solvers.py 文件源码

def gain_substitution_vector(gain, x, xwt):
    nants, nchan, nrec, _ = gain.shape
    newgain = numpy.ones_like(gain, dtype='complex')
    if nrec > 0:
        newgain[..., 0, 1] = 0.0
        newgain[..., 1, 0] = 0.0

    gwt = numpy.zeros_like(gain, dtype='float')

    # We are going to work with Jones 2x2 matrix formalism so everything has to be
    # converted to that format
    x = x.reshape(nants, nants, nchan, nrec, nrec)
    xwt = xwt.reshape(nants, nants, nchan, nrec, nrec)

    if nrec > 0:
        gain[..., 0, 1] = 0.0
        gain[..., 1, 0] = 0.0

    for ant1 in range(nants):
        for chan in range(nchan):
            # Loop over e.g. 'RR', 'LL, or 'xx', 'YY' ignoring cross terms
            for rec in range(nrec):
                top = numpy.sum(x[:, ant1, chan, rec, rec] *
                                gain[:, chan, rec, rec] * xwt[:, ant1, chan, rec, rec], axis=0)
                bot = numpy.sum((gain[:, chan, rec, rec] * numpy.conjugate(gain[:, chan, rec, rec]) *
                                 xwt[:, ant1, chan, rec, rec]).real, axis=0)

                if bot > 0.0:
                    newgain[ant1, chan, rec, rec] = top / bot
                    gwt[ant1, chan, rec, rec] = bot
                else:
                    newgain[ant1, chan, rec, rec] = 0.0
                    gwt[ant1, chan, rec, rec] = 0.0

    return newgain, gwt