Chapter 6. Analysis of data and curves

Table of Contents

Fast Fourier Transform
Correlation
Convolution
Deconvolution
The Fit Wizard
Fitting to specific curves
Fitting to a line
Fitting to a polynome
Fitting to a Bolzmann function
Fitting to a Gauss function
Fitting to a Lorentz function
Multi-Peaks fitting
Filtering of data curves
FFT low pass filter
FFT high pass filter
FFT band pass filter
FFT block band filter
Interpolation

Fast Fourier Transform

This function can be accessed by the command FFT.... It can be found in the Analysis Menu when a table or a plot is selected. The Fourier transform decomposes a signal in its elementary components by assuming that the signal x(t) can be describe as a sum:

Equation 6.1. Fourier equation

in which are the frequencies, an are the amplitudes of each frequency and are the phase corresponding frequency. QtiPlot will compute these parameters and build a new plot of the amplitude as a function of the frequency.

Figure 6.1. An example of a inverse FFT.

FFT performed on a curve to extract the characteristic frequencies. The signal is on the bottom plot, while the amplitude-frequency plot is on the top layer. In this example, the amplitude curve has been normalized, and the frequencies have been shifted to obtain a centered x-scale.

An example of a inverse FFT.

Some parameters of the FFT can be modified in the FFT dialog.