How to use the drizzlepac.astrodrizzle.ablot function in drizzlepac

wcs.pscale = np.sqrt(wcs.wcs.cd[0,0]**2 +
                                 wcs.wcs.cd[1,0]**2)*3600.
            
            #print 'IDCSCALE: %.3f' %(wcs.idcscale)
            
        #print refimage.filename(), ref_wcs.idcscale, ref_wcs.wcs.cd, flt_wcs.idcscale, ref_wcs.orientat
            
        if segmentation:
            #print '\nSEGMENTATION\n\n',(seg_ones+1).dtype, refdata.dtype, ref_wcs, flt_wcs
            ### +1 here is a hack for some memory issues
            blotted_seg = astrodrizzle.ablot.do_blot(refdata+0, ref_wcs,
                                flt_wcs, 1, coeffs=True, interp='nearest',
                                sinscl=1.0, stepsize=1, wcsmap=None)
            
            blotted_ones = astrodrizzle.ablot.do_blot(seg_ones+1, ref_wcs,
                                flt_wcs, 1, coeffs=True, interp='nearest',
                                sinscl=1.0, stepsize=1, wcsmap=None)
            
            blotted_ones[blotted_ones == 0] = 1
            ratio = np.round(blotted_seg/blotted_ones)
            grow = nd.maximum_filter(ratio, size=3, mode='constant', cval=0)
            ratio[ratio == 0] = grow[ratio == 0]
            blotted = ratio
            
        else:
            #print '\nREFDATA\n\n', refdata.dtype, ref_wcs, flt_wcs
            blotted = astrodrizzle.ablot.do_blot(refdata, ref_wcs, flt_wcs, 1, coeffs=True, interp='poly5', sinscl=1.0, stepsize=10, wcsmap=None)
        
        return blotted

if wcs.idcscale is None:
                    wcs.idcscale = np.sqrt(np.sum(wcs.wcs.cd[0,:]**2))*3600.
            else:
                wcs.idcscale = np.sqrt(np.sum(wcs.wcs.cd[0,:]**2))*3600.
            
            wcs.pscale = np.sqrt(wcs.wcs.cd[0,0]**2 +
                                 wcs.wcs.cd[1,0]**2)*3600.
            
            #print 'IDCSCALE: %.3f' %(wcs.idcscale)
            
        #print refimage.filename(), ref_wcs.idcscale, ref_wcs.wcs.cd, flt_wcs.idcscale, ref_wcs.orientat
            
        if segmentation:
            #print '\nSEGMENTATION\n\n',(seg_ones+1).dtype, refdata.dtype, ref_wcs, flt_wcs
            ### +1 here is a hack for some memory issues
            blotted_seg = astrodrizzle.ablot.do_blot(refdata+0, ref_wcs,
                                flt_wcs, 1, coeffs=True, interp='nearest',
                                sinscl=1.0, stepsize=1, wcsmap=None)
            
            blotted_ones = astrodrizzle.ablot.do_blot(seg_ones+1, ref_wcs,
                                flt_wcs, 1, coeffs=True, interp='nearest',
                                sinscl=1.0, stepsize=1, wcsmap=None)
            
            blotted_ones[blotted_ones == 0] = 1
            ratio = np.round(blotted_seg/blotted_ones)
            grow = nd.maximum_filter(ratio, size=3, mode='constant', cval=0)
            ratio[ratio == 0] = grow[ratio == 0]
            blotted = ratio
            
        else:
            #print '\nREFDATA\n\n', refdata.dtype, ref_wcs, flt_wcs
            blotted = astrodrizzle.ablot.do_blot(refdata, ref_wcs, flt_wcs, 1, coeffs=True, interp='poly5', sinscl=1.0, stepsize=10, wcsmap=None)

ctx = pyfits.open(visit['product']+'_drc_ctx.fits')
    bits = np.log2(ctx[0].data)
    mask = ctx[0].data == 0
    single_image = np.cast[np.float32]((np.cast[int](bits) == bits) & (~mask))
    ctx_wcs = pywcs.WCS(ctx[0].header)
    ctx_wcs.pscale = utils.get_wcs_pscale(ctx_wcs)
    
    for file in visit['files']:
        flt = pyfits.open(file, mode='update')
        for ext in [1,2]:
            
            flt_wcs = pywcs.WCS(flt['SCI',ext].header, fobj=flt, relax=True)
            flt_wcs.pscale = utils.get_wcs_pscale(flt_wcs)
            
            blotted = astrodrizzle.ablot.do_blot(single_image, ctx_wcs,
                            flt_wcs, 1, coeffs=True, interp='nearest',
                            sinscl=1.0, stepsize=10, wcsmap=None)
            
            ctx_mask = blotted > 0
            
            sci = flt['SCI',ext].data
            dq = flt['DQ',ext].data

            if simple_mask:
                print('{0}: Mask image without overlaps, extension {1:d}'.format(file, ext))
                dq[ctx_mask] |= 1024
            else:
                print('{0}: Clean CRs with LACosmic, extension {1:d}'.format(file, ext))

                if with_ctx_mask:
                    inmask = blotted == 0