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()
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
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()