@docfiller<EOL>def minimum_filter1d(input, size, axis=-<NUM_LIT:1>, output=None,<EOL>mode="<STR_LIT>", cval=<NUM_LIT:0.0>, origin=<NUM_LIT:0>):
input = numpy.asarray(input)<EOL>if numpy.iscomplexobj(input):<EOL><INDENT>raise TypeError('<STR_LIT>')<EOL><DEDENT>axis = _ni_support._check_axis(axis, input.ndim)<EOL>if size < <NUM_LIT:1>:<EOL><INDENT>raise RuntimeError('<STR_LIT>')<EOL><DEDENT>output, return_value = _ni_support._get_output(output, input)<EOL>if (size // <NUM_LIT:2> + origin < <NUM_LIT:0>) or (size // <NUM_LIT:2> + origin >= size):<EOL><INDENT>raise ValueError('<STR_LIT>')<EOL><DEDENT>mode = _ni_support._extend_mode_to_code(mode)<EOL>_nd_image.min_or_max_filter1d(input, size, axis, output, mode, cval,<EOL>origin, <NUM_LIT:1>)<EOL>return return_value<EOL>
Calculate a one-dimensional minimum filter along the given axis.

    The lines of the array along the given axis are filtered with a
    minimum filter of given size.

    Parameters
    ----------
    %(input)s
    size : int
        length along which to calculate 1D minimum
    %(axis)s
    %(output)s
    %(mode)s
    %(cval)s
    %(origin)s

    Notes
    -----
    This function implements the MINLIST algorithm [1]_, as described by
    Richard Harter [2]_, and has a guaranteed O(n) performance, `n` being
    the `input` length, regardless of filter size.

    References
    ----------
    .. [1] http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.42.2777
    .. [2] http://www.richardhartersworld.com/cri/2001/slidingmin.html