Correlate.py 文件源码

def get_rv(vel, corr, Npix=None, **fit_kws):
    """
    Get the best radial velocity, with errors.
    This will only work if the ccf was made with the maximum likelihood method!
    Uses the formula given in Zucker (2003) MNRAS, 342, 4  for the rv error.

    Parameters:
    ===========
    - vel:   numpy.ndarray
             The velocities
    - corr:  numpy.ndarray
             The ccf values. Should be the same size as vel
    - Npix:  integer
             The number of pixels used in the CCF.

    Returns:
    ========
    -rv:     float
             The best radial velocity, in km/s
    -rv_err: float
             Uncertainty in the velocity, in km/s
    -ccf:    float
             The CCF power at the maximum velocity.
    """
    vel = np.atleast_1d(vel)
    corr = np.atleast_1d(corr)
    sorter = np.argsort(vel)
    fcn = spline(vel[sorter], corr[sorter])
    fcn_prime = fcn.derivative(1)
    fcn_2prime = fcn.derivative(2)

    guess = vel[np.argmax(corr)]

    def errfcn(v):
        ll = 1e6*fcn_prime(v)**2
        return ll

    out = minimize_scalar(errfcn, bounds=(guess-2, guess+2), method='bounded')
    rv = out.x
    if Npix is None:
        Npix = vel.size

    rv_var = -(Npix * fcn_2prime(rv) * (fcn(rv) / (1 - fcn(rv) ** 2))) ** (-1)
    return rv, np.sqrt(rv_var), fcn(rv)