def METHOD_NAME(d, axis=None, mask=None):
    """
    Find the median of a numpy array. If an axis is provided, then find the median along
    the given axis. If mask is included, elements in d that have a non-zero mask value will
    be ignored.
    Parameters
    ----------
    d : float32 numpy array
        Input array to find the median.
    axis : int (default is None)
           Index of the array to take the median
    mask : unit8 or boolean numpy array (default is None)
          Numpy array of bitmask values. Non-zero values are ignored when calculating the median.
    Returns
    -------
    med : float32 numpy array
        The median value. If axis is None, then we return a single float.
    Notes
    -----
    Makes extensive use of the quick select algorithm written in C, included in quick_select.c.
    If all of the elements in the array are masked (or all of the elements of the axis of interest
    are masked), we return zero.
    """
    if axis is None:
        if mask is not None:
            median_mask = mask.ravel()
        else:
            median_mask = np.zeros(d.size, dtype=np.uint8)
        output_median = median_utils.median1d(np.ascontiguousarray(d.ravel(), dtype=np.float32),
                                              np.ascontiguousarray(median_mask, dtype=np.uint8))
    else:
        nx = d.shape[axis]
        ny = d.size // nx
        output_shape = np.delete(d.shape, axis)
        if mask is not None:
            median_mask = np.rollaxis(mask, axis, len(d.shape)).reshape(ny, nx).astype(np.uint8)
        else:
            median_mask = np.zeros((ny, nx), dtype=np.uint8)
        med = median_utils.median2d(np.ascontiguousarray(np.rollaxis(d, axis, len(d.shape)).reshape(ny, nx), dtype=np.float32),
                                    mask=np.ascontiguousarray(median_mask, dtype=np.uint8))
        median_array = np.array(med)
        output_median = median_array.reshape(output_shape)
    return output_median