示例#1
0
def residual(params, t, y, y_err, inclination, prec_period):
    alpha = params['alpha'].value
    delta = params['delta'].value
    rho1 = params['rho1'].value
    rho2 = params['rho2'].value
    T1 = params['T1'].value


    cos_phi0_1 = width_rl(t, alpha, delta, T1, rho1, inclination, prec_period)
# For the second pole, we assume that it is directly opposite to the first pole.
# The other parameters delta and T1 are universal to the system
    cos_phi0_2 = width_rl(t, (np.pi-alpha), delta, T1, rho2, inclination, prec_period)

    cos_phi0 = np.array([cos_phi0_1, cos_phi0_2])
    
    
 #   print y
 #   print cos_phi0
 #   print y_err
    # Divide by data uncertainty, so that minimizer will minimize
    # sum((y-cos_phi0)**2/y_err**2)
    # Flatten all arrays (2D) to get 1D arrays before calculating residuals:
    return np.abs(y.flatten()-cos_phi0.flatten())/y_err.flatten()
示例#2
0
def residual(params, t, y, y_err, inclination, prec_period):
    alpha = params['alpha'].value
    delta = params['delta'].value
    rho = params['rho'].value
    T1 = params['T1'].value


    cos_phi0 = width_rl(t, alpha, delta, T1, rho, inclination, prec_period)
    
 #   print y
 #   print cos_phi0
 #   print y_err
    # Divide by data uncertainty, so that minimizer will minimize
    # sum((y-cos_phi0)**2/y_err**2)
    return np.abs(y-cos_phi0)/y_err
示例#3
0
def residual(params, t, y, y_err, n_rho, inclination, prec_period):
    alpha = params['alpha'].value
    delta = params['delta'].value
    rho = []
    T1 = params['T1'].value

    # Will do successive calculations with same parameters, changing only the 
    # value of rho to be fit.
    cos_phi0 = []
    for i_rho in np.arange(n_rho):
        rho_str = 'rho_'+str(i_rho)
        rho = params[rho_str].value
   #    rho2 = params['rho2'].value
        cos_phi0.append(width_rl(t[i_rho], alpha, delta, T1, rho, 
                            inclination, prec_period))
    
    cos_phi0 = np.array(cos_phi0)

    ###cos_phi0_1 = width_rl(t, alpha, delta, T1, rho1, inclination, prec_period)
# For the second pole, we assume that it is directly opposite to the first pole.
# The other parameters delta and T1 are universal to the system
#    cos_phi0_2 = width_rl(t, (np.pi-alpha), delta, T1, rho2, inclination, prec_period)

# Will do successive calculations with same parameters, changing only the value
# of rho to be fit.
    ###cos_phi0_2 = width_rl(t, (np.pi-alpha), delta, T1, rho2, inclination, prec_period)

    #  cos_phi0 = np.array([cos_phi0_1, cos_phi0_2])
    
    
 #   print y
 #   print cos_phi0
 #   print y_err
    # Divide by data uncertainty, so that minimizer will minimize
    # sum((y-cos_phi0)**2/y_err**2)
    # Flatten all arrays (2D) to get 1D arrays before calculating residuals:
    return np.abs(y.flatten()-cos_phi0.flatten())/y_err.flatten()