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			137 lines
		
	
	
		
			6.9 KiB
		
	
	
	
		
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								<!-- This manual is for FFTW
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								(version 3.3.10, 10 December 2020).
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								Copyright (C) 2003 Matteo Frigo.
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								Copyright (C) 2003 Massachusetts Institute of Technology.
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								<title>The Halfcomplex-format DFT (FFTW 3.3.10)</title>
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								<meta name="description" content="The Halfcomplex-format DFT (FFTW 3.3.10)">
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								<link href="Real-even_002fodd-DFTs-_0028cosine_002fsine-transforms_0029.html" rel="next" title="Real even/odd DFTs (cosine/sine transforms)">
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								<span id="The-Halfcomplex_002dformat-DFT"></span><div class="header">
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								<p>
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								Next: <a href="Real-even_002fodd-DFTs-_0028cosine_002fsine-transforms_0029.html" accesskey="n" rel="next">Real even/odd DFTs (cosine/sine transforms)</a>, Previous: <a href="More-DFTs-of-Real-Data.html" accesskey="p" rel="prev">More DFTs of Real Data</a>, Up: <a href="More-DFTs-of-Real-Data.html" accesskey="u" rel="up">More DFTs of Real Data</a>   [<a href="index.html#SEC_Contents" title="Table of contents" rel="contents">Contents</a>][<a href="Concept-Index.html" title="Index" rel="index">Index</a>]</p>
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								<hr>
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								<span id="The-Halfcomplex_002dformat-DFT-1"></span><h4 class="subsection">2.5.1 The Halfcomplex-format DFT</h4>
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								<p>An r2r kind of <code>FFTW_R2HC</code> (<em>r2hc</em>) corresponds to an r2c DFT
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								<span id="index-FFTW_005fR2HC"></span>
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								<span id="index-r2c-1"></span>
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								<span id="index-r2hc"></span>
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								(see <a href="One_002dDimensional-DFTs-of-Real-Data.html">One-Dimensional DFTs of Real Data</a>) but with “halfcomplex”
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								format output, and may sometimes be faster and/or more convenient than
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								the latter.
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								<span id="index-halfcomplex-format-1"></span>
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								The inverse <em>hc2r</em> transform is of kind <code>FFTW_HC2R</code>.
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								<span id="index-FFTW_005fHC2R"></span>
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								<span id="index-hc2r"></span>
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								This consists of the non-redundant half of the complex output for a 1d
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								real-input DFT of size <code>n</code>, stored as a sequence of <code>n</code> real
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								numbers (<code>double</code>) in the format:
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								</p>
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								<p align=center>
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								r<sub>0</sub>, r<sub>1</sub>, r<sub>2</sub>, ..., r<sub>n/2</sub>, i<sub>(n+1)/2-1</sub>, ..., i<sub>2</sub>, i<sub>1</sub>
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								</p>
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								<p>Here,
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								r<sub>k</sub>
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								is the real part of the <em>k</em>th output, and
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								i<sub>k</sub>
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								is the imaginary part.  (Division by 2 is rounded down.) For a
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								halfcomplex array <code>hc[n]</code>, the <em>k</em>th component thus has its
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								real part in <code>hc[k]</code> and its imaginary part in <code>hc[n-k]</code>, with
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								the exception of <code>k</code> <code>==</code> <code>0</code> or <code>n/2</code> (the latter
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								only if <code>n</code> is even)—in these two cases, the imaginary part is
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								zero due to symmetries of the real-input DFT, and is not stored.
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								Thus, the r2hc transform of <code>n</code> real values is a halfcomplex array of
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								length <code>n</code>, and vice versa for hc2r.
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								<span id="index-normalization-2"></span>
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								</p>
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								<p>Aside from the differing format, the output of
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								<code>FFTW_R2HC</code>/<code>FFTW_HC2R</code> is otherwise exactly the same as for
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								the corresponding 1d r2c/c2r transform
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								(i.e. <code>FFTW_FORWARD</code>/<code>FFTW_BACKWARD</code> transforms, respectively).
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								Recall that these transforms are unnormalized, so r2hc followed by hc2r
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								will result in the original data multiplied by <code>n</code>.  Furthermore,
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								like the c2r transform, an out-of-place hc2r transform will
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								<em>destroy its input</em> array.
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								</p>
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								<p>Although these halfcomplex transforms can be used with the
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								multi-dimensional r2r interface, the interpretation of such a separable
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								product of transforms along each dimension is problematic.  For example,
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								consider a two-dimensional <code>n0</code> by <code>n1</code>, r2hc by r2hc
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								transform planned by <code>fftw_plan_r2r_2d(n0, n1, in, out, FFTW_R2HC,
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								FFTW_R2HC, FFTW_MEASURE)</code>.  Conceptually, FFTW first transforms the rows
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								(of size <code>n1</code>) to produce halfcomplex rows, and then transforms the
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								columns (of size <code>n0</code>).  Half of these column transforms, however,
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								are of imaginary parts, and should therefore be multiplied by <em>i</em>
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								and combined with the r2hc transforms of the real columns to produce the
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								2d DFT amplitudes; FFTW’s r2r transform does <em>not</em> perform this
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								combination for you.  Thus, if a multi-dimensional real-input/output DFT
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								is required, we recommend using the ordinary r2c/c2r
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								interface (see <a href="Multi_002dDimensional-DFTs-of-Real-Data.html">Multi-Dimensional DFTs of Real Data</a>).
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								</p>
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								<hr>
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								<div class="header">
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								<p>
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								Next: <a href="Real-even_002fodd-DFTs-_0028cosine_002fsine-transforms_0029.html" accesskey="n" rel="next">Real even/odd DFTs (cosine/sine transforms)</a>, Previous: <a href="More-DFTs-of-Real-Data.html" accesskey="p" rel="prev">More DFTs of Real Data</a>, Up: <a href="More-DFTs-of-Real-Data.html" accesskey="u" rel="up">More DFTs of Real Data</a>   [<a href="index.html#SEC_Contents" title="Table of contents" rel="contents">Contents</a>][<a href="Concept-Index.html" title="Index" rel="index">Index</a>]</p>
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