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Current fftw source
author Chris Cannam <cannam@all-day-breakfast.com>
date Tue, 18 Oct 2016 13:40:26 +0100
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cannam@127 3 <!-- This manual is for FFTW
cannam@127 4 (version 3.3.5, 30 July 2016).
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cannam@127 6 Copyright (C) 2003 Matteo Frigo.
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cannam@127 24 <head>
cannam@127 25 <title>FFTW 3.3.5: Multi-Dimensional DFTs of Real Data</title>
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cannam@127 69 </head>
cannam@127 70
cannam@127 71 <body lang="en" bgcolor="#FFFFFF" text="#000000" link="#0000FF" vlink="#800080" alink="#FF0000">
cannam@127 72 <a name="Multi_002dDimensional-DFTs-of-Real-Data"></a>
cannam@127 73 <div class="header">
cannam@127 74 <p>
cannam@127 75 Next: <a href="More-DFTs-of-Real-Data.html#More-DFTs-of-Real-Data" accesskey="n" rel="next">More DFTs of Real Data</a>, Previous: <a href="One_002dDimensional-DFTs-of-Real-Data.html#One_002dDimensional-DFTs-of-Real-Data" accesskey="p" rel="prev">One-Dimensional DFTs of Real Data</a>, Up: <a href="Tutorial.html#Tutorial" accesskey="u" rel="up">Tutorial</a> &nbsp; [<a href="index.html#SEC_Contents" title="Table of contents" rel="contents">Contents</a>][<a href="Concept-Index.html#Concept-Index" title="Index" rel="index">Index</a>]</p>
cannam@127 76 </div>
cannam@127 77 <hr>
cannam@127 78 <a name="Multi_002dDimensional-DFTs-of-Real-Data-1"></a>
cannam@127 79 <h3 class="section">2.4 Multi-Dimensional DFTs of Real Data</h3>
cannam@127 80
cannam@127 81 <p>Multi-dimensional DFTs of real data use the following planner routines:
cannam@127 82 </p>
cannam@127 83 <div class="example">
cannam@127 84 <pre class="example">fftw_plan fftw_plan_dft_r2c_2d(int n0, int n1,
cannam@127 85 double *in, fftw_complex *out,
cannam@127 86 unsigned flags);
cannam@127 87 fftw_plan fftw_plan_dft_r2c_3d(int n0, int n1, int n2,
cannam@127 88 double *in, fftw_complex *out,
cannam@127 89 unsigned flags);
cannam@127 90 fftw_plan fftw_plan_dft_r2c(int rank, const int *n,
cannam@127 91 double *in, fftw_complex *out,
cannam@127 92 unsigned flags);
cannam@127 93 </pre></div>
cannam@127 94 <a name="index-fftw_005fplan_005fdft_005fr2c_005f2d"></a>
cannam@127 95 <a name="index-fftw_005fplan_005fdft_005fr2c_005f3d"></a>
cannam@127 96 <a name="index-fftw_005fplan_005fdft_005fr2c"></a>
cannam@127 97
cannam@127 98 <p>as well as the corresponding <code>c2r</code> routines with the input/output
cannam@127 99 types swapped. These routines work similarly to their complex
cannam@127 100 analogues, except for the fact that here the complex output array is cut
cannam@127 101 roughly in half and the real array requires padding for in-place
cannam@127 102 transforms (as in 1d, above).
cannam@127 103 </p>
cannam@127 104 <p>As before, <code>n</code> is the logical size of the array, and the
cannam@127 105 consequences of this on the the format of the complex arrays deserve
cannam@127 106 careful attention.
cannam@127 107 <a name="index-r2c_002fc2r-multi_002ddimensional-array-format"></a>
cannam@127 108 Suppose that the real data has dimensions n<sub>0</sub>&nbsp;&times;&nbsp;n<sub>1</sub>&nbsp;&times;&nbsp;n<sub>2</sub>&nbsp;&times;&nbsp;&hellip;&nbsp;&times;&nbsp;n<sub>d-1</sub> (in row-major order).
cannam@127 109 Then, after an r2c transform, the output is an n<sub>0</sub>&nbsp;&times;&nbsp;n<sub>1</sub>&nbsp;&times;&nbsp;n<sub>2</sub>&nbsp;&times;&nbsp;&hellip;&nbsp;&times;&nbsp;(n<sub>d-1</sub>/2 + 1) array of
cannam@127 110 <code>fftw_complex</code> values in row-major order, corresponding to slightly
cannam@127 111 over half of the output of the corresponding complex DFT. (The division
cannam@127 112 is rounded down.) The ordering of the data is otherwise exactly the
cannam@127 113 same as in the complex-DFT case.
cannam@127 114 </p>
cannam@127 115 <p>For out-of-place transforms, this is the end of the story: the real
cannam@127 116 data is stored as a row-major array of size n<sub>0</sub>&nbsp;&times;&nbsp;n<sub>1</sub>&nbsp;&times;&nbsp;n<sub>2</sub>&nbsp;&times;&nbsp;&hellip;&nbsp;&times;&nbsp;n<sub>d-1</sub> and the complex
cannam@127 117 data is stored as a row-major array of size n<sub>0</sub>&nbsp;&times;&nbsp;n<sub>1</sub>&nbsp;&times;&nbsp;n<sub>2</sub>&nbsp;&times;&nbsp;&hellip;&nbsp;&times;&nbsp;(n<sub>d-1</sub>/2 + 1).
cannam@127 118 </p>
cannam@127 119 <p>For in-place transforms, however, extra padding of the real-data array
cannam@127 120 is necessary because the complex array is larger than the real array,
cannam@127 121 and the two arrays share the same memory locations. Thus, for
cannam@127 122 in-place transforms, the final dimension of the real-data array must
cannam@127 123 be padded with extra values to accommodate the size of the complex
cannam@127 124 data&mdash;two values if the last dimension is even and one if it is odd.
cannam@127 125 <a name="index-padding-1"></a>
cannam@127 126 That is, the last dimension of the real data must physically contain
cannam@127 127 2 * (n<sub>d-1</sub>/2+1)<code>double</code> values (exactly enough to hold the complex data).
cannam@127 128 This physical array size does not, however, change the <em>logical</em>
cannam@127 129 array size&mdash;only
cannam@127 130 n<sub>d-1</sub>values are actually stored in the last dimension, and
cannam@127 131 n<sub>d-1</sub>is the last dimension passed to the plan-creation routine.
cannam@127 132 </p>
cannam@127 133 <p>For example, consider the transform of a two-dimensional real array of
cannam@127 134 size <code>n0</code> by <code>n1</code>. The output of the r2c transform is a
cannam@127 135 two-dimensional complex array of size <code>n0</code> by <code>n1/2+1</code>, where
cannam@127 136 the <code>y</code> dimension has been cut nearly in half because of
cannam@127 137 redundancies in the output. Because <code>fftw_complex</code> is twice the
cannam@127 138 size of <code>double</code>, the output array is slightly bigger than the
cannam@127 139 input array. Thus, if we want to compute the transform in place, we
cannam@127 140 must <em>pad</em> the input array so that it is of size <code>n0</code> by
cannam@127 141 <code>2*(n1/2+1)</code>. If <code>n1</code> is even, then there are two padding
cannam@127 142 elements at the end of each row (which need not be initialized, as they
cannam@127 143 are only used for output).
cannam@127 144 </p>
cannam@127 145 <p>The following illustration depicts the input and output arrays just
cannam@127 146 described, for both the out-of-place and in-place transforms (with the
cannam@127 147 arrows indicating consecutive memory locations):
cannam@127 148 <img src="rfftwnd-for-html.png" alt="rfftwnd-for-html">
cannam@127 149 </p>
cannam@127 150 <p>These transforms are unnormalized, so an r2c followed by a c2r
cannam@127 151 transform (or vice versa) will result in the original data scaled by
cannam@127 152 the number of real data elements&mdash;that is, the product of the
cannam@127 153 (logical) dimensions of the real data.
cannam@127 154 <a name="index-normalization-1"></a>
cannam@127 155 </p>
cannam@127 156
cannam@127 157 <p>(Because the last dimension is treated specially, if it is equal to
cannam@127 158 <code>1</code> the transform is <em>not</em> equivalent to a lower-dimensional
cannam@127 159 r2c/c2r transform. In that case, the last complex dimension also has
cannam@127 160 size <code>1</code> (<code>=1/2+1</code>), and no advantage is gained over the
cannam@127 161 complex transforms.)
cannam@127 162 </p>
cannam@127 163 <hr>
cannam@127 164 <div class="header">
cannam@127 165 <p>
cannam@127 166 Next: <a href="More-DFTs-of-Real-Data.html#More-DFTs-of-Real-Data" accesskey="n" rel="next">More DFTs of Real Data</a>, Previous: <a href="One_002dDimensional-DFTs-of-Real-Data.html#One_002dDimensional-DFTs-of-Real-Data" accesskey="p" rel="prev">One-Dimensional DFTs of Real Data</a>, Up: <a href="Tutorial.html#Tutorial" accesskey="u" rel="up">Tutorial</a> &nbsp; [<a href="index.html#SEC_Contents" title="Table of contents" rel="contents">Contents</a>][<a href="Concept-Index.html#Concept-Index" title="Index" rel="index">Index</a>]</p>
cannam@127 167 </div>
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