Metadata-Version: 2.1
Name: vmdpy
Version: 0.1
Summary: Variational Mode Decomposition (VMD) algorithm
Home-page: http://github.com/vrcarva/vmdpy
Author: Vinicius Rezende Carvalho
Author-email: vrcarva@ufmg.br
License: UNKNOWN
Description: # Variational mode decomposition Python Package
        
        Function for calculating Variational Mode Decomposition (Dragomiretskiy and Zosso, 2014) of a signal  
        Original VMD paper:  
        Dragomiretskiy, K. and Zosso, D. (2014) â€˜Variational Mode Decompositionâ€™, 
        IEEE Transactions on Signal Processing, 62(3), pp. 531â€“544. doi: 10.1109/TSP.2013.2288675.
        
        original MATLAB code: https://www.mathworks.com/matlabcentral/fileexchange/44765-variational-mode-decomposition  
        
        
        ## Installation 
        
        1) Dowload the project from https://github.com/vrcarva/vmdpy, then run "python setup.py install" from the project folder
        
        OR
        
        2) pip install vmdpy
        
        ## Citation and Contact
        If you find this package useful, we kindly ask you to cite it in your work.   
        Vinicius Carvalho (2019-), Variational Mode Decomposition in Python  
        
        A paper will soon be submitted and linked here.  
        
        contact: vrcarva@ufmg.br  
        VinÃ­cius Rezende Carvalho  
        Programa de PÃ³s-GraduaÃ§Ã£o em Engenharia ElÃ©trica â€“ Universidade Federal de Minas Gerais, Belo Horizonte, Brasil  
        NÃºcleo de NeurociÃªncias - Universidade Federal de Minas Gerais  
        
        
        ## Example script
        ```python
        #%% Simple example  
        import numpy as np  
        import matplotlib.pyplot as plt  
        from vmdpy import VMD  
        
        #. Time Domain 0 to T  
        T = 1000  
        fs = 1/T  
        t = np.arange(1,T+1)/T  
        freqs = 2*np.pi*(t-0.5-fs)/(fs)  
        
        #. center frequencies of components  
        f_1 = 2  
        f_2 = 24  
        f_3 = 288  
        
        #. modes  
        v_1 = (np.cos(2*np.pi*f_1*t))  
        v_2 = 1/4*(np.cos(2*np.pi*f_2*t))  
        v_3 = 1/16*(np.cos(2*np.pi*f_3*t))  
        
        f = v_1 + v_2 + v_3 + 0.1*np.random.randn(v_1.size)  
        
        #. some sample parameters for VMD  
        alpha = 2000       # moderate bandwidth constraint  
        tau = 0.            # noise-tolerance (no strict fidelity enforcement)  
        K = 3              # 3 modes  
        DC = 0             # no DC part imposed  
        init = 1           # initialize omegas uniformly  
        tol = 1e-7  
        
        
        #. Run actual VMD code  
        u, u_hat, omega = VMD(f, alpha, tau, K, DC, init, tol)  
        ```
Keywords: VMD,variational,decomposition
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
