MWDI - Morlet-Wave Damping Identification
------------------------------------------
This is the Python implementation of the Morlet-Wave damping identification method, see [1]_ and [2]_ for details.
This package is based on the `MorletDamping`_ code developed by WANG Longqi and was created within the
MSCA IF project `NOSTRADAMUS`_.
Simple example
---------------
A simple example how to identify damping using MWDI method:
.. code-block:: python
import mwdi as mw
import numpy as np
# set time domain
fs = 5000 # sampling frequency [Hz]
N = 5000 # number of data points of time signal
time = np.arange(N) / fs # time vector
# generate a free response of a SDOF damped mechanical system
w_n = 2*np.pi * 100 # undamped natural frequency
d = 0.01 # damping ratio
x = 1 # amplitude
phi = 0.3 # phase
response = x * np.exp(-d * w_n * time) * np.cos(w_n * np.sqrt(1 - d**2) * time - phi)
# set MWDI object identifier
identifier = mw.MorletWave(free_response=response, fs=fs)
# identify damping
dmp = identifier.identify_damping(w=w_n, root_finding='Newton')
print(dmp)
References
----------
.. [1] J\. Slavič, M. Boltežar, Damping identification with the Morlet-wave, Mechanical Systems and Signal Processing, 25 (2011) 1632–1645, doi: `10.1016/j.ymssp.2011.01.008`_.
.. [2] I\. Tomac, J. Slavič, Damping identification based on a high-speed camera. Mechanical Systems and Signal Processing, 166 (2022) 108485–108497, doi: `10.1016/j.ymssp.2021.108485`_.
.. _NOSTRADAMUS: http://ladisk.si/?what=incfl&flnm=nostradamus.php
.. _MorletDamping: https://github.com/wanglongqi/MorletDamping
.. _10.1016/j.ymssp.2011.01.008: https://doi.org/10.1016/j.ymssp.2011.01.008
.. _10.1016/j.ymssp.2021.108485: https://doi.org/10.1016/j.ymssp.2021.108485
|Build Status| |Zenodo|
.. |Build Status| image:: https://travis-ci.com/ladisk/mwdi.svg?branch=main
:target: https://travis-ci.com/ladisk/mwdi
.. |Zenodo| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.7002813.svg
:target: https://doi.org/10.5281/zenodo.7002813
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"description": "MWDI - Morlet-Wave Damping Identification \n------------------------------------------\nThis is the Python implementation of the Morlet-Wave damping identification method, see [1]_ and [2]_ for details.\n\nThis package is based on the `MorletDamping`_ code developed by WANG Longqi and was created within the \nMSCA IF project `NOSTRADAMUS`_.\n\n\nSimple example\n---------------\n\nA simple example how to identify damping using MWDI method:\n\n.. code-block:: python\n\n import mwdi as mw\n import numpy as np\n\n # set time domain\n fs = 5000 # sampling frequency [Hz]\n N = 5000 # number of data points of time signal\n time = np.arange(N) / fs # time vector\n\n # generate a free response of a SDOF damped mechanical system\n w_n = 2*np.pi * 100 # undamped natural frequency\n d = 0.01 # damping ratio\n x = 1 # amplitude\n phi = 0.3 # phase\n response = x * np.exp(-d * w_n * time) * np.cos(w_n * np.sqrt(1 - d**2) * time - phi)\n\n # set MWDI object identifier\n identifier = mw.MorletWave(free_response=response, fs=fs)\n\n # identify damping\n dmp = identifier.identify_damping(w=w_n, root_finding='Newton')\n print(dmp)\n\nReferences\n----------\n.. [1] J\\. Slavi\u010d, M. Bolte\u017ear, Damping identification with the Morlet-wave, Mechanical Systems and Signal Processing, 25 (2011) 1632\u20131645, doi: `10.1016/j.ymssp.2011.01.008`_.\n.. [2] I\\. Tomac, J. Slavi\u010d, Damping identification based on a high-speed camera. Mechanical Systems and Signal Processing, 166 (2022) 108485\u2013108497, doi: `10.1016/j.ymssp.2021.108485`_.\n\n.. _NOSTRADAMUS: http://ladisk.si/?what=incfl&flnm=nostradamus.php\n.. _MorletDamping: https://github.com/wanglongqi/MorletDamping\n.. _10.1016/j.ymssp.2011.01.008: https://doi.org/10.1016/j.ymssp.2011.01.008\n.. _10.1016/j.ymssp.2021.108485: https://doi.org/10.1016/j.ymssp.2021.108485\n\n|Build Status| |Zenodo|\n\n.. |Build Status| image:: https://travis-ci.com/ladisk/mwdi.svg?branch=main\n :target: https://travis-ci.com/ladisk/mwdi\n \n.. |Zenodo| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.7002813.svg\n :target: https://doi.org/10.5281/zenodo.7002813\n",
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