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author Chris Cannam <cannam@all-day-breakfast.com>
date Wed, 20 Mar 2013 15:35:50 +0000
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cannam@95 3 <title>Real-data DFT Array Format - FFTW 3.3.3</title>
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cannam@95 49 <a name="Real-data-DFT-Array-Format"></a>
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cannam@95 52 Next:&nbsp;<a rel="next" accesskey="n" href="Real_002dto_002dReal-Transforms.html#Real_002dto_002dReal-Transforms">Real-to-Real Transforms</a>,
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cannam@95 57
cannam@95 58 <h4 class="subsection">4.3.4 Real-data DFT Array Format</h4>
cannam@95 59
cannam@95 60 <p><a name="index-r2c_002fc2r-multi_002ddimensional-array-format-199"></a>
cannam@95 61 The output of a DFT of real data (r2c) contains symmetries that, in
cannam@95 62 principle, make half of the outputs redundant (see <a href="What-FFTW-Really-Computes.html#What-FFTW-Really-Computes">What FFTW Really Computes</a>). (Similarly for the input of an inverse c2r transform.) In
cannam@95 63 practice, it is not possible to entirely realize these savings in an
cannam@95 64 efficient and understandable format that generalizes to
cannam@95 65 multi-dimensional transforms. Instead, the output of the r2c
cannam@95 66 transforms is <em>slightly</em> over half of the output of the
cannam@95 67 corresponding complex transform. We do not &ldquo;pack&rdquo; the data in any
cannam@95 68 way, but store it as an ordinary array of <code>fftw_complex</code> values.
cannam@95 69 In fact, this data is simply a subsection of what would be the array in
cannam@95 70 the corresponding complex transform.
cannam@95 71
cannam@95 72 <p>Specifically, for a real transform of d (= <code>rank</code>)
cannam@95 73 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>, the complex data 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@95 74 <code>fftw_complex</code> values in row-major order (with the division rounded
cannam@95 75 down). That is, we only store the <em>lower</em> half (non-negative
cannam@95 76 frequencies), plus one element, of the last dimension of the data from
cannam@95 77 the ordinary complex transform. (We could have instead taken half of
cannam@95 78 any other dimension, but implementation turns out to be simpler if the
cannam@95 79 last, contiguous, dimension is used.)
cannam@95 80
cannam@95 81 <p><a name="index-out_002dof_002dplace-200"></a>For an out-of-place transform, the real data is simply an array with
cannam@95 82 physical 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@95 83
cannam@95 84 <p><a name="index-in_002dplace-201"></a><a name="index-padding-202"></a>For an in-place transform, some complications arise since the complex data
cannam@95 85 is slightly larger than the real data. In this case, the final
cannam@95 86 dimension of the real data must be <em>padded</em> with extra values to
cannam@95 87 accommodate the size of the complex data&mdash;two extra if the last
cannam@95 88 dimension is even and one if it is odd. That is, the last dimension of
cannam@95 89 the real data must physically contain
cannam@95 90 2 * (n<sub>d-1</sub>/2+1)<code>double</code> values (exactly enough to hold the complex data). This
cannam@95 91 physical array size does not, however, change the <em>logical</em> array
cannam@95 92 size&mdash;only
cannam@95 93 n<sub>d-1</sub>values are actually stored in the last dimension, and
cannam@95 94 n<sub>d-1</sub>is the last dimension passed to the planner.
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