class:: MantissaMask
summary:: Reduce precision.
categories::  UGens>Filters>Nonlinear


Description::

Masks off bits in the mantissa of the floating point sample value.
This introduces a quantization noise, but is less severe than linearly
quantizing the signal.


classmethods::

method::ar, kr

argument::in
The input signal.

argument::bits

The number of mantissa bits to preserve. A number from 0 to 23.


argument::mul
Output will be multiplied by this value.

argument::add
This value will be added to the output.

Examples::

code::

// preserve only 3 bits of mantissa.
{ MantissaMask.ar(SinOsc.ar(SinOsc.kr(0.2,0,400,500), 0, 0.4), 3) }.play

// the original
{ SinOsc.ar(SinOsc.kr(0.2,0,400,500), 0, 0.4) }.play

// the difference.
(
{
	var in;
	in = SinOsc.ar(SinOsc.kr(0.2,0,400,500), 0, 0.4);
	Out.ar(0, in - MantissaMask.ar(in, 3));
}.play
)


// preserve 7 bits of mantissa.
// This makes the lower 16 bits of the floating point number become zero.
{ MantissaMask.ar(SinOsc.ar(SinOsc.kr(0.2,0,400,500), 0, 0.4), 7) }.play

// the original
{ SinOsc.ar(SinOsc.kr(0.2,0,400,500), 0, 0.4) }.play

// the difference.
(
{
	var in;
	in = SinOsc.ar(SinOsc.kr(0.2,0,400,500), 0, 0.4);
	Out.ar(0, in - MantissaMask.ar(in, 7));
}.play
)

::