Metadata-Version: 2.4
Name: MorletWaveModal
Version: 0.6.4
Summary: Morlet Wave Modal Identification.
Project-URL: homepage, https://github.com/ladisk/MorletWaveModal
Project-URL: source, https://github.com/ladisk/MorletWaveModal
Author-email: "Ivan Tomac, Janko Slavič" <itomac@fesb.hr>
Maintainer-email: "Ivan Tomac, Janko Slavič" <itomac@fesb.hr>
License-Expression: MIT
License-File: LICENSE
Keywords: identification,modal parameters,morlet-wave
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.10
Requires-Dist: mwdi==0.73
Requires-Dist: numpy>=2.0.0
Requires-Dist: scipy>=1.0.0
Provides-Extra: dev
Requires-Dist: build; extra == 'dev'
Requires-Dist: pytest; extra == 'dev'
Requires-Dist: twine; extra == 'dev'
Description-Content-Type: text/x-rst

MWModal - Morlet-Wave Modal Identification 
------------------------------------------
This is the Python implementation of the method based on the Morlet-wave integral, which is developed for identification of modal parameters from short, noisy and low-dynamic-range signals [1]_.

This package is created within the H2020-MSCA-IF-2020 project `NOSTRADAMUS`_.


Simple example
---------------
A simple example how to identify modal parameters using Morlet-Wave Modal package:

.. code-block:: python

    import mwmodal as mwm
    import numpy as np

    # set time domain
    fs = 5000 # sampling frequency [Hz]
    T = 2 # signal duration [s]
    time = np.arange(T*fs) / fs # time vector

    # generate a free response of a SDOF damped mechanical system
    w_d = 2*np.pi * 100 # damped natural frequency
    d = 0.01 # damping ratio
    x = 1 # amplitude
    phi = 0.3 # phase
    response = x * np.exp(-d * w_d / np.sqrt(1 - d**2) * time) * np.cos(w_d * time - phi)

    # set MorletWaveModal object identifier
    identifier = mwm.MorletWaveModal(free_response=response, fs=fs)

    #  set initial natural frequency, estimate damping ratio and identify modal parameters
    identifier.identify_modal_parameters(omega_estimated=w_n, damping_estimated=0.005)

References
----------
.. [1] I\. Tomac, J. Slavič, Morlet-wave-based modal identification in the time domain, Mechanical Systems and Signal Processing 192C (2023) 110243, doi: `10.1016/j.ymssp.2023.110243`_.


.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.7002904.svg
   :target: https://doi.org/10.5281/zenodo.7002904

.. _NOSTRADAMUS: http://ladisk.si/?what=incfl&flnm=nostradamus.php
.. _10.1016/j.ymssp.2023.110243: https://authors.elsevier.com/sd/article/S0888-3270(23)00150-4

.. image:: https://github.com/ladisk/MorletWaveModal/actions/workflows/python-package.yml/badge.svg
    :target: https://github.com/ladisk/MorletWaveModal/actions