xi.py
def eig_analysis_xi(xi, postfix = ''):
    """
    Analyze the eigenvalues of the transformation matrix xi

    sp: scipy or numpy
    la: linalg
    """
    size = min(xi.shape)
    xi_eigval, xi_eigvec = la.eig(xi[0 : size, 0 : size])
    # Now I plot the abs of eigenvalues
    out_eigval = sorted(abs(xi_eigval), reverse = True)
    plt.stem(out_eigval)
    plt.xlim([-1, size])
    #plt.savefig('test_xi_eig.eps', format = 'eps', dpi = 1000)
    plt.savefig('xi_eig{0}.png'.format(postfix), format = 'png')
    plt.close()
    sp.savetxt('xi_eig{0}.dat'.format(postfix), out_eigval, delimiter = ' ')
    # Analyze eigenvalues and return a message
    msg = ''
    return msg

    Analyze the eigenvalues of the transformation matrix xi

    sp: scipy or numpy
    la: linalg
    
Analyze the eigenvalues of the transformation matrix xi
sp: scipy or numpy
la: linalg
Analyze the eigenvalues of the transformation matrix xi sp : scipy or numpy la : linalg
def eig_analysis_xi(xi, postfix = ''):
    size = min(xi.shape)
    xi_eigval, xi_eigvec = la.eig(xi[0 : size, 0 : size])
    out_eigval = sorted(abs(xi_eigval), reverse = True)
    plt.stem(out_eigval)
    plt.xlim([-1, size])
    plt.savefig('xi_eig{0}.png'.format(postfix), format = 'png')
    plt.close()
    sp.savetxt('xi_eig{0}.dat'.format(postfix), out_eigval, delimiter = ' ')
    msg = ''
    return msg
def eig_analysis_xi ( xi , postfix = '' ) : size = min ( xi . shape ) xi_eigval , xi_eigvec = la . eig ( xi [ 0 : size , 0 : size ] ) out_eigval = sorted ( abs ( xi_eigval ) , reverse = True ) plt . stem ( out_eigval ) plt . xlim ( [ - 1 , size ] ) plt . savefig ( 'xi_eig{0}.png' . format ( postfix ) , format = 'png' ) plt . close ( ) sp . savetxt ( 'xi_eig{0}.dat' . format ( postfix ) , out_eigval , delimiter = ' ' ) msg = '' return msg
Analyze the eigenvalues of the transformation matrix xi
sp: scipy or numpy
la: linalg
Analyze the eigenvalues of the transformation matrix xi sp : scipy or numpy la : linalg
"""
    Analyze the eigenvalues of the transformation matrix xi

    sp: scipy or numpy
    la: linalg
    """ # Now I plot the abs of eigenvalues #plt.savefig('test_xi_eig.eps', format = 'eps', dpi = 1000) # Analyze eigenvalues and return a message
xi
postfix