Discrete Fourier Transform: How does independent varialbe spacing change?

In summary, the independent variable in the Fourier transform will have units of 1/meter, with a spacing of 1/11 cycles/meter between each consecutive point in the transformed list.
  • #1
4ierTrans4m3
2
0
Hey guys,

I was imagining that I have a sine function: y = sin(x) where x represents a distance in meters for instance. Now let us say that I sample the function at x = 0,1,2,3...,10 (meters) producing a list of values: {sin(1), sin(2), sin(3),...,sin(10)} = {0.000, 0.841, 0.909, 0.141, -0.756, -0.958, -0.279, 0.656, 0.989, 0.412, -0.544}. Obviously I know that the "spacing" between each of these consecutive points would be 1 meter.

Now I take the DFT using Mathematica (using standard Fourier parameters), to get:

Fourier[{sin(1), sin(2), sin(3),...,sin(10)}] = {0.425 + 0.000*I, 0.570 - 0.270*I, -0.860 + 1.098*I, -0.034 + 0.218*I, 0.045 + 0.095*I, 0.066 + 0.028*I, 0.066 - 0.028*I,
0.045 - 0.095*I, -0.034 - 0.218*I, -0.860 - 1.098*I, 0.570 + 0.270*I}

Which is now a list of 11 complex numbers. It is my understanding that the independent variable will now have units of (1/meter)? Which would be a wavenumber? Let us just call this new independent variable k. Here is my real question. Imagine that now I plot the absolute value of this list of complex values on one axis vs k on the other axis. What would be the "spacing" (along the k axis) between each consecutive point in my transformed list?

Thanks for any help
 
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  • #2
I don't fully understand your question. However, the argument of a trig function must be a non-dimensional number - it can't be in meters, without an appropriate 1/meter factor.
 
  • #3
Is not the Fourier transform of something in the time domain something in the frequency domain? I know that it does not make sense to put numbers with units into the argument of the sine function. But just imagine that the x variable "represents" a distance even though it is a pure number.
 
  • #4
Before transforming, you had N = 11 samples with spacing dx = 1 m, with a total interval L = N dx = 11 m.

An FFT will return 11 Fourier coefficients corresponding to a spacing: dk = 1 / L = 1 / 11 [cycles/m].

However, the wavenumbers will be numbered (since this is an odd FFT) like this:
(-5, -4, ..., -1, 0, 1, 2, ..., 5)/11 [cycles/meter].
 

Related to Discrete Fourier Transform: How does independent varialbe spacing change?

1. How does changing the spacing of independent variables affect the Discrete Fourier Transform?

Changing the spacing of independent variables can affect the Discrete Fourier Transform in several ways. Firstly, it can change the frequency resolution of the transform, meaning that smaller spacing between variables can result in a more precise representation of the frequency components in the signal. Additionally, changing the spacing can also impact the computational complexity of the transform, potentially making it faster or slower depending on the chosen spacing.

2. Can changing the spacing of independent variables improve the accuracy of the Discrete Fourier Transform?

In some cases, changing the spacing of independent variables can improve the accuracy of the Discrete Fourier Transform. This is because smaller spacing can result in a more detailed representation of the signal, allowing for a more precise identification of frequency components. However, this is not always the case and the chosen spacing should be carefully considered based on the specific signal and desired outcome.

3. Is there a limit to how small or large the spacing of independent variables can be for the Discrete Fourier Transform?

Yes, there are limits to how small or large the spacing of independent variables can be for the Discrete Fourier Transform. If the spacing is too small, the transform may become computationally intensive and may also introduce errors due to finite precision. On the other hand, if the spacing is too large, it may result in a loss of information and a less accurate representation of the signal.

4. How does the number of data points affect the choice of independent variable spacing for the Discrete Fourier Transform?

The number of data points in a signal can play a role in determining the optimal spacing of independent variables for the Discrete Fourier Transform. Generally, a larger number of data points may allow for smaller spacing to be chosen, as there is more information available to accurately represent the signal's frequency components. However, the specific characteristics of the signal should also be taken into consideration when determining the best spacing.

5. Can changing the spacing of independent variables impact the interpretation of the Discrete Fourier Transform results?

Yes, changing the spacing of independent variables can impact the interpretation of the Discrete Fourier Transform results. This is because different choices of spacing can result in different representations of the same signal, potentially highlighting different frequency components or levels of detail. It is important to consider the chosen spacing when interpreting the results of the transform and to choose a spacing that best suits the desired outcome.

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