DFT Meaning of k's Greetings: Understanding Wavelength & Interval Length

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In summary, the conversation discusses the use of k in the expression of the Discrete Fourier Transform (DFT) and its relationship to wavenumbers. The value of k depends on the length of the x-interval that holds the signal and is not necessarily a wavenumber. The conversation also mentions a different representation of the DFT and the intuitive understanding of decomposing a signal into "eigen-wavenumbers". The speaker's view on this concept may be oversimplified.
  • #1
SchroedingersLion
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Question to DFTs
Greetings!

I am getting started with Python's Fast Fourier Transform, and I noticed a missunderstanding on my part.
I always thought that the k's in the expression of the DFT resemble actual wavenumbers ## \frac{2\pi}{\lambda}## that form the waves the signal is composed of.
But the actual meaning seems to depend on the length of the x-interval that holds the signal.

Wiki:
$$
X_{k} = \sum_{n=0}^{N-1}x_n e^{-2\pi i k \frac n N} ~~~~(1) \\

x_n = \frac 1 N \sum_{k=0}^{N-1}X_k e^{2\pi i k \frac n N}~~~~ (2),
$$

where the ##x_n## are the ##N## signal values that are equidistantly positioned at the points ##\frac n N ##, i.e. in the interval ## [0, 1] ##.
The expressions are ##N-##periodic in ##k##, so it makes no difference whether one uses ##k\in [0,N-1]## or ##k\in [-\frac N 2 +1, \frac N 2]## (suppose ##N## is even).

In this representation, however, the ##k## are no wavenumbers. Rather, they are wavenumbers divided by ##2\pi##, i.e. ##k=\frac 1 \lambda ##.In another derivation of a book, the points ##x_n## are equidistantly spaced across ##[0, 2\pi]##.
(1) then becomes
$$ X_{k} = \sum_{n=0}^{N-1}x_n e^{-i k x_n}. $$

Here, however, it is ##k=\frac {2\pi} {\lambda}##.
Now I was wondering, is it generally true that the ##k## are given by ## \frac L \lambda ## with ##L## as the length of the ##x-##interval?

It somehow makes sense, as the sampling interval does not enter anywhere else in these formulas.SL

 
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  • #2
k is simply an index. How it is defined depends entirely on the context, as you noticed.
 
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  • #3
Alright, I think I understand now, thanks!

On another note, I wonder whether my intuitive understanding is correct:
Given my grid of data points, I know which wavenumbers k can be distinguished on the grid.
I want to decompose my signal into a sum over these "eigen-wavenumbers".
If I imagine the complex plane and discrete lines that go from 0 to ##e^{ik}## for the different ##k##, then ##X_k## is simply a point on the line to the corresponding ##k##, correct?

I then try to imagine that eq. (1) projects each data point onto that line and sums over these projections in order to find the total ##X_k##. Is this idea correct? If so, I don't really see how the term ##x_n e^{-2\pi ik\frac n N}## performs that task. If I was correct, I would simply write ##x_n e^{ik}##. So my view seems to be a bit oversimplified.
 
  • #5
k is not wavenumber, it’s simply an integer index, as is n. All context is lost once your data are sampled and digitized. Context about units (time, position, frequency or other variables) must be tracked with external bookkeeping.
 
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FAQ: DFT Meaning of k's Greetings: Understanding Wavelength & Interval Length

What is the meaning of DFT?

DFT stands for Discrete Fourier Transform. It is a mathematical operation that converts a signal from its original domain (often time or space) to a representation in the frequency domain.

What do the k's represent in DFT?

The k's in DFT represent the frequency components of a signal. Each k value corresponds to a specific frequency in the signal's spectrum.

How do you calculate the wavelength in DFT?

The wavelength in DFT can be calculated by dividing the sampling rate by the k value. This will give you the length of one full cycle of the corresponding frequency component.

What is the meaning of "Greetings" in DFT?

"Greetings" in DFT refers to the initial greeting or introduction of the signal. It is often used as a placeholder for the first data point in the signal, representing the starting point of the signal.

How do you determine the interval length in DFT?

The interval length in DFT is determined by the number of data points in the signal. It is equal to the total length of the signal divided by the number of data points.

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