcons, mean of the Gaussian distribution stddev - realcons, standard deviation of the Gaussian distribution

Options

 • container : Array, predefined Array for holding results
 • seed : nonnegint, seed for the pseudo-random number generator

Description

 • The GenerateGaussian( n, mean, stddev, seed ) command generates n floating-point samples for a random signal with a Gaussian distribution of mean mean and with standard deviation stddev. The results are returned in an Array with datatype float[8].
 • If the container=C option is provided, then the results are put into C and C is returned. With this option, no additional memory is allocated to store the result. The container must be an Array of size n having datatype float[8].
 • If the seed option is provided, the given value is used as the seed for the pseudo-random number generator.

 • The SignalProcessing[GenerateGaussian] command is thread-safe as of Maple 17.

Examples

 > with( SignalProcessing ):
 > GenerateGaussian( 10, 0, 0.3 );
 $\left[\begin{array}{cccccccccc}{0.0495371873412778}& {-0.0661056974613580}& {0.354875385184700}& {-1.01149036815707}& {0.288902907818719}& {-0.0812277011513979}& {0.587902082619430}& {0.384853190715909}& {0.0884179640720818}& {-0.105533578711073}\end{array}\right]$ (1)
 > c := Array( 1 .. 10, 'datatype' = 'float'[ 8 ], 'order' = 'C_order' ):
 > GenerateGaussian( 10, 0, 0.3, 'container' = c );
 $\left[\begin{array}{cccccccccc}{0.193439942966889}& {0.432921279364537}& {-0.00923099297881528}& {-0.0525675926804104}& {-0.283722360626314}& {-0.254513167668317}& {-0.278375701180696}& {0.0309625011601372}& {-0.262806836671325}& {0.350352787464030}\end{array}\right]$ (2)
 > c;
 $\left[\begin{array}{cccccccccc}{0.193439942966889}& {0.432921279364537}& {-0.00923099297881528}& {-0.0525675926804104}& {-0.283722360626314}& {-0.254513167668317}& {-0.278375701180696}& {0.0309625011601372}& {-0.262806836671325}& {0.350352787464030}\end{array}\right]$ (3)
 > SignalPlot( c );

Compatibility

 • The SignalProcessing[GenerateGaussian] command was introduced in Maple 17.
 • For more information on Maple 17 changes, see Updates in Maple 17.