ebe_scripts/analysis_multi_particle_correlation_oversample_UrQMDevent.py
def calcualte_vn_2_delta_eta(vn_A_data_array, vn_B_data_array):
    """
        this function computes vn{2} and its stat. err.
        sample A and B are assumed to have no overlap
        no self correlation substraction needed
    """
    vn_A_data_array = array(vn_A_data_array)
    vn_B_data_array = array(vn_B_data_array)
    nev = len(vn_A_data_array[:, 0])
    dN_A = vn_A_data_array[:, 0]
    dN_A = dN_A.reshape(len(dN_A), 1)
    dN_B = vn_B_data_array[:, 0]
    dN_B = dN_B.reshape(len(dN_B), 1)
    Qn_A_array = dN_A*vn_A_data_array[:, 1:]
    Qn_B_array = dN_B*vn_B_data_array[:, 1:]
    corr = 1./(dN_A*dN_B)*Qn_A_array*conj(Qn_B_array)
    vn_2 = sqrt(real(mean(corr, 0))) + 1e-30
    vn_2_err = std(real(corr), 0)/sqrt(nev)/2./vn_2
    return(vn_2, vn_2_err)

        this function computes vn{2} and its stat. err.
        sample A and B are assumed to have no overlap
        no self correlation substraction needed
    
this function computes vn{2} and its stat. err.
sample A and B are assumed to have no overlap
no self correlation substraction needed
this function computes vn { 2 } and its stat . err . sample A and B are assumed to have no overlap no self correlation substraction needed
def calcualte_vn_2_delta_eta(vn_A_data_array, vn_B_data_array):
    vn_A_data_array = array(vn_A_data_array)
    vn_B_data_array = array(vn_B_data_array)
    nev = len(vn_A_data_array[:, 0])
    dN_A = vn_A_data_array[:, 0]
    dN_A = dN_A.reshape(len(dN_A), 1)
    dN_B = vn_B_data_array[:, 0]
    dN_B = dN_B.reshape(len(dN_B), 1)
    Qn_A_array = dN_A*vn_A_data_array[:, 1:]
    Qn_B_array = dN_B*vn_B_data_array[:, 1:]
    corr = 1./(dN_A*dN_B)*Qn_A_array*conj(Qn_B_array)
    vn_2 = sqrt(real(mean(corr, 0))) + 1e-30
    vn_2_err = std(real(corr), 0)/sqrt(nev)/2./vn_2
    return(vn_2, vn_2_err)
def calcualte_vn_2_delta_eta ( vn_A_data_array , vn_B_data_array ) : vn_A_data_array = array ( vn_A_data_array ) vn_B_data_array = array ( vn_B_data_array ) nev = len ( vn_A_data_array [ : , 0 ] ) dN_A = vn_A_data_array [ : , 0 ] dN_A = dN_A . reshape ( len ( dN_A ) , 1 ) dN_B = vn_B_data_array [ : , 0 ] dN_B = dN_B . reshape ( len ( dN_B ) , 1 ) Qn_A_array = dN_A * vn_A_data_array [ : , 1 : ] Qn_B_array = dN_B * vn_B_data_array [ : , 1 : ] corr = 1. / ( dN_A * dN_B ) * Qn_A_array * conj ( Qn_B_array ) vn_2 = sqrt ( real ( mean ( corr , 0 ) ) ) + 1e-30 vn_2_err = std ( real ( corr ) , 0 ) / sqrt ( nev ) / 2. / vn_2 return ( vn_2 , vn_2_err )
this function computes vn{2} and its stat.
this function computes vn { 2 } and its stat .
"""
        this function computes vn{2} and its stat. err.
        sample A and B are assumed to have no overlap
        no self correlation substraction needed
    """
vn_A_data_array
vn_B_data_array