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Try a double-precision kissfft
author Chris Cannam
date Wed, 07 Sep 2016 10:40:32 +0100
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Chris@19 49 <a name="Real-data-DFT-Array-Format"></a>
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Chris@19 57
Chris@19 58 <h4 class="subsection">4.3.4 Real-data DFT Array Format</h4>
Chris@19 59
Chris@19 60 <p><a name="index-r2c_002fc2r-multi_002ddimensional-array-format-201"></a>
Chris@19 61 The output of a DFT of real data (r2c) contains symmetries that, in
Chris@19 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
Chris@19 63 practice, it is not possible to entirely realize these savings in an
Chris@19 64 efficient and understandable format that generalizes to
Chris@19 65 multi-dimensional transforms. Instead, the output of the r2c
Chris@19 66 transforms is <em>slightly</em> over half of the output of the
Chris@19 67 corresponding complex transform. We do not &ldquo;pack&rdquo; the data in any
Chris@19 68 way, but store it as an ordinary array of <code>fftw_complex</code> values.
Chris@19 69 In fact, this data is simply a subsection of what would be the array in
Chris@19 70 the corresponding complex transform.
Chris@19 71
Chris@19 72 <p>Specifically, for a real transform of d (= <code>rank</code>)
Chris@19 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
Chris@19 74 <code>fftw_complex</code> values in row-major order (with the division rounded
Chris@19 75 down). That is, we only store the <em>lower</em> half (non-negative
Chris@19 76 frequencies), plus one element, of the last dimension of the data from
Chris@19 77 the ordinary complex transform. (We could have instead taken half of
Chris@19 78 any other dimension, but implementation turns out to be simpler if the
Chris@19 79 last, contiguous, dimension is used.)
Chris@19 80
Chris@19 81 <p><a name="index-out_002dof_002dplace-202"></a>For an out-of-place transform, the real data is simply an array with
Chris@19 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.
Chris@19 83
Chris@19 84 <p><a name="index-in_002dplace-203"></a><a name="index-padding-204"></a>For an in-place transform, some complications arise since the complex data
Chris@19 85 is slightly larger than the real data. In this case, the final
Chris@19 86 dimension of the real data must be <em>padded</em> with extra values to
Chris@19 87 accommodate the size of the complex data&mdash;two extra if the last
Chris@19 88 dimension is even and one if it is odd. That is, the last dimension of
Chris@19 89 the real data must physically contain
Chris@19 90 2 * (n<sub>d-1</sub>/2+1)<code>double</code> values (exactly enough to hold the complex data). This
Chris@19 91 physical array size does not, however, change the <em>logical</em> array
Chris@19 92 size&mdash;only
Chris@19 93 n<sub>d-1</sub>values are actually stored in the last dimension, and
Chris@19 94 n<sub>d-1</sub>is the last dimension passed to the planner.
Chris@19 95
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