pyExSi - Excitation signals as used in structural dynamics and vibration fatigue
--------------------------------------------------------------------------------
Supported excitation signals are:
- pulse (e.g. half-sine)
- random:
- uniform random distribution
- normal random distribution
- pseudorandom distribution
- random, defined by power spectral density (PSD):
- stationary Gaussian
- stationary non-Gaussian
- non-stationary non-Gaussian random process
- burst random
- sine sweep
Simple example
---------------
A simple example on how to generate random signals on PSD basis:
.. code-block:: python
import pyExSi as es
import numpy as np
N = 2**16 # number of data points of time signal
fs = 1024 # sampling frequency [Hz]
t = np.arange(0,N)/fs # time vector
# define frequency vector and one-sided flat-shaped PSD
M = N//2 + 1 # number of data points of frequency vector
freq = np.arange(0, M, 1) * fs / N # frequency vector
freq_lower = 50 # PSD lower frequency limit [Hz]
freq_upper = 100 # PSD upper frequency limit [Hz]
PSD = es.get_psd(freq, freq_lower, freq_upper) # one-sided flat-shaped PSD
#get gaussian stationary signal
gausian_signal = es.random_gaussian((N, PSD, fs)
#get non-gaussian non-stationary signal, with kurtosis k_u=10
#amplitude modulation, modulating signal defined by PSD
PSD_modulating = es.get_psd(freq, freq_lower=1, freq_upper=10)
#define array of parameters delta_m and p
delta_m_list = np.arange(.1,2.1,.5)
p_list = np.arange(.1,2.1,.5)
#get signal
nongaussian_nonstationary_signal = es.nonstationary_signal(N,PSD,fs,k_u=5,modulating_signal=('PSD', PSD_modulating),param1_list=p_list,param2_list=delta_m_list)
|DOI| |Build Status| |Docs Status|
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:target: https://doi.org/10.5281/zenodo.4431844
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"description": "pyExSi - Excitation signals as used in structural dynamics and vibration fatigue\n--------------------------------------------------------------------------------\nSupported excitation signals are:\n\n- pulse (e.g. half-sine)\n- random:\n\n - uniform random distribution\n - normal random distribution\n - pseudorandom distribution\n\n- random, defined by power spectral density (PSD):\n\n - stationary Gaussian\n - stationary non-Gaussian\n - non-stationary non-Gaussian random process\n\n- burst random\n- sine sweep\n\n\nSimple example\n---------------\n\nA simple example on how to generate random signals on PSD basis:\n\n.. code-block:: python\n\n import pyExSi as es\n import numpy as np\n\n\n N = 2**16 # number of data points of time signal\n fs = 1024 # sampling frequency [Hz]\n t = np.arange(0,N)/fs # time vector\n\n # define frequency vector and one-sided flat-shaped PSD\n M = N//2 + 1 # number of data points of frequency vector\n freq = np.arange(0, M, 1) * fs / N # frequency vector\n freq_lower = 50 # PSD lower frequency limit [Hz]\n freq_upper = 100 # PSD upper frequency limit [Hz]\n PSD = es.get_psd(freq, freq_lower, freq_upper) # one-sided flat-shaped PSD\n\n #get gaussian stationary signal\n gausian_signal = es.random_gaussian((N, PSD, fs)\n\n #get non-gaussian non-stationary signal, with kurtosis k_u=10\n #amplitude modulation, modulating signal defined by PSD\n PSD_modulating = es.get_psd(freq, freq_lower=1, freq_upper=10) \n #define array of parameters delta_m and p\n delta_m_list = np.arange(.1,2.1,.5) \n p_list = np.arange(.1,2.1,.5)\n #get signal \n nongaussian_nonstationary_signal = es.nonstationary_signal(N,PSD,fs,k_u=5,modulating_signal=('PSD', PSD_modulating),param1_list=p_list,param2_list=delta_m_list)\n\n|DOI| |Build Status| |Docs Status|\n\n.. |Docs Status| image:: https://readthedocs.org/projects/pyexsi/badge/\n :target: https://pyexsi.readthedocs.io\n\n.. |Build Status| image:: https://travis-ci.com/ladisk/pyExSi.svg?branch=main\n :target: https://travis-ci.com/ladisk/pyExSi\n\n.. |DOI| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.4431844.svg\n :target: https://doi.org/10.5281/zenodo.4431844\n\n",
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