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I have a 3D grid in real space of grid spacing $L$ and say 21 grid points in each direction, containing e.g. a charge distribution. This is stored as a numpy array of shape (21, 21, 21). The grid points themselves are of course vectors and so are stored as a numpy array of shape (3, 21, 21, 21).

I also have a set of plane waves that form a basis for reciprocal space. These are of the form $$ \psi_{\alpha}(\vec r) = \exp\left(i\vec G_\alpha\cdot \vec r\right) $$ such that $|\vec G_\alpha|^2 \le \text{cutoff}$, so as to form a finite set. For reference, all the $\vec G_\alpha$ are vertically stacked to form an $\alpha\times3$ matrix, which I'll call $\mathbb G$. Now we can expand our charge density $n(\vec r)$ in terms of the basis: $$ n(\vec r) = c_\alpha\psi_\alpha(\vec r) $$

This means that the computational representation for the charge density is an ordered list of the $\alpha$ numbers $c_\alpha$, i.e. an $\alpha$-length vector $\vec c$. My current objective is to reconstruct the real-space charge density from $\vec c$.

The way I currently do this is through the following code:

I = np.exp(1j * G @ real_grid)
density_reconstruction = c @ I

In other words, I precompute the tensor $\mathbb I$ of shape (α, 21, 21, 21) which is essentially the value of each of the basis functions on each of the grid points. I then matrix multiply/contract along the $\alpha$-dimension with $\vec c$ to get a (21, 21, 21) array - this is the reconstructed charge density. Although this method works, the matrix multiplication in the second step is very expensive.

Question: I have heard that, instead of using a costly matrix multiplication, it is possible to do perform this step using a Fourier transform (in particular, FFT), owing to the form of the plane wave. How can I implement this, e.g. using the scipy.fft package? How does it work for the case where the number of basis functions $\ne$ the number of real-space grid points?

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The whole point of the FFT is to avoid the costly matrix-vector multiplication of the DFT, which has an effort of $\mathcal O(N^2)$ (where $N = N_x N_y N_z$), and perform the unitary transformation iteratively to obtain $\mathcal O(N \log N)$. What you do is, in the first step, simply Fast-Fourier-Transform your data to get the frequency components (which are often ordered a bit differently as one would expect, see the documentation of your chosen FFT). Moreover, you can save another factors of two if you have real and/or even/odd grid-data.

For the case, where the number of plane waves $\neq$ grid-points, you can apply the normal FFT, and interpolate the frequency-points to a new grid using trigonometric interpolation (which should be preferably smaller or equal than the number of gridpoints, otherwise you fake a resolution which is not there). This scales as long as the number of constrained frequency-points is not much smaller than the original number of gridpoints (if that is the case, one can return to normal DFT again).

No scipy.fft interpolation, sorry.


A few more notes about the interpolation approach (the discussion was brought up by @ConvexHull, thanks again for that).

If you do a Fourier transform of a $2\pi$-periodic function $f(x)$, you'll basically calculate:

$$c_k = \frac{1}{2\pi} \int_{0}^{2\pi} f(x)\, e^{-i k x} \, dx$$

to obtain the Fourier transform

$$ f(x)=\sum_{k=-\infty}^\infty c_k\, e^{i k x} = \sum_{k=-\infty}^\infty c_k\, z^k $$

with $z=e^{ikx}$. If you'll approximate this for convenience by an odd number of Fourier modes $(2N+1)$, you'll get

$$ f_N(x)=\sum_{k=-N}^N c_k\, z^k $$

The coefficients $c_k$ is what the DFT/FFT yields for a grid of size $(2N+1)$. The way to up- and down-sample this term to a function $f_M(x), M\neq N$ is particularly clear: for $M>N$, set all $c_{k}=0$ where $|k|>N$. And for $M<N$, simply truncate the $c_{k}$ with $|k|>M$. This is what was suggested by @ConvexHull, as far as I understood, and it's correct.

The same effect would be obtained by trigonometric interpolation in the real/nodal space down to $M$ equally spaced gridpoints, and thereafter performing the FFT on this reduced grid. The trigonometric interpolation in barycentric form is given, e.g., here. However, as this trigonometric interpolation requires an effort of $\mathcal O(MN)$, there is no advantage in using it over the truncation of the Fourier series.

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  • $\begingroup$ The interpolation in the frequency space sounds a bit weird, however should work in some sense. A more correct way is to truncate/pad your trigonometric expansion (modal representation). Otherwise you would introduce aliasing in the real space or would violate the integral mean. $\endgroup$
    – ConvexHull
    Jun 20 at 18:05
  • $\begingroup$ @ConvexHull: Nope, interpolation in frequency space is imo exactly the right tool. You get a trigonometric polynomial, which you can re-sample at any frequency-nodes as long as the number is smaller than the origonal nodes. You then get a trigonometric polynomial of smaller degree, which can be represented exactly. Chebyshev interpolation does that in excatly the same way, but for non-periodic functions. $\endgroup$
    – davidhigh
    Jun 20 at 18:10
  • $\begingroup$ One brainteaser: What would happen with the real space data if your zero'th mode would not be included after the interpolation in frequency space? $\endgroup$
    – ConvexHull
    Jun 20 at 18:16
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    $\begingroup$ @ConvexHull: sorry, you're absolutely right ... I was thinking the wrong direction (upsampling). If you down-sample a polynomial, you'll end up with an approximate lower-degree polynomial where the integral, for example, is not constant. And you''l get aliasing, yes. Thanks for pointing. $\endgroup$
    – davidhigh
    Jun 20 at 18:29
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    $\begingroup$ I have to correct myself. The results are identical because the underlying polynomials are trigonometric and not hierarchical. $\endgroup$
    – ConvexHull
    Jun 23 at 11:15

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