import numpy
import perfplot


def unique_axis(data):
    return numpy.unique(data, axis=0)


def unique_row_view(data):
    b = numpy.ascontiguousarray(data).view(
        numpy.dtype((numpy.void, data.dtype.itemsize * data.shape[1]))
    )
    u = numpy.unique(b).view(data.dtype).reshape(-1, data.shape[1])
    return u


def unique_scikit(ar):
    if ar.ndim != 2:
        raise ValueError("unique_rows() only makes sense for 2D arrays, "
                         "got %dd" % ar.ndim)
    # the view in the next line only works if the array is C-contiguous
    ar = numpy.ascontiguousarray(ar)
    # np.unique() finds identical items in a raveled array. To make it
    # see each row as a single item, we create a view of each row as a
    # byte string of length itemsize times number of columns in `ar`
    ar_row_view = ar.view('|S%d' % (ar.itemsize * ar.shape[1]))
    _, unique_row_indices = numpy.unique(ar_row_view, return_index=True)
    ar_out = ar[unique_row_indices]
    return ar_out


perfplot.save(
    "unique.png",
    setup=lambda n: numpy.random.randint(0, 100, (n, 2)),
    kernels=[unique_axis, unique_row_view, unique_scikit],
    n_range=[2 ** k for k in range(20)],
)