Estimate power spectral density characteristics using Welch's method

# 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}}
}


## Project details

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