def collapsed_spectrum(fitsfile, ns1, ns2,
                       method='mean', nwin_background=0,
                       reverse=False, out_sp=None, debugplot=0):
    """Compute a collapsed spectrum from a 2D image using scans in [ns1,ns2].

    Parameters
    ----------
    fitsfile : file object
        File name of FITS file containing the spectra to be calibrated.
    ns1 : int
        First scan (from 1 to NAXIS2).
    ns2 : int
        Last scan (from 1 to NAXIS2).
    method : string
        Indicates collapsing method. Possible values are "mean" or
        "median".
    nwin_background : int
        Window size for the computation of background using a median
        filtering with that window width. This background is computed
        and subtracted only if this parameter is > 0.
    reverse : bool
        If True, reserve wavelength direction prior to wavelength
        calibration.
    out_sp : string or None
        File name to save the selected spectrum in FITS format before
        performing the wavelength calibration.
    debugplot : int
        Determines whether intermediate computations and/or plots
        are displayed. The valid codes are defined in
        numina.array.display.pause_debugplot.

    Returns
    -------
    sp : 1d numpy array
        Collapsed spectrum.

    """

    # read FITS file
    with fits.open(fitsfile) as hdulist:
        image2d = hdulist[0].data
    naxis2, naxis1 = image2d.shape
    if abs(debugplot) >= 10:
        print('>>> Reading file:', fitsfile.name)
        print('>>> NAXIS1:', naxis1)
        print('>>> NAXIS2:', naxis2)

    if 1 <= ns1 <= ns2 <= naxis2:
        # extract spectrum
        if method == "mean":
            sp = np.mean(image2d[(ns1 - 1):ns2], axis=0)
        elif method == "median":
            sp = np.median(image2d[(ns1 - 1):ns2], axis=0)
        else:
            raise ValueError("Invalid method '" + str(method) + "'")

        # reverse spectrum if necessary
        if reverse:
            sp = sp[::-1]

        # fit and subtract background
        if nwin_background > 0:
            background = ndimage.filters.median_filter(
                sp, size=nwin_background
            )
            sp -= background

        # save spectrum before wavelength calibration in external
        # FITS file
        if out_sp is not None:
            hdu = fits.PrimaryHDU(sp)
            hdu.writeto(out_sp, overwrite=True)
    else:
        raise ValueError("Invalid ns1=" + str(ns1) + ", ns2=" + str(ns2) +
                         " values")

    return sp