Analyzing the frequency components of a signal with a Fast Fourier Transform
In this recipe, we will show how to use a Fast Fourier Transform (FFT) to compute the spectral density of a signal. The spectrum represents the energy associated to frequencies (encoding periodic fluctuations in a signal). It is obtained with a Fourier transform, which is a frequency representation of a time-dependent signal. A signal can be transformed back and forth from one representation to the other without information loss.
In this recipe, we will illustrate several aspects of the Fourier Transform. We will apply this tool to weather data spanning 20 years in France obtained from the US National Climatic Data Center.
Getting ready
Download the Weather dataset from the book's GitHub repository at http://github.com/ipython-books/cookbook-data, and extract it in the current directory.
The data has been obtained from www.ncdc.noaa.gov/cdo-web/datasets#GHCND.
How to do it...
Let's import the packages, including
scipy...
