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author Chris Cannam
date Wed, 20 Mar 2013 15:35:50 +0000
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+<title>One-Dimensional DFTs of Real Data - FFTW 3.3.3</title>
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+<p>
+Next:&nbsp;<a rel="next" accesskey="n" href="Multi_002dDimensional-DFTs-of-Real-Data.html#Multi_002dDimensional-DFTs-of-Real-Data">Multi-Dimensional DFTs of Real Data</a>,
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+
+<h3 class="section">2.3 One-Dimensional DFTs of Real Data</h3>
+
+<p>In many practical applications, the input data <code>in[i]</code> are purely
+real numbers, in which case the DFT output satisfies the &ldquo;Hermitian&rdquo;
+<a name="index-Hermitian-46"></a>redundancy: <code>out[i]</code> is the conjugate of <code>out[n-i]</code>.  It is
+possible to take advantage of these circumstances in order to achieve
+roughly a factor of two improvement in both speed and memory usage.
+
+   <p>In exchange for these speed and space advantages, the user sacrifices
+some of the simplicity of FFTW's complex transforms. First of all, the
+input and output arrays are of <em>different sizes and types</em>: the
+input is <code>n</code> real numbers, while the output is <code>n/2+1</code>
+complex numbers (the non-redundant outputs); this also requires slight
+&ldquo;padding&rdquo; of the input array for
+<a name="index-padding-47"></a>in-place transforms.  Second, the inverse transform (complex to real)
+has the side-effect of <em>overwriting its input array</em>, by default. 
+Neither of these inconveniences should pose a serious problem for
+users, but it is important to be aware of them.
+
+   <p>The routines to perform real-data transforms are almost the same as
+those for complex transforms: you allocate arrays of <code>double</code>
+and/or <code>fftw_complex</code> (preferably using <code>fftw_malloc</code> or
+<code>fftw_alloc_complex</code>), create an <code>fftw_plan</code>, execute it as
+many times as you want with <code>fftw_execute(plan)</code>, and clean up
+with <code>fftw_destroy_plan(plan)</code> (and <code>fftw_free</code>).  The only
+differences are that the input (or output) is of type <code>double</code>
+and there are new routines to create the plan.  In one dimension:
+
+<pre class="example">     fftw_plan fftw_plan_dft_r2c_1d(int n, double *in, fftw_complex *out,
+                                    unsigned flags);
+     fftw_plan fftw_plan_dft_c2r_1d(int n, fftw_complex *in, double *out,
+                                    unsigned flags);
+</pre>
+   <p><a name="index-fftw_005fplan_005fdft_005fr2c_005f1d-48"></a><a name="index-fftw_005fplan_005fdft_005fc2r_005f1d-49"></a>
+for the real input to complex-Hermitian output (<dfn>r2c</dfn>) and
+complex-Hermitian input to real output (<dfn>c2r</dfn>) transforms. 
+<a name="index-r2c-50"></a><a name="index-c2r-51"></a>Unlike the complex DFT planner, there is no <code>sign</code> argument. 
+Instead, r2c DFTs are always <code>FFTW_FORWARD</code> and c2r DFTs are
+always <code>FFTW_BACKWARD</code>. 
+<a name="index-FFTW_005fFORWARD-52"></a><a name="index-FFTW_005fBACKWARD-53"></a>(For single/long-double precision
+<code>fftwf</code> and <code>fftwl</code>, <code>double</code> should be replaced by
+<code>float</code> and <code>long double</code>, respectively.) 
+<a name="index-precision-54"></a>
+
+   <p>Here, <code>n</code> is the &ldquo;logical&rdquo; size of the DFT, not necessarily the
+physical size of the array.  In particular, the real (<code>double</code>)
+array has <code>n</code> elements, while the complex (<code>fftw_complex</code>)
+array has <code>n/2+1</code> elements (where the division is rounded down). 
+For an in-place transform,
+<a name="index-in_002dplace-55"></a><code>in</code> and <code>out</code> are aliased to the same array, which must be
+big enough to hold both; so, the real array would actually have
+<code>2*(n/2+1)</code> elements, where the elements beyond the first
+<code>n</code> are unused padding.  (Note that this is very different from
+the concept of &ldquo;zero-padding&rdquo; a transform to a larger length, which
+changes the logical size of the DFT by actually adding new input
+data.)  The kth element of the complex array is exactly the
+same as the kth element of the corresponding complex DFT.  All
+positive <code>n</code> are supported; products of small factors are most
+efficient, but an <i>O</i>(<i>n</i>&nbsp;log&nbsp;<i>n</i>) algorithm is used even for prime sizes.
+
+   <p>As noted above, the c2r transform destroys its input array even for
+out-of-place transforms.  This can be prevented, if necessary, by
+including <code>FFTW_PRESERVE_INPUT</code> in the <code>flags</code>, with
+unfortunately some sacrifice in performance. 
+<a name="index-flags-56"></a><a name="index-FFTW_005fPRESERVE_005fINPUT-57"></a>This flag is also not currently supported for multi-dimensional real
+DFTs (next section).
+
+   <p>Readers familiar with DFTs of real data will recall that the 0th (the
+&ldquo;DC&rdquo;) and <code>n/2</code>-th (the &ldquo;Nyquist&rdquo; frequency, when <code>n</code> is
+even) elements of the complex output are purely real.  Some
+implementations therefore store the Nyquist element where the DC
+imaginary part would go, in order to make the input and output arrays
+the same size.  Such packing, however, does not generalize well to
+multi-dimensional transforms, and the space savings are miniscule in
+any case; FFTW does not support it.
+
+   <p>An alternative interface for one-dimensional r2c and c2r DFTs can be
+found in the &lsquo;<samp><span class="samp">r2r</span></samp>&rsquo; interface (see <a href="The-Halfcomplex_002dformat-DFT.html#The-Halfcomplex_002dformat-DFT">The Halfcomplex-format DFT</a>), with &ldquo;halfcomplex&rdquo;-format output that <em>is</em> the same size
+(and type) as the input array. 
+<a name="index-halfcomplex-format-58"></a>That interface, although it is not very useful for multi-dimensional
+transforms, may sometimes yield better performance.
+
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