3.5

FFT v1

../_images/FFT-v1_dlg.png

FFT dialog.

Summary

Performs complex Fast Fourier Transform

Properties

Name Direction Type Default Description
InputWorkspace Input MatrixWorkspace Mandatory The name of the input workspace.
OutputWorkspace Output MatrixWorkspace Mandatory The name of the output workspace.
InputImagWorkspace Input MatrixWorkspace   The name of the input workspace for the imaginary part. Leave blank if same as InputWorkspace
Real Input number 0 Spectrum number to use as real part for transform
Imaginary Input number Optional Spectrum number to use as imaginary part for transform
Transform Input string Forward Direction of the transform: forward or backward. Allowed values: [‘Forward’, ‘Backward’]
Shift Input number 0 Apply an extra phase equal to this quantity times 2*pi to the transform

Description

The FFT algorithm performs discrete Fourier transform of complex data using the Fast Fourier Transform algorithm. It uses the GSL Fourier transform functions to do the calculations. Due to the nature of the fast fourier transform the input spectra must have linear x axes. If the imaginary part is not set the data is considered real. The “Transform” property defines the direction of the transform: direct (“Forward”) or inverse (“Backward”).

Note that the input data is shifted before the transform along the x axis to place the origin in the middle of the x-value range. It means that for the data defined on an interval [A,B] the output F(\xi_k) must be multiplied by e^{-2\pi ix_0\xi_k}, where x_0=\tfrac{1}{2}(A+B), \xi_k is the frequency.

Details

The Fourier transform of a complex function f(x) is defined by equation:

F(\xi)=\int_{-\infty}^\infty f(x)e^{-2\pi ix\xi} dx

For discrete data with equally spaced x_n and only non-zero on an interval [A,B] the integral can be approximated by a sum:

F(\xi)=\Delta x\sum_{n=0}^{N-1}f(x_n)e^{-2\pi ix_n\xi}

Here \Delta x is the bin width, x_n=A+\Delta xn. If we are looking for values of the transformed function F at discrete frequencies \xi_k=\Delta\xi k with

\Delta\xi=\frac{1}{B-A}=\frac{1}{L}=\frac{1}{N\Delta x}

the equation can be rewritten:

F_k=e^{-2\pi iA\xi_k}\Delta x\sum_{n=0}^{N-1}f_ne^{-\tfrac{2\pi i}{N}nk}

Here f_n=f(x_n) and F_k=F(\xi_k). The formula

\tilde{F}_k=\Delta x\sum_{n=0}^{N-1}f_ne^{-\tfrac{2\pi i}{N}nk}

is the Discrete Fourier Transform (DFT) and can be efficiently evaluated using the Fast Fourier Transform algorithm. The DFT formula calculates the Fourier transform of data on the interval [0,L]. It should be noted that it is periodic in k with period N. If we also assume that f_n is also periodic with period N the DFT can be used to transform data on the interval [-L/2,L/2]. To do this we swap the two halves of the data array f_n. If we denote the modified array as \bar{f}_n, its transform will be

\bar{F}_k=\Delta x\sum_{n=0}^{N-1}\bar{f}_ne^{-\tfrac{2\pi i}{N}nk}

The Mantid FFT algorithm returns the complex array \bar{F}_K as Y values of two spectra in the output workspace, one for the real and the other for the imaginary part of the transform. The X values are set to the transform frequencies and have the range approximately equal to [-N/L,N/L]. The actual limits depend sllightly on whether N is even or odd and whether the input spectra are histograms or point data. The variations are of the order of \Delta\xi. The zero frequency is always in the bin with index k=int(N/2).

Example 1

In this example the input data were calculated using function \exp(-(x-1)^2) in the range [-5,5].

Gaussian

Gaussian

FFT of a Gaussian

FFT of a Gaussian

Because the x=0 is in the middle of the data array the transform shown is the exact DFT of the input data.

Example 2

In this example the input data were calculated using function \exp(-x^2) in the range [-6,4].

Gaussian

Gaussian

FFT of a Gaussian

FFT of a Gaussian

Because the x=0 is not in the middle of the data array the transform shown includes a shifting factor of \exp(2\pi i\xi). To remove it the output must be mulitplied by \exp(-2\pi i\xi). The corrected transform will be:

FFT of a Gaussian

FFT of a Gaussian

It should be noted that in a case like this, i.e. when the input is a real positive even function, the correction can be done by finding the transform’s modulus (Re^2+Im^2)^{1/2}. The output workspace includes the modulus of the transform.

Output

The output workspace for a direct (“Forward”) transform contains either three or six spectra, depending on whether the input function is complex or purely real. If the input function has an imaginary part, the transform is written to three spectra with indexes 0, 1, and 2. Indexes 0 and 1 are the real and imaginary parts, while index 2 contains the modulus \sqrt{Re^2+Im^2}. If the input function does not contain an spectrum for the imaginary part (purely real function), the actual transform is written to spectra with indexes 3 and 4 which are the real and imaginary parts, respectively. The last spectrum (index 5) has the modulus of the transform. The spectra from 0 to 2 repeat these results for positive frequencies only.

Output for the case of input function containing imaginary part:

Workspace index Description
0 Complete real part
1 Complete imaginary part
2 Complete transform modulus

Output for the case of input function containing no imaginary part:

Workspace index Description
0 Real part, positive frequencies
1 Imaginary part, positive frequencies
2 Modulus, positive frequencies
3 Complete real part
4 Complete imaginary part
5 Complete transform modulus

The output workspace for an inverse (“Backward”) transform has 3 spectra for the real (0), imaginary (1) parts, and the modulus (2).

Workspace index Description
0 Real part
1 Imaginary part
2 Modulus

Usage

Example: Applying FFT algorithm

#Create Sample Workspace
ws = CreateSampleWorkspace(WorkspaceType = 'Event', NumBanks = 1, Function = 'Exp Decay', BankPixelWidth = 1, NumEvents = 100)

#apply the FFT algorithm
outworkspace = FFT(InputWorkspace = ws, Transform = 'Backward')

#print statements
print "DataX(0)[0] equals DataX(0)[100]? : " + str((round(abs(outworkspace.dataX(0)[0]), 3)) == (round(outworkspace.dataX(0)[100], 3)))
print "DataX(0)[10] equals DataX(0)[90]? : " + str((round(abs(outworkspace.dataX(0)[10]), 3)) == (round(outworkspace.dataX(0)[90], 3)))
print "DataX((0)[50] equals 0? : " + str((round(abs(outworkspace.dataX(0)[50]), 3)) == 0)
print "DataY(0)[40] equals DataY(0)[60]? : " + str((round(abs(outworkspace.dataY(0)[40]), 5)) == (round(outworkspace.dataY(0)[60], 5)))

Output:

DataX(0)[0] equals DataX(0)[100]? : True
DataX(0)[10] equals DataX(0)[90]? : True
DataX((0)[50] equals 0? : True
DataY(0)[40] equals DataY(0)[60]? : True

Categories: Algorithms | Arithmetic | FFT

Source

C++ source: FFT.cpp

C++ header: FFT.h