voronoi_2d_binning.py 文件源码

def _reassign_bad_bins(classe, x, y):
    """
    Implements steps (vi)-(vii) in section 5.1 of Cappellari & Copin (2003)

    """
    # Find the centroid of all successful bins.
    # CLASS = 0 are unbinned pixels which are excluded.
    #
    good = np.unique(classe[classe > 0])
    xnode = ndimage.mean(x, labels=classe, index=good)
    ynode = ndimage.mean(y, labels=classe, index=good)

    # Reassign pixels of bins with S/N < targetSN
    # to the closest centroid of a good bin
    #
    bad = classe == 0
    index = voronoi_tessellation(x[bad], y[bad], xnode, ynode, [1])
    classe[bad] = good[index]

    # Recompute all centroids of the reassigned bins.
    # These will be used as starting points for the CVT.
    #
    good = np.unique(classe)
    xnode = ndimage.mean(x, labels=classe, index=good)
    ynode = ndimage.mean(y, labels=classe, index=good)

    return xnode, ynode

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