How the heck are you supposed to use FFT?

  • Thread starter Thread starter Focus
  • Start date Start date
  • Tags Tags
    Fft
Click For Summary

Discussion Overview

The discussion centers around the application of the Fast Fourier Transform (FFT) in numerical analysis, specifically in relation to obtaining values from a characteristic function of a distribution. Participants explore how to utilize FFT to compute the distribution function from given data.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant expresses confusion about using FFT for a single value and seeks clarification on its application to distributions.
  • Another participant explains that the Fourier transform is a function transform, while the FFT operates on discrete vectors, questioning the intent behind applying FFT to a single value.
  • A later reply introduces a specific function and asks how to sample values to obtain the distribution function using FFT.
  • Further, a participant outlines the process of using the inverse Fourier transform (IFT) to find the distribution function, emphasizing the importance of sampling and the conditions under which the function is bandlimited.
  • It is noted that if the function is not bandlimited, aliasing may occur, potentially affecting the accuracy of results.

Areas of Agreement / Disagreement

Participants do not reach a consensus on the application of FFT to the problem posed. There are multiple competing views regarding the nature of the function and the implications of bandlimited versus non-bandlimited conditions.

Contextual Notes

Participants mention the necessity of adhering to the sampling theorem and the implications of using a power of 2 for the number of points in FFT. There is also a distinction made between the sampling of the spectrum and the time function, which remains unresolved.

Focus
Messages
285
Reaction score
3
Hey,

This may seem very stupid but how the heck are you supposed to use FFT? The FFT transformations take a vector as in input but I want to use it to work out FT of a perticular value. I have the charecteristic function of a distribution and wish to numericaly work out values for the distribution function. Hope this makes sense.

Thanks in advance
 
Physics news on Phys.org


The FT gives the complex spectrum of the input signal, the FFT gives the discrete complex spectrum of an input sequence or vector. In other words, it decomposes the input into frequency components. I don't follow how you intend to apply this to distributions.
 


The Fourier transform itself is a transform of a function not a number. The FFT (Fast Fourier Transform) thinks of the function as being given by a discrete "vector" of values at different points. I have no idea what you could mean by taking the FFT of a single value.
 


Sorry maybe I was not so clear on this. I have a function
[tex]\psi(\lambda)=\int_{-\infty}^{\infty}e^{i \lambda t}f(t) dt[/tex]

I was wondering if you could tell me how I could use the FFT (i.e. what values I should sample) to obtain [tex]f(\alpha)[/tex] for some [tex]\alpha[/tex] I choose?

Thanks
 


EDIT: I couldn't get equations in this post to render properly, please see the next.
 
Last edited:


So you know [tex]\psi(\lambda)[/tex] and want to find [tex]f(\alpha)[/tex], which is given by the continuous inverse FT or IFT
[tex]f(\alpha)=\int_{-\infty}^{\infty}e^{-i \lambda \alpha}\psi(\lambda) d\lambda[/tex]

You want to know how to evaluate this using the FFT, right? This is a classic problem in digital signal processing, and the approach depends on the nature of f and psi.

A) f(t) is "bandlimited", that is, it is zero for [tex]t > t_c[/tex] where [tex]t_c[/tex] is a "cutoff time." (Because you are doing an IFT, the usual conventions of sampling a time function to find frequency are swapped here--you are sampling a spectrum to find a time function). An example is f(t)=gaussian, which is effectively zero after 5 standard deviations in time. This is the cutoff time t_c. Psi is also limited in frequency, after, say [tex]5\sigma[/tex]. So you sample psi uniformly over the interval
[tex]|\lambda| \leq 5\sigma[/tex], at a rate [tex]\Delta\lambda[/tex] that satisfies the sampling theorem

[tex]\Delta\lambda \geq \frac{2}{t_c}[/tex].

Use a number of points N that is a power of 2 if you want to use an FFT, otherwise compute with the DFT, and take the value closest to [tex]\alpha[/tex].

B) If you are not bandlimited, you will alias and your results will suffer in accuracy. We won't discuss this unless it's necessary.
 

Similar threads

  • · Replies 16 ·
Replies
16
Views
15K
  • · Replies 8 ·
Replies
8
Views
3K
  • · Replies 5 ·
Replies
5
Views
11K
Replies
8
Views
2K
  • · Replies 3 ·
Replies
3
Views
3K
  • · Replies 2 ·
Replies
2
Views
2K
  • · Replies 1 ·
Replies
1
Views
3K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 3 ·
Replies
3
Views
6K
  • · Replies 1 ·
Replies
1
Views
21K