Ejemplo n.º 1
0
def runOffPolyISCE(offField):
    '''
    Estimate polynomial here.
    '''

    inArr = np.array(offField.unpackOffsets())
    x = inArr[:, 0]
    y = inArr[:, 2]
    dx = inArr[:, 1]
    dy = inArr[:, 3]
    sig = inArr[:, 4]

    obj = Offsetpoly()
    obj.setLocationAcross(list(x))
    obj.setLocationDown(list(y))
    obj.setSNR(list(sig))
    obj.setOffset(list(dy))
    obj.offsetpoly()
    val = obj.offsetPoly

    #    print('Range: ', val)
    azpol = Polynomial(rangeOrder=2, azimuthOrder=2)
    azpol.setCoeffs([[val[0], val[1], val[4]], [val[2], val[3]], [val[5]]])

    obj.setOffset(list(dx))
    obj.offsetpoly()
    val = obj.offsetPoly

    #    print('Azimuth: ', val)

    rgpol = Polynomial(rangeOrder=2, azimuthOrder=2)
    rgpol.setCoeffs([[val[0], val[1], val[4]], [val[2], val[3]], [val[5]]])

    return azpol, rgpol
Ejemplo n.º 2
0
def runOffPoly(offField):
    '''
    Estimate polynomial here.
    '''

    inArr = np.array(offField.unpackOffsets())
    x = inArr[:, 0]
    y = inArr[:, 2]
    dx = inArr[:, 1]
    dy = inArr[:, 3]
    sig = inArr[:, 4]
    snr = 1.0 + 1.0 / sig

    xOrder = 2
    yOrder = 2

    #####Normalization factors
    ymin = np.min(y)
    ynorm = np.max(y) - ymin
    if ynorm == 0:
        ynorm = 1.0

    yoff = np.int(np.round(np.mean(dy)))
    y = (y - ymin) / ynorm

    xmin = np.min(x)
    xnorm = np.max(x) - xmin
    if xnorm == 0:
        xnorm = 1.0

    x = (x - xmin) / xnorm

    arrList = []
    for ii in range(yOrder + 1):
        yfact = np.power(y, ii)
        for jj in range(yOrder + 1 - ii):
            temp = np.power(x, jj) * yfact
            arrList.append(temp.reshape((temp.size, 1)))

    A = np.hstack(arrList)

    A = A / snr[:, None]
    b = dy / snr

    val, res, rank, eigs = np.linalg.lstsq(A, b, rcond=1.0e-12)
    print('Az Chi : ', np.sqrt(res / (1.0 * len(b))))

    azpol = Polynomial(rangeOrder=2, azimuthOrder=2)
    azpol.setCoeffs([val[0:3], val[3:5], val[5:]])
    azpol._meanRange = xmin
    azpol._normRange = xnorm
    azpol._meanAzimuth = ymin
    azpol._normAzimuth = ynorm

    b = dx / snr
    val, res, rank, eigs = np.linalg.lstsq(A, b, rcond=1.0e-12)
    print('Rg chi : ', np.sqrt(res / (1.0 * len(b))))

    rgpol = Polynomial(rangeOrder=2, azimuthOrder=2)
    rgpol.setCoeffs([val[0:3], val[3:5], val[5:]])
    rgpol._meanRange = xmin
    rgpol._normRange = xnorm
    rgpol._meanAzimuth = ymin
    rgpol._normAzimuth = ynorm

    return azpol, rgpol