Fourier Series Approximations to Roots of Unity

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This Demonstration shows Fourier series approximations to functions representing the roots of unity, for . The "number of vertices" is equal to , with the roots of unity shown in blue. Each (gray) vector, representing the progressing Fourier series, is displayed along with the (orange) circle within which it rotates. The approximation is the point at the end of the final vector, indicated with a red dot. The "number of terms" equals the number of (gray) vectors/(orange) circles. You can select the angle that the vector from the origin makes with the ray () with the "angle" control. The checkbox lets you plot the partial Fourier sum (green), as the angle is swept from to .

Contributed by: Walden Freedman (August 2022)
After work by: Grant Sanderson (3Blue1Brown)
Open content licensed under CC BY-NC-SA



Let be the -periodic function defined by: for for . It can be shown that the Fourier series for is given by

where and is the ceiling function. This Demonstration shows the partial sums with between one and 50 terms.

With a smaller number of vertices and terms, you can see how the circular motions combine. Try a slowed-down animation with a smaller number of vertices, say 2, 3 or 4, and 5, 6 or 7 terms. With more terms, the approximation improves in accuracy.

This Demonstration is inspired by the videos on Fourier Series by Grant Sanderson of 3Blue1Brown. There are other related animations online, such as:

Snapshot 1: with , we see that for large values of , the first term with dominates and the green curve approximates the unit circle

Snapshot 2: With and an odd number of terms (7 in this case), we get an approximation between two "official" partial sums. The parametrization is not strictly real valued as it would be with an even number of terms; the number of vertices of the star-like figures one sees at is equal to the odd number of terms.

Snapshot 3: making the red dot larger can be helpful when viewing a large number of terms

Snapshot 4: viewing or animating slowly with a smaller number of terms and vertices can help one appreciate the mechanics of the motion


[1] E. M. Stein and R. Shakarchi, Fourier Analysis: An Introduction, Princeton, NJ: Princeton University Press, 2003.

[2] F. A. Farris, Creating Symmetry: The Artful Mathematics of Wallpaper Patterns, Princeton, NJ: Princeton University Press, 2015.

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