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11.1: Conversion of Continuous Fourier Transform to DFT

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    34864
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    The DFT is commonly encountered when discretizing formulas involving Fourier integrals. Recall the definition of the Fourier transform: given a function \(f(x)\), where \(x \in (-\infty, \infty)\), the Fourier transform is a function \(F(k)\), where \(k \in (-\infty, \infty)\), and these two functions are related by a pair of integral formulas:

    \[\begin{align} F(k) &= \displaystyle \;\; \int_{-\infty}^\infty dx\; e^{-ikx}\, f(x) \\ f(x) &=\; \displaystyle \int_{-\infty}^\infty \frac{dk}{2\pi}\; e^{ikx}\, F(k).\end{align}\]

    Typically, a computer simulation or experimental measurement will produce values of \(f(x)\) at certain values of \(x\) that are discrete and evenly-spaced. Suppose these points are \(\{x_0, x_1, \dots, x_{N-1}\}\), where the spacing is \(\Delta x = x_{m+1}-x_m\); the corresponding data points are \(\{f(x_0), \dots, f(x_{N-1})\}\). We are then interested in finding the Fourier spectrum, i.e. plotting either \(|F(k)|\) or \(|F(k)|^2\) versus \(k\). To do this, we can approximate the Fourier integral by using the mid-point rule:

    \[F(k) \approx \Delta x \sum_{m=0}^{N-1} e^{-ik x_m} \, f(x_m).\]

    Note that this necessitates a truncation of the Fourier integral. The Fourier integral ran over \(-\infty < x < \infty\), but our numerical integral runs over a finite range \(x_0 \lesssim x \lesssim x_{N-1}\). This truncation will have important consequences later. Now, we have to decide the values of \(k\) at which to find \(F(k)\). Let us choose a set of \(N\) equally-spaced points,

    \[k_n \equiv \frac{2\pi n}{N\Delta x}.\]

    At these points, the discretized Fourier integral takes the form

    \[\begin{align}F(k_n) &\approx \Delta x\, \sum_{m=0}^{N-1} \exp\left[- \frac{2 \pi i n (x_0 + m \Delta x)}{N \Delta x}\right] \, f(x_m) \\ &= \Delta x\, \exp\left[- \frac{2 \pi i n x_0}{N \Delta x}\right] \sum_{m=0}^{N-1} e^{-i 2 \pi n m / N} \, f(x_m) \\ &= \Delta x\, \exp\left[- \frac{2 \pi i n x_0}{N \Delta x}\right] \mathrm{DFT}\{f(x_m)\}_n.\end{align}\]

    Here \(\mathrm{DFT}\{f(x_m)\}_n\) denotes the \(n\)-th element of the Discrete Fourier Transform (DFT). The \(m\) index inside the curly brackets is a dummy index, indicating that the DFT involves an internal sum over this index (we're slightly abusing mathematical notation here). The phase factor, \(\exp[- 2 \pi i n x_0/N \Delta x]\), is determined by the choice of "origin" for the spatial coordinates; it does not affect \(|F(k_n)|^2\) (which is what's used to plot the Fourier spectrum).

    The DFT and IDFT can be computed very efficiently, in \(O(N\log N)\) time, using an algorithm called the Fast Fourier Transform (FFT). We will not discuss the FFT algorithm in this article, but many good explanations can be found elsewhere online. In Python, you can perform an FFT (fast DFT) by calling fft, and an inverse FFT (fast IDFT) by calling ifft


    11.1: Conversion of Continuous Fourier Transform to DFT is shared under a CC BY-SA 4.0 license and was authored, remixed, and/or curated by Y. D. Chong via source content that was edited to conform to the style and standards of the LibreTexts platform; a detailed edit history is available upon request.