timeslice.py 文件源码

def predict_timeslice_single(vis: Visibility, model: Image, predict=predict_2d_base, **kwargs) -> Visibility:
    """ Predict using a single time slices.

    This fits a single plane and corrects the image geometry.

    :param vis: Visibility to be predicted
    :param model: model image
    :return: resulting visibility (in place works)
    """
    log.debug("predict_timeslice_single: predicting using single time slice")

    inchan, inpol, ny, nx = model.shape

    vis.data['vis'] *= 0.0

    if not isinstance(vis, Visibility):
        avis = coalesce_visibility(vis, **kwargs)
    else:
        avis = vis

    # Fit and remove best fitting plane for this slice
    avis, p, q = fit_uvwplane(avis, remove=False)

    # Calculate nominal and distorted coordinate systems. We will convert the model
    # from nominal to distorted before predicting.
    workimage = copy_image(model)

    # Use griddata to do the conversion. This could be improved. Only cubic is possible in griddata.
    # The interpolation is ok for invert since the image is smooth but for clean images the
    # interpolation is particularly poor, leading to speckle in the residual image.
    lnominal, mnominal, ldistorted, mdistorted = lm_distortion(model, -p, -q)
    for chan in range(inchan):
        for pol in range(inpol):
            workimage.data[chan, pol, ...] = \
                griddata((mnominal.flatten(), lnominal.flatten()),
                         values=workimage.data[chan, pol, ...].flatten(),
                         xi=(mdistorted.flatten(), ldistorted.flatten()),
                         method='cubic',
                         fill_value=0.0,
                         rescale=True).reshape(workimage.data[chan, pol, ...].shape)

    vis = predict(vis, workimage, **kwargs)

    return vis