Discrete Fourier transform: Difference between revisions
(julia example) |
(J implementation) |
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The inverse transform is given by: |
The inverse transform is given by: |
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<math>x_n = \sum_{k=0}^{N-1} X_k\cdot e^{i \frac{2 \pi}{N} k n}</math> |
<math>x_n = \sum_{k=0}^{N-1} X_k\cdot e^{i \frac{2 \pi}{N} k n}</math> |
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=={{header|J}}=== |
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Implementation: |
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<lang j>fourier=: # %~ ] +/@:* ^@:(0j_2p1 * */~@i.@# % #) |
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ifourier=: +/@:* ^@:(0j2p1 * */~@i.@# % #) |
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require'general/misc/numeric' |
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clean=: 1e_9 round | NB. assume complex component is insignificant</lang> |
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Example use: |
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<lang j> clean ifourier fourier 2 3 5 7 11 |
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2 3 5 7 11 |
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clean ifourier 2 * fourier 2 3 5 7 11 |
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4 6 10 14 22 |
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clean ifourier 2 + fourier 2 3 5 7 11 |
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12 3 5 7 11</lang> |
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=={{header|Julia}}== |
=={{header|Julia}}== |
Revision as of 07:09, 27 April 2021
The discrete Fourier transform is a linear, invertible transformation which transforms an arbitrary sequence of complex numbers to another sequence of complex numbers of the same length. The Fast Fourier transform (FFT) is an efficient implementation of this mechanism, but one which only works for sequences which have a length which is a power of 2.
The discrete Fourier transform is a useful testing mechanism to verify the correctness of code bases which use or implement the FFT.
For this task:
- Implement the discrete fourier transform
- Implement the inverse fourier transform
- (optional) implement a cleaning mechanism to remove small errors introduced by floating point representation.
- Verify the correctness of your implementation using a small sequence of integers, such as 2 3 5 7 11
The fourier transform of a sequence of length is given by:
The inverse transform is given by:
J=
Implementation: <lang j>fourier=: # %~ ] +/@:* ^@:(0j_2p1 * */~@i.@# % #) ifourier=: +/@:* ^@:(0j2p1 * */~@i.@# % #)
require'general/misc/numeric' clean=: 1e_9 round | NB. assume complex component is insignificant</lang>
Example use:
<lang j> clean ifourier fourier 2 3 5 7 11 2 3 5 7 11
clean ifourier 2 * fourier 2 3 5 7 11
4 6 10 14 22
clean ifourier 2 + fourier 2 3 5 7 11
12 3 5 7 11</lang>
Julia
<lang julia>function dft(A::AbstractArray{T,N}) where {T,N}
F = zeros(complex(float(T)), size(A)...) for k in CartesianIndices(F), n in CartesianIndices(A) F[k] += cispi(-2 * sum(d -> (k[d] - 1) * (n[d] - 1) / real(eltype(F))(size(A, d)), ntuple(identity, Val{N}()))) * A[n] end return F
end
function idft(A::AbstractArray{T,N}) where {T,N}
F = zeros(complex(float(T)), size(A)...) for k in CartesianIndices(F), n in CartesianIndices(A) F[k] += cispi(2 * sum(d -> (k[d] - 1) * (n[d] - 1) / real(eltype(F))(size(A, d)), ntuple(identity, Val{N}()))) * A[n] end return F ./ length(A)
end
const seq = [2, 3, 5, 7, 11]
const fseq = dft(seq)
const newseq = idft(fseq)
println("$seq =>\n$fseq =>\n$newseq =>\n", Int.(round.(newseq)))
</lang>
- Output:
[2, 3, 5, 7, 11] => ComplexF64[28.0 + 0.0im, -3.3819660112501033 + 8.784022634946172im, -5.618033988749888 + 2.800168985749483im, -5.618033988749888 - 2.800168985749483im, -3.381966011250112 - 8.78402263494618im] => ComplexF64[2.0000000000000013 - 1.4210854715202005e-15im, 2.999999999999996 + 7.993605777301127e-16im, 5.000000000000002 + 2.1316282072803005e-15im, 6.999999999999998 - 8.881784197001252e-16im, 11.0 + 0.0im] => [2, 3, 5, 7, 11]