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<h1 id="reducemapslib">reducemaps.lib</h1>
<p>A library providing reduce/map operations in Faust. Its official prefix is
<code>rm</code>. The basic idea behind <em>reduce</em> operations is to combine several values
into a single one by repeatedly applying a binary operation. A typical
example is finding the maximum of a set of values by repeatedly applying the
binary operation <code>max</code>.</p>
<p>In this reducemaps library, you'll find two types of <em>reduce</em>, depending on
whether you want to reduce n consecutive samples of the same signal or a set
of n parallel signals.</p>
<h4 id="references">References</h4>
<ul>
<li><a href="https://github.com/grame-cncm/faustlibraries/blob/master/reducemaps.lib">https://github.com/grame-cncm/faustlibraries/blob/master/reducemaps.lib</a></li>
</ul>
<hr />
<h3 id="rmparreduce"><code>(rm.)parReduce</code></h3>
<p><code>parReduce(op,N)</code> combines a set of <code>N</code> parallel signals into a single one
using a binary operation <code>op</code>.</p>
<p>With <code>parReduce</code>, this reduction process simultaneously occurs on each half
of the incoming signals. In other words, <code>parReduce(max,256)</code> is equivalent
to <code>parReduce(max,128),parReduce(max,128) : max</code>.</p>
<p>To be used with <code>parReduce</code>, binary operation <code>op</code> must be associative.
Additionally, the concept of a binary operation extends to operations
that have <code>2*n</code> inputs and <code>n</code> outputs. For example, complex signals can be
simulated using two signals for the real and imaginary parts. In
such case, a binary operation would have 4 inputs and 2 outputs.</p>
<p>Please note also that <code>parReduce</code> is faster than <code>topReduce</code> or <code>botReduce</code>
for large number of signals. It is therefore the recommended operation
whenever <code>op</code> is associative.</p>
<h4 id="usage">Usage</h4>
<pre><code>_,...,_ : parReduce(op, N) : _
</code></pre>
<p>Where:</p>
<ul>
<li><code>op</code>: is a binary operation </li>
<li><code>N</code>: is the number of incomming signals (<code>N>0</code>). We use a capital letter
here to indicate that the number of incomming signals must be constant and
known at compile time.</li>
</ul>
<hr />
<h3 id="rmtopreduce"><code>(rm.)topReduce</code></h3>
<p><code>topReduce(op,N)</code> involves combining a set of <code>N</code> parallel signals into a
single one using a binary operation <code>op</code>. With <code>topReduce</code>, the reduction
process starts from the top two incoming signals, down to the bottom. In
other words, <code>topReduce(max,256)</code> is equivalent to <code>topReduce(max,255),_ : max</code>.</p>
<p>Contrary to <code>parReduce</code>, the binary operation <code>op</code> doesn't have to be
associative here. Like with <code>parReduce</code> the concept of a binary operation can be
extended to operations that have 2*n inputs and n outputs. For example,
complex signals can be simulated using two signals representing the real and
imaginary parts. In such cases, a binary operation would have 4 inputs and 2
outputs.</p>
<h4 id="usage_1">Usage</h4>
<pre><code> _,...,_ : topReduce(op, N) : _
</code></pre>
<p>Where:</p>
<ul>
<li><code>op</code>: is a binary operation</li>
<li><code>N</code>: is the number of incomming signals (<code>N>0</code>). We use a capital letter
here to indicate that the number of incomming signals must be constant and
known at compile time.</li>
</ul>
<hr />
<h3 id="rmbotreduce"><code>(rm.)botReduce</code></h3>
<p><code>botReduce(op,N)</code> combines a set of <code>N</code> parallel signals into a single one
using a binary operation <code>op</code>. With <code>botReduce</code>, the reduction process starts
from the bottom two incoming signals, up to the top. In other words,
<code>botReduce(max,256)</code> is equivalent to <code>_,botReduce(max,255): max</code>.</p>
<p>Contrary to <code>parReduce</code>, the binary operation <code>op</code> doesn't have to be
associative here. Like with <code>parReduce</code> the concept of a binary operation can be
extended to operations that have 2*n inputs and n outputs. For example,
complex signals can be simulated using two signals representing the real and
imaginary parts. In such cases, a binary operation would have 4 inputs and 2
outputs.</p>
<h4 id="usage_2">Usage</h4>
<pre><code> _,...,_ : botReduce(op, N) : _
</code></pre>
<p>Where:</p>
<ul>
<li>op: is a binary operation</li>
<li>N: is the number of incomming signals (<code>N>0</code>). We use a capital letter
here to indicate that the number of incomming signals must be constant and
known at compile time.</li>
</ul>
<hr />
<h3 id="rmreduce"><code>(rm.)reduce</code></h3>
<p>Reduce a block of <code>n</code> consecutive samples of the incomming signal using a
binary operation <code>op</code>. For example: <code>reduce(max,128)</code> will compute the
maximun value of each block of 128 samples. Please note that the resulting
value, while computed continuously, will be constant for the duration of a
block. A new value is only produced at the end of a block. Note also that
blocks should be of at least one sample (n>0).</p>
<h4 id="usage_3">Usage</h4>
<pre><code>_ : reduce(op, n) : _
</code></pre>
<p>Where:</p>
<ul>
<li><code>op</code>: is a binary operation</li>
<li><code>n</code>: is the number of consecutive samples in a block. </li>
</ul>
<hr />
<h3 id="rmreducemap"><code>(rm.)reducemap</code></h3>
<p>Like <code>reduce</code> but a <code>foo</code> function is applied to the result. From
a mathematical point of view:
<code>reducemap(op,foo,n)</code> is equivalent to <code>reduce(op,n):foo</code>
but more efficient.</p>
<h4 id="usage_4">Usage</h4>
<pre><code>_ : reducemap(op, foo, n) : _
</code></pre>
<p>Where:</p>
<ul>
<li><code>op</code>: is a binary operation</li>
<li><code>foo</code>: is a function applied to the result of the reduction</li>
<li><code>n</code>: is the number of consecutive samples in a block. </li>
</ul></div>
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