Consider a series of $N$ measurements $x_n$ that are made at equally spaced time intervals $\Delta t$. The total time to make the measurement series is $N\Delta t$. A discrete Fourier transform can be used to find a periodic function $x(t)$ with a fundamental period $N\Delta t$ that passes through all of the points. This function can be expressed as a Fourier series in terms of sines and cosines,
\[ \begin{equation} \label{eq:sines} x(t) = \sum\limits_{n=0}^{n < N/2}\left[c_n\cos\left(\frac{2\pi nt}{N\Delta t}\right)+s_n\sin\left(\frac{2\pi nt}{N\Delta t}\right)\right]. \end{equation} \]Data for $x_n$ can be input in the textbox below. When the 'Calculate Fourier Coefficients' button is pressed, the periodic function $x(t)$ is plotted through the data points. The Fourier coefficients are tabulated and plotted as well. The fft algorithm first checks if the number of data points is a poweroftwo. If so, it calculates the discrete Fourier transform using a CooleyTukey decimationintime radix2 algorithm. If the number of data points is not a poweroftwo, it uses Bluestein's chirp ztransform algorithm. The fft code was taken from Project Nayuki.
$x_n$ 
 
Fourier coefficients calculated 

The power spectrum $S_{xx}$ specifies the power there is in the signal in every frequency interval. This quantity is sometimes called the power spectral density (PSD).
\[ \begin{equation} S_{xx} = c_n^2+s_n^2. \end{equation} \]The notation comes from problems with more variables where $S_{xx}$ is the power spectrum of variable $x$, $S_{yy}$ is the power spectrum of variable $y$, and $S_{xy}$ describes the cross correlations of variables $x$ and $y$. The units of the power spectrum are $[x]^2/\text{Hz}$, where $[x]$ are the units of the variable $x$. If the measurement points represent a voltage, the units of $S_{xx}$ are $\text{V}^2/\text{Hz}$. Sometimes a noise signal is specified as the square root of $S_{xx}$. A voltage amplifier manufacturer might specify the voltage noise as $10 \text{nV}/\sqrt{\text{Hz}}$ @ 1 kHz. If they leave the specification @ 1 kHz out, it implies they are using the frequency at which the noise is the lowest.
$f$ $S_{xx}$ $\sqrt{S_{xx}}$ For the plot, the $\sqrt{S_{xx}}$ curve was 

A digital filter multiplies the Fourier coefficients by a filter function that can selectively enhance or suppress some frequency components of the signal. Some buttons are provided for filters but it is possible to use any mathematical expression involving $f$, $f_0$, and $f_1$ as the filter function.
 
Filtered fit function $x(t)$ $t$ $x$ 

Often in an experiment, a preamplifier acts like a digital filter. Usually an amplifier acts like a lowpass filter where the cutoff frequency is lower for higher amplification settings. Sometimes a capacitor is put in series with the amplifier to achieve accoupling. In this case the amplifier acts like a bandpass filter.
The Fourier series can be used to estimate the derivative and the integral of the data series. The derivative is,
\[ \begin{equation} \frac{dx}{dt} = \sum\limits_{n=1}^{n < N/2}\frac{2\pi n}{N\Delta t}\left[c_n\sin\left(\frac{2\pi nt}{N\Delta t}\right)+s_n\cos\left(\frac{2\pi nt}{N\Delta t}\right)\right]. \end{equation} \]The high frequency components get amplified by a factor of $f$ when the derivative is taken so it is often useful to filter out the high frequency noise before differentiating. The derivative of the filtered fit is tabulated and plotted below.
$t$ $\frac{dx}{dt}$ 

The integral of the Fourier series is,
\[ \begin{equation} \int\limits_0^{t}x(t')dt' = a_0t+\sum\limits_{n=1}^{n < N/2}\frac{N\Delta t}{2\pi n}\left[c_n\sin\left(\frac{2\pi nt}{N\Delta t}\right)s_n\left(\cos\left(\frac{2\pi nt}{N\Delta t}\right)1\right)\right]. \end{equation} \]Here the integration constant was chosen so that the integral is zero at $t=0$. The integral of the filtered fit is tabulated and plotted below.
$t$ $\int\limits_0^{t}xdt'$ 
