Data Science Day2

Fourier Transformation and Noise

I have learned Fourier transformation in maths classes, but i never know what is used for except decompose sin(x) and cos(x).
yesterday i knew Fourier Transformation can be used in Python to analyze noises. And there are many types of noises, which are differed by colors, white, blue and pink noises.

We did a Python code to plot White noise and Blue Noise.

Fourier transformation can decomposes a function of signal into frequencies,
Fourier Transform , then we can plot the frequencies in python phase_spectrum to detect the noise patterns and decide what methods we can reduce or filter some  noise under special circumstances.

Noise data has systematic error  and indeterminate(random) error.

Systematic error: error due to data collection procedures, miss read the scale. Those errors can be reduced.
Indeterminate(Random) error: statistical errors, such as Noise, which can not be reduced.

White Noise: Almost perfectly normal distributed around mean value, See below.

Capture

Blue Noise: Biased to higher noise.

Capture1

Pink Noise: Decreased to lower noise. We can use Pink Noise to help sleep.

Capture2

Interesting fact: if we have a huge spike around 60Hz; it is mostly due toElectric frequency.

Data used : voltage_3.

Detailed Code:Python voltage code

Thanks for Reading!

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