#### Tutorials > Numerical Computing in Julia

# FFT Derivative

## Introduction

In many applications, we want to take advantage that the derivative operator is transformed into a multiplication operator in Fourier space. By applying a subsequent inverse transform, we can expect to obtain the derivative of a function using FFTs, using the well-known relation:

$$ \mathcal{F}[g’](\xi) = \frac{2\pi}{L} \xi \mathcal{F}[g](\xi) $$

However, this is not so simple to translate into the discrete setting.

## Common Gotcha: Alising

For example, let’s compute the FFT-derivative of a periodic function defined in \( (0,L) \). The following code yields an incorrect result:

```
using FFTW
using plots
N = 20;
L = 1;
xj = (0:N-1)*L/N;
f = sin.(2π*xj)
df = 2π*cos.(2π*xj)
k = 1:N
df_fft = ifft( 2π/L * k.* fft(f) )
plot(xj,real(df),label="Exact derivative")
plot!(xj,real(df_fft),label="incorrect FFT derivative",markershape=:circle)
```

**The reason this doesn’t work is related to aliasing**, which we discussed in the previous section. In detail, what’s going on under the hood is that, even though **in our grid, we have the equality**

$$ \sin(5 x_j) = \frac{e^{-5 i x_j} + e^{5 i x_j}}{2i} + \frac{e^{15 i x_j} + e^{5 i x_j}}{2i}, $$

clearly, the frequency corresponding to k=15 should not be employed when computing the derivative.

**The correct derivative algorithm uses the negative frequencies mentioned in the previous section**.

## Correct FFT-Derivative Algorithm

In order to obtain the correct result, we to correct these two lines:

```
k = fftfreq(N)*N;
df_fft = ifft( 2π*im/L * k.* fft(f) );
```

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