Tendency

Extension

Creates a new Tendency object. Any of the values below may be a SimpleNumber, an Env or a Function. If a Function is used, the current time will be passed into the Function.

Arguments:

Returns:

Basic usage of Tendency.

Arguments:

Returns:

Discussion:

Uniform distribution. Convenience method for .at(time, \uniform).a = Tendency.new(1.0, 0.0); z = 500.collect({arg i; a.at(i)}); z.plot(discrete: true);

Lowpass distribution. Convenience method for .at(time, \lpRand).a = Tendency.new(1.0, 0.0); z = 500.collect({arg i; a.at(i, \lpRand)}); z.plot(discrete: true);

Highpass distribution. Convenience method for .at(time, \hpRand).a = Tendency.new(1.0, 0.0); z = 500.collect({arg i; a.at(i, \hpRand)}); z.plot(discrete: true);

Mean (bandpass) distribution. Convenience method for .at(time, \meanRand).a = Tendency.new(1.0, 0.0); z = 500.collect({arg i; a.at(i, \meanRand)}); z.plot(discrete: true);

Exponential distribution. Convenience method for .at(time, \expRand). Note that we need to avoid 0.0.a = Tendency.new(0.0001, 1.0); z = 500.collect({arg i; a.at(i, \expRand)}); z.plot(discrete: true);

Exponential distribution. Convenience method for .at(time, \exponential). 'parX' parameter control on density.a = Tendency.new(0.5); z = 500.collect({arg i; a.at(i, \exponential)}); z.plot(discrete: true, minval: 0, maxval: 5); a = Tendency.new(1); z = 500.collect({arg i; a.at(i, \exponential)}); z.plot(discrete: true, minval: 0, maxval: 5); a = Tendency.new(2); z = 500.collect({arg i; a.at(i, \exponential)}); z.plot(discrete: true, minval: 0, maxval: 5);

Gamma distribution. Convenience method for .at(time, \gamma). 'parX' controls mode or peak.a = Tendency.new(2.0); z = 500.collect({arg i; a.at(i, \gamma)}); z.plot(discrete: true, minval: 0, maxval: 12); a = Tendency.new(4.0); z = 500.collect({arg i; a.at(i, \gamma)}); z.plot(discrete: true, minval: 0, maxval: 12); a = Tendency.new(8.0); z = 500.collect({arg i; a.at(i, \gamma)}); z.plot(discrete: true, minval: 0, maxval: 12);

Laplace distribution. Convenience method for .at(time, \laplace). 'parX' value controls dispersion.a = Tendency.new(0.01); z = 500.collect({arg i; a.at(i, \laplace)}); z.plot(discrete: true, minval: -3, maxval: 3); a = Tendency.new(0.1, 0); z = 500.collect({arg i; a.at(i, \laplace)}); z.plot(discrete: true, minval: -3, maxval: 3); a = Tendency.new(1.0, 0); z = 500.collect({arg i; a.at(i, \laplace)}); z.plot(discrete: true, minval: -3, maxval: 3);

Special case of laplace distribution, creates a gap in the centerv (anti-laplace). Convenience method for .at(time, \alaplace). 'parX' value controls dispersion.a = Tendency.new(0.01); z = 500.collect({arg i; a.at(i, \alaplace)}); z.plot(discrete: true, minval: -3, maxval: 3); a = Tendency.new(0.1); z = 500.collect({arg i; a.at(i, \alaplace)}); z.plot(discrete: true, minval: -3, maxval: 3); a = Tendency.new(1.0, 0); z = 500.collect({arg i; a.at(i, \alaplace)}); z.plot(discrete: true, minval: -3, maxval: 3);

Hyperbolic cosine distribution. Convenience method for .at(time, \hcos). 'parX' value controls dispersion.a = Tendency.new(0.01); z = 500.collect({arg i; a.at(i, \hcos)}); z.plot(discrete: true, minval: -3, maxval: 3); a = Tendency.new(0.1); z = 500.collect({arg i; a.at(i, \hcos)}); z.plot(discrete: true, minval: -3, maxval: 3); a = Tendency.new(1.0); z = 500.collect({arg i; a.at(i, \hcos)}); z.plot(discrete: true, minval: -3, maxval: 3);

Logistic distribution. Convenience method for .at(time, \logistic). 'parX' value controls dispersion.a = Tendency.new(0.01); z = 500.collect({arg i; a.at(i, \logistic)}); z.plot(discrete: true, minval: -3, maxval: 3); a = Tendency.new(0.1); z = 500.collect({arg i; a.at(i, \logistic)}); z.plot(discrete: true, minval: -3, maxval: 3); a = Tendency.new(1.0); z = 500.collect({arg i; a.at(i, \logistic)}); z.plot(discrete: true, minval: -3, maxval: 3);

Poisson distribution. Convenience method for .at(time, \poisson). 'parX' value is the mean of the distribution. This distribution has a discrete histogram (not continuous). It returns always integer values.a = Tendency.new(4); z = 500.collect({arg i; a.at(i, \poisson)}); z.plot(discrete: true); z.histo.plot(discrete: true, bounds: Rect(0,0,400,300), name: "histogram"); a = Tendency.new(6); z = 500.collect({arg i; a.at(i, \poisson)}); z.plot(discrete: true); z.histo.plot(discrete: true, bounds: Rect(0,0,400,300), name: "histogram");

Arcsin distribution. Convenience method for .at(time, \arcsin). 'parX' value controls dispersion (spread). Output range is between 0 and this value. Histogram shape is similar to beta with parA=parB=0.5.a = Tendency.new(0.01); z = 500.collect({arg i; a.at(i, \arcsin)}); z.maxItem; z.plot(discrete: true, minval: 0, maxval: 10); z.histo.plot(discrete: true, bounds: Rect(0,0,400,300), name: "histogram"); a = Tendency.new(0.1); z = 500.collect({arg i; a.at(i, \arcsin)}); z.maxItem; z.plot(discrete: true, minval: 0, maxval: 1); a = Tendency.new(1.0); z = 500.collect({arg i; a.at(i, \arcsin)}); z.maxItem; z.plot(discrete: true, minval: 0, maxval: 1)

Beta distribution. Convenience method for .at(time, \betaRand). This distribution takes two extra parameters parA and parB to describe where the likelihood that a random value will occur near parX (parA) or parY (parB).// parA = parB = 0.5: histogram has symmetrical shape a = Tendency.new(1.0, 0.0, 0.5, 0.5); z = 500.collect({arg i; a.at(i, \betaRand)}); z.plot(discrete: true); z.histo.plot(discrete: true, bounds: Rect(0,0,400,300), name: "histogram"); // parA = 0.5, parB = 0.25: histogram lopsided towards parX end a = Tendency.new(1.0, 0.0, 0.5, 0.25); z = 500.collect({arg i; a.at(i, \betaRand)}); z.plot(discrete: true); z.histo.plot(discrete: true, bounds: Rect(0,0,400,300), name: "histogram"); // parA = 0.25, parB = 0.5: histogram lopsided towards low end a = Tendency.new(1.0, 0.0, 0.25, 0.5); z = 500.collect({arg i; a.at(i, \betaRand)}); z.plot(discrete: true); z.histo.plot(discrete: true, bounds: Rect(0,0,400,300), name: "histogram");

Cauchy distribution. Convenience method for .at(time, \cauchy). This distribution has a symmetrical histogram around 0. For this implementation 'parX' controls its dispersion. If parA = 1, only positive values are returned.// small dispersion around 0.0 a = Tendency.new(0.01); z = 500.collect({arg i; a.at(i, \cauchy)}); z.plot(discrete: true, minval: -3, maxval: 3); // larger dispersion around 0.0 a = Tendency.new(0.1); z = 500.collect({arg i; a.at(i, \cauchy)}); z.plot(discrete: true, minval: -3, maxval: 3); // even larger dispersion around 0.0, return only positive half a = Tendency.new(1.0, parA: 1); z = 500.collect({arg i; a.at(i, \cauchy)}); z.plot(discrete: true, minval: -3, maxval: 3);

Gaussian distribution. Convenience method for .at(time, \gauss). This distribution has a bell-shaped symmetrical histogram. 'parX' is the deviation or width of the bell.// small deviation a = Tendency.new(0.01); z = 500.collect({arg i; a.at(i, \gauss)}); z.plot(discrete: true, minval: -3, maxval: 3); // larger deviation a = Tendency.new(0.1); z = 500.collect({arg i; a.at(i, \gauss)}); z.plot(discrete: true, minval: -3, maxval: 3); // even larger deviation a = Tendency.new(1.0); z = 500.collect({arg i; a.at(i, \gauss)}); z.plot(discrete: true, minval: -3, maxval: 3);

Use this Tendency like a Stream.( SynthDef(\tendency, {arg freq; Out.ar(0, SinOsc.ar(freq, 0, XLine.kr(0.1, 0.0001, 1, doneAction: 2))); }).send(s); ) ( Routine.run({ g = Tendency.new(1760.0, 440.0, Env([0.01, 2.0], [10], \sin), Env([0.01, 2.0], [10], \sin)).asStream(\betaRand); t = Main.elapsedTime; while({ Synth(\tendency, [\freq, g.next]); 0.02.wait; (Main.elapsedTime - t) < 10; }) }) )

Returns:

Returns:

Returns:

Arguments:

Returns:

Arguments:

Returns: