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Estimate power spectral density characteristics using Welch's method

Project description

psd2

Estimation of power spectral density characteristics using Welch's method

The function psd2.py from Python module psd2 estimates power spectral density characteristics using Welch's method. This function is just a wrap of the scipy.signal.welch function with estimation of some frequency characteristics and a plot. The psd2.py returns power spectral density data, frequency percentiles of the power spectral density (for example, Fpcntile[50] gives the median power frequency in Hz); mean power frequency; maximum power frequency; total power, and plots power spectral density data.

Installation

pip install psd2

Or

conda install -c duartexyz psd2

Examples

#Generate a test signal, a 2 Vrms sine wave at 1234 Hz, corrupted by
# 0.001 V**2/Hz of white noise sampled at 10 kHz and calculate the PSD:
>>> fs = 10e3
>>> N = 1e5
>>> amp = 2*np.sqrt(2)
>>> freq = 1234.0
>>> noise_power = 0.001 * fs / 2
>>> time = np.arange(N) / fs
>>> x = amp*np.sin(2*np.pi*freq*time)
>>> x += np.random.normal(scale=np.sqrt(noise_power), size=time.shape)
>>> psd2(x, fs=freq);

How to cite this work

Here is a suggestion to cite this GitHub repository:

Duarte, M. (2020) psd2: A Python module for estimation of power spectral density characteristics using Welch's method. GitHub repository, https://github.com/demotu/psd2.

And a possible BibTeX entry:

@misc{Duarte2020,  
    author = {Duarte, M.},
    title = {psd2: A Python module for estimation of power spectral density characteristics using Welch's method},  
    year = {2020},  
    publisher = {GitHub},  
    journal = {GitHub repository},  
    howpublished = {\url{https://github.com/demotu/psd2}}  
}

License

The non-software content of this project is licensed under a Creative Commons Attribution 4.0 International License, and the software code is licensed under the MIT license.

Project details


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