solvers.py 文件源码

def gain_substitution_matrix(gain, x, xwt):
    nants, nchan, nrec, _ = gain.shape
    newgain = numpy.ones_like(gain, dtype='complex')
    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)

    # Write these loops out explicitly. Derivation of these vector equations is tedious but they are
    # structurally identical to the scalar case with the following changes
    # Vis -> 2x2 coherency vector, g-> 2x2 Jones matrix, *-> matmul, conjugate->Hermitean transpose (.H)
    for ant1 in range(nants):
        for chan in range(nchan):
            top = 0.0
            bot = 0.0
            for ant2 in range(nants):
                if ant1 != ant2:
                    xmat = x[ant2, ant1, chan]
                    xwtmat = xwt[ant2, ant1, chan]
                    g2 = gain[ant2, chan]
                    top += xmat * xwtmat * g2
                    bot += numpy.conjugate(g2) * xwtmat * g2
            newgain[ant1, chan][bot > 0.0] = top[bot > 0.0] / bot[bot > 0.0]
            newgain[ant1, chan][bot <= 0.0] = 0.0
            gwt[ant1, chan] = bot.real
    return newgain, gwt